Enhancing Traffic Safety Through V2X-Based Deep Reinforcement Learning

Sammanfattning

As a core component of an ITS, CAVs have garnered significant attention and expect to play an important role in enhancing traffic safety. To enable (future) practical deployment of CAVs, safety strategies must be incorporated into the control design. This includes, in particular, strategies for ensuring avoidance or mitigation of multi-vehicle collisions. Conventional control strategies exhibit some inherent limitations that limit their ability to improve efficiency and safety beyond a certain point. This thesis proposes DRL as strategy or means to go beyond the limited performance of classic control strategies. Our preliminary results demonstrate the potential of using DRL for design of emergency braking profiles in vehicle-following scenarios. In the studied scenarios, three vehicles are involved, where the middle vehicle shall decelerate in such a way that collective harm is minimized for the three vehicles involved. Based on the developed DRL-approach, this thesis further provides a hybrid approach that combines DRL with an existing method based on analytical expressions for selecting optimal constant deceleration for the middle vehicle. By combining DRL with the previous method, the proposed hybrid approach increases the reliability compared to standalone DRL, while achieving superior performance in comparison to the optimal constant deceleration approach in terms of overall harm reduction and collision avoidance.