小马智行表示，运营时段充分满足市民包括早晚高峰在内的日常通勤及休闲出行需求，同时运营线路覆 盖浦东新区人民政府、啦啦宝都购物中心、世纪公园、浦东足球场地铁站等核心商圈、地标场所及热门 地铁站，有效连接区域内工作、生活、休闲三大场景，让自动驾驶技术真正服务于市民的日常出行需 求。 责任编辑：王翔 消息称，小马智行联合锦江出租率先在浦东金桥和花木地区推出了面向公众常态化运营的Robotaxi服 务。每周一至周五早7：30至晚9：30，市民通过"小马智行"手机App或小程序，均可呼叫Robotaxi作为 出行交通工具。运营线路覆盖浦东新区人民政府、啦啦宝都购物中心、世纪公园、浦东足球场地铁站等 核心商圈、地标场所及热门地铁站。 8月4日消息，小马智行宣布，8月1日起，小马智行在上海浦东推出了面向公众常态化运营的Robotaxi服 务。 ...

作者 | 王小迪MLE 编辑 | 自动驾驶之心 原文链接： https://www.zhihu.com/question/279973696/answer/3535722816 点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近15个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 本文只做学术分享，如有侵权，联系删文 最近end2end风头正盛，BEV已成标准范式，但planning进展仍然焦灼。究其原因，interaction modelling是planning的深水区，涉及博弈和不确定性建模，监督学习仍然 不能很好得描述此类问题。这次报告以interaction的建模和求解为切口，分析了近些年常用的框架范式，比如将ego trajectory和agent trajectory的关系加入loss function或 constraint中，ego/agent trajectory从lane routing或neural network生成等。 - (We already have) Reactive: surrounding agents influenc ...

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Core Viewpoint - The article discusses a new detection framework called Butter, designed to improve target detection in autonomous driving scenarios by addressing the challenges of multi-scale semantic information modeling and enhancing detection robustness and deployment efficiency [3][11]. Group 1: Framework Innovations - Butter introduces two core innovations in the Neck layer: the Frequency Consistency Enhancement Module (FAFCE) and the Progressive Hierarchical Feature Fusion Network (PHFFNet) [3][15]. - FAFCE enhances boundary resolution by integrating high-frequency detail enhancement with low-frequency noise suppression, while PHFFNet progressively fuses semantic information to strengthen multi-scale feature representation [3][15]. Group 2: Performance Metrics - Butter outperforms existing state-of-the-art (SOTA) methods in detection accuracy with significantly lower parameter counts, achieving a mean Average Precision (mAP@50) of 94.4% on the KITTI dataset, surpassing the previous best by 1.2 percentage points while using only about one-third of the computational load [32][34]. - On the BDD100K and Cityscapes datasets, Butter achieved mAP@50 scores of 53.7% and 53.2%, respectively, demonstrating superior performance compared to other lightweight models, particularly with a 1.6 percentage point improvement on Cityscapes [32][34]. Group 3: Structural Challenges - Existing Neck structures often face issues such as frequency aliasing and rigid fusion processes, which compromise feature expression and detection accuracy, particularly for small targets in complex environments [9][10]. - Butter's design addresses these structural bottlenecks by decoupling frequency modeling and multi-scale fusion, achieving a balance between accuracy and efficiency [11][12]. Group 4: Methodology Overview - The Butter framework begins with a 640×640 monocular image, extracting initial features through a lightweight Backbone module, followed by refinement through various lightweight blocks before entering the Neck module [16][17]. - The model employs a four-output head in the Head layer to generate final detection results, including class labels, confidence scores, and bounding boxes [16][17]. Group 5: Feature Fusion Techniques - FAFCE enhances feature fusion accuracy and robustness by employing high-frequency amplification and low-frequency damping mechanisms, which improve the consistency and precision of multi-scale feature integration [20][27]. - PHFFNet implements a hierarchical fusion strategy that alleviates semantic discrepancies between non-adjacent layers, significantly enhancing detection accuracy and alignment in scenarios requiring precise boundary detection [29][30].

WeRide plans to ramp up spending, even as it continues to post losses, as the Chinese autonomous driving company bets that its ambitious international expansion plans will eventually see it reach profitability https://t.co/OzOplQ7iFe ...

