The current test of autonomous driving technology requires extensive experimental
verification, whether in simulation or on real roads. Netherthless, how to test autonomous …

One remaining challenge for Automated Driving (AD) that remains unclear to this day is its
assessment for market release. The application of previous strategies derived from the V …

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

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

Driving test is critical to the deployment of autonomous vehicles. It is necessary to review the
related works since the methodologies summaries are rare, which will help to set up an …

Automotive cyber-physical systems are constantly increasing in complexity, especially due
to innovations like sophisticated advanced driver assistance features. The increase in …

Simulation-based tests are more cost-effective and less dangerous than field tests; hence,
they are becoming the norm for thoroughly testing self-driving cars in virtual environments …

Safety is one of the main challenges that prohibit autonomous vehicles (AV), requiring them
to be well tested ahead of being allowed on the road. In comparison with road tests …

Many approaches for testing automated and autonomous driving systems in dynamic traffic
scenarios rely on the reuse of test cases, eg, recording test scenarios during real test drives …

The safety validation of Automated Driving Systems (ADSs) needs a combination of tools to
ensure testing in a broad range of traffic scenarios. Among the others, virtual testing is …