A Method for Predicting Diverse Lane-Changing Trajectories of Surrounding Vehicles Based on Early Detection of Lane Change

Yuan-Yuan Ren,Xue-Lian Zheng,Lan Zhao,Xian-Sheng Li

doi:10.1109/access.2022.3149269

Abstract

The trajectory prediction of surrounding vehicles is the basis for reasonable decision-making of autonomous vehicles (AV), which is helpful for improving their safety and comfort. Aiming to predict lane-changing trajectories, we propose a behavior-based method of predicting diverse lane-changing trajectories of surrounding vehicles, which includes two parts: lane-changing behavior recognition and diverse lane-changing trajectory prediction. Firstly, a lane-changing behavior recognition model based on the Continuous Hidden Markov Model (CHMM) is established to identify the lane-changing behavior of surrounding vehicles. Secondly, considering the driving styles will lead to diverse lane-changing patterns, a diverse lane-changing trajectory prediction method based on LSTM is proposed to predict three lane-changing trajectories when the driving style is unknown, which is composed of three LSTM trajectory generators representing three lane-changing patterns. Finally, the Next Generation Simulation (NGSIM) dataset is used to train, validate and test the behavior recognition model and the trajectory prediction model. The results show good accuracy and anticipative ability of the behavior recognition model. The average accuracy of surrounding vehicle behavior detection is 98.98%, the accuracy of surrounding vehicle behavior detection in 2s before lane change point is above 95%, the average anticipation time of left and right lane-changing behavior recognition is 3.24s and 3.71s, the average proportion of anticipation time in the lane-changing duration time is 46.78% and 55.54%. In the trajectory prediction section, with considering the diversity of lane changing trajectory caused by driving style, the proposed method for predicting diverse lane-changing trajectories reduces the error between the predicted and actual trajectories. The Root Mean Square Error (RMSE) and the Final Displacement Error (FDE) of the longitudinal and lateral positions are reduced by more than 21% over a 5s time horizon. In conclusion, the diverse trajectory prediction method based on the early detection of lane-changing behavior can provide AV with future trajectory of other vehicle under different driving styles, which is conducive to a more comprehensive and accurate driving risk assessment.

Highlights

The structure of the autonomous driving system includes perception, prediction, decision-making, action planning and control
We believe that the lane-changing duration time reflects the diversity of the lanechange process to a certain extent, so we propose a diverse lane-changing trajectory prediction model considering the lane-changing duration time
This paper mainly focuses on early detection of lane change and trajectory prediction of surrounding vehicles

Summary

Introduction

The structure of the autonomous driving system includes perception, prediction, decision-making, action planning and control. Determining how to accurately and reasonably predict the trajectories of surrounding vehicles on the road is very important, especially for the action planning of autonomous driving, which can make it more safe, efficient and comfortable. The early trajectory prediction method was physics-based trajectory prediction, which consists of three steps It assumes that the future trajectory of the vehicle is VOLUME XX, 2017. One is the I-80 freeway in Emeryville, California, the segment covered being approximately 500m in length and 6 lanes (3.66m or 12ft each) in width (see Fig.). The other is the US101 freeway in Los Angeles, California, the segment covered being approximately 640m in length and 5 mainline lanes in width (see Fig.). The original data has certain error and noise, the symmetric exponential moving average filtering algorithm(sEMA) [27] is used to filter the original data

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