Group 1 - The article discusses the importance of the autumn recruitment season, highlighting a student's experience of receiving an offer from a tier 1 company but feeling unfulfilled due to a desire to transition to a more advanced algorithm position [1] - The article encourages perseverance and self-challenge, emphasizing that pushing oneself can reveal personal limits and potential [2] Group 2 - A significant learning package is introduced, including a 299 yuan discount card for a year of courses at a 30% discount, various course benefits, and hardware discounts [4][6] - The focus is on cutting-edge autonomous driving technologies for 2025, particularly end-to-end (E2E) and VLA autonomous driving systems, which are becoming central to the industry [7][8] Group 3 - The article outlines the development of end-to-end autonomous driving algorithms, emphasizing the need for knowledge in multimodal large models, BEV perception, reinforcement learning, and more [8] - It highlights the challenges faced by beginners in synthesizing knowledge from fragmented research papers and the lack of practical guidance in transitioning from theory to practice [8] Group 4 - The introduction of a new course on automated 4D annotation algorithms is aimed at addressing the increasing complexity of training data requirements for autonomous driving systems [11][12] - The course is designed to help students navigate the challenges of data annotation and improve the efficiency of data loops in autonomous driving [12] Group 5 - The article discusses the emergence of multimodal large models in autonomous driving, noting the rapid growth of job opportunities in this area and the need for a structured learning platform [14] - It emphasizes the importance of practical experience and project involvement for job seekers in the autonomous driving sector [21] Group 6 - The article mentions various specialized courses available, including those focused on perception, model deployment, planning control, and simulation in autonomous driving [16][18][20] - It highlights the importance of community engagement and support through dedicated VIP groups for course participants [26]

Core Viewpoint - The article emphasizes the importance of active safety technology in smart electric vehicles, highlighting Xiaopeng's advancements in this area to regain a competitive edge in the market [2][3][14]. Group 1: Active Safety Technology Developments - Xiaopeng has demonstrated its AEB (Automatic Emergency Braking) capabilities at speeds of up to 130 km/h in various challenging conditions, including night and wet roads [2][4]. - The company has redefined its active safety architecture and software, with daily updates to enhance performance and address market competition [2][3]. - Xiaopeng's AEB system is designed to operate effectively in a full speed range of 0-150 km/h, with a focus on real-world scenarios [4][5]. Group 2: Technical Innovations - Xiaopeng employs a "two-stage braking" strategy to enhance user comfort during emergency braking, initially applying a moderate deceleration before engaging full braking if necessary [5][6]. - The introduction of the AES (Automatic Emergency Steering) system allows vehicles to navigate around obstacles on slippery surfaces, utilizing a unique "single-side braking" technique [8][9]. - The company aims to tackle complex driving conditions, such as icy roads, to ensure stability and effective obstacle avoidance [9][10]. Group 3: Strategic Focus and Team Dynamics - Xiaopeng has established dedicated teams and "war rooms" to enhance collaboration and expedite the development of active safety features [15][16]. - The company has shifted its focus to prioritize active safety, responding to consumer demand for improved vehicle safety [14][18]. - The development process has been streamlined to ensure rapid iteration and effective communication among team members [16][17]. Group 4: Market Position and Future Goals - Xiaopeng's advancements in active safety are part of a broader strategy to maintain its leadership in the smart driving sector amid increasing competition [14][18]. - The ultimate goal of the active safety technology is to achieve "zero collisions" by expanding the coverage of AEB and AES systems [22][23]. - Future efforts will focus on enhancing scene coverage based on real-world collision data to prioritize high-frequency and high-severity scenarios [23][24].

Core Viewpoint - The article emphasizes the importance of understanding end-to-end (E2E) algorithms and Visual Language Models (VLA) in the context of autonomous driving, highlighting the rapid development and complexity of the technology stack involved [2][32]. Summary by Sections Introduction to End-to-End and VLA - The article discusses the evolution of large language models over the past five years, indicating a significant technological advancement in the field [2]. Technical Foundations - The Transformer architecture is introduced as a fundamental component for understanding large models, with a focus on attention mechanisms and multi-head attention [8][12]. - Tokenization methods such as BPE (Byte Pair Encoding) and positional encoding are explained as essential for processing sequences in models [13][9]. Course Overview - A new course titled "End-to-End and VLA Autonomous Driving" is launched, aimed at providing a comprehensive understanding of the technology stack and practical applications in autonomous driving [21][33]. - The course is structured into five chapters, covering topics from basic E2E algorithms to advanced VLA methods, including practical assignments [36][48]. Key Learning Objectives - The course aims to equip participants with the ability to classify research papers, extract innovative points, and develop their own research frameworks [34]. - Emphasis is placed on the integration of theory and practice, ensuring that learners can apply their knowledge effectively [35]. Industry Demand and Career Opportunities - The demand for VLA/VLM algorithm experts is highlighted, with salary ranges between 40K to 70K for positions requiring 3-5 years of experience [29]. - The course is positioned as a pathway for individuals looking to transition into roles focused on autonomous driving algorithms, particularly in the context of emerging technologies [28].

