Core Viewpoint - The integration of artificial intelligence (AI) with transportation is transforming production and lifestyle, enhancing precision, reliability, and efficiency in traffic management and infrastructure development [1] Group 1: Implementation and Goals - The Ministry of Transport and six other departments issued implementation opinions on "AI + Transportation," outlining 16 specific tasks across four areas, aiming for a fully advanced intelligent transportation network by 2030 with self-controlled core technologies [1][2] - The goal includes achieving a 20% increase in traffic efficiency and a 30% improvement in emergency response efficiency through intelligent monitoring and management systems [3] Group 2: Technological Development - The focus is on application technology breakthroughs, innovation in intelligent products, and the construction of a comprehensive transportation model, which will support the intelligent transformation of the industry [2] - The establishment of a transportation big model, which includes high-quality datasets and algorithm libraries, is crucial for promoting technological sharing and collaborative innovation [2] Group 3: Application Scenarios - The implementation opinions cover seven key areas for intelligent applications, including combined auxiliary driving, smart railways, and intelligent shipping, providing ample testing grounds for new technologies and products [4] - In the water transport sector, 52 automated terminals have been established, and intelligent systems are being applied in over ten domestic and international ports [4] Group 4: Infrastructure Support - The integration of AI in transportation relies on robust infrastructure, with specific deployments in computing power, data, and network capabilities to ensure effective support for intelligent systems [7] - The construction of a comprehensive transportation big data center is underway to enhance data sharing and the creation of high-quality datasets, which are essential for AI model training and application [7]

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].