每经AI快讯，1月5日，在英特尔旗下自动驾驶技术公司Mobileye与一家美国大型汽车制造商达成交易 后，该公司股价在盘前交易中延续涨势，最新涨幅接近7%。 ...

招股书显示，驭势科技是大中华区的技术驱动型龙头企业，专注于无人化L4级自动驾驶技术。公司目前专注于封闭场景(特别是在机场及厂区)中的商 用车，旗下解决方案为全场景通用，已应用于各种开放及封闭场景，覆盖物流、营运及机动车辆，囊括L2级至L4级自动驾驶级别。 智通财经APP获悉，1月5日，中国证监会国际合作司发布《关于驭势科技(北京)股份有限公司境外发行上市及境内未上市股份"全流通"备案通知书》。 公司拟发行不超过18,914,150股境外上市普通股并在香港联合交易所上市。公司41名股东拟将所持合计112,264,250股境内未上市股份转为境外上市股 份，并在香港联合交易所上市流通。 | 20 | 北京二期中科创星硬科技创业投资合伙企业(有限合伙) | 1,950,370 | | --- | --- | --- | | 21 | 北京中关村龙门基金投资中心(有限合伙) | 1,950,370 | | 22 | 北京智慧云城投资基金中心(有限合伙) | 1,949,910 | | 23 | CAS-Tech Fund I L.P. | 1,824,170 | | 24 | 嘉兴喜耀创业投资合伙企业(有限合伙) | ...

Group 1 - The Hong Kong stock market started 2026 with a significant rise of over 700 points on the first trading day, indicating a positive outlook for the year ahead [1] - The performance of the Hong Kong stock market in the fourth quarter of the previous year lagged behind major markets due to profit-taking at year-end, but optimism remains for 2026 [1] - Key factors expected to boost the market include important meetings in mainland China scheduled for March and April, and the uncertainty surrounding the next Federal Reserve chair, with a target for the Hang Seng Index set at 29,500 points for the year [1] Group 2 - The mainland AI industry is broad, covering multiple sectors, and is expected to see increased spending on innovation and development as it enters the "14th Five-Year Plan" [1] - The competition between China and the U.S. in AI is anticipated to drive growth in industries such as new energy and autonomous driving, with cloud computing and AI applications also benefiting [1] - There is a positive outlook for high-dividend stocks, including traditional sectors like Chinese telecommunications, banks, insurance, and energy, as well as consumer staples with yields above 3% after valuation corrections [1]

Core Viewpoint - The article discusses the advancements in autonomous driving technology, particularly focusing on the Vision-Language-Action (VLA) model, which integrates visual perception, semantic understanding, and logical decision-making to enhance the capabilities of autonomous vehicles. The introduction of the AutoDRRT 3.0 framework aims to address the challenges of real-time processing and system optimization for VLA models in automotive applications [2][3][8]. Summary by Sections VLA Model and Challenges - The VLA model is becoming the preferred solution for autonomous driving, enabling vehicles to understand and reason like humans. However, the model's parameter scale has increased to billions, leading to processing delays exceeding 100ms, necessitating optimization of hardware and software systems for real-time performance [2][5][6]. AutoDRRT 3.0 Framework - The AutoDRRT 3.0 framework, developed by Inspur Information, is an open-source solution designed to accelerate the deployment of VLA models in vehicles. It reduces the end-to-end latency of VLA models from 8000ms to 78ms, achieving a performance improvement of 102 times [3][13][23]. Innovations in Computation - AutoDRRT 3.0 introduces several computational innovations, including parallel decoding, visual pruning, and operator fusion. These techniques significantly enhance the efficiency of the VLA model's inference process, allowing for smoother and faster action outputs [9][12][13]. Communication Mechanism - The framework also features a high-performance communication mechanism that optimizes data transfer between heterogeneous computing units, reducing latency and improving the overall responsiveness of the system. This mechanism allows for zero-copy data transfer, enhancing efficiency during large data loads [16][17][23]. Scheduling Innovations - AutoDRRT 3.0 implements a unified scheduling framework for heterogeneous computing resources, ensuring efficient task management and resource allocation. This approach minimizes idle computing time and enhances the overall system stability and performance [18][21][20]. Future Prospects - The article concludes that the AutoDRRT 3.0 framework not only validates the feasibility of real-time operation of VLA models in vehicles but also lays a solid foundation for the transition of autonomous driving technology towards scalable and replicable solutions across various applications [23].

招商证券指出，小马智行-W属L4领域先行者，技术及商业化能力构筑护城河。公司依托于世界模型和 虚拟司机技术公司在L4领域深耕，更在商业化应用方面取得了实质性突破，尤其是去年第三季度在广 州实现的Robotaxi单车盈利转正，标志着其商业化迎来阶段性的里程碑式时刻。该行认为，小马智行凭 借先发优势和行业地位，在自动驾驶出行服务和自动驾驶货车领域占据重要的市场份额，随着第七代车 型的加速部署和车队规模扩大，强劲的增长势头有望延续。 智通财经获悉，小马智行-W(02026)高开逾6%，截至发稿，涨6.29%，报123.4港元，成交额76.51万港 元。 本文源自：智通财经网 消息面上，近期，小马智行联合创始人、CFO王皓俊在接受采访时透露，依托第七代Robotaxi的规模化 投放，公司已在广州实现单车盈利(UE 转正)，日均营收每车达299元，2025年底车队规模将突破1000 辆，2026年目标扩容至3000辆，远期计划2030年达成10万辆运营规模。此次在广州市场实现单车盈利的 核心支撑，来自第七代Robotaxi的技术突破与成本优化。 ...

