Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions

TL;DR

This paper investigates how environmental variability, such as weather and road types, causes domain shifts in radar-based 3D object detection, highlighting dataset sensitivities and the importance of diverse data collection.

Contribution

It provides the first comprehensive empirical analysis of domain shift effects on 4D radar-based object detection across different environmental conditions.

Findings

Distinct domain shifts observed across weather scenarios

Road type transitions cause significant detection performance changes

Radar point cloud generation is critical for robustness

Abstract

The rapid evolution of deep learning and its integration with autonomous driving systems have led to substantial advancements in 3D perception using multimodal sensors. Notably, radar sensors show greater robustness compared to cameras and lidar under adverse weather and varying illumination conditions. This study delves into the often-overlooked yet crucial issue of domain shift in 4D radar-based object detection, examining how varying environmental conditions, such as different weather patterns and road types, impact 3D object detection performance. Our findings highlight distinct domain shifts across various weather scenarios, revealing unique dataset sensitivities that underscore the critical role of radar point cloud generation. Additionally, we demonstrate that transitioning between different road types, especially from highways to urban settings, introduces notable domain shifts, emphasizing the necessity for diverse data collection across varied road environments. To the best of our knowledge, this is the first comprehensive analysis of domain shift effects on 4D radar-based object detection. We believe this empirical study contributes to understanding the complex nature of domain shifts in radar data and suggests paths forward for data collection strategy in the face of environmental variability.