新浪科技讯 1月9日上午消息，地平线与欧摩威集团合资成立的智驾大陆（neueHCT）近日完成近2亿美 元融资。本次融资由云锋基金、达晨（达晨财智、国晨创投）、混沌投资、碧鸿投资、宁波通商基金 （甬宁高芯基金）等领先产业资本及市场化基金联合投资，战略股东欧摩威集团（前大陆集团汽车子集 团）与地平线继续追加投资。 云锋基金执行董事董怀谨Andrea表示："neueHCT兼具地平线领先的端到端智能辅助驾驶技术能力，以 及欧摩威集团深厚的全球产业资源优势，在国内市场已快速获得多款车型定点，海外业务亦实现量产突 破，具备成长为全球智能驾驶领域领军企业的坚实基础，期待与neueHCT携手合作，推动具备全球竞争 力的智能辅助驾驶解决方案加速走向国际市场"。 责任编辑：宋雅芳 新浪科技讯 1月9日上午消息，地平线与欧摩威集团合资成立的智驾大陆（neueHCT）近日完成近2亿美 元融资。本次融资由云锋基金、达晨（达晨财智、国晨创投）、混沌投资、碧鸿投资、宁波通商基金 （甬宁高芯基金）等领先产业资本及市场化基金联合投资，战略股东欧摩威集团（前大陆集团汽车子集 团）与地平线继续追加投资。 云锋基金执行董事董怀谨Andrea表示：" ...

北京时间1月6日，在2026年美国消费电子展（CES 2026）上，千里智驾与吉利共同宣布，面向全球市 场发布全新辅助驾驶品牌——G-ASD（Geely Afari Smart Driving，千里浩瀚）。其中，"G"代表吉 利，"ASD"代表千里智驾。G-ASD是双方联合研发的高含模量智能辅助驾驶解决方案，将全面覆盖从 L2到L4级别的智能驾驶能力。目前，该系统首版本已搭载于极氪、领克两大品牌旗下共16款车型，覆 盖车辆超30万辆，并计划在未来逐步在吉利汽车旗下更多车型上搭载。 ...

关注 「电动车公社」 《今日新闻》将会每天给大家带来几条当日重磅新闻，并附上社长的简单评论。关注「电动车公社」，新能源圈大事小事 ，看我们就够啦～ 近日，吉利旗下 千里智驾核心业务与技术体系整合完成， 将聚焦前沿智驾技术和产品，加速推动创新和交付效率，致力于打造更强的智能辅助驾驶解决方案。 这次"国家队"车企和新势力品牌的合作前所未有，一汽拥有先进的供应链能力、内燃机研发能力和庞大的渠道网络，零跑拥有全域自研的智能化和电气化技 术，双方合作取长补短，对双方来说无疑是一场双赢。 2、 吉利千里智驾完成整合 和我们一起重新思考汽车 一汽正式入股零跑； 吉利千里智驾完成整合； 今日新闻要点： 1、 一汽正式入股零跑 12月29日 ， 零跑汽车在港交所发布公告， 一汽集团以37.44亿元入股零跑汽车5%股权。 吉利在辅助驾驶领域投入了巨大的研发成本，但从实际的用户反馈来看，体验似乎并没有对友商实现碾压级的领先 。 这次吉利将 分散的团队整合在一起， 形成合力，集中力量办大事，未来有可能在激烈的竞争中进一步取得优势。 ...

2025年11月4日，在上海证券交易所的指导下，华福证券国家级投资者教育基地联合重庆邹容路证券营业部，共同组织投资者走进上市公司 千里科技 ，开展 了一场聚焦企业发展动态与上市公司运营的深度交流活动。此次活动为投资者搭建了近距离考察平台，助力其全面了解该公司从传统制造向智能化转型的最 新进展。 上市公司简介： 重庆千里科技股份有限公司聚焦"AI+车"核心战略，创 新产业 发展模式，致力于打造面向全球市场的智能出行科技品牌。公司核心业务包括终端业务与科 技业务两大板块，其中终端业务覆盖摩托车、汽车领域，旗下摩托车品牌"力帆、派方"和 新能源 汽车品牌"睿蓝"形成多元化产品矩阵；科技业务聚焦研发 行业领先的智能驾驶和智能座舱解决方案，打造AI智行开放平台。 在参观环节中，千里科技高管向投资者展示了最新的技术成果，投资者现场见证了公司智能辅助驾驶解决方案及联合生态伙伴推出的 智能座舱 Agent OS 系 统。这套系统基于AI Agent原生打造，能够为AI Agent的运行、调用与协作提供底层基础设施与服务。体验后，有投资者表示："从传统的 汽车制造 到如今 的AI驱动，AI故事不仅写在资料里，更烙在生产线上的每 ...

