DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
*Examiner Note: Claim language is bolded. Cited References are italicized. Examiner interpretations are preceded with an asterisk *.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 11/25/2025 has been entered.
Response to Arguments
Applicant's arguments filed November 25, 2025 have been fully considered but they are moot because the amendments made have presented a combination of elements directed towards newly added elements that have necessitated new grounds of rejection.
Response to Amendment
Regarding the previous rejections, the amendments made to the claims fail to overcome the prior art and have necessitated new grounds of rejection as outlined below. While the new ground of rejection may rely on some of the previous reference applied in the prior rejection of record, new additional references have been added to the combination and introduced for Applicant’s consideration given the amended independent claims. The new grounds of rejection are outlined below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 4, 6, 9, 11, 14 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over Yu (US 2019/0272433 A1) in view of Gross (US 2014/0244111A1) in view of Damerow (US 2015/0344030A1) and further in view of Fritsch (US 2013/0179382A1).
Regarding claim 1, Yu discloses A driver assistance system (see at least para. [0035] of Yu
which discloses “the autonomous control unit may be configured to control the vehicle 105 for operation without a driver or to provide driver assistance in controlling the vehicle 105”) comprising:
at least one processor (Fig. 14, 702 and see at least para. [0080] of Yu which discloses “a data processor 702”); and
a program memory (Fig. 14, 704 and see at least para. [0080] of Yu which discloses “a memory 704”) communicatively coupled (see at least para. [0043] of Yu which discloses “The device elements that make up vehicle 105 could be communicatively coupled together”) to the at least one processor (see at least para. [0080] of Yu which discloses “a data processor 702 (e.g., a System-on-a-Chip (SoC), general processing core, graphics core, and optionally other processing logic) and a memory 704, which can communicate with each other via a bus or other data transfer system 706”), the program memory storing a plurality of executable instructions (see at least para. [0039] of Yu which discloses “the data storage device 172 may contain processing instructions (e.g., program logic) executable by the data processor 171 to perform various functions” and see at least para. [0079] of Yu which discloses “a machine in the example form of a computing system 700 within which a set of instructions when executed and/or processing logic when activated may cause the machine to perform any one or more of the methodologies described”) configured to cause the at least one processor to:
extract risk target information related to a potential risk target (see at least para. [0035] of Yu which discloses “a control system configured to identify, evaluate, and avoid or otherwise negotiate potential obstacles in the environment of the vehicle”, *Specifically, Yu discloses a control system configured to identify and evaluate potential obstacles in the environment of the vehicle (see para. [0035]), wherein such potential obstacles constitute potential risk targets) that is present in front of a vehicle and creates a blind spot (see at least para. [0047] of Yu which discloses “the occlusion status of each vehicle captured in a static image or an image sequence. An example embodiment formulates vehicle occlusion detection as a classification problem in computer vision. In the example embodiment, for one vehicle, we classify the vehicle's occlusion status into one or more of four classes: Class 0—the vehicle occludes other vehicles, Class 1—the vehicle is occluded by other vehicles, Class 2—the vehicle is separate from other vehicles, and Class 3—the vehicle is in between two other vehicles”,*A vehicle that occludes other vehicles necessarily creates an occluded region behind it relative to the vehicle, corresponding to a blind spot) for the vehicle from information related to a peripheral situation of the vehicle (see at least para. [0068] of Yu which discloses “the dynamic features of the vehicle object are extracted and provided to the second classifier 212, which determines the occlusion status for the vehicle object (operation block 542) based on the dynamic features of the vehicle object”, *This corresponds to extracting risk target information related to a potential risk target that creates a blind spot from information related to a peripheral situation of the vehicle);
Yu may not explicitly disclose obtain natural phenomenon information related to a natural
phenomenon that affects visibility of the vehicle from a pedestrian hiding in the blind spot of the potential risk target; determine a risk parameter that quantifies the collision risk such that the collision risk increases as the visibility worsens, based on the risk target information and the natural phenomenon information; determine a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter; control the movement of the vehicle, in response to determining the manipulated variable of the actuator, to follow a trajectory; list candidates for a predicted risk vector based on the risk target information; and calculate a prediction accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration.
