Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions
This addresses the challenge of robust perception for autonomous driving systems under diverse conditions, but it is incremental as it focuses on analysis rather than new solutions.
This study tackled the problem of domain shift in 4D radar-based 3D object detection by examining how varying environmental conditions like weather and road types impact performance, revealing distinct sensitivities and emphasizing the need for diverse data collection.
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.