4.7 Review

Self-driving cars: A survey

期刊

EXPERT SYSTEMS WITH APPLICATIONS
卷 165, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.113816

关键词

Self-driving cars; Robot localization; Occupancy grid mapping; Road mapping; Moving objects detection; Moving objects tracking; Traffic signalization detection; Traffic signalization recognition; Route planning; Behavior selection; Motion planning; Obstacle avoidance; Robot control

资金

  1. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Brazil [311654/2019-3, 311504/2017-5]
  2. Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES), Brazil [001]
  3. Fundacao de Amparo a Pesquisa do Espirito Santo (FAPES), Brazil [84412844/2018]
  4. Vale company, Brazil
  5. FAPES, Brazil [75537958/16]
  6. Embraer company, Brazil [GDT0017-18]

向作者/读者索取更多资源

The research survey examined literature on self-driving cars, focusing on the architecture of autonomy system, perception, and decision-making methods. It also provided a detailed description of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES). Additionally, prominent self-driving car research platforms developed by academia and technology companies were listed.
We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

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