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: Core Insights - The "2025 New Energy Smart Vehicle New Quality Development Forum" was successfully held in Changchun, focusing on the theme of "New Quality Leading, Intelligent Creation of the Future" and discussing the technological evolution, ecological reconstruction, and future trends of new energy smart vehicles [2] - The forum highlighted the rapid development of new energy vehicles and the deep restructuring of the global automotive landscape, emphasizing the need for open cooperation and collaborative innovation to secure future success [4] - Key tasks identified include accelerating the popularization of assisted driving from 2025 to 2030 and setting ambitious goals for L3 and higher-level autonomous driving technology [4][10] Group 2: Technological Innovations - Experts discussed the dual paths of safety technology in smart vehicles: rule-driven and data-driven approaches, with a proposed "cognitive-driven" route to overcome key technological bottlenecks [6] - The shift towards higher voltage charging systems (1000V to 1500V) and the adoption of wide bandgap power devices like silicon carbide were noted as trends in electric drive systems [8] - The automotive industry's competitive focus is shifting towards smart and AI capabilities, with mechanical costs expected to drop from 70% to below 30% while electronic and software costs rise to 70% [10] Group 3: Industry Practices and Strategies - Automotive companies are urged to innovate technologically, manage relationships as partnerships, and build long-term brand trust to transition from low-level competition to high-quality development [14] - The collaboration between Jianghuai Automobile and Huawei exemplifies deep integration across the entire value chain, aiming for breakthroughs in the ultra-luxury smart new energy sector [16] - The evolution of AI in automotive applications is leading to a transition from traditional software-defined vehicles to "AI-defined vehicles," presenting challenges in hardware and software compatibility [18] Group 4: Supply Chain and Ecosystem Collaboration - AI's impact on the automotive industry is seen as an incremental enhancement rather than a replacement, with the software supply chain maturing to meet rapid iteration demands [22] - The importance of software in defining vehicles is highlighted, with trends in electric vehicles, smart driving, and personalized features becoming increasingly significant [24] - The interaction between vehicles and the grid is viewed as a means to alleviate pressure from large-scale electric vehicle adoption and support the transition to a low-carbon energy structure [30] Group 5: Future Directions - The forum showcased the collaborative trends in smart vehicle development across four dimensions: intelligence, localization, software integration, and ecological fusion, indicating a vibrant and innovative landscape in China's new energy smart vehicle industry [34] - The integration of heterogeneous information fusion technologies is expected to enhance the safety performance of smart driving systems, making advanced technology more accessible to a broader market [34]

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]

Group 1: Industry Overview - In the first half of the year, China's new energy vehicle (NEV) production and sales reached 6.968 million and 6.937 million units, respectively, marking year-on-year growth of 41.4% and 40.3%, with NEVs accounting for 44.3% of total new car sales [1] - The global automotive industry is undergoing a critical transition from electrification to intelligence, with China having established a first-mover advantage in terminal penetration and technology application scenarios, although challenges remain in high-level autonomous driving technology and core supply chain autonomy [1] Group 2: Technological Innovation - The safety and reliability of smart vehicles are central to their advancement, with a shift from low-level to high-level autonomous driving revealing technical shortcomings in the "perception-cognition-decision" chain [2] - A new "cognitive-driven" technical route is proposed, integrating rule-based interpretability with data-driven learning capabilities to enhance the adaptability of smart vehicles in extreme scenarios [2] Group 3: AI Operating Systems - The evolution of AI operating systems (AIOS) is outlined in three stages: AI as an application, AI optimizing systems, and AI defining systems, with a focus on creating a robust control hardware layer and efficient development frameworks [3] - The NeuSAR OS supports over 80 chip adaptations, enabling rapid new chip iterations for automotive companies [3] Group 4: Chip Development - Domestic smart cockpit and vehicle control chips are rapidly advancing, with the X9 series cockpit chips covering over 50 models and the E3 series MCU filling high-end gaps, with over 8 million units shipped [4] - The next-generation X10 series will support local deployment of large AI models, enhancing multi-modal intelligent interaction capabilities [4] Group 5: Industry Collaboration - The competition in the global smart vehicle market has shifted from single technology to ecosystem capabilities, with China showing significant advantages in terminal applications and deep binding models between vehicle and component manufacturers [5] - The cost structure of electric vehicles is expected to shift significantly towards electronics and software by 2030, with consumer preferences increasingly focusing on smart features and cost [5] Group 6: Global Strategy - China's automotive industry is transitioning from "product export" to "technology and standard output," with local R&D teams compressing development cycles and supporting global competitiveness [6] - Open innovation is highlighted as a key strategy for penetrating high-end markets, with companies focusing on user experience rather than just technical specifications [6] Group 7: Market Expansion - Anhui Jianghuai Automobile Group is entering the ultra-luxury market through cross-industry collaboration, achieving significant pre-orders for its joint product with Huawei [7] - Continuous innovation and deep collaboration across the value chain are emphasized as essential for Chinese brands to ascend the global value chain [7]

Core Viewpoint - Intelligent automotive technology is a key solution for traffic safety, which remains a perpetual theme in the development of smart vehicles [1] Group 1: Current State of Intelligent Vehicles - Low-level intelligent vehicles have achieved a high market penetration rate, but accidents still occur as the industry transitions to higher levels of autonomous driving [1] - There are significant challenges in safety technology that need to be addressed in the context of complex long-tail scenarios [1] Group 2: Technological Approaches - The early development of intelligent vehicles relied on rule-driven approaches, while current mainstream autonomous driving methods include data-driven techniques [4] - Rule-driven systems are observable and interpretable but are inflexible in complex environments, whereas data-driven systems utilize deep learning but suffer from a "black box" nature that obscures decision-making processes [4] - A proposed third route, "cognitive-driven," aims to combine the interpretability of rule-driven systems with the learning capabilities of data-driven systems, enhancing adaptability and transparency [4][5] Group 3: Cognitive-Driven Architecture - The cognitive-driven approach is based on a deep understanding of the interactions between humans, vehicles, and roads, leading to accurate modeling and digital representation of system characteristics [5] - The architecture consists of three layers: perception, cognition, and decision-making, integrating physical state estimation, semantic understanding, and human-like adaptive decision generation [5][6] Group 4: Future Trends and Goals - The evolution of autonomous driving is shifting from rule-driven and data-driven methods to cognitive-driven systems, focusing on human-like cognition, learning, and evolution [5] - A new paradigm of "self-learning + prior knowledge" is necessary to enhance environmental understanding and reasoning capabilities, improving safety and generalization in long-tail scenarios [5] - The ultimate goal is to develop a high-level intelligent driving system that possesses self-learning, self-reflection, and adaptive capabilities, ensuring safety and verifiability [6]

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].