Core Viewpoint - Leap Motor reported strong financial results for the first half of 2025, achieving revenue of 24.25 billion yuan, a year-on-year increase of 174%, and becoming the second Chinese new energy vehicle manufacturer to achieve half-year profitability [1] Financial Performance - The company's gross margin reached a historical high of 14.1%, with net profit attributable to shareholders at 30 million yuan, and adjusted net profit at 330 million yuan [1] - The actual net profit for Q2 2025 exceeded market expectations by 115 million yuan, with a reported net profit of 163 million yuan [1] - Operating cash flow generated during the period was 2.86 billion yuan, with cash reserves amounting to 29.58 billion yuan [6] Sales and Market Position - Leap Motor delivered approximately 222,000 vehicles in the first half of 2025, marking a year-on-year growth of 155.7%, leading the new energy vehicle sector in terms of delivery volume [1] - The company achieved a record monthly delivery of 50,129 vehicles in July 2025, maintaining its position as the top-selling new energy vehicle brand in China [4] - The B series and C series models have been well-received, with the C10 and C16 models leading sales in their respective categories [4] Product Strategy and Innovation - Leap Motor has adopted a high-end strategy, increasing the average selling price of its vehicles by 76% year-on-year, contrasting with the overall market trend of declining prices [5] - The company has invested heavily in R&D, with a nearly 100% increase in its autonomous driving team and computing resources [7] - The B10 model, launched in April 2025, features advanced technology and has quickly become a best-seller, achieving over 10,000 deliveries in its first month [8] International Expansion - Leap Motor exported 24,980 vehicles from January to July 2025, leading among new energy vehicle brands in China [9][12] - The company has established over 600 sales and service outlets across approximately 30 international markets, with plans to set up a local production base in Europe by the end of 2026 [12] - The stock performance has been strong, with a 125% increase in market value this year, and several investment banks have raised their target prices for the company [12]

Core Viewpoint - Leap Motor has achieved profitability in its mid-term results and has raised its annual net profit guidance to between RMB 500 million and RMB 1 billion, while also increasing its annual sales target to 580,000 to 650,000 units [1][2] Group 1: Financial Performance - In the first half of 2025, Leap Motor reported a net profit of RMB 30 million, with an adjusted net profit of RMB 330 million [1] - The company delivered 221,700 vehicles in the first half of 2025, marking a 155.7% increase compared to the same period in 2024 [1] - Revenue reached RMB 24.25 billion, a 174% increase year-on-year, with a gross margin of 14.1% [1] - The gross margin decreased from 14.9% in Q1 to below 14% in Q2 [1][2] Group 2: Sales and Production Goals - Leap Motor aims for a monthly sales target of 60,000 units in the next five months to meet its revised annual sales goal [2] - The company has completed sales of 271,800 units in the first seven months of the year, indicating a significant ramp-up in sales for the latter half of the year [2] - Leap Motor plans to challenge a sales target of 1 million units in the following year [2] Group 3: Strategic Initiatives - Leap Motor has initiated a strategic cooperation with China FAW to jointly develop new energy passenger vehicles and components [2] - The company has exported 24,980 vehicles in the first seven months, with strong performance in the European market [2] - Leap Motor plans to establish a localized production base in Europe by the end of 2026 to enhance its global market presence and optimize cost structure [2]

Core Viewpoint - The article emphasizes the evolving landscape of autonomous driving, particularly the integration of traditional planning and control (PnC) with end-to-end systems, highlighting the necessity for professionals to adapt to these changes in order to remain competitive in the job market [2][4][29]. Group 1: Industry Trends - The shift towards end-to-end and VLA (Vision-Language Alignment) systems is impacting traditional PnC roles, which are now required to incorporate more advanced algorithms and frameworks [2][4]. - As of 2025, end-to-end systems are expected to become more prevalent, yet traditional PnC methods will still play a crucial role, especially in safety-critical applications like Level 4 autonomous driving [4][29]. - The article discusses the importance of understanding both traditional and modern approaches to planning and control, as they are increasingly being integrated in practical applications [4][29]. Group 2: Educational Offerings - The company has launched specialized courses aimed at bridging the gap between theoretical knowledge and practical application in the field of autonomous driving, focusing on real-world challenges and interview preparation [5][7]. - The courses are designed to provide hands-on experience with current industry practices, including classic and innovative solutions in PnC, and are tailored for individuals with some background in the field [8][12]. - The curriculum includes modules on foundational algorithms, decision-making frameworks, and advanced topics such as contingency planning and interactive planning, which are critical for modern autonomous driving systems [20][21][24][26][29]. Group 3: Career Development - The courses not only focus on technical skills but also offer support in job application processes, including resume reviews and mock interviews, to enhance employability [9][10][31]. - Previous participants have successfully secured positions at major companies in the autonomous driving sector, indicating the effectiveness of the training provided [10][12]. - The program aims to equip participants with the skills necessary to construct decision-making systems and address real-world challenges in autonomous driving, thereby enhancing their career prospects [13][29].

