Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This office action is in response to an amendment filed on 11/11/2025. Claims 1-11 are pending.
Response to Amendments
Amendments filed on 11/11/2025 are under consideration. Claims 1, 10, and 11 are amended.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Anderson et al. (US 2018/0154723 Al). in view of Giovanardi et al. (US 2023/0017774 Al) and in further view of Alleva (US 2024/0185646 A1)
Regarding Claim 1 Anderson teaches A vehicle position estimation method comprising: and performing preview control, (Pg. 11 – [0022] – “Some aspects relate to vehicle systems that utilize topographical maps of the road surface. Such maps include positional information as well as road surface information such as road height. These maps may be highly granular in detail, showing individual road imperfections, bumps, potholes, and the like. These maps may be generated by a variety of means, including vision camera sensors, LID AR, radar, and other planar or three-dimensional scanning sensors, and the like. The maps may also be generated by sensor information post-encounter, such as the front suspension actuators determining information about the road as they traverse terrain. These topographical maps may also be communicated from vehicle to vehicle over a network, or may be downloaded from servers in communication with the vehicle such as over a cellular network. The topographical maps may be used for a variety of control purposes, such as: adapting driving behavior ( changing speed such as slowing down on a rough road;” (equates to and performing preview control as the quote shows the use of topographical maps to control the vehicles speed in response to a rough rod being detected ahead and thus the car is preemptively controlled to alleviate the roughness of the ride) ) acquiring a reference unsprung displacement around the vehicle, from an unsprung displacement map indicating a correspondence relationship between unsprung displacement and a position (Pg. 15 – Fig. 15-1- 15-104 & See Also Pg. 13 – [0037] – “A driving plan 15-200 is calculated based on a route planning algorithm and using stored maps 15-202. As the vehicle traverses terrain, road condition data 15-204 is acquired such as vertical accelerometer data, road surface information from a forward-looking vision system, data from a stored topographical map, GPS-indexed data, data from other vehicles, and a measure of at least one state variable from an electronic suspension system (such as accelerometer, velocity, and position data from each actuator or semi-active damper).” (equates to acquiring the unsprung displacement around the vehicle, from a unsprung displacement map indicating a correspondence relationship between the unsprung displacement and a position as the unsprung displacement of vertical acceleration is calculated as seen from the above quote corresponding to the unsprung displacement map between the unsprung displacement, and position as the GPS indexed data is collected while the vehicle is traveling along the route.)) estimating a vehicle position of the vehicle by estimating a position of the actual trajectory (Pg. 15 – [0061] – “In FIG. 15-7, a vehicle state estimator 15-700 determines a vehicle's kinematic state based on a number of sensors such as accelerometers, steering angle, vehicle velocity (wheel speed sensors, GPS, etc.). This functional unit calculates how the vehicle is moving across the terrain, and outputs a change in (x, y, z) coordinates for each time step.” (equates to estimating a vehicle position of the vehicle by estimating a position of the actual trajectory as the quote shows the estimation of the vehicle position via a gps installed into the vehicle and does the position collection over the actual trajectory as seen by the vehicle moving across the terrain at each time step.) ) recognizing road surface roughness representing roughness of a road surface around the vehicle in a lateral direction by using a recognition sensor installed on the vehicle; (Pg. 11 – [0022] – “Some aspects relate to vehicle systems that utilize topographical maps of the road surface. Such maps include positional information as well as road surface information such as road height. These maps may be highly granular in detail, showing individual road imperfections, bumps, potholes, and the like. These maps may be generated by a variety of means, including vision camera sensors, LID AR, radar, and other planar or three-dimensional scanning sensors, and the like.” (equates to recognizing road surface roughness representing roughness of a road surface around the vehicle in a lateral direction by using a recognition sensor installed on the vehicle; as the quote shows the use of various recognition sensors to ensure the road height is stored and generated into a topographical map.)) recognizing a lateral position of the vehicle in a road by using the recognition sensor; (Pg. 12 – [0032] – “Several suitable look-ahead systems exist such as mono or stereo vision camera systems, radar, sonar, LIDAR, and other planar or three dimensional scanning systems. In some embodiments multiple look-ahead sensors are used in conjunction through a secondary fusion system in order to obtain a more accurate estimate of road conditions. These sensors may build a topographical map that expands beyond road surface conditions: they may detect curbs, edges of roads, street signs, other vehicles, pedestrians, buildings, etc.” (equates to recognizing a lateral position of the vehicle in a road by using the recognition sensor as the system is able to build a map and thus actualize the vehicle’s position on the map it’s built.)) replacing a component of the lateral position of the estimated vehicle position with the lateral position recognized by using the recognition sensor, (Pg. 11 – [0022] – “These maps may be highly granular in detail, showing individual road imperfections, bumps, potholes, and the like. These maps may be generated by a variety of means, including vision camera sensors” & See Also Pg. 2 – Fig. 15-1 & See Also Pg. 11 – [0027] – “Output from the sensor fusion system is a position metric that serves as either an index to the topographical map 15-100, or serves to transform the topographical map at each time update” (equates to replacing a component of the lateral position of the estimated vehicle position with the lateral position recognized by using the recognition sensor as the look ahead sensor is equivalent to the recognition sensor in the lateral direction, and it replaces and updates the map and thus the lateral position of the vehicle as a result of the sensor fusion.)) and the preview control comprises: determining a current position of the wheel of the vehicle; (Pg. 11 – [0022] – “Aspects also relate to plotting a trajectory of the vehicle and its elements ( e.g. individual wheels) across the topographical map” & See Also Pg. 11 – [0026] – “multiple relative maps about parts of the vehicle (for example, relative maps about each wheel), an absolute map comprising absolute positions (for example, GPS coordinates), or any other means of associated terrain height Z information or similar.” (equates to determining a current position of the wheel of the vehicle as the first quote shows a trajectory of the wheel being plotting and the second quote further demonstrates that the z position and thus the position of the wheel being attained. ) ) calculating an expected passage position of the wheel of the vehicle after a preview time; (Pg. 11 – [0029] – “The active suspension then calculates an incidence time to each point corresponding with each wheel of the vehicle for which an active suspension actuator is disposed” & See Also Pg. 11 – [0029] – “In one embodiment, the active suspension system receives data from the topological map and determines an incidence time and correction. In a simple implementation, a path may be calculated that represents a path through a plurality of points in the topographical map 15-100.” (equates to calculating an expected passage position of the wheel of the vehicle after a preview time as the first quote shows the incidence time or preview time calculation wherein each wheel will be position in reference to a detected disturbance and thus the passage point is calculated when the active suspension is disposed for each wheel.) ) determining the unsprung displacement of the wheel of the vehicle at the expected passage position based on the unsprung displacement map; (Pg. 11 – [0029] – “the active suspension system receives data from the topological map and determines an incidence time and correction. In a simple implementation, a path may be calculated that represents a path through a plurality of points in the topographical map 15-100. This path may be a function of current steering angle and speed, or be based on a planned route. The planned route may be a combination of GPS/maps route planning and any obstacle avoidance procedures being employed by the self driving vehicle to plan vehicle travel. The path may comprise of a single trajectory in a lower resolution map, of two paths, each representing a path of travel of the left and right sides of the vehicle respectively, or four paths, with each representing a path of travel of a wheel of the vehicle (in the case of a two axle vehicle)” (equates to determining the unsprung displacement of the wheel of the vehicle at the expected passage position based on the unsprung displacement map; as the quote shows a path wherein the topographical data is taken showing the bumps / disturbances the car will experience wherein the map may be made for each wheel at each expected passage position.) ) calculating a target control force of an actuator of a suspension based on the unsprung displacement of the wheel at the expected passage position; (Pg. 11 – [0029] – “The active suspension then calculates a correction, which comprises a force or position setting of the actuator at each wheel so as to mitigate impact of the event on the trajectory.” & See Also Pg. 11 – [0029] – “The active suspension then calculates an incidence time to each point corresponding with each wheel of the vehicle for which an active suspension actuator is disposed” & See Also Pg. 11 – [0029] – “In one embodiment, the active suspension system receives data from the topological map and determines an incidence time and correction. In a simple implementation, a path may be calculated that represents a path through a plurality of points in the topographical map 15-100.” (equates to calculating a target control force of an actuator of a suspension based on the unsprung displacement of the wheel at the expected passage position as the first quote shows the calculation of a force for an actuator controlling the suspension position based on a trajectory wherein the trajectory is shown to be the displacement t of the wheel wherein that displacement coincides with an incident time at a passage position. ) ) and controlling the actuator to generate the target control force at a timing when the wheel passes the expected passage position; (Pg. 11 – [0029] – “The active suspension then calculates an incidence time to each point corresponding with each wheel of the vehicle for which an active suspension actuator is disposed. The active suspension then calculates a correction, which comprises a force or position setting of the actuator at each wheel so as to mitigate impact of the event on the trajectory.” & See Also Pg. 11 – [0022] – “The topographical maps may be used for a variety of control purposes, such as: adapting driving behavior ( changing speed such as slowing down on a rough road; changing vehicle course such as choosing a less bumpy road to reach the destination, etc.); adapting active suspension system behavior ( controlling actuator force/position in a predictive manner in response to road perturbations ahead” (equates to controlling the actuator to generate the target control force at a timing when the wheel passes the expected passage position as the first quote shows the correction force generated in response to the ahead trajectory determined, and the second quote shows how specifically the suspension is controlled by inputting said force to the suspension.)) recognizing road surface roughness representing roughness of a road surface around the vehicle in a lateral direction by using a recognition sensor installed on the vehicle; (Pg. 12 – [0032] – “These are particularly useful due to their ability to sense road conditions prior to encountering them with the wheels of the vehicle. Several suitable look-ahead systems exist such as mono or stereo vision camera systems, radar, sonar, LIDAR, and other planar or three dimensional scanning systems. In some embodiments multiple look-ahead sensors are used in conjunction through a secondary fusion system in order to obtain a more accurate estimate of road conditions.” (equates to recognizing road surface roughness representing roughness of a road surface around the vehicle in a lateral direction by using a recognition sensor installed on the vehicle as the system is able to build a map and thus actualize the vehicle’s position on the map it’s built thus establish a position and senses road conditions such as road roughness to help build this map.))
Yet Anderson fails to teach performing localization that estimates a vehicle position without using Global Navigation Satellite System; wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map; when the road surface roughness is less than a threshold.
