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

Robotic systems are becoming pervasive and adopted in increasingly many domains, such
as manufacturing, healthcare, and space exploration. To this end, engineering software has …

Unmanned aerial vehicles (UAVs), also known as drones, are acquiring increasing
autonomy. With their commercial adoption, the problem of testing their functional and non …

Offline model-level testing of autonomous driving software is much cheaper, faster, and
diversified than in-field, online system-level testing. Hence, researchers have compared …

With the increasing complexity of robot systems, it is necessary to simulate them before
deployment. To do this, a model of the robot's kinematics or dynamics is required. One of the …

The complexity of robot systems is rising which makes it increasingly effective to simulate
before deployment. To do this, a model of the robot's kinematics or dynamics is required …

Recent advances in Deep Neural Networks (DNNs) and sensor technologies are enabling
autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However …

Simulation-based testing of automated driving systems (ADS) is the industry standard, being
a controlled, safe, and cost-effective alternative to real-world testing. Despite these …

Simulation environments are essential for the continuous development of complex cyber-
physical systems such as self-driving cars (SDCs). Previous results on simulation-based …

Testing plays an essential role in ensuring the safety and quality of cyberphysical systems
(CPSs). One of the main challenges in automated and software-in-the-loop simulation …