Intelligent Vehicle Development and Application

Dear Colleagues,

The rapid advancement in intelligent vehicle technologies, driven by breakthroughs in artificial intelligence, sensing, connectivity, and automation, is fundamentally reshaping the future of transportation and mobility. From advanced driver assistance systems (ADAS) to highly automated and autonomous vehicles, intelligent vehicles are evolving from isolated intelligent agents into deeply interconnected, data-driven cyber–physical systems. This evolution places unprecedented demands on perception, decision-making, control, and system integration, while also raising new challenges in safety, reliability, scalability, and real-world deployment.

Artificial intelligence—spanning machine learning, deep learning, reinforcement learning, and large-scale foundation models—has become a cornerstone of intelligent vehicle development and application. AI enables vehicles to perceive complex traffic environments; understand multimodal information; make optimal decisions under uncertainty; and cooperate with infrastructure, other vehicles, and cloud/edge platforms. At the same time, practical deployment requires addressing challenges such as long-tail scenarios, data sparsity, real-time constraints, energy efficiency, explainability, and compliance with safety and regulatory standards.

This Special Issue on “Intelligent Vehicle Development and Application” aims to provide a comprehensive platform for presenting state-of-the-art research, system architectures, algorithms, and real-world applications related to intelligent vehicles. We seek contributions that advance both fundamental theories and practical implementations, bridging the gap between laboratory research and large-scale deployment. Particular emphasis is placed on intelligent vehicle technologies that operate robustly in complex, dynamic, and uncertain environments, and that support sustainable, safe, and efficient transportation systems.

Topics of interest include, but are not limited to, the following:

• Intelligent perception and sensor fusion for autonomous and assisted driving (camera, LiDAR, radar, V2X, and multimodal fusion);
• AI-driven decision-making, behavior planning, and trajectory optimization for intelligent vehicles;
• Learning-based vehicle control, motion planning, and human–machine shared control;
• Intelligent vehicle development for autonomous driving, ADAS, and intelligent chassis systems;
• V2X-enabled intelligent vehicles and cooperative driving applications;
• Intelligent vehicle applications in smart transportation systems and smart cities;
• Edge–cloud collaborative intelligence for connected and autonomous vehicles;
• Data-driven modeling, simulation, and digital twins for intelligent vehicle development;
• Safety, reliability, explainability, and verification of AI algorithms in intelligent vehicles;
• Energy-efficient and low-latency computing for intelligent vehicle platforms;
• Intelligent vehicle applications in logistics, public transportation, and special vehicles;
• Human-centered intelligent vehicle design, driver monitoring, and human–vehicle interaction;
• Security, privacy, and resilience of intelligent vehicle systems;
• Large-scale deployment, testing, and evaluation of intelligent vehicle technologies in real-world scenarios.

By bringing together researchers, engineers, and practitioners from academia and industry, this SIssueseeks to foster interdisciplinary collaboration and accelerate the development and application of intelligent vehicle technologies. We believe that the contributions gathered under this theme will not only advance the scientific foundations of intelligent vehicles but also promote their safe, reliable, and sustainable integration into future mobility systems.

Dr. Zhenyu Zhang
Dr. Yong Wang
Dr. Bing Lu
Guest Editors

Manuscript Submission Information

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• intelligent vehicles
• autonomous driving
• artificial intelligence
• machine learning
• deep learning
• reinforcement learning
• ADAS