In this era of active development of autonomous vehicles, it becomes crucial to provide driving systems with the capacity to explain their decisions. In this work, we focus on generating high-level driving explanations as the vehicle drives. We present BEEF, for BEhavior Explanation with Fusion, a deep architecture which explains the behavior of a trajectory prediction model. Supervised by annotations of human driving decisions justifications, BEEF learns to fuse features from multiple levels. Leveraging recent advances in the multi-modal fusion literature, BEEF is carefully designed to model the correlations between high-level decisions features and mid-level perceptual features. The flexibility and efficiency of our approach are validated with extensive experiments on the HDD and BDD-X datasets.
@article{arxiv.2012.04983,
title = {Driving Behavior Explanation with Multi-level Fusion},
author = {Hédi Ben-Younes and Éloi Zablocki and Patrick Pérez and Matthieu Cord},
journal= {arXiv preprint arXiv:2012.04983},
year = {2021}
}