However, in the same field of endeavor, Gross discloses obtain natural phenomenon
information related to a natural phenomenon that affects visibility of the vehicle from a pedestrian hiding in the blind spot of the potential risk target (see at least para. [0026] of Gross which discloses “a low visibility condition (e.g. presence of rain, snow, or fog). Recognizing this, in situations where the controller 12 receives one or more inputs indicating the presence of a low visibility condition, the controller 12 can modify the speed threshold, as shown in step 300. The controller 12 tends to decrease the speed threshold when a low visibility is present“, *Such natural phenomenon information directly relates to visibility degradation that would impact a pedestrian hiding in a blind spot. Therefore, Gross teaches obtaining natural phenomenon information related to a natural phenomenon that affects visibility of the vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the driver assistance system of Yu to include obtaining natural phenomenon information related to a natural phenomenon that affects visibility of the vehicle from a pedestrian hiding in the blind spot of the potential risk target; as taught in Gross with a reasonable expectation of success in order to improve accuracy and reliability of collision risk assessment under low-visibility conditions, since degraded visibility caused by natural phenomena such as rain, snow or fog increases uncertainty in detecting pedestrians hidden by blind spots and ultimately increases collision risk. See para. [0026] of Gross for motivation.
Yu, as modified by Gross may not explicitly disclose determine a risk parameter that
quantifies the collision risk such that the collision risk increases as the visibility worsens, based on the risk target information and the natural phenomenon information; determine a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter; control the movement of the vehicle, in response to determining the manipulated variable of the actuator, to follow a trajectory; list candidates for a predicted risk vector based on the risk target information; and calculate a prediction accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration.
However, in the same field of endeavor, Damerow discloses determine a risk parameter
that quantifies (see at least para. [0028] of Damerow which discloses “two trajectories are used to calculate a momentary risk indicator, which quantifies the risk probability for that exact moment in time”) the collision risk (see at least para. [0028] of Damerow which discloses “we say that the collision probability is 1. The collision risk can then be calculated using further states at that moment in time, like the angles with which they collided and the velocities and masses involved”, *Assigning a numerical value of 1 corresponds to quantifying the collision risk) such that the collision risk increases as the visibility worsens (see at least para. [0026] of Gross which discloses “a low visibility condition (e.g. presence of rain, snow, or fog)”, *a low visibility condition corresponds to worsening visibility), based on the risk target information and the natural phenomenon information (As discussed, above Gross teaches obtaining natural phenomenon information indicating low-visibility conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the low-visibility information of Gross into the collision risk determination of Damerow such that the quantified collision risk increases as visibility worsens, because reduced visibility increases uncertainty in detecting risk targets and therefore increases the chances of a collision); determine a manipulated variable of an actuator for controlling movement of the vehicle (see at least para. [0016] of Damerow which discloses “a control signal is generated on the basis of the analysis of the risk map. This signal either includes information about the risk on an intended travel path which can be used for driver warning or it includes information about an action that is to be taken by vehicle control systems like motor management or brake systems for autonomously accelerating or deceleration the vehicle”, *Vehicle brakes, steering mechanisms and throttle systems are actuators that control vehicle movement and determining braking force, steering angle or acceleration constitutes determining manipulated variables of actuators) to reduce the collision risk based on the risk parameter (see at least para. [0044] of Damerow which discloses “any situation has an inherent risk, especially when extrapolated into some future, even if the current state combination of e.g. an ego-car and another car does not lead to a collision. Continuous risk indicators depend on the classical parameters that are associated with physical risk, e.g. distance between cars, the current relative heading angles, the masses and velocities (as e.g. needed for an impact calculation), but also single car indicators like centrifugal acceleration at a certain curve point for a certain velocity, etc.). The underlying assumption is that by the continuous risk measures we capture the inherent uncertainty in e.g. the sensor measurements, the state estimation of others, the behavior variability, etc.”, *Adjusting speed, braking or steering necessarily involves determining manipulated variables (i.e., braking force, acceleration or steering angles) for actuators that control vehicle movement. Therefore, Damerow teaches determining a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter); control the movement of the vehicle (see at least para. [0014] of Damerow which discloses “control systems for executing autonomous driving actions” and see at least para. [0016] of Damerow which discloses “control systems like motor management or brake systems for autonomously accelerating or deceleration the vehicle”), in response to determining the manipulated variable of the actuator (see at least para. [0060] of Damerow which discloses “The chosen path will then again be the basis for actuation support, control, or situation and risk dependent warning”), to follow a trajectory (see at least para. [0063] of Damerow which discloses “the evaluation or analysis of the risk map a control signal is output. This control signal either includes an information about risks on the intended travel path (corresponding to the predicted trajectory) … the driving state of the vehicle is controlled in such a way that the selected preferred path through the risk map is followed”, *the risk map preferred path corresponds to an example of a trajectory); list (see at least para. [0026] of Damerow which discloses “Trajectory: A set of state vectors (a list of values that quantify selected states of scene elements) over discrete or continuous points in time”) candidates (see at least para. [0060] of Damerow which discloses “possible candidates according to some criteria (like a mixture between evidence for the class and past experienced risk for the involved class”) for a predicted risk vector based on the risk target information (see at least para. [0060] of Damerow which discloses “criteria (like a mixture between evidence for the class and past experienced risk for the involved class). Typically, the situations represent discrete behavioral choices of other traffic participants. For each chosen situation candidate, we build a separate risk map”, *these criteria correspond to a type of risk target information).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the driver assistance system of Yu, as modified by Gross to include determining a risk parameter that quantifies the collision risk such that the collision risk increases as the visibility worsens, based on the risk target information and the natural phenomenon information; determining a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter; controlling the movement of the vehicle, in response to determining the manipulated variable of the actuator, to follow a trajectory; listing candidates for a predicted risk vector based on the risk target information; as taught in Damerow with a reasonable expectation of success in order to facilitate the driver assistance system’s evaluation of multiple candidate future risk scenarios and selection and execution of a vehicle control trajectory that reduces collision risk under reduced visibility conditions, since incorporating quantified risk assessment and risk based actuator control into an occlusion aware driver assistance system will improve safety and decision making ability.
