Real-Time Vehicle Trajectory Prediction for Traffic Conflict Detection at Unsignalized Intersections

Abstract

Real-time prediction of vehicle trajectory at unsignalized intersections is important for real-time traffic conflict detection and early warning to improve traffic safety at unsignalized intersections. In this study, we propose a robust real-time prediction method for turning movements and vehicle trajectories using deep neural networks. Firstly, a vision-based vehicle trajectory extraction system is developed to collect vehicle trajectories and their left-turn, go straight, and right-turn labels to train turning recognition models and multilayer LSTM deep neural networks for the prediction task. Then, when performing vehicle trajectory prediction, we propose the vehicle heading angle change trend method to recognize the future move of the target vehicle to turn left, go straight, and turn right based on the trajectory data characteristics of the target vehicle before passing the stop line. Finally, we use the trained multilayer LSTM models of turning left, going straight, and turning right to predict the trajectory of the target vehicle through the intersection. Based on the TensorFlow-GPU platform, we use Yolov5-DeepSort to automatically extract vehicle trajectory data at unsignalized intersections. The experimental results show that the proposed method performs well and has a good performance in both speed and accuracy evaluation.

Vehicle Trajectory Prediction Method Based on “Current” Statistical Model and Cubature Kalman Filter

“…The method of CA-UKF, CS-UKF, and CS-CKF (this paper proposed) models are applied to track and predict the vehicle maneuvering state. As the intersection is a high incidence place of traffic accidents, tracking the vehicle trajectory in the intersection has more practical importance [22]. We selected the intersection of Yingbin Avenue crossed Qiche Avenue in Nanchang as the research object.…”

A Comprehensive Review on Deep Learning-Based Motion Planning and End-to-End Learning for Self-Driving Vehicle

“…Where Yi is the anticipated position of the vehicle at time step i, Yi* is the equivalent ground truth position, and N is the number of time steps in the predicted trajectory. Previously discussed models CNN_Raw-RNN [38], Four layer LSTM [40] and U net (6 layer) [43] have achieved MAE values are 0.113, 0.29 and 0.38 in meters respectively. We observed that CNN_Raw-RNN [38] has a less MAE value when compared with other models which indicate that it better performed.…”

“…In addition, to detect traffic conflicts at unsignalized crossings, Qianxia Cao et. al [40] have developed a real-time vehicle trajectory estimation technique. To forecast each vehicle's upcoming motion as it approaches an intersection, the system employs a DL-based method.…”

Behavior trajectory prediction research of adjacent vehicles based on LSTM

“…The encoder module is used for hidden layer vector computation. The obtained vehicle trajectory information x, y can be extracted into the LSTM encoder after being processed by increasing the dimension [7]. For any time point t, its motion and trajectory information can be expressed as Equation 10.…”