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:
Cited references are bold italicized. Examiner interpretations are preceded with an asterisk *.
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.
Claims 1-3 and 5-20 are rejected under 35 U.S.C. 103 as being unpatentable over in Takahashi et al. (US 20250050895 A1; hereinafter Takahashi) view of Han et al. (US 20190265060 A1; hereinafter Han).
Regarding claim 1, Takahashi teaches a system (see at least, Fig 1, [0015] The control device 1) comprising: one or more processors; and one or more non-transitory computer-readable media storing instructions executable by the one or more processors (see at least, [0014] a non-transitory computer readable medium (storage medium) that can be read by a machine such as a computer that stores such a program), wherein the instructions, when executed, cause the system to perform operations (see at least, [0046] The controller 11 includes a CPU…configured to execute arbitrary information processing based on a program and various data) comprising: receiving sensor data from a sensor associated (see at least, [0025] The environment is an event observed at least on the mobile body M itself and its surroundings…at least a portion of the environment may be observed by one or more sensors S disposed inside or outside the mobile body M ) with an autonomous vehicle (see at least, [0002] a mobile body such as an autonomous driving vehicle); a first planned trajectory usable to control the autonomous vehicle in an environment at a future time; a second planned trajectory usable to control the autonomous vehicle in the environment at the future time (see at least, Fig 1, [0015] Each control model (30, 35) is configured to derive a path to perform automatic control of the movement of the mobile body M; [0022] the paths (40, 45, 50) are configured to indicate the future travel path of the mobile body M and may be used to derive one or more control commands), determining a first cost to use the first planned trajectory at the future time; determining a second cost to use the second planned trajectory at the future time (see at least, Fig 1, [0037] the control device 1
may determine the ratio of each path (40, 45) according to the conformability criteria 20; [0022] the paths (40, 45, 50) are configured to indicate the future travel path of the mobile body M and may be used to derive one or more control commands); determining a bias value associated with a preference for following the first planned trajectory or the second planned trajectory (see at least, Fig 1, [0019] the conformability criteria 20 are adjusted to give greater weight to the use of the …second control model 35…i.e., to give priority to the second path 45); determining, as a control trajectory and based at least in part on the first cost, the second cost, and the bias value, one of the first planned trajectory or the second planned trajectory (see at least, Fig 1, [0037] Evaluating the deviation with the conformability criteria 20 may include determining a ratio of integration of each path (40, 45) according to the conformability criteria 20… if the deviation in speed between the first path 40 and the second path 45 exceeds the threshold of the conformability criteria 20…if not, may select the first path 40 as the final path 50); and controlling the autonomous vehicle in the environment based at least in part on the control trajectory (see at least, Fig 1, [0017] the control device 1 controls the operation (movement) of the mobile body M according to the generated final path 50).
[AltContent: connector][AltContent: textbox (Control trajectory )][AltContent: connector][AltContent: textbox (Second planned trajectory)][AltContent: connector][AltContent: connector][AltContent: textbox (Bias Value)][AltContent: textbox (First planned trajectory)]
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Takahashi does not explicitly teach determining, by a first model and based at least in part on the sensor data and a set of costs, determining, by a second model and based at least in part on the sensor data and the second model trained to determine the second planned trajectory independent of the set of costs. However, Han teaches these limitations.
Han teaches determining, by a first model and based at least in part on the sensor data and a set of costs (see at least, [0013] first driving route to the destination based on the destination information and the traffic information provided to a trained first mode; [0016] The first driving route may be one of a minimum time driving route, a shortest distance driving route, and a least cost driving route from departure to destination), determining, by a second model and based at least in part on the sensor data (see at least, [0060] a second driving route based on, for example, and without limitation, the destination information and the user's driving history provided to the trained second model) and the second model trained to determine the second planned trajectory independent of the set of costs (see at least, [0060] a second driving route based on, for example, and without limitation, the destination information and the user's driving history provided to the trained second model. The second driving route may, for example, be a driving route usually preferred by a user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Takahashi to include determining, by a first model and based at least in part on the sensor data and a set of costs, determining, by a second model and based at least in part on the sensor data and the second model trained to determine the second planned trajectory independent of the set of costs as taught by Han in order to determines an optimal route from departure to destination, and drives a vehicle along the determined optimal route (Han, [0008]).
