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

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 with high 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. Therefore, we also perform a comparative overview of 45 public driving datasets that enable learning and publish this dataset index at https://sites.google.com/view/driveability-surveydatasets. 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.

Survey on Scenario-Based Safety Assessment of Automated Vehicles

“…Many institutions and companies have their own, non-accessible data sets, but in recent years, publicly accessible data sets have increasingly been made available by various organizations. An overview of available data sets can be found in [35], [36]. Zhu et al [37] also show an overview of data sets and try to unify them.…”

Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and the Way Forward

“…An autoencoder-based approach for automatic identification of unusual events using small dashcam video and the inertial sensor is presented in [228] that can potentially be used to develop a robust autonomous driving system. Various factors and challenges impacting driveability of autonomous vehicles along with an overview of available datasets for training selfdriving is presented in [229] and challenges in designing such datasets are described in [230]. Furthermore, Dreossi et al suggested that while robustifying the ML systems, the effect of adversarial ML should be studied by considering the semantics and context of the whole system [231].…”

OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

“…In fact, the training of deep models requires datasets with a huge number of labeled data though collecting such amount of data is not an obvious task. Hence, this requirement has led to the development of several new sophisticated autonomous driving datasets [18]. In this section, we review various existing public monomodal and multimodal environment perception databases by detailing and observing the characteristics of each one.…”