DETAILED ACTION
Claims 1-15 are currently pending and have been examined in this application. Claim 15 is New.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is made FINAL in response to the “amendment” and “response” filed 01/14/2026.
Claim Objections
Claim 5 objected to because of the following informalities:
Claim 5: Delete repeated word.
“…including at least one of the [[the]] first partial situation and the second partial situation, with further partial situations.”
Appropriate correction is required.
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) 1-3, 5-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Danzl (US20160200317) in view of Censi (US20190079527) further in view of Rausch (US20200406928).
Claim 1:
Danzl explicitly teaches:
A computer-implemented method for behavior planning of an at least partially automated EGO vehicle using: (i) a database of pre-defined partial situations and at least one evaluation model for each of the partial situations, and
(Danzl) – “A motor vehicle comprising at least one driver assistance system (2) to pre-calculate future driving situations of the motor vehicle (1) for a specified time interval by evaluating ego data related to the motor vehicle (1) and environmental data related to the motor vehicle environment, wherein the motor vehicle (1) is controllable by a driver in a first operating mode of the driver assistance system (2), wherein the driver assistance system (2) is designed, upon fulfillment of one switchover condition dependent at least upon future driving situations, to be temporarily switched over into a second operating mode in which the motor vehicle (1) is autonomously controlled by the driver assistance system (2) without the possibility of intervention by the driver, wherein driving is continued in the second operating mode.” (Abstract)
“With autonomous driving in the second operating mode, it is possible to control the motor vehicle in an at least section-by-section, pre-calculated trajectory. It is therefore advantageous when the driver assistance system is designed to determine a control trajectory between the current position of the motor vehicle and the target position, and to control the motor vehicle along the control trajectory in the second operating mode.” (Para 0018)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“In addition to the ego data, the most exact environmental model possible is required to calculate the potential trajectories. Parameters of the environment may be obtained, for example, from a database integrated into the navigation system 4; however, it is also possible to use other data in the vehicle or external databases. Such stored data may also be supplemented by data from the vehicle sensors, particularly by data from a camera.” (Para 0097)
“Data about moving obstacles—particularly about other motor vehicles or pedestrians—can also be recorded, wherein future movements of moving obstacles can be depicted in a movement model.” (Para 0124)
Examiner Note: Per BRI, partial situations may correspond with a wide variety of situations or trajectories or scenarios.
the method comprising the following steps performed by the EGO vehicle:
a. aggregating situation-specific information from the vehicle's own information sources and from information sources outside the vehicle;
(Danzl) – “In addition to the information regarding the motor vehicle itself, data regarding the environment of the motor vehicle is also necessary to predict future driving situations. This information can be obtained, in particular, by using sensors on the motor vehicle and/or from information stored in the navigation system. It is advantageous when data about the road is recorded—for example, at least one curve radius and/or one local road gradient and/or a local road slope and/or a local friction coefficient and/or a road width. In addition, information should be obtained regarding potential obstacles such as other motor vehicles, pedestrians, and fixed obstacles. To do this, data may also be determined for moving obstacles in particular, such as motor vehicles and pedestrians, that enables the movement of said obstacles to be predicted. Processes and devices for obtaining environmental data are known in the prior art and will therefore not be discussed further.” (Para 0010)
“In addition to the ego data, the most exact environmental model possible is required to calculate the potential trajectories. Parameters of the environment may be obtained, for example, from a database integrated into the navigation system 4; however, it is also possible to use other data in the vehicle or external databases. Such stored data may also be supplemented by data from the vehicle sensors, particularly by data from a camera.” (Para 0097)
b. generating an environment model of the given situation based on the situation-specific information;
(Danzl) – “In addition to the information regarding the motor vehicle itself, data regarding the environment of the motor vehicle is also necessary to predict future driving situations. This information can be obtained, in particular, by using sensors on the motor vehicle and/or from information stored in the navigation system. It is advantageous when data about the road is recorded—for example, at least one curve radius and/or one local road gradient and/or a local road slope and/or a local friction coefficient and/or a road width. In addition, information should be obtained regarding potential obstacles such as other motor vehicles, pedestrians, and fixed obstacles. To do this, data may also be determined for moving obstacles in particular, such as motor vehicles and pedestrians, that enables the movement of said obstacles to be predicted. Processes and devices for obtaining environmental data are known in the prior art and will therefore not be discussed further.” (Para 0010)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“In addition to the ego data, the most exact environmental model possible is required to calculate the potential trajectories. Parameters of the environment may be obtained, for example, from a database integrated into the navigation system 4; however, it is also possible to use other data in the vehicle or external databases. Such stored data may also be supplemented by data from the vehicle sensors, particularly by data from a camera.” (Para 0097)
c. analyzing the environment model to identify first and second partial situations of the pre-defined partial situations in the database that are contained within the given situation]
(Danzl) – “A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
“The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
d. generating, using the situation-specific information, at least one instance for each respective identified partial situation;
(Danzl) – “A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
Examiner Note: Per BRI, an “instance” may correspond with an individual trajectory.
e. analyzing all generated instances using the at least one evaluation model for the respective identified partial situation to determine boundary conditions for possible behaviors of the EGO vehicle in the given situation, the analyzing all generated instances including combining the generated instances corresponding to the first partial situation and the second partial situation; and
(Danzl) – “The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
f. prioritizing the possible behaviors of the EGO vehicle based on the determined boundary conditions [in conjunction with the rule set].
(Danzl) – “The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
Examiner Note: Bracketed text not explicitly taught by primary reference, but is taught by non-primary reference later in the rejection.
