A Survey of Autonomous Driving: Common Practices and Emerging Technologies
It addresses the need for improved robustness in autonomous driving systems to reduce fatalities and enhance safety, but it is incremental as it primarily reviews and compares existing technologies.
This paper surveys the technical aspects of autonomous driving systems, identifying unsolved problems and reviewing challenges, architectures, methodologies, and core functions, with implementation and comparison of state-of-the-art algorithms on a real-world platform.
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.