Lane change identification and prediction with roadside LiDAR data. (March 2020)

• Record Type: Journal Article
• Authors: Cui, Yuepeng; Wu, Jianqing; Xu, Hao; Wang, Aobo
• Is Part Of: Optics & laser technology. Volume 123(2020)
• Publication Date: 2020-03
• Subjects: Lane change -- Roadside LiDAR -- Lane boundary -- Vehicle trajectory; Optics -- Periodicals; Lasers -- Periodicals; Electronic journals; 621.366
• DOI: 10.1016/j.optlastec.2019.105934
• Languages: English
• ISSNs: 0030-3992

Abstract

Highlights: Lane Change was identified with Roadside LiDAR Data. Vehicle Trajectories from Roadside LiDAR were used for Lane Change Prediction. Method was evaluated with the real-world data. Abstract: Lane change identification and lane change prediction are important tasks for the Connected-Vehicle (CV) technologies. Since both connected vehicles and non-connected vehicles may exist on the roads for a long time, the real-time information of the unconnected traffic could not be obtained by the current CV network. Therefore, lane change identification and lane change prediction could not be achieved with the missing traffic information of the unconnected vehicles. The roadside Light Detection and Ranging (LiDAR) provides a solution to fill the data gap under the mixed traffic situation. This paper developed the methods of lane change identification and prediction based on the vehicle trajectories extracted from the roadside LiDAR data. Lane boundaries were used to enhance the accuracy of lane change identification. The proposed method was evaluated using real-world data. The results showed that the proposed method can achieve the relatively high accuracy. The lane change information can be used to develop the lane-change warning system for the CV network.