In autonomous driving, relying solely on frame-based cameras can lead to inaccuracies caused by factors like long exposure times, high-speed motion, and challenging lighting conditions. To address these issues, we introduce a bio-inspired vision sensor known as the event camera. Unlike conventional cameras, event cameras capture sparse, asynchronous events that provide a complementary modality to mitigate these challenges. In this work, we propose an energy-aware imitation learning framework for steering prediction that leverages both events and frames. Specifically, we design an Energy-driven Cross-modality Fusion Module (ECFM) and an energy-aware decoder to produce reliable and safe predictions. Extensive experiments on two public real-world datasets, DDD20 and DRFuser, demonstrate that our method outperforms existing state-of-the-art (SOTA) approaches. The codes and trained models will be released upon acceptance.
@article{arxiv.2603.28008,
title = {Energy-Aware Imitation Learning for Steering Prediction Using Events and Frames},
author = {Hu Cao and Jiong Liu and Xingzhuo Yan and Rui Song and Yan Xia and Walter Zimmer and Guang Chen and Alois Knoll},
journal= {arXiv preprint arXiv:2603.28008},
year = {2026}
}