Operational Design Domain (ODD) attributes define the environmental conditions under which Automated Driving Systems (ADS) can safely operate. These attributes include factors such as road and lighting conditions, as well as infrastructure elements, such as lane markings and road conditions. However, existing ODD definitions are often ambiguous and lack specificity, making it challenging to validate their presence in datasets.The absence of precise ODD definitions and robust validation mechanisms poses significant challenges, as it remains unclear whether ADS training and testing datasets adequately represent real-world operating conditions. This gap introduces risks that could compromise the safe deployment of ADS in diverse environments.To address this issue, we introduce FODSE (Framing ODDs as Domain Specifications for Evaluation), a semi-automated AI-powered approach that refines ODD attributes into structured, context-aware domain specifications and systematically evaluates their presence in datasets. FODSE leverages Retrieval-Augmented Generation (RAG), multimodal AI, and prompt learning to enhance specification clarity and dataset completeness.Experimental evaluation on two commonly adopted datasets in ADS demonstrates that FODSE significantly improves dataset validation accuracy, achieving up to 96.8% classification accuracy for an extended set of lane marking variants and 97.8% for roadway users—two key ODD attributes. Expert assessments confirm that FODSE effectively reduces ambiguity and enhances contextual adaptability, reinforcing its potential to improve dataset integrity and ensure safer, more reliable ADS training and validation.

Simulink/Stateflow (SL/SF) is the primary modeling notation for the development of control systems in automotive and aerospace industries. In model based testing, test cases derived from a design model are used to show model-code conformance. Safety standards such as ISO 26262 recommend model based testing to show the conformance of a software with the corresponding model. From our experiments with various test generation techniques, we have observed that their coverage capabilities are complementary in nature. With this observation in mind, we have developed a new tool called SmartTestGen which integrates different test generation techniques. In this paper, we discuss SmartTestGen and the different test generation techniques utilized - random testing, constraint solving, model checking and heuristics. We experimented with 20 production-quality SL/SF models and compared the performance of our tool with that of two prominent commercial tools.

SUMMARY This paper is concerned with test case generation from Simulink/Stateflow (SL/SF) models with a focus on coverage of SF model elements. Coverage of the SF component in a model is a difficult task because of two primary reasons: (i) the SF component itself may lie deep in the SL/SF model in which case, inputs have to pass through a complex chain of SL blocks to reach the SF block and (ii) nonlinear constraints in the model are difficult to solve using constraint solvers. Hierarchy and parallelism in the SF model add further complexity to the problem. The existing approaches flatten such SF elements, and generate test cases from the flattened finite state machines. Handling of issues (i) and (ii) has already been discussed in earlier research. In this paper, we present a method of covering SF components, which does not require to flatten any hierarchy or parallelism in the components. This not only makes the test case generation problem efficient but also addresses the problem of scalability. We have implemented this method and performed a number of medium‐sized case studies. The results show improved performance over the results obtained by some commercial tools. Copyright © 2011 John Wiley & Sons, Ltd.

Symbolic property verification is an increasingly popular debugging method based on Binary Decision Diagrams (BDDs). The lack of optimization of the state space search is often responsible for the excessive growth of the BDDs. In this paper we present an accelerated symbolic property verification by means of a new guiding technique that automatically finds the set of interesting variables by exploiting the property and the transition relation of a design. Our property based state space guiding can substantially speed up the verification process. The heuristic picks up the interesting state or the input variables automatically and utilizes them in guiding the state space traversal. This guiding approach is a novel one as it is automatic, efficient and stable for fast falsification. Furthermore it does not degrade as much for full validation.