Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment

Side collisions caused by sudden vehicle cut-ins comprise a significant proportion of traffic accidents. Due to the complex and dynamic nature of traffic environments, the warning algorithms in advanced driving assistant systems (ADAS) often misjudge and misdiagnose risk and omit necessary warnings, because they rely solely on the sensing information of the single vehicle equipped with ADAS and have limited insights from and communication with the surrounding vehicles and traffic environment. To improve the effectiveness of ADAS in cut-in scenarios, this study established a collision warning model in a vehicle-to-vehicle (V2V) communication environment. Firstly, based on the support vector machine-recursive feature elimination (SVM-RFE) lane-change intent-recognition model, the lane-change feasibility and the change rate of the lateral offset, the logical and was used to establish a lane-change behavior prediction model, and a trajectory prediction model was established based on the long short-term memory (LSTM). Then, based on the proposed comprehensive prediction model for lane-change behavior, the driving trajectory prediction model, and the oriented bounding box (OBB) detection algorithm, a collision warning model was established for a V2V environment. Finally, based on a driving simulation platform and a real-world vehicle test, a cut-in experiment in a V2V environment was designed and implemented. By comparing the warning confusion matrix and warning time, it was found that the proposed cut-in collision warning model is superior to the traditional collision warning model. The results of this study can provide new modeling ideas and a theoretical basis for ADAS to further optimize for a cut-in scenario.

Automated summary

This study establishes a collision warning model in a vehicle-to-vehicle (V2V) communication environment to improve the effectiveness of advanced driving assistant systems (ADAS) in cut-in scenarios. Using support vector machine-recursive feature elimination and long short-term memory models, the study predicts lane-change behavior and driving trajectories, while incorporating an oriented bounding box detection algorithm to establish a collision warning model.