Performance Comparison of Deep RL Algorithms for Mixed Traffic Cooperative Lane-Changing

TL;DR

This paper compares deep reinforcement learning algorithms for cooperative lane-changing in mixed traffic, showing PPO's superior performance in safety, efficiency, comfort, and ecological impact.

Contribution

It enhances a previous CLCMT mechanism by incorporating uncertainty and microscopic interactions, and evaluates multiple DRL algorithms for lane-changing strategies.

Findings

PPO outperforms DDPG, TD3, and SAC in safety and efficiency.

DRL algorithms effectively handle traffic uncertainty.

PPO achieves higher rewards with fewer crashes.

Abstract

Lane-changing (LC) is a challenging scenario for connected and automated vehicles (CAVs) because of the complex dynamics and high uncertainty of the traffic environment. This challenge can be handled by deep reinforcement learning (DRL) approaches, leveraging their data-driven and model-free nature. Our previous work proposed a cooperative lane-changing in mixed traffic (CLCMT) mechanism based on TD3 to facilitate an optimal lane-changing strategy. This study enhances the current CLCMT mechanism by considering both the uncertainty of the human-driven vehicles (HVs) and the microscopic interactions between HVs and CAVs. The state-of-the-art (SOTA) DRL algorithms including DDPG, TD3, SAC, and PPO are utilized to deal with the formulated MDP with continuous actions. Performance comparison among the four DRL algorithms demonstrates that DDPG, TD3, and PPO algorithms can deal with uncertainty in traffic environments and learn well-performed LC strategies in terms of safety, efficiency, comfort, and ecology. The PPO algorithm outperforms the other three algorithms, regarding a higher reward, fewer exploration mistakes and crashes, and a more comfortable and ecology LC strategy. The improvements promise CLCMT mechanism greater advantages in the LC motion planning of CAVs.