A Cooperation-Aware Lane Change Method for Autonomous Vehicles

TL;DR

This paper introduces a cooperation-aware lane change method for autonomous vehicles that improves safety, efficiency, and comfort by modeling vehicle interactions and using an MPC-based motion planning algorithm.

Contribution

It presents a novel interactive trajectory prediction and decision-making framework that leverages vehicle cooperation to enhance lane change performance.

Findings

Increases driving efficiency of AVs by 14.8%

Enhances overall traffic flow with 2.6% efficiency gain for other vehicles

Reduces conservatism in AV lane change strategies

Abstract

Lane change for autonomous vehicles (AVs) is an important but challenging task in complex dynamic traffic environments. Due to difficulties in guarantee safety as well as a high efficiency, AVs are inclined to choose relatively conservative strategies for lane change. To avoid the conservatism, this paper presents a cooperation-aware lane change method utilizing interactions between vehicles. We first propose an interactive trajectory prediction method to explore possible cooperations between an AV and the others. Further, an evaluation is designed to make a decision on lane change, in which safety, efficiency and comfort are taken into consideration. Thereafter, we propose a motion planning algorithm based on model predictive control (MPC), which incorporates AV's decision and surrounding vehicles' interactive behaviors into constraints so as to avoid collisions during lane change. Quantitative testing results show that compared with the methods without an interactive prediction, our method enhances driving efficiencies of the AV and other vehicles by 14.8$\%$ and 2.6$\%$ respectively, which indicates that a proper utilization of vehicle interactions can effectively reduce the conservatism of the AV and promote the cooperation between the AV and others.