Giovanardi teaches a similar vehicle position estimation method (abstract). Giovanardi teaches performing localization that estimates a vehicle position without using Global Navigation Satellite System; (Pg. 27 - [0067] In some embodiments, localization system 152 may comprise GPS systems, terrain-based localization systems, and/or any other appropriate localization system capable of providing a location of a vehicle on a road surface to the processor 150.” (equates to performing localization that estimates a vehicle position without using Global Navigation Satellite System as the quote shows a localization system that may be one of terrain based localization type wherein this is known as not using GPS signal but rather onboard sensors to detect and understand the environment around the vehicle.))) when the road surface roughness is less than a threshold (Pg. 10 – Fig. 5 –“ when a first magnitude associated with the information regarding the travel surface is less than a threshold” ).
Yet both fail to teach wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map;
Alleva teaches wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; (Pg. 2 – Fig. 2 & See Also Pg. 9 – [0063] – “first vehicle geo-referencing data of the measured first vertical acceleration values (i.e., data indicative of two/three-dimensional (2D/3D) positions over time corresponding to the measured first vertical acceleration values-e.g., positions provided by Global Navigation Satellite System (GNSS) receivers, such as Global Positioning System (GPS) positions), and first vehicle speed data indicative of the given constant speed (s) associated with the measured first vertical acceleration values;” & See Also Pg. 9 – [0067-0069]– “schematically illustrates an IRI estimation step ( denoted as a whole by 20) of an IRI estimation method according to a preferred embodiment of the present invention. [0068] In particular, the IRI estimation step 20 comprises: [0069] acquiring (block 21 in FIG. 2) second vehicle vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road/road segment” (equates to wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; as the unsprung displacement is calculated using the vehicle vertical velocity, acceleration and position and each is gathered as seen from the first quote, and the first figure and second quote is showing the generation of the time series data based on the vehicle actually traveling over the said trajectory.) ) trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map (Pg. 10 – [0093] – “Additionally, FIG. 6 shows an example of comparison between real IRI values of a road or road segment, and IRI values estimated by carrying out the IRI estimation method according to the present invention” & See Also pg. 5 – Fig. 6 & See Also Pg. 9 – [0067-0069]– “schematically illustrates an IRI estimation step ( denoted as a whole by 20) of an IRI estimation method according to a preferred embodiment of the present invention. [0068] In particular, the IRI estimation step 20 comprises: [0069] acquiring (block 21 in FIG. 2) second vehicle vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road/road segment” & See Also Pg. 2 – Fig. 1 – 11 – “collecting vehicle vertical accelerations and vehicle geo-referencing and speed data”(equates to trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map as the figure shows the comparison between the measured rough roughness index and historical chart and thus an actual trajectory and its associated road roughness is calculated and then subsequently compared to real road roughness index value. The last quote showing how the road roughness being calculated is equivalent to the calculated unsprung displacement of this art wherein vertical acceleration, position and speed are calculated for determination of rough roughness. ) ) It would have been an advantageous addition to the method disclosed by Anderson-Giovanardi to include wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map ensuring the road and trajectory travelled upon by the vehicle is the road being considered for data collection and subsequently allowing for comparison between the actual displacement data of the vehicle and the historical displacement data of the vehicle over the same road.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map as these limitations allow for the displacement data to be compared between the road the vehicle is currently driving upon and the data gathered of the same road and allows for an accurate position updating to happen based on the displacement being experienced.
Regarding Claim 2 Anderson- Giovanardi-Alleva teaches (Anderson discloses the following limitation:) The vehicle position estimation method according to claim 1, further comprising: employing the estimated vehicle position, when the road surface roughness is equal to or greater than the threshold. (Pg. 13 – [0036] – “Based on the measure of road roughness, the driving plan 15-200 is altered to either bias against rough roads by employing a weight factor directly in the route-planning algorithm, or by avoiding roads that have a road roughness above a certain threshold. ” (equates to employing the estimated vehicle position, when the road surface roughness is equal to or greater than the threshold as the threshold in this art has exceeded the limit and a new driving course has been implemented thus employing an estimated position.))
Regarding Claim 3 Anderson- Giovanardi-Alleva teaches The vehicle position estimation method according to claim 1, as mapped above. (Anderson discloses the following limitations:) wherein the recognizing the road surface roughness includes: using the recognition sensor to measure road surface displacements around the vehicle; (Pg. 11 –- [0028] - “the topographical map represents a local measure of terrain about the vehicle, and a method for accurately interpreting and using results from look-ahead sensors 15-108 by the active suspension system 15-110.” (equates to using the recognition sensor to measure road surface displacements around the vehicle as the sensor data between the suspension and the look ahead sensor give the displacements used for the topographical map.)) and calculating the road surface roughness based on a variance of the road surface displacements in the lateral direction. (Pg. 11 –[0026] – “A topographical map 15-100 comprises high-resolution terrain data for the vehicle.” & See Also Pg. 11 – [0026] – “In addition to or instead of terrain height data, the topological map may contain a generalized roughness metric” & See Also Pg. 11 – [0027] – “This map may start as populated, unpopulated, or partially populated. In order to use a high resolution topographical map, the vehicle needs an accurate method of localizing with respect to the map” (equates to calculating the road surface roughness based on a variance of the road surface displacements in the lateral direction as the map information contains road roughness information and terrain height , and considering the map can be unpopulated the car can use localization techniques to understand the road surface roughness and terrain height.)).
Regarding Claim 4 Anderson-Giovanardi-Alleva teaches (Anderson discloses the following limitations:) The vehicle position estimation method according to claim 1, and estimating the vehicle position based on a position of the selected one imaginary trajectory (Pg. 11 - [0022] - " Aspects also relate to plotting a trajectory of the vehicle and its elements ( e.g. individual wheels) across the topographical map." (equates to estimating the vehicle position based on a position of the selected one imaginary trajectory as the trajectory selected by the vehicle is then subsequently mapped on this art's topographical map and thus a position of the vehicle is estimated. ))
Yet Anderson fails to teach wherein the estimating the vehicle position includes: setting a plurality of imaginary trajectories of the wheel around the vehicle; acquiring the time-series data of the reference unsprung displacement along each of the plurality of imaginary trajectories from the unsprung displacement map; selecting, from the plurality of imaginary trajectories, one imaginary trajectory that yields a highest correlation between the time-series data of the unsprung displacement and the time-series data of the reference unsprung displacement.
Giovanardi teaches a similar vehicle position estimation method (abstract). Giovanardi teaches wherein the estimating the vehicle position includes: setting a plurality of imaginary trajectories of the wheel around the vehicle (Pg. 31 – [0104] – “a trajectory may be set by improving the performance of the system within the constraints, taking into account the upcoming disturbance. For example, in some embodiments of a suspension system, a third derivative of the vehicle superstructure's motion, jerk, may be a metric related to the comfort of the occupants. A controller may filter an upcoming road disturbance at variable filter frequencies to reduce or substantially minimize jerk while satisfying system constraints. For example, an algorithm may define a cost function that penalizes constraint violations, and values low jerk levels. Alternatively or additionally, the algorithm may first consider the highest violation in an upcoming road segment.”(equates to : setting a plurality of imaginary trajectories of the wheel around the vehicle as the trajectory setting in this prior art takes into account multiple values of expected jerk and generates trajectories corresponding to the expected jerk values.) ) acquiring the time-series data of the reference unsprung displacement along each of the plurality of imaginary trajectories from the unsprung displacement map; Pg. 31 – [0104] – “a trajectory may be set by improving the performance of the system within the constraints, taking into account the upcoming disturbance. For example, in some embodiments of a suspension system, a third derivative of the vehicle superstructure's motion, jerk, may be a metric related to the comfort of the occupants. A controller may filter an upcoming road disturbance at variable filter frequencies to reduce or substantially minimize jerk while satisfying system constraints. For example, an algorithm may define a cost function that penalizes constraint violations, and values low jerk levels. Alternatively or additionally, the algorithm may first consider the highest violation in an upcoming road segment.”(equates to acquiring the time-series data of the reference unsprung displacement along each of the plurality of imaginary trajectories from the unsprung displacement map as the upcoming disturbance that causes the vertical motion or the unsprung displacement of the vehicle to change to and jerk values are subsequently mitigated to. Based on the filter frequency sleeted the jerk is mitigated and thus multiple imaginary trajectories unsprung displacements are taken into account to preemptively actuate the suspension for comfort.)) selecting, from the plurality of imaginary trajectories, one imaginary trajectory that yields a highest correlation between the time-series data of the unsprung displacement and the time-series data of the reference unsprung displacement; (Pg. 31 - [0106-0107] - "With a priori knowledge of the appropriate trajectories, conflicts can be avoided while maintaining a high level of performance. [0107] The description above applies to many different types of control systems. When a priori knowledge of upcoming disturbances is available, performance targets and operational constraints are known, this process may be used to plan out a strategy for a proactive controller to follow" & See also Pg. 31 - [109] - "The proactive control calculation block shown on the left calculates two outputs. First, it calculates an actuator command that is sized such that it creates a desired performance in terms of the response of the plant to the disturbance. As a second output, the resulting expected sensor signal is calculated, and supplied to the controller as a reference command… If the actuator command from the proactive control results in the expected reference output from the sensors, then the feedback loop will see no error and thus take no action. If, on the other hand, there is an error, due for example to inaccuracies in the expected disturbance, then the feedback loop may work to correct the resulting motion" (equates to selecting, from the plurality of imaginary trajectories, one imaginary trajectory that yields a highest correlation between the time-series data of the unsprung displacement and the time-series data of the reference unsprung displacement as the trajectories are known and then a proactive controller takes in this information to accurately control the suspension system for a smooth ride. Quote 2 shows the controller then using the detected unsprung displacement and reference unsprung displacement to deliver the smooth ride and takes action to ensure the reference matches the measured unsprung displacement.)) It would have been an advantageous addition to the system disclosed by Anderson to include setting a plurality of imaginary trajectories of the wheel around the vehicle; acquiring the time-series data of the reference unsprung displacement along each of the plurality of imaginary trajectories from the unsprung displacement map; selecting, from the plurality of imaginary trajectories, one imaginary trajectory that yields a highest correlation between the time-series data of the unsprung displacement and the time-series data of the reference unsprung displacement as this allows for multiple ways for the wheel to be guided throughout the terrain allowing for a variety of system responses to happen where only one get selected to meet the current need of the occupants and the reference unsprung displacement instilled into the method.
