Deep learning based motion control of a group of autonomous vehicles

A computer program code comprising instructions, which, when executed by at least one processor, cause the at least one processor to implement a controller for an agent (Ai) of a group of agents (An), the controller comprising:

• a temporal deep network configured to calculate a desired trajectory for the agent (Ai);
• a nonlinear model predictive controller configured to calculate commands for the agent (Ai) based at least in part on the desired trajectory and desired trajectories of other agents (An) of the group of agents (An);
• and
• an augmented memory configured to integrate historic system states of the group of agents (An) for the temporal deep network.

A computer program code comprising instructions, which, when executed by at least one processor, cause the at least one processor to implement a temporal deep network to:

• calculate a desired trajectory for an agent (Ai) based at least in part on historic observations of the agent (Ai);
• calculate a reference trajectory for the agent (Ai);
• and
• calculate historic states of all agents (An).

An autonomous or semi-autonomous vehicle, characterized in that the autonomous or semi-autonomous vehicle comprises:

• a controller comprising:
• a temporal deep network configured to calculate a desired trajectory for an agent (Ai);
• a nonlinear model predictive controller configured to calculate commands for the agent (Ai) based at least in part on the desired trajectory and desired trajectories of other agents (An) of a group of agents (An);
• and
• an augmented memory configured to integrate historic system states of the group of agents (An) for the temporal deep network.