Financial Data and Key Metrics Changes - Total revenue for Q2 2025 reached RMB 127.2 million, a 60.8% increase year over year, driven by strong growth in both product and service segments [20] - Product revenue surged by 309.6% year over year to RMB 59.8 million, with robotaxi revenue hitting a record high of RMB 45.9 million, up 836.7% year over year, contributing 36.1% to total revenue [21] - Group level gross profit increased by 40.6% to RMB 35.7 million, with a gross margin of 28.1% [23] - Net loss decreased by 1.7% to RMB 406.4 million, while on a non-IFRS basis, net loss increased to RMB 306.6 million due to ongoing R&D investments [26] Business Line Data and Key Metrics Changes - Robotaxi revenue accounted for 36% of total revenue, indicating a strong momentum that is expected to continue [36] - Service revenue grew by 4.3% year over year to RMB 67.4 million, supported by intelligent data services and L4 operational support [22] Market Data and Key Metrics Changes - The company operates the largest public commercial robotaxi fleet outside the US and China, with significant expansion in the Middle East, particularly in Abu Dhabi and Saudi Arabia [11][12] - The robotaxi fleet in Dubai has tripled in size, covering approximately 50% of core areas, with plans to further scale the fleet [12] Company Strategy and Development Direction - The company aims to transform future mobility through safe and accessible driverless solutions, with a focus on expanding global robotaxi operations [10] - A multi-product strategy is employed, leveraging a universal platform that supports various urban mobility applications, enhancing data collection and operational flexibility [30][32] - The Middle East is identified as a strategic priority for growth, with plans to expand robotaxi services to 15 additional cities [13] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the company's ability to convert global potential into long-term value, citing successful partnerships and regulatory momentum [26] - The company is optimistic about the future, with plans to scale operations and enhance user experience through advanced technology [49] Other Important Information - The company has received autonomous driving permits in six countries, demonstrating its technology's compliance with high global safety standards [22][19] - The newly launched HPC 3.0 computing platform is expected to cut costs by 50% and enhance the performance of autonomous vehicles [17][48] Q&A Session Summary Question: Could you elaborate on the company's multi-product strategy and the relationship between Robotaxi and other products? - The company emphasizes a multi-product strategy that allows for data sharing across different autonomous vehicle types, enhancing system improvement and market adaptability [30][32] Question: Is the revenue contribution from the Robotaxi business expected to sustain in the coming quarters? - Management believes the strong momentum in Robotaxi revenue will continue, driven by fleet expansion and operational scaling in key markets [36][37] Question: When will the HPC 3.0 platform be deployed in the next generation robotaxi? - The HPC 3.0 platform is already in use, with the Robotaxi GXR being the first mass-produced L4 autonomous vehicle utilizing this technology [45][48] Question: How many permits are currently in the pipeline and what are the expansion targets for Robotaxi? - The company has over 1,300 autonomous vehicles globally, with plans to add hundreds more by the end of the year, focusing on markets with strong unit economics [54][56] Question: What challenges does the company face in deploying robotaxi services in new markets? - Challenges include adapting technology to local conditions and navigating regulatory frameworks, but the company has built a strong foundation for successful deployment [90][92] Question: How does the company view the competitive landscape of robotaxi operations? - The company is confident in its competitive edge due to its extensive operational experience, safety track record, and advanced technology [75][78] Question: How does the company leverage new AI models for future technology trends? - The company is integrating advanced AI models into its autonomous driving systems, enhancing training and operational capabilities [81][84]

