4D radar is recognized for its resilience and cost-effectiveness under adverse weather conditions, thus playing a pivotal role in autonomous driving. While cameras and LiDAR are typically the primary sensors used in perception modules for autonomous vehicles, radar serves as a valuable supplementary sensor. Unlike LiDAR and cameras, radar remains unimpaired by harsh weather conditions, thereby offering a dependable alternative in challenging environments. Developing radar-based 3D object detection not only augments the competency of autonomous vehicles but also provides economic benefits. In response, we propose the Multi-View Feature Assisted Network (\textit{MVFAN}), an end-to-end, anchor-free, and single-stage framework for 4D-radar-based 3D object detection for autonomous vehicles. We tackle the issue of insufficient feature utilization by introducing a novel Position Map Generation module to enhance feature learning by reweighing foreground and background points, and their features, considering the irregular distribution of radar point clouds. Additionally, we propose a pioneering backbone, the Radar Feature Assisted backbone, explicitly crafted to fully exploit the valuable Doppler velocity and reflectivity data provided by the 4D radar sensor. Comprehensive experiments and ablation studies carried out on Astyx and VoD datasets attest to the efficacy of our framework. The incorporation of Doppler velocity and RCS reflectivity dramatically improves the detection performance for small moving objects such as pedestrians and cyclists. Consequently, our approach culminates in a highly optimized 4D-radar-based 3D object detection capability for autonomous driving systems, setting a new standard in the field.

翻译：4D雷达因其在恶劣天气条件下的鲁棒性和成本效益而被公认为自动驾驶中的关键传感器。尽管摄像头和激光雷达通常是自动驾驶感知模块中的主要传感器，但雷达仍可作为有价值的辅助传感器。与激光雷达和摄像头不同，雷达不受恶劣天气条件影响，因此在复杂环境中提供了可靠的替代方案。研发基于雷达的3D目标检测不仅能增强自动驾驶系统的能力，还能带来经济效益。为此，我们提出多视角特征辅助网络（MVFAN），一种面向自动驾驶车辆基于4D雷达的3D目标检测的端到端、无锚框单阶段框架。为解决特征利用不足的问题，我们引入新颖的位置图生成模块，通过重新加权前景与背景点及其特征来增强特征学习，以应对雷达点云不规则分布的特性。此外，我们提出一种开创性的骨干网络——雷达特征辅助骨干，专门设计用于充分利用4D雷达传感器提供的多普勒速度和反射率数据。在Astyx和VoD数据集上的综合实验与消融研究验证了该框架的有效性。多普勒速度与RCS反射率的引入显著提升了行人、骑行者等小型运动目标的检测性能。最终，我们的方法为自动驾驶系统实现了高度优化的基于4D雷达的3D目标检测能力，为该领域树立了新标杆。