Multi-Modal 3D Object Detection in Autonomous Driving: A New Survey

Engineering notes

This survey systematically reviews state-of-the-art multi-modal 3D detection techniques for autonomous vehicles, outlining fundamental principles and domain challenges.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Recent progress in autonomous driving has underscored the critical importance of multi-modal 3D object detection, which addresses the limitations of single-sensor methods such as cameras or LiDAR. This survey systematically reviews state-of-the-art multi-modal 3D detection techniques for autonomous vehicles, outlining fundamental principles and domain challenges. It categorizes existing approaches through distinct classification schemes and evaluates detection performance and safety across object types. Unlike previous surveys, this work focuses on challenges and solutions in pedestrian detection, long-range detection, road camera integration, and collaborative perception. The robustness of these methods under varying weather and terrain conditions is also analyzed. Using multiple datasets and experiments, the survey compares classification methods and concludes with promising research directions to guide future developments. This work aims to be a definitive reference for advancing 3D object detection in autonomous driving.

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