Danzl does not explicitly teach the following limitations:
(ii) a pre-defined rule set for evaluating possible behaviors of the EGO vehicle in a given situation, … first and second partial situations of the pre-defined partial situations in the database that are contained within the given situation … the analyzing all generated instances including combining the generated instances corresponding to the first partial situation and the second partial situation … in conjunction with the rule set
Censi, in the same field of endeavor of vehicle control, teaches:
(ii) a pre-defined rule set for evaluating possible behaviors of the EGO vehicle in a given situation, … in conjunction with the rule set
(Censi) – “The term “basic principle” is used broadly to include, for example, any factor that guides, influences, or otherwise constrains motions of an autonomous system to, e.g., conform to social, cultural, legal, ethical, moral, or other behavioral rules, principles, or other norms, including or related to, for example, traffic laws, traffic rules, driving cultures, rules of using roads, preferred driving styles, pedestrian behavior, driving experiences, ethics, or boundaries of drivable areas, or combinations of them.” (Para 0097)
“In general, in one aspect, a method may include: (1) analyzing data associated with basic principles applicable to one or more motion actions of a machine to generate logical expressions associated with the basic principles, and (2) using the logical expressions to plan motion actions for the machine.” (Para 0004)
“generating logical expressions associated with the basic principles may comprise statistically evaluating occurrences of conditions, proper actions, and deviations from proper actions, or adjusting one or more logical expressions based on the occurrences, or both.” (Para 0010)
“In some implementations, planning motion actions may comprise one or more or all of the following: identifying candidate trajectories, specifying sequences of the motion actions along a trajectory, evaluating a priority level of a motion action of the machine, evaluating a motion action of the machine complying with a proper action of a logical expression, or evaluating a deviation metric of a motion action of the machine deviating from a proper action of a logical expression.” (Para 0040)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the motion planning of Censi. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, in order to “benefit vehicles in any levels, ranging from fully autonomous vehicles to human-operated vehicles.” (Censi Para 0103)
Censi does not explicitly teach the following limitations:
first and second partial situations of the pre-defined partial situations in the database that are contained within the given situation … the analyzing all generated instances including combining the generated instances corresponding to the first partial situation and the second partial situation
Rausch, in the same field of endeavor of vehicle control, teaches:
first and second partial situations of the pre-defined partial situations in the database that are contained within the given situation … the analyzing all generated instances including combining the generated instances corresponding to the first partial situation and the second partial situation
(Rausch) – “The example methods described herein are very flexible and modular. Important influence factors may be added in an additive manner, existing scenarios are maintained, but may also be automatically expanded by the new influence factors. The model-based approach allows for simple transferability to other countries. A modular description of the individual effects that contribute to a complex behavior decision (for example, consideration of a pedestrian crosswalk separate from an intersection at which it is located) is enabled by an abstraction of traffic situations into logical zones. This results in a high reduction of the complexity and a high degree of reusability.” (Para 0026)
“The zones in the zone graphs, and thus the zone graphs, represent abstractions of the respective situations in such a way that they are separate from specific structures of the digital map, such as curve radii or angles of an intersection. These logical zones may be mapped as physical zones onto the map.” (Para 0057)
“With a behavior analysis, it is then possible to analyze systematically and fully the scope of the possible scenarios and to fully and consistently divide them into equivalence classes. An equivalence class in this case includes all situations, in which the vehicle under consideration behaves identically or should behave identically.” (Para 0061)
“Analyses are preferably carried out for particular, generic road segments (simple road segments, simple intersections, simple traffic circle, simple parking lot, etc.) and stored as models in a corresponding model library. Relatively few such generic models are sufficient for covering a large portion of all possible or actually existing road segments. With a selection of the relevant characteristics of such generic road segments, it is thus possible to create a small configurable and parameterizable model library, on the basis of which more complex road maps may then be easily calculated. Thus, for example, T-intersections of different types may be produced from the generic model of a four-way intersection by omitting one branch of the intersection. The angles between the branches of the intersection, the lane widths, the number of lanes, the placement of yield signs and traffic signals may, in particular, be parameters in the models.” (Para 0139)
“Two road map segments are shown in FIG. 9 in the form of road schemes, which may be combined. The road map segments include: the vehicle under consideration starting on the left side below, whose intended maneuver is marked by a sequence of thin arrows and guides the vehicle on the right side below, additional road users (vehicles with intended maneuvers marked by thick arrows, pedestrians as boxes), traffic lights and traffic signs, pedestrian crosswalks.” (Para 0140-0144)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the method for controlling a vehicle of Rausch. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, because “This results in a high reduction of the complexity and a high degree of reusability.” (Rausch Para 0026)
Claim 2:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl further teaches:
wherein each of the partial situations is defined as a situation class which is at least partially determined by at least one of the following elements: the EGO vehicle, at least one transport infrastructure element, at least one other road user, and a general situation context; and
wherein each of the partial situations can be instantiated by data-loading with situation- specific information.
(Danzl) – “A motor vehicle comprising at least one driver assistance system (2) to pre-calculate future driving situations of the motor vehicle (1) for a specified time interval by evaluating ego data related to the motor vehicle (1) and environmental data related to the motor vehicle environment, wherein the motor vehicle (1) is controllable by a driver in a first operating mode of the driver assistance system (2), wherein the driver assistance system (2) is designed, upon fulfillment of one switchover condition dependent at least upon future driving situations, to be temporarily switched over into a second operating mode in which the motor vehicle (1) is autonomously controlled by the driver assistance system (2) without the possibility of intervention by the driver, wherein driving is continued in the second operating mode.” (Abstract)
“With autonomous driving in the second operating mode, it is possible to control the motor vehicle in an at least section-by-section, pre-calculated trajectory. It is therefore advantageous when the driver assistance system is designed to determine a control trajectory between the current position of the motor vehicle and the target position, and to control the motor vehicle along the control trajectory in the second operating mode.” (Para 0018)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“In addition to the ego data, the most exact environmental model possible is required to calculate the potential trajectories. Parameters of the environment may be obtained, for example, from a database integrated into the navigation system 4; however, it is also possible to use other data in the vehicle or external databases. Such stored data may also be supplemented by data from the vehicle sensors, particularly by data from a camera.” (Para 0097)
“Data about moving obstacles—particularly about other motor vehicles or pedestrians—can also be recorded, wherein future movements of moving obstacles can be depicted in a movement model.” (Para 0124)
“The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
Claim 3:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl further teaches:
wherein the pre-defined partial situations of the database are selected such that an environment model generated based on situation-specific information can be represented by a combination of instantiated partial situations of the database.
(Danzl) – “If a control trajectory is being recalculated, or if multiple trajectories are known for driving through the critical situation, an optimal control trajectory can be determined or selected. The control trajectory can be optimized, in particular, with respect to parameters relevant to safety, such as, for example, the safe distance with respect to obstacles or the spacing of the acceleration forces necessary to drive the trajectory at a maximum value for the acceleration forces. In many cases, the space of usable trajectories for the motor vehicle is sufficiently large that other parameters may also be considered, particularly for minimizing the stress on the driver during optimization. In particular, attempts can be made to minimize acceleration in the longitudinal and/or lateral directions. In addition, an attempt may be made to determine a trajectory that enables the quickest return of control of the vehicle to the driver. It is often possible to safely drive through critical driving situations using a control trajectory that deviates minimally from a predicted intent of the driver. Obviously, in calculating the control trajectory, the physical limits of the motor vehicle are considered. It is also possible, however, to consider, to the extent possible, the comfort limits of the driver.” (Para 0020)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“In addition to the ego data, the most exact environmental model possible is required to calculate the potential trajectories. Parameters of the environment may be obtained, for example, from a database integrated into the navigation system 4; however, it is also possible to use other data in the vehicle or external databases. Such stored data may also be supplemented by data from the vehicle sensors, particularly by data from a camera.” (Para 0097)
Examiner Note: Per BRI, composition may correspond with any form of composition or composite nature of situations, including the optimized trajectory detailed above.
Claim 5:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl further teaches:
wherein at least some of the evaluation models include combination rules for combining relevant partial situations, [including at least one of the the first partial situation and the second partial situation, with further partial situations].
