WeRide (WRD) Q2 2025 Earnings Call July 31, 2025 08:00 AM ET Company ParticipantsTony Han - Founder, Chairman & CEOJennifer Li - CFOLiping Zhao - Vice PresidentConference Call ParticipantsTim Hsiao - AnalystJiajie Shen - Equity Research AnalystNone - AnalystOlivia Niu - Senior Equity Research AnalystOperatorGood morning, good lady, and good evening, ladies and gentlemen. Thank you for standing by, and welcome to WeRide's Second Quarter twenty twenty five Earnings Conference Call. At this time, all participa ...

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Group 1 - The article discusses recent advancements in autonomous driving technologies, highlighting several innovative frameworks and models [3][9][21][33][45] - GS-Occ3D achieves state-of-the-art (SOTA) geometric accuracy with a 0.56 corner distance (CD) on the Waymo dataset, demonstrating superior performance over LiDAR-based methods [3][5] - BEV-LLM introduces a lightweight multimodal scene description model that outperforms existing models by 5% in BLEU-4 score, showcasing the integration of LiDAR and multi-view images [9][10] - CoopTrack presents an end-to-end cooperative perception framework that sets new SOTA performance on the V2X-Seq dataset with 39.0% mAP and 32.8% AMOTA [21][22] - The Diffusion-FS model achieves a 0.7767 IoU in free-space prediction, marking a significant improvement in multimodal driving channel prediction [45][48] Group 2 - GS-Occ3D's contributions include a scalable visual occupancy label generation pipeline that eliminates reliance on LiDAR annotations, enhancing the training efficiency for downstream models [5][6] - BEV-LLM utilizes BEVFusion to combine 360-degree panoramic images with LiDAR point clouds, improving the accuracy of scene descriptions [10][12] - CoopTrack's innovative instance-level end-to-end framework integrates cooperative tracking and perception, enhancing the learning capabilities across agents [22][26] - The ContourDiff model introduces a novel self-supervised method for generating free-space samples, reducing dependency on dense annotated data [48][49]

AI and Engineering Landscape - AI is rapidly transforming various aspects of life, from personalized recommendations to predictive maintenance [1][4][5][14][15][16] - The integration of AI raises concerns about job displacement for engineers and the need for continuous upskilling [7][11] - Engineers play a crucial role in designing AI systems with ethics, logic, and empathy, ensuring they benefit humanity [9][12][18][19] - AI is smart but lacks wisdom, highlighting the importance of human oversight and ethical considerations in AI development [18][19] Essential Skills for Engineers in the AI Era - Critical thinking is paramount for engineers to approach problems from different perspectives and innovate [21][22] - Creativity is essential for transforming dreams into innovative solutions [23] - Collaboration is crucial for engineers from diverse fields to work together effectively [24][25] - Communication skills are vital for conveying ideas, solving problems, and programming AI systems [25][26] Real-world Applications and Future Outlook - AI can be used to monitor driver behavior in real-time to prevent accidents [27][30] - A project to monitor driver behavior is expected to be commercialized within 6 months, with 97,000 ringgit awarded by the Malaysian Communications and Multimedia Commission (MCMC) [28] - Malaysia needs 30,000 more engineers in the next 10 years, with 508% of students taking STEM educations [31]

Core Insights - WeRide and Uber have launched their Robotaxi service in Al Reem and Al Maryah Islands, aiming to double ride volumes and cover half of Abu Dhabi's core areas [1][4][5] Company Developments - The Robotaxi fleet has tripled in size since its launch in December 2024, with plans to expand to hundreds of vehicles in Abu Dhabi [4][5] - WeRide's GXR model is a mass-produced autonomous vehicle designed for commercial deployment, accommodating up to five passengers [3][9] - The service is expected to average dozens of trips per vehicle per day, with rides typically exceeding six kilometers [3] Industry Impact - The expansion into high-demand areas like Al Reem and Al Maryah Islands aligns with Abu Dhabi's vision for a smarter mobility system, enhancing the transport experience for residents [6][7] - The deployment of autonomous vehicles is part of a broader strategy to provide sustainable and innovative mobility solutions in the region [6][11] - Fully driverless testing of WeRide's Robotaxis is currently underway, with public access anticipated in the near future [8]

Core Viewpoint - The article emphasizes the establishment of a leading communication platform for autonomous driving technology in China, focusing on industry, academic, and career development aspects [1]. Group 1 - The platform, named "Autonomous Driving Heart," aims to facilitate discussions and exchanges among professionals in various fields related to autonomous driving technology [1]. - The technical discussion group covers a wide range of topics including large models, end-to-end systems, VLA, BEV perception, multi-modal perception, occupancy, online mapping, 3DGS, multi-sensor fusion, transformers, point cloud processing, SLAM, depth estimation, trajectory prediction, high-precision maps, NeRF, planning control, model deployment, autonomous driving simulation testing, product management, hardware configuration, and AI job exchange [1]. - Interested individuals are encouraged to join the community by adding a WeChat assistant and providing their company/school, nickname, and research direction [1].

