mlsanityData to 14 Oct 2026

Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control

arXiv 2411.10031 ↗First posted 15 Nov 2024eess.SY cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

It is recognized that the control of mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. Among existing methods, Multi-Agent Reinforcement Learning (MARL) appears to be a promising control strategy because it can manage complex scenarios in real time. However, current research on MARL-based mixed-autonomy platoon control suffers from several limitations. First, existing MARL approaches address safety by penalizing safety violations in the reward function, thus lacking theoretical safety guarantees due to the black-box nature of RL. Second, few studies have explored the cooperative safety of multi-CAV platoons, where CAVs can be coordinated to further enhance the system-level safety involving the safety of both CAVs and HDVs. Third, existing work tends to make an unrealistic assumption that the behavior of HDVs and CAVs is publicly known and rationale. To bridge the research gaps, we propose a safe MARL framework for mixed-autonomy platoons. Specifically, this framework (i) characterizes cooperative safety by designing a cooperative Control Barrier Function (CBF), enabling CAVs to collaboratively improve the safety of the entire platoon, (ii) provides a safety guarantee to the MARL-based controller by integrating the CBF-based safety constraints into MARL through a differentiable quadratic programming (QP) layer, and (iii) incorporates a conformal prediction module that enables each CAV to estimate the unknown behaviors of the surrounding vehicles with uncertainty qualification. Simulation results show that our proposed control strategy can effectively enhance the system-level safety through CAV cooperation of a mixed-autonomy platoon with a minimal impact on control performance.

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 21 of its 59 references that are in the map. First posted on arXiv 15 Nov 2024.

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21 of 59 references.

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Same direction, newest first

  1. 2024Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning20 Jan

Other references · 20

  1. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  3. 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 537
  4. 2015Risk-Constrained Reinforcement Learning with Percentile Risk Criteriacited by 337
  5. 2018A Lyapunov-based Approach to Safe Reinforcement Learningcited by 297
  6. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 289
  7. 2022Safe Planning in Dynamic Environments using Conformal Predictioncited by 119
  8. 2021Safe Reinforcement Learning Using Robust Control Barrier Functionscited by 59
  9. 2018Data-Enabled Predictive Control: In the Shallows of the DeePCcited by 38
  10. 2022Safe Reinforcement Learning Using Black-Box Reachability Analysiscited by 26
  11. 2024Language-Driven Policy Distillation for Cooperative Driving in Multi-Agent Reinforcement Learningcited by 14
  12. 2022DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flowcited by 8
8 more
  1. 2024Interaction-Aware Trajectory Prediction for Safe Motion Planning in Autonomous Driving: A Transformer-Transfer Learning Approachcited by 7
  2. 2020Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stabilitycited by 4
  3. 2024A Parameter Privacy-Preserving Strategy for Mixed-Autonomy Platoon Controlcited by 4
  4. 2024Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehiclescited by 3
  5. 2022Energy-efficient Connected Cruise Control with Lean Penetration of Connected Vehiclescited by 2
  6. 2024Decentralized Robust Data-driven Predictive Control for Smoothing Mixed Traffic Flowcited by 2
  7. 2024Towards Safe and Robust Autonomous Vehicle Platooning: A Self-Organizing Cooperative Control Frameworkcited by 2
  8. 2022Cumulative Flow Diagram-Based Fixed-Time Signal Timing Optimization at Isolated Intersections Using Connected Vehicle Trajectory Datacited by 1

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  1. 2025Safe and Efficient CAV Lane Changing using Decentralised Safety Shields
  2. 2025MARL-OT: Multi-Agent Reinforcement Learning Guided Online Fuzzing to Detect Safety Violation in Autonomous Driving Systems
  3. 2026Reinforcement Learning-Based Control of CAV Platoon Joining Maneuvers in Mixed Traffic
  4. 2025Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems
  5. 2026Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles
  6. 2026A Collaborative Safety Shield for Safe and Efficient CAV Lane Changes in Congested On-Ramp Merging
  7. 2022Learning-based social coordination to improve safety and robustness of cooperative autonomous vehicles in mixed traffic
  8. 2022Spatial-Temporal-Aware Safe Multi-Agent Reinforcement Learning of Connected Autonomous Vehicles in Challenging Scenarios