(Danzl) – “The parameters describing the trajectories—particularly the location coordinates—are typically continuous variables. Even with a technically constrained final solution, very many individual trajectories would thereby have to be calculated to find the possible trajectories. In order to reduce the number of trajectories, two approaches in particular are possible. Thus, it is possible for the driver assistance system to be designed to calculate the drivable trajectories as at least one parameterized band of trajectories from a multitude of drivable trajectories and/or as multiple individual drivable trajectories that are spaced apart in their parameters by a specified or adjustable distance—particularly the location coordinates. In the first case, the trajectories are thus at least partially indicated as a continuous band of trajectories. For example, when using pure location trajectories, two limitation trajectories can thus be determined from which the further intermediate trajectories can be calculated as a function of at least one parameter. A corresponding parameterization for multiple parameters is obviously simultaneously possible.” (Para 0029)
“A multitude of approaches is possible for calculating the trajectories. As previously mentioned, it is particularly simple to initially exclusively specify three-dimensional curves as the trajectories. In this case, vehicle paths are initially determined within which there is no obstacle. A vehicle path in this case is an area that can be completely covered by drivable trajectories. Such a vehicle path and the trajectories lying between the limits of the vehicle path can be described in that two geometric limiting trajectories are initially determined for the vehicle path that limit the vehicle path. Such limiting trajectories typically have a specified distance from the edge of a driving area. This means that they proceed at a specified safe distance from obstacles in the environment of the vehicle. If there is no obstacle between two of these limiting trajectories, a vehicle path is typically formed by these two limiting trajectories, if all of the trajectories between them are drivable. In addition to this, an ideal trajectory can be determined within the path of the vehicle that describes, for example, the shortest route to a specified point or section of the course, which is drivable at the highest speed or similar.” (Para 0031)
“The other trajectories of the vehicle path can then be calculated by interpolating between the two limiting trajectories or between the limiting trajectories on the ideal trajectory. As an alternative, it is also possible to vary the ideal trajectory, for example, in that trajectories are calculated adjacent to the ideal trajectory that deviate in only one direction from the ideal trajectory and preferably have precisely the same number of turning points or fewer.” (Para 0032)
Danzl does not explicitly teach the following limitations:
including at least one of the the first partial situation and the second partial situation, with further partial situations
Rausch, in the same field of endeavor of vehicle control, teaches:
including at least one of the the first partial situation and the second partial situation, with further partial situations
(Rausch) – “The example methods described herein are very flexible and modular. Important influence factors may be added in an additive manner, existing scenarios are maintained, but may also be automatically expanded by the new influence factors. The model-based approach allows for simple transferability to other countries. A modular description of the individual effects that contribute to a complex behavior decision (for example, consideration of a pedestrian crosswalk separate from an intersection at which it is located) is enabled by an abstraction of traffic situations into logical zones. This results in a high reduction of the complexity and a high degree of reusability.” (Para 0026)
“The zones in the zone graphs, and thus the zone graphs, represent abstractions of the respective situations in such a way that they are separate from specific structures of the digital map, such as curve radii or angles of an intersection. These logical zones may be mapped as physical zones onto the map.” (Para 0057)
“With a behavior analysis, it is then possible to analyze systematically and fully the scope of the possible scenarios and to fully and consistently divide them into equivalence classes. An equivalence class in this case includes all situations, in which the vehicle under consideration behaves identically or should behave identically.” (Para 0061)
“Analyses are preferably carried out for particular, generic road segments (simple road segments, simple intersections, simple traffic circle, simple parking lot, etc.) and stored as models in a corresponding model library. Relatively few such generic models are sufficient for covering a large portion of all possible or actually existing road segments. With a selection of the relevant characteristics of such generic road segments, it is thus possible to create a small configurable and parameterizable model library, on the basis of which more complex road maps may then be easily calculated. Thus, for example, T-intersections of different types may be produced from the generic model of a four-way intersection by omitting one branch of the intersection. The angles between the branches of the intersection, the lane widths, the number of lanes, the placement of yield signs and traffic signals may, in particular, be parameters in the models.” (Para 0139)
“Two road map segments are shown in FIG. 9 in the form of road schemes, which may be combined. The road map segments include: the vehicle under consideration starting on the left side below, whose intended maneuver is marked by a sequence of thin arrows and guides the vehicle on the right side below, additional road users (vehicles with intended maneuvers marked by thick arrows, pedestrians as boxes), traffic lights and traffic signs, pedestrian crosswalks.” (Para 0140-0144)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the method for controlling a vehicle of Rausch. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, because “This results in a high reduction of the complexity and a high degree of reusability.” (Rausch Para 0026)
Claim 6:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl does not explicitly teach the following limitations in full. Censi further teaches:
wherein the pre-defined rule set includes and prioritizes safety requirements and/or traffic regulations and/or comfort requirements and/or vehicle-related boundary conditions. (Censi) (Censi) – “The term “basic principle” is used broadly to include, for example, any factor that guides, influences, or otherwise constrains motions of an autonomous system to, e.g., conform to social, cultural, legal, ethical, moral, or other behavioral rules, principles, or other norms, including or related to, for example, traffic laws, traffic rules, driving cultures, rules of using roads, preferred driving styles, pedestrian behavior, driving experiences, ethics, or boundaries of drivable areas, or combinations of them.” (Para 0097)
“In general, in one aspect, a method may include: (1) analyzing data associated with basic principles applicable to one or more motion actions of a machine to generate logical expressions associated with the basic principles, and (2) using the logical expressions to plan motion actions for the machine.” (Para 0004)
“generating logical expressions associated with the basic principles may comprise statistically evaluating occurrences of conditions, proper actions, and deviations from proper actions, or adjusting one or more logical expressions based on the occurrences, or both.” (Para 0010)
“In some implementations, planning motion actions may comprise one or more or all of the following: identifying candidate trajectories, specifying sequences of the motion actions along a trajectory, evaluating a priority level of a motion action of the machine, evaluating a motion action of the machine complying with a proper action of a logical expression, or evaluating a deviation metric of a motion action of the machine deviating from a proper action of a logical expression.” (Para 0040)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the motion planning of Censi. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, in order to “benefit vehicles in any levels, ranging from fully autonomous vehicles to human-operated vehicles.” (Censi Para 0103)
Claim 7:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl further teaches:
wherein each of the generated instance is analyzed separately to generate instance boundary conditions for the possible behaviors of the EGO vehicle for the generated instance, wherein the at least one evaluation model and a situation context of an underlying partial situation are used for the analysis.
