Adaptive autonomous vehicle planner logic

1. A method comprising:

receiving path data with which to guide motion of an autonomous vehicle from a first geographic location to a second geographic location;

generating, based, at least in part, on the path data, a trajectory;

generating, based, at least in part, on the path data, a contingent trajectory;

controlling the autonomous vehicle according to the trajectory; and

controlling the autonomous vehicle according to the contingent trajectory subsequent to an impairment of the trajectory.

2. The method of claim 1 , further comprising receiving sensor data from a sensor of the autonomous vehicle and wherein generating the contingent trajectory comprises:

determining, based, at least in part, on the path data and the sensor data, multiple contingent trajectories;

comparing the multiple contingent trajectories; and

selecting, based, at least in part, on the comparison, one of the multiple contingent trajectories as the contingent trajectory.

3. The method of claim 1 , wherein generating the contingent trajectory, comprises generating a nominal contingent trajectory and generating a safe-stop contingent trajectory.

4. The method of claim 3 , wherein generating the nominal contingent trajectory comprises iteratively generating nominal contingent trajectories based, at least in part, on a receding horizon, and

wherein the safe-stop contingent trajectory is configured to bring the autonomous vehicle to a safe stop.

5. The method of claim 1 , wherein generating the contingent trajectory comprises generating the contingent trajectory using an artificial intelligence layer, and

wherein controlling the autonomous vehicle according to the contingent trajectory comprises controlling the autonomous vehicle using a real time operating system (RTOS) layer.

6. The method of claim 5 , wherein controlling the autonomous vehicle according to the contingent trajectory is based at least in part on detecting a failure or a degradation of the artificial intelligence layer.

7. The method of claim 1 , further comprising determining whether contingency data is within a range of predicted values, the contingency data comprising data for controlling the autonomous vehicle according to the contingent trajectory, uncertainties associated with the contingent trajectory, or probabilities associated with the contingent trajectory.

8. The method of claim 1 , wherein the impairment comprises an inoperable high-level logic that a trajectory evaluator determines is not executable by at least one of a processor or a component of the autonomous vehicle, a failure of the trajectory evaluator, or a degradation of the trajectory evaluator.

9. A system comprising:

one or more processors

memory storing processor-executable instructions that, when executed by the one or more processors, cause the system to:

receive path data with which to guide motion of an autonomous vehicle from a first geographic location to a second geographic location;

generate a trajectory with which to control motion of the autonomous vehicle based, at least in part, on the path data;

generate a contingent trajectory;

monitor generation of the trajectory;

detect an impairment of the generation of the trajectory; and

control motion of the autonomous vehicle according to the contingent trajectory responsive to detecting the impairment.

10. The system of claim 9 , wherein:

the system further comprises a sensor configured to capture sensor data, wherein generating the contingent trajectory comprises:

determining, based, at least in part, on the sensor data, multiple contingent trajectories;

comparing the multiple contingent trajectories; and

selecting, based, at least in part, on the comparison, one of the multiple contingent trajectories as the contingent trajectory.

11. The system of claim 9 , wherein the instructions, when executed, further cause the system to:

receive static map data;

receive current object state data and predicted object state data;

receive local pose data;

determine a confidence level associated with the trajectory based, at least in part, on at least one of the static map data, the current object state data, the predicted object state data, the local pose data, or the path data; and

determine, based, at least in part, on the confidence level, an operational state of the autonomous vehicle,

wherein detecting the impairment comprises determining the operational state is a non-normative operational state.

12. The system of claim 9 , wherein detecting the impairment comprises detecting cessation of generation of the trajectory or detecting that the trajectory is associated with inoperable high-level logic.

13. The system of claim 9 , further comprising:

an artificial intelligence layer comprising the one or more processors and the memory; and

a real time operating system (RTOS) layer, the RTOS layer configured to cause the autonomous vehicle to traverse the trajectory or the contingent trajectory.

14. The system of claim 9 , the instructions, when executed, further cause the system to generate a nominal contingent trajectory, based, at least in part, on a receding horizon,

wherein the contingent trajectory comprises a safe stop maneuver, and

further wherein controlling motion of the autonomous vehicle according to the contingent trajectory comprises causing the autonomous vehicle to traverse the contingent trajectory or the nominal contingent trajectory.

15. The system of claim 14 , wherein generating the trajectory comprises:

generating, at a trajectory generator, a plurality of trajectories;

determining, at a trajectory evaluator, a set of confidence levels associated with the plurality of trajectories; and

selecting, as the trajectory, a trajectory of the plurality of trajectories having a highest confidence level,

wherein detecting the impairment comprises detecting a failure of the trajectory generator, a failure of the trajectory evaluator, a degradation of the trajectory generator, or a degradation of the trajectory evaluator.

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

receiving path data with which to guide motion of an autonomous vehicle from a first geographic location to a second geographic location;

generating, based, at least in part, on the path data, multiple trajectories, the multiple trajectories including a trajectory with which to control motion of the autonomous vehicle and a contingent trajectory;

directing motion of the autonomous vehicle according to the trajectory;

detecting an impairment of the generation of the trajectory; and

responsive to detecting the impairment of the generation of the trajectory, directing motion of the autonomous vehicle according to the contingent trajectory.

17. The one or more non-transitory computer-readable media of claim 16 , wherein:

the trajectory is generated to move the autonomous vehicle at least a portion of a distance between the first geographic location and the second geographic location; and

the contingent trajectory is generated to control motion of the autonomous vehicle to a third geographic location responsive to detecting that generation of the trajectory is impaired.

18. The one or more non-transitory computer-readable media of claim 16 , wherein generating the contingent trajectory comprises:

receiving sensor data from a sensor of the autonomous vehicle;

determining, based, at least in part, on the sensor data and a receding horizon technique, multiple contingent trajectories;

comparing the multiple contingent trajectories; and

selecting, based, at least in part, on the comparison, one of the multiple contingent trajectories as the contingent trajectory.

19. The one or more non-transitory computer-readable media of claim 16 , wherein detecting the impairment comprises at least one of:

detecting cessation of generation of the trajectory,

detecting that the trajectory includes or is associated with inoperable high-level logic, or

detecting a failure or a degradation of an artificial intelligence layer of the autonomous vehicle.

20. The one or more non-transitory computer-readable media of claim 16 , the operations further comprising:

receiving static map data;

receiving current object state data and predicted object state data;

receiving local pose data;

determining a confidence level associated with the trajectory, based, at least in part, on at least one of the static map data, the current object state data, the predicted object state data, the local pose data, or the path data; and

determining, based, at least in part, on the confidence level, an operational state of the autonomous vehicle,

wherein detecting the impairment comprises determining the operational state is a non-normative operational state, based, at least in part, on the confidence level meeting or exceeding a threshold confidence.