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.
Highlights
According to a recent technical report by the National Highway Traffic Safety Administration (NHTSA), 94% of road accidents are caused by human errors [1]
The accumulated knowledge in vehicle dynamics, breakthroughs in computer vision caused by the advent of deep learning [4] and availability of new sensor modalities, such as lidar [5], catalyzed Automated Driving Systems (ADSs) research and industrial implementation
One of the key findings of this study is that even though human drivers are still better at reasoning in general, the perception capability of ADSs with sensor-fusion can exceed humans, especially in degraded conditions such as insufficient illumination
Summary
According to a recent technical report by the National Highway Traffic Safety Administration (NHTSA), 94% of road accidents are caused by human errors [1]. A survey that covers: present challenges, available and emerging high-level system architectures, individual core functions such as localization, mapping, perception, planning, vehicle control, and human-machine interface altogether does not exist. Ego-only is the most common approach amongst the state-of-the-art ADSs [15], [47], [51]–[56], [56]–[58] We believe this is due to the practicality of having a self-sufficient platform for development and the additional challenges of connected systems. Developing individual modules separately divides the challenging task of automated driving into an easier-to-solve set of problems [69] These sub-tasks have their corresponding literature in robotics [70], computer vision [71] and vehicle dynamics [36], which makes the accumulated know-how and expertise directly transferable. A control safety governor design was proposed in [108] to prevent jackknifing while reversing
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