BEV高频面试问题汇总！（纯视觉&多模态融合算法）

Core Viewpoint - The article discusses the rapid advancements in BEV (Bird's Eye View) perception technology, highlighting its significance in the autonomous driving industry and the various companies investing in its development [2]. Group 1: BEV Perception Technology - BEV perception has become a competitive area in visual perception, with various models like BEVDet, PETR, and InternBEV gaining traction since the introduction of BEVFormer [2]. - The technology is being integrated into production by companies such as Horizon, WeRide, XPeng, BYD, and Haomo, indicating a shift towards practical applications in autonomous driving [2]. Group 2: Technical Insights - In BEVFormer, the temporal and spatial self-attention modules utilize BEV queries, with keys and values derived from historical BEV information and image features [3]. - The grid_sample warp in BEVDet4D is explained as a method for transforming coordinates based on camera parameters and predefined BEV grids, facilitating pixel mapping from 2D images to BEV space [3]. Group 3: Algorithm and Performance - Lightweight BEV algorithms such as fast-bev and TRT versions of BEVDet and BEVDepth are noted for their deployment in vehicle systems [5]. - The physical space size corresponding to a BEV bird's eye matrix is typically around 50 meters, with pure visual solutions achieving stable performance up to this distance [6]. Group 4: Community and Collaboration - The article mentions the establishment of a knowledge-sharing platform for the autonomous driving industry, aimed at fostering technical exchanges among students and professionals from various prestigious universities and companies [8].