Core Insights - The rise of AI is leading to a new era characterized by embodied intelligence and intelligent assisted driving, marking the beginning of a competitive landscape in the Agentic AI domain [1] - Companies are increasingly focusing on the transition from modular to end-to-end architectures in intelligent driving technology, which is seen as a paradigm shift [2][3] - The demand for data and computational power is growing exponentially, posing significant challenges for the industry [3][4] Group 1: Industry Trends - The shift to "end-to-end" architecture in autonomous driving has been a game changer, allowing for rapid iteration and adaptation to complex scenarios [2] - The traditional modular approach has been limited by its reliance on manually defined rules and case-by-case adjustments, while the new VLA architecture integrates visual, linguistic, and action capabilities [2] - Companies are investing heavily in cloud infrastructure to support the increasing demands of data processing and model training in both intelligent driving and embodied intelligence sectors [4][5] Group 2: Technological Developments - The integration of large-scale data management and advanced AI infrastructure is crucial for the success of intelligent driving and embodied intelligence applications [4][5] - Alibaba Cloud has upgraded its intelligent assisted driving solutions, achieving significant improvements in data management and model training efficiency [5] - The collaboration between Alibaba Cloud and NVIDIA aims to enhance the capabilities of Physical AI, providing a comprehensive platform for data processing, simulation, and model training [6][8] Group 3: Future Outlook - The future of AI is expected to involve widespread deployment of agents and robots in various sectors, necessitating substantial computational resources [8] - The competition for building "super AI clouds" is intensifying, with a focus on creating robust infrastructure to support advanced AI applications [8] - The industry is moving towards a model where only a few major cloud platforms will dominate, driven by the need for extensive resources and capabilities [8]

Core Viewpoint - The rise of AI is leading to a new era characterized by embodied intelligence and intelligent assisted driving, marking the beginning of a competitive landscape in the Agentic AI era [1] Group 1: End-to-End Transformation - The transition from modular to end-to-end architectures in intelligent assisted driving is a significant shift, allowing for rapid iteration and adaptation to complex scenarios [3][4] - The end-to-end architecture requires exponential growth in data volume and computational power, with current mainstream intelligent driving companies needing 10P-30P of data for single model training [4] - The embodied intelligence sector faces even greater complexity, requiring machines to understand physical laws and execute intricate actions, which presents unique challenges in data handling and computational needs [4] Group 2: Cloud Infrastructure and AI Integration - Companies are increasingly seeking a unified cloud AI infrastructure that integrates computational power, big data, and AI platforms to support embodied intelligence [5] - Alibaba Cloud has upgraded its intelligent assisted driving solutions, achieving significant efficiency improvements, such as a 2-3 times increase in task management and scheduling capabilities [7] - The integration of NVIDIA's tools into Alibaba Cloud's AI platform aims to enhance the development of embodied intelligence applications, showcasing a comprehensive support system for data processing and model training [9] Group 3: High Demand and Future Outlook - The demand for high availability and extreme communication capabilities in embodied intelligence is pushing cloud providers to innovate beyond traditional models, leading to a new "network-storage-computation integration" requirement [10] - Alibaba Cloud is positioning itself as a leader in the AI infrastructure space, with ambitions to build a super AI cloud that can support the future needs of various industries [11][12] - The future may see a limited number of super cloud computing platforms, with Alibaba Cloud aiming to be a key player through substantial investments in AI infrastructure [11]