Therefor it would have been obvious to one of ordinary skill in the art before the effective filing date to include setting a plurality of imaginary trajectories of the wheel around the vehicle; acquiring the time-series data of the reference unsprung displacement along each of the plurality of imaginary trajectories from the unsprung displacement map; selecting, from the plurality of imaginary trajectories, one imaginary trajectory that yields a highest correlation between the time-series data of the unsprung displacement and the time-series data of the reference unsprung displacement as this allows for more possibilities of the wheel going over the detected rough patch to handle the rough patch and use data from the same vehicle or other vehicles that have previously travelled along the road to be able to handle the rough patch with confidence that others before have done it with similar vertical motion unsprung displacements being instilled in the method.
Regarding Claim 5 Anderson-Giovanardi-- Alleva teaches The vehicle position estimation method according to claim 1, as mapped above. (Anderson discloses the following limitation:) wherein the unsprung displacement is calculated based on sensor-based information obtained by a sensor installed on the vehicle, (Pg. 13 – [0037] – “As the vehicle traverses terrain, road condition data 15-204 is acquired such as vertical accelerometer data,” (equates to wherein the unsprung displacement is calculated based on sensor-based information obtained by a sensor installed on the vehicle as the quote shows the unsprung displacement of the vehicle’s vertical motion is captured by an accelerometer installed in the vehicle.)) the acquiring the time-series data of the unsprung displacement while the vehicle is traveling includes a first filtering process that applies a first filter to time-series data of the sensor-based information or the unsprung displacement, (Pg. 12 – [0036] – “In some embodiments this metric may comprise a measure of vertical acceleration on the chassis of the vehicle. In one embodiment, vertical acceleration on the vehicle chassis or in the passenger compartment may be band-pass filtered to cut out frequencies significantly below body frequency and frequencies significantly above wheel frequency” & See Also Pg. 5 – [n0021] – “In FIG. 15-7, a vehicle state estimator 15-700 determines a vehicle's kinematic state based on a number of sensors such as accelerometers, steering angle, vehicle velocity (wheel speed sensors, GPS, etc.). This functional unit calculates how the vehicle is moving across the terrain, and outputs a change in (x, y, z) coordinates for each time step.” (equates to the acquiring the time-series data of the unsprung displacement while the vehicle is traveling includes a first filtering process that applies a first filter to time-series data of the sensor-based information or the unsprung displacement as the unsprung displacement is collected and subsequently filters out extraneous values and the second quote shows how the data collected is done at each time step thus making it time series type data.)) unsprung displacement in the unsprung displacement map also is calculated (Pg. 13 – [0037] –“A driving plan 15-200 is calculated based on a route planning algorithm and using stored maps 15-202. As the vehicle traverses terrain, road condition data 15-204 is acquired such as vertical accelerometer data, road surface information from a forward-looking vision system, data from a stored topographical map, GPS-indexed data, data from other vehicles, and a measure of at least one state variable from an electronic suspension system (such as accelerometer, velocity, and position data from each actuator or semi-active damper).” (equates to the unsprung displacement in the unsprung displacement map also is calculated as the vertical motion unsprung displacement is calculated from the electronic suspension which is in the unsprung displacement map as the unsprung displacement and vehicle location are a part of the same data that get fused for the absolute position measurement. ))
Yet Anderson fails to teach and the unsprung displacement also is calculated through the first filtering process using the first filter.
Giovanardi teaches a similar vehicle position estimation method (abstract). Giovanardi teaches the unsprung displacement also is calculated through the first filtering process using the first filter. (Pg. 28 – [0081] – “FIG. 9 shows an exemplary results of both a causal filter and a zero-phase filter on a given input signal” & See Also Pg. 10 – Fig. 9 & see Also Pg. 27 – [0068] – “In some embodiments, inputs 154 to the processor 150 may include sensor inputs and/or inputs from various systems of a vehicle which may include, a velocimeter output of a vehicle, a velocity sensor, shaft encoders, steering inputs, braking inputs, and/or any other appropriate type of input from a sensor or system included in a vehicle” & See Also Pg. 26 – [0052] – “For example, where the component to be controlled is a suspension, an objective related to operation of the suspension or vehicle may be limiting the number and/or magnitude (and/or other suitable characteristics) of vertical… Accordingly, in some embodiments in which a suspension is controlled based on applying filters with different frequencies to information regarding… suspension” (equates to the unsprung displacement also is calculated through the first filtering process using the first filter as the filtering can be done to any given input signal in this art and the following quotes show how vertical motion and thus the unsprung displacement is calculated to be controlled in this art and thus the unsprung displacement can be attained and calculated through filtering.) ). It would have been an advantageous addition to the system disclosed by Anderson to include the unsprung displacement also is calculated through the first filtering process using the first filter as this allows for the vertical motion noise to be filtered out of the system and the data used to control the unsprung displacement of the system can be based on frequencies that are plausible and meant to use for the control aspect.
Therefor it would have been obvious to one of ordinary skill in the art before the effective filing date to include the unsprung displacement also is calculated through the first filtering process using the first filter as this ensures the unsprung displacement we are modulating the system with is based on solid data that doesn’t include any noise in the calculation leading to smoother control and overall better passenger comfort.
Regarding Claim 6 Anderson-Giovanardi- Alleva teaches The vehicle position estimation method according to claim 1, as mapped above. (Anderson discloses the following limitations:) wherein the unsprung displacement is calculated based on sensor-based information obtained by a sensor installed on the vehicle, (Pg. 12 – [0036] – “In some embodiments this metric may comprise a measure of vertical acceleration on the chassis of the vehicle.” & See Also Pg. 12 – [0032] – “Such data may comprise accelerometer data representing wheel or body movement,” (equates to wherein the unsprung displacement is calculated based on sensor-based information obtained by a sensor installed on the vehicle as the quote shows the unsprung displacement of the vehicle’s vertical motion is captured by an accelerometer sensor installed in the vehicle.)) the acquiring the time-series data of the unsprung displacement while the vehicle is traveling includes a first filtering process that applies a first filter to time-series data of the sensor-based information or the unsprung displacement, (Pg. 12 – [0036] – “In some embodiments this metric may comprise a measure of vertical acceleration on the chassis of the vehicle. In one embodiment, vertical acceleration on the vehicle chassis or in the passenger compartment may be band-pass filtered” & See Also Pg. 13 – [0037] – “For the a posteriori embodiment, the system operates in real time while executing (i.e. driving) the driving plan 15-200. A driving plan 15-200 is calculated based on a route planning algorithm and using stored maps 15-202. As the vehicle traverses terrain, road condition data 15-204 is acquired such as vertical accelerometer data” (equates to the acquiring the time-series data of the unsprung displacement while the vehicle is traveling includes a first filtering process that applies a first filter to time-series data of the sensor-based information or the unsprung displacement as the unsprung displacement is the vertical acceleration of the body and this sensor reading is put through a band pass filter and the second quote shows how this can happen in real time and thus the unsprung displacement being filtered is based on time series data.)). the estimating the vehicle position uses the time-series data of the reference unsprung displacement after the first filtering process. (Pg. 9 – [0007] – “A sensor fusion system such as a Kalman Filter may combine the location data and relative data to obtain an accurate estimate of absolute position. For example, a sensor fusion system may bias the location sensor over the long term, but bias the relative sensor over the short term. Similarly, the sensor fusion system may eliminate extraneous points (for example, ignore a GPS coordinate reading if it has moved significantly farther than the vehicle could have moved given the current speed sensor reading)” & See Also Pg. 12 – [0036] – “For the embodiment with an advanced route planning correction, the a priori driving plan 15-200 is calculated based on a route planning algorithm such as an A* algorithm or any other suitable route planning method. This is then compared to road condition data 15-204 that has been stored from previous driving data, from other vehicles, or from a database. The road condition data is processed or has already been processed and stored to include a road roughness impact 15-206 metric. In some embodiments this metric may comprise a measure of vertical acceleration on the chassis of the vehicle” (equates to estimating the vehicle position uses the time-series data of the reference unsprung displacement after the first filtering process as the reference unsprung displacement is the vertical acceleration of this art and this is factored into the road condition data which is used to determine the route for the vehicle and thus used for the position attaining as seen in quote 1 where the filtering is done after the necessary data is collected and fused.) )
Yet Anderson fails to teach the unsprung displacement in the unsprung displacement map is calculated by applying a zero-phase filter to time-series data of the sensor-based information or the unsprung displacement, the acquiring the reference unsprung displacement includes performing the first filtering process with respect to the time-series data of the reference unsprung displacement acquired from the unsprung displacement map.
