新一轮智驾PK，迈入实战时刻

Core Viewpoint - The recent surge in advancements in intelligent driving technology is driven by several key factors, leading to a new competitive landscape in the industry [2][3][20]. Group 1: Industry Dynamics - Major players in the intelligent driving sector have recently launched their latest capabilities, reminiscent of past industry waves driven by end-to-end models [2][3]. - The competition has intensified due to regulatory pressures and public sentiment, which have delayed some companies' planned releases [6][20]. - Companies are prioritizing the release of "basic versions" of their technologies to avoid being outpaced by competitors [6][20]. Group 2: Technological Advancements - The VLA model has surpassed the capabilities of previous end-to-end models, with its lower limits exceeding the upper limits of earlier technologies [3][5]. - The transition from CNN to Transformer technology has significantly enhanced the model's ability to mimic human cognitive processes [8][10]. - The VLA model incorporates a chain of thought (CoT) capability, allowing for more logical and reliable decision-making in complex driving scenarios [11][12]. Group 3: Model Features and Performance - The VLA model's architecture is designed to handle perception, reasoning, and action execution, making it more suitable for intelligent driving applications compared to previous models [9][10]. - The model's ability to analyze and respond to potential risks has improved, enabling it to exhibit a form of "defensive driving" akin to human behavior [12][13][17]. - The VLA model can interpret traffic signs and respond to verbal commands, enhancing user interaction and operational efficiency [17][20]. Group 4: Future Outlook - The VLA model is still in the optimization phase, with significant improvements needed to fully realize its CoT capabilities [18][19]. - The industry is expected to focus on data collection and refining the performance of intelligent driving models in the coming period [19][20]. - The cost of implementing VLA technology varies, with higher-end models being more adaptable, while lower-cost models may require further optimization [20].