Public defence in Spatial planning and transportation engineering, M.Sc. Francesco Vitale

Title of the thesis: Hierarchical control for Connected and Automated Vehicles: From safe vehicle navigation to network congestion alleviation

Doctoral student: M.Sc. Francesco Vitale

Opponents: 
Professor Monica Menendez, New York University, Abu Dhabi
Associate Professor Jack Haddad, Israel Institute of Technology

Custos: Associate Professor Claudio Roncoli, School of Engineering, Department of Built Environment

A new framework for improving urban traffic with CAVs, focusing on real-time safety, coordination, and congestion reduction

This thesis focuses on improving urban traffic control through connected and automated vehicles (CAVs). It introduces methods for real-time collision avoidance, optimizing vehicle movement at intersections, and reducing congestion. The goal is to enhance urban traffic efficiency, safety, and sustainability by improving traffic flow, reducing delays, and facilitating smoother interactions between CAVs and human-driven vehicles.

The study addresses challenges like real-time decision-making in dynamic traffic environments and cooperation among CAVs to reduce congestion and travel time. It demonstrates that CAVs can avoid obstacles in real time without waiting for full trajectory replanning, significantly improving reaction times and safety. This allows vehicles to promptly respond to unexpected obstacles while refining their paths.

Additionally, the research proposes a distributed system for optimizing time slots at intersections, reducing travel time and delays. CAVs cooperate to ensure smooth passage through intersections, without overlapping, while maintaining high traffic efficiency. The congestion reduction method cuts total travel time by up to 33% and delays by up to 67%. It also adapts to different levels of CAV usage in the network, preventing full congestion on key roads.

The research offers new insights by integrating real-time responsiveness with cooperative behavior, providing innovative solutions to urban traffic management, even with partial CAV integration. The proposed methods are applicable in cities deploying CAVs to improve traffic management, reduce fuel consumption, and enhance road safety. Additionally, the approach holds potential for applications in automation and robotics.

The results indicate that real-time, distributed optimization significantly enhances urban traffic conditions. The strategies developed in this thesis pave the way for smoother and safer driving conditions for both CAVs and human drivers. As cities continue adopting CAV technology, these findings will contribute to smarter, more sustainable urban transport networks.

Keywords: Connected and automated vehicles, urban traffic control, distributed optimization, trajectory planning and control, cooperative vehicle routing

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/

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