The automotive industry is a pillar of the national economy. Its intelligent and connected transformation represents a crucial direction for the industry's development, converging core technologies from multiple fields including automotive engineering, electronics, communications, and artificial intelligence. Intelligent vehicles on the road, on one hand, share vehicle control with the driver, and on the other hand, interact with and influence the traffic flow, further intensifying the coupling between humans, vehicles, and other traffic participants. Focusing on this international frontier, and with the goal of establishing a China-standard intelligent and connected cloud control system and the Smart City-Smart Traffic-Smart Vehicle (SCSTSV) systems engineering framework, our team has conducted research on key technologies such as multi-source sensor fusion, high-definition mapping and localization, driving safety risk assessment, collective learning and intelligent decision-making, longitudinal and lateral motion control, and evaluation of autonomous vehicles. This has formed a distinctive technological profile characterized by the "Human-Vehicle-Road" integration and intelligent vehicle technology integrating "Fused Perception, Human-like Decision-making, and Coordinated Control." In recent years, the following innovative achievements have been made:

（1）In Fused Perception: Breakthroughs were achieved in holographic perception enhancement technology through deep fusion of multi-source heterogeneous sensors, achieving internationally leading performance on open-source benchmarks. A trustworthy and continuously evolving perception system based on unified perception representation was invented, ranking first in international autonomous driving challenges. Results have been published in top-tier journals such as Nature Machine Intelligence. These outcomes supported the all-weather, multi-vehicle-type, vehicle-road cooperative high-level autonomous driving demonstrations for the Beijing 2022 Science & Technology Winter Olympics. They have also been applied on a large scale by several mainstream automakers including GAC, BYD, Dongfeng, and Li Auto, supporting the front-fitment of 1.8 million intelligent driving systems.

（2）In Brain-like Decision-making: A series of common challenges in end-to-end autonomous driving systems were overcome, achieving full-chain integration from "data generation - model training - optimization deployment," filling a domestic technological gap. The research work has made prominent achievements in reinforcement learning algorithms, neural network design, and the localization of industrial software, providing solid support for the development of high-level autonomous driving systems in China. Parts of this research have been published in top-tier journals in the field such as IEEE TPAMI, IEEE TNNLS, and IEEE TITS, with cover paper selections twice, receiving one IET Journal Best Paper Award, and seven outstanding paper awards at domestic and international academic conferences. The outcomes were among the first to be successfully deployed on real-vehicle platforms of enterprises like GAC and Horizon Robotics, enabling open-road testing.

（3）In Coordinated Control: A three-level safety control framework encompassing multi-vehicle multi-domain coordination, multi-controller coordination, and multi-objective coordination was realized. The generation and evolution mechanisms of driving risks were elucidated, methods for risk quantitative assessment and safety control were invented, a multi-vehicle multi-domain control model and coordination architecture were established, and a coordinated safety arbitration method combining deliberative optimization and reactive safety controllers was proposed. Breakthroughs were made in multi-objective coordinated optimal control technology based on the principle of least action. The similarity between control outcomes and those of expert drivers reached 84%, user adoption rates increased by 25.8% compared to traditional safety systems, and the average accident rate per 100 million vehicle-kilometers was reduced by 32.9%.

（4）Vehicle-Road-Cloud Integrated Complex System and Core Applications: Methodologies for complex systems engineering were advanced, and a logically coordinated, architecturally unified cloud control platform framework was created. Technologies for continuous target tracking via road-cloud coordination and for hierarchical decision-making and control via vehicle-cloud coordination were conquered, enabling interaction and coordination between vehicles and traffic signals. Applications developed for vehicle models from Shaanxi Auto and Dongfeng achieved, while ensuring safety, a 6% improvement in intersection traffic efficiency and an 8% reduction in single-vehicle energy consumption. These achievements received the First Prize for Science and Technology Progress from the China Intelligent Transportation Systems Association. The developed cloud control platform software has been applied in the Beijing Connected and Cloud-controlled High-Level Autonomous Driving Demonstration Zone, the Chongqing Intelligent and Connected Vehicle Demonstration Zone, among others.