多年来，迈凯伦是超豪华品牌中对 SUV 最坚定的「拒绝者」。 其前 CEO Mike Flewitt 曾多次强调，「SUV 不符合迈凯伦的 DNA」。在迈凯伦的自我叙事中，轻量化、中置引擎与驾驶纯粹性，长期被视为其最重要的 工程原则，而 SUV 的高重心、大尺寸与重车身，似乎天然与之相悖。 然而，这一立场正在悄然瓦解。 据海外知名汽车媒体 motor1 报道，迈凯伦上个月向其经销商展示了一款采用混合动力的五座 SUV 车型，内部代号「P47」。 尽管迈凯伦官方尚未正式确认，但种种迹象表明：这台车已进入工程验证后期，预计将于 2028 年亮相。 曾经的「绝不」，如今变成了「即将」 迈凯伦对 SUV 的抗拒曾有其底气——这份底气，源于它从诞生之初就与赛道深度绑定的血统，以及对工程极致近乎偏执的追求。 1963 年，布鲁斯·迈凯伦（Bruce McLaren）创立车队时，关注的目标就是如何打造真正为胜利而生的竞赛机器，无论是在 F1、耐力赛还是印第体系中， 对极限性能的追逐始终居于迈凯伦体系的核心位置。 2009 年，迈凯伦发布 MP4-12C 正式重返民用跑车市场。这辆车并未沿用传统超跑依赖大排量自然吸气或奢华内 ...

Core Insights - The article discusses the rapid advancements in end-to-end autonomous driving, particularly focusing on Vision-Language-Action (VLA) models and their applications in the industry [2][3]. Group 1: VLA Model Developments - The introduction of AutoVLA, a new VLA model that integrates reasoning and action generation for end-to-end autonomous driving, shows promising results in semantic reasoning and trajectory planning [3][4]. - ReCogDrive, another VLA model, addresses performance issues in rare and long-tail scenarios by utilizing a three-stage training framework that combines visual language models with diffusion planners [7][9]. - Impromptu VLA introduces a dataset aimed at improving VLA models' performance in unstructured extreme conditions, demonstrating significant performance improvements in established benchmarks [14][24]. Group 2: Experimental Results - AutoVLA achieved competitive performance metrics in various scenarios, with the best-of-N method reaching a PDMS score of 92.12, indicating its effectiveness in planning and execution [5]. - ReCogDrive set a new state-of-the-art PDMS score of 89.6 on the NAVSIM benchmark, showcasing its robustness and safety in driving trajectories [9][10]. - The OpenDriveVLA model demonstrated superior results in open-loop trajectory planning and driving-related question-answering tasks, outperforming previous methods on the nuScenes dataset [28][32]. Group 3: Industry Trends - The article highlights a trend among major automotive manufacturers, such as Li Auto, Xiaomi, and XPeng, to invest heavily in VLA model research and development, indicating a competitive landscape in autonomous driving technology [2][3]. - The integration of large language models (LLMs) with VLA frameworks is becoming a focal point for enhancing decision-making capabilities in autonomous vehicles, as seen in models like ORION and VLM-RL [33][39].