Detection of surrounding objects and their motion prediction are critical components of a self-driving system. Recently proposed models that jointly address these tasks rely on a number of sensors to achieve state-of-the-art performance. However, this increases system complexity and may result in a brittle model that overfits to any single sensor modality while ignoring others, leading to reduced generalization. We focus on this important problem and analyze the contribution of sensor modalities towards the model performance. In addition, we investigate the use of sensor dropout to mitigate the above-mentioned issues, leading to a more robust, better-performing model on real-world driving data.

翻译：探测周围物体及其运动预测是自驾系统的关键组成部分。最近提出的联合处理这些任务的模型依靠若干传感器来达到最新性能。然而,这增加了系统的复杂性,并可能导致一个过于适合任何单一传感器模式而忽视其他传感器模式的微弱模型,从而降低一般化程度。我们集中关注这一重要问题,分析传感器模式对模型性能的贡献。此外,我们调查使用传感器辍学来缓解上述问题,从而形成一个更健全、更完善的现实世界驱动数据模型。