Group 1 - The development of data space in the automotive industry presents significant opportunities, focusing on distributed and point-to-point data circulation rather than traditional centralized platforms [1] - Data space features three key characteristics: trustworthiness, controllability, and measurability, effectively supporting the "data not leaving the domain, usable but invisible" circulation model [1] - Applications of data space in the automotive sector include carbon footprint management, battery passports, intelligent driving insurance, and residual value assessment, showcasing its potential [1] Group 2 - Data has become a core element driving the intelligent transformation of the automotive industry, but issues such as data silos, compliance risks, and unclear responsibilities remain prominent [2] - Automotive companies should take primary responsibility for data governance by clarifying data ownership, establishing regulations, and enhancing data security design [2] - The Ministry of Industry and Information Technology is constructing a data security governance system for the automotive sector, implementing mechanisms for regulatory oversight [2] Group 3 - AI in the automotive field poses significantly higher safety risks compared to consumer electronics, with three main applications: driving automation, intelligent cockpit, and smart manufacturing [3] - The evolution of technology is characterized by end-to-end technology, multi-modal large models, and integrated vehicle-cloud iterations, but it also introduces inherent risks such as algorithm opacity and robustness issues [3] - The government is accelerating the establishment of policies for AI technology parameters, safety guidelines, and standard systems to balance innovation and safety in the automotive industry [3]