Group 1: Industry Transformation - The global automotive industry is undergoing profound changes driven by the "new four modernizations," with a focus on the transition from electrification to intelligence and from local market dominance to global value chain restructuring [1] - The period from now until 2030 is critical for cultivating intelligent driving culture and popularizing lower-level intelligent driving technologies, necessitating clear development goals and strategies from major companies [1][2] Group 2: Safety and Technology Challenges - The penetration rate of L2-level intelligent vehicles in China has surpassed 50%, leading the world, but recent serious traffic accidents related to intelligent driving have raised safety concerns [2][3] - Current intelligent vehicle safety technologies are evolving along two main paths: "rule-driven" and "data-driven," each with its own advantages and limitations [3][4] Group 3: Cognitive-Driven Approach - A "cognitive-driven" approach is proposed to combine the advantages of both "rule-driven" and "data-driven" systems, enhancing adaptability and transparency in decision-making processes [4][5] - The stability of automotive safety heavily relies on the performance of automotive-grade chips, which must meet stringent reliability standards [5][6] Group 4: Competitive Landscape - The cost structure of vehicles is shifting, with electronic hardware and AI becoming increasingly significant, projected to rise from less than 25% to 70% by 2030 [7][8] - Companies are encouraged to break traditional industry boundaries and collaborate with technology firms to enhance their competitive edge in the intelligent and AI-driven automotive landscape [8][9]

Group 1 - The current development of autonomous driving systems is significantly lagging behind expectations, facing numerous challenges, particularly in achieving safety and advancing from L3 to L4 and L5 levels [1][2] - Traditional "data feeding" methods are insufficient for complex scenarios, necessitating a new paradigm of "self-learning + prior knowledge" to enhance safety and generalization in high-level autonomous driving [1][5] - The focus is shifting towards a human-centered technology approach, emphasizing the construction of cognitive capabilities that surpass human abilities [1][9] Group 2 - Intelligent vehicle safety is a critical national demand, especially in China, where complex road traffic scenarios and frequent accidents pose significant challenges [2][3] - Low-level intelligent vehicles have achieved high market penetration, but there are still many safety challenges to overcome as the industry moves towards higher levels of automation [2][3] - A complete "perception-cognition-decision" technology system is essential for rapid perception, accurate judgment, and efficient response to complex dynamic scenarios [2][3] Group 3 - Current intelligent vehicles struggle with accurate perception, cognition, and safety decision-making in unpredictable and complex situations [3][4] - The rule-driven approach is limited to known structured scenarios, while the data-driven approach suffers from a lack of interpretability and generalization capabilities, making it inadequate for L4+ level autonomous driving [3][4] - Both rule-driven and data-driven methods face critical challenges in adapting to complex environments and ensuring safety [4][5] Group 4 - To address the limitations of existing methods, a cognitive-driven approach is proposed, which combines the interpretability of rule-driven systems with the learning capabilities of data-driven systems [5][6] - This cognitive-driven approach aims to enhance the system's ability to generalize, evolve, and make reliable decisions by understanding the interactions and dynamics of the human-vehicle-road system [5][6] Group 5 - The cognitive-driven architecture encompasses three main layers: perception, cognition, and decision-making, integrating both rule-based and data-driven elements [6][7] - The first layer focuses on environmental perception, the second on risk cognition and prediction, and the third on adaptive safety decision-making [6][7] - This comprehensive approach aims to create a cognitive autonomous driving system capable of handling complex and unknown scenarios effectively [6][7] Group 6 - The future of intelligent vehicles is expected to evolve from rule-driven and data-driven approaches to a cognitive-driven model, enhancing generalization and safety in unknown and long-tail scenarios [7][8] - A "three verticals and three horizontals" technical architecture is proposed to support the systematic evolution of intelligent vehicles, focusing on key vehicle technologies, advanced information technologies, and foundational support technologies [8][9] - The emphasis is on ensuring that "smart cars" are also "safe cars," necessitating a transition to a brain-like cognitive architecture for intelligent vehicle safety [9]

Core Viewpoint - The development of intelligent vehicle safety technology is crucial for addressing the complex traffic scenarios and frequent accidents in China, with a focus on a cognitive-driven innovation route for high-level autonomous driving [4][5][6]. Group 1: Current Challenges in Intelligent Vehicle Safety Technology - The existing technology faces limitations due to the complexity of traffic scenarios and uncontrollable factors such as vehicle malfunctions and environmental disturbances [4]. - Current mainstream technology routes, including rule-driven and data-driven approaches, have shortcomings that hinder their effectiveness in high-level autonomous driving [4]. - Specific incidents involving Tesla, Waymo, and Uber highlight the technical shortcomings in handling unexpected and complex scenarios [4]. Group 2: Cognitive-Driven Technology as a Solution - The cognitive-driven approach is proposed as a third technological route that combines the interpretability of rule-based systems with the learning capabilities of data-driven systems [5]. - This approach emphasizes a deep understanding of the interactions between humans, vehicles, and roads, aiming to create precise models of their characteristics and operational rules [5]. - The cognitive-driven architecture consists of three layers: perception, cognition, and decision-making, enhancing reliability and adaptability in complex environments [5]. Group 3: Future Outlook for Autonomous Driving - The evolution of autonomous driving is shifting from rule-driven and data-driven methods to cognitive-driven capabilities, focusing on human-like cognition, learning, and evolution [6]. - A "three vertical, three horizontal" technical architecture is proposed to support the large-scale development of intelligent vehicles [6]. - The ultimate goal is to enhance the self-learning, self-reflective, and adaptive capabilities of autonomous driving systems, creating high-level intelligent driving systems with human-like reasoning and safety verification [6].