Autonomous driving is a rapidly evolving technology, whose ultimate goal is the complete replacement of the human driver of vehicles (cars, trucks, etc.) with an automated driving system. This system is based on a combination of sensors, actuators, and automation solutions to ensure safer and more reliable driving than human-based one. Despite the progress made, there are still several technical and legal challenges to overcome before fully autonomous driving. However, autonomous driving offers several possibilities, such as vehicle platooning, which is becoming a widespread reality. This thesis is focused on the study and implementation of autonomous driving control algorithms, with a particular emphasis on platooning. Specifically, a scalable interface for controlling a fleet of autonomous vehicles, is developed. This includes the definition and implementation of low- and high-level control strategies that leverage communication between vehicles to improve the efficiency of the platoon. The implemented system shows promising results for both the easy coordination of autonomous vehicles, especially in the context of vehicle platooning, and the simulation and experimental validation of solutions relevant to vehicle communication and fleet management.The present work has been developed thanks to the collaboration between the Decision and Control Laboratory (D\&C Lab) of Polytechnic of Bari (Italy) and the COSMOS (Control of Smart Multi-agent systems Operating autonomously and Synergistically) research group of University of Surrey (United Kingdom). All tests are carried out experimentally in the COSMOS laboratory at the Faculty of Engineering and Physical Sciences of University of Surrey in Guildford , using fully autonomous-scale prototype vehicles (i.e., the QCar produced by Quanser).
La guida autonoma è una tecnologia in rapida evoluzione, il cui obiettivo ultimo è la completa sostituzione del conducente umano dei veicoli (auto, camion, ecc.) con un sistema di guida automatizzato. Questo sistema si basa su una combinazione di sensori, attuatori e soluzioni di automazione per garantire una guida più sicura e affidabile rispetto a quella umana. Nonostante i progressi compiuti, ci sono ancora diverse sfide tecniche e legali da superare prima di raggiungere la guida completamente autonoma. Tuttavia, la guida autonoma offre diverse possibilità, come ad esempio il convogliamento dei veicoli, che sta diventando una realtà sempre più diffusa. Questa tesi è incentrata sullo studio e l'implementazione degli algoritmi di controllo della guida autonoma, con un particolare enfasi sul convogliamento. In particolare, viene sviluppata un'interfaccia scalabile per il controllo di una flotta di veicoli autonomi. Questo include la definizione e l'implementazione di strategie di controllo a basso e alto livello che sfruttano la comunicazione tra i veicoli per migliorare l'efficienza del convogliamento. Il sistema implementato mostra risultati promettenti sia per la coordinazione agevole dei veicoli autonomi, specialmente nel contesto del convogliamento, sia per la simulazione e la validazione sperimentale di soluzioni rilevanti per la comunicazione tra veicoli e la gestione della flotta. Il presente lavoro è stato sviluppato grazie alla collaborazione tra il Laboratorio di Decisione e Controllo (D&C Lab) del Politecnico di Bari (Italia) e il gruppo di ricerca COSMOS (Control of Smart Multi-agent systems Operating autonomously and Synergistically) dell'Università di Surrey (Regno Unito). Tutti i test sono stati condotti sperimentalmente nel laboratorio COSMOS presso la Facoltà di Ingegneria e Scienze Fisiche dell'Università di Surrey a Guildford, utilizzando veicoli prototipo completamente autonomi su scala reale (ad esempio, la QCar prodotta da Quanser).
DESIGN, SIMULATION, AND EXPERIMENTAL EVALUATION OF DECENTRALIZED CONTROL FOR VEHICLE PLATOONING UNDER CONSTANT SPACING POLICY
TANGORRA, MICHELE
2022/2023
Abstract
Autonomous driving is a rapidly evolving technology, whose ultimate goal is the complete replacement of the human driver of vehicles (cars, trucks, etc.) with an automated driving system. This system is based on a combination of sensors, actuators, and automation solutions to ensure safer and more reliable driving than human-based one. Despite the progress made, there are still several technical and legal challenges to overcome before fully autonomous driving. However, autonomous driving offers several possibilities, such as vehicle platooning, which is becoming a widespread reality. This thesis is focused on the study and implementation of autonomous driving control algorithms, with a particular emphasis on platooning. Specifically, a scalable interface for controlling a fleet of autonomous vehicles, is developed. This includes the definition and implementation of low- and high-level control strategies that leverage communication between vehicles to improve the efficiency of the platoon. The implemented system shows promising results for both the easy coordination of autonomous vehicles, especially in the context of vehicle platooning, and the simulation and experimental validation of solutions relevant to vehicle communication and fleet management.The present work has been developed thanks to the collaboration between the Decision and Control Laboratory (D\&C Lab) of Polytechnic of Bari (Italy) and the COSMOS (Control of Smart Multi-agent systems Operating autonomously and Synergistically) research group of University of Surrey (United Kingdom). All tests are carried out experimentally in the COSMOS laboratory at the Faculty of Engineering and Physical Sciences of University of Surrey in Guildford , using fully autonomous-scale prototype vehicles (i.e., the QCar produced by Quanser).I documenti in UNITESI sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/20.500.14253/84818