  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.