Core Viewpoint - The article discusses the impact of end-to-end autonomous driving technology on traditional rule-based control (PNC) methods, highlighting the shift towards data-driven approaches and the complementary relationship between the two systems [2][6]. Summary by Sections Differences Between Approaches - Traditional PNC relies on manually coded rules and logic for vehicle planning and control, utilizing algorithms like PID, LQR, and various path planning methods. Its advantages include clear algorithms and strong interpretability, suitable for stable applications [4]. - End-to-end algorithms aim to directly map raw sensor data to control commands, reducing system complexity and enabling the model to learn human driving behavior through large-scale data training. This approach allows for joint optimization of the entire driving process [4]. Advantages and Disadvantages - **End-to-End Approach**: - Advantages include reduced system complexity, natural driving style emulation, and minimized information loss between modules [4]. - Disadvantages involve challenges in traceability of decision processes, high data scale requirements, and the need for rule-based fallback in extreme scenarios [4]. - **PNC Approach**: - Advantages include clear module functions, ease of debugging, and stable performance in known scenarios, making it suitable for high safety requirements [5]. - Disadvantages consist of high development costs and potential difficulties in handling complex scenarios without suitable rules [5]. Complementary Relationship - The analysis indicates that end-to-end systems require PNC for certain scenarios, while PNC can benefit from the efficiencies of end-to-end approaches. This suggests a complementary rather than adversarial relationship between the two methodologies [6]. Job Opportunities - The article highlights job openings in both end-to-end and traditional PNC roles, indicating a demand for skilled professionals in these areas with competitive salaries ranging from 30k to 100k per month depending on the position and location [8][10][12][14].

Core Insights - The article discusses the results of the 2025 Waymo Open Dataset End-to-End Driving Challenge, highlighting the advancements in end-to-end autonomous driving systems and the shift towards using large-scale public datasets for training models [2][18]. Group 1: Competition Results - The champion of the competition was the EPFL team, which utilized the DiffusionDrive model, nuPlan data, and an ensembling strategy [1]. - The runner-up was a collaboration between Nvidia and Tubingen teams, which also referenced DiffusionDrive and SmartRefine, employing multiple datasets to demonstrate the importance of training data quality [1][22]. - The third place was secured by Hanyang University from South Korea, which focused on a simplified structure using only front-view input and vehicle state [1][3]. Group 2: Methodology - The UniPlan framework was introduced, leveraging large-scale public driving datasets to enhance generalization in rare long-tail scenarios, achieving competitive results without relying on expensive multimodal large language models [3][18]. - The model architecture is based on DiffusionDrive, which employs a truncated diffusion strategy for efficient and diverse trajectory generation [4][6]. - The diffusion decoder utilizes a cross-attention mechanism to refine trajectory predictions based on scene context [5][6]. Group 3: Data Processing - The nuPlan dataset was processed to create a diverse training set, resulting in 90,000 samples by applying a sliding window approach [7]. - A similar filtering strategy was used for the WOD-E2E dataset, generating 35,000 training samples and 10,000 validation samples [8]. - The model was trained on a powerful computing setup with four H100 GPUs, achieving significant training efficiency [10]. Group 4: Experimental Results - The performance was evaluated using Rater Feedback Score (RFS) and Average Displacement Error (ADE), with various configurations tested [12][17]. - The results indicated that the combined training of WOD-E2E and nuPlan datasets led to slight improvements in average RFS, particularly in long-tail categories [23]. - The analysis showed that while additional datasets generally provide benefits, the quality of the data sources is more critical than quantity [39]. Group 5: Conclusion - The article emphasizes the potential of data-centric approaches in enhancing the robustness of autonomous driving systems, as demonstrated by the competitive results achieved with the UniPlan framework [18][39].

