Project description

As autonomous vehicles (AVs), including the rapidly evolving segment of electric vehicles, become more prevalent on our roads, the blend of AVs with traditional human-driven vehicles introduces distinct challenges and opportunities in traffic management and road safety. The proposed research is designed to tackle these challenges by crafting and validating state-of-the-art autonomous vehicle control and traffic management models and algorithms specifically for environments where autonomous and conventional vehicles share the roadway. This research harnesses machine learning, data analytics, and vehicle-to everything (V2X) communication technologies to forge a unified system aimed at enhancing traffic efficiency, alleviating congestion, and reducing the incidence of accidents in varied urban and suburban settings.

Employing a holistic research methodology that integrates machine learning, control theory, optimization techniques, and multi-agent systems, this project aims to comprehensively understand and accurately forecast the interactions between autonomous and human-driven vehicles. Through the simulation of diverse traffic conditions and the analysis of real traffic network data, the effectiveness of the developed models will be rigorously evaluated. The focus will be on the implementation of innovative autonomous vehicle control and traffic management strategies, such as adaptive signal control, dynamic lane assignment, and strategic speed adjustments, all tailored to accommodate mixed vehicle flows seamlessly.

Additionally, this project will delve into the realm of cooperative driving strategies enhanced by V2X communication capabilities. Such strategies empower AVs to proactively navigate the unpredictable nature of human driving behaviors, thereby smoothing overall traffic flow and mitigating the risk of congestion and collisions. An essential goal of this research is to identify the critical thresholds within which AVs exert a substantial influence on traffic dynamics. This insight is aimed at informing future policy directives related to traffic network design and advocating for solutions that uphold the principles of safety and accessibility for all participants in the transportation ecosystem.

This research endeavor represents a pivotal stride towards realizing a harmonized traffic frame-work that gracefully integrates the operational paradigms of both autonomous and traditional vehicles. By establishing robust, flexible, and scalable traffic management approaches, this project lays the groundwork for advancing urban mobility into a safer, more efficient, and universally accessible future.