Toward Driver Intention Prediction for Intelligent Vehicles: A Deep Learning Approach

High-level scene understanding and situational awareness are fundamental for autonomous vehicles before being widely used on public roads in a thoroughly efficient and safe manner. These tasks involve not only perceiving the current surrounding states but also predicting the future behavior of nearby human-driven vehicles. A large amount of vehicular sensing data can be collected using sensors and networking systems in vehicles. Moreover, with the advent of Dedicated Short Range Communication, vehicles can further transfer intention information to surrounding vehicles. However, real-time intention inference of nearby drivers is still challenging. Our primary focus is to present a novel deep learning-based approach to predict drivers driving behavior at unsignalized T-junctions. We use temporal convolutional networks to analyze sequences of trajectory data for a vehicle approaching the T-junction and classify the maneuver seconds before actual maneuver occurrence. The cross-validation results demonstrate the efficiency of the proposed methodology.