Pedestrian crossing prediction is a crucial task for autonomous driving. Numerous studies show that an early estimation of the pedestrian's intention can decrease or even avoid a high percentage of accidents. In this paper, different variations of a deep learning system are proposed to attempt to solve this problem. The proposed models are composed of two parts: a CNN-based feature extractor and an RNN module. All the models were trained and tested on the JAAD dataset. The results obtained indicate that the choice of the features extraction method, the inclusion of additional variables such as pedestrian gaze direction and discrete orientation, and the chosen RNN type have a significant impact on the final performance.
@article{arxiv.2008.11647,
title = {RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features},
author = {Javier Lorenzo and Ignacio Parra and Florian Wirth and Christoph Stiller and David Fernandez Llorca and Miguel Angel Sotelo},
journal= {arXiv preprint arXiv:2008.11647},
year = {2020}
}
Comments
6 pages, 5 figures. This work has been accepted for publication at IEEE Intelligent Vehicle Symposium 2020