Performance and Challenges of 3D Object Detection Methods in Complex Scenes for Autonomous Driving

How to ensure robust and accurate 3D object detection under various environment is essential for autonomous driving (AD) environment perception. While, until now, most of the existing 3D object detection methods are based on the ordinary driving scenes provided by the mainstream dataset. The researches on actual complex scenes (adverse illumination, inclement weather, distant or small objects, etc.) have been ignored and there is still no comprehensive review of the recent progress in this field. Thence, this paper aims to gain a deep insight on the performance and challenges of 3D object detection methods under complex scenes for AD. Firstly, we discuss the complex driving environments in actual and the perception limitations of mainstream sensors (LIDAR and camera). Then we analyze the performance and challenges of single-modality 3D object detection methods. Therefore, in order to improve the accuracy and robustness of 3D object detection methods in some complex AD scenes, the fusion of L-C (LIDAR-camera) is recommended and systematically analyzed. Finally, some suitable datasets and potential directions are comparatively summarized to support the relative research in complex driving scenes. We hope that this review could facilitate people's research and look forward to more progress in this timely and crucial problem field.