Related papers: Integrating Formal Verification and Simulation-bas…
Autonomous Vehicles (AVs) are complex systems that drive in uncertain environments and potentially navigate unforeseeable situations. Safety of these systems requires not only an absence of malfunctions but also high performance of…
The emergence of the Industrial Internet results in an increasing number of complicated temporal interdependencies between automation systems and the processes to be controlled. There is a need for verification methods that scale better…
A fail-operational system for highly automated driving must complete the driving task even in the presence of a failure. This requires redundant architectures and a mechanism to reconfigure the system in case of a failure. Therefore, an…
Development of quality assured software-intensive systems, such as automotive embedded systems, is an increasing challenge as the complexity of these systems significantly increases. EAST-ADL is an architecture description language…
One of the challenges in designing safe, reliable and trustworthy Autonomous Vehicles (AVs) is to ensure that the AVs abide by traffic rules. For this, the AVs need to be able to understand and reason about traffic rules. In previous work,…
Driver models play a vital role in developing and verifying autonomous vehicles (AVs). Previously, they are mainly applied in traffic flow simulation to model driver behavior. With the development of AVs, driver models attract much…
Autonomous Driving Systems (ADS) are critical dynamic reconfigurable agent systems whose specification and validation raises extremely challenging problems. The paper presents a multilevel semantic framework for the specification of ADS and…
In this paper we discuss the growing need for system behaviour to be validated and verified (V&V'ed) early in model-based systems engineering. Several aspects push companies towards integration of techniques, methods, and processes that…
The widescale deployment of Autonomous Vehicles (AV) appears to be imminent despite many safety challenges that are yet to be resolved. It is well-known that there are no universally agreed Verification and Validation (VV) methodologies…
Simulation is a crucial step in ensuring accurate, efficient, and realistic Connected and Autonomous Vehicles (CAVs) testing and validation. As the adoption of CAV accelerates, the integration of real-world data into simulation environments…
Developing safety and efficiency applications for Connected and Automated Vehicles (CAVs) require a great deal of testing and evaluation. The need for the operation of these systems in critical and dangerous situations makes the burden of…
Fact verification (FV) aims to assess the veracity of a claim based on relevant evidence. The traditional approach for automated FV includes a three-part pipeline relying on short evidence snippets and encoder-only inference models. More…
The safety of Automated Vehicles (AVs) must be assured before their release and deployment. The current approach to evaluation relies primarily on (i) testing AVs on public roads or (ii) track testing with scenarios defined in a test…
Safety verification for autonomous vehicles (AVs) and ground robots is crucial for ensuring reliable operation given their uncertain environments. Formal language tools provide a robust and sound method to verify safety rules for such…
Cyber-Physical Systems (CPSs) have become an intrinsic part of the 21st century world. Systems like Smart Grids, Transportation, and Healthcare help us run our lives and businesses smoothly, successfully and safely. Since malfunctions in…
Current validation methods often rely on recorded data and basic functional checks, which may not be sufficient to encompass the scenarios an autonomous vehicle might encounter. In addition, there is a growing need for complex scenarios…
The safety of single-vehicle autonomous driving technology is limited due to the limits of perception capability of on-board sensors. In contrast, vehicle-road collaboration technology can overcome those limits and improve the traffic…
Comprehensive and efficient validation of connected and automated vehicles (CAVs) is critical prior to real-world deployment. While simulation-based testing offers scalability, existing approaches often lack seamless integration with real…
Modern AI technologies enable autonomous vehicles to perceive complex scenes, predict human behavior, and make real-time driving decisions. However, these data-driven components often operate as black boxes, lacking interpretability and…
Scientific and Technical Intelligence (S&TI) analysis requires verifying complex technical claims across rapidly growing literature, where existing approaches fail to bridge the verification gap between surface-level accuracy and deeper…