Regarding claim 2, the combination of Takahashi and Han teaches the system of claim 1. Takahashi further teaches wherein: the bias value represents a weight associated with one of: the first cost or the second cost to indicate a preference for the first planned trajectory or the second planned trajectory (see at least, [0019] the conformability criteria 20 are adjusted to give greater weight to the use of the …second control model 35…i.e., to give priority to the second path 45), and determining to use one of: the first planned trajectory or the second planned trajectory based at least in part on comparing the first cost, the second cost, and the weight (see at least, [0037] Evaluating the deviation with the conformability criteria 20 may include determining a ratio of integration of each path (40, 45) according to the conformability criteria 20… if the deviation in speed between the first path 40 and the second path 45 exceeds the threshold of the conformability criteria 20…if not, may select the first path 40 as the final path 50).
Regarding claim 3, the combination of Takahashi and Han teaches the system of claim 1. Takahashi further teaches the operations further comprising: receiving one of: map data associated with the environment or log data associated with the autonomous vehicle (see at least, [0042] user feedback …may be immediately reflected in the conformability criteria 20…may be accumulated as an intervention archival record 60); and determining the bias value based at least in part on the map data or the log data (see at least, [0042] the obtained intervention archival record 60 may be reflected in the conformability criteria 20 after the fact. User feedback may be reflected in adjustments to the conformability criteria 20 (e.g., updating the threshold values) after identifying at least one of the moving scenes and the moving environment).
Regarding claim 5, the combination of Takahashi and Han teaches the system of claim 1. Han further teaches wherein: the second model is a machine learned model that determines the second planned trajectory based at least in part on driving data associated with a human driver (see at least, [0060] a second driving route based on…the destination information and the user's driving history provided to the trained second model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Takahashi to include the second model is a machine learned model that determines the second planned trajectory based at least in part on driving data associated with a human driver as taught by Han in order to train a driving route preferred by a user and a driving habit of the user thereby to provide a driving route desired by the user (Han, [0028]).
Takahashi further teaches and the first cost or the second cost comprises one of: an intersection cost indicating a likelihood for an object in the environment to intersect with the autonomous vehicle, a safety cost indicating a level of safety associated with a corresponding trajectory, a progress cost indicating an amount of progress by the autonomous vehicle using the corresponding trajectory, or a comfort cost indicating a comfort level for a passenger of the autonomous vehicle (see at least, [0019] the operation 55 of the intervention by the user is used as an index to evaluate whether or not the control by…first control model 30…is conformable with the user. When intervention occurs, the control by the new control model is evaluated as not being suitable for the user, and the conformability criteria 20 are adjusted to give greater weight to the use of the…second control model 35).
Regarding claim 6, the combination of Takahashi and Han teaches the system of claim 1. Takahashi further teaches wherein: the bias value is determined based at least in part on one or more of: vehicle state data of the autonomous vehicle, a number of objects within a threshold distance of the autonomous vehicle, presence of a construction zone, or a distance between the autonomous vehicle and a destination in the environment (see at least, [0034] the conformability criteria 20 may comprise a threshold value for a physical index…may comprise a threshold value for speed, acceleration, timing of change, or a combination thereof for movement…straight ahead, right turn, left turn, etc).
