A Recurrent Neural Network Solution for Predicting Driver Intention at Unsignalized Intersections

• Published in: IEEE robotics and automation letters Vol. 3; no. 3; pp. 1759 - 1764
• Main Authors: Zyner, Alex, Worrall, Stewart, Nebot, Eduardo
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Advanced driver assistance systems; Algorithms; Automobiles; Datasets; deep learning in robotics and automation; Driver behavior; Drivers; Intelligent transportation systems; Intelligent vehicles; Laser radar; Neural networks; Predictions; Recurrent neural networks; Roads; Roundabouts; Sensors; Task analysis; Tracking systems; Traffic intersections; Urban areas
• ISSN: 2377-3766; 2377-3766
• DOI: 10.1109/LRA.2018.2805314

In this letter, we present a system capable of inferring intent from observed vehicles traversing an unsignalized intersection, a task critical for the safe driving of autonomous vehicles, and beneficial for advanced driver assistance systems. We present a prediction method based on recurrent neural networks that takes data from a Lidar-based tracking system similar to those expected in future smart vehicles. The model is validated on a roundabout, a popular style of unsignalized intersection in urban areas. We also present a very large naturalistic dataset recorded in a typical intersection during two days of operation. This comprehensive dataset is used to demonstrate the performance of the algorithm introduced in this letter. The system produces excellent results, giving a significant 1.3-s prediction window before any potential conflict occurs.