(Danzl) – “The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
“If a control trajectory is being recalculated, or if multiple trajectories are known for driving through the critical situation, an optimal control trajectory can be determined or selected. The control trajectory can be optimized, in particular, with respect to parameters relevant to safety, such as, for example, the safe distance with respect to obstacles or the spacing of the acceleration forces necessary to drive the trajectory at a maximum value for the acceleration forces. In many cases, the space of usable trajectories for the motor vehicle is sufficiently large that other parameters may also be considered, particularly for minimizing the stress on the driver during optimization. In particular, attempts can be made to minimize acceleration in the longitudinal and/or lateral directions. In addition, an attempt may be made to determine a trajectory that enables the quickest return of control of the vehicle to the driver. It is often possible to safely drive through critical driving situations using a control trajectory that deviates minimally from a predicted intent of the driver. Obviously, in calculating the control trajectory, the physical limits of the motor vehicle are considered. It is also possible, however, to consider, to the extent possible, the comfort limits of the driver.” (Para 0020)
Examiner Note: Per BRI, “analyzed separately” may correspond with any form of analysis which relates to the individual situation.
Claim 8:
Danzl in combination with the references relied upon in Claim 7 teach those respective limitations. Danzl further teaches:
wherein at least some of the evaluation models include combination rules for combining relevant partial situations, including at least one of the the first partial situation and the second partial situation, with further partial situations, and
(Danzl) – “The parameters describing the trajectories—particularly the location coordinates—are typically continuous variables. Even with a technically constrained final solution, very many individual trajectories would thereby have to be calculated to find the possible trajectories. In order to reduce the number of trajectories, two approaches in particular are possible. Thus, it is possible for the driver assistance system to be designed to calculate the drivable trajectories as at least one parameterized band of trajectories from a multitude of drivable trajectories and/or as multiple individual drivable trajectories that are spaced apart in their parameters by a specified or adjustable distance—particularly the location coordinates. In the first case, the trajectories are thus at least partially indicated as a continuous band of trajectories. For example, when using pure location trajectories, two limitation trajectories can thus be determined from which the further intermediate trajectories can be calculated as a function of at least one parameter. A corresponding parameterization for multiple parameters is obviously simultaneously possible.” (Para 0029)
“A multitude of approaches is possible for calculating the trajectories. As previously mentioned, it is particularly simple to initially exclusively specify three-dimensional curves as the trajectories. In this case, vehicle paths are initially determined within which there is no obstacle. A vehicle path in this case is an area that can be completely covered by drivable trajectories. Such a vehicle path and the trajectories lying between the limits of the vehicle path can be described in that two geometric limiting trajectories are initially determined for the vehicle path that limit the vehicle path. Such limiting trajectories typically have a specified distance from the edge of a driving area. This means that they proceed at a specified safe distance from obstacles in the environment of the vehicle. If there is no obstacle between two of these limiting trajectories, a vehicle path is typically formed by these two limiting trajectories, if all of the trajectories between them are drivable. In addition to this, an ideal trajectory can be determined within the path of the vehicle that describes, for example, the shortest route to a specified point or section of the course, which is drivable at the highest speed or similar.” (Para 0031)
“The other trajectories of the vehicle path can then be calculated by interpolating between the two limiting trajectories or between the limiting trajectories on the ideal trajectory. As an alternative, it is also possible to vary the ideal trajectory, for example, in that trajectories are calculated adjacent to the ideal trajectory that deviate in only one direction from the ideal trajectory and preferably have precisely the same number of turning points or fewer.” (Para 0032)
wherein the boundary conditions for the possible behaviors of the EGO vehicle are determined in the given situation by combining at least some of the generated instance boundary conditions, taking into account combination rules of underlying evaluation models.
(Danzl) – “The parameters describing the trajectories—particularly the location coordinates—are typically continuous variables. Even with a technically constrained final solution, very many individual trajectories would thereby have to be calculated to find the possible trajectories. In order to reduce the number of trajectories, two approaches in particular are possible. Thus, it is possible for the driver assistance system to be designed to calculate the drivable trajectories as at least one parameterized band of trajectories from a multitude of drivable trajectories and/or as multiple individual drivable trajectories that are spaced apart in their parameters by a specified or adjustable distance—particularly the location coordinates. In the first case, the trajectories are thus at least partially indicated as a continuous band of trajectories. For example, when using pure location trajectories, two limitation trajectories can thus be determined from which the further intermediate trajectories can be calculated as a function of at least one parameter. A corresponding parameterization for multiple parameters is obviously simultaneously possible.” (Para 0029)
“A multitude of approaches is possible for calculating the trajectories. As previously mentioned, it is particularly simple to initially exclusively specify three-dimensional curves as the trajectories. In this case, vehicle paths are initially determined within which there is no obstacle. A vehicle path in this case is an area that can be completely covered by drivable trajectories. Such a vehicle path and the trajectories lying between the limits of the vehicle path can be described in that two geometric limiting trajectories are initially determined for the vehicle path that limit the vehicle path. Such limiting trajectories typically have a specified distance from the edge of a driving area. This means that they proceed at a specified safe distance from obstacles in the environment of the vehicle. If there is no obstacle between two of these limiting trajectories, a vehicle path is typically formed by these two limiting trajectories, if all of the trajectories between them are drivable. In addition to this, an ideal trajectory can be determined within the path of the vehicle that describes, for example, the shortest route to a specified point or section of the course, which is drivable at the highest speed or similar.” (Para 0031)
“The other trajectories of the vehicle path can then be calculated by interpolating between the two limiting trajectories or between the limiting trajectories on the ideal trajectory. As an alternative, it is also possible to vary the ideal trajectory, for example, in that trajectories are calculated adjacent to the ideal trajectory that deviate in only one direction from the ideal trajectory and preferably have precisely the same number of turning points or fewer.” (Para 0032)
Danzl does not explicitly teach the following limitations:
including at least one of the the first partial situation and the second partial situation, with further partial situations
Rausch, in the same field of endeavor of vehicle control, teaches:
including at least one of the the first partial situation and the second partial situation, with further partial situations
(Rausch) – “The example methods described herein are very flexible and modular. Important influence factors may be added in an additive manner, existing scenarios are maintained, but may also be automatically expanded by the new influence factors. The model-based approach allows for simple transferability to other countries. A modular description of the individual effects that contribute to a complex behavior decision (for example, consideration of a pedestrian crosswalk separate from an intersection at which it is located) is enabled by an abstraction of traffic situations into logical zones. This results in a high reduction of the complexity and a high degree of reusability.” (Para 0026)
“The zones in the zone graphs, and thus the zone graphs, represent abstractions of the respective situations in such a way that they are separate from specific structures of the digital map, such as curve radii or angles of an intersection. These logical zones may be mapped as physical zones onto the map.” (Para 0057)
“With a behavior analysis, it is then possible to analyze systematically and fully the scope of the possible scenarios and to fully and consistently divide them into equivalence classes. An equivalence class in this case includes all situations, in which the vehicle under consideration behaves identically or should behave identically.” (Para 0061)
“Analyses are preferably carried out for particular, generic road segments (simple road segments, simple intersections, simple traffic circle, simple parking lot, etc.) and stored as models in a corresponding model library. Relatively few such generic models are sufficient for covering a large portion of all possible or actually existing road segments. With a selection of the relevant characteristics of such generic road segments, it is thus possible to create a small configurable and parameterizable model library, on the basis of which more complex road maps may then be easily calculated. Thus, for example, T-intersections of different types may be produced from the generic model of a four-way intersection by omitting one branch of the intersection. The angles between the branches of the intersection, the lane widths, the number of lanes, the placement of yield signs and traffic signals may, in particular, be parameters in the models.” (Para 0139)
“Two road map segments are shown in FIG. 9 in the form of road schemes, which may be combined. The road map segments include: the vehicle under consideration starting on the left side below, whose intended maneuver is marked by a sequence of thin arrows and guides the vehicle on the right side below, additional road users (vehicles with intended maneuvers marked by thick arrows, pedestrians as boxes), traffic lights and traffic signs, pedestrian crosswalks.” (Para 0140-0144)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the method for controlling a vehicle of Rausch. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, because “This results in a high reduction of the complexity and a high degree of reusability.” (Rausch Para 0026)
Claim 9:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl further teaches:
wherien the determined boundary conditions for the possible behaviors of the EGO vehicle are compared [with the rules of the rule set], and the possible behaviors of the EGO vehicle are sorted by priorities [based on the comparison].
