Core Viewpoint - The article discusses the current state and future directions of autonomous driving technology, highlighting the maturity of certain technologies, the challenges that remain, and the emerging trends in the industry. Group 1: Current Technology Maturity - The introduction of BEV (Bird's Eye View) and OCC (Occupancy) perception methods has matured, with no major players claiming that BEV is unusable [2][13] - The main challenge remains corner cases, where 99% of scenarios are manageable, but complex situations like rural roads and large intersections still pose difficulties [13] - E2E (End-to-End) models have not yet demonstrated clear advantages over two-stage models in practical applications, despite their theoretical appeal [4][5] Group 2: Emerging Technologies - VLA (Vision-Language Alignment) is gaining attention as it simplifies tasks and potentially addresses corner cases more effectively than traditional methods [5][6] - The efficiency of models is a critical issue, with discussions around using smaller models to achieve performance close to larger ones [6][30] - Reinforcement learning has not yet proven to be significantly impactful in autonomous driving, with a need for better simulation environments to validate its effectiveness [7][51] Group 3: Future Directions - There is a consensus that VLA and VLM (Vision-Language Model) will be key areas for future development, focusing on enhancing reasoning capabilities and safety [45][48] - The industry is moving towards a more data-driven approach, where the efficiency of data collection, cleaning, and training will determine competitive advantage [28][40] - The integration of world models and closed-loop simulations is seen as essential for advancing autonomous driving technologies [47][50] Group 4: Industry Perspectives - The shift towards VLA/VLM is viewed as a necessary evolution, with the potential to improve user experience and safety in autonomous vehicles [28][45] - The debate between deepening expertise in autonomous driving versus transitioning to embodied intelligence reflects the industry's evolving landscape and personal career choices [22][27] - The current focus on safety and robustness in L4 (Level 4) autonomous driving indicates a divergence in technical approaches between L2+ and L4 players [25][36]

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