Introducing Complexity Metric for Test Scenarios for Automated Driving Systems

Virtual Testing of Automated Driving Systems through Credible Simulations

Simulation is increasingly used to support safety-related decision-making in road transport, particularly for the assessment and approval of automated driving systems (ADS). The complexity of ADS behavior and size of their operational design domains make exclusive reliance on physical testing impractical, leading to ex...

Region-specific highway driving scenarios generation for accelerating automated driving systems validation: A large-language-model assisted framework

This work proposes a 14-dimension highway driving behavioral taxonomy and builds an automated framework to compare naturalistic driving datasets from China and Germany, with every contrast computed within matched traffic states.

A Calculation Method for Automated Driving Test Scenario Complexity

Scenario-based validation requires efficient screening of large test libraries, but existing complexity metrics often combine traffic hazard and automated-driving-system workload into a single construct. This study proposes a two-dimensional framework that separates scenario hazardousness from system processing difficu...

Scalable Distributed Simulation-Based Testing for Automated Driving Systems

An end-to-end, DevOps-driven framework that automates build, deployment, and distributed execution of CARLA-based scenario tests of an ADS on a lightweight Kubernetes cluster and demonstrates that the approach provides a strong foundation not only for scalable simulation testing, but also for generating traceable evide...

BUILDING SCIENTIFIC CONSENSUS ON THE CRASH SAFETY PERFORMANCE OF AUTOMATED DRIVING SYSTEMS

Coverage-Driven Verification for Safety-by-Design in AI-Based Collision Avoidance Systems

This work presents a method for representativeness assessment of AI/ML constituent ODDs in the context of aviation safety assurance and illustrates how statistical distribution comparison methods can support the assessment of representativeness for safety-critical AI applications.