Fast Rule-Based Clutter Detection in Automotive Radar Data

TL;DR

This paper introduces a fast, rule-based algorithm for detecting and filtering clutter in automotive radar data by modeling common wave propagation effects, improving perception accuracy in real-world traffic scenarios.

Contribution

The paper presents a novel, efficient method for identifying radar clutter based on modeling specific wave propagation effects, with real-world evaluation.

Findings

High clutter detection accuracy with low false positives

Fast execution suitable for real-time applications

Effective in complex environments like near guardrails or walls

Abstract

Automotive radar sensors output a lot of unwanted clutter or ghost detections, whose position and velocity do not correspond to any real object in the sensor's field of view. This poses a substantial challenge for environment perception methods like object detection or tracking. Especially problematic are clutter detections that occur in groups or at similar locations in multiple consecutive measurements. In this paper, a new algorithm for identifying such erroneous detections is presented. It is mainly based on the modeling of specific commonly occurring wave propagation paths that lead to clutter. In particular, the three effects explicitly covered are reflections at the underbody of a car or truck, signals traveling back and forth between the vehicle on which the sensor is mounted and another object, and multipath propagation via specular reflection. The latter often occurs near guardrails, concrete walls or similar reflective surfaces. Each of these effects is described both theoretically and regarding a method for identifying the corresponding clutter detections. Identification is done by analyzing detections generated from a single sensor measurement only. The final algorithm is evaluated on recordings of real extra-urban traffic. For labeling, a semi-automatic process is employed. The results are promising, both in terms of performance and regarding the very low execution time. Typically, a large part of clutter is found, while only a small ratio of detections corresponding to real objects are falsely classified by the algorithm.