稳健性(进化)
计算机科学
感知
新兴技术
系统工程
数据科学
人机交互
人工智能
工程类
生物化学
生物
基因
神经科学
化学
作者
Ekim Yurtsever,Jacob Lambert,Alexander Carballo,Kazuya Takeda
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:8: 58443-58469
被引量:1150
标识
DOI:10.1109/access.2020.2983149
摘要
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 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: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.
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