Recent visual autonomous perception systems achieve remarkable performances with deep representation learning. However, they fail in scenarios with challenging illumination.While event cameras can mitigate this problem, there is a lack of a large-scale dataset to develop event-enhanced deep visual perception models in autonomous driving scenes. To address the gap, we present the eAP (event-enhanced Autonomous Perception) dataset, the largest dataset with event cameras for autonomous perception. We demonstrate how eAP can facilitate the study of different autonomous perception tasks, including 3D vehicle detection and object time-to-contact (TTC) estimation, through deep representation learning. Based on eAP, we demonstrate the ffrst successful use of events to improve a popular 3D vehicle detection network in challenging illumination scenarios. eAP also enables a devoted study of the representation learning problem of object TTC estimation. We show how a geometryaware representation learning framework leads to the best eventbased object TTC estimation network that operates at 200 FPS. The dataset, code, and pre-trained models will be made publicly available for future research.

翻译：近期视觉自主感知系统通过深度表征学习取得了显著性能。然而，在具有挑战性光照的场景中，这些系统仍存在不足。尽管事件相机能够缓解这一问题，但目前缺乏用于开发自动驾驶场景中事件增强深度视觉感知模型的大规模数据集。为填补这一空白，我们提出了eAP（事件增强自主感知）数据集，这是目前面向自主感知的最大规模事件相机数据集。我们通过深度表征学习展示了eAP如何促进不同自主感知任务的研究，包括三维车辆检测和物体碰撞时间估计。基于eAP，我们首次成功利用事件数据改进了流行三维车辆检测网络在挑战性光照场景下的性能。eAP还支持对物体碰撞时间估计的表征学习问题进行专项研究。我们展示了几何感知表征学习框架如何构建出当前最优的基于事件的物体碰撞时间估计网络，其运行速度可达200 FPS。该数据集、代码与预训练模型将公开发布以供后续研究。