Core Insights - The article highlights significant developments in the autonomous driving industry, particularly the performance of Horizon HSD and the advancements in Xiaopeng's VLA2.0, indicating a shift towards end-to-end production models [1][3]. Group 1: Industry Developments - Horizon HSD's performance has exceeded expectations, marking a return to the industry's focus on one-stage end-to-end production, which has a high potential ceiling [1]. - Xiaopeng's VLA2.0, which integrates visual and language inputs, reinforces the notion that value-added (VA) capabilities are central to autonomous driving technology [1]. Group 2: Educational Initiatives - The article discusses a new course titled "Practical Class for End-to-End Production," aimed at sharing production experiences in autonomous driving, focusing on various methodologies including one-stage and two-stage frameworks, reinforcement learning, and trajectory optimization [3][8]. - The course is limited to 40 participants, emphasizing a targeted approach to skill development in the industry [3][5]. Group 3: Course Structure - The course consists of eight chapters covering topics such as end-to-end task overview, two-stage and one-stage algorithm frameworks, navigation information applications, reinforcement learning algorithms, trajectory output optimization, fallback solutions, and production experience sharing [8][9][10][11][12][13][14][15]. - Each chapter is designed to build upon the previous one, providing a comprehensive understanding of the end-to-end production process in autonomous driving [16]. Group 4: Target Audience and Requirements - The course is aimed at advanced learners with a background in autonomous driving algorithms, reinforcement learning, and programming skills, although it is also accessible to those with less experience [16][17]. - Participants are required to have a GPU with recommended specifications and a foundational understanding of relevant mathematical concepts [17].

Investment Rating - The report maintains a positive outlook on the industry, particularly highlighting the advancements represented by Xiaopeng's VLA2.0 in enhancing algorithmic capabilities for intelligent driving [5][6]. Core Insights - The VLA2.0 demonstrates significantly improved generalization capabilities, achieving human-like feedback in certain scenarios, such as navigating complex roads with minimal human intervention [5][6]. - The report emphasizes the potential for technology spillover from VLA2.0 into other embodied intelligence fields, including robotics and low-altitude economies [5][31]. - Key investment targets identified include Xiaopeng Motors, Desay SV, Xizhi Jia, and Tianzhun Technology [5][38]. Summary by Sections Section 1: Xiaopeng's VLA2.0 Release - Xiaopeng's VLA2.0 was launched with claims of higher efficiency and faster response times, capable of handling various road conditions seamlessly [12][15]. - The second-generation VLA eliminates the traditional language translation step, allowing for direct conversion from visual input to action, which retains more information [15][16]. Section 2: Algorithm Development - The VLA model has a clear historical evolution, enhancing its capabilities over time from single-modal processing to multi-modal understanding and execution [19][24]. - The report notes the ongoing divergence in investment strategies within the market due to the non-convergence of technical solutions, highlighting the impact of the second-generation VLA on industry applications [29][30]. Section 3: Computing Power - Turing Chip - The Turing chip, which supports the VLA2.0, features independent ISP and enhanced perception capabilities, crucial for recognizing challenging environmental conditions [34][36]. - The chip is designed to support low bandwidth, which is beneficial for AI inference and autonomous driving, resulting in lower power consumption and reduced latency [34][36]. Section 4: Investment Targets - The report identifies key investment targets including Xiaopeng Motors, Desay SV, Xizhi Jia, and Tianzhun Technology, indicating their relevance in the intelligent driving sector [38][39].