System and method for updating an autonomous vehicle driving model based on the vehicle driving model becoming statistically incorrect

Abstract

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.

What is claimed is: 
     
         1 . A method comprising:
 receiving a statistical accuracy associated with a first autonomous vehicle model;   determining that the statistical accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect; and   updating the first autonomous vehicle model to a second autonomous vehicle model based on the determination that the first autonomous vehicle model is statistically incorrect.   
     
     
         2 . The method of  claim 1 , further comprising:
 receiving at least one update for the first autonomous vehicle model;   applying the at least one update to the first autonomous vehicle model; and   generating the second autonomous vehicle model based on the at least one update.   
     
     
         3 . The method of  claim 2 , wherein the at least one update includes one or more model parameters for a portion of the first autonomous vehicle model. 
     
     
         4 . The method of  claim 1 , wherein the statistical accuracy associated with the first autonomous vehicle model is based on at least one of a quantity of course corrections or a quantity of course deviations. 
     
     
         5 . The method of  claim 4 , wherein a course correction includes determining that an input associated with a manual override was received. 
     
     
         6 . The method of  claim 4 , 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. 
     
     
         7 . The method of  claim 1 , wherein determining that the statistical accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect includes determining that the statistical accuracy associated with the first autonomous vehicle model is less than a threshold. 
     
     
         8 . The method of  claim 1 , further comprising:
 providing the second autonomous vehicle model to an autonomous vehicle.   
     
     
         9 . The method of  claim 8 , further comprising:
 providing the second autonomous vehicle model to a second autonomous vehicle.   
     
     
         10 . The method of  claim 1 , wherein the second autonomous vehicle model is generated at an autonomous vehicle. 
     
     
         11 . 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 statistical accuracy associated with a first autonomous vehicle model;   determining that the statistical accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect; and   updating the first autonomous vehicle model to a second autonomous vehicle model based on the determination that the first autonomous vehicle model is statistically incorrect.   
     
     
         12 . The system of  claim 11 , wherein the method includes:
 receiving at least one update for the first autonomous vehicle model;   applying the at least one update to the first autonomous vehicle model; and   generating the second autonomous vehicle model based on the at least one update.   
     
     
         13 . The system of  claim 12 , wherein the at least one update includes one or more model parameters for a portion of the first autonomous vehicle model. 
     
     
         14 . The system of  claim 11 , wherein the statistical accuracy associated with the first autonomous vehicle model is based on at least one of a quantity of course corrections or a quantity of course deviations. 
     
     
         15 . The system of  claim 14 , wherein a course correction includes determining that an input associated with a manual override was received. 
     
     
         16 . 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 statistical accuracy associated with a first autonomous vehicle model;   determining that the statistical accuracy associated with the first autonomous vehicle model indicates that the first autonomous vehicle model is statistically incorrect; and   updating the first autonomous vehicle model to a second autonomous vehicle model based on the determination that the first autonomous vehicle model is statistically incorrect.   
     
     
         17 . The non-transitory computer readable medium of  claim 16 , wherein the method includes:
 receiving at least one update for the first autonomous vehicle model;   applying the at least one update to the first autonomous vehicle model; and   generating the second autonomous vehicle model based on the at least one update.   
     
     
         18 . The non-transitory computer readable medium of  claim 17 , wherein the at least one update includes one or more model parameters for a portion of the first autonomous vehicle model. 
     
     
         19 . The non-transitory computer readable medium of  claim 16 , wherein the statistical accuracy associated with the first autonomous vehicle model is based on at least one of a quantity of course corrections or a quantity of course deviations. 
     
     
         20 . The non-transitory computer readable medium of  claim 19 , wherein a course correction includes determining that an input associated with a manual override was received.