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

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.

Keywords

• Autonomous vehicle
• behavior recognition
• trajectory diversity
• trajectory prediction
• CHMM
• LSTM