TVT Call for Papers: Special Issue on Advanced Driving Intelligence for Autonomous Vehicles

With the advancement of automation level, autonomous vehicles require more intelligent decision-making and control strategies to operate safely and efficiently in various complex traffic scenarios, particularly in long-tail scenarios (e.g., construction zones, accident-prone areas, and pedestrian crossings) and challenging road sections (e.g., intersections, roundabouts, and merging lanes). Under these complex and uncertain conditions, it is highly valuable for autonomous vehicles to operate safely and autonomously without human intervention.  

Experienced human drivers can adeptly and effortlessly resolve such situations using their accumulated experience and commonsense knowledge. However, for autonomous vehicles, understanding the driving environment in a human-like manner and making decisions through commonsense reasoning in specific driving scenarios remains a significant challenge. Consequently, the design of autonomous driving decision-making and control strategies should enhance these capabilities to further improve the level of automation.  

Therefore, recent advances in multimodal perception, scene understanding, decision-making, robust control, artificial intelligence, foundation models such as vision-language models (VLMs) and large language models (LLMs), wireless networking, vehicle-to-everything (V2X) communications, and integrated sensing and communications (ISAC) provide new opportunities for enabling human-like driving in automotive vehicles. At the same time, issues related to security, privacy, data availability, simulation and test platforms, standardization, and industrial deployment are becoming increasingly important for the successful development and real-world adoption of intelligent driving systems. Motivated by these developments, this Special Issue aims to provide a timely forum for original and high-quality contributions on intelligent human-like driving for automotive vehicles in complex traffic scenarios, covering both fundamental methodologies and practical system-level advances. Topics of Interest include, but are not limited to: 

• Foundation models, including vision-language models (VLMs) and large language models (LLMs), for interpretable and explainable driving intelligence 
• Multimodal end-to-end autonomous driving
• Neuro-symbolic AI for explainable, safe, and trustworthy autonomous driving
• Multimodal/multi agent perception and scene understanding for autonomous vehicles 
• Human-like decision-making and planning in complex traffic scenarios 
• Context-aware localization, mapping, and state estimation 
• Datasets, benchmarks, simulation frameworks, digital twins, testbeds, and experimental platforms for autonomous driving 
• Standardization efforts, industry practices, field deployment, and real-world validation of intelligent driving systems 
• Security, privacy, trust, and resilience in intelligent and connected autonomous driving systems 
• Wireless networking and V2X communications for cooperative driving 
• Integrated sensing and communications for intelligent vehicles and transportation systems

Prospective authors should submit their manuscripts following the IEEE TVT Instructions for Authors. Submissions are accepted as Regular papers (up to 14 pages). Authors should submit the manuscripts through IEEE Transactions on Vehicular Technology Author Portal.

Important Dates  

Manuscript Submission Deadline: 1 August 2026 

First Notification: 15 November 2026 

Revised Manuscript Due: 15 January 2027 

Acceptance Notification: 15 March 2027 

Final Manuscript Due: 1 April 2027 

Guest Editors 

• Xin Xia, University of Michigan-Dearborn, USA 

• Yi Lu Murphey, University of Michigan-Dearborn, USA 

• Mohammad H. (Vahid) Mamduhi, University of Birmingham, UK 

• Neel P. Bhatt, University of Texas at Dallas, USA 

• Xiao Zhang, University of Michigan-Dearborn, USA 

• Evangelos Mintsis, Hellenic Institute of Transport, Greece 

• Ehsan Hashemi, University of Alberta, Canada 

For questions related to this Special Issue, please contact the Guest Editors at (Click to show email).