Density-Aware Federated Imitation Learning for Connected and Automated Vehicles with Unsignalized Intersection

TL;DR

This paper introduces a privacy-preserving federated imitation learning framework for connected vehicles at unsignalized intersections, enhancing safety and reducing communication costs through density-aware aggregation and experience selection.

Contribution

It proposes a novel density-aware federated imitation learning framework with collision avoidance and communication efficiency improvements for intelligent transportation systems.

Findings

Collision avoidance capability improved by 55.71%.

Discomfort reduced by 41.37%.

Communication overhead decreased by 12.80%.

Abstract

Intelligent Transportation System (ITS) has become one of the essential components in Industry 4.0. As one of the critical indicators of ITS, efficiency has attracted wide attention from researchers. However, the next generation of urban traffic carried by multiple transport service providers may prohibit the raw data interaction among multiple regions for privacy reasons, easily ignored in the existing research. This paper puts forward a federated learning-based vehicle control framework to solve the above problem, including interactors, trainers, and an aggregator. In addition, the density-aware model aggregation method is utilized in this framework to improve vehicle control. What is more, to promote the performance of the end-to-end learning algorithm in the safety aspect, this paper proposes an imitation learning algorithm, which can obtain collision avoidance capabilities from a set of collision avoidance rules. Furthermore, a loss-aware experience selection strategy is also explored, reducing the communication overhead between the interactors and the trainers via extra computing. Finally, the experiment results demonstrate that the proposed imitation learning algorithm obtains the ability to avoid collisions and reduces discomfort by 55.71%. Besides, density-aware model aggregation can further reduce discomfort by 41.37%, and the experience selection scheme can reduce the communication overhead by 12.80% while ensuring model convergence.