A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Обзор автономного вождения: общепринятые практики и новые технологии
Ekim Yurtsever, Jacob Lambert, Alexander Carballo, Kazuya Takeda
2020-01-01

automated driving systemsautonomous drivinglocalization and mappingperception and planningstate-of-the-art algorithms
Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.
1
Automated driving systems promise safer, more comfortable, and more efficient transportation, but fatalities involving ADS-equipped vehicles are increasing.
2
It reviews unresolved challenges, system architectures, emerging methodologies, and core functions including localization, mapping, perception, planning, and human–machine interfaces.
3
Numerous state-of-the-art algorithms were implemented and compared on the authors’ platform in real-world driving conditions.
4
The paper summarizes available datasets and development tools for automated driving systems.
5
The survey identifies robustness limitations in state-of-the-art automated driving systems as a major barrier to realizing their full potential.

Automated driving systems (ADSs)

technical challenges, architectures, methodologies, core functions, algorithms, datasets, and tools for improving the robustness and development of ADSs

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Publication Date
2020-01-01
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Authors
Ekim Yurtsever
Jacob Lambert
Alexander Carballo
Kazuya Takeda
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