Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change
On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic
Abstract (arXiv)
Autonomous lane-change, a key feature of advanced driver-assistance systems, can enhance traffic efficiency and reduce the incidence of accidents. However, safe driving of autonomous vehicles remains challenging in complex environments. How to perform safe and appropriate lane change is a popular topic of research in the field of autonomous driving. Currently, few papers consider the safety of reinforcement learning in autonomous lane-change scenarios. We introduce safe hybrid-action reinforcement learning into discretionary lane change for the first time and propose Parameterized Soft Actor-Critic with PID Lagrangian (PASAC-PIDLag) algorithm. Furthermore, we conduct a comparative analysis of the Parameterized Soft Actor-Critic (PASAC), which is an unsafe version of PASAC-PIDLag. Both algorithms are employed to train the lane-change strategy of autonomous vehicles to output discrete lane-change decision and longitudinal vehicle acceleration. Our simulation results indicate that at a traffic density of 15 vehicles per kilometer (15 veh/km), the PASAC-PIDLag algorithm exhibits superior safety with a collision rate of 0%, outperforming the PASAC algorithm, which has a collision rate of 1%. The outcomes of the generalization assessments reveal that at low traffic density levels, both the PASAC-PIDLag and PASAC algorithms are proficient in attaining a 0% collision rate. Under conditions of high traffic flow density, the PASAC-PIDLag algorithm surpasses PASAC in terms of both safety and optimality.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 12 of its 38 references that are in the map. First posted on arXiv 1 Mar 2024.
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- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2018Soft Actor-Critic Algorithms and Applicationscited by 1,240
- 2017Constrained Policy Optimizationcited by 983
- 2020Responsive Safety in Reinforcement Learning by PID Lagrangian Methodscited by 316
- 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
- 2018A Reinforcement Learning Based Approach for Automated Lane Change Maneuverscited by 46
- 2018Automated Speed and Lane Change Decision Making using Deep Reinforcement Learningcited by 36
- 2019Driving in Dense Traffic with Model-Free Reinforcement Learningcited by 35
- 2019Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environmentcited by 21
- 2020An Integrated Framework of Decision Making and Motion Planning for Autonomous Vehicles Considering Social Behaviorscited by 17
- 2019Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Modelcited by 14
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- 2020Meta Reinforcement Learning-Based Lane Change Strategy for Autonomous Vehicles
- 2025A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
- 2025Safe and Efficient CAV Lane Changing using Decentralised Safety Shields
- 2021Minimizing Safety Interference for Safe and Comfortable Automated Driving with Distributional Reinforcement Learning
- 2022SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving
- 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic