Core Insights - Goldman Sachs' report titled "2026 Outlook: Navigating Divergence" highlights 2026 as a pivotal year for the adoption of Battery Electric Vehicles (BEVs) and Navigation on Autopilot (NOA), suggesting that these technologies may develop into separate standards [1] - The report indicates a slowdown in BEV sales in Europe and the U.S., with consumers showing interest in advanced NOA but being cautious about purchasing BEVs [1][7] - In China, the integration of NOA in vehicles has led to a significant increase in sales, showcasing the advantages of BEVs in utilizing NOA, while traditional fuel vehicles are losing market share [1][5] Group 1: Market Trends - The market share of domestic brands in China has risen from 43.9% in 2017 to 51.9% in 2023, with the penetration rate of new energy vehicles increasing from 2.7% to 31.6% during the same period [3] - The report notes that the global electricity consumption of BEVs is expected to grow from 0.7% in 2024 to 2.5% by 2030, despite a stabilization in new BEV sales [7] - The competition landscape is shifting as NOA enhances the recognition and purchase intent for domestic brands, narrowing the gap with joint venture brands [5][6] Group 2: Technological Developments - Many automotive companies are focusing on applying NOA in hybrid models, although Goldman Sachs expresses skepticism about the medium-term effectiveness of this strategy [13] - The report lists various global automakers' progress in developing electronic and electrical architectures and end-to-end autonomous driving technologies, with companies like Tesla and BYD having completed their developments [14] - Traditional fuel vehicle manufacturers are exploring NOA applications in hybrid vehicles, but face challenges due to the inherent complexities of integrating NOA into their existing systems [15][16] Group 3: Future Outlook - The report suggests that by 2026, NOA may not solely rely on BEVs for growth, as traditional vehicles could also play a role in developing their own systems [19] - Concerns are raised about whether hybrid vehicles will be able to catch up with BEVs in terms of NOA capabilities, especially as BEVs are already testing Level 3 autonomous driving [19] - The future of NOA in traditional vehicles will depend on their ability to attract consumers and demonstrate value, as the market for traditional fuel vehicles remains substantial [19]

Core Insights - The automatic driving industry is experiencing a battle for narrative control over next-generation technologies, with companies like Li Auto and XPeng betting on VLA (Visual Language Action) as the future architecture, while Huawei criticizes it as a shortcut and promotes its own WA (World Behavior Architecture) [1][2][3] - The rapid emergence of jargon in the industry reflects the struggle for technological branding, as hardware becomes increasingly homogeneous and intelligent driving capabilities become the key differentiator [1][2][3] Group 1: Evolution of Terminology - Before 2022, the automatic driving industry's technical evolution was primarily defined by Tesla and Waymo, with terms being objective descriptions of specific functions [3] - Tesla's AI Day events in 2021 and 2022 significantly influenced the industry, introducing the BEV+Transformer architecture, which improved perception capabilities by integrating multiple camera inputs into a unified 3D view [3][4] - The transition to an "end-to-end" paradigm began in 2022, breaking down the barriers between perception and planning, with Tesla's FSD Beta V12 showcasing a large neural network that processes both simultaneously [5][6] Group 2: Technological Developments - Chinese automakers quickly adopted Tesla's advancements, with companies like XPeng and NIO implementing their own versions of the BEV+Transformer architecture for mass production [4][6] - The industry is moving towards a more integrated approach, with XPeng and Huawei adopting multi-stage end-to-end systems, while NIO is restructuring to focus on end-to-end development [7][8] - The introduction of VLA and world models into the automatic driving sector reflects a shift towards more sophisticated AI models that can understand and respond to complex driving scenarios [9][10][13] Group 3: Competitive Landscape - The competition in computing power is intensifying, with XPeng and Li Auto investing heavily in both vehicle and cloud computing capabilities, aiming to develop larger parameter models for their systems [11][12][36] - The VLA model, initially developed for robotics, is being adapted for automatic driving, with companies like Yuanrong Qixing leading the charge in applying this technology [10][31] - NIO and Huawei are taking a more aggressive approach by deploying world models directly in vehicles for real-time control, although the technology is still in the experimental stage [14][15] Group 4: Future Directions - The evolution of automatic driving terminology indicates a broader exploration of technology, with each new term representing a step in the industry's journey [16] - The ultimate success in the automatic driving sector may hinge on the ability to translate technological promises into tangible user experiences, rather than merely introducing new concepts [16]

