autonomous-driving

Autonomous Car Following by Deep Reinforcement Learning

This on-going project aims to develop autonomous car-following strategies based on deep reinforcement learning.The demo animation shows the car-following learning process of the RL agents (cars). In early training episodes, cars become red frequently, indicating penalties caused by bad performances in car following, e.g., rear-end crashes. As training converges, the RL agents maintain steady car-following headways and receive fewer penalties.

Human-Like Driving by Inverse Reinforcement Learning

This on-going project is inspired by Levine et al. (2012). It aims to imitate human driving patterns. The assumption is that drivers act based on a utility (reward) function, which represents the preference of the driver and elicits the driving behavior. The role of inverse reinforcement learning is to infer or discover the latent utility function from driver (expert) demonstrations and thus generalize the driving policy to unobserved situations.