Cooperative lane-changing in mixed traffic
a deep reinforcement learning approach
Xue Yao (Transport and Planning)
Zhaocheng Du (McGill University)
Zhanbo Sun (Southwest Jiaotong University)
Simeon C. Calvert (Transport and Planning)
Ang ji (Southwest Jiaotong University)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Deep Reinforcement Learning (DRL) has made remarkable progress in autonomous vehicle decision-making and execution control to improve traffic performance. This paper introduces a DRL-based mechanism for cooperative lane changing in mixed traffic (CLCMT) for connected and automated vehicles (CAVs). The uncertainty of human-driven vehicles (HVs) and the microscopic interactions between HVs and CAVs are explicitly modelled, and different leader-follower compositions are considered in CLCMT, which provides a high-fidelity DRL learning environment. A feedback module is established to enable interactions between the decision-making layer and the manoeuvre control layer. Simulation results show that the increase in CAV penetration leads to safer, more comfort, and eco-friendly lane-changing behaviours. A CAV-CAV lane-changing scenario can enhance safety by 24.5%–35.8%, improve comfort by 8%–9%, and reduce fuel consumption and emissions by 5.2%–12.9%. The proposed CLCMT promises advantages in the lateral decision-making and motion control of CAVs.