We survey research on self-driving cars published in the literature focusing on autonomous
cars developed since the DARPA challenges, which are equipped with an autonomy system …

The connected automated vehicle has been often touted as a technology that will become
pervasive in society in the near future. One can view an automated vehicle as having …

Humans drive in a holistic fashion which entails, in particular, understanding dynamic road
events and their evolution. Injecting these capabilities in autonomous vehicles can thus take …

The core obstacle towards a large-scale deployment of autonomous vehicles currently lies
in the long tail of rare events. These are extremely challenging since they do not occur often …

Automated Driving (AD) datasets, when used in combination with deep learning techniques,
have enabled significant progress on difficult AD tasks such as perception, trajectory …

Abstract Automated Vehicles (AVs) are designed and programmed to follow traffic rules.
However, there is no separate and comprehensive regulatory framework dedicated to AVs …

For the classification of traffic scenes, a description model is necessary that can describe the
scene in a uniform way, independent of its domain. A model to describe a traffic scene in a …

The verification and validation of autonomous driving vehicles remains a major challenge
due to the high complexity of autonomous driving functions. Scenario-based testing is a …

Deep learning methods have made remarkable breakthroughs in machine learning in
general and in automated driving (AD) in particular. However, there are still unsolved …

There are billions of devices worldwide deployed, connected, and communicating to other
systems. Sensors and actuators, which can be stationary or movable devices. These Edge …