Yu, as modified by Gross and Damerow may not explicitly disclose calculate a prediction
accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration.
However, in the same field of endeavor, Fritsch discloses calculate a prediction accuracy
(see at least para. [0039] of Fritsch which discloses “accurate probability distributions of the potential future states of other traffic participants” and see at least para. [0069] of Fritsch which discloses “generating more accurate state estimations. More accurate state estimations of other agents can again be used to better determine the maximally appropriate ego-car control or also to deduce more effective and valid warning signals”) for each predicted risk vector in which the natural phenomenon information is not taken into consideration (Fritsch discloses generating expected future state for detected traffic objects using probability distributions that represent possible behaviors, where each predicted behavior observation is associated with a probability indicating the likelihood of that behavior. Such probability values function as a measure of prediction accuracy for each candidate prediction. The probability based predictions are determined from object/motion position data and don’t rely on natural phenomenon information such as weather conditions. Accordingly, Fritsch teaches calculating a prediction accuracy for each predicted risk vector in which the natural information is not taken into consideration).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the driver assistance system of Yu as modified by Gross and Damerow to include calculate a prediction accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration; as taught in Fritsch with a reasonable expectation of success in order to evaluate the reliability of multiple predicted risk scenarios independently of environmental visibility conditions and thereby improve selection of appropriate vehicle control actions, since determining a confidence or accuracy associated with each predicted outcome is a known technique for improving decision making in risk based vehicle control systems.
Regarding claim 4, Yu, as modified by Gross, Damerow and Fritsch discloses wherein the
obtaining the natural phenomenon information comprises obtaining information related to weather (see at least para. [0026] of Gross which discloses “a low visibility condition (e.g. presence of rain, snow, or fog). Recognizing this, in situations where the controller 12 receives one or more inputs indicating the presence of a low visibility condition, the controller 12 can modify the speed threshold, as shown in step 300. The controller 12 tends to decrease the speed threshold when a low visibility is present“, *Such natural phenomenon information directly relates to visibility degradation that would impact a pedestrian hiding in a blind spot. Therefore, Gross teaches obtaining natural phenomenon information that is weather related and relates to a natural phenomenon that affects visibility of the vehicle).
Regarding claim 6, Yu discloses A driver assistance method (see at least para. [0035] of Yu
which discloses “the autonomous control unit may be configured to control the vehicle 105 for operation without a driver or to provide driver assistance in controlling the vehicle 105”) comprising: extracting risk target information related to a potential risk target (see at least para. [0035] of Yu which discloses “a control system configured to identify, evaluate, and avoid or otherwise negotiate potential obstacles in the environment of the vehicle”, *Specifically, Yu discloses a control system configured to identify and evaluate potential obstacles in the environment of the vehicle (see para. [0035]), wherein such potential obstacles constitute potential risk targets) that is present in front of a vehicle and creates a blind spot (see at least para. [0047] of Yu which discloses “the occlusion status of each vehicle captured in a static image or an image sequence. An example embodiment formulates vehicle occlusion detection as a classification problem in computer vision. In the example embodiment, for one vehicle, we classify the vehicle's occlusion status into one or more of four classes: Class 0—the vehicle occludes other vehicles, Class 1—the vehicle is occluded by other vehicles, Class 2—the vehicle is separate from other vehicles, and Class 3—the vehicle is in between two other vehicles”,*A vehicle that occludes other vehicles necessarily creates an occluded region behind it relative to the vehicle, corresponding to a blind spot) for the vehicle from information related to a peripheral situation of the vehicle (see at least para. [0068] of Yu which discloses “the dynamic features of the vehicle object are extracted and provided to the second classifier 212, which determines the occlusion status for the vehicle object (operation block 542) based on the dynamic features of the vehicle object”, *This corresponds to extracting risk target information related to a potential risk target that creates a blind spot from information related to a peripheral situation of the vehicle).
