Is it Safe to Drive? An Overview of Factors, Challenges, and Datasets for Driveability Assessment in Autonomous Driving

Is it Safe to Drive? Abstract--With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered and unseen environments withhigh uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors, that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize Driveability in some quantified form. To this end, this paper aims to create a framework for investigating different factors that can impact driveability. Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios. The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning. Specifically,we categorize the datasets according to use cases, and highlight the datasets that capture complicated and hazardous driving conditions which can be better used for training robust driving models. Furthermore, by discussions of what driving scenarios are not covered by existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments. I. INTRODUCTION Despite testing autonomous cars in highly controlled settings, thesecars still occasionally fail in making correct decisions, often with catastrophic results According to the accident records, the failures are most likely to happen in complex or unseen driving environments. The fact remains that while autonomous cars can operate well in controlled or structured environments such as highways, they are still far from reliable when operating in cluttered, unstructured or unseen environments [2]. These apply to autonomous vehicles in general. Thesetwo different application fields also suggest that driveability could be quantified in different forms, either as a single metric or a composition of metrics. For example, with ADAS and current Level 2 or 3 autonomy, a scene can be simply defined as driveable if the car can operate safely in autonomous mode. When a non-driveable scene is detected, the autonomous car can hand over control to the human driver in a timely manner [4].