Feedback-based control model generation for an autonomous vehicle

Patent metadata

Field	Value
Publication number	US-10449958-B2
Application number	US-201715433781-A
Country	US
Kind code	B2
Filing date	Feb 15, 2017
Priority date	Feb 15, 2017
Publication date	Oct 22, 2019
Grant date	Oct 22, 2019

Abstract

A computer system receives data sets for a driving maneuver at a location, such as data regarding a state of a vehicle, an external environment indicated by external sensors, and user feedback regarding the driving maneuver. Data sets may be collected from users having various demographic and behavioral attributes. Users may be selected using design of experiment (DOE) algorithms to cover a wide range of possible combinations. A stochastic response surface model (SRSM) is generated that relates sensor data (vehicle state and environmental) to user feedback regarding safety and comfort. The SRSM may be generated using Gaussian process regression (GPR) in order to model uncertainty. The SRSM is then used to generate a control model using an optimization under uncertainty (OUU) algorithm.

First claim

The invention claimed is:

1. A method comprising, by a computer system:
receiving feedback from one or more passengers of one or more autonomous vehicles;
receiving sensor data and control data from the one or more autonomous vehicles;
generating a behavioral model according to the feedback, sensor data, and control data;
determining uncertainty for the behavioral model; and
generating a control model according to the behavioral model and the uncertainty for the behavioral model;
wherein generating the behavior model according to the feedback and sensor data comprises generating a stochastic response surface model (SRSM) according to the feedback and sensor data, the SRSM relating the feedback to environmental factors indicated by the sensor data, status of the one or more autonomous vehicles indicated in the sensor data, and control actions taken by the autonomous vehicle indicated in the control data.

2. The method of claim 1 , wherein the feedback includes one or more driving maneuver ratings, each rating including a location.

3. The method of claim 2 , wherein receiving feedback includes receiving feedback from a plurality of passengers.

4. The method of claim 3 , further comprising selecting, by the computer system, the plurality of passengers according to design of experiment criteria.

5. The method of claim 3 , further comprising selecting, by the computer system, the plurality of passengers according to a design of experiment algorithm that processes potential passengers for selection according to demographic attributes of the potential passengers and driving preferences of the potential passengers.

6. The method of claim 1 , further comprising:
evaluating, by the computer system, the SRSM according to a portion of the feedback, sensor data, and control data;
determining, by the computer system, that the SRSM does not satisfactorily correspond to the portion of the feedback, sensor data, and control data;
in response to determining that the SRSM does not satisfactorily correspond to the portion of the feedback, sensor data, and control data, collecting additional feedback, additional sensor data, and additional control data; and
generating an updated SRSM using the additional feedback, additional sensor data, and additional control data.

7. The method of claim 1 , wherein generating the control model according to the behavior model and the uncertainty for the model comprises performing a multi-objective optimization under uncertainty (OUU) algorithm using the behavioral model and the uncertainty for the behavioral model.

8. The method of claim 1 , wherein the sensor data includes outputs of at least one of a light detection and ranging (LIDAR) sensor, a radio detection and ranging (RADAR) sensor, and one or more cameras.

9. The method of claim 1 , further comprising:
uploading the control model into an autonomous vehicle;
receiving, by a controller of the autonomous vehicle, outputs of one or more sensors; and
autonomously driving, by the controller, the autonomous vehicle using the outputs processed according to the control model.

10. A system comprising one or more processing devices and one or more memory devices operably coupled to the one or more processing devices, the one or more memory devices storing executable code effective to cause the one or more processing devices to:
receive feedback from one or more passengers of one or more autonomous vehicles;
receive sensor data and control data from the one or more autonomous vehicles;
generate a behavioral model according to the feedback, sensor data, and control data;
determine uncertainty for the behavioral model; and
generate a control model according to the behavioral model and the uncertainty for the behavioral model;
wherein the executable code is further effective to cause the one or more processing devices to generate the behavior model according to the feedback and sensor data by generating a stochastic response surface model (SRSM) according to the feedback and sensor data, the SRSM relating the feedback to environmental factors indicated by the sensor data, status of the one or more autonomous vehicles indicated in the sensor data, and control actions taken by the autonomous vehicle indicated in the control data.

11. The system of claim 10 , wherein the feedback includes one or more driving maneuver ratings, each rating including a location.

12. The system of claim 11 , wherein the one or more passengers include a plurality of passengers.

13. The system of claim 12 , wherein the executable code is further effective to cause the one or more processing devices to select the plurality of passengers according to design of experiment criteria.

14. The system of claim 12 , wherein the executable code is further effective to cause the one or more processing devices to select the plurality of passengers according to a design of experiment algorithm that processes potential passengers for selection according to demographic attributes of the potential passengers and driving preferences of the potential passengers.

15. The system of claim 10 , wherein the executable code is further effective to cause the one or more processing devices to:
evaluate the SRSM according to a portion of the feedback, sensor data, and control data;
if the SRSM does not satisfactorily correspond to the portion of the feedback, sensor data, and control data:
collect additional feedback, additional sensor data, and additional control data; and
generate an updated SRSM using the additional feedback, additional sensor data, and additional control data.

16. The system of claim 10 , wherein the executable code is further effective to cause the one or more processing devices to generate the control model according to the behavior model and the uncertainty for the model by performing a multi-objective optimization under uncertainty (OUU) algorithm using the behavioral model and the uncertainty for the behavioral model.

17. The system of claim 10 , wherein the sensor data includes outputs of at least one of a light detection and ranging (LIDAR) sensor, a radio detection and ranging (RADAR) sensor, and one or more cameras.

18. The system of claim 10 , wherein the executable code is further effective to cause the one or more processing devices to upload the control model into an autonomous vehicle.

Assignees

• Ford Global Tech Llc

Inventors

• Jiang Zhen
• Fu Yan
• Li Yuzhou

Classifications

• B60W2540/215

  Selection or confirmation of options · CPC title

• B60W2050/0088

  Adaptive recalibration · CPC title

• B60W2050/0031

  Mathematical model of the vehicle · CPC title

• B60W40/10

  related to vehicle motion · CPC title

• B60W50/0098

  Details of control systems ensuring comfort, safety or stability not otherwise provided for · CPC title