Problem
Research questions and friction points this paper is trying to address.
4D radar
preprocessing
autonomous driving
perception systems
real-time performance
Accuracy- and Real-Time-Aware 4D Radar Preprocessing for Autonomous Driving Perception Systems
🤖 AI Summary
为提高4D雷达在自动驾驶感知系统中的应用，提出了一种包括P3DP、MF-KDE和ENS的预处理框架，以兼顾准确性、实时性和计算复杂度。
📝 Abstract
4D radar has emerged as a promising next-generation sensor for improving the robustness of autonomous driving perception systems because of its stable sensing capability under adverse weather conditions. However, deploying 4D radar in embedded environments with limited hardware resources requires radar-representation preprocessing that jointly considers perception accuracy, real-time performance, and computational complexity. This paper proposes a preprocessing framework for 4D-radar-based 3D object detection. First, Percentile-based 3D Shape Preservation (P3DP) extracts point clouds from radar tensors while preserving object-shape information and suppressing noise and false alarms. Second, Multi-frame-based Noise Point Discrimination using Kernel Density Estimation (MF-KDE) improves the density and reliability of sparse radar point clouds. Finally, Embedded \& NetScore (ENS) evaluates suitability for embedded deployment by jointly considering accuracy, real-time performance, adverse-weather robustness, and model complexity.
Innovation
Methods, ideas, or system contributions that make the work stand out.
P3DP
MF-KDE
ENS
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