US 2026/0225623 A1Published application

Determining costs based on scenario probability

AssigneeZoox, Inc.

InventorsShakeeb Ahmad, Gowtham Garimella, Carl-Johan Hoel, Xianan Huang, Marin Kobilarov, Sahil Narang, Jeremy Schwartz, Ke Sun, Linjun Zhang

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Abstract

Techniques for controlling a vehicle based on predicted driving scenarios are described herein. A vehicle may generate candidate actions for the vehicle to follow. Further, the vehicle may receive a predicted driving scenario that includes an object trajectory probability and a predicted scenario probability. The vehicle may analyze the candidate actions by determining costs of the candidate actions based on the predicted scenario. The vehicle may scale some or all of the cost values by a ratio of the object trajectory probability and the predicted scenario probability. Based on the weighted cost values, the vehicle can determine one or more of the candidate actions to follow.

Claims20

Dependent claims

A system comprising:

one or more processors;
and
one or more non-transitory computer-readable media storing computer-executable instructions that, when executed, cause the system to perform operations comprising:
generating a candidate action for an autonomous vehicle to follow;
receiving a predicted driving scenario that includes:
an object,
a predicted object trajectory,
a probability of the predicted object trajectory, and
a probability of the predicted driving scenario;
determining, based at least in part on the predicted driving scenario, a plurality of costs associated with following the candidate action;
determining a subset of costs of the plurality of costs, the subset of costs being less than all of the plurality of costs;
determining, for a cost value of the subset of costs and based at least in part on the probability of the predicted object trajectory and the probability of the predicted driving scenario, a modified cost value;
determining, based at least in part on the modified cost value, to follow the candidate action;
and
controlling the autonomous vehicle based at least in part on the candidate action.

12 more elements

One or more non-transitory computer-readable media storing instructions executable by one or more processors, wherein the instructions, when executed, cause a system to perform operations comprising:

generating a candidate action for a vehicle to follow;
receiving a predicted driving scenario that includes:
a probability of a predicted object trajectory, and
a probability of the predicted driving scenario;
determining, based at least in part on the predicted driving scenario, a cost value associated with following the candidate action;
determining, for the cost value and based at least in part on the probability of the predicted object trajectory and the probability of the predicted driving scenario, a modified cost value;
and
determining, based at least in part on the modified cost value, to follow the candidate action.

5 more elements

A method comprising:

generating a candidate action for a vehicle to follow;
receiving a predicted driving scenario that includes:
a probability of a predicted object trajectory, and
a probability of the predicted driving scenario;
determining, based at least in part on the predicted driving scenario, a cost value associated with following the candidate action;
determining, for the cost value and based at least in part on the probability of the predicted object trajectory and the probability of the predicted driving scenario, a modified cost value;
and
determining, based at least in part on the modified cost value, to follow the candidate action.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 19/042,481Filed 31 Jan 2025
Published: 6 Aug 2026
Term ends: Not estimatedThe source records don’t support an estimate.

Classification11 codes

B60W 60/0011
Planning or execution of driving tasks: involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
B60W 60/0027
Planning or execution of driving tasks: using trajectory prediction for other traffic participants
B60W 30/0956
Predicting travel path or likelihood of collision: the prediction being responsive to traffic or environmental parameters
B60W 50/0097
Details of control systems for road vehicle drive control not related to the control of a particular sub-unit , e.g. process diagnostic or…