上岸自动驾驶多传感融合感知，1v6小班课！

Core Viewpoint - The rapid development of fields such as autonomous driving, robotic navigation, and intelligent monitoring necessitates the integration of multiple sensors (like LiDAR, millimeter-wave radar, and cameras) to create a robust environmental perception system, overcoming the limitations of single sensors [1][2]. Group 1: Multi-Modal Sensor Fusion - The integration of various sensors allows for all-weather and all-scenario reliable perception, significantly enhancing the robustness and safety of autonomous driving systems [1]. - Current mainstream approaches include mid-term fusion based on Bird's-Eye View (BEV) and end-to-end fusion using Transformer architectures, which improve the efficiency and robustness of feature interaction [2]. - Traditional fusion methods face challenges such as sensor calibration, data synchronization, and the need for efficient algorithms to handle heterogeneous data [3]. Group 2: Course Outline and Content - The course aims to provide a comprehensive understanding of multi-modal fusion technology, covering classic and cutting-edge papers, innovative points, baseline models, and dataset usage [4][32]. - The course structure includes 12 weeks of online group research, 2 weeks of paper guidance, and 10 weeks of paper maintenance, ensuring a thorough learning experience [4][32]. - Participants will gain insights into research methodologies, experimental methods, writing techniques, and submission advice, enhancing their academic skills [8][14]. Group 3: Learning Requirements and Support - The program is designed for individuals with a basic understanding of deep learning and Python, providing foundational courses to support learning [15][25]. - A structured support system is in place, including mentorship from experienced instructors and a focus on academic integrity and research quality [20][32]. - Participants will have access to datasets and baseline code relevant to multi-modal fusion tasks, facilitating practical application of theoretical knowledge [18][33].