Topic: Integrated Intelligent Transportation: Advances in Collaborative Perception, Autonomous Decision-Making and Intelligent Control

With the rapid development of Intelligent Transportation Systems (ITS) and the deep integration of artificial intelligence and big data technologies, integrated transportation has entered a new era of intelligence and digitization. Nevertheless, integrated transportation features the coexistence of multiple traffic participants, dynamic navigation conditions and heterogeneous data sources, imposing severe challenges to the intelligent and autonomous operation of intelligent agents.

The booming development of artificial intelligence and large models has significantly boosted the iterative upgrading of integrated intelligent transportation systems, delivering robust theoretical foundations and core technical underpinnings for key research directions including multi-source collaborative perception, high-safety collision avoidance decision-making, and high-precision intelligent motion control. Through deep feature learning and representation mining from massive traffic data, large models empower integrated transportation agents to achieve efficient multi-source information fusion, optimized path planning, trustworthy autonomous decision-making for collision avoidance, and refined intelligent control, addressing the practical bottlenecks of current intelligent transportation applications.

This Special Issue aims to showcase state-of-the-art advances, theoretical innovations and practical applications in collaborative perception, autonomous decision-making, path planning and motion control within integrated intelligent transportation systems. It serves as a dedicated platform for researchers, engineers and industry practitioners to exchange academic viewpoints, share frontier research findings, and further advance the technological development and industrial implementation of integrated intelligent transportation systems.

Topics of interest include, but are not limited to:

● Systems and frameworks of integrated transportation;

● Applications of large models in the transportation domain;

● Collaborative perception strategies of traffic intelligent agents in integrated transportation scenarios;

● Multi-source information fusion methods for improving perceptual accuracy of traffic intelligent agents;

● Autonomous navigation of intelligent agents based on multi-source data;

● Collision avoidance decision-making and motion control algorithms for traffic intelligent agents;

● Practical deployment and validation of intelligent transportation systems, et al.

Integrated transportation, traffic intelligent agent, transportation large model, collaborative perception of traffic intelligent agents, decision-making and motion control, autonomous driving or autonomous navigation, multi-source information fusion, motion planning and control