By way of example, the technology disclosed by this document may be implemented in a method that includes aggregating local sensor data from vehicle system sensors, detecting a driver action using the local sensor data, and extracting features related to predicting driver action from the local sensor data during the operation of the vehicle. The method may include adapting a stock machine learning-based driver action prediction model to a customized machine learning-based driver action prediction model using one or more of the extracted features and the detected driver action, the stock machine learning-based driver action prediction model initially generated using a generic model configured to be applicable to a generalized driving populace. In some instances, the method may also include predicting a driver action using the customized machine learning-based driver action prediction model and the extracted features.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method, the method comprising: aggregating local sensor data from a plurality of vehicle system sensors during operation of vehicle by a driver; detecting, during the operation of the vehicle, a driver action using the local sensor data; extracting, during the operation of the vehicle, features related to predicting driver action from the local sensor data; adapting, during operation of the vehicle, a stock machine learning-based driver action prediction model to a customized machine learning-based driver action prediction model using one or more of the extracted features and the detected driver action, the stock machine learning-based driver action prediction model initially generated using a generic model configured to be applicable to a generalized driving populace; and predicting a driver action using the customized machine learning-based driver action prediction model and the extracted features. 2. The computer-implemented method of claim 1, wherein detecting the driver action using the local sensor data includes labeling the driver action, extracting the features related to predicting driver action from the local sensor data includes generating one or more extracted features vectors including the extracted features, and the method of further includes synchronizing the labeled driver action with the one or more extracted features vectors. 3. The computer-implemented method of claim 2, further comprising: determining a driver action prediction duration, wherein the features are extracted from the local sensor data over the driver action prediction duration. 4. The computer-implemented method of claim 2, wherein synchronizing the labeled driver action with the one or more extracted feature vectors includes labeling the features of the one or more extracted feature vectors and determining which of the extracted features from the one or more extracted feature vectors to use in adapting the machine learning-based driver action prediction model. 5. The computer-implemented method of claim 1, wherein the local sensor data includes one or more of internal sensor data from sensors located inside a cabin of the vehicle, external sensor data from sensors located outside of the cabin of the vehicle, and network-communicated sensor data from one or more of adjacent vehicles and roadway infrastructure equipment. 6. The computer-implemented method of claim 1, wherein the local sensor data includes one or more of braking data describing braking actions by the driver, steering data describing steering actions by the driver, turn indicator data describing turning actions by the driver, acceleration data describing acceleration actions by the driver, control panel data describing control panel actions by the driver, vehicle-to-vehicle data, and vehicle-to-infrastructure data. 7. The computer-implemented method of claim 1, wherein adapting the stock machine learning-based driver action prediction model includes iteratively updating the stock machine learning-based driver action prediction model using sets of newly received local sensor data. 8. The computer-implemented method of claim 1, wherein aggregating the local sensor data includes receiving localized data from one or more other adjacent vehicles reflecting local conditions of a surrounding environment surrounding the vehicle, and adapting the stock machine learning-based driver action prediction model includes training the stock machine learning-based driver action prediction model using the localized data. 9. A computing system comprising: one or more computer processors; and one or more non-transitory memories storing instructions that, when executed by the one or more computer processors, cause the computer system to perform operations comprising: aggregating local sensor data from a plurality of vehicle system sensors during operation of vehicle by a driver; detecting, during the operation of the vehicle, a driver action using the local sensor data; extracting, during the operation of the vehicle, features related to predicting driver action from the local sensor data; adapting, during operation of the vehicle, a stock machine learning-based driver action prediction model to a customized machine learning-based driver action prediction model using one or more of the extracted features and the detected driver action, the stock machine learning-based driver action prediction model initially generated using a generic model configured to be applicable to a generalized driving populace; and predicting a driver action using the customized machine learning-based driver action prediction model and the extracted features. 10. The computing system of claim 9, wherein detecting the driver action using the local sensor data includes labeling the driver action, extracting the features related to predicting driver action from the local sensor data includes generating one or more extracted features vectors including the extracted features, and the method of further includes synchronizing the labeled driver action with the one or more extracted features vectors. 11. The computing system of claim 10, wherein the operations further comprise: determining a driver action prediction duration, wherein the features are extracted from the local sensor data over the driver action prediction duration. 12. The computing system of claim 10, wherein synchronizing the labeled driver action with the one or more extracted feature vectors includes labeling the features of the one or more extracted feature vectors and determining which of the extracted features from the one or more extracted feature vectors to use in adapting the machine learning-based driver action prediction model. 13. The computing system of claim 9, wherein the local sensor data includes one or more of internal sensor data from sensors located inside a cabin of the vehicle, external sensor data from sensors located outside of the cabin of the vehicle, and network-communicated sensor data from one or more of adjacent vehicles and roadway infrastructure equipment. 14. The computing system of claim 9, wherein the local sensor data includes one or more of braking data describing braking actions by the driver, steering data describing steering actions by the driver, turn indicator data describing turning actions by the driver, acceleration data describing acceleration actions by the driver, control panel data describing control panel actions by the driver, vehicle-to-vehicle data, and vehicle-to-infrastructure data. 15. The computing system of claim 9, wherein adapting the stock machine learning-based driver action prediction model includes iteratively updating the stock machine learning-based driver action prediction model using sets of newly received local sensor data. 16. The computing system of claim 9, wherein aggregating the local sensor data includes receiving localized data from one or more other adjacent vehicles reflecting local conditions of a surrounding environment surrounding the vehicle, and adapting the stock machine learning-based driver action prediction model includes training the stock machine learning-based driver action prediction model using the localized data. 17. A computer-implemented method, the method comprising: receiving a stock machine learning-based driver action prediction model prior to operation of a vehicle, the stock machine learning-based driver action prediction model having been initially generated using one or more generic training examples, the one or more generic training examples being configured to be applicable to a generalized set of users; detecting a driver action of a specific user during the operation of the vehicle using local sensor data; extracting, during the operation of the vehicle, features related to the driver action from the local sensor data; generating, during the operation of the vehicle, training examples using the extracted features related to the driver action and the extracted features; generating, during the operation of the vehicle, a customized machine learning-based driver action prediction model by updating the stock machine learning-based driver action prediction model using the training examples; and predicting, during the operation of the vehicle, a future driver action using the customized machine learning-based driver action prediction model. 18. The computer-implemented method of claim 17, wherein the stock machine learning-based driver action prediction model is a neural network-based computer learning model. 19. The computer-implemented method of claim 17, wherein detecting the driver action includes generating a recognized driver action label using a machine learning based-recognition model. 20. The computer-implemented method of claim 17, further comprising: linking the customized machine learning-based driver action prediction model to the specific user; and providing the customized machine learning-based driver action prediction model to a remote computing device of a second vehicle for use in predicting future driver actions of the specific user relating the second vehicle.