Regarding claim 7, Takahashi teaches one or more non-transitory computer-readable media storing instructions executable by one or more processors (see at least, [0014] a non-transitory computer readable medium (storage medium) that can be read by a machine such as a computer that stores such a program), wherein the instructions, when executed, cause the one or more processors to perform operations see at least, [0046] The controller 11 includes a CPU…configured to execute arbitrary information processing based on a program and various data) comprising:, a first planned trajectory usable to control a vehicle in an environment at a future time; a second planned trajectory usable to control the vehicle in the environment at the future time (see at least, Fig 1, [0015] Each control model (30, 35) is configured to derive a path to perform automatic control of the movement of the mobile body M; [0022] the paths (40, 45, 50) are configured to indicate the future travel path of the mobile body M and may be used to derive one or more control commands), the second model different from the first model (see at least, Fig 1, First Control Model-30 and Second Control Model-45); determining a bias value indicative of a preference for controlling a vehicle according to the first or second planned trajectory (see at least, Fig 1, [0019] the conformability criteria 20 are adjusted to give greater weight to the use of the …second control model 35…i.e., to give priority to the second path 45); determining, as a control trajectory and based at least in part on the bias value, one of the first planned trajectory or the second planned trajectory (see at least, Fig 1, [0037] Evaluating the deviation with the conformability criteria 20 may include determining a ratio of integration of each path (40, 45) according to the conformability criteria 20… if the deviation in speed between the first path 40 and the second path 45 exceeds the threshold of the conformability criteria 20…if not, may select the first path 40 as the final path 50); and controlling the autonomous vehicle in the environment based at least in part on the control trajectory (see at least, Fig 1, [0017] the control device 1 controls the operation (movement) of the mobile body M according to the generated final path 50).
[AltContent: connector][AltContent: textbox (Control trajectory )][AltContent: connector][AltContent: textbox (Second planned trajectory)][AltContent: connector][AltContent: connector][AltContent: textbox (Bias Value)][AltContent: textbox (First planned trajectory)]
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Takahashi does not explicitly teach determining, by a first model and based at least in part on sensor data from one or more sensors determining, by a second model and based at least in part on the sensor data. However, Han teaches these limitations.
Han teaches determining, by a first model and based at least in part on sensor data from one or more sensors (see at least, [0013] first driving route to the destination based on the destination information and the traffic information provided to a trained first mode) and determining, by a second model and based at least in part on the sensor data (see at least, [0060] a second driving route based on, for example, and without limitation, the destination information and the user's driving history provided to the trained second model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Takahashi to include determining, by a first model and based at least in part on sensor data from one or more sensors determining, by a second model and based at least in part on the sensor data as taught by Han in order to determines an optimal route from departure to destination, and drives a vehicle along the determined optimal route (Han, [0008]).
Regarding claim 8, the combination of Takahashi and Han teaches the one or more non-transitory computer-readable media of claim 7. Takahashi further teaches wherein determining the bias value is based at least in part on one or more of: vehicle state data of the vehicle, a number of objects within a threshold distance of the vehicle, presence of a construction zone, or a distance between the vehicle and a destination in the environment (see at least, [0034] the conformability criteria 20 may comprise a threshold value for a physical index…may comprise a threshold value for speed, acceleration, timing of change, or a combination thereof for movement…straight ahead, right turn, left turn, etc).
Regarding claim 9, the combination of Takahashi and Han teaches the one or more non-transitory computer-readable media of claim 7. Takahashi further teaches the operations further comprising: receiving one of: map data associated with the environment or log data associated with the vehicle (see at least, [0042] user feedback …may be immediately reflected in the conformability criteria 20…may be accumulated as an intervention archival record 60); and determining the bias value based at least in part on the map data or the log data (see at least, [0042] the obtained intervention archival record 60 may be reflected in the conformability criteria 20 after the fact. User feedback may be reflected in adjustments to the conformability criteria 20 (e.g., updating the threshold values) after identifying at least one of the moving scenes and the moving environment).
Regarding claim 10, the combination of Takahashi and Han teaches the one or more non-transitory computer-readable media of claim 7. Han further teaches wherein: the second model is a machine learned model that determines the second planned trajectory based at least in part on driving data associated with a human driver (see at least, [0060] a second driving route based on…the destination information and the user's driving history provided to the trained second model).
