🤖 AI Summary
This study addresses the challenge of coordination failures in connected and automated vehicles during cooperative maneuvers—such as lane changes—caused by inaccurate inference of other vehicles’ intentions. To overcome the limitations of conventional trajectory prediction methods that rely solely on kinematic data, this work proposes directly sharing driving intentions via V2X communication. The paper presents the first systematic quantitative comparison between intention sharing and trajectory prediction in the context of cooperative lane changing. Results demonstrate that intention sharing significantly improves the success rate of coordinated lane changes. These findings underscore the critical role of explicit intention exchange in enhancing vehicular cooperation and provide both empirical justification and technical foundation for the design of future maneuver coordination protocols.
📝 Abstract
This paper examines the critical role of intent-sharing in enabling effective maneuver coordination for connected and automated vehicles (CAVs). Successful maneuver coordinations require vehicles to accurately know other vehicles' driving intentions. Intent-sharing can be achieved by the remote vehicles directly communicating their plans with the ego vehicle, as opposed to the ego vehicle predicting the trajectory on the remote vehicles' behalf. In this paper, we investigate the potential of intent-sharing on maneuver coordination effectiveness by quantifying the percentage of successful coordinations. We analyze the potential of intent-sharing by comparing its effectiveness for coordinated lane changes in a highway scenario with the effectiveness of a trajectory prediction method based on current kinematic data. Our analysis demonstrates in two scenarios substantial improvements in maneuver coordination when CAVs have direct access to the nearby vehicles' driving intentions through intent sharing. These findings highlight the importance of including intent-sharing in the maneuver coordination protocol.