(Danzl) – “The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
“If a control trajectory is being recalculated, or if multiple trajectories are known for driving through the critical situation, an optimal control trajectory can be determined or selected. The control trajectory can be optimized, in particular, with respect to parameters relevant to safety, such as, for example, the safe distance with respect to obstacles or the spacing of the acceleration forces necessary to drive the trajectory at a maximum value for the acceleration forces. In many cases, the space of usable trajectories for the motor vehicle is sufficiently large that other parameters may also be considered, particularly for minimizing the stress on the driver during optimization. In particular, attempts can be made to minimize acceleration in the longitudinal and/or lateral directions. In addition, an attempt may be made to determine a trajectory that enables the quickest return of control of the vehicle to the driver. It is often possible to safely drive through critical driving situations using a control trajectory that deviates minimally from a predicted intent of the driver. Obviously, in calculating the control trajectory, the physical limits of the motor vehicle are considered. It is also possible, however, to consider, to the extent possible, the comfort limits of the driver.” (Para 0020)
Examiner Note: Bracketed text not explicitly taught by primary reference, but is taught by non-primary reference later in the rejection.
Danzl does not explicitly teach the following limitations:
with the rules of the rule set…based on the comparison
Censi, in the same field of endeavor of vehicle control, teaches:
with the rules of the rule set…based on the comparison
(Censi) – “The term “basic principle” is used broadly to include, for example, any factor that guides, influences, or otherwise constrains motions of an autonomous system to, e.g., conform to social, cultural, legal, ethical, moral, or other behavioral rules, principles, or other norms, including or related to, for example, traffic laws, traffic rules, driving cultures, rules of using roads, preferred driving styles, pedestrian behavior, driving experiences, ethics, or boundaries of drivable areas, or combinations of them.” (Para 0097)
“In general, in one aspect, a method may include: (1) analyzing data associated with basic principles applicable to one or more motion actions of a machine to generate logical expressions associated with the basic principles, and (2) using the logical expressions to plan motion actions for the machine.” (Para 0004)
“generating logical expressions associated with the basic principles may comprise statistically evaluating occurrences of conditions, proper actions, and deviations from proper actions, or adjusting one or more logical expressions based on the occurrences, or both.” (Para 0010)
“In some implementations, planning motion actions may comprise one or more or all of the following: identifying candidate trajectories, specifying sequences of the motion actions along a trajectory, evaluating a priority level of a motion action of the machine, evaluating a motion action of the machine complying with a proper action of a logical expression, or evaluating a deviation metric of a motion action of the machine deviating from a proper action of a logical expression.” (Para 0040)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the motion planning of Censi. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, in order to “benefit vehicles in any levels, ranging from fully autonomous vehicles to human-operated vehicles.” (Censi Para 0103)
Claim 10:
Danzl in combination with the references relied upon in Claim 9 teach those respective limitations. Danzl does not explicitly teach the following limitations in full. Censi further teaches:
wherein the comparison of the boundary conditions for the possible behaviors of the EGO vehicle with the rules of the rule set is logged.
(Censi) – “In some implementations, the method includes generating a report of motion actions of the machine. In some implementations, generating a report may comprise one or more or all of the following: identifying a basic principle guiding a motion action, identifying an overwriting principle associated with a motion action, recording trajectories and motion actions, recording decisions on planning the motion actions for the machine, recording logical expressions used to plan the motion actions for the machine, recording deviation metrics of a motion action of the machine, detecting a risky event, generating an alert regarding the risky event, transmitting the report to a remote computing device, detecting a deviation away from a logical expression or away from a basic principle, determining a liability, integrating the report to the data associated with basic principles, or using the report to adjust processes of generating logical expressions.” (Para 0013)
“generating logical expressions associated with the basic principles may comprise statistically evaluating occurrences of conditions, proper actions, and deviations from proper actions, or adjusting one or more logical expressions based on the occurrences, or both.” (Para 0010)
“In some implementations, planning motion actions may comprise one or more or all of the following: identifying candidate trajectories, specifying sequences of the motion actions along a trajectory, evaluating a priority level of a motion action of the machine, evaluating a motion action of the machine complying with a proper action of a logical expression, or evaluating a deviation metric of a motion action of the machine deviating from a proper action of a logical expression.” (Para 0040)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the motion planning of Censi. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, in order to “benefit vehicles in any levels, ranging from fully autonomous vehicles to human-operated vehicles.” (Censi Para 0103)
Claim 11:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl further teaches:
wherein at least one of the possible behaviors of the EGO vehicle in the given situation is defined by: at least some of the determined boundary conditions, or a set of behavior instructions that implement at least some of the determined boundary conditions, or a reference trajectory which satisfies at least some of the determined boundary conditions.
(Danzl) – “The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
“If a control trajectory is being recalculated, or if multiple trajectories are known for driving through the critical situation, an optimal control trajectory can be determined or selected. The control trajectory can be optimized, in particular, with respect to parameters relevant to safety, such as, for example, the safe distance with respect to obstacles or the spacing of the acceleration forces necessary to drive the trajectory at a maximum value for the acceleration forces. In many cases, the space of usable trajectories for the motor vehicle is sufficiently large that other parameters may also be considered, particularly for minimizing the stress on the driver during optimization. In particular, attempts can be made to minimize acceleration in the longitudinal and/or lateral directions. In addition, an attempt may be made to determine a trajectory that enables the quickest return of control of the vehicle to the driver. It is often possible to safely drive through critical driving situations using a control trajectory that deviates minimally from a predicted intent of the driver. Obviously, in calculating the control trajectory, the physical limits of the motor vehicle are considered. It is also possible, however, to consider, to the extent possible, the comfort limits of the driver.” (Para 0020)
Claim 12:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl further teaches:
wherein a possible behavior of the EGO vehicle to be implemented in the given situation is detailed and/or optimized with respect to a pre-specified quality function only after prioritization of behaviors previously determined as possible.
