收到很多同学关于自驾方向选择的咨询......

Core Insights - The article discusses various cutting-edge directions in autonomous driving research, emphasizing the importance of deep learning and traditional methods for students in related fields [2][3]. Group 1: Research Directions - Key areas of focus include VLA, end-to-end learning, reinforcement learning, 3D goal detection, and occupancy networks, which are recommended for students in computer science and automation [2][3]. - For mechanical and vehicle engineering students, traditional methods like PnC and 3DGS are suggested as they require lower computational power and are easier to start with [2]. Group 2: Guidance and Support - The article announces the launch of a paper guidance service that offers support in various research areas, including multi-sensor fusion, trajectory prediction, and semantic segmentation [3][6]. - Services provided include topic selection, full process guidance, and experimental support, aimed at enhancing the research capabilities of students [6][7]. Group 3: Publication Opportunities - The guidance service has a high acceptance rate for papers submitted to top conferences and journals, including CVPR, AAAI, and ICLR [7]. - The article highlights the availability of support for various publication levels, including CCF-A, CCF-B, and SCI indexed journals [10].