Yu may not explicitly disclose obtaining natural phenomenon information related to a
natural phenomenon that affects visibility of the vehicle from a pedestrian hiding in the blind spot of the potential risk target; determining a risk parameter that quantifies the collision risk such that the collision risk increases as the visibility worsens, based on the risk target information and the natural phenomenon information; determining a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter; controlling the movement of the vehicle, in response to determining the manipulated variable of the actuator, to follow a trajectory; listing candidates for a predicted risk vector based on the risk target information; and calculating a prediction accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration.
However, in the same field of endeavor, Gross discloses obtaining natural phenomenon
information related to a natural phenomenon that affects visibility of the vehicle from a pedestrian hiding in the blind spot of the potential risk target (see at least para. [0026] of Gross which discloses “a low visibility condition (e.g. presence of rain, snow, or fog). Recognizing this, in situations where the controller 12 receives one or more inputs indicating the presence of a low visibility condition, the controller 12 can modify the speed threshold, as shown in step 300. The controller 12 tends to decrease the speed threshold when a low visibility is present“, *Such natural phenomenon information directly relates to visibility degradation that would impact a pedestrian hiding in a blind spot. Therefore, Gross teaches obtaining natural phenomenon information related to a natural phenomenon that affects visibility of the vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the driver assistance system of Yu to include obtaining natural phenomenon information related to a natural phenomenon that affects visibility of the vehicle from a pedestrian hiding in the blind spot of the potential risk target; as taught in Gross with a reasonable expectation of success in order to improve accuracy and reliability of collision risk assessment under low-visibility conditions, since degraded visibility caused by natural phenomena such as rain, snow or fog increases uncertainty in detecting pedestrians hidden by blind spots and ultimately increases collision risk. See para. [0026] of Gross for motivation.
Yu, as modified by Gross may not explicitly disclose determining a risk parameter that
quantifies the collision risk such that the collision risk increases as the visibility worsens, based on the risk target information and the natural phenomenon information; determining a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter; controlling the movement of the vehicle, in response to determining the manipulated variable of the actuator, to follow a trajectory; listing candidates for a predicted risk vector based on the risk target information; and calculating a prediction accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration.
However, in the same field of endeavor, Damerow discloses determining a risk parameter
that quantifies (see at least para. [0028] of Damerow which discloses “two trajectories are used to calculate a momentary risk indicator, which quantifies the risk probability for that exact moment in time”) the collision risk (see at least para. [0028] of Damerow which discloses “we say that the collision probability is 1. The collision risk can then be calculated using further states at that moment in time, like the angles with which they collided and the velocities and masses involved”, *Assigning a numerical value of 1 corresponds to quantifying the collision risk) such that the collision risk increases as the visibility worsens (see at least para. [0026] of Gross which discloses “a low visibility condition (e.g. presence of rain, snow, or fog)”, *a low visibility condition corresponds to worsening visibility), based on the risk target information and the natural phenomenon information (As discussed, above Gross teaches obtaining natural phenomenon information indicating low-visibility conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the low-visibility information of Gross into the collision risk determination of Damerow such that the quantified collision risk increases as visibility worsens, because reduced visibility increases uncertainty in detecting risk targets and therefore increases the chances of a collision); determining a manipulated variable of an actuator for controlling movement of the vehicle (see at least para. [0016] of Damerow which discloses “a control signal is generated on the basis of the analysis of the risk map. This signal either includes information about the risk on an intended travel path which can be used for driver warning or it includes information about an action that is to be taken by vehicle control systems like motor management or brake systems for autonomously accelerating or deceleration the vehicle”, *Vehicle brakes, steering mechanisms and throttle systems are actuators that control vehicle movement and determining braking force, steering angle or acceleration constitutes determining manipulated variables of actuators) to reduce the collision risk based on the risk parameter (see at least para. [0044] of Damerow which discloses “any situation has an inherent risk, especially when extrapolated into some future, even if the current state combination of e.g. an ego-car and another car does not lead to a collision. Continuous risk indicators depend on the classical parameters that are associated with physical risk, e.g. distance between cars, the current relative heading angles, the masses and velocities (as e.g. needed for an impact calculation), but also single car indicators like centrifugal acceleration at a certain curve point for a certain velocity, etc.). The underlying assumption is that by the continuous risk measures we capture the inherent uncertainty in e.g. the sensor measurements, the state estimation of others, the behavior variability, etc.”, *Adjusting speed, braking or steering necessarily involves determining manipulated variables (i.e., braking force, acceleration