Key points are not available for this paper at this time.
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.
Building similarity graph...
Analyzing shared references across papers
Loading...
Ekim Yurtsever
Jacob Lambert
Alexander Carballo
IEEE Access
SHILAP Revista de lepidopterología
Nagoya University
Building similarity graph...
Analyzing shared references across papers
Loading...
Yurtsever et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d851298c03fbaff8beef4b — DOI: https://doi.org/10.1109/access.2020.2983149