Core Insights - The article discusses the implementation of the LLaMA Factory framework for fine-tuning large models in the context of autonomous driving, utilizing a small dataset of 400 images and a GPU 3090 with 24GB memory [1][2]. Group 1: LLaMA Factory Overview - LLaMA Factory is an open-source low-code framework for fine-tuning large models, gaining popularity in the open-source community with over 40,000 stars on GitHub [1]. - The framework integrates widely used fine-tuning techniques and is designed to facilitate the training of models suitable for visual-language tasks in autonomous driving scenarios [1]. Group 2: Qwen2.5-VL Model - The Qwen2.5-VL model serves as the foundational model for the project, achieving significant breakthroughs in visual recognition, object localization, document parsing, and long video understanding [2]. - It offers three model sizes, with the flagship Qwen2.5-VL-72B performing comparably to advanced models like GPT-4o and Claude 3.5 Sonnet, while smaller versions excel in resource-constrained environments [2]. Group 3: CoVLA Dataset - The CoVLA dataset, comprising 10,000 real driving scenes and over 80 hours of video, is utilized for training and evaluating visual-language-action models [3]. - This dataset surpasses existing datasets in scale and annotation richness, providing a comprehensive platform for developing safer and more reliable autonomous driving systems [3]. Group 4: Model and Dataset Installation - Instructions for downloading and installing LLaMA Factory and the Qwen2.5-VL model are provided, including commands for cloning the repository and installing necessary dependencies [4][5][6]. - The CoVLA dataset can also be downloaded from Hugging Face, with configurations to speed up the download process [8][9]. Group 5: Fine-tuning Process - The fine-tuning process involves using the SwanLab tool for visual tracking of the training, with commands provided for installation and setup [14]. - After configuring parameters and starting the fine-tuning task, logs of the training process are displayed, and the fine-tuned model is saved for future use [17][20]. Group 6: Model Testing and Evaluation - Post-training, the fine-tuned model is tested through a web UI, allowing users to input questions related to autonomous driving risks and receive more relevant answers compared to the original model [22]. - The original model, while informative, may provide less relevant responses, highlighting the benefits of fine-tuning for specific applications [22].

人工智能： 其他： 1. 阿里云：通义万相2.2正式开源。 2. 智谱发布首款SOTA级原生智能体大模型。 3. 上海：发放6亿元算力券，降低智能算力使用成本。 4. 微软在Edge加入AI Agent，自动化搜索、预测、整合 5. Anthropic：从8月28日起，将为Claude Pro和Max推出新的每周使用限制。 金十数据整理：每日科技要闻速递（7月29日） 1. 美商务部酝酿专利新收费机制：按专利总价值1%-5%收取。 2. 三星与特斯拉达成165亿美元芯片供应协议，将在美国生产AI6芯片。 3. 抖音：网传"员工离职后被字节跳动随意取消期权"与事实不符。 4. 工信部：巩固新能源汽车行业"内卷式"竞争综合整治成效。 5. 上海市经信委：争取年内实现浦东除陆家嘴外全域开放自动驾驶。 6. 上海智能网联汽车示范运营牌照发放，文远知行等8家公司为首批获准企业。 7. 2025世界智能网联汽车大会将于10月16日至18日召开，多款自动驾驶系统车辆将亮相现场。 ...

Group 1 - Waymo plans to launch a robotaxi service in Dallas next year, expanding its commercial operations which already include Los Angeles and San Francisco [1][6] - The company is partnering with Avis Budget Group to manage its fleet of all-electric autonomous Jaguar I-Pace vehicles, with Avis handling depot operations such as charging and maintenance [1][2] - Waymo has previously partnered with companies like Uber and Moove, but this marks the first collaboration with a rental car company, indicating a potential for future expansions to other cities [2] Group 2 - Waymo has conducted preliminary testing in Dallas, including mapping the city and testing autonomous vehicles on public roads with a human safety operator [3] - The fleet is expected to scale to hundreds of vehicles over time, although specific launch dates and initial fleet size have not been disclosed [4] - Waymo currently operates commercially in five cities and plans to launch services in Washington, D.C., and Miami next year [6]

Core Insights - WeRide has received Saudi Arabia's first Robotaxi autonomous driving permit, making it the only technology company with such permits in six countries [1][9] - The company is authorized to operate an autonomous vehicle business and deploy Robotaxis nationwide, starting pilot operations in Riyadh in collaboration with Uber and local partner Ai Driver [2][3] Company Developments - The pilot program launched covers key locations in Riyadh, including King Khalid International Airport and major highways, with a full-scale commercial service expected by the end of 2025 [3][4] - WeRide's Robotaxi completed the TGA's Regulatory Sandbox for AV Piloting, involving rigorous testing and assessments to ensure safety and performance [5] - The permit is seen as a significant step in WeRide's global expansion, enabling large-scale deployment and new revenue streams in Saudi Arabia [6] Regional Expansion - WeRide entered the Saudi market in May 2025 and has been testing its Robobus in various locations, including King Fahad Medical City and Aramco residential communities [7] - The company is also expanding its Robotaxi services in the Middle East, with ongoing fully driverless testing in Abu Dhabi and plans to extend services to Dubai [8] Industry Position - WeRide is recognized as a leader in the autonomous driving industry, being the first publicly traded Robotaxi company and having tested vehicles in over 30 cities across 10 countries [9]