or steering angles) for actuators that control vehicle movement. Therefore, Damerow teaches determining a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter); controlling the movement of the vehicle (see at least para. [0014] of Damerow which discloses “control systems for executing autonomous driving actions” and see at least para. [0016] of Damerow which discloses “control systems like motor management or brake systems for autonomously accelerating or deceleration the vehicle”), in response to determining the manipulated variable of the actuator (see at least para. [0060] of Damerow which discloses “The chosen path will then again be the basis for actuation support, control, or situation and risk dependent warning”), to follow a trajectory (see at least para. [0063] of Damerow which discloses “the evaluation or analysis of the risk map a control signal is output. This control signal either includes an information about risks on the intended travel path (corresponding to the predicted trajectory) … the driving state of the vehicle is controlled in such a way that the selected preferred path through the risk map is followed”, *the risk map preferred path corresponds to an example of a trajectory); listing (see at least para. [0026] of Damerow which discloses “Trajectory: A set of state vectors (a list of values that quantify selected states of scene elements) over discrete or continuous points in time”) candidates (see at least para. [0060] of Damerow which discloses “possible candidates according to some criteria (like a mixture between evidence for the class and past experienced risk for the involved class”) for a predicted risk vector based on the risk target information (see at least para. [0060] of Damerow which discloses “criteria (like a mixture between evidence for the class and past experienced risk for the involved class). Typically, the situations represent discrete behavioral choices of other traffic participants. For each chosen situation candidate, we build a separate risk map”, *these criteria correspond to a type of risk target information). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the driver assistance system of Yu, as modified by Gross to include determining a risk parameter that quantifies the collision risk such that the collision risk increases as the visibility worsens, based on the risk target information and the natural phenomenon information; determining a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter; controlling the movement of the vehicle, in response to determining the manipulated variable of the actuator, to follow a trajectory; listing candidates for a predicted risk vector based on the risk target information; as taught in Damerow with a reasonable expectation of success in order to facilitate the driver assistance system’s evaluation of multiple candidate future risk scenarios and selection and execution of a vehicle control trajectory that reduces collision risk under reduced visibility conditions, since incorporating quantified risk assessment and risk based actuator control into an occlusion aware driver assistance system will improve safety and decision making ability.
Yu, as modified by Gross and Damerow may not explicitly disclose calculating a prediction
accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration.
However, in the same field of endeavor, Fritsch discloses calculating a prediction accuracy
(see at least para. [0039] of Fritsch which discloses “accurate probability distributions of the potential future states of other traffic participants” and see at least para. [0069] of Fritsch which discloses “generating more accurate state estimations. More accurate state estimations of other agents can again be used to better determine the maximally appropriate ego-car control or also to deduce more effective and valid warning signals”) for each predicted risk vector in which the natural phenomenon information is not taken into consideration (Fritsch discloses generating expected future state for detected traffic objects using probability distributions that represent possible behaviors, where each predicted behavior observation is associated with a probability indicating the likelihood of that behavior. Such probability values function as a measure of prediction accuracy for each candidate prediction. The probability based predictions are determined from object/motion position data and don’t rely on natural phenomenon information such as weather conditions. Accordingly, Fritsch teaches calculating a prediction accuracy for each predicted risk vector in which the natural information is not taken into consideration).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the driver assistance system of Yu as modified by Gross and Damerow to include calculating a prediction accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration; as taught in Fritsch with a reasonable expectation of success in order to evaluate the reliability of multiple predicted risk scenarios independently of environmental visibility conditions and thereby improve selection of appropriate vehicle control actions, since determining a confidence or accuracy associated with each predicted outcome is a known technique for improving decision making in risk based vehicle control systems.
Regarding claim 9, Yu, as modified by Gross, Damerow and Fritsch discloses wherein the
obtaining the natural phenomenon information comprises obtaining information related to weather (see at least para. [0026] of Gross which discloses “a low visibility condition (e.g. presence of rain, snow, or fog). Recognizing this, in situations where the controller 12 receives one or more inputs indicating the presence of a low visibility condition, the controller 12 can modify the speed threshold, as shown in step 300. The controller 12 tends to decrease the speed threshold when a low visibility is present“, *Such natural phenomenon information directly relates to visibility degradation that would impact a pedestrian hiding in a blind spot. Therefore, Gross teaches obtaining natural phenomenon information that is weather related and relates to a natural phenomenon that affects visibility of the vehicle).
