借助智能建造装备，交通工程建设作业更精准、质量更可靠；搭载智能交通系统，事故发生减少、交通 更顺畅；依托智能设计算法，线路更合理、出行更绿色……"人工智能+交通运输"改变着生产生活方 式。 人工智能与交通运输深度融合，关键在于丰富的应用场景。意见系统部署了组合辅助驾驶、智能铁路、 智慧航运等7个重点领域的智能化应用场景，加速创新场景赋能。"这些场景几乎覆盖了交通运输的所有 细分领域，将为新技术、新产品提供更加丰富的试验场和应用空间。"王云鹏说。 先看水运。自动化码头建成52座、自主研发的全自动化集装箱码头智能操作系统已在国内外10多个码头 应用、航道电子图成功应用于长江干线和支线航道……港口、航道、船舶智能化转型提速，有力释放水 运效能。 我国内河航运面临航道等级整体偏低、航运市场"小、散、杂"等问题。"进一步释放运输效能，需对航 道进行升级改造，推动航道、港口、助导航设施等航运系统要素有机衔接，这就需要加大人工智能的应 用。"中国工程院院士、武汉理工大学教授严新平建议，我国正在推进平陆运河、三峡水运新通道等重 大水运工程建设，应以此为抓手，加快推动航运系统从单点突破向"船—货—港、人—机—环"系统变 革。 ...

Core Viewpoint - The article discusses the implementation opinions on "Artificial Intelligence + Transportation" issued by the Ministry of Transport and six other departments, aiming to accelerate the innovative application of AI in the transportation sector by 2027 and 2030 [1][2]. Group 1: Key Deployments - Accelerate the research and development of intelligent driving systems and remote driving cockpits, supporting innovation resource aggregation in key regions [3][8]. - Encourage the development of new equipment such as drones and all-terrain vehicles [4]. - Promote the opening of urban scenarios and road networks to scale up the application of new delivery devices and intelligent delivery services [5][9]. - Build high-quality datasets, algorithm libraries, and toolchains for "Artificial Intelligence + Transportation" to support the construction of an intelligent comprehensive transportation network [5][10]. - Expedite the formulation and revision of standards and norms in key areas like intelligent driving and smart shipping [6][10]. - Encourage enterprises to lead the development of standards around new products, technologies, and business models [6]. Group 2: Accelerating Innovation - The transportation sector is identified as a key area for AI application due to its diverse scenarios and rich data. The opinions outline systematic deployments to enhance key technology supply, accelerate innovation scene empowerment, and optimize the industrial development ecosystem [7]. - Specific tasks include the development of intelligent driving systems, smart rail equipment, and intelligent shipping devices, as well as the establishment of a comprehensive transportation model [8]. - The article emphasizes the importance of creating replicable and scalable application cases in various fields such as assisted driving, smart railways, and intelligent logistics [8]. Group 3: Optimizing the Innovation Environment - The opinions propose optimizing computing power supply, accelerating the construction of high-quality datasets, and promoting ubiquitous network infrastructure [10][11]. - It highlights the need for a robust data resource system and a reliable traffic data transmission network to support AI applications [10]. - The establishment of a transportation big model innovation and industry alliance is encouraged to integrate leading AI companies, industry enterprises, and academic institutions [11].