Enhancing Sensing and Decision-Making of Automated Driving Systems With Multi-Access Edge Computing and Machine Learning
Allan M. de Souza, Horácio F. Oliveira, Zhongliang Zhao, Torsten Braun, Antônio A. F. Loureiro, Leandro A. Villas
Abstract
Emerging self-driving vehicles are now capable of sensing the environment and performing autonomous operations, paving the way to a more efficient, safer, and greener transportation system. On the other hand, emerging technologies such as vehicle-to-everything communications, 5G, and edge computing can expand even more the potential of automated driving vehicles, especially when combined with machine learning techniques. In this article, we explore how these emerging technologies can be used to enhance automated driving systems from different perspectives, such as driving safety and transportation efficiency. We conduct a case study using real-world data to show how these technologies can be used together to provide a more reliable path planning service considering predicted future urban dynamics.