Regarding claim 11, Yu discloses A non-transitory computer-readable storage medium
storing a driver assistance program configured to cause a computer to execute processing (see at least para. [0039] of Yu which discloses “The computing system 170 may include at least one data processor 171 (which can include at least one microprocessor) that executes processing instructions stored in a non-transitory computer readable medium, such as the data storage device 172”), the processing comprising: extracting risk target information related to a potential risk target (see at least para. [0035] of Yu which discloses “a control system configured to identify, evaluate, and avoid or otherwise negotiate potential obstacles in the environment of the vehicle”, *Specifically, Yu discloses a control system configured to identify and evaluate potential obstacles in the environment of the vehicle (see para. [0035]), wherein such potential obstacles constitute potential risk targets) that is present in front of a vehicle and creates a blind spot (see at least para. [0047] of Yu which discloses “the occlusion status of each vehicle captured in a static image or an image sequence. An example embodiment formulates vehicle occlusion detection as a classification problem in computer vision. In the example embodiment, for one vehicle, we classify the vehicle's occlusion status into one or more of four classes: Class 0—the vehicle occludes other vehicles, Class 1—the vehicle is occluded by other vehicles, Class 2—the vehicle is separate from other vehicles, and Class 3—the vehicle is in between two other vehicles”,*A vehicle that occludes other vehicles necessarily creates an occluded region behind it relative to the vehicle, corresponding to a blind spot) for the vehicle from information related to a peripheral situation of the vehicle (see at least para. [0068] of Yu which discloses “the dynamic features of the vehicle object are extracted and provided to the second classifier 212, which determines the occlusion status for the vehicle object (operation block 542) based on the dynamic features of the vehicle object”, *This corresponds to extracting risk target information related to a potential risk target that creates a blind spot from information related to a peripheral situation of the vehicle).
Yu may not explicitly disclose obtaining natural phenomenon information related to a
natural phenomenon that affects visibility of the vehicle from a pedestrian hiding in the blind spot of the potential risk target; determining a risk parameter that quantifies the collision risk such that the collision risk increases as the visibility worsens, based on the risk target information and the natural phenomenon information; determining a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter; controlling the movement of the vehicle, in response to determining the manipulated variable of the actuator, to follow a trajectory; listing candidates for a predicted risk vector based on the risk target information; and calculating a prediction accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration.
However, in the same field of endeavor, Gross discloses obtaining natural phenomenon
information related to a natural phenomenon that affects visibility of the vehicle from a pedestrian hiding in the blind spot of the potential risk target (see at least para. [0026] of Gross which discloses “a low visibility condition (e.g. presence of rain, snow, or fog). Recognizing this, in situations where the controller 12 receives one or more inputs indicating the presence of a low visibility condition, the controller 12 can modify the speed threshold, as shown in step 300. The controller 12 tends to decrease the speed threshold when a low visibility is present“, *Such natural phenomenon information directly relates to visibility degradation that would impact a pedestrian hiding in a blind spot. Therefore, Gross teaches obtaining natural phenomenon information related to a natural phenomenon that affects visibility of the vehicle).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the driver assistance system of Yu to include obtaining natural phenomenon information related to a natural phenomenon that affects visibility of the vehicle from a pedestrian hiding in the blind spot of the potential risk target; as taught in Gross with a reasonable expectation of success in order to improve accuracy and reliability of collision risk assessment under low-visibility conditions, since degraded visibility caused by natural phenomena such as rain, snow or fog increases uncertainty in detecting pedestrians hidden by blind spots and ultimately increases collision risk. See para. [0026] of Gross for motivation.
Yu, as modified by Gross may not explicitly disclose determining a risk parameter that
quantifies the collision risk such that the collision risk increases as the visibility worsens, based on the risk target information and the natural phenomenon information; determining a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter; controlling the movement of the vehicle, in response to determining the manipulated variable of the actuator, to follow a trajectory; listing candidates for a predicted risk vector based on the risk target information; and calculating a prediction accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration.
