Online generation of trajectories for autonomous vehicles using a multi-agent system

Autonomous vehicles are frequently deployed in environments where only certain trajectories are feasible. Classical trajectory generation methods attempt to find a feasible trajectory that satisfies a set of constraints. In some cases the optimal trajectory may be known, but it is hidden from the autonomous vehicle. Under such circumstance the vehicle must discover a feasible trajectory. This paper describes a multi-agent system that uses a combination of reinforcement learning and differential evolution to generate a trajectory that is ε-close to a target trajectory that is hidden.