Giovanardi teaches a similar vehicle position estimation method (abstract). Giovanardi teaches the unsprung displacement in the unsprung displacement map is calculated by applying a zero-phase filter to time-series data of the sensor-based information or the unsprung displacement, (Pg. 28 – [0081] – “FIG. 9 shows an exemplary results of both a causal filter and a zero-phase filter on a given input signal” & See Also Pg. 10 – Fig. 9 & see Also Pg. 27 – [0068] – “In some embodiments, inputs 154 to the processor 150 may include sensor inputs and/or inputs from various systems of a vehicle which may include, a velocimeter output of a vehicle, a velocity sensor, shaft encoders, steering inputs, braking inputs, and/or any other appropriate type of input from a sensor or system included in a vehicle” & See Also Pg. 26 – [0052] – “For example, where the component to be controlled is a suspension, an objective related to operation of the suspension or vehicle may be limiting the number and/or magnitude (and/or other suitable characteristics) of vertical… Accordingly, in some embodiments in which a suspension is controlled based on applying filters with different frequencies to information regarding… suspension” (equates to the unsprung displacement in the unsprung displacement map is calculated by applying a zero-phase filter to time-series data of the sensor-based information or the unsprung displacement as the zero phase filtering can be done to any given input signal in this art and the following quotes show how vertical motion and thus the unsprung displacement is controlled in this art and thus the unsprung displacement can be attained and put through the zero-phase filtering.) ) the acquiring the reference unsprung displacement includes performing the first filtering process with respect to the time-series data of the reference unsprung displacement acquired from the unsprung displacement map; (Pg. 28 – [0081] – “FIG. 9 shows an exemplary results of both a causal filter and a zero-phase filter on a given input signal” & See Also Pg. 10 – Fig. 9 & see Also Pg. 27 – [0068] – “In some embodiments, inputs 154 to the processor 150 may include sensor inputs and/or inputs from various systems of a vehicle which may include, a velocimeter output of a vehicle, a velocity sensor, shaft encoders, steering inputs, braking inputs, and/or any other appropriate type of input from a sensor or system included in a vehicle” & See Also Pg. 26 – [0052] – “For example, where the component to be controlled is a suspension, an objective related to operation of the suspension or vehicle may be limiting the number and/or magnitude (and/or other suitable characteristics) of vertical… Accordingly, in some embodiments in which a suspension is controlled based on applying filters with different frequencies to information regarding… suspension” & See Also Pg. 31 – [0105] – “Accordingly, the process described above may be calculated as a function of speed…” (equates to the acquiring the reference unsprung displacement includes performing the first filtering process with respect to the time-series data of the reference unsprung displacement acquired from the unsprung displacement map as the las quote shows the trajectory is calculated on a function of speed where the trajectory is about modulating a vertical motion unsprung displacement and thus the unsprung displacement is a reference unsprung displacement and the first quote and figure show how any input value can be put through the filtering process that is time series data.) ). It would have been an advantageous addition to the method disclosed by Anderson to include the unsprung displacement in the unsprung displacement map is calculated by applying a zero-phase filter to time-series data of the sensor-based information or the unsprung displacement and the acquiring the reference unsprung displacement includes performing the first filtering process with respect to the time-series data of the reference unsprung displacement acquired from the unsprung displacement map as the zero phase filtering can be done to track the input and thus no delay time is required for the filtered values to come back and benefit the system thus improving the response time. And it would have been advantageous to filter the reference unsprung displacement as the it based on the time series data and thus the reference unsprung displacement can be updated to match real time and noise can’t be included in the value the actual unsprung displacement gets compared to thus leading to better control over the vertical motion.
Therefor it would have been obvious to one of ordinary skill in the art before the effective filing date to include the unsprung displacement in the unsprung displacement map is calculated by applying a zero-phase filter to time-series data of the sensor-based information or the unsprung displacement and the acquiring the reference unsprung displacement includes performing the first filtering process with respect to the time-series data of the reference unsprung displacement acquired from the unsprung displacement map as better tracking and thus control of the unsprung displacement can happen with the zero phase filter, and the reference unsprung displacement being filtered allows for better comparison to be had with the unsprung displacement ensuring the value is accurate based on time and not being compared to any noise in the signal that may exist.
Regarding Claim 7 Anderson-Giovanardi- Alleva teaches (Anderson discloses the following limitations:)The vehicle position estimation method according to claim 1, wherein the unsprung displacement map indicates a correspondence relationship between the unsprung displacement, the position, and a vehicle speed, (Pg. 11 – [ 0027] – “the vehicle has at least one relative position sensor 15-104 such as an IMU, accelerometers, steering angle, vehicle speed, and/or other suitable sensors onboard. A sensor fusion system 15-106 processes the absolute position data using the relative position data to determine an accurate estimate of current location” & See Also Pg. 12 – [0036] – “In some embodiments this metric may comprise a measure of vertical acceleration on the chassis of the vehicle.” (equates to wherein the unsprung displacement map indicates a correspondence relationship between the unsprung displacement, the position, and a vehicle speed as the unsprung displacement map in this case shows a relation between the vehicle speed, vertical acceleration (as the acceleration from the first quote is the vertical acceleration from the second quote, and the position is calculated based on the fused sensor data thus a unsprung displacement map between the three claimed variables is seen in this prior art.))
Yet both Anderson fails to teach and the acquiring the reference unsprung displacement includes acquiring the reference unsprung displacement according to the vehicle speed of the vehicle from the unsprung displacement map.
Giovanardi teaches a similar vehicle position estimation method (abstract). Giovanardi teaches the acquiring the reference unsprung displacement includes acquiring the reference unsprung displacement according to the vehicle speed of the vehicle from the unsprung displacement map. (Pg. 31 - [0104] - "trajectory may be set by improving the performance of the system within the constraints, taking into account the upcoming disturbance. For example, in some embodiments of a suspension system, a third derivative of the vehicle superstructure's motion, jerk, may be a metric related to the comfort of the occupants. A controller may filter an upcoming road disturbance at variable filter frequencies to reduce or substantially minimize jerk while satisfying system constraints" & See Also Pg. 31 - [0105] - "In some embodiments, the improved trajectory may be time-dependent, since the dynamics of the structure being controlled may have an impact on the estimation of the constraints. For example, for a road vehicle, when the vehicle travels at low forward speed, even large road inputs may feel relatively smooth and the vehicle may easily traverse the large road inputs without exceeding suspension travel limits. When traversing the same road at a higher speed, the dynamics of the vehicle may cause the vehicle to exceed travel limits even though at lower speed this was not a concern. Accordingly, the process described above may be calculated as a function of speed, " (equates to acquiring the reference unsprung displacement includes acquiring the reference unsprung displacement according to the vehicle speed of the vehicle from the unsprung displacement map as the vertical motion reference unsprung displacement is tracked in the first quote to ensure maximal ride comfort for the passenger and the second quote shows how the speed of the vehicle plays a factor in determining how to actuate the system against the disturbances and thus the reference unsprung displacement changes as a function of speed to ensure the vertical motion unsprung displacement is optimal for passenger comfort.)). It would have been an advantageous addition to the method disclosed by Anderson to include the acquiring the reference unsprung displacement includes acquiring the reference unsprung displacement according to the vehicle speed of the vehicle from the unsprung displacement map as this allows for multiple speed types to be able to dictate the vehicle response when facing rough patches in the road. The vehicle’s response and thus spring response should and will differ based on the speed the vehicle is traveling over the said patches thus speed must be incorporated for maximal passenger comfort.
Therefor it would have been obvious to one of ordinary skill in the art before the effective filing date to include the acquiring the reference unsprung displacement includes acquiring the reference unsprung displacement according to the vehicle speed of the vehicle from the unsprung displacement map as this allows for the best passenger experience as the unsprung displacement that is acquired will be used to actuate the vehicle suspension and the seed at which the vehicle travels will dictate the need or lack thereof to actuate the suspension accordingly.
Regarding Claim 8 Anderson-Giovanardi- Alleva teaches (Anderson discloses the following limitations:) The vehicle position estimation method according to claim 7, wherein the unsprung displacement map indicates the correspondence relationship between the unsprung displacement and the position (Pg. 13 – [0037] – “A driving plan 15-200 is calculated based on a route planning algorithm and using stored maps 15-202. As the vehicle traverses terrain, road condition data 15-204 is acquired such as vertical accelerometer data, road surface information from a forward-looking vision system, data from a stored topographical map, GPS-indexed data, data from other vehicles, and a measure of at least one state variable from an electronic suspension system (such as accelerometer, velocity, and position data from each actuator or semi-active damper).” (equates to acquiring the unsprung displacement around the vehicle, as a reference unsprung displacement, from a unsprung displacement map indicating a correspondence relationship between the unsprung displacement and a position as the unsprung displacement of vertical acceleration is calculated as seen from the above quote corresponding to the unsprung displacement map between the unsprung displacement and position as the GPS indexed data is collected while the vehicle is traveling along the route.)) and the acquiring the reference unsprung displacement includes: selecting the unsprung displacement map (Pg. 9 – [0007] – “sensor fusion system such as a Kalman Filter may combine the location data and relative data to obtain an accurate estimate of absolute position.” (equates to selecting the unsprung displacement map as the unsprung displacement map contains the relative data and it is previously been mapped as having a vertical motion component, speed, and the position of the vehicle is found within.))
Yet both Anderson-Giovanardi fail to teach for each vehicle speed range, and the acquiring the reference unsprung displacement includes: for the vehicle speed range to which the vehicle speed of the vehicle belongs; and acquiring the reference unsprung displacement from the selected unsprung displacement map.
Alleva teaches a similar vehicle position estimation method (abstract.) Alleva teaches for each vehicle speed range (Pg. 3 – Fig. 4 – (equates to for each vehicle speed range as the figure shows roughness index associated with vehicle speeds.)) and the acquiring the reference unsprung displacement includes: for the vehicle speed range to which the vehicle speed of the vehicle belongs; (Pg. 3 – Fig. 4 & See Also Pg. 9 – [0063] – “first vehicle geo-referencing data of the measured first vertical acceleration values (i.e., data indicative of two/three-dimensional (2D/3D) positions over time corresponding to the measured first vertical acceleration values-e.g., positions provided by Global Navigation Satellite System (GNSS) receivers, such as Global Positioning System (GPS) positions), and first vehicle speed data indicative of the given constant speed (s) associated with the measured first vertical acceleration values;” (equates to the acquiring the reference unsprung displacement includes: for the vehicle speed range to which the vehicle speed of the vehicle belongs as the first figure shows the speed ranges to roughness indices are calculated and the second quote show how the vertical motion or reference unsprung displacement is calculated based on position, and vehicle speed..)) and acquiring the reference unsprung displacement from the selected unsprung displacement map. (Pg. 9 – [0083] – “Conveniently, the preliminary step 10 further comprises selecting vehicle "good" passages on the road segments at given speed range,” & See Also Pg. 9 – [0063] – “first vehicle geo-referencing data of the measured first vertical acceleration values (i.e., data indicative of two/three-dimensional (2D/3D) positions over time corresponding to the measured first vertical acceleration values-e.g., positions provided by Global Navigation Satellite System (GNSS) receivers, such as Global Positioning System (GPS) positions), and first vehicle speed data indicative of the given constant speed (s) associated with the measured first vertical acceleration values;” (equates to and acquiring the reference unsprung displacement from the selected unsprung displacement map as the reference unsprung displacement is based on the vehicle speed, a vehicle position and vertical motion and the unsprung displacement map is in the selected speed range as seen from the first quote.)). It would have been an advantageous addition to the method disclosed by Anderson-Giovanardi to include for each vehicle speed range, and the acquiring the reference unsprung displacement includes: for the vehicle speed range to which the vehicle speed of the vehicle belongs; and acquiring the reference unsprung displacement from the selected unsprung displacement map as this ensures that the vehicle has a range of speeds over which the reference unsprung displacements are collected ensuring the value we are modulating the unsprung displacement with is suitable to the driving experience and conditions experienced from the varied vehicle speeds, considering the different behaviors of the vehicle at different speeds the reference unsprung displacements should consider the vehicle speed in which it is traveling at to accurately damp out any road roughness experienced.
