  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.
