US 11,999,380 B1Granted

Autonomous vehicle trajectory generation and optimization

AssigneeZoox, Inc.

InventorsJoseph Funke, Steven Cheng Qian, Kazuhide Okamoto, Jacob Patrick Thalman, Sriram Narayanan

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Abstract

Techniques are discussed for generating and optimizing trajectories for controlling autonomous vehicles in performing on-route and off-route actions within a driving environment. A planning component of an autonomous vehicle can receive or generate time-discretized (or temporal) trajectories for the autonomous vehicle to traverse an environment. Trajectories can be optimized, for example, based on the lateral and longitudinal dynamics of the vehicle, using loss functions and/or costs. In some examples, the temporal optimization of a trajectory may include resampling a previous trajectory based on the differences in the time sequences of the temporal trajectories, to ensure temporal consistency of trajectories across planning cycles. Constraints also may be applied during temporal optimization in some examples, to control or restrict driving maneuvers that are not supported by the autonomous vehicle.

Claims18

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:
determining a first trajectory for a vehicle, wherein the first trajectory includes a first set of time-discretized vehicle states associated with a first set of times;
controlling the vehicle for a first duration of time based at least in part on the first trajectory;
receiving an updated trajectory defined by a second set of time-discretized vehicle states associated with a second set of times, the second set of times differing from the first set of times;
determining, based at least in part on the first trajectory and the second set of times, a resampled trajectory including a third set of time-discretized vehicle states associated with the second set of times, wherein determining the resampled trajectory comprises applying an offset value to the first set of time-discretized vehicle states, wherein each of the third set of vehicle states is associated with a fixed time and is independent of a fixed physical location, and wherein the resampled trajectory comprises a trajectory for moving the vehicle from an off-route current vehicle state to a target vehicle state located on a driving lane of a route-based driving environment;
determining a consistency metric between the updated trajectory and the resampled trajectory, based at least in part on a vehicle state difference determined based on the second and third sets of time-discretized vehicle states;
determining a control trajectory for the vehicle, based at least in part on the consistency metric;
and
controlling the vehicle based at least in part on the control trajectory.

8 more elements

A method comprising:

determining a first trajectory for a vehicle, wherein the first trajectory includes a first set of vehicle states spaced temporally from one another;
resampling the first trajectory into a second set of vehicle states spaced temporally from one another and differing in time from the first set of vehicle states, wherein resampling the first trajectory comprises determining the second set of vehicle states by applying an offset value to the first set of vehicle states, wherein each of the second set of vehicle states is associated with a fixed time and is independent of a fixed physical location;
determining an optimal trajectory for moving the vehicle from an off-route current vehicle state to a target vehicle state located on a driving lane of a route-based driving environment, the optimal trajectory comprising a third set of vehicle states spaced temporally from one another, wherein the third set of vehicle states correspond in time to the second set of vehicle states;
determining a consistency metric between the first trajectory and the optimal trajectory, based at least in part on a vehicle state difference determined based on the second and thirds set of vehicle states;
determining a control trajectory for the vehicle, based at least in part on the consistency metric;
and
controlling the vehicle based at least in part on the control trajectory.

4 more elements

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

determining a first trajectory for a vehicle, wherein the first trajectory includes a first set of vehicle states spaced temporally from one another;
resampling the first trajectory into a second set of vehicle states spaced temporally from one another and differing in time from the first set of vehicle states, wherein resampling the first trajectory comprises determining the second set of vehicle states by applying an offset value to the first set of vehicle states, wherein each of the second set of vehicle states is associated with a fixed time and is independent of a fixed physical location;
determining an optimal trajectory for moving the vehicle from an off-route current vehicle state to a target vehicle state located on a driving lane of a route-based driving environment, the optimal trajectory comprising a third set of vehicle states spaced temporally from one another, wherein the third set of vehicle states correspond in time to the second set of vehicle states;
determining a consistency metric between the first trajectory and the optimal trajectory, based at least in part on a vehicle state difference determined based on the second and thirds set of vehicle states;
determining a control trajectory for the vehicle, based at least in part on the consistency metric;
and
controlling the vehicle based at least in part on the control trajectory.

4 more elements

Dependencies are inferred from the claim text.

Facts

Application: 17/554,693Filed 17 Dec 2021
Granted: 4 Jun 2024
Term ends: Not estimatedThe source records don’t support an estimate.

Classification5 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 2520/06
Input parameters relating to overall vehicle dynamics: direction of travel
B60W 2520/10
Input parameters relating to overall vehicle dynamics: longitudinal speed
B60W 2520/12
Input parameters relating to overall vehicle dynamics: lateral speed

Family

No related US applications are recorded in the indexed filing history.

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