Safe and Efficient CAV Lane Changing using Decentralised Safety Shields

• URL: http://arxiv.org/abs/2505.01453v1
• Date: Wed, 30 Apr 2025 09:11:09 GMT
• Title: Safe and Efficient CAV Lane Changing using Decentralised Safety Shields
• Authors: Bharathkumar Hegde, Melanie Bouroche,
• Abstract summary: Lane changing is a complex decision-making problem for Connected and Autonomous Vehicles (CAVs)<n>We propose a decentralised Hybrid Safety Shield (HSS) that combines optimisation and a rule-based approach to guarantee safety.<n>Our method applies control barrier functions to constrain longitudinal and lateral control inputs of a CAV to ensure safe manoeuvres.
• Score: 2.07180164747172
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Trust-MARL: Trust-Based Multi-Agent Reinforcement Learning Framework for Cooperative On-Ramp Merging Control in Heterogeneous Traffic Flow [8.452082837356162]
  This study proposes a trust-based multi-agent reinforcement learning (Trust-MARL) framework to address the challenge of cooperative on-ramp merging.<n>Trust-MARL enhances global traffic efficiency by leveraging inter-agent trust to improve bottleneck throughput and mitigate traffic shockwave.<n>An extensive set of ablation studies and comparative experiments validates the effectiveness of the proposed Trust-MARL approach.
  arXiv  Detail & Related papers  (2025-06-14T18:35:10Z)
• Shape it Up! Restoring LLM Safety during Finetuning [66.46166656543761]
  Finetuning large language models (LLMs) enables user-specific customization but introduces critical safety risks.<n>We propose dynamic safety shaping (DSS), a framework that uses fine-grained safety signals to reinforce learning from safe segments of a response while suppressing unsafe content.<n>We present STAR-DSS, guided by STAR scores, that robustly mitigates finetuning risks and delivers substantial safety improvements across diverse threats, datasets, and model families.
  arXiv  Detail & Related papers  (2025-05-22T18:05:16Z)
• SCANS: Mitigating the Exaggerated Safety for LLMs via Safety-Conscious Activation Steering [56.92068213969036]
  Safety alignment is indispensable for Large Language Models (LLMs) to defend threats from malicious instructions.<n>Recent researches reveal safety-aligned LLMs prone to reject benign queries due to the exaggerated safety issue.<n>We propose a Safety-Conscious Activation Steering (SCANS) method to mitigate the exaggerated safety concerns.
  arXiv  Detail & Related papers  (2024-08-21T10:01:34Z)
• Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning [57.84059344739159]
  "Shielding" is a popular technique to enforce safety inReinforcement Learning (RL) We propose a new permissibility-based framework to deal with safety and shield construction.
  arXiv  Detail & Related papers  (2024-05-29T18:00:21Z)
• Towards Comprehensive Post Safety Alignment of Large Language Models via Safety Patching [74.62818936088065]
  textscSafePatching is a novel framework for comprehensive PSA.<n>textscSafePatching achieves a more comprehensive PSA than baseline methods.<n>textscSafePatching demonstrates its superiority in continual PSA scenarios.
  arXiv  Detail & Related papers  (2024-05-22T16:51:07Z)
• Safe Hybrid-Action Reinforcement Learning-Based Decision and Control for Discretionary Lane Change [6.221047868628538]
  How to perform safe and appropriate lane change is a popular topic of research in the field of autonomous driving. We introduce safe hybrid reinforcementaction learning into discretionary lane change for the first time. 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%.
  arXiv  Detail & Related papers  (2024-03-01T11:03:17Z)
• SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control [5.862792724739738]
  One-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing. We propose a safety-enhanced residual reinforcement learning method (SafeLight) Our method can significantly reduce collisions while increasing traffic mobility.
  arXiv  Detail & Related papers  (2022-11-20T05:09:12Z)
• Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios [10.37986799561165]
  Communication technologies enable coordination among connected and autonomous vehicles (CAVs) We propose a framework of constrained multi-agent reinforcement learning (MARL) with a parallel safety shield for CAVs. Results show that our proposed methodology significantly increases system safety and efficiency in challenging scenarios.
  arXiv  Detail & Related papers  (2022-10-05T14:39:07Z)
• Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments [84.3830478851369]
  We propose a safe reinforcement learning approach that can jointly learn the environment and optimize the control policy. Our approach can effectively enforce hard safety constraints and significantly outperform CMDP-based baseline methods in system safe rate measured via simulations.
  arXiv  Detail & Related papers  (2022-09-29T20:49:25Z)
• Enhancing Safe Exploration Using Safety State Augmentation [71.00929878212382]
  We tackle the problem of safe exploration in model-free reinforcement learning. We derive policies for scheduling the safety budget during training. We show that Simmer can stabilize training and improve the performance of safe RL with average constraints.
  arXiv  Detail & Related papers  (2022-06-06T15:23:07Z)
• Decision-Making under On-Ramp merge Scenarios by Distributional Soft Actor-Critic Algorithm [10.258474373022075]
  We propose an RL-based end-to-end decision-making method under a framework of offline training and online correction, called the Shielded Distributional Soft Actor-critic (SDSAC) The results show that the SDSAC has the best safety performance compared to baseline algorithms and efficient driving simultaneously.
  arXiv  Detail & Related papers  (2021-03-08T03:57:32Z)
• SAINT-ACC: Safety-Aware Intelligent Adaptive Cruise Control for Autonomous Vehicles Using Deep Reinforcement Learning [17.412117389855226]
  SAINT-ACC: Setyaf-Aware Intelligent ACC system (SAINT-ACC) is designed to achieve simultaneous optimization of traffic efficiency, driving safety, and driving comfort. A novel dual RL agent-based approach is developed to seek and adapt the optimal balance between traffic efficiency and driving safety/comfort.
  arXiv  Detail & Related papers  (2021-03-06T14:01:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.