With the development of autonomous driving, offline testing remains an important process
allowing low-cost and efficient validation of vehicle performance and vehicle control …

In our days, vehicle automation is in a continuous evolutionary phase consisting of
experiments, testing and validation. Accelerating the development and deployment of …

Current advances in the research field of autonomous driving demand advanced simulation
methods for testing and validation. By combining versatile foci of different simulations, we …

Testing autonomous driving algorithms on real autonomous vehicles is extremely costly and
many researchers and developers in the field cannot afford a real car and the corresponding …

This paper presents a simulation-based testing and validation framework for ADAS
(Advanced Driver Assistant System) and AD (Autonomous Driving) technology development …

The number and complexity of newly developed automated driving systems has been
constantly rising over the past decade. Especially the introduction of vehicle-to-everything …

This paper presents the CAT Vehicle (Cognitive and Autonomous Test Vehicle) Testbed: a
research testbed comprised of a distributed simulation-based autonomous vehicle, with …

This paper presents a Model in the Loop (MiL) framework to validate embedded
autonomous driving (AD) and advanced driver assistant systems (ADAS) algorithms …

Developing control and perception algorithms for autonomous vehicle is a time-consuming
activity if one performs it directly on vehicles hardware level. Moreover, some test cases are …

Automotive industry is currently shifting from automated driving assistance systems to
conditionally automated vehicles. Traditional automotive component testing methodologies …