Regarding claim 11, the combination of Takahashi and Han teaches the one or more non-transitory computer-readable media of claim 7. Takahashi further teaches the operations further comprising: determining a first cost to use the first planned trajectory at the future time; and determining a second cost to use the second planned trajectory at the future time (see at least, Fig 1, [0037] the control device 1 may determine the ratio of each path (40, 45) according to the conformability criteria 20; [0022] the paths (40, 45, 50) are configured to indicate the future travel path of the mobile body M and may be used to derive one or more control commands), wherein determining the bias value is based at least in part on the first cost and the second cost (see at least, Fig 1, [0037] Evaluating the deviation with the conformability criteria 20 may include determining a ratio of integration of each path (40, 45) according to the conformability criteria 20… if the deviation in speed between the first path 40 and the second path 45 exceeds the threshold of the conformability criteria 20…if not, may select the first path 40 as the final path 50).
Regarding claim 12, the combination of Takahashi and Han teaches the one or more non-transitory computer-readable media of claim 7. Han further teaches wherein: the first model is trained ([0060] obtain at least one first driving route to destination based on destination information and traffic information to the trained first model) to determine the first planned trajectory based at least in part on a set of costs comprising one or more of: a progress cost, a follow cost, a lane change cost, a blinker cost, an intersection cost, a safety cost, an active object cost, or an inactive object cost [0016] The first driving route may be one of a minimum time driving route, a shortest distance driving route, and a least cost driving route from departure to destination), and the second model is trained to determine the second planned trajectory independent of the set of costs (see at least, [0060] a second driving route based on, for example, and without limitation, the destination information and the user's driving history provided to the trained second model. The second driving route may, for example, be a driving route usually preferred by a user).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Takahashi to include the first model is trained to determine the first planned trajectory based at least in part on a set of costs comprising one or more of: a progress cost, a follow cost, a lane change cost, a blinker cost, an intersection cost, a safety cost, an active object cost, or an inactive object cost, and the second model is trained to determine the second planned trajectory independent of the set of costs as taught by Han in order to determines an optimal route from departure to destination, and drives a vehicle along the determined optimal route (Han, [0008]).
Regarding claim 13, the combination of Takahashi and Han teaches the one or more non-transitory computer-readable media of claim 7. Han further teaches wherein: the vehicle navigates to a destination at a first time (see at least, [0112] the autonomous driving apparatus 100 may determine a driving route at a predetermined time interval or at a predetermined distance); and the bias value changes from a first bias value to a second bias value at a second time based at least in part on a position of the vehicle being within a threshold distance of the destination (see at least, [0112] The autonomous driving apparatus 100 may determine a new driving route when a plurality of roads included in the driving route are changed).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Takahashi to include the vehicle navigates to a destination at a first time; and the bias value changes from a first bias value to a second bias value at a second time based at least in part on a position of the vehicle being within a threshold distance of the destination as taught by Han in order to determines an optimal route from departure to destination, and drives a vehicle along the determined optimal route (Han, [0008]).
Regarding claim 14, the combination of Takahashi and Han teaches the one or more non-transitory computer-readable media of claim 7. Takahashi further teaches wherein: the first cost or the second cost comprises one of: an intersection cost indicating a likelihood for an object in the environment to intersect with the vehicle, a safety cost indicating a level of safety associated with a corresponding trajectory, a progress cost indicating an amount of progress by the vehicle using the corresponding trajectory, or a comfort cost indicating a comfort level for a passenger of the vehicle (see at least, [0019] the operation 55 of the intervention by the user is used as an index to evaluate whether or not the control by…first control model 30…is conformable with the user. When intervention occurs, the control by the new control model is evaluated as not being suitable for the user, and the conformability criteria 20 are adjusted to give greater weight to the use of the…second control model 35).
Regarding claim 15, the combination of Takahashi and Han teaches the one or more non-transitory computer-readable media of claim 7. Han further teaches wherein: the first model determines the first planned trajectory at approximately a same time as the second model determines the second planned trajectory (see at least, [0110] The autonomous driving apparatus 100 may determine an optimal driving route 620 to destination in real time during driving along the determined driving route 610; [0079] the display 140 may display a UI including a first driving route determined by a first model, and a second driving route determined by a second model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Takahashi to include the first model determines the first planned trajectory at approximately a same time as the second model determines the second planned trajectory as taught by Han in order to determines an optimal route from departure to destination, and drives a vehicle along the determined optimal route (Han, [0008]).
