A Recurrent Neural Network Solution for Predicting Driver Intention at Unsignalized Intersections
Alex Zyner, Stewart Worrall, Eduardo M. Nebot
Abstract
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.
BibTeX
@inproceedings{ral2018_arecurrentneural,
title = {A Recurrent Neural Network Solution for Predicting Driver Intention at Unsignalized Intersections},
author = {Alex Zyner and Stewart Worrall and Eduardo M. Nebot},
booktitle = {RA-L 2018},
year = {2018}
}