FORESEE: A Cooperative Lane Change Model for Connected and Automated Driving

2026-06-23

AI Summary

This study addresses the limitations of conventional autonomous vehicles that rely on local, short-term information for lane changes, often causing traffic disturbances, speed fluctuations, and reduced energy efficiency. To overcome these issues, this work proposes a V2X-enabled cooperative lane-changing model that leverages V2X communication to proactively predict traffic states and dynamically allocate lanes according to target vehicle speeds. By doing so, the approach significantly reduces unnecessary lane changes, enhances traffic flow homogeneity, and improves ride comfort. Experimental results demonstrate that, compared to non-cooperative strategies, the proposed method effectively increases average vehicle speed and energy efficiency, mitigates acceleration fluctuations, and exhibits greater robustness against disturbances such as obstacles.

Abstract

This paper presents FORESEE, a novel cooperative lane change model for connected and automated driving. FORESEE leverages Vehicle-to-Everything (V2X) data to anticipate traffic conditions and effectively organize lane changes. Specifically, it uses V2X data to organize vehicles into lanes based on their desired speeds, which helps to homogenize traffic flow and reduce disturbances caused by speed differences among vehicles within the same lane. The study demonstrates that implementing cooperative lane changes with FORESEE enhances average vehicle speed and energy efficiency compared to non-cooperative lane changes, which typically rely on short-term and local information about the ego vehicle and its immediate neighbors. This is achieved through fewer but more effective lane changes. Additionally, vehicles can maintain speeds closer to their desired speeds, resulting in fewer fluctuations in speed and acceleration and enhanced driving comfort. Moreover, cooperative lane changes can better manage road traffic disturbances, such as obstacles, by anticipating traffic conditions and organizing lane changes ahead. FORESEE serves as a valuable framework for the future design and testing of V2X-based maneuver coordinations as their effectiveness depends on how vehicles change lanes and their ability to plan and organize maneuvers in consideration of the upcoming traffic conditions.

Problem

• cooperative lane change
• connected and automated driving
• V2X
• traffic homogenization
• maneuver coordination

Innovation

• cooperative lane change
• V2X
• traffic homogenization
• maneuver coordination
• connected and automated driving