Therefor it would have been obvious to one of ordinary skill in the art before the effective filing date to include for each vehicle speed range, and the acquiring the reference unsprung displacement includes: for the vehicle speed range to which the vehicle speed of the vehicle belongs; and acquiring the reference unsprung displacement from the selected unsprung displacement map as this ensures the reference unsprung displacement is based on speed value similar to current travelling conditions of the vehicle and thus the vertical motion actuation can better account for the actual vehicle motion and thus the passenger comfort can be improved.
Regarding Claim 9 Anderson-Giovanardi- Alleva teaches (Anderson Discloses the following limitations:) The vehicle position estimation method according to claim 1, and the estimating the vehicle position (Pg. 11 - [0027] - "in this embodiment, the topographical map 15-100 is indexed by the current position. This map may start as populated, unpopulated, or partially populated. In order to use a high resolution topographical map, the vehicle needs an accurate method of localizing with respect to the map" (equates to estimating the vehicle position as the topographical map is unpopulated and the vehicle then localizing itself within the map to populate the map as well as understand its surroundings.))
Yet Anderson fails to teach includes making the comparison between the time-series data of the unsprung displacement and the time-series data of the reference unsprung displacement in a common effective frequency range in which the effective frequency range of the unsprung displacement and the effective frequency range of the reference unsprung displacement overlap each other.
Giovanardi teaches a similar vehicle position estimation method (abstract). Giovanardi teaches wherein an effective frequency range of the unsprung displacement is inversely proportional to a vehicle speed, (Pg. 29 – [0087] – “According to the exemplary embodiment, the preview time may then be identified to be at least as long as a multiple of the period corresponding to the inverse of the lowest of the selected vibration mode frequencies.” & See Also Pg. 31 – [0105] – “In some embodiments, the improved trajectory may be time-dependent, since the dynamics of the structure being controlled may have an impact on the estimation of the constraints. For example, for a road vehicle, when the vehicle travels at low forward speed, even large road inputs may feel relatively smooth and the vehicle may easily traverse the large road inputs without exceeding suspension travel limits” (equates to an effective frequency range of the unsprung displacement is inversely proportional to a vehicle speed as the speed used for the improved trajectory is time dependent and the inverse of the frequency is mapped to the period thus the time dependent speed is inversely proportional to the frequency.)) includes making the comparison between the time-series data of the unsprung displacement and the time-series data of the reference unsprung displacement in a common effective frequency range in which the effective frequency range of the unsprung displacement and the effective frequency range of the reference unsprung displacement overlap each other. ( Pg. 24 - [0006] - "…the at least one component comprises a suspension of the automobile and the at least one physical constraint of the system comprises a travel limit of the suspension" & See Also Pg. 34 - [0141] -" The process flow 600 may be performed by a controller of the system… . At step 602, for frequency content identified by the controller to be below a threshold frequency, the controller controls the at least one component of the system to track the frequency content below the threshold frequency…both steps 602 and 604 are performed for detected frequencies in both ranges" & See Also Pg. 31 - [0109] - "In the embodiment of FIG. 13, the feedback loop is similar to the feedback loop described in FIG. 8. The proactive control calculation block shown on the left calculates two outputs. First, it calculates an actuator command that is sized such that it creates a desired performance in terms of the response of the plant to the disturbance. As a second output, the resulting expected sensor signal is calculated, and supplied to the controller as a reference command" (equates to includes making the comparison between the time-series data of the unsprung displacement and the time-series data of the reference unsprung displacement in a common effective frequency range in which the effective frequency range of the unsprung displacement and the effective frequency range of the reference unsprung displacement overlap each other as the last quote show how the measured component of the vertical motion is compared to a reference by means of the proactive controller. Quote 2 shows the method of quote 2 is done with any controller of this art, including the proactive controller, and the quote 1 shows that the component being the vertical motion wherein quote 2 further compares the frequency range of the measured component within a frequency range, wherein the reference unsprung displacement would be within the same frequency range to ensure the proper error value is being read by the set point of the feedback loop.)). It would have been an advantageous addition to the system disclosed by Anderson to include wherein an effective frequency range of the unsprung displacement is inversely proportional to a vehicle speed as this ensures effective sampling is happening based on the vehicle speed, and making the comparison between the time-series data of the unsprung displacement and the time-series data of the reference unsprung displacement in a common effective frequency range in which the effective frequency range of the unsprung displacement and the effective frequency range of the reference unsprung displacement overlap each other as this ensures the data being supplied to the system is happening from a similar situation to that being taken and when taking the position the reference data needs to match the measured to ensure an accurate vehicular position is being taken.
Therefor it is obvious to one of ordinary skill in the art to include teach wherein an effective frequency range of the unsprung displacement is inversely proportional to a vehicle speed as this allows the unsprung displacement data to accurately reflect the driving conditions and thus reflect how to actuate the suspension based on the driving experience, and wherein an effective frequency range of the unsprung displacement is inversely proportional to a vehicle speed, includes making the comparison between the time-series data of the unsprung displacement and the time-series data of the reference unsprung displacement in a common effective frequency range in which the effective frequency range of the unsprung displacement and the effective frequency range of the reference unsprung displacement overlap each other
Regarding Claim 10 Anderson teaches A vehicle position estimation method comprising: and performing preview control, (Pg. 11 – [0022] – “Some aspects relate to vehicle systems that utilize topographical maps of the road surface. Such maps include positional information as well as road surface information such as road height. These maps may be highly granular in detail, showing individual road imperfections, bumps, potholes, and the like. These maps may be generated by a variety of means, including vision camera sensors, LID AR, radar, and other planar or three-dimensional scanning sensors, and the like. The maps may also be generated by sensor information post-encounter, such as the front suspension actuators determining information about the road as they traverse terrain. These topographical maps may also be communicated from vehicle to vehicle over a network, or may be downloaded from servers in communication with the vehicle such as over a cellular network. The topographical maps may be used for a variety of control purposes, such as: adapting driving behavior ( changing speed such as slowing down on a rough road;” (equates to and performing preview control as the quote shows the use of topographical maps to control the vehicles speed in response to a rough rod being detected ahead and thus the car is preemptively controlled to alleviate the roughness of the ride) ) acquiring reference unsprung displacement around the vehicle, from a unsprung displacement map indicating a correspondence relationship between the unsprung displacement and a position; (Pg. 13 – [0037] – “A driving plan 15-200 is calculated based on a route planning algorithm and using stored maps 15-202. As the vehicle traverses terrain, road condition data 15-204 is acquired such as vertical accelerometer data, road surface information from a forward-looking vision system, data from a stored topographical map, GPS-indexed data, data from other vehicles, and a measure of at least one state variable from an electronic suspension system (such as accelerometer, velocity, and position data from each actuator or semi-active damper).” (equates to acquiring the unsprung displacement around the vehicle, as a reference parameter, from a unsprung displacement map indicating a correspondence relationship between the unsprung displacement and a position as the unsprung displacement of vertical acceleration is calculated as seen from the above quote corresponding to the unsprung displacement map between the unsprung displacement and position as the GPS indexed data is collected while the vehicle is traveling along the route.)) estimating a vehicle position of the vehicle by estimating a position of the actual trajectory ((Pg. 15 – [0061] – “In FIG. 15-7, a vehicle state estimator 15-700 determines a vehicle's kinematic state based on a number of sensors such as accelerometers, steering angle, vehicle velocity (wheel speed sensors, GPS, etc.). This functional unit calculates how the vehicle is moving across the terrain, and outputs a change in (x, y, z) coordinates for each time step.” (equates to estimating a vehicle position of the vehicle by estimating a position of the actual trajectory as the quote shows the estimation of the vehicle position via a gps installed into the vehicle and does the position collection over the actual trajectory as seen by the vehicle moving across the terrain at each time step.) )) wherein the unsprung displacement is calculated based on sensor-based information obtained by a sensor installed on the vehicle, (Pg. 12 – [0036] – “In some embodiments this metric may comprise a measure of vertical acceleration on the chassis of the vehicle.” & See Also Pg. 12 – [0032] – “Such data may comprise accelerometer data representing wheel or body movement,” (equates to wherein the unsprung displacement is calculated based on sensor-based information obtained by a sensor installed on the vehicle as the quote shows the unsprung displacement of the vehicle’s vertical motion is captured by an accelerometer sensor installed in the vehicle.)), the acquiring the time-series data of the unsprung displacement while the vehicle is traveling includes a first filtering process that applies a first filter to time-series data of the sensor-based information or the unsprung displacement, (Pg. 12 – [0036] – “In some embodiments this metric may comprise a measure of vertical acceleration on the chassis of the vehicle. In one embodiment, vertical acceleration on the vehicle chassis or in the passenger compartment may be band-pass filtered” & See Also Pg. 13 – [0037] – “For the a posteriori embodiment, the system operates in real time while executing (i.e. driving) the driving plan 15-200. A driving plan 15-200 is calculated based on a route planning algorithm and using stored maps 15-202. As the vehicle traverses terrain, road condition data 15-204 is acquired such as vertical accelerometer data” (equates to the acquiring the time-series data of the unsprung displacement while the vehicle is traveling includes a first filtering process that applies a first filter to time-series data of the sensor-based information or the unsprung displacement as the unsprung displacement is the vertical acceleration of the body and this sensor reading is put through a band pass filter and the second quote shows how this can happen in real time and thus the unsprung displacement being filtered is based on time series data.)) the unsprung displacement in the unsprung displacement map also is calculated (Pg. 13 – [0037] –“A driving plan 15-200 is calculated based