Investment Rating - The report assigns a "Buy" rating for Horizon Robotics (9660.HK) [3] Core Insights - Horizon Robotics is positioned as a leader in the new generation of automotive intelligent chips and a world-class AI algorithm company, focusing on software-defined principles and exploring new boundaries in intelligent driving [5][14] - The intelligent driving market is expected to grow significantly, with the AD market projected to take over from ADAS as the main growth driver, achieving a market size of 407 billion yuan by 2030 [12][37] - The company has a leading market share in the intelligent driving computing solutions market, with a 28.65% share in the first half of 2024, and is expected to further increase its share in the OEM ADAS and AD markets [11][57] Summary by Sections Company Overview - Horizon Robotics focuses on intelligent driving chip platforms, full-scene intelligent driving solutions, and supporting toolchains, establishing itself as a comprehensive supplier in the industry [5][14] - The company has launched several intelligent driving chips, including J2, J3, J5, and J6, and has developed a self-adaptive BPU computing unit that maximizes computational efficiency [14] Market Growth - The AD+ADAS market in China has seen a compound annual growth rate (CAGR) of 57.8% from 2019 to 2023, with the AD market growing at a CAGR of 144.2% [12][37] - By 2030, the AD market is expected to reach a size of 407 billion yuan, with a CAGR of 48.8% from 2025 to 2030 [12][37] Competitive Position - Horizon Robotics has a steadily increasing market share, with 41% in the ADAS market and over 30% in the AD market among Chinese OEMs by the end of 2024 [12][57] - The company has established partnerships with major OEMs, including BYD, Geely, and Chery, to support their intelligent driving strategies [61][69] Financial Projections - Revenue projections for Horizon Robotics are expected to reach 36.10 billion yuan in 2025, 56.97 billion yuan in 2026, and 80.53 billion yuan in 2027, with corresponding growth rates of 51%, 58%, and 41% respectively [6] - The company anticipates a return to profitability by 2027, with a projected net profit of 668 million yuan [6] Customer Base and Partnerships - Horizon Robotics has a broad customer base, covering major domestic automakers and new energy vehicle manufacturers, which positions it well for future growth as the demand for intelligent driving solutions increases [69]

Core Viewpoint - The report from Shenwan Hongyuan indicates that SUTENG JUCHUANG (02498) is expected to experience significant revenue growth from 2025 to 2027, with projected revenues of 2.62 billion, 3.66 billion, and 4.70 billion yuan respectively, while the net profit is forecasted to be -238 million, 106 million, and 320 million yuan respectively. The company is currently not profitable, leading to a PS valuation method being employed for its assessment [1]. Group 1 - The company is rapidly leading the global LiDAR industry, focusing on providing quality solutions in the field of embodied intelligence. The sales of LiDAR products have seen a non-linear high growth, confirming the explosive demand from automotive companies for LiDAR configurations under the trend of increasing intelligence [2]. - In 2024, the total sales of LiDAR products are expected to reach approximately 544,000 units, representing a significant year-on-year increase of 109.6%. The sales of LiDAR products for ADAS applications are projected to be around 520,000 units. The company is expected to maintain a leading market share of 26% in 2024, ranking first globally [2]. - The product matrix of the company is comprehensive, covering various technical paths including mechanical, semi-solid, and solid-state LiDAR, with performance ranging from short to ultra-long distances and low to high beam configurations. This allows the company to meet a wide range of demands across different price segments [2]. Group 2 - The first driving force is the end-to-end vehicle integration and equalization of intelligent driving. The previous debate over LiDAR configurations in vehicles has been influenced by Tesla's insistence on a pure vision and neural network approach. With advancements in computing power and the maturity of end-to-end algorithms, the integration of multi-sensor fusion with pure vision is becoming more feasible [3]. - The LiDAR industry is expected to enter the "thousand-yuan machine era" by 2025, with prices dropping to the range of 25,000 to 30,000 yuan. This price reduction is anticipated to significantly increase the configuration rate of LiDAR as an "invisible safety airbag" for autonomous driving [3]. - The global market for LiDAR in passenger vehicles is estimated to reach approximately 7 billion yuan by 2025, with the Chinese market accounting for about 6.3 billion yuan. The overseas market is expected to gradually open up and grow rapidly, representing an important direction for LiDAR's incremental growth [3]. Group 3 - The second driving force is the strategic positioning of the robotics technology platform. The company focuses on the development of incremental components such as robotic vision and dexterous hands, launching solutions based on hand-eye coordination for upper body operations and lower body mobility [4]. - The year 2025 is viewed as the year of mass production for humanoid robots, with companies like Tesla aiming to produce 5,000 units of Optimus this year, and domestic companies like Zhiyuan Robotics achieving deliveries in the thousands [4]. - In the niche market of robotic lawn mowers, the demand for LiDAR products is projected to exceed 400,000 units by 2025 and is expected to surpass 900,000 units by 2028 [4].