  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.
