Safe and Efficient CAV Lane Changing using Decentralised Safety Shields

TL;DR

This paper introduces a decentralised Hybrid Safety Shield (HSS) integrated with Multi-Agent Reinforcement Learning (MARL) to ensure safe lane changes for Connected and Autonomous Vehicles (CAVs), balancing safety and traffic efficiency.

Contribution

It proposes a novel HSS combining optimisation and rule-based safety guarantees, integrated with MARL for safe, efficient lane changing in CAVs.

Findings

HSS guarantees safety by enforcing dynamic safety constraints.

MARL-HSS learns stable policies with zero crashes.

Achieves comparable speeds with enhanced safety in simulations.

Abstract

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.