| CPC B60W 30/16 (2013.01) [B60W 2554/80 (2020.02); B60W 2754/10 (2020.02); G05B 13/0285 (2013.01); G05B 13/048 (2013.01)] | 18 Claims |
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1. A computer-implemented method for adaptively controlling spacing between a vehicle and a moving object in an operating environment of the vehicle, the method comprising:
training a machine learning model using reinforcement learning to select vehicle responses based on predicted vehicle states and safety values, wherein the machine learning model is trained using a general value function (GVF) framework;
determining a current vehicle state of the vehicle and a current object state of the moving object based on sensor data captured by sensors of the vehicle;
predicting, by learned predictive functions of the machine learning model, based on the current vehicle state and current object state, for each response in a set of multiple alternative responses, a future state for the vehicle corresponding to the response;
predicting, by learned predictive functions of the machine learning model, based on the current vehicle state and current object state, for each response in the set of multiple alternative responses, a first zone future safety value corresponding to a first safety zone of the vehicle, the first zone future safety value indicating a safety level of the vehicle for the first safety zone if the response is performed by the vehicle;
selecting, based on the predicted future states and predicted first zone future safety values, one of the alternative responses for the vehicle; and
providing the selected alternative response to a drive control system of the vehicle to cause the drive control system of the vehicle to correspondingly adjust the spacing between the vehicle and the moving object.
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