Automated and autonomous driving systems (ADS) are a transformational technology in the
mobility sector. Current practice for testing ADS uses virtual tests in computer simulations; …

Extracting interesting scenarios from real-world data as well as generating failure cases is
important for the development and testing of autonomous systems. We propose efficient …

It is apparent that one cannot rely solely on physical test drives for ensuring the correct
functionality of autonomous vehicles. Since physical test drives are costly and time …

One of the expectations from fully or partially automated vehicles is to never cause an
accident and actively avoid dangerous situations. However, an automated vehicle may not …

In this paper, we outline the use of a genetic algorithm for test parameter optimization in the
context of autonomous and automated driving. Our approach iteratively optimizes test …

Functional specifications and real drive data are typically used to derive parameterized
scenarios for scenario-based testing of driving systems. The domains of the parameters …

This paper proposes AV-FUZZER, a testing framework, to find the safety violations of an
autonomous vehicle (AV) in the presence of an evolving traffic environment. We perturb the …

To ensure safety and functional correctness of automated and autonomous driving systems,
virtual scenario-based testing is used. Experts derive traffic scenario types and generate …

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 …

Ensuring the safety of self-driving cars is important, but neither industry nor authorities have
settled on a standard way to test them. Deploying self-driving cars for testing in regular traffic …