(Danzl) – “The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
“If a control trajectory is being recalculated, or if multiple trajectories are known for driving through the critical situation, an optimal control trajectory can be determined or selected. The control trajectory can be optimized, in particular, with respect to parameters relevant to safety, such as, for example, the safe distance with respect to obstacles or the spacing of the acceleration forces necessary to drive the trajectory at a maximum value for the acceleration forces. In many cases, the space of usable trajectories for the motor vehicle is sufficiently large that other parameters may also be considered, particularly for minimizing the stress on the driver during optimization. In particular, attempts can be made to minimize acceleration in the longitudinal and/or lateral directions. In addition, an attempt may be made to determine a trajectory that enables the quickest return of control of the vehicle to the driver. It is often possible to safely drive through critical driving situations using a control trajectory that deviates minimally from a predicted intent of the driver. Obviously, in calculating the control trajectory, the physical limits of the motor vehicle are considered. It is also possible, however, to consider, to the extent possible, the comfort limits of the driver.” (Para 0020)
Claim 13:
Danzl explicitly teaches:
A computer-implemented system for behavior planning of an at least partially automated EGO vehicle, comprising: at least one processor configured to: aggregate situation-specific information from the vehicle's own information sources and from information sources outside the vehicle;
(Danzl) – “In addition to the information regarding the motor vehicle itself, data regarding the environment of the motor vehicle is also necessary to predict future driving situations. This information can be obtained, in particular, by using sensors on the motor vehicle and/or from information stored in the navigation system. It is advantageous when data about the road is recorded—for example, at least one curve radius and/or one local road gradient and/or a local road slope and/or a local friction coefficient and/or a road width. In addition, information should be obtained regarding potential obstacles such as other motor vehicles, pedestrians, and fixed obstacles. To do this, data may also be determined for moving obstacles in particular, such as motor vehicles and pedestrians, that enables the movement of said obstacles to be predicted. Processes and devices for obtaining environmental data are known in the prior art and will therefore not be discussed further.” (Para 0010)
“In addition to the ego data, the most exact environmental model possible is required to calculate the potential trajectories. Parameters of the environment may be obtained, for example, from a database integrated into the navigation system 4; however, it is also possible to use other data in the vehicle or external databases. Such stored data may also be supplemented by data from the vehicle sensors, particularly by data from a camera.” (Para 0097)
“FIG. 1 shows a motor vehicle 1 having a driver assistance system 2, which, when a critical situation is determined, is designed for the autonomous control of the motor vehicle 1” (Para 0080)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2.” (Para 0084)
generate an environment model of a given situation of the vehicle based on the situation-specific information;
(Danzl) – “In addition to the information regarding the motor vehicle itself, data regarding the environment of the motor vehicle is also necessary to predict future driving situations. This information can be obtained, in particular, by using sensors on the motor vehicle and/or from information stored in the navigation system. It is advantageous when data about the road is recorded—for example, at least one curve radius and/or one local road gradient and/or a local road slope and/or a local friction coefficient and/or a road width. In addition, information should be obtained regarding potential obstacles such as other motor vehicles, pedestrians, and fixed obstacles. To do this, data may also be determined for moving obstacles in particular, such as motor vehicles and pedestrians, that enables the movement of said obstacles to be predicted. Processes and devices for obtaining environmental data are known in the prior art and will therefore not be discussed further.” (Para 0010)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“In addition to the ego data, the most exact environmental model possible is required to calculate the potential trajectories. Parameters of the environment may be obtained, for example, from a database integrated into the navigation system 4; however, it is also possible to use other data in the vehicle or external databases. Such stored data may also be supplemented by data from the vehicle sensors, particularly by data from a camera.” (Para 0097)
analyze the environment model to identify [first and second partial situations associated with pre-defined partial situations in a database that are contained within the given situation], the database comprising of the pre-defined partial situations and at least one evaluation model for each of the partial situations;
(Danzl) – “A motor vehicle comprising at least one driver assistance system (2) to pre-calculate future driving situations of the motor vehicle (1) for a specified time interval by evaluating ego data related to the motor vehicle (1) and environmental data related to the motor vehicle environment, wherein the motor vehicle (1) is controllable by a driver in a first operating mode of the driver assistance system (2), wherein the driver assistance system (2) is designed, upon fulfillment of one switchover condition dependent at least upon future driving situations, to be temporarily switched over into a second operating mode in which the motor vehicle (1) is autonomously controlled by the driver assistance system (2) without the possibility of intervention by the driver, wherein driving is continued in the second operating mode.” (Abstract)
“With autonomous driving in the second operating mode, it is possible to control the motor vehicle in an at least section-by-section, pre-calculated trajectory. It is therefore advantageous when the driver assistance system is designed to determine a control trajectory between the current position of the motor vehicle and the target position, and to control the motor vehicle along the control trajectory in the second operating mode.” (Para 0018)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“In addition to the ego data, the most exact environmental model possible is required to calculate the potential trajectories. Parameters of the environment may be obtained, for example, from a database integrated into the navigation system 4; however, it is also possible to use other data in the vehicle or external databases. Such stored data may also be supplemented by data from the vehicle sensors, particularly by data from a camera.” (Para 0097)
“Data about moving obstacles—particularly about other motor vehicles or pedestrians—can also be recorded, wherein future movements of moving obstacles can be depicted in a movement model.” (Para 0124)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
“The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
Examiner Note: Per BRI, partial situations may correspond with a wide variety of situations or trajectories or scenarios.
generate at least one instance for each respective identified partial situation using the situation-specific information;
(Danzl) – “A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
Examiner Note: Per BRI, an “instance” may correspond with an individual trajectory.
analyze all generated instances using the at least one evaluation model for the respective identified partial situation to determine boundary conditions for possible behaviors of the EGO vehicle in the given situation, [the analyzing all generated instances including combining the generated instances corresponding to the first partial situation and the second partial situation]; and
(Danzl) – “The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
“A dynamic model for the particular motor vehicle, as well as a detailed environmental model, can be calculated from the substantial amount of ego data on the motor vehicle 1 and the substantial amount of environmental data, wherein, in particular, the movement of other motor vehicles can be predicted by means of another dynamic model. The driver assistance system 2 can determine potential future driving situations, in that multiple possible trajectories are determined for the motor vehicle 1. Such trajectories, in particular, can proceed on a target route of the motor vehicle determined by the driver assistance system 2. The duration or spatial length of the trajectories may depend on the traffic density and/or on the speed of the vehicle.” (Para 0084)
“it is also possible for trajectories to be calculated even within the scope of the pre-calculation of future driving situations. As previously explained, switching into the second operating mode preferably only takes place if at least one possibility exists for driving through the critical situation. Thus, there is at least one known trajectory that is safely navigable by the motor vehicle. Therefore, when the future driving situations have been calculated with the assistance of trajectories, it is possible to use the corresponding trajectories directly afterwards to control the motor vehicle, i.e., as a control trajectory.” (Para 0019)
Danzl does not explicitly teach the following limitations:
first and second partial situations of the pre-defined partial situations in a database that are contained within the given situation … the analyzing all generated instances including combining the generated instances corresponding to the first partial situation and the second partial situation … evaluate and prioritize the possible behaviors based on the boundary conditions and according to a pre-defined rule set used for evaluating the possible behaviors of the EGO vehicle.
