Robustness-Aware 3D Object Detection in Autonomous Driving: A Review and
  Outlook
Ziying Song Lin Liu Feiyang Jia Yadan Luo Guoxin Zhang Lei Yang Li Wang Caiyan Jia
In the realm of modern autonomous driving, the perception system is
indispensable for accurately assessing the state of the surrounding
environment, thereby enabling informed prediction and planning. Key to this
system is 3D object detection methods, that utilize vehicle-mounted sensors
such as LiDAR and cameras to identify the size, category, and location of
nearby objects. Despite the surge in 3D object detection methods aimed at
enhancing detection precision and efficiency, there is a gap in the literature
that systematically examines their resilience against environmental variations,
noise, and weather changes. This study emphasizes the importance of robustness,
alongside accuracy and latency, in evaluating perception systems under
practical scenarios. Our work presents an extensive survey of camera-based,
LiDAR-based, and multimodal 3D object detection algorithms, thoroughly
evaluating their trade-off between accuracy, latency, and robustness,
particularly on datasets like KITTI-C and nuScenes-C to ensure fair
comparisons. Among these,multimodal 3D detection approaches exhibit superior
robustness and a novel taxonomy is introduced to reorganize its literature for
enhanced clarity. This survey aims to offer a more practical perspective on the
current capabilities and constraints of 3D object detection algorithms in
real-world applications, thus steering future research towards
robustness-centric advancements