每经AI快讯，小马智行-W(02026.HK)高开逾6%，截至发稿，涨6.29%，报123.4港元，成交额76.51万港 元。 ...

Core Viewpoint - The company, Pony.ai-W (02026), has achieved single-vehicle profitability in Guangzhou with its seventh-generation Robotaxi, marking a significant milestone in its commercialization efforts [1] Group 1: Financial Performance - The stock opened over 6% higher, currently up 6.29% at HKD 123.4, with a trading volume of HKD 765,100 [1] - Daily revenue per vehicle has reached CNY 299 [1] Group 2: Growth Projections - The company plans to expand its fleet to over 1,000 vehicles by the end of 2025 and aims for 3,000 vehicles by 2026, with a long-term goal of 100,000 vehicles by 2030 [1] Group 3: Technological Advancements - The profitability in Guangzhou is supported by technological breakthroughs and cost optimization from the seventh-generation Robotaxi [1] - The company is recognized as a pioneer in the Level 4 (L4) autonomous driving sector, leveraging world models and virtual driver technology [1] Group 4: Market Position - According to China Merchants Securities, Pony.ai-W has established a competitive moat through its technological and commercialization capabilities [1] - The company holds significant market share in both autonomous ride-hailing services and autonomous trucking, benefiting from its first-mover advantage and industry position [1]

Core Viewpoint - Pony.ai has achieved single-vehicle profitability in Guangzhou with its seventh-generation Robotaxi, marking a significant milestone in its commercialization efforts [1] Group 1: Financial Performance - The company reported an average daily revenue of 299 yuan per vehicle [1] - The stock price increased by 6.29%, reaching 123.4 HKD, with a trading volume of 765,100 HKD [1] Group 2: Growth Plans - By the end of 2025, the fleet size is expected to exceed 1,000 vehicles, with a target of expanding to 3,000 vehicles by 2026 [1] - The long-term goal is to achieve an operational scale of 100,000 vehicles by 2030 [1] Group 3: Technological Advancements - The profitability in Guangzhou is supported by technological breakthroughs and cost optimization from the seventh-generation Robotaxi [1] - The company is recognized as a pioneer in the L4 autonomous driving sector, leveraging world models and virtual driver technology [1] Group 4: Market Position - According to招商证券, Pony.ai's early mover advantage and industry position allow it to capture significant market share in autonomous ride-hailing and freight services [1] - The acceleration of the deployment of the seventh-generation vehicles is expected to sustain strong growth momentum [1]

Core Viewpoint - The Hong Kong stock market experienced a significant rise last Friday, marking a positive start to 2026, with major indices showing mixed performance but overall upward trends in technology and commodity sectors [1] Group 1: Market Performance - The Hang Seng Index opened slightly higher by 0.09%, while the Hang Seng China Enterprises Index opened down by 0.03%, and the Hang Seng Tech Index increased by 0.33% [1] - Major technology stocks saw continued gains, with Kuaishou rising approximately 6% and Alibaba increasing by 1.4% [1] Group 2: Sector Performance - Gold stocks experienced a collective rise amid geopolitical tensions, while the copper and other non-ferrous metal sectors were active [1] - Shipping stocks, robotics concept stocks, and autonomous driving concept stocks all saw increases [1] - Conversely, oil stocks, wind power stocks, and Chinese brokerage stocks mostly declined, with Goldwind Technology dropping by 4.5% and CNOOC falling by 3.65% [1]

Core Viewpoint - The article discusses the development of FastDriveVLA, a new framework for efficient visual token pruning in end-to-end autonomous driving systems, which significantly reduces computational costs and improves inference efficiency [1][8]. Group 1: Research Background and Problem - End-to-end autonomous driving shows great potential to transform future transportation systems, learning the entire driving process within a unified framework, thus reducing errors in information transfer between modules [7]. - Existing VLA models convert visual inputs into a large number of visual tokens, leading to significant computational overhead and increased inference latency, posing challenges for real-world deployment [7][8]. - Previous research aimed at reducing visual tokens has limitations in autonomous driving scenarios, as new designs often require retraining the entire model, and pruning strategies based on attention or similarity may retain irrelevant information [7][8]. Group 2: Methodology and Innovations - FastDriveVLA introduces a novel, reconstruction-based visual token pruning framework specifically tailored for end-to-end autonomous driving [8]. - The research team hypothesized that visual tokens related to foreground information are more valuable than those related to background content, leading to the creation of the nuScenes-FG dataset, which includes 241,000 images with foreground annotations [2][13]. - The lightweight, plug-and-play pruning tool, ReconPruner, is designed to effectively identify and select meaningful foreground visual tokens, utilizing a masked image modeling approach for pixel reconstruction [16][19]. Group 3: Experimental Results - FastDriveVLA achieved state-of-the-art (SOTA) performance in open-loop planning benchmarks on the nuScenes dataset, demonstrating significant efficiency improvements [2][20]. - When the number of visual tokens was reduced from 3,249 to 812, FastDriveVLA's FLOPs decreased by approximately 7.5 times, and it reduced prefill time by 3.7 times and decode time by 1.3 times, enhancing inference efficiency [26][27]. - The framework outperformed existing methods across various pruning ratios, particularly at a 50% pruning rate, where it maintained a balanced performance across all metrics [25][28]. Group 4: Efficiency Analysis - FastDriveVLA's efficiency was analyzed in terms of FLOPs and CUDA latency, showing a significant reduction in computational requirements while maintaining high performance [26][27]. - At a 25% pruning rate, FastDriveVLA demonstrated the best performance across all evaluation metrics, indicating that focusing on foreground-related visual tokens is crucial for enhancing autonomous driving performance [28].