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

TL;DR

This paper proposes a shared information-based multi-agent reinforcement learning framework for connected autonomous vehicles, enhancing traffic safety and efficiency through V2V communication and safe decision-making.

Contribution

It introduces an integrated approach combining CNN-based data processing, information sharing, and safe actor-critic algorithms for improved CAV behavior planning.

Findings

Improves average velocity and comfort in traffic scenarios.

Ensures safety by avoiding unsafe actions and maintaining safe distances.

Enables earlier obstacle detection through shared vision data.

Abstract

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.