Trajectory prediction of straight vehicles at signalized intersections considering interference from right-turning vehicles

The disturbance of straight vehicle trajectories under the interference of right turning vehicles on intersecting roads has become a safety hazard for traffic operation at signalized intersections on urban roads. To improve the ability of direct driving drivers to respond to right-turning vehicles and make correct decisions, it is crucial to reliably predict the disturbance trajectory of direct driving vehicles. This article associated the trajectory distribution characteristics of straight vehicles in different states with the motion information of right-turning vehicles on crossed approach. On the basis of identifying vehicle disturbance trajectories, the time to collision (TTC) was added as the input layer to the model, and a three-layer Gaussian Mixture Module-Input and Output Hidden Markov Model (GMM-IOHMM) was constructed. A method for predicting the disturbance trajectory of through vehicles was proposed, which considers the degree to which right-turning vehicles on the crossed road have an impact on the direct traffic on this surface at signalized intersections. The experimental results showed that the improved model can better fit actual trajectory data during model training compared with traditional HMM, and the fitting effect of GMM-IOHMM has been significantly improved compared with traditional time series models. And TTC is less than or equal to 4. 5 s and yaw angle is greater than 2. 35 degrees can be used as a criterion to determine whether a straight ahead vehicle is disturbed. The trajectory prediction results can more accurately determine the possibility of conflicts between direct vehicles and surrounding vehicles, and can serve as an important basis for the design of assisted driving systems for disturbed direct vehicles and other vehicles traveling together. © 2024 Harbin Institute of Technology. All rights reserved.