Methodology for Test Case Allocation based on a Formalized ODD
- URL: http://arxiv.org/abs/2509.02311v1
- Date: Tue, 02 Sep 2025 13:33:24 GMT
- Title: Methodology for Test Case Allocation based on a Formalized ODD
- Authors: Martin Skoglund, Fredrik Warg, Anders Thoren, Sasikumar Punnekkat, Hans Hansson,
- Abstract summary: This paper presents a method for evaluating the suitability of test case allocation to various test environments by drawing on and extending an existing Operational Design Domain (ODD) formalization.<n>The resulting construct integrates ODD parameters and additional test attributes to capture a given test environments relevant capabilities.
- Score: 0.4349640169711269
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The emergence of Connected, Cooperative, and Automated Mobility (CCAM) systems has significantly transformed the safety assessment landscape. Because they integrate automated vehicle functions beyond those managed by a human driver, new methods are required to evaluate their safety. Approaches that compile evidence from multiple test environments have been proposed for type-approval and similar evaluations, emphasizing scenario coverage within the systems Operational Design Domain (ODD). However, aligning diverse test environment requirements with distinct testing capabilities remains challenging. This paper presents a method for evaluating the suitability of test case allocation to various test environments by drawing on and extending an existing ODD formalization with key testing attributes. The resulting construct integrates ODD parameters and additional test attributes to capture a given test environments relevant capabilities. This approach supports automatic suitability evaluation and is demonstrated through a case study on an automated reversing truck function. The system's implementation fidelity is tied to ODD parameters, facilitating automated test case allocation based on each environments capacity for object-detection sensor assessment.
Related papers
- MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving [63.875372281596576]
MetAdv is a novel adversarial testing platform that enables realistic, dynamic, and interactive evaluation.<n>It supports flexible 3D vehicle modeling and seamless transitions between simulated and physical environments.<n>It enables real-time capture of physiological signals and behavioral feedback from drivers.
arXiv Detail & Related papers (2025-08-04T03:07:54Z) - Continual Adaptation: Environment-Conditional Parameter Generation for Object Detection in Dynamic Scenarios [54.58186816693791]
environments constantly change over time and space, posing significant challenges for object detectors trained based on a closed-set assumption.<n>We propose a new mechanism, converting the fine-tuning process to a specific- parameter generation.<n>In particular, we first design a dual-path LoRA-based domain-aware adapter that disentangles features into domain-invariant and domain-specific components.
arXiv Detail & Related papers (2025-06-30T17:14:12Z) - AI-Augmented Metamorphic Testing for Comprehensive Validation of Autonomous Vehicles [7.237068561453082]
Self-driving cars have the potential to revolutionize transportation, but ensuring their safety remains a significant challenge.<n> Conventional testing methodologies face critical limitations, including the oracle problem determining whether the systems behavior is correct.<n>We propose enhancing Metamorphic Testing (MT) by integrating AI-driven image generation tools, such as Stable Diffusion.
arXiv Detail & Related papers (2025-02-16T23:31:59Z) - Human-Calibrated Automated Testing and Validation of Generative Language Models [3.2855317710497633]
This paper introduces a comprehensive framework for the evaluation and validation of generative language models (GLMs)<n>It focuses on Retrieval-Augmented Generation (RAG) systems deployed in high-stakes domains such as banking.
arXiv Detail & Related papers (2024-11-25T13:53:36Z) - SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities [50.6382396309597]
Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift.<n>We present a complete and fair evaluation of existing shallow algorithms, including reweighting, mapping, and subspace alignment.<n>Our benchmark highlights the importance of realistic validation and provides practical guidance for real-life applications.
arXiv Detail & Related papers (2024-07-16T12:52:29Z) - DARTH: Holistic Test-time Adaptation for Multiple Object Tracking [87.72019733473562]
Multiple object tracking (MOT) is a fundamental component of perception systems for autonomous driving.
Despite the urge of safety in driving systems, no solution to the MOT adaptation problem to domain shift in test-time conditions has ever been proposed.
We introduce DARTH, a holistic test-time adaptation framework for MOT.
arXiv Detail & Related papers (2023-10-03T10:10:42Z) - Simulative Performance Analysis of an AD Function with Road Network
Variation [0.0]
We propose a method to automatically test a set of scenarios in many variations.
Those variations are not applied to traffic participants around the ADF, but to the road network to show that parameters regarding the road topology also influence the performance of such an ADF.
arXiv Detail & Related papers (2023-08-01T15:25:51Z) - Generalizable Metric Network for Cross-domain Person Re-identification [55.71632958027289]
Cross-domain (i.e., domain generalization) scene presents a challenge in Re-ID tasks.
Most existing methods aim to learn domain-invariant or robust features for all domains.
We propose a Generalizable Metric Network (GMN) to explore sample similarity in the sample-pair space.
arXiv Detail & Related papers (2023-06-21T03:05:25Z) - Position: AI Evaluation Should Learn from How We Test Humans [65.36614996495983]
We argue that psychometrics, a theory originating in the 20th century for human assessment, could be a powerful solution to the challenges in today's AI evaluations.
arXiv Detail & Related papers (2023-06-18T09:54:33Z) - Identifying and Explaining Safety-critical Scenarios for Autonomous
Vehicles via Key Features [5.634825161148484]
This paper uses Instance Space Analysis (ISA) to identify the significant features of test scenarios that affect their ability to reveal the unsafe behaviour of AVs.
ISA identifies the features that best differentiate safety-critical scenarios from normal driving and visualises the impact of these features on test scenario outcomes (safe/unsafe) in 2D.
To test the predictive ability of the identified features, we train five Machine Learning classifiers to classify test scenarios as safe or unsafe.
arXiv Detail & Related papers (2022-12-15T00:52:47Z) - Testing Autonomous Systems with Believed Equivalence Refinement [1.370633147306388]
We propose believed equivalence, where the establishment of an equivalence class is initially based on expert belief.
We focus on modules implemented using deep neural networks where every category partitions an input over the real domain.
arXiv Detail & Related papers (2021-03-08T07:25:20Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.