Regarding claim 16, the combination of Takahashi and Han teaches the 16. Takahashi further teaches one or more non-transitory computer-readable media of claim 14, further comprising: receiving vehicle state data associated with the vehicle, the vehicle state data comprising one or more of: position data, orientation data, heading data, velocity data, speed data, acceleration data, yaw rate data, or turning rate data (see at least, [0026] At least one of the control models (30, 35) may be configured to further accept input of arbitrary information such as…set speed….may include driving data); and determining the bias value based at least in part on the vehicle state data (see at least, [0034] the conformability criteria 20 may comprise a threshold value for speed, acceleration, timing of change, or a combination thereof for movement (straight ahead, right turn, left turn, etc).
Regarding claim 17, Takahashi teaches a method comprising: a first planned trajectory usable to control a vehicle in an environment at a future time; a second planned trajectory usable to control the vehicle in the environment at the future time (see at least, Fig 1, [0015] Each control model (30, 35) is configured to derive a path to perform automatic control of the movement of the mobile body M; [0022] the paths (40, 45, 50) are configured to indicate the future travel path of the mobile body M and may be used to derive one or more control commands), the second model different from the first model (see at least, Fig 1, First Control Model-30 and Second Control Model-45); determining a bias value indicative of a preference for controlling a vehicle according to the first or second planned trajectory (see at least, Fig 1, [0019] the conformability criteria 20 are adjusted to give greater weight to the use of the …second control model 35…i.e., to give priority to the second path 45); determining, as a control trajectory and based at least in part on the bias value, one of the first planned trajectory or the second planned trajectory (see at least, Fig 1, [0037] Evaluating the deviation with the conformability criteria 20 may include determining a ratio of integration of each path (40, 45) according to the conformability criteria 20… if the deviation in speed between the first path 40 and the second path 45 exceeds the threshold of the conformability criteria 20…if not, may select the first path 40 as the final path 50); and controlling the vehicle in the environment based at least in part on the control trajectory (see at least, Fig 1, [0017] the control device 1 controls the operation (movement) of the mobile body M according to the generated final path 50).
[AltContent: connector][AltContent: textbox (Control trajectory )][AltContent: connector][AltContent: textbox (Second planned trajectory)][AltContent: connector][AltContent: connector][AltContent: textbox (Bias Value)][AltContent: textbox (First planned trajectory)]
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Takahashi does not explicitly teach determining, by a first model and based at least in part on sensor data from one or more sensors determining, by a second model and based at least in part on the sensor data. However, Han teaches these limitations.
Han teaches determining, by a first model and based at least in part on sensor data from one or more sensors (see at least, [0013] first driving route to the destination based on the destination information and the traffic information provided to a trained first mode) and determining, by a second model and based at least in part on the sensor data (see at least, [0060] a second driving route based on, for example, and without limitation, the destination information and the user's driving history provided to the trained second model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Takahashi to include determining, by a first model and based at least in part on sensor data from one or more sensors determining, by a second model and based at least in part on the sensor data as taught by Han in order to determines an optimal route from departure to destination, and drives a vehicle along the determined optimal route (Han, [0008]).
Regarding claim 18, the combination of Takahashi and Han teaches the method of claim 17. Takahashi further teaches wherein determining the bias value is based at least in part on one or more of: vehicle state data of the vehicle, a number of objects within a threshold distance of the vehicle, presence of a construction zone, or a distance between the vehicle and a destination in the environment (see at least, [0034] the conformability criteria 20 may comprise a threshold value for a physical index…may comprise a threshold value for speed, acceleration, timing of change, or a combination thereof for movement…straight ahead, right turn, left turn, etc).
Regarding claim 19, the combination of Takahashi and Han teaches the method of claim 17. Takahashi further teaches comprising: receiving one of: map data associated with the environment or log data associated with the autonomous vehicle (see at least, [0042] user feedback …may be immediately reflected in the conformability criteria 20…may be accumulated as an intervention archival record 60); and determining the bias value based at least in part on the map data or the log data (see at least, [0042] the obtained intervention archival record 60 may be reflected in the conformability criteria 20 after the fact. User feedback may be reflected in adjustments to the conformability criteria 20 (e.g., updating the threshold values) after identifying at least one of the moving scenes and the moving environment).
