Doutorado em Ciência da Computação

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Navegando Doutorado em Ciência da Computação por Autor "Azevedo, Pedro Henrique Vieira de Oliveira"

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    A planning pipeline for software systems of autonomous vehicles
    (Universidade Federal do Espírito Santo, 2023-09-04) Azevedo, Pedro Henrique Vieira de Oliveira; Gonçalves, Claudine Santos Baduê; https://orcid.org/0000-0003-1810-8581; http://lattes.cnpq.br/1359531672303446; http://lattes.cnpq.br/5958251435708396; Komati, Karin Satie; https://orcid.org/0000-0001-5677-4724; http://lattes.cnpq.br/9860697624155451; Santos, Thiago Oliveira dos; https://orcid.org/0000-0001-7607-635X; http://lattes.cnpq.br/5117339495064254; Boeres, Maria Claudia Silva; https://orcid.org/0000-0001-9801-2410; http://lattes.cnpq.br/0528154281423964; Wolf, Denis Fernando
    The use of autonomous vehicles on public roads and in industrial environments has been growing in recent years and to achieve the higher level of autonomy, where no human intervention is needed, the vehicle must know the roads of the environment where it will operate, which allows it to travel from an initial location to a desired destination dealing with the uncertainties of the environments, such as static obstacles. In this work, we propose a planning pipeline for autonomous vehicle software systems. The pipeline is composed of an offline process and an online process. The offline process consists of two modules: (i) the Waypoints Editor, used to edit or even create entire new paths using an open-source vector graphics editor. (ii) The Multi-Level Road Network Generator constructs a representation of the environment in two levels of representation where the first one is used to guide the vehicle in the autonomous mission and the second one represents the paths and is used is route computation. The online process consists of three modules: (i) the Route Planner module to compute routes, (ii) the Off-Road Planner module to compute short paths in order to bring the car to the start of the route or to take the car from the end of the route to the final goal, and (iii) the Frenét Frames Path Planner to generate alternative paths to the right and left of the route and, in the presence of static obstacles, overtake them. We evaluated the performance of the proposed planning pipeline through simulations in three different real-world datasets using our Autonomous Vehicle Simulator module. Simulated experimental results showed that the proposed planning pipeline allowed the autonomous vehicle to know the environment as much as the user of the system wants and successfully execute missions from an initial position to a desired destination computing faster routes using the second level of the Multi-Level Road Network, dealing with static obstacles in the environment obeying the overtake safe distance restrictions imposed by the autonomous vehicle system.