Core Insights - The automatic driving industry is experiencing a battle for narrative control over next-generation technologies, with companies like Li Auto and XPeng betting on VLA (Visual Language Action) as the future architecture, while Huawei promotes its self-developed WA (World Behavior Architecture) [1][2][20] - The rapid emergence of jargon in the industry reflects the struggle for technological branding and user perception, as hardware and configurations become increasingly homogeneous [1][2][27] Group 1: Evolution of Technology - Before 2022, the evolution of automatic driving technology was primarily defined by Tesla and Waymo, with terminology focused on objective descriptions of specific functions [3] - Tesla's introduction of the BEV+Transformer architecture in 2021 marked a significant shift from rule-based systems to AI-driven approaches, enhancing perception capabilities [4][5][6] - The transition to an end-to-end paradigm was catalyzed by Tesla's AI DAY in 2022, which integrated perception and planning into a single neural network, significantly improving obstacle recognition [9][10] Group 2: Adoption of New Models - Chinese automakers quickly adopted Tesla's technology, with companies like XPeng and NIO implementing their own versions of the BEV+Transformer model for mass production [8][10] - The industry is moving towards end-to-end systems, with XPeng and Huawei initially adopting a multi-stage approach for safety reasons, before transitioning to fully integrated models [10][12] - The introduction of VLA and world models into automatic driving systems represents a new frontier, with companies like Yuanrong Qixing and NIO leading the charge in applying these concepts [17][20] Group 3: Competitive Landscape - The competition among companies is not only about technology but also about computational power, with XPeng and Li Auto investing heavily in cloud computing capabilities, boasting figures of 10 EFlops and over 13 EFlops respectively [18][19][55] - The race for computational resources extends to both vehicle and cloud platforms, with Tesla's Dojo and other companies ramping up their AI training capabilities [18][57] - The rapid evolution of VLA and world models is indicative of a broader trend where companies are leveraging advanced AI techniques to enhance their automatic driving systems [20][46] Group 4: Future Directions - The world model concept, initially used for simulation, is now being applied in real-time vehicle control by companies like NIO and Huawei, aiming for more predictive and human-like driving experiences [20][24][25] - The emergence of terms like VLA and world models highlights the industry's shift towards integrating language understanding and real-time decision-making into automatic driving systems [46][59] - The ultimate success in this competitive landscape may hinge on a company's ability to translate technological promises into tangible user experiences, rather than merely marketing jargon [30][29]

Core Insights - The concept of "smart driving" has gained significant attention since 2025, with companies like BYD pushing the narrative of "equal rights in smart driving" [1] - The initial purpose of smart driving was to enhance safety and reduce driver fatigue, but it has been misrepresented by some companies, leading to user misconceptions about its capabilities [1] - A recent test conducted by an automotive platform revealed a low pass rate for smart driving systems, raising concerns about the reliability of such technologies [3] Group 1: Testing and Results - A total of 36 vehicles underwent 183 tests, with only 44 passing, resulting in an overall pass rate of 24% [3] - In urban scenarios, 26 vehicles were tested 233 times, achieving a pass rate of approximately 44.2% [3] - The release of the test results has sparked public debate, with some experts criticizing the methodology and suggesting it was a marketing stunt [3][4] Group 2: Regulatory Response - The Ministry of Industry and Information Technology has emphasized the need for standardized marketing practices to avoid misleading consumers, including banning terms like "automatic" and "high-level smart driving" [8] - The police have stated that current smart driving systems do not possess true autonomous driving capabilities, reinforcing that human drivers remain responsible [10] - Regulatory scrutiny is increasing, with a focus on clarifying responsibilities and enhancing safety measures in the smart driving sector [10][14] Group 3: Safety and Limitations - All Level 2 smart driving systems are still fundamentally reliant on human drivers, and cannot guarantee absolute safety [11] - There is a significant difference between Level 2 and Level 3 systems in terms of design and safety protocols, with Level 2 systems lacking comprehensive redundancy [13] - The recent tests highlight the limitations of smart driving technologies in real-world conditions, urging consumers to remain aware of their responsibilities while driving [14]