on a route planning algorithm and using stored maps 15-202. As the vehicle traverses terrain, road condition data 15-204 is acquired such as vertical accelerometer data, road surface information from a forward-looking vision system, data from a stored topographical map, GPS-indexed data, data from other vehicles, and a measure of at least one state variable from an electronic suspension system (such as accelerometer, velocity, and position data from each actuator or semi-active damper).” (equates to the unsprung displacement in the unsprung displacement map also is calculated as the vertical motion unsprung displacement is calculated from the electronic suspension which is in the unsprung displacement map as the unsprung displacement and vehicle location are a part of the same data that get fused for the absolute position measurement. )) and the preview control comprises: determining a current position of the wheel of the vehicle; (Pg. 11 – [0022] – “Aspects also relate to plotting a trajectory of the vehicle and its elements ( e.g. individual wheels) across the topographical map” & See Also Pg. 11 – [0026] – “multiple relative maps about parts of the vehicle (for example, relative maps about each wheel), an absolute map comprising absolute positions (for example, GPS coordinates), or any other means of associated terrain height Z information or similar.” (equates to determining a current position of the wheel of the vehicle as the first quote shows a trajectory of the wheel being plotting and the second quote further demonstrates that the z position and thus the position of the wheel being attained. ) ) calculating an expected passage position of the wheel of the vehicle after a preview time; (Pg. 11 – [0029] – “The active suspension then calculates an incidence time to each point corresponding with each wheel of the vehicle for which an active suspension actuator is disposed” & See Also Pg. 11 – [0029] – “In one embodiment, the active suspension system receives data from the topological map and determines an incidence time and correction. In a simple implementation, a path may be calculated that represents a path through a plurality of points in the topographical map 15-100.” (equates to calculating an expected passage position of the wheel of the vehicle after a preview time as the first quote shows the incidence time or preview time calculation wherein each wheel will be position in reference to a detected disturbance and thus the passage point is calculated when the active suspension is disposed for each wheel.) ) determining the unsprung displacement of the wheel of the vehicle at the expected passage position based on the unsprung displacement map; (Pg. 11 – [0029] – “the active suspension system receives data from the topological map and determines an incidence time and correction. In a simple implementation, a path may be calculated that represents a path through a plurality of points in the topographical map 15-100. This path may be a function of current steering angle and speed, or be based on a planned route. The planned route may be a combination of GPS/maps route planning and any obstacle avoidance procedures being employed by the self driving vehicle to plan vehicle travel. The path may comprise of a single trajectory in a lower resolution map, of two paths, each representing a path of travel of the left and right sides of the vehicle respectively, or four paths, with each representing a path of travel of a wheel of the vehicle (in the case of a two axle vehicle)” (equates to determining the unsprung displacement of the wheel of the vehicle at the expected passage position based on the unsprung displacement map; as the quote shows a path wherein the topographical data is taken showing the bumps / disturbances the car will experience wherein the map may be made for each wheel at each expected passage position.) ) calculating a target control force of an actuator of a suspension based on the unsprung displacement of the wheel at the expected passage position; (Pg. 11 – [0029] – “The active suspension then calculates a correction, which comprises a force or position setting of the actuator at each wheel so as to mitigate impact of the event on the trajectory.” & See Also Pg. 11 – [0029] – “The active suspension then calculates an incidence time to each point corresponding with each wheel of the vehicle for which an active suspension actuator is disposed” & See Also Pg. 11 – [0029] – “In one embodiment, the active suspension system receives data from the topological map and determines an incidence time and correction. In a simple implementation, a path may be calculated that represents a path through a plurality of points in the topographical map 15-100.” (equates to calculating a target control force of an actuator of a suspension based on the unsprung displacement of the wheel at the expected passage position as the first quote shows the calculation of a force for an actuator controlling the suspension position based on a trajectory wherein the trajectory is shown to be the displacement t of the wheel wherein that displacement coincides with an incident time at a passage position. ) ) and controlling the actuator to generate the target control force at a timing when the wheel passes the expected passage position; (Pg. 11 – [0029] – “The active suspension then calculates an incidence time to each point corresponding with each wheel of the vehicle for which an active suspension actuator is disposed. The active suspension then calculates a correction, which comprises a force or position setting of the actuator at each wheel so as to mitigate impact of the event on the trajectory.” & See Also Pg. 11 – [0022] – “The topographical maps may be used for a variety of control purposes, such as: adapting driving behavior ( changing speed such as slowing down on a rough road; changing vehicle course such as choosing a less bumpy road to reach the destination, etc.); adapting active suspension system behavior ( controlling actuator force/position in a predictive manner in response to road perturbations ahead” (equates to controlling the actuator to generate the target control force at a timing when the wheel passes the expected passage position as the first quote shows the correction force generated in response to the ahead trajectory determined, and the second quote shows how specifically the suspension is controlled by inputting said force to the suspension.))
Yet Anderson fails to teach performing localization that estimates a vehicle position without using Global Navigation Satellite System; wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map; and the unsprung displacement also is calculated through the first filtering process using the first filter.
Giovanardi teaches a similar vehicle position estimation method (abstract). Giovanardi teaches performing localization that estimates a vehicle position without using Global Navigation Satellite System; (Pg. 27 - [0067] In some embodiments, localization system 152 may comprise GPS systems, terrain-based localization systems, and/or any other appropriate localization system capable of providing a location of a vehicle on a road surface to the processor 150.” (equates to performing localization that estimates a vehicle position without using Global Navigation Satellite System as the quote shows a localization system that may be one of terrain based localization type wherein this is known as not using GPS signal but rather onboard sensors to detect and understand the environment around the vehicle.))) the unsprung displacement also is calculated through the first filtering process using the first filter. (Pg. 28 – [0081] – “FIG. 9 shows an exemplary results of both a causal filter and a zero-phase filter on a given input signal” & See Also Pg. 10 – Fig. 9 & see Also Pg. 27 – [0068] – “In some embodiments, inputs 154 to the processor 150 may include sensor inputs and/or inputs from various systems of a vehicle which may include, a velocimeter output of a vehicle, a velocity sensor, shaft encoders, steering inputs, braking inputs, and/or any other appropriate type of input from a sensor or system included in a vehicle” & See Also Pg. 26 – [0052] – “For example, where the component to be controlled is a suspension, an objective related to operation of the suspension or vehicle may be limiting the number and/or magnitude (and/or other suitable characteristics) of vertical… Accordingly, in some embodiments in which a suspension is controlled based on applying filters with different frequencies to information regarding… suspension” (equates to the unsprung displacement also is calculated through the first filtering process using the first filter as the filtering can be done to any given input signal in this art and the following quotes show how vertical motion and thus the unsprung displacement is calculated to be controlled in this art and thus the unsprung displacement can be attained and calculated through filtering.) ).
Yet both fail to teach wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map;
Alleva teaches wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; (Pg. 2 – Fig. 2 & See Also Pg. 9 – [0063] – “first vehicle geo-referencing data of the measured first vertical acceleration values (i.e., data indicative of two/three-dimensional (2D/3D) positions over time corresponding to the measured first vertical acceleration values-e.g., positions provided by Global Navigation Satellite System (GNSS) receivers, such as Global Positioning System (GPS) positions), and first vehicle speed data indicative of the given constant speed (s) associated with the measured first vertical acceleration values;” & See Also Pg. 9 – [0067-0069]– “schematically illustrates an IRI estimation step ( denoted as a whole by 20) of an IRI estimation method according to a preferred embodiment of the present invention. [0068] In particular, the IRI estimation step 20 comprises: [0069] acquiring (block 21 in FIG. 2) second vehicle vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road/road segment” (equates to wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; as the unsprung displacement is calculated using the vehicle vertical velocity, acceleration and position and each is gathered as seen from the first quote, and the first figure and second quote is showing the generation of the time series data based on the vehicle actually traveling over the said trajectory.) ) trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map; (Pg. 10 – [0093] – “Additionally, FIG. 6 shows an example of comparison between real IRI values of a road or road segment, and IRI values estimated by carrying out the IRI estimation method according to the present invention” & See Also pg. 5 – Fig. 6 & See Also Pg. 9 – [0067-0069]– “schematically illustrates an IRI estimation step ( denoted as a whole by 20) of an IRI estimation method according to a preferred embodiment of the present invention. [0068] In particular, the IRI estimation step 20 comprises: [0069] acquiring (block 21 in FIG. 2) second vehicle vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road/road segment” & See Also Pg. 2 – Fig. 1 – 11 – “collecting vehicle vertical accelerations and vehicle geo-referencing and speed data”(equates to trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map as the figure shows the comparison between the measured rough roughness index and historical chart and thus an actual trajectory and its associated road roughness is calculated and then subsequently compared to real road roughness index value. The last quote showing how the road roughness being calculated is equivalent to the calculated unsprung displacement of this art wherein vertical acceleration, position and speed are calculated for determination of rough roughness. ) ) It would have been an advantageous addition to the method disclosed by Anderson-Giovanardi to include wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map ensuring the road and trajectory travelled upon by the vehicle is the road being considered for data collection and subsequently allowing for comparison between the actual displacement data of the vehicle and the historical displacement data of the vehicle over the same road.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map as these limitations allow for the displacement data to be compared between the road the vehicle is currently driving upon and the data gathered of the same road and allows for an accurate position updating to happen based on the displacement being experienced.
