A method includes receiving sensed vehicle-state data, actuation-command data, and surface-coefficient data from a plurality of remote vehicles, inputting the sensed vehicle-state data, the actuation-command data, and the surface-coefficient data into a self-supervised recurrent neural network (RNN) to predict vehicle states of a host vehicle in a plurality of driving scenarios, and commanding the host vehicle to move autonomously according to a trajectory determined using the vehicle states predicted using the self-supervised RNN.
Unified self-supervisory learnable vehicle motion control policy
2025-04-01
Patent
Electronic Resource
English
UNIFIED SELF-SUPERVISORY LEARNABLE VEHICLE MOTION CONTROL POLICY
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