Censi, in the same field of endeavor of vehicle control, teaches:
evaluate and prioritize the possible behaviors based on the boundary conditions and according to a pre-defined rule set used for evaluating the possible behaviors of the EGO vehicle.
(Censi) – “The term “basic principle” is used broadly to include, for example, any factor that guides, influences, or otherwise constrains motions of an autonomous system to, e.g., conform to social, cultural, legal, ethical, moral, or other behavioral rules, principles, or other norms, including or related to, for example, traffic laws, traffic rules, driving cultures, rules of using roads, preferred driving styles, pedestrian behavior, driving experiences, ethics, or boundaries of drivable areas, or combinations of them.” (Para 0097)
“In general, in one aspect, a method may include: (1) analyzing data associated with basic principles applicable to one or more motion actions of a machine to generate logical expressions associated with the basic principles, and (2) using the logical expressions to plan motion actions for the machine.” (Para 0004)
“generating logical expressions associated with the basic principles may comprise statistically evaluating occurrences of conditions, proper actions, and deviations from proper actions, or adjusting one or more logical expressions based on the occurrences, or both.” (Para 0010)
“In some implementations, planning motion actions may comprise one or more or all of the following: identifying candidate trajectories, specifying sequences of the motion actions along a trajectory, evaluating a priority level of a motion action of the machine, evaluating a motion action of the machine complying with a proper action of a logical expression, or evaluating a deviation metric of a motion action of the machine deviating from a proper action of a logical expression.” (Para 0040)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the motion planning of Censi. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, in order to “benefit vehicles in any levels, ranging from fully autonomous vehicles to human-operated vehicles.” (Censi Para 0103)
Censi does not explicitly teach the following limitations:
first and second partial situations of the pre-defined partial situations in a database that are contained within the given situation … the analyzing all generated instances including combining the generated instances corresponding to the first partial situation and the second partial situation
Rausch, in the same field of endeavor of vehicle control, teaches:
first and second partial situations of the pre-defined partial situations in a database that are contained within the given situation … the analyzing all generated instances including combining the generated instances corresponding to the first partial situation and the second partial situation
(Rausch) – “The example methods described herein are very flexible and modular. Important influence factors may be added in an additive manner, existing scenarios are maintained, but may also be automatically expanded by the new influence factors. The model-based approach allows for simple transferability to other countries. A modular description of the individual effects that contribute to a complex behavior decision (for example, consideration of a pedestrian crosswalk separate from an intersection at which it is located) is enabled by an abstraction of traffic situations into logical zones. This results in a high reduction of the complexity and a high degree of reusability.” (Para 0026)
“The zones in the zone graphs, and thus the zone graphs, represent abstractions of the respective situations in such a way that they are separate from specific structures of the digital map, such as curve radii or angles of an intersection. These logical zones may be mapped as physical zones onto the map.” (Para 0057)
“With a behavior analysis, it is then possible to analyze systematically and fully the scope of the possible scenarios and to fully and consistently divide them into equivalence classes. An equivalence class in this case includes all situations, in which the vehicle under consideration behaves identically or should behave identically.” (Para 0061)
“Analyses are preferably carried out for particular, generic road segments (simple road segments, simple intersections, simple traffic circle, simple parking lot, etc.) and stored as models in a corresponding model library. Relatively few such generic models are sufficient for covering a large portion of all possible or actually existing road segments. With a selection of the relevant characteristics of such generic road segments, it is thus possible to create a small configurable and parameterizable model library, on the basis of which more complex road maps may then be easily calculated. Thus, for example, T-intersections of different types may be produced from the generic model of a four-way intersection by omitting one branch of the intersection. The angles between the branches of the intersection, the lane widths, the number of lanes, the placement of yield signs and traffic signals may, in particular, be parameters in the models.” (Para 0139)
“Two road map segments are shown in FIG. 9 in the form of road schemes, which may be combined. The road map segments include: the vehicle under consideration starting on the left side below, whose intended maneuver is marked by a sequence of thin arrows and guides the vehicle on the right side below, additional road users (vehicles with intended maneuvers marked by thick arrows, pedestrians as boxes), traffic lights and traffic signs, pedestrian crosswalks.” (Para 0140-0144)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the method for controlling a vehicle of Rausch. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, because “This results in a high reduction of the complexity and a high degree of reusability.” (Rausch Para 0026)
Claim 14:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl further teaches:
wherein the determined boundary conditions for the possible behaviors of the EGO vehicle are used as filters for present trajectory candidates, in order to sort out the trajectory candidates that are not compatible with the determined boundary conditions.
(Danzl) – “The driver assistance system may be designed in particular to calculate multiple drivable trajectories based upon the current position of the motor vehicle, with at least one boundary condition determined from ego data and/or from environmental data, and the switchover condition may be designed to evaluate the drivable trajectories. The prediction of future driving situations in detecting switchover conditions, particularly the fact that a driving situation requires driving in the range of physical limits or beyond the comfort range of the driver, can also be accomplished by calculating a multitude of potential trajectories and evaluating the trajectories. In particular, those trajectories are considered to be drivable trajectories which may be executed without negatively impacting people, the motor vehicle, and other objects. In particular, when determining drivable trajectories, parameters of the motor vehicle itself may also be considered, such as a maximum steering angle, maximum possible accelerations, the friction coefficient between tires and road, or the like. The drivability of the trajectories can also be ensured by applying the boundary conditions.” (Para 0022)
Claim 15:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl does not explicitly teach the following limitations. Rausch further teaches:
wherein the first partial situation represents a pedestrian crossing a path of the EGO vehicle and the second partial situation represents another vehicle crossing the path of the EGO vehicle.