Regarding Claim 11 Anderson teaches A vehicle position estimation method comprising: and performing preview control, (Pg. 11 – [0022] – “Some aspects relate to vehicle systems that utilize topographical maps of the road surface. Such maps include positional information as well as road surface information such as road height. These maps may be highly granular in detail, showing individual road imperfections, bumps, potholes, and the like. These maps may be generated by a variety of means, including vision camera sensors, LID AR, radar, and other planar or three-dimensional scanning sensors, and the like. The maps may also be generated by sensor information post-encounter, such as the front suspension actuators determining information about the road as they traverse terrain. These topographical maps may also be communicated from vehicle to vehicle over a network, or may be downloaded from servers in communication with the vehicle such as over a cellular network. The topographical maps may be used for a variety of control purposes, such as: adapting driving behavior ( changing speed such as slowing down on a rough road;” (equates to and performing preview control as the quote shows the use of topographical maps to control the vehicles speed in response to a rough rod being detected ahead and thus the car is preemptively controlled to alleviate the roughness of the ride) ) acquiring the reference unsprung displacement around the vehicle, from a unsprung displacement map indicating a correspondence relationship between the unsprung displacement and a position; (Pg. 13 – [0037] – “A driving plan 15-200 is calculated based on a route planning algorithm and using stored maps 15-202. As the vehicle traverses terrain, road condition data 15-204 is acquired such as vertical accelerometer data, road surface information from a forward-looking vision system, data from a stored topographical map, GPS-indexed data, data from other vehicles, and a measure of at least one state variable from an electronic suspension system (such as accelerometer, velocity, and position data from each actuator or semi-active damper).” (equates to acquiring the unsprung displacement around the vehicle, as a parameter, from a unsprung displacement map indicating a correspondence relationship between the unsprung displacement and a position as the unsprung displacement of vertical acceleration is calculated as seen from the above quote corresponding to the unsprung displacement map between the unsprung displacement and position as the GPS indexed data is collected while the vehicle is traveling along the route.)) estimating a vehicle position of the vehicle by estimating a position of the actual trajectory ((Pg. 15 – [0061] – “In FIG. 15-7, a vehicle state estimator 15-700 determines a vehicle's kinematic state based on a number of sensors such as accelerometers, steering angle, vehicle velocity (wheel speed sensors, GPS, etc.). This functional unit calculates how the vehicle is moving across the terrain, and outputs a change in (x, y, z) coordinates for each time step.” (equates to estimating a vehicle position of the vehicle by estimating a position of the actual trajectory as the quote shows the estimation of the vehicle position via a gps installed into the vehicle and does the position collection over the actual trajectory as seen by the vehicle moving across the terrain at each time step.) )) wherein the unsprung displacement map indicates a correspondence relationship between the unsprung displacement, the position, and a vehicle speed, (Pg. 11 – [ 0027] – “the vehicle has at least one relative position sensor 15-104 such as an IMU, accelerometers, steering angle, vehicle speed, and/or other suitable sensors onboard. A sensor fusion system 15-106 processes the absolute position data using the relative position data to determine an accurate estimate of current location” & See Also Pg. 12 – [0036] – “In some embodiments this metric may comprise a measure of vertical acceleration on the chassis of the vehicle.” (equates to wherein the unsprung displacement map indicates a correspondence relationship between the unsprung displacement, the position, and a vehicle speed as the unsprung displacement map in this case shows a relation between the vehicle speed, vertical acceleration (as the acceleration from the first quote is the vertical acceleration from the second quote, and the position is calculated based on the fused sensor data thus a unsprung displacement map between the three claimed variables is seen in this prior art.)) and the preview control comprises: determining a current position of the wheel of the vehicle; (Pg. 11 – [0022] – “Aspects also relate to plotting a trajectory of the vehicle and its elements ( e.g. individual wheels) across the topographical map” & See Also Pg. 11 – [0026] – “multiple relative maps about parts of the vehicle (for example, relative maps about each wheel), an absolute map comprising absolute positions (for example, GPS coordinates), or any other means of associated terrain height Z information or similar.” (equates to determining a current position of the wheel of the vehicle as the first quote shows a trajectory of the wheel being plotting and the second quote further demonstrates that the z position and thus the position of the wheel being attained. ) ) calculating an expected passage position of the wheel of the vehicle after a preview time; (Pg. 11 – [0029] – “The active suspension then calculates an incidence time to each point corresponding with each wheel of the vehicle for which an active suspension actuator is disposed” & See Also Pg. 11 – [0029] – “In one embodiment, the active suspension system receives data from the topological map and determines an incidence time and correction. In a simple implementation, a path may be calculated that represents a path through a plurality of points in the topographical map 15-100.” (equates to calculating an expected passage position of the wheel of the vehicle after a preview time as the first quote shows the incidence time or preview time calculation wherein each wheel will be position in reference to a detected disturbance and thus the passage point is calculated when the active suspension is disposed for each wheel.) ) determining the unsprung displacement of the wheel of the vehicle at the expected passage position; (Pg. 11 – [0029] – “the active suspension system receives data from the topological map and determines an incidence time and correction. In a simple implementation, a path may be calculated that represents a path through a plurality of points in the topographical map 15-100. This path may be a function of current steering angle and speed, or be based on a planned route. The planned route may be a combination of GPS/maps route planning and any obstacle avoidance procedures being employed by the self driving vehicle to plan vehicle travel. The path may comprise of a single trajectory in a lower resolution map, of two paths, each representing a path of travel of the left and right sides of the vehicle respectively, or four paths, with each representing a path of travel of a wheel of the vehicle (in the case of a two axle vehicle)” (equates to determining the unsprung displacement of the wheel of the vehicle at the expected passage position as the quote shows a path wherein the topographical data is taken showing the bumps / disturbances the car will experience wherein the map may be made for each wheel at each expected passage position.) ) calculating a target control force of an actuator of a suspension based on the unsprung displacement of the wheel at the expected passage position; (Pg. 11 – [0029] – “The active suspension then calculates a correction, which comprises a force or position setting of the actuator at each wheel so as to mitigate impact of the event on the trajectory.” & See Also Pg. 11 – [0029] – “The active suspension then calculates an incidence time to each point corresponding with each wheel of the vehicle for which an active suspension actuator is disposed” & See Also Pg. 11 – [0029] – “In one embodiment, the active suspension system receives data from the topological map and determines an incidence time and correction. In a simple implementation, a path may be calculated that represents a path through a plurality of points in the topographical map 15-100.” (equates to calculating a target control force of an actuator of a suspension based on the unsprung displacement of the wheel at the expected passage position as the first quote shows the calculation of a force for an actuator controlling the suspension position based on a trajectory wherein the trajectory is shown to be the displacement t of the wheel wherein that displacement coincides with an incident time at a passage position. ) ) controlling the actuator to generate the target control force at a timing when the wheel passes the expected passage position; (Pg. 11 – [0029] – “The active suspension then calculates an incidence time to each point corresponding with each wheel of the vehicle for which an active suspension actuator is disposed. The active suspension then calculates a correction, which comprises a force or position setting of the actuator at each wheel so as to mitigate impact of the event on the trajectory.” & See Also Pg. 11 – [0022] – “The topographical maps may be used for a variety of control purposes, such as: adapting driving behavior ( changing speed such as slowing down on a rough road; changing vehicle course such as choosing a less bumpy road to reach the destination, etc.); adapting active suspension system behavior ( controlling actuator force/position in a predictive manner in response to road perturbations ahead” (equates to controlling the actuator to generate the target control force at a timing when the wheel passes the expected passage position as the first quote shows the correction force generated in response to the ahead trajectory determined, and the second quote shows how specifically the suspension is controlled by inputting said force to the suspension.))
Yet Anderson fails to teach performing localization that estimates a vehicle position without using Global Navigation Satellite System; and the acquiring the reference unsprung displacement includes acquiring the reference unsprung displacement according to the vehicle speed of the vehicle from the unsprung displacement map.
Giovanardi teaches a similar vehicle position estimation method (abstract). Giovanardi teaches performing localization that estimates a vehicle position without using Global Navigation Satellite System; (Pg. 27 - [0067] In some embodiments, localization system 152 may comprise GPS systems, terrain-based localization systems, and/or any other appropriate localization system capable of providing a location of a vehicle on a road surface to the processor 150.” (equates to performing localization that estimates a vehicle position without using Global Navigation Satellite System as the quote shows a localization system that may be one of terrain based localization type wherein this is known as not using GPS signal but rather onboard sensors to detect and understand the environment around the vehicle.))) the acquiring the reference unsprung displacement includes acquiring the reference unsprung displacement according to the vehicle speed of the vehicle from the unsprung displacement map. (Pg. 31 - [0104] - "trajectory may be set by improving the performance of the system within the constraints, taking into account the upcoming disturbance. For example, in some embodiments of a suspension system, a third derivative of the vehicle superstructure's motion, jerk, may be a metric related to the comfort of the occupants. A controller may filter an upcoming road disturbance at variable filter frequencies to reduce or substantially minimize jerk while satisfying system constraints" & See Also Pg. 31 - [0105] - "In some embodiments, the improved trajectory may be time-dependent, since the dynamics of the structure being controlled may have an impact on the estimation of the constraints. For example, for a road vehicle, when the vehicle travels at low forward speed, even large road inputs may feel relatively smooth and the vehicle may easily traverse the large road inputs without exceeding suspension travel limits. When traversing the same road at a higher speed, the dynamics of the vehicle may cause the vehicle to exceed travel limits even though at lower speed this was not a concern. Accordingly, the process described above may be calculated as a function of speed, " (equates to acquiring the reference unsprung displacement includes acquiring the reference unsprung displacement according to the vehicle speed of the vehicle from the unsprung displacement map as the vertical motion reference unsprung displacement is tracked in the first quote to ensure maximal ride comfort for the passenger and the second quote shows how the speed of the vehicle plays a factor in determining how to actuate the system against the disturbances and thus the reference unsprung displacement changes as a function of speed to ensure the vertical motion unsprung displacement is optimal for passenger comfort.)). It would have been an advantageous addition to the method disclosed by Anderson to include the acquiring the reference unsprung displacement includes acquiring the reference unsprung displacement according to the vehicle speed of the vehicle from the unsprung displacement map as this allows for multiple speed types to be able to dictate the vehicle response when facing rough patches in the road. The vehicle’s response and thus spring response should and will differ based on the speed the vehicle is traveling over the said patches thus speed must be incorporated for maximal passenger comfort.
Therefor it would have been obvious to one of ordinary skill in the art before the effective filing date to include the acquiring the reference unsprung displacement includes acquiring the reference unsprung displacement according to the vehicle speed of the vehicle from the unsprung displacement map as this allows for the best passenger experience as the unsprung displacement that is acquired will be used to actuate the vehicle suspension and the seed at which the vehicle travels will dictate the need or lack thereof to actuate the suspension accordingly.
