Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlusion and over long ranges, and robust performance in adverse weather conditions. However, the usage of radar data presents some challenges: it is characterized by low resolution, sparsity, clutter, high uncertainty, and lack of good datasets. These challenges have limited radar deep learning research. As a result, current radar models are often influenced by lidar and vision models, which are focused on optical features that are relatively weak in radar data, thus resulting in under-utilization of radar's capabilities and diminishing its contribution to autonomous perception. This review seeks to encourage further deep learning research on autonomous radar data by 1) identifying key research themes, and 2) offering a comprehensive overview of current opportunities and challenges in the field. Topics covered include early and late fusion, occupancy flow estimation, uncertainty modeling, and multipath detection. The paper also discusses radar fundamentals and data representation, presents a curated list of recent radar datasets, and reviews state-of-the-art lidar and vision models relevant for radar research. For a summary of the paper and more results, visit the website: autonomous-radars.github.io.

翻译：雷达是用于自动驾驶车辆安全可靠导航的感知传感器套件中的关键组件。其独特能力包括高分辨率速度成像、遮挡及远距离目标检测，以及在恶劣天气条件下的稳健性能。然而，雷达数据的使用面临若干挑战：分辨率低、稀疏性、杂波干扰、高不确定性以及缺乏优质数据集。这些问题限制了雷达深度学习研究的发展。因此，当前雷达模型常受激光雷达和视觉模型影响，这些模型侧重于光学特征，而此类特征在雷达数据中相对较弱，导致雷达能力未被充分挖掘，并在自主感知中贡献度降低。本综述旨在通过以下方式推动基于自主雷达数据的深度学习研究：1) 识别关键研究主题，2) 提供该领域当前机遇与挑战的全面概述。涵盖主题包括早期与晚期融合、占用流估计、不确定性建模及多径检测。本文还讨论了雷达基础原理与数据表征，整理了近期雷达数据集清单，并回顾了与雷达研究相关的先进激光雷达与视觉模型。论文总结及更多结果请访问网站：autonomous-radars.github.io。