A Novel Intelligent Approach to Lane-Change Behavior Prediction for Intelligent and Connected Vehicles

Abstract

The prediction of lane-change behavior is a challenging issue in intelligent and connected vehicles (ICVs), which can help vehicles predict in advance and change lanes safely. In this paper, a novel intelligent approach, which considering both the driving style-based lane-change environment and the driving trajectory-related parameters of the ICV and surrounding vehicles, is proposed to predict the lane-change behaviors for ICVs. By analyzing the characteristics of the lane-change behavior of the vehicle, a modified dataset for the prediction of lane-change behavior was established based on the Next-Generation Simulation (NGSIM) dataset, which is made up of real vehicle trajectories collected by camera. In the proposed approach, the hidden Markov model (HMM)-based model is designed to judge whether the current environment is suitable for lane change according to the driving environment parameters around the vehicle; then according to the driving state of the vehicle, a learning-based prediction-then-judgment model is proposed and designed to realize the prediction of the ICV’s lane-change behavior. Experiments are implemented by using the modified dataset. From the experimental results, the lane-change probability value on the target lane in the truth of the lane-change behavior calculated by the designed HMM-based model is basically above 0.5, indicating that the model can make a more accurate judgment on the surrounding lane-change environment. The proposed learning-based prediction-then-judgment model has an accuracy of 99.32% for the prediction of lane-change behavior, and the accuracy of the lane-change detection algorithm in the model is 99.56%. The experimental results show that the proposed approach has a good performance in the prediction of lane-change behavior, which could effectively assist ICVs to change lanes safely.

An innovative method for scene recognition and target tracking for adaptive cruise control in complex traffic environment

“…It is important to distinguish the different driving maneuvers of the target vehicles for target tracking and behavior prediction. 28,29 However, the curve driving and lane changing maneuvers are very similar and difficult to be recognized. The curve entry/exit and cut in/out maneuvers are the four most common scenes which are discussed detailed in this paper.…”

Lane-Changing Model of Intelligent Connected Vehicle Considering the Factor of Turn Signal

“…The lane-changing behavior of vehicles has aroused fierce discussion among scholars due to its characteristics of commonness, uncertainty and complexity [6] and its tendency to cause traffic accidents [7]. Based on the analysis of the vehicle's lane-changing environment and driving trajectory, the lane-changing behavior of intelligent networked vehicles was predicted by Du et al [8]. And its experimental results show that the prediction model based on machine learning has high accuracy in predicting the lane-changing behavior and can effectively assist the lane-changing behavior decision-making of intelligent networked vehicles.…”