@article{ref1,
title="Naturalistic driver intention and path prediction using recurrent neural networks",
journal="IEEE transactions on intelligent transportation systems",
year="2020",
author="Zyner, Alex and Worrall, Stewart and Nebot, Eduardo",
volume="21",
number="4",
pages="1584-1594",
abstract="Understanding the intentions of drivers at intersections is a critical component for autonomous vehicles. Urban intersections that do not have traffic signals are a common epicenter of highly variable vehicle movement and interactions. We present a method for predicting driver intent at urban intersections through multi-modal trajectory prediction with uncertainty. Our method is based on recurrent neural networks combined with a mixture density network output layer. To consolidate the multi-modal nature of the output probability distribution, we introduce a clustering algorithm that extracts the set of possible paths that exist in the prediction output and ranks them according to probability. To verify the method's performance and generalizability, we present a real-world dataset that consists of over 23 000 vehicles traversing five different intersections, collected using a vehicle-mounted lidar-based tracking system. An array of metrics is used to demonstrate the performance of the model against several baselines.<p /> <p>Language: en</p>",
language="en",
issn="1524-9050",
doi="10.1109/TITS.2019.2913166",
url="http://dx.doi.org/10.1109/TITS.2019.2913166"
}