  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. The key step to
this system is related to 3D object detection that utilizes vehicle-mounted
sensors such as LiDAR and cameras to identify the size, the category, and the
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-only, LiDAR-only, and multi-modal 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, multi-modal 3D detection approaches exhibit superior
robustness, and a novel taxonomy is introduced to reorganize the literature for
enhanced clarity. This survey aims to offer a more practical perspective on the
current capabilities and the constraints of 3D object detection algorithms in
real-world applications, thus steering future research towards
robustness-centric advancements.
