Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Driving

Understand

A model used for velocity control during car following was proposed based on deep reinforcement learning (RL).

• To fulfil the multi-objectives of car following, a reward function reflecting driving safety, efficiency, and comfort was constructed.
• With the reward function, the RL agent learns to control vehicle speed in a fashion that maximizes cumulative rewards, through trials and errors in the simulation environment.
• A total of 1,341 car-following events extracted from the Next Generation Simulation (NGSIM) dataset were used to train the model.