随到随学！端到端与VLA自动驾驶小班课正式结课

Core Viewpoint - 2023 marks the year of end-to-end production, with 2024 expected to be a significant year for end-to-end production in the automotive industry, as leading new forces and manufacturers have already achieved end-to-end production [1][3]. Group 1: End-to-End Production Development - The automotive industry has two main paradigms: single-stage and two-stage, with UniAD being a representative of the single-stage approach that directly models vehicle trajectories from sensor inputs [1]. - Since last year, the single-stage end-to-end development has rapidly advanced, leading to various derivatives such as perception-based, world model-based, diffusion model-based, and VLA-based single-stage methods [3][5]. - Major players in the autonomous driving sector, including both solution providers and car manufacturers, are focusing on self-research and production of end-to-end autonomous driving technologies [3]. Group 2: Course Overview - A course titled "End-to-End and VLA Autonomous Driving" has been launched, aimed at teaching cutting-edge algorithms in both single-stage and two-stage end-to-end approaches, with a focus on the latest developments in the industry and academia [5][14]. - The course is structured into several chapters, starting with an introduction to end-to-end algorithms, followed by background knowledge on various technologies such as VLA, diffusion models, and reinforcement learning [8][9]. - The second chapter is highlighted as containing the most frequently asked technical keywords for job interviews in the next two years [9]. Group 3: Technical Focus Areas - The course covers various subfields of single-stage end-to-end methods, including perception-based (UniAD), world model-based, diffusion model-based, and the currently popular VLA-based approaches [10][12]. - The curriculum includes practical assignments, such as RLHF fine-tuning, and aims to provide students with hands-on experience in building and experimenting with pre-trained and reinforcement learning modules [11][12]. - The course emphasizes the importance of understanding BEV perception, multi-modal large models, and the latest advancements in diffusion models, which are crucial for the future of autonomous driving [12][16].