Regarding claim 20, the combination of Takahashi and Han teaches the method of claim 17. Han further teaches wherein: the second model is a machine learned model that determines the second planned trajectory based at least in part on driving data associated with a human driver (see at least, [0060] a second driving route based on…the destination information and the user's driving history provided to the trained second model).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Takahashi to include the second model is a machine learned model that determines the second planned trajectory based at least in part on driving data associated with a human driver as taught by Han in order to train a driving route preferred by a user and a driving habit of the user thereby to provide a driving route desired by the user (Han, [0028]).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over in Takahashi et al. (US 20250050895 A1; hereinafter Takahashi) view of Han et al. (US 20190265060 A1; hereinafter Han) in further view of Rao et al. (US 20180143641 A1; hereinafter Rao).
Regarding claim 4, the combination of Takahashi and Han teaches the system of claim 1. The combination does not explicitly teach the operations further comprising: determining that the first cost and the second cost are a same value; identifying, based at least in part on the first cost and the second cost being the same value, a third cost associated with the first planned trajectory and a fourth cost associated with the second planned trajectory; and comparing the third cost associated with the first planned trajectory to the fourth cost associated with the second planned trajectory; wherein determining, as the control trajectory, the first planned trajectory or the second planned trajectory is based at least in part on comparing the third cost and the fourth cost. However, Rao teaches these limitations.
Rao teaches the operations further comprising: determining that the first cost and the second cost are a same value (see at least, [0082] the first cost value is equal to…the second cost value); identifying, based at least in part on the first cost and the second cost being the same value, a third cost associated with the first planned trajectory (see at least, [0073] the information of a third path and the first cost value in a same response message ) and a fourth cost associated with the second planned trajectory (see at least, [0078] the computing device calculates the second cost value according to the forth path); and comparing the third cost associated with the first planned trajectory to the fourth cost associated with the second planned trajectory (see at least, [0080] When the second cost is greater than MAX, it means the cost for the autonomous vehicle adjusting the movement to enable the
third path is too expensive…enabling the third path…enable the first path); wherein determining, as the control trajectory, the first planned trajectory or the second planned trajectory is based at least in part on comparing the third cost and the fourth cost (see at least, [0060] Trajectory planner 320 may generate multiple possible paths based on the current position of the autonomous vehicle and then select an optimism path).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have further modified the combination of Takahashi and Han to include determining that the first cost and the second cost are a same value; identifying, based at least in part on the first cost and the second cost being the same value, a third cost associated with the first planned trajectory and a fourth cost associated with the second planned trajectory; and comparing the third cost associated with the first planned trajectory to the fourth cost associated with the second planned trajectory; wherein determining, as the control trajectory, the first planned trajectory or the second planned trajectory is based at least in part on comparing the third cost and the fourth cost as taught by Rao so that the computing device may control the autonomous vehicle stay at the particular for some second to let the target vehicle passing by the autonomous vehicle to improve driving safety (Rao, [0092]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Srinivasan et al. (US 20220128370 A1) discloses determining, by a first model and based at least in part on the sensor data and a set of costs, a first planned trajectory usable to control the autonomous vehicle in an environment at a future time; determining, by a second model and based at least in part on the sensor data, a second planned trajectory usable to control the autonomous vehicle in the environment at the future time (e.g. [0067] the training module 102 is configured to generate a first performance measurement for the first routing algorithm based on the first set of routes using the trained safety risk model and to generate a second performance measurement for the second routing algorithm based on the second set of routes using the trained safety risk model).
Refaat et al. (US 20220169278 A1) discloses determining a bias value associated with a preference for following the first planned trajectory or the second planned trajectory (e.g. [0015] The set of probability scores can indicate respective likelihoods that the autonomous vehicle will travel along different ones of the predicted trajectories).
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/TOYA PETTIEGREW/Primary Examiner, Art Unit 3662
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