Core Insights - WeRide Inc. reported a significant growth in total revenue, which increased by 60.8% year-over-year to RMB127.2 million (US$17.8 million) for the second quarter of 2025, compared to RMB79.1 million in the same period of 2024 [7] - The company achieved its highest-ever quarterly robotaxi revenue, which surged by 836.7% year-over-year to RMB45.9 million (US$6.4 million), accounting for 36.1% of total revenue in 2Q2025 [3][11] - WeRide launched its High-Performance Computing (HPC) 3.0 platform, powered by NVIDIA DRIVE AGX Thor chips, which reduces autonomous driving suite costs by 50% and is fully automotive-grade [3][6] Financial Performance - Total revenue for 2Q2025 was RMB127.2 million (US$17.8 million), a 60.8% increase from RMB79.1 million in 2Q2024 [7] - Gross profit rose by 40.6% year-over-year to RMB35.7 million (US$5.0 million), with a gross margin of 28.1% [3][9] - The net loss for the quarter was RMB406.4 million (US$56.7 million), slightly improved from a net loss of RMB413.6 million in the same period of 2024 [12][16] Business Developments - WeRide expanded its robotaxi operations, launching Saudi Arabia's first-ever robotaxi pilot in Riyadh and becoming the only technology company with autonomous driving permits in six countries [3][5] - The WeRide-Uber robotaxi fleet in Abu Dhabi has tripled in size since its launch in December 2024, now covering about 50% of the city's core area [3][4] - The company showcased its new generation robotaxi model, CER, during the World Artificial Intelligence Conference in Shanghai, featuring advanced safety systems and a spacious interior [3][5] Strategic Partnerships - WeRide partnered with Chery Group to develop the new generation robotaxi model, CER, which is built on WeRide's universal autonomous driving platform [3][5] - The company aims to grow its robotaxi fleet in Abu Dhabi to hundreds of vehicles, with plans to extend services to additional areas later this year [5][6] - WeRide's collaboration with Uber continues to expand, with plans for commercial robotaxi services in Saudi Arabia by the end of 2025 [5][6]

Core Viewpoint - The article discusses advancements in trajectory prediction models, specifically focusing on three papers: "Query-Centric Trajectory Prediction," "SmartRefine," and "DONUT," highlighting their methodologies and improvements over previous models [1]. Summary of Related Papers Query-Centric Trajectory Prediction - Introduces a query-centric scene encoding paradigm that allows the model to learn representations independent of global spatiotemporal coordinates, enabling reuse of past computations without re-normalization [2][3]. - Proposes a two-stage trajectory decoding paradigm, where an anchor-free query generates trajectory proposals, which are then refined using a refiner based on anchor points [2][3]. SmartRefine - Enhances the refinement process in trajectory prediction by introducing adaptive anchor selection and context range acquisition, allowing for more efficient computation by segmenting future trajectory points [28][30]. - Implements anchor-centric context encoding, transforming surrounding context features into the corresponding anchor point's coordinate system to capture more relevant scene information [34]. - Adopts a recurrent and multi-iteration refinement approach, where each trajectory is divided into segments, and each segment undergoes refinement iteratively, improving overall prediction quality [35][37]. DONUT - Builds upon the QCNet architecture, introducing a proposer and refiner module along with an overprediction mechanism to enhance trajectory prediction accuracy [40][41]. - The model segments trajectories into sub-trajectories, predicting future segments based on previous predictions and adjusting reference points for refinement [41][46]. - Achieves state-of-the-art performance in single-agent trajectory prediction on the Argoverse v2 dataset, demonstrating significant improvements over previous models [48].

Core Insights - Aurora Innovation, Inc. (AUR) is expected to report its second-quarter 2025 results on July 30, with an anticipated loss of 12 cents per share, consistent with the previous year's results [1][9] Group 1: Financial Performance and Expectations - The company's earnings missed the Zacks Consensus Estimate by 10% in the previous quarter [1] - For the upcoming quarter, the Zacks Consensus Estimate indicates a loss of 12 cents per share, which is flat compared to the year-ago actual [1][9] - Aurora anticipates average quarterly cash use between $175 million and $185 million for the remainder of 2025, reflecting an increase in capital expenditures [2] Group 2: Strategic Focus and Operational Plans - Following a significant commercial launch milestone, Aurora is shifting its strategic focus to expand driverless operations, aiming to demonstrate the reliability and cost-effectiveness of its Aurora Driver technology [3] - The company plans to provide updates on new pilot programs, fleet expansion, or partnerships that validate its roadmap during the second quarter [4] - The quarter is seen as pivotal for Aurora to determine if it remains on track to achieve its scaling and profitability ambitions [4] Group 3: Market Position and Earnings Prediction - The current model does not predict an earnings beat for AUR, with an Earnings ESP of 0.00% and a Zacks Rank of 4 (Sell) [5]

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]