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End-to-end learning for autonomous vehicles: a narrow approach

Heringer, Adauton Machado

Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Matemática e Estatística 2023-06-23

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  • Título:
    End-to-end learning for autonomous vehicles: a narrow approach
  • Autor: Heringer, Adauton Machado
  • Orientador: Silva, Flavio Soares Correa da
  • Assuntos: Aprendizado End-To-End; Redes Neurais Convolucionais; Inteligência Artificial Geral; Imaginário Sociotécnico; Autonomia Restrita; Veículos Autônomos; Convolutional Neural Networks; End-To-End Learning; Autonomous Vehicles; Narrow Autonomy; Artificial General Intelligence; Sociotechnical Imaginary
  • Notas: Dissertação (Mestrado)
  • Descrição: Autonomous vehicles are long promised to revolutionize our civilization. Nevertheless, it has consistently failed to meet expectations in the past two decades. Based on the fundamental difference between narrow and general artificial intelligence and equipped with the theoretical approach of sociotechnical imaginaries, we criticize general autonomy: the study of autonomous vehicles as envisaged by its artificially fabricated sociotechnical imaginary utopia. By contrast, we conceptualize narrow autonomy as the study of context-limited autonomous vehicles. Accordingly, we propose a narrow approach: instead of training a vehicle in a context-free environment, we set clear boundaries for the path the vehicle is supposed to drive. Using the latest advancements in end-to-end deep learning, we trained a convolutional neural network to map images and high-level commands straight to vehicle control, such as steering angle, throttle, and brake, in a simulated environment. Although this is a multidisciplinary conceptual work, our results indicate that by delimiting its path we can significantly improve performance and contribute to the advancements of autonomous technology.
  • DOI: 10.11606/D.45.2023.tde-19072023-053510
  • Editor: Biblioteca Digital de Teses e Dissertações da USP; Universidade de São Paulo; Instituto de Matemática e Estatística
  • Data de criação/publicação: 2023-06-23
  • Formato: Adobe PDF
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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