# A Survey of Autonomous Driving: Common Practices and Emerging Technologies

## TL;DR

This survey reviews current challenges, architectures, and emerging technologies in autonomous driving, highlighting the need for improved robustness and comparing algorithms in real-world tests.

## Contribution

It provides a comprehensive overview of unsolved problems, system components, and emerging methods, including real-world algorithm comparisons and dataset overviews.

## Key findings

- Identified key challenges in autonomous driving systems.
- Compared various algorithms in real-world scenarios.
- Reviewed datasets and tools for ADS development.

## Abstract

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.