Leveraging on Deep Reinforcement Learning for Autonomous Safe Decision-Making in Highway On-ramp Merging (Student Abstract)

Abstract High-speed highway on-ramp merging is one of the most difficult and critical tasks for any autonomous driving system. This work studies this problem by combining deep deterministic policy gradient (DDPG) reinforcement learning with drivers’ intentions prediction. Our proposed solution is based on an artificial neural network to predict drivers’ intentions, used as an input state to the DDPG agent that outputs the longitudinal acceleration to the merging vehicle. We show that this solution improves safety performances.