Method and system for adaptively controlling object spacing

An adaptive spacing predictive control system for controlling a vehicle to adaptively control spacing between the vehicle and a moving object in an operating environment of the vehicle, comprising:

• a processor system;
• a memory coupled to the processor system, the memory tangibly storing thereon executable instructions that, when executed by the processor system, cause the processor system to:
• train 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;
• determine 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;
• predict, 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;
• predict, 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;
• select, based on the predicted future states and predicted first zone future safety values, one of the alternative responses for the vehicle;
• and
• provide 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.

A computer program product comprising a non-transitory medium tangibly storing thereon executable instructions that, when executed by a processor system in a vehicle, cause the processor system to control a vehicle to adaptively control spacing between the vehicle and a moving object in an operating environment of the vehicle by:

• 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.