Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

Autonomous, or self-driving, cars are emerging as the solution to several problems primarily
caused by humans on roads, such as accidents and traffic congestion. However, those …

Deep learning (DL) techniques are rapidly developed and have been widely adopted in
practice. However, similar to traditional software systems, DL systems also contain bugs …

Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field
testing. Most testing techniques consider virtualized SDCs within a simulation environment …

Deep Neural Networks (DNNs) are the core component of modern autonomous driving
systems. To date, it is still unrealistic that a DNN will generalize correctly to all driving …

Automated Driving Systems (ADS) have made great achievements in recent years thanks to
the efforts from both academia and industry. A typical ADS is composed of multiple modules …

Automated online recognition of unexpected conditions is an indispensable component of
autonomous vehicles to ensure safety even in unknown and uncertain situations. In this …

The race for deploying AI-enabled autonomous vehicles (AVs) on public roads is based on
the promise that such self-driving cars will be as safe as or safer than human drivers …

Autonomous Driving System (ADS) is one of the most promising and valuable large-scale
machine learning (ML) powered systems. Hence, ADS has attracted much attention from …

With the development and the increasing interests in ML/DL fields, companies are eager to
apply Machine Learning/Deep Learning approaches to increase service quality and …