mlsanityData to 14 Oct 2026

Shared Information-Based Safe And Efficient Behavior Planning For Connected Autonomous Vehicles

arXiv 2302.04321 ↗First posted 8 Feb 2023cs.RO cs.AIcited by 0

On the map: End-to-end autonomous driving → Cooperative and V2X driving

Abstract (arXiv)

The recent advancements in wireless technology enable connected autonomous vehicles (CAVs) to gather data via vehicle-to-vehicle (V2V) communication, such as processed LIDAR and camera data from other vehicles. In this work, we design an integrated information sharing and safe multi-agent reinforcement learning (MARL) framework for CAVs, to take advantage of the extra information when making decisions to improve traffic efficiency and safety. We first use weight pruned convolutional neural networks (CNN) to process the raw image and point cloud LIDAR data locally at each autonomous vehicle, and share CNN-output data with neighboring CAVs. We then design a safe actor-critic algorithm that utilizes both a vehicle's local observation and the information received via V2V communication to explore an efficient behavior planning policy with safety guarantees. Using the CARLA simulator for experiments, we show that our approach improves the CAV system's efficiency in terms of average velocity and comfort under different CAV ratios and different traffic densities. We also show that our approach avoids the execution of unsafe actions and always maintains a safe distance from other vehicles. We construct an obstacle-at-corner scenario to show that the shared vision can help CAVs to observe obstacles earlier and take action to avoid traffic jams.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving. Builds on 30 of its 53 references that are in the map. First posted on arXiv 8 Feb 2023.

Builds on

30 of 53 references.

References · 30

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18 more
  1. 2017Safe Model-based Reinforcement Learning with Stability Guaranteescited by 366
  2. 2016Safe, Multi-Agent, Reinforcement Learning for Autonomous Drivingcited by 311
  3. 2019Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networkscited by 293
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  13. 2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificatescited by 74
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  15. 2019Robust Model Predictive Shielding for Safe Reinforcement Learning with Stochastic Dynamicscited by 47
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  17. 2019MAMPS: Safe Multi-Agent Reinforcement Learning via Model Predictive Shieldingcited by 20
  18. 2021Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Changecited by 9

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