The rapid evolution of connected and cooperative automated mobility (CCAM) is transforming urban transportation. Advanced autonomous vehicle technologies offer significant opportunities to improve traffic safety, efficiency, and sustainability. However, modern urban mobility faces complex challenges, such as minimizing collision risks and enhancing the resilience of dense traffic networks.

This Research Topic aims to explore innovative solutions in autonomous vehicle technologies for active traffic management systems. The scope includes, but is not limited to:

o Deep Reinforcement Learning-based variable speed limit strategies

o Real-time traffic flow control and incident management

o Vehicle-to-everything (V2X) communication for cooperative manoeuvres

o Integration of autonomous and connected vehicles in smart transportation systems

o Impact analysis on road safety, congestion reduction, and network resilience

o Sustainable mobility solutions enabled by intelligent vehicle systems

This Research Topic welcomes manuscripts which look to:

o Investigate advanced, AI-driven approaches for autonomous traffic management.

o Assess how integration of CCAM and DRL technology can mitigate collision risks and build robust transportation infrastructures.

o Collect case studies demonstrating the application and effectiveness of cutting-edge autonomous vehicle technologies.

o Align technological advancements with sustainable development goals in urban transportation.

This collection supports the journal’s focus on innovative approaches in transportation, mobility, and smart city systems. By highlighting both technical advances and interdisciplinary perspectives, this Research Topic will serve as a comprehensive resource for researchers, practitioners, and policymakers shaping the future of autonomous mobility.

Are you interested in contributing a manuscript to this Research Topic?

Autonomous Vehicles: Revolutionizing Traffic Safety with Intelligent Active Traffic Management