Yet both fail to teach wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map;
Alleva teaches wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; (Pg. 2 – Fig. 2 & See Also Pg. 9 – [0063] – “first vehicle geo-referencing data of the measured first vertical acceleration values (i.e., data indicative of two/three-dimensional (2D/3D) positions over time corresponding to the measured first vertical acceleration values-e.g., positions provided by Global Navigation Satellite System (GNSS) receivers, such as Global Positioning System (GPS) positions), and first vehicle speed data indicative of the given constant speed (s) associated with the measured first vertical acceleration values;” & See Also Pg. 9 – [0067-0069]– “schematically illustrates an IRI estimation step ( denoted as a whole by 20) of an IRI estimation method according to a preferred embodiment of the present invention. [0068] In particular, the IRI estimation step 20 comprises: [0069] acquiring (block 21 in FIG. 2) second vehicle vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road/road segment” (equates to wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; as the unsprung displacement is calculated using the vehicle vertical velocity, acceleration and position and each is gathered as seen from the first quote, and the first figure and second quote is showing the generation of the time series data based on the vehicle actually traveling over the said trajectory.) ) trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map; (Pg. 10 – [0093] – “Additionally, FIG. 6 shows an example of comparison between real IRI values of a road or road segment, and IRI values estimated by carrying out the IRI estimation method according to the present invention” & See Also pg. 5 – Fig. 6 & See Also Pg. 9 – [0067-0069]– “schematically illustrates an IRI estimation step ( denoted as a whole by 20) of an IRI estimation method according to a preferred embodiment of the present invention. [0068] In particular, the IRI estimation step 20 comprises: [0069] acquiring (block 21 in FIG. 2) second vehicle vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road/road segment” & See Also Pg. 2 – Fig. 1 – 11 – “collecting vehicle vertical accelerations and vehicle geo-referencing and speed data”(equates to trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map as the figure shows the comparison between the measured rough roughness index and historical chart and thus an actual trajectory and its associated road roughness is calculated and then subsequently compared to real road roughness index value. The last quote showing how the road roughness being calculated is equivalent to the calculated unsprung displacement of this art wherein vertical acceleration, position and speed are calculated for determination of rough roughness. ) ) It would have been an advantageous addition to the method disclosed by Anderson-Giovanardi to include wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map ensuring the road and trajectory travelled upon by the vehicle is the road being considered for data collection and subsequently allowing for comparison between the actual displacement data of the vehicle and the historical displacement data of the vehicle over the same road.
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date to include wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map as these limitations allow for the displacement data to be compared between the road the vehicle is currently driving upon and the data gathered of the same road and allows for an accurate position updating to happen based on the displacement being experienced.
Response to Arguments
Response to 35 U.S.C. § 103 rejection of claims 1-11 applicant’s amendments to the claim changes the scope. Applicant’s arguments have been considered but are not persuasive.
Applicant argues on pages 1-2, “As noted hereinabove, claims 1-11 have been rejected under 35 U.S.C. § 103 as allegedly being unpatentable over Anderson in view of Giovanardi and Alleva. The rejections are respectfully traversed. In particular, the cited references fail to teach or suggest the features regarding "performing localization that estimates a vehicle position without using Global Navigation Satellite System" and "acquiring time-series data of an unsprung displacement along an actual trajectory through which a wheel of a vehicle has actually passed while the vehicle is traveling", as recited in amended independent claim 1, and similarly recited in amended independent claims 10 and 11.
The Examiner relies on Alleva to teach these features. Office Action, Pgs. 8-9. However, Alleva merely discloses measuring vertical acceleration values over time provided by GPS and first vehicle speed data. Alleva, Para. [0063]. Applicant respectfully submits that vertical acceleration values provided by GPS cannot reasonably be interpreted as acquiring time-series data of an unsprung displacement. In particular, the specification refers to unsprung displacement
as a vertical motion parameter. See Application, Para. [0019]. Applicant respectfully submits that a vertical motion parameter cannot be reasonably interpreted as vertical acceleration values. Furthermore, Alleva explicitly discloses that the vertical acceleration values are provided by GPS. Accordingly, Applicant respectfully submits that the cited references fail to teach performing localization that estimates a vehicle position without using Global Navigation Satellite System.
Furthermore, Alleva fails to teach "estimating a vehicle position of the vehicle by estimating a position of the actual trajectory by comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map", as recited in amended independent claim 1 and similarly recited in amended independent claims 10 and 11.
The Examiner relies on Alleva to teach this feature. Office Action, Pgs. 9-10. In particular, the Examiner relies on Paras. [0067]-[0069] and [0093] of Alleva to teach this feature. Id. Alleva discloses an International Roughness Index (IRI) estimation system. Alleva, Para. [0067]. The method comprises acquiring vehicle vertical acceleration values for a motor vehicle, computing root mean square values of the vertical acceleration values, and estimating an IRI value based on one or more vehicle transfer functions and the root mean square values and driving speed of the motor vehicle. Id. at Paras. [0069]-[0071]. FIG. 6 shows an example of comparison between real IRI values of a road or road segment and IRI values estimated by carrying the disclosed IRI estimation method. Id. at Para. [0093].
The Examiner equates the comparison, in Alleva, between the calculated IRT values and the real IRI values to the claimed feature of comparing the time-series data of the unsprung displacement along the actual trajectory and time-series data of the reference unsprung displacement acquired from the unsprung displacement map. Office Action, Pgs. 9-10. However, Alleva fails to teach an unsprung displacement map. Anderson and Giovanardi also fail to teach the use of an unsprung displacement map. Accordingly, Applicant respectfully submits that the cited references fail to teach the features of amended independent claims 1, 10 and 11.
Based on the foregoing, Applicant respectfully submits that independent claims 1, 10 and 11, and all claims that depend thereon, can no longer be rejected as unpatentable over any combination of the cited references. Withdrawal of the rejections and a timely notice of allowance are respectfully requested.” – As to point (b) the examiner respectfully disagrees. Applicant asserts that Anderson in view of Giovandari and Alleva fails to teach “performing localization that estimates a vehicle position without using Global Navigation Satellite System”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is acquiring the vehicle position by means that do not include global positioning satellites. Giovanardi teaches a localization technique of the vehicle in which GPS does not specifically have to be used but rather terrain based localization techniques can be implemented instead. Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Giovanardi teaches “performing localization that estimates a vehicle position without using Global Navigation Satellite System” (Pg. 27 - [0067] In some embodiments, localization system 152 may comprise GPS systems, terrain-based localization systems, and/or any other appropriate localization system capable of providing a location of a vehicle on a road surface to the processor 150.” (equates to performing localization that estimates a vehicle position without using Global Navigation Satellite System as the quote shows a localization system that may be one of terrain based localization type wherein this is known as not using GPS signal but rather onboard sensors to detect and understand the environment around the vehicle.)))
Similarly, Applicant asserts that Anderson in view of Giovandari and Alleva fails to teach “the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which a wheel of a vehicle has actually passed while the vehicle is traveling;”. During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is acquiring a parameter (unsprung displacement which is equivalent to vertical motion of wheel see the applications specification: [0075] As an example, in the following description, a case where the vertical motion parameter is the unsprung displacement Zu will be considered. When generalizing, the “unsprung displacement” in the following description shall be replaced by the “vertical motion parameter.”) while the vehicle is currently traveling over a route, and the positioning data of the vehicle is being acquired at the same time the data is collected. Alleva teaches a system in which vertical displacement or a road roughness calculation of the vehicle travelling over a road is simultaneously referenced with GPS or GNSS positioning. Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Alleva teaches “the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which a wheel of a vehicle has actually passed while the vehicle is traveling” (Pg. 2 – Fig. 2 & See Also Pg. 9 – [0063] – “first vehicle geo-referencing data of the measured first vertical acceleration values (i.e., data indicative of two/three-dimensional (2D/3D) positions over time corresponding to the measured first vertical acceleration values-e.g., positions provided by Global Navigation Satellite System (GNSS) receivers, such as Global Positioning System (GPS) positions), and first vehicle speed data indicative of the given constant speed (s) associated with the measured first vertical acceleration values;” & See Also Pg. 9 – [0067-0069]– “schematically illustrates an IRI estimation step ( denoted as a whole by 20) of an IRI estimation method according to a preferred embodiment of the present invention. [0068] In particular, the IRI estimation step 20 comprises: [0069] acquiring (block 21 in FIG. 2) second vehicle vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road/road segment” (equates to wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; as the unsprung displacement is calculated using the vehicle vertical velocity, acceleration and position and each is gathered as seen from the first quote, and the first figure and second quote is showing the generation of the time series data based on the vehicle actually traveling over the said trajectory.) )
Similarly Applicant Argues Anderson-Giovanardi-Alleva fails to teach “acquiring time-series data of an unsprung displacement along an actual trajectory through which a wheel of a vehicle has actually passed while the vehicle is traveling” During Patent Examination, pending claims must be given their broadest reasonable interpretation consistent with the specification (see MPEP 2111). The broadest reasonable interpretation of the aforementioned amendment is acquiring a displacement parameter of the vehicle as previously cited to be vertical motion of the vehicle and correlate the position of the vehicle with the value of the displacement experience by the vehicle and to do so while the vehicle is actively travelling. Alleva teaches collection of a variety of vertical motion parameters of the vehicle while it is travelling and simultaneously collecting the position of the vehicle. Therefor the Examiner respectfully disagrees with the applicants arguments and assert that Alleva teaches “acquiring time-series data of an unsprung displacement along an actual trajectory through which a wheel of a vehicle has actually passed while the vehicle is traveling” (Pg. 2 – Fig. 2 & See Also Pg. 9 – [0063] – “first vehicle geo-referencing data of the measured first vertical acceleration values (i.e., data indicative of two/three-dimensional (2D/3D) positions over time corresponding to the measured first vertical acceleration values-e.g., positions provided by Global Navigation Satellite System (GNSS) receivers, such as Global Positioning System (GPS) positions), and first vehicle speed data indicative of the given constant speed (s) associated with the measured first vertical acceleration values;” & See Also Pg. 9 – [0067-0069]– “schematically illustrates an IRI estimation step ( denoted as a whole by 20) of an IRI estimation method according to a preferred embodiment of the present invention. [0068] In particular, the IRI estimation step 20 comprises: [0069] acquiring (block 21 in FIG. 2) second vehicle vertical acceleration values measured on a given motor vehicle driven at a driving speed on a given road/road segment” (equates to wherein the localization comprises: acquiring time-series data of an unsprung displacement along an actual trajectory through which of a wheel has actually passed vehicle while the vehicle is traveling; as the unsprung displacement is calculated using the vehicle vertical velocity, acceleration and position and each is gathered as seen from the first quote, and the first figure and second quote is showing the generation of the time series data based on the vehicle actually traveling over the said trajectory.) )
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US20200249031A1 - A method for determining a position of a vehicle in a digital map, including at least the following: a) determining motion information about the motion of the vehicle, b) determining course information which is characteristic for the course of a stretch of road traveled by the vehicle, using motion information determined in a), and c) matching course information determined in b) to map information which is characteristic for the course of roads stored in a digital map.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/R.A.W./Examiner, Art Unit 3667
/Hitesh Patel/Supervisory Patent Examiner, Art Unit 3667
3/25/26