Core Viewpoint - The article discusses the launch of the Li Auto i8, which features the new VLA driver model that significantly enhances its assisted driving capabilities through advanced technologies such as the Vision-Language-Action model and NVIDIA's Thor-U chip [2][20]. Group 1: VLA Driver Model Development - The VLA driver model represents a paradigm shift in assisted driving, moving from traditional methods to a more integrated approach that combines visual, language, and behavioral understanding [2][6]. - Li Auto's assisted driving technology has evolved significantly, with the MPI (Mile Per Intervention) level improving from a few kilometers to 100 kilometers within a year, indicating a tenfold increase in performance [5][24]. - The company has implemented "super alignment" techniques to enhance model output and has improved data selection standards, resulting in a twofold increase in model performance from March to May [5][24]. Group 2: Technical Enhancements - The VLA model incorporates reasoning capabilities, allowing for better decision-making and understanding of driving scenarios, which was a limitation in previous models [6][11]. - The system can now process environmental data at a speed of 10Hz, translating sensor inputs into actionable driving decisions [11][13]. - The driving style has shifted from imitating "experienced drivers" to a more stable approach akin to "chauffeur drivers," which is expected to be more appealing to users [15][20]. Group 3: User Interaction and Experience - The VLA model allows for natural language interaction, enabling users to give commands directly to the vehicle, enhancing the overall user experience [9][17]. - The system's memory capabilities allow it to remember user preferences for specific routes, improving personalization [17][20]. - The VLA model has learned defensive driving techniques, enabling it to anticipate potential hazards and react accordingly, which enhances safety [20][21]. Group 4: Data and Simulation - Li Auto has accumulated 4.3 billion kilometers of user driving data, with 1.2 billion kilometers of effective data collected by July this year, which is crucial for training the VLA model [24][25]. - The company employs data synthesis techniques to create balanced datasets for rare driving scenarios, improving the model's performance in complex situations [25][26]. - The use of simulation environments has drastically reduced testing costs and time, allowing for rapid iteration and improvement of the assisted driving system [28][29]. Group 5: Future Prospects - Li Auto aims to provide a "personal driver" experience to a broader user base, with expectations of achieving a 1000 km MPI in the near future [20][32]. - The company has established a fully simulated environment at its headquarters to enhance training efficiency, indicating a commitment to advancing its technology [32][34].
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