Machine learning system for modifying advanced driver assistance systems (adas) behavior to provide optimum vehicle trajectory in a region

A system comprising a plurality of connected vehicles, each connected vehicle configured to:

• exchange Vehicle-to-Everything (V2X) wireless messages, each V2X message containing customized data describing driving preferences of a transmitting vehicle with associated preference weights and driving intentions of the transmitting vehicle with associated intention weights;
• determine relative priorities by comparing the preference weights and the intention weights across the plurality of connected vehicles;
• and
• cooperatively negotiate, via the V2X wireless messages, individual vehicle behaviors based on the relative priorities so that execution of the individual vehicle behaviors achieves an overall optimum behavior of a region containing the plurality of connected vehicles.

A computer program product comprising a non-transitory memory storing computer-executable code that, when executed by a processor, causes the processor to:

• exchange Vehicle-to-Everything (V2X) wireless messages with a plurality of connected vehicles in a region, each V2X message containing customized data describing driving preferences of a transmitting vehicle with associated preference weights and driving intentions of the transmitting vehicle with associated intention weights;
• and
• determine relative priorities by comparing the preference weights and the intention weights across the plurality of connected vehicles;
• and
• cooperatively negotiate individual vehicle behaviors based on the relative priorities so that execution of the individual vehicle behaviors achieves an overall optimum behavior of the region.