Vehicle Turning Intention Prediction Based on Data-Driven Method with Roadside Radar and Vision Sensor

The trajectory tracking and turning intention prediction of vehicles at intersections are a vital part of smart traffic safety. With the height limitation, traffic objects are often obscured by other vehicles that easily results in blind spots for the visual sensing system. This paper summarizes the author's practice of a roadside unit composed of monitors and radar sensors to track and predict behavioral intentions of traffic objects, and develop a stable system based on the fusion of radar and image sensing information to reduce the danger caused by the steering of other vehicles that are not predicted by the driving sight and the blind angle of the on-board sensor. The roadside unit is installed at the intersection to collect vehicle data on the road, such as position, speed, and direction. An artificial neural network based on LSTM-GAN is used to process data and predict vehicle turning intention. The research case shows that the proposed model has about 91% prediction accuracy.