Safe and Efficient CAV Lane Changing using Decentralised Safety Shields - Hegde, Bharathkumar Shripad; Bouroche, Melanie (My University, 2025)
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.
IEEE Intelligent Vehicles Symposium 2025
PUBLISHED
Cluj-Napoca, Romania
Keywords: Machine Learning; Multi-Agent Systems; Autonomous Vehicles; Connected Autonomous Vehicles (CAVs); Multi- Agent Reinforcement Learning (MARL); Control Barrier Func- tions (CBFs); Safe control; Lane change; Deep learning (Machine learning); Reinforcement learning; Automated vehicles; Nonlinear control theory; Creative Technologies; Smart & Sustainable Planet; Artificial Intelligence; Distributed systems; MACHINE LEARNING; autonomous vehicles