scenario.center: Methods from Real-world Data to a Scenario Database

• URL: http://arxiv.org/abs/2404.02561v3
• Date: Fri, 19 Apr 2024 08:25:05 GMT
• Title: scenario.center: Methods from Real-world Data to a Scenario Database
• Authors: Michael Schuldes, Christoph Glasmacher, Lutz Eckstein,
• Abstract summary: This paper presents the scenario database scenario.center to process and manage scenario data. A common input format with defined quality requirements is defined. For evaluation, the methodology is compared to state-of-the-art scenario databases.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scenario-based testing is a promising method to develop, verify and validate automated driving systems (ADS) since pure on-road testing seems inefficient for complex traffic environments. A major challenge for this approach is the provision and management of a sufficient number of scenarios to test a system. The provision, generation, and management of scenario at scale is investigated in current research. This paper presents the scenario database scenario.center ( https://scenario.center ) to process and manage scenario data covering the needs of scenario-based testing approaches comprehensively and automatically. Thereby, requirements for such databases are described. Based on those, a four-step approach is proposed. Firstly, a common input format with defined quality requirements is defined. This is utilized for detecting events and base scenarios automatically. Furthermore, methods for searchability, evaluation of data quality and different scenario generation methods are proposed to allow a broad applicability serving different needs. For evaluation, the methodology is compared to state-of-the-art scenario databases. Finally, the application and capabilities of the database are shown by applying the methodology to the inD dataset. A public demonstration of the database interface is provided at https://scenario.center .

Related papers

• SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation [55.87169702896249]
  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. We propose a framework to evaluate DA methods and present a fair evaluation of existing shallow algorithms, including reweighting, mapping, and subspace alignment. 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)
• Better Practices for Domain Adaptation [62.70267990659201]
  Domain adaptation (DA) aims to provide frameworks for adapting models to deployment data without using labels. Unclear validation protocol for DA has led to bad practices in the literature. We show challenges across all three branches of domain adaptation methodology.
  arXiv  Detail & Related papers  (2023-09-07T17:44:18Z)
• Is Scenario Generation Ready for SOTIF? A Systematic Literature Review [3.1491385041570146]
  We perform a Systematic Literature Review to identify techniques that generate scenarios complying with requirements of the SOTIF-standard. We investigate which details of the real-world are covered by generated scenarios, whether scenarios are specific for a system under test or generic, and whether scenarios are designed to minimize the set of unknown and hazardous scenarios.
  arXiv  Detail & Related papers  (2023-08-04T11:59:21Z)
• Acquire Driving Scenarios Efficiently: A Framework for Prospective Assessment of Cost-Optimal Scenario Acquisition [0.1999925939110439]
  This paper proposes a methodology to quantify the cost-optimal usage of scenario generation approaches to reach a certainly complete scenario space coverage. A methodology is proposed to fit the meta model including the prediction of reachable complete coverage, quality criteria, and costs.
  arXiv  Detail & Related papers  (2023-07-21T15:26:08Z)
• Tree-Based Scenario Classification: A Formal Framework for Coverage Analysis on Test Drives of Autonomous Vehicles [0.0]
  In scenario-based testing, relevant (driving) scenarios are the basis of tests. We address the open challenges of classifying sets of scenarios and measuring coverage of theses scenarios in recorded test drives.
  arXiv  Detail & Related papers  (2023-07-11T08:30:57Z)
• Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions [69.9674326582747]
  This paper proposes an approach to access a production systems' event data based on the event data's context. The approach extracts filtered event logs from a database system by combining: 1) a semantic model of a production system's hierarchical structure, 2) a formalized process description and 3) an OPC UA information model.
  arXiv  Detail & Related papers  (2022-07-25T15:13:44Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• UMAD: Universal Model Adaptation under Domain and Category Shift [138.12678159620248]
  Universal Model ADaptation (UMAD) framework handles both UDA scenarios without access to source data. We develop an informative consistency score to help distinguish unknown samples from known samples. Experiments on open-set and open-partial-set UDA scenarios demonstrate that UMAD exhibits comparable, if not superior, performance to state-of-the-art data-dependent methods.
  arXiv  Detail & Related papers  (2021-12-16T01:22:59Z)
• Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts [52.9168275057997]
  This paper presents Bellamy, a novel modeling approach that combines scale-outs, dataset sizes, and runtimes with additional descriptive properties of a dataflow job. We evaluate our approach on two publicly available datasets consisting of execution data from various dataflow jobs carried out in different environments.
  arXiv  Detail & Related papers  (2021-07-29T11:57:38Z)
• Unsupervised Lane-Change Identification for On-Ramp Merge Analysis in Naturalistic Driving Data [0.0]
  A scenario-driven approach has gained acceptance for CAVs emphasizing the requirement of a solid data basis of scenarios. This work proposes a framework for on-ramp scenario identification that also enables for scenario categorization and assessment. The efficacy of the framework is shown with a dataset collected on the Test Bed Lower Saxony.
  arXiv  Detail & Related papers  (2021-04-12T17:32:22Z)
• Real-World Scenario Mining for the Assessment of Automated Vehicles [12.962830182937035]
  We propose a new method to capture scenarios from real-world data using a two-step approach. The method is not specific for one type of scenario and, therefore, it can be applied to a large variety of scenarios.
  arXiv  Detail & Related papers  (2020-05-31T10:10:39Z)

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.