However, in the same field of endeavor, Damerow discloses determining a risk parameter
that quantifies (see at least para. [0028] of Damerow which discloses “two trajectories are used to calculate a momentary risk indicator, which quantifies the risk probability for that exact moment in time”) the collision risk (see at least para. [0028] of Damerow which discloses “we say that the collision probability is 1. The collision risk can then be calculated using further states at that moment in time, like the angles with which they collided and the velocities and masses involved”, *Assigning a numerical value of 1 corresponds to quantifying the collision risk) such that the collision risk increases as the visibility worsens (see at least para. [0026] of Gross which discloses “a low visibility condition (e.g. presence of rain, snow, or fog)”, *a low visibility condition corresponds to worsening visibility), based on the risk target information and the natural phenomenon information (As discussed, above Gross teaches obtaining natural phenomenon information indicating low-visibility conditions. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the low-visibility information of Gross into the collision risk determination of Damerow such that the quantified collision risk increases as visibility worsens, because reduced visibility increases uncertainty in detecting risk targets and therefore increases the chances of a collision); determining a manipulated variable of an actuator for controlling movement of the vehicle (see at least para. [0016] of Damerow which discloses “a control signal is generated on the basis of the analysis of the risk map. This signal either includes information about the risk on an intended travel path which can be used for driver warning or it includes information about an action that is to be taken by vehicle control systems like motor management or brake systems for autonomously accelerating or deceleration the vehicle”, *Vehicle brakes, steering mechanisms and throttle systems are actuators that control vehicle movement and determining braking force, steering angle or acceleration constitutes determining manipulated variables of actuators) to reduce the collision risk based on the risk parameter (see at least para. [0044] of Damerow which discloses “any situation has an inherent risk, especially when extrapolated into some future, even if the current state combination of e.g. an ego-car and another car does not lead to a collision. Continuous risk indicators depend on the classical parameters that are associated with physical risk, e.g. distance between cars, the current relative heading angles, the masses and velocities (as e.g. needed for an impact calculation), but also single car indicators like centrifugal acceleration at a certain curve point for a certain velocity, etc.). The underlying assumption is that by the continuous risk measures we capture the inherent uncertainty in e.g. the sensor measurements, the state estimation of others, the behavior variability, etc.”, *Adjusting speed, braking or steering necessarily involves determining manipulated variables (i.e., braking force, acceleration or steering angles) for actuators that control vehicle movement. Therefore, Damerow teaches determining a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter); controlling the movement of the vehicle (see at least para. [0014] of Damerow which discloses “control systems for executing autonomous driving actions” and see at least para. [0016] of Damerow which discloses “control systems like motor management or brake systems for autonomously accelerating or deceleration the vehicle”), in response to determining the manipulated variable of the actuator (see at least para. [0060] of Damerow which discloses “The chosen path will then again be the basis for actuation support, control, or situation and risk dependent warning”), to follow a trajectory (see at least para. [0063] of Damerow which discloses “the evaluation or analysis of the risk map a control signal is output. This control signal either includes an information about risks on the intended travel path (corresponding to the predicted trajectory) … the driving state of the vehicle is controlled in such a way that the selected preferred path through the risk map is followed”, *the risk map preferred path corresponds to an example of a trajectory); listing (see at least para. [0026] of Damerow which discloses “Trajectory: A set of state vectors (a list of values that quantify selected states of scene elements) over discrete or continuous points in time”) candidates (see at least para. [0060] of Damerow which discloses “possible candidates according to some criteria (like a mixture between evidence for the class and past experienced risk for the involved class”) for a predicted risk vector based on the risk target information (see at least para. [0060] of Damerow which discloses “criteria (like a mixture between evidence for the class and past experienced risk for the involved class). Typically, the situations represent discrete behavioral choices of other traffic participants. For each chosen situation candidate, we build a separate risk map”, *these criteria correspond to a type of risk target information).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the driver assistance system of Yu, as modified by Gross to include determining a risk parameter that quantifies the collision risk such that the collision risk increases as the visibility worsens, based on the risk target information and the natural phenomenon information; determining a manipulated variable of an actuator for controlling movement of the vehicle to reduce the collision risk based on the risk parameter; controlling the movement of the vehicle, in response to determining the manipulated variable of the actuator, to follow a trajectory; listing candidates for a predicted risk vector based on the risk target information; as taught in Damerow with a reasonable expectation of success in order to facilitate the driver assistance system’s evaluation of multiple candidate future risk scenarios and selection and execution of a vehicle control trajectory that reduces collision risk under reduced visibility conditions, since incorporating quantified risk assessment and risk based actuator control into an occlusion aware driver assistance system will improve safety and decision making ability.
Yu, as modified by Gross and Damerow may not explicitly disclose calculating a prediction
accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration.
However, in the same field of endeavor, Fritsch discloses calculating a prediction accuracy
(see at least para. [0039] of Fritsch which discloses “accurate probability distributions of the potential future states of other traffic participants” and see at least para. [0069] of Fritsch which discloses “generating more accurate state estimations. More accurate state estimations of other agents can again be used to better determine the maximally appropriate ego-car control or also to deduce more effective and valid warning signals”) for each predicted risk vector in which the natural phenomenon information is not taken into consideration (Fritsch discloses generating expected future state for detected traffic objects using probability distributions that represent possible behaviors, where each predicted behavior observation is associated with a probability indicating the likelihood of that behavior. Such probability values function as a measure of prediction accuracy for each candidate prediction. The probability based predictions are determined from object/motion position data and don’t rely on natural phenomenon information such as weather conditions. Accordingly, Fritsch teaches calculating a prediction accuracy for each predicted risk vector in which the natural information is not taken into consideration).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to modify the driver assistance system of Yu as modified by Gross and Damerow to include calculating a prediction accuracy for each predicted risk vector in which the natural phenomenon information is not taken into consideration; as taught in Fritsch with a reasonable expectation of success in order to evaluate the reliability of multiple predicted risk scenarios independently of environmental visibility conditions and thereby improve selection of appropriate vehicle control actions, since determining a confidence or accuracy associated with each predicted outcome is a known technique for improving decision making in risk based vehicle control systems.
