Decentralized cooperative driving automation: a reinforcement learning framework using genetic fuzzy systems

Abstract

Cooperative Automated Driving System (ADS)-equipped Vehicles (CoAVs) can be a promising solution to traffic congestion. We present a reinforcement learning strategy using Genetic Fuzzy Systems (GFS) for cooperative merge of vehicles onto a highway in high density, mixed-autonomy traffic conditions. The CoAVs are trained to make their own decisions purely based on information available from its surrounding vehicles, thus making this a decentralized system. The GFS module in each CoAV makes recommendations on the acceleration and lane-change decisions. The CoAVs are trained on different ADS behavioral parameters, such as aggressiveness of lane change, different CoAV market penetration rates (MPRs) and traffic congestion levels. The results show the effectiveness of increasing the MPR. It is noticed that the CoAVs trained with different ADS behavioral parameters can generate completely different cooperative maneuvers. The results also show that the trained CoAVs can cooperate even with human-driven vehicles to improve the overall traffic performance.