2025/04/30 by Bharathkumar Hegde, Hegde, Bharathkumar, Mélanie Bouroche +1
Engineering · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #Constraint (computer-aided design) #Control (management) #Controller (irrigation) #FOS: Computer and information sciences #FOS: Electrical engineering #Multiagent Systems (cs.MA) #Reinforcement learning #Road traffic control #Road traffic safety #Robotics (cs.RO) #Systems and Control (eess.SY) #Traffic control and management #Vehicle Dynamics and Control Systems #Vehicle dynamics #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2505.01453
openalex publication_date 2025/04/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Lane changing is a complex decision-making problem for Connected and Autonomous Vehicles (CAVs) as it requires balancing traffic efficiency with safety. Although traffic efficiency can be improved by using vehicular communication for training lane change controllers using Multi-Agent Reinforcement Learning (MARL), ensuring safety is difficult. To address this issue, we propose a decentralised Hybrid Safety Shield (HSS) that combines optimisation and a rule-based approach to guarantee safety. Our method applies control barrier functions to constrain longitudinal and lateral control inputs of a CAV to ensure safe manoeuvres. Additionally, we present an architecture to integrate HSS with MARL, called MARL-HSS, to improve traffic efficiency while ensuring safety. We evaluate MARL-HSS using a gym-like environment that simulates an on-ramp merging scenario with two levels of traffic densities, such as light and moderate densities. The results show that HSS provides a safety guarantee by strictly enforcing a dynamic safety constraint defined on a time headway, even in moderate traffic density that offers challenging lane change scenarios. Moreover, the proposed method learns stable policies compared to the baseline, a state-of-the-art MARL lane change controller without a safety shield. Further policy evaluation shows that our method achieves a balance between safety and traffic efficiency with zero crashes and comparable average speeds in light and moderate traffic densities.