Regarding claim 14, Yu, as modified by Gross, Damerow and Fritsch discloses wherein the
obtaining the natural phenomenon information comprises obtaining information related to weather (see at least para. [0026] of Gross which discloses “a low visibility condition (e.g. presence of rain, snow, or fog). Recognizing this, in situations where the controller 12 receives one or more inputs indicating the presence of a low visibility condition, the controller 12 can modify the speed threshold, as shown in step 300. The controller 12 tends to decrease the speed threshold when a low visibility is present“, *Such natural phenomenon information directly relates to visibility degradation that would impact a pedestrian hiding in a blind spot. Therefore, Gross teaches obtaining natural phenomenon information that is weather related and relates to a natural phenomenon that affects visibility of the vehicle).
Regarding claim 16, the combination of Yu, as modified by Gross, Damerow and Fritsch
discloses wherein the at least one processor is further configured to generate a target trajectory of the vehicle based on the predicted risk vector, the target trajectory including the trajectory along which the vehicle travels on and includes a set of target positions of the vehicle and a target speed at each target point (see at least para. [0057] of Damerow which discloses “a desired travel speed which is set for example be a cruise control system. For paths that require a greater deviation from the desired speed the benefit will be decreased. The final analysis of the risk map thus finds a compromise between the accepted risk and the efficiency. This avoids that the methods locks the ego vehicle in a minimum of the risk map with speed 0”, *Damerow further teaches selecting a preferred future path for the vehicle and planning motion along that path, which necessarily includes a sequence of target positions over time and corresponding target speeds along the trajectory. Therefore, Damerow teaches generating a target trajectory include a set of target positions and a target speed at each point, as broadly as recited).
It would have been obvious to one of ordinary skill in the art before the effective filing date
of the claimed invention to further modify the driver assistance system of Yu, as modified by Gross in order to implement risk based vehicle control using known trajectory planning techniques.
Regarding claim 17, Yu, as modified by Gross, Damerow and Fritsch discloses wherein the at
least one processor is further configured to increase a run-out speed in a case that a pedestrian or an oncoming vehicle run out into a traveling area of the vehicle (see at least para. [0033] of Yu which discloses “the RADAR unit may additionally be configured to sense the speed and the heading of the objects proximate to the vehicle 105”) by increasing a magnitude of the predicted risk vector heading toward a traveling lane of the vehicle (see at least para. [0051] of Damerow which discloses “In addition to the risk calculation for each timestep, in one particular embodiment we discount for the assumption that predictions lying more ahead in the future are less certain. This has the effect that maxima in the risk map lying in the more distant future are lower or broader than maxima in the near future and they will sharpen (they will get narrower) and increase as they get closer in time” and see at least para. [0053] of Damerow which discloses “an (overly simplified) situation of the ego-car entering a crossing approaching a car driving on the same road and in the same direction but slower, and with another car passing the crossing. The situation is shown in FIG. 7. The variation of the ego-car trajectory parameters may e.g. occur by a variation of the ego-car velocity. For high velocities, the ego-car might be able to pass before the other car comes, with relatively low risk. But then the ego-car will relatively fast approach the preceding car which causes another risk. For medium ego-car velocities, it might collide with or get very close to the other car somewhere in the intersection, and for very low ego-car velocities, the other car passes first and the ego-car can afterwards enter the crossing without danger”), *Increasing a run-out speed reasonably means increasing the systems predicted rate at which a pedestrian or oncoming vehicle is moving into the lane, reflected by a larger risk vector directed toward that lane. Yu further discloses detecting occluded objects and assessing the likelihood that such objects may emerge into the ego lane, corresponding to a pedestrian or oncoming vehicle that could run out into the travel area. Damerow teaches evaluating the predicted motion of surrounding objects and increasing the magnitude of a risk metric when an object’s predicted trajectory enters the vehicle’s travel lane. Specifically, Damerow teaches assigning higher collision risk values to predicted scenarios in which another vehicle or pedestrian moves into the path of the ego vehicle and adjusting vehicle control accordingly).
Additional Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Mazuir et al. (US 10,112,528 B1) disclose a driver assistance and warning system that includes sensors that may also gather information on relative speed between the vehicle and a following vehicle, information on risks of a collision between a vehicle and an external object. Lee et al. (US 2018/0148053 A1) discloses a driver assistance system that includes a vehicle velocity control apparatus that determines a maximum distance within which a visible range is obtained to be a visibility distance, in order to prevent a potential risk of collision.
Conclusion
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/DANA D IVEY/Examiner, Art Unit 3662 /D.D.I/February 2, 2026
/JELANI A SMITH/Supervisory Patent Examiner, Art Unit 3662