智能驾驶2.0：自主应对极端场景

Core Insights - The report emphasizes the evolution of technology architecture in the autonomous driving sector, highlighting the shift from traditional methods to advanced models like the Visual Language Action (VLA) model, which focuses on user experience and data-driven approaches [2][3]. Technical Architecture Iteration Analysis - The transition from no-image solutions to end-to-end VLA models is noted, with a focus on enhancing user experience through features like voice control and visualizing system reasoning processes. The VLA model is set to be launched in September 2024, with training divided into three phases: base pre-training, action fine-tuning (imitation learning), and reinforcement learning optimization [3]. - Key players like Yuanrong Qixing are focusing on the VLA route, leveraging "thinking chain reasoning capabilities" to address traditional black-box issues and enhance generalization through integrated knowledge bases. Their goal is to achieve a high-level autonomous driving deployment of 1 million units by 2026, emphasizing the importance of data scale [3]. - Huawei's ADS 4.0 introduces a world model (WEWA architecture), shifting from data-driven to scenario-driven approaches, relying on cloud-based simulation engines for training in complex scenarios. Their roadmap includes commercializing L3 autonomous driving on highways by 2026 and piloting L4 in urban areas [3]. - Horizon Robotics and Momenta adopt a pragmatic approach, focusing on "end-to-end + reinforcement learning" while accommodating diverse customer computing power configurations. Momenta collaborates with over 160 vehicle models, including partnerships with joint ventures, state-owned enterprises, and private companies [3]. Business Model Transformation: Acceleration of Robotaxi Business - The trend indicates that by 2026, the Robotaxi business based on mass-produced vehicles will become a focal point, with a gradual approach (as seen with Tesla and Xiaopeng) being preferred over a leapfrogging strategy (as seen with Waymo and WeRide) [4]. Future Trends and Industry Directions - The report identifies the transition of AI autonomous driving into its 2.0 phase, with 2024 marking the establishment of the "end-to-end paradigm" as a 1.0 milestone. The industry is moving towards a stage of "intelligent emergence," capable of autonomously handling extreme scenarios, with 2026 being a critical juncture for technological architecture, hardware iteration, and the commercialization of Robotaxi [5]. - Tesla is highlighted as a global leader, with advancements in its Full Self-Driving (FSD) system, including the V12 version establishing the end-to-end paradigm and the V14 version featuring a new software architecture with exponentially increased parameter scale. The FSD has accumulated 6 billion miles of driving data, with a penetration rate of approximately 12% [5]. - The report outlines Tesla's business model and ecosystem, noting that its Robotaxi fleet has driven over 250,000 miles without a safety driver. Production of new vehicles is set to ramp up from 500,000 to 2-5 million units annually by April 2026 [5]. - The development stages of autonomous driving are categorized into three phases: rule-driven, hybrid systems, and pure data-driven, with Tesla's FSD V14 representing the highest potential ceiling [5]. - Key players like Li Auto are leveraging data feedback from Robotaxi to optimize mass-produced vehicle systems, reusing hardware and algorithms to significantly reduce deployment costs, and strategically positioning themselves in the Mobility as a Service (MaaS) ecosystem [6]. - The report also notes that high-end autonomous driving solutions will rapidly penetrate the economy vehicle segment, ultimately achieving "optimal experience standardization" [6]. - Companies with diverse real-world testing data and robust R&D resources are expected to have a competitive advantage in the evolving landscape [6].