自动驾驶多传感器融合感知1v6小班课来了（视觉/激光雷达/毫米波雷达）

Core Insights - The article emphasizes the necessity of multi-modal sensor fusion in autonomous driving to overcome the limitations of single sensors like cameras, LiDAR, and millimeter-wave radar, enhancing robustness and safety in various environmental conditions [1][34]. Group 1: Multi-Modal Sensor Fusion - Multi-modal sensor fusion combines the strengths of different sensors: cameras provide semantic information, LiDAR offers high-precision 3D point clouds, and millimeter-wave radar excels in adverse weather conditions [1][34]. - Current mainstream fusion techniques include mid-level fusion based on Bird's Eye View (BEV) and end-to-end fusion using Transformer architectures, which significantly improve the performance of autonomous driving systems [2][34]. Group 2: Challenges in Sensor Fusion - Key challenges in multi-modal sensor fusion include sensor calibration, data synchronization, and the design of efficient algorithms to handle the heterogeneity and redundancy of sensor data [3][34]. - Ensuring high-precision spatial and temporal alignment of different sensors is critical for successful fusion [3]. Group 3: Course Structure and Content - The course outlined in the article spans 12 weeks of online group research, followed by 2 weeks of paper guidance and 10 weeks of paper maintenance, focusing on classic and cutting-edge papers, innovative ideas, and practical coding implementations [4][34]. - Participants will gain insights into research methodologies, experimental methods, and writing techniques, ultimately producing a draft paper [4][34].