Generalizable intention prediction of human drivers at intersections

Abstract

Effective navigation of urban environments is a primary challenge remaining in the development of autonomous vehicles. Intersections come in many shapes and forms, making it difficult to find features and models that generalize across intersection types. New and traditional features are used to train several intersection intention models on real-world intersection data, and a new class of recurrent neural networks, Long Short Term Memory networks (LSTMs), are shown to outperform the state of the art. The models predict whether a driver will turn left, turn right, or continue straight up to 150 m with consistent accuracy before reaching the intersection. The results show promise for further use of LSTMs, with the mean cross validated prediction accuracy averaging over 85% for both three and four-way intersections, obtaining 83% for the highest throughput intersection.