(Rausch) – “The example methods described herein are very flexible and modular. Important influence factors may be added in an additive manner, existing scenarios are maintained, but may also be automatically expanded by the new influence factors. The model-based approach allows for simple transferability to other countries. A modular description of the individual effects that contribute to a complex behavior decision (for example, consideration of a pedestrian crosswalk separate from an intersection at which it is located) is enabled by an abstraction of traffic situations into logical zones. This results in a high reduction of the complexity and a high degree of reusability.” (Para 0026)
“The zones in the zone graphs, and thus the zone graphs, represent abstractions of the respective situations in such a way that they are separate from specific structures of the digital map, such as curve radii or angles of an intersection. These logical zones may be mapped as physical zones onto the map.” (Para 0057)
“With a behavior analysis, it is then possible to analyze systematically and fully the scope of the possible scenarios and to fully and consistently divide them into equivalence classes. An equivalence class in this case includes all situations, in which the vehicle under consideration behaves identically or should behave identically.” (Para 0061)
“Analyses are preferably carried out for particular, generic road segments (simple road segments, simple intersections, simple traffic circle, simple parking lot, etc.) and stored as models in a corresponding model library. Relatively few such generic models are sufficient for covering a large portion of all possible or actually existing road segments. With a selection of the relevant characteristics of such generic road segments, it is thus possible to create a small configurable and parameterizable model library, on the basis of which more complex road maps may then be easily calculated. Thus, for example, T-intersections of different types may be produced from the generic model of a four-way intersection by omitting one branch of the intersection. The angles between the branches of the intersection, the lane widths, the number of lanes, the placement of yield signs and traffic signals may, in particular, be parameters in the models.” (Para 0139)
“Two road map segments are shown in FIG. 9 in the form of road schemes, which may be combined. The road map segments include: the vehicle under consideration starting on the left side below, whose intended maneuver is marked by a sequence of thin arrows and guides the vehicle on the right side below, additional road users (vehicles with intended maneuvers marked by thick arrows, pedestrians as boxes), traffic lights and traffic signs, pedestrian crosswalks.” (Para 0140-0144)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the method for controlling a vehicle of Rausch. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, because “This results in a high reduction of the complexity and a high degree of reusability.” (Rausch Para 0026)
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Danzl (US20160200317) in view of Censi (US20190079527) further in view of Rausch (US20200406928) further in view of Neurohr (“Criticality Analysis for the Verification and Validation of Automated Vehicles”).
Claim 4:
Danzl in combination with the references relied upon in Claim 1 teach those respective limitations. Danzl does not explicitly teach the following limitations. Rausch, in the same field of endeavor of vehicle control, teaches:
wherein at least some of the evaluation models are based on a decomposition of relevant partial situations into zone graphs and
(Rausch) – “The example methods described herein are very flexible and modular. Important influence factors may be added in an additive manner, existing scenarios are maintained, but may also be automatically expanded by the new influence factors. The model-based approach allows for simple transferability to other countries. A modular description of the individual effects that contribute to a complex behavior decision (for example, consideration of a pedestrian crosswalk separate from an intersection at which it is located) is enabled by an abstraction of traffic situations into logical zones. This results in a high reduction of the complexity and a high degree of reusability.” (Para 0026)
“The zones in the zone graphs, and thus the zone graphs, represent abstractions of the respective situations in such a way that they are separate from specific structures of the digital map, such as curve radii or angles of an intersection. These logical zones may be mapped as physical zones onto the map.” (Para 0057)
“With a behavior analysis, it is then possible to analyze systematically and fully the scope of the possible scenarios and to fully and consistently divide them into equivalence classes. An equivalence class in this case includes all situations, in which the vehicle under consideration behaves identically or should behave identically.” (Para 0061)
“Analyses are preferably carried out for particular, generic road segments (simple road segments, simple intersections, simple traffic circle, simple parking lot, etc.) and stored as models in a corresponding model library. Relatively few such generic models are sufficient for covering a large portion of all possible or actually existing road segments. With a selection of the relevant characteristics of such generic road segments, it is thus possible to create a small configurable and parameterizable model library, on the basis of which more complex road maps may then be easily calculated. Thus, for example, T-intersections of different types may be produced from the generic model of a four-way intersection by omitting one branch of the intersection. The angles between the branches of the intersection, the lane widths, the number of lanes, the placement of yield signs and traffic signals may, in particular, be parameters in the models.” (Para 0139)
“Two road map segments are shown in FIG. 9 in the form of road schemes, which may be combined. The road map segments include: the vehicle under consideration starting on the left side below, whose intended maneuver is marked by a sequence of thin arrows and guides the vehicle on the right side below, additional road users (vehicles with intended maneuvers marked by thick arrows, pedestrians as boxes), traffic lights and traffic signs, pedestrian crosswalks.” (Para 0140-0144)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the method for controlling a vehicle of Rausch. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, because “This results in a high reduction of the complexity and a high degree of reusability.” (Rausch Para 0026)
Rausch does not explicitly teach the following limitations:
a morphological behavior analysis of road users involved.
Neurohr, in the same field of endeavor of vehicle control, teaches:
a morphological behavior analysis of road users involved.
(Neurohr) – “The SOCA method introduced by Rittel et al. [14] is based on an abstraction from concrete road geometries and concrete population with objects as well as other traffic participants called zone graphs. This concept is used together with the SCODE method for the description and analysis of traffic situations. SCODE is a morphological analysis used at BOSCH,
which is based on the essential analysis described in [59] as well as the morphological analysis of F. Zwicky [90]. Figure 17 shows the zone graph for the running example on a sketch of a T-intersection, displaying the different types of zones that have to be considered in that situation. The zones the ego will be passing along its intended path, marked by the red arrows, are called driving zones (Y, F, G and H in this example). The position zones K and L denote the areas where
other vehicles can get in conflict with the driving path.” (Pg. 18036)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the driver assistance system of Danzl with the criticality analysis of Neurohr. One of ordinary skill in the art would have been motivated to make these modifications, with a reasonable expectation of success, in order to “to derive safety principles and mitigation mechanisms for automated driving and to set up a coherent safety argument for the homologation process” (Neurohr Abstract)
Response to Arguments
The Claim Objections mailed 11/10/2025 have been withdrawn because the “amendment” and “response” filed 01/14/2026 satisfactorily overcome this objection.
The 35 U.S.C. 112 rejections mailed 11/10/2025 have been withdrawn because the “amendment” and “response” filed 01/14/2026 satisfactorily overcome this objection.
Applicant’s arguments with respect to the 35 U.S.C. 103 rejections mailed 11/10/2025 have been considered but are not convincing. Specifically, all claims are now rejected further in view of Rausch as necessitated by amendment. Examiner maintains that Rausch resolves any deficiencies of the previously recited prior art as evidenced in the amended rejection rationale.
Therefore, all outstanding claims remain rejected over the prior art.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Schoeggl (US20190111933) teaches rule-based driver assistance.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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 mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID RUBEN PEDERSEN whose telephone number is (571)272-9696. The examiner can normally be reached M-Th: 07:00 -16:00 Eastern.
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/DAVID RUBEN PEDERSEN/Examiner, Art Unit 3658
/Ramon A. Mercado/Supervisory Patent Examiner, Art Unit 3658
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