mlsanityData to 14 Oct 2026

Beyond Safety Filtering: Control Barrier Function-Informed Reinforcement Learning for Connected and Automated Vehicles

arXiv 2605.16894 ↗First posted 16 May 2026cs.RO cs.SY eess.SYcited by 0

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

Abstract (arXiv)

Reinforcement Learning (RL) uses rewards to guide learning, yet reward design is typically hand-crafted using heuristics that can be difficult to tune. We propose a Control Barrier Function (CBF)-informed reward design for Multi-Agent RL (MARL) that converts CBF constraint values under joint MARL actions into a reward signal that explicitly guides safe learning. We compare against two heuristic reward baselines in a four-way multi-lane intersection with connected and automated vehicles. Results show that our method achieves the highest task performance and is less sensitive to reward hyperparameters, yielding consistently strong performance across the tested hyperparameter range. Code for reproducing the experimental results and a video demonstration are available at https://github.com/bassamlab/SigmaRL.

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 19 of its 29 references that are in the map. First posted on arXiv 16 May 2026.

Builds on

19 of 29 references.

References · 19

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  2. 2020Deep Reinforcement Learning for Autonomous Driving: A Surveycited by 721
  3. 2019Control Barrier Functions: Theory and Applicationscited by 372
  4. 2019End-to-End Safe Reinforcement Learning through Barrier Functions for Safety-Critical Continuous Control Taskscited by 289
  5. 2018A predictive safety filter for learning-based control of constrained nonlinear dynamical systemscited by 118
  6. 2021Reward (Mis)design for Autonomous Drivingcited by 104
  7. 2020Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learningcited by 103
  8. 2019Model-free Deep Reinforcement Learning for Urban Autonomous Drivingcited by 98
  9. 2019Learning for Safety-Critical Control with Control Barrier Functionscited by 69
  10. 2019Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous Drivingcited by 59
  11. 2023Not Only Rewards But Also Constraints: Applications on Legged Robot Locomotioncited by 47
  12. 2017Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Mergecited by 19
7 more
  1. 2024A Learning Framework for Diverse Legged Robot Locomotion Using Barrier-Based Style Rewardscited by 12
  2. 2024Barrier Functions Inspired Reward Shaping for Reinforcement Learningcited by 11
  3. 2021Episodic Learning for Safe Bipedal Locomotion with Control Barrier Functions and Projection-to-State Safetycited by 7
  4. 2024SigmaRL: A Sample-Efficient and Generalizable Multi-Agent Reinforcement Learning Framework for Motion Planningcited by 3
  5. 2025A Real-Time Control Barrier Function-Based Safety Filter for Motion Planning with Arbitrary Road Boundary Constraintscited by 3
  6. 2024A Learning-Based Control Barrier Function for Car-Like Robots: Toward Less Conservative Collision Avoidancecited by 2
  7. 2026TTCBF: A Truncated Taylor Control Barrier Function for High-Order Safety Constraintscited by 1

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  2. 2025CBF-RL: Safety Filtering Reinforcement Learning in Training with Control Barrier Functions
  3. 2025Barrier Function Overrides For Non-Convex Fixed Wing Flight Control and Self-Driving Cars
  4. 2025Balancing Progress and Safety: A Novel Risk-Aware Objective for RL in Autonomous Driving
  5. 2025Guided by Guardrails: Control Barrier Functions as Safety Instructors for Robotic Learning
  6. 2024Disturbance Observer-based Control Barrier Functions with Residual Model Learning for Safe Reinforcement Learning
  7. 2024Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control
  8. 2024Generating and Evolving Reward Functions for Highway Driving with Large Language Models