The performance of perception systems developed for autonomous driving vehicles has seen significant improvements over the last few years. This improvement was associated with the increasing use of LiDAR sensors and point cloud data to facilitate the task of object detection and recognition in autonomous driving. However, LiDAR and camera systems show deteriorating performances when used in unfavorable conditions like dusty and rainy weather. Radars on the other hand operate on relatively longer wavelengths which allows for much more robust measurements in these conditions. Despite that, radar-centric data sets do not get a lot of attention in the development of deep learning techniques for radar perception. In this work, we consider the radar object detection problem, in which the radar frequency data is the only input into the detection framework. We further investigate the challenges of using radar-only data in deep learning models. We propose a transformers-based model, named RadarFormer, that utilizes state-of-the-art developments in vision deep learning. Our model also introduces a channel-chirp-time merging module that reduces the size and complexity of our models by more than 10 times without compromising accuracy. Comprehensive experiments on the CRUW radar dataset demonstrate the advantages of the proposed method. Our RadarFormer performs favorably against the state-of-the-art methods while being 2x faster during inference and requiring only one-tenth of their model parameters. The code associated with this paper is available at https://github.com/YahiDar/RadarFormer.

翻译：近年来，面向自动驾驶汽车的感知系统性能取得了显著提升。这一进展与激光雷达（LiDAR）传感器和点云数据的广泛应用密切相关，以促进自动驾驶中的目标检测与识别任务。然而，在沙尘、雨雪等不利环境下，LiDAR和摄像头系统的性能会下降。相比之下，雷达因工作在较长波长上，能在这些条件下实现更鲁棒的测量。尽管如此，以雷达为中心的数据集在雷达感知深度学习技术的发展中并未获得足够关注。本研究聚焦于雷达目标检测问题，其中雷达频率数据是检测框架的唯一输入。我们进一步探讨了在深度学习模型中仅使用雷达数据所面临的挑战。我们提出了一种基于Transformer的模型，命名为RadarFormer，该模型融合了视觉深度学习领域的最新进展。我们的模型还引入了一个通道-啁啾-时间（channel-chirp-time）合并模块，在不牺牲精度的情况下将模型规模与复杂度降低了10倍以上。在CRUW雷达数据集上的全面实验证明了所提方法的优势。我们的RadarFormer在性能上优于现有最先进方法，同时推理速度提升2倍，且仅需其十分之一的模型参数。本文相关代码已开源至https://github.com/YahiDar/RadarFormer。