聊过十多位大佬后的暴论：自动驾驶还有很多事情没做，转行具身大可不必！

Core Viewpoint - The article discusses the current state and future directions of autonomous driving technology, emphasizing the maturity of certain technologies like BEV and the emerging focus on VLA/VLM, while highlighting the challenges in corner case handling and the need for robust models [2][11][37]. Group 1: Current Technology Maturity - The BEV (Bird's Eye View) perception model is considered fully mature and widely adopted in the industry, effectively handling dynamic and static perception tasks [11][45]. - The introduction of VLA (Vision-Language Alignment) is seen as a promising approach to address corner cases, although its practical effectiveness remains under scrutiny [4][28]. - There is a consensus that while end-to-end models are usable, they cannot be solely relied upon for production due to their limitations in handling complex scenarios [37][45]. Group 2: Emerging Technologies - New technological directions such as VLA/VLM (Vision-Language Model) and diffusion models are being explored to enhance the capabilities of autonomous driving systems, particularly in complex environments [16][18][42]. - The integration of world models is recognized as essential for improving data generation and model training, addressing the high costs associated with real data collection [42][49]. - The industry is also focusing on closed-loop simulations to validate models before deployment, which is crucial for ensuring safety and reliability [44][48]. Group 3: Challenges and Gaps - A significant challenge remains in effectively addressing corner cases, with many companies still struggling to demonstrate robust performance in these scenarios [11][33]. - There is a noted gap between academic research and industrial application, particularly in data sharing and validation of new models like VLA [4][28]. - The efficiency of models is a critical concern, as larger models may not meet latency requirements while smaller models may lack necessary capabilities [5][37]. Group 4: Future Directions - The future of autonomous driving technology is expected to focus on enhancing safety, user experience, and comprehensive scene coverage, with a shift towards data-driven approaches [26][30]. - The industry is likely to see a transition from algorithm-centric development to data-driven efficiency, emphasizing the importance of robust data operations [26][30]. - There is an ongoing debate about whether to deepen expertise in autonomous driving or pivot towards embodied intelligence, with both fields offering unique opportunities [21][41].