Abstract
Road safety remains a critical global concern. Human-driven motor vehicles contribute significantly to the high number of accidents, highlighting the urgent need for effective solutions. Traffic congestion intensifies road safety issues, with studies showing a direct correlation between congestion and accident frequency. Despite improvements in road infrastructure, challenges persist, particularly in navigating complex road segments like roundabouts and intersections. Cooperative, Connected, and Automated Mobility (CCAM) technologies emerge as a promising solution to enhance transportation efficiency and safety. CCAM aims to optimize traffic flow, mitigate congestion, and improve safety through the integration of Connected Automated Vehicles (CAVs) with advanced infrastructure technologies and algorithms.Cooperative maneuvers play a pivotal role in the realization of CCAM technologies, offering a strategic approach to address challenges associated with complex road segments. By using advanced communication systems, CAVs can exchange real-time data about their positions, speed, and intentions, allowing for coordination and decision-making during maneuvers. Moreover, cooperative maneuvers not only enhance traffic safety but also optimize transport flow, thereby reducing congestion and improving overall transportation efficiency. As such, the development and implementation of cooperative maneuvers represent a crucial step towards achieving the overarching goals of CCAM technologies in revolutionizing the future of transportation.
Furthermore, as these technologies advance, ensuring robust cyber-security measures becomes imperative to safeguard against potential cyber-threats. The integration of CAVs with sophisticated communication systems exposes them to vulnerabilities, making them susceptible to cyber-attacks that could compromise vehicle control and endanger road safety. By fortifying cyber-security frameworks, initiatives aim to bolster the protection of CAVs and their communication networks, thereby fostering trust and reliability in the deployment of cooperative maneuvers. As CCAM continues to evolve, cyber-security remains a critical aspect that requires ongoing attention and investment to uphold the integrity and safety of future transportation systems.
Aligned with the aforementioned premises, this Ph.D. thesis aims to address the coordination of multiple CAVs to execute various cooperative maneuvers across diverse testing environments, while also delving briefly into cyber-security frameworks based on Internet of Things (IoT) solutions. To fulfill this objective, the thesis embarks with a comprehensive review of the State-of-the-Art (SoA), focusing on the advancements in driving architecture constituting the CCAM systems, alongside the current state of cooperation in its domain. Following the identification of challenges pertinent, the thesis introduces the Automated Driving Core (AUDRIC) architecture, serving as the foundational framework. Particular emphasis is placed on integrating the SerIoT and IoTAC systems into the infrastructure module.
Subsequently, the thesis delineates the validation platforms, encompassing both simulated and real environments, along with the various proving grounds where these tests were conducted. It then elucidates the decision and control algorithms developed for executing cooperative maneuvers. The primary algorithm pertains to car following, comprising Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC) technologies, supported by a feedback/feedforward + Proportional Derivative (PD) controller framework. Additionally, two decision strategies are elaborated upon: Hybrid Trajectory Planning (HYTP), using Bézier Curves and predictive control, and Real-Time Trajectory Planning (RTTP), employing high-definition maps and a Finite State Machine (FSM) capable of managing cooperative and non-cooperative scenarios, thus facilitating real-time trajectory planning.
The thesis proceeds to validate cooperative maneuvers, including car following, roundabout merging, platoon lane merging, fleet management, and intersection management, across a diverse array of test environments utilizing the developed algorithms. Based on the results obtained, the thesis concludes by emphasizing the efficacy of the planned strategies and advocating for the expansion of cooperative maneuvers, given their pivotal role in achieving optimal connected and automated driving.
| Date of Award | 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Estibaliz Asua Uriarte (Supervisor) & Pérez Rastelli (Supervisor) |