Connected and Automated Vehicle (“CAV”) is well recognized as a crucial component of the modern intelligent transportation system. The application of CAV can relieve traffic congestion, reduce traffic emission and more importantly, enhance the overall operation efficiency of the transportation system, based on the real-time data exchange of vehicle trajectory using vehicle-to-vehicle (“V2V”) and vehicle-to-infrastructure (“V2I”) information and communication technology. Although promising great enhancements to traffic operation, CAV can induce great challenges to traffic safety. In particular, driver distraction is one of the leading contributing factors in collisions involving CAV. It is necessary to examine the role of the driver and their contribution to safety hazards. Furthermore, it is necessary to consider the effects of road environment and traffic conditions (mixed traffic composition of traditional vehicles and CAVs) on driver behavior and driving safety.

Additionally, automated driving is an emerging transport technology, which would become the ultimate form of CAV. However, achieving the ultimate goal of fully automated driving is still a long way off. “Human-Machine Cooperation Stage” refers to the transition stage between fully automated and conventional manual driving. Currently, human drivers still play a major role in traffic operation and safety. It is necessary to explore the fundamental issues of driving performance in the "Human-Machine Cooperation Stage" from the perspectives of driver’s behavior, physiological attributes, mental workload, and safety perception. Therefore, there is still a requirement for the implementation of cost-effective measures through public policy, vehicle technology, infrastructure development, legislation, and education.

This Special Issue aims to provide a platform for researchers and practitioners to exchange the idea of traffic safety in an intelligent and connected environment. Original research and review articles will be considered. All aspects of statistical analysis, machine learning, driving simulator experiments, and naturalistic driving experiments are of interest.

Potential topics include but are not limited to the following:

• Traffic Safety Analysis of Connected and Automated Vehicles and Traditional Vehicles in Mixed State
• Driving Behavior and Physiological Characteristics in an Intelligent and Connected Environment
• Research on the Impact of Drivers' Risk Perception in Intelligent and Connected Environment
• Research on Road Traffic Control in Intelligent and Connected Environment
• Travel Behavior of Other Traffic Participants (such as pedestrians, cyclists, passengers, etc.) In an Intelligent and Connected Environment
• Analysis of Factors Influencing Acceptance and Willingness toward Automated Driving
• Interaction between Autonomous Vehicles and Other Traffic Participants
• Optimization Method of Road Traffic Safety Facilities under Automated Driving Environment
• Analysis of Collision Accidents Involving Autonomous Vehicles
• Research on the Willingness and Manner of Taking Over During Human-Machine Cooperation for Automated Driving
• Take Over Training Method during Human-Machine Cooperation for Automated Driving
• Driving Simulation and Natural Driving Experiment under Human-Machine Cooperation Environment