Group 1 - BMW Group announced a partnership with Momenta to develop next-generation intelligent driving assistance solutions specifically for the Chinese market, focusing on AI-based software for driving assistance functions [2] - The new driving assistance system will support full-scenario, point-to-point navigation assistance, enabling cross-city travel from parking to destination [2] - Currently, BMW's existing models only support basic driving assistance features, with advanced functionalities like highway ramp access and traffic light navigation not yet available [2] Group 2 - At the Shanghai International Auto Show, several multinational car manufacturers, including Buick and Toyota, announced strategic partnerships with Momenta, highlighting the company's prominence in intelligent driving solutions [3] - Buick's L2 urban driving assistance technology will utilize a data-driven approach without relying on high-precision maps, showcasing the integration of advanced AI models [3] - The collaboration between multinational car manufacturers and local tech firms like Huawei is indicative of a trend where foreign brands are increasingly adopting local innovations in smart driving technology [3][4] Group 3 - The penetration rate of L2-level intelligent driving assistance vehicles in China reached 55.7% in the first half of last year, reflecting a growing consumer demand for smart features in vehicles [5] - The rising consumer expectations for intelligent cabins and personalized configurations are reshaping global automotive technology innovation [5] - The validation of local suppliers' capabilities is attracting more multinational car manufacturers to collaborate with Chinese tech firms [5]

Core Viewpoint - The autonomous driving industry is experiencing strong regulatory oversight, yet companies are actively developing and promoting high-level autonomous vehicles, indicating a commitment to innovation and safety [2][3][4]. Regulatory Environment - The Ministry of Industry and Information Technology has mandated that automotive manufacturers conduct comprehensive testing for driving assistance systems, ensuring clear definitions of system capabilities and safety measures [2]. - Strong regulations are seen as beneficial for the healthy development of the autonomous driving industry, promoting market order and enhancing consumer trust in product safety [5]. Market Dynamics - The demand for high-level autonomous driving is driven by market competition, with companies needing to innovate to stay relevant [4]. - The shift from B2B to B2C markets is anticipated, with increasing acceptance of autonomous driving technology among consumers [4][6]. Technological Advancements - Significant advancements in AI, sensors, and communication technologies are providing a solid foundation for the development of high-level autonomous vehicles [5]. - Companies like Huawei are leveraging their expertise in communication and AI to enhance autonomous driving solutions [5]. Consumer Sentiment - Consumers show high interest in L4 autonomous vehicles, particularly in China and India, with younger generations expressing a strong desire for advanced driving technologies [9][10]. - Despite enthusiasm, safety concerns remain paramount, with over 70% of consumers citing safety and stability as potential barriers to adoption [10]. Commercialization Challenges - The B2B market for high-level autonomous driving is beginning to open, particularly in sectors like mining and logistics, where the technology can significantly enhance efficiency [12][13]. - Cost reduction and improved value propositions are critical for the profitability of high-level autonomous vehicles, with companies focusing on optimizing production and supply chain management [13][20]. Company Accountability - Companies like GAC have committed to taking responsibility for the safety of their L4 autonomous vehicles, which is expected to boost consumer confidence [18]. - The commitment to accountability may drive companies to enhance their technological capabilities and safety measures [19]. Future Outlook - The industry is expected to see a gradual shift towards more comprehensive regulations and insurance frameworks to support the deployment of high-level autonomous vehicles [20]. - The integration of autonomous driving technology into everyday scenarios is anticipated to evolve, with a focus on enhancing operational efficiency and user experience [17].

Core Insights - Momenta has become a prominent player in the automotive industry by securing strategic partnerships with six major car manufacturers at the Shanghai Auto Show, including General Motors Buick, FAW Toyota, Honda China, Cadillac, SAIC Audi, and Zhiji [1] - The company has demonstrated significant growth in its production model deliveries, increasing from 1 model in 2022 to 8 models in 2023, and projected to reach 26 models in 2024, with over 130 cumulative production models to date [2] - Momenta's innovative "flywheel model" is a key factor in its appeal to multiple car manufacturers, showcasing continuous technological advancements and industry-leading capabilities [3] Partnership and Market Expansion - The partnerships with global mainstream car manufacturers highlight Momenta's extensive reach and influence in the automotive sector [2] - The company has achieved rapid growth in vehicle deployment, completing its first 100,000 units in two years and the second in just six months, with expectations to reach a third 100,000 units by May [2] Technological Advancements - Momenta's upcoming R6 flywheel model, based on reinforcement learning, aims to enhance driving safety and performance beyond human capabilities [3] - The company's "no-map" technology ensures adaptability across various global markets, including Europe, Australia, New Zealand, the Middle East, and Japan, enabling a "global driving" solution [3] Strategic Focus - Momenta is focusing on the development of Robotaxi services, addressing safety standards that must be met for large-scale deployment, with plans to launch the first mass-produced Robotaxi solution by the end of 2025 [4] - The company employs a dual strategy of smart assisted driving and Robotaxi development, leveraging data-driven approaches to solve long-tail problems and accumulate extensive driving data [5]