System and method for detecting a condition prompting an update to an autonomous vehicle driving model
Systems and methods for implementing one or more autonomous features for autonomous and semi-autonomous control of one or more vehicles are provided. More specifically, image data may be obtained from an image acquisition device and processed utilizing one or more machine learning models to identify, track, and extract one or more features of the image utilized in decision making processes for providing steering angle and/or acceleration/deceleration input to one or more vehicle controllers. In some instances, techniques may be employed such that the autonomous and semi-autonomous control of a vehicle may change between vehicle follow and lane follow modes. In some instances, at least a portion of the machine learning model may be updated based on one or more conditions.
1. A method comprising:
receiving a first autonomous vehicle model;
autonomously driving a path generated by the first autonomous vehicle model;
detecting two or more manual overrides of the generated path;
determining that at least one of a quantity of manual overrides associated with course corrections or a quantity of manual overrides associated with course deviations exceeded a threshold within a period of time, wherein the course correction includes determining that an input associated with a manual override was received, and wherein the input associated with the manual override includes one of a manual velocity change or manual steering angle change;
in response to the determination that course corrections or course deviations exceeded the threshold, determining that the first autonomous vehicle model should be updated based on the detected condition;
receiving at least one update for the first autonomous vehicle model;
applying the at least one update;
generating a second autonomous vehicle model based on the at least one update; and
autonomously driving a second path generated by the second autonomous vehicle model.
2. The method of claim 1 , wherein detecting a condition associated with the performance of the first autonomous vehicle model includes determining that at least one of a quantity of course corrections or a quantity of course deviations exceeded a threshold within a period of time.
3. The method of claim 2 , wherein a course deviation includes determining that a path traveled by an autonomous vehicle is different from a projected path to be traveled by the autonomous vehicle.
4. The method of claim 2 , wherein the threshold varies based on one or more of time, location, date, or weather condition.
5. The method of claim 1 , wherein the at least one update includes one or more model parameters for a portion of the first autonomous vehicle model.
6. The method of claim 1 , further comprising:
recording a location associated with one or more course corrections and/or course deviations.
7. The method of claim 1 , wherein the second autonomous vehicle model is received at an autonomous vehicle.
8. The method of claim 1 , wherein the second autonomous vehicle model is generated at an autonomous vehicle.
9. A system comprising:
a memory;
a processor in communication with the memory, wherein the processor executes instructions stored in the memory, which cause the processor to execute a method, the method comprising:
receiving a first autonomous vehicle model, wherein a first autonomous vehicle drives a path generated by the first autonomous vehicle model;
detecting two or more manual overrides of the generated path;
determining that at least one of a quantity of manual overrides associated with course corrections or a quantity of manual overrides associated with course deviations exceeded a threshold within a period of time, wherein the course correction includes determining that an input associated with a manual override was received, and wherein the input associated with the manual override includes one of a manual velocity change or manual steering angle change;
in response to the determination that course corrections or course deviations exceeded the threshold, determining that the first autonomous vehicle model should be updated based on the detected condition;
receiving at least one update for the first autonomous vehicle model;
applying the at least one update;
generating a second autonomous vehicle model based on the at least one update; and
autonomously driving a second path generated by the second autonomous vehicle model.
10. The system of claim 9 , wherein detecting a condition associated with the performance of the first autonomous vehicle model includes determining that at least one of a quantity of course corrections or a quantity of course deviations exceeded a threshold within a period of time.
11. The system of claim 10 , wherein a course deviation includes determining that a path traveled by an autonomous vehicle is different from a projected path traveled by the autonomous vehicle.
12. A non-transitory computer readable medium having stored thereon instructions, which when executed by a processor cause the processor to execute a method, the method comprising:
receiving a first autonomous vehicle model, wherein a first autonomous vehicle drives a path generated by the first autonomous vehicle model;
detecting two or more manual overrides of the generated path;
determining that at least one of a quantity of manual overrides associated with course corrections or a quantity of manual overrides associated with course deviations exceeded a threshold within a period of time, wherein the course correction includes determining that an input associated with a manual override was received, and wherein the input associated with the manual override includes one of a manual velocity change or manual steering angle change;
in response to the determination that course corrections or course deviations exceeded the threshold, determining that the first autonomous vehicle model should be updated based on the detected condition;
receiving at least one update for the first autonomous vehicle model;
applying the at least one update;
generating a second autonomous vehicle model based on the at least one update; and
autonomously driving a second path generated by the second autonomous vehicle model.
13. The non-transitory computer readable medium of claim 12 , wherein detecting a condition associated with the performance of the first autonomous vehicle model includes determining that at least one of a quantity of course corrections or a quantity of course deviations exceeded a threshold within a period of time.
14. The non-transitory computer readable medium of claim 12 , wherein a course deviation includes determining that a path traveled by an autonomous vehicle is different from a projected path traveled by the autonomous vehicle.