An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving

• URL: http://arxiv.org/abs/2409.06607v2
• Date: Fri, 11 Oct 2024 17:02:19 GMT
• Title: An Ontology-based Approach Towards Traceable Behavior Specifications in Automated Driving
• Authors: Nayel Fabian Salem, Marcus Nolte, Veronica Haber, Till Menzel, Hans Steege, Robert Graubohm, Markus Maurer,
• Abstract summary: We propose a Semantic Norm Behavior Analysis as an approach to specify the behavior for an Automated Driving System equipped vehicle. We use to formally represent specified behavior for a targeted operational environment, and to establish traceability between specified behavior and the stakeholder needs. Our evaluation shows that the explicit documentation of assumptions in the behavior specification supports both the identification of specification insufficiencies and their treatment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vehicles in public traffic that are equipped with Automated Driving Systems are subject to a number of expectations: Among other aspects, their behavior should be safe, conforming to the rules of the road and provide mobility to their users. This poses challenges for the developers of such systems: Developers are responsible for specifying this behavior, for example, in terms of requirements at system design time. As we will discuss in the article, this specification always involves the need for assumptions and trade-offs. As a result, insufficiencies in such a behavior specification can occur that can potentially lead to unsafe system behavior. In order to support the identification of specification insufficiencies, requirements and respective assumptions need to be made explicit. In this article, we propose the Semantic Norm Behavior Analysis as an ontology-based approach to specify the behavior for an Automated Driving System equipped vehicle. We use ontologies to formally represent specified behavior for a targeted operational environment, and to establish traceability between specified behavior and the addressed stakeholder needs. Furthermore, we illustrate the application of the Semantic Norm Behavior Analysis in a German legal context with two example scenarios and evaluate our results. Our evaluation shows that the explicit documentation of assumptions in the behavior specification supports both the identification of specification insufficiencies and their treatment. Therefore, this article provides requirements, terminology and an according methodology to facilitate ontology-based behavior specifications in automated driving.

Related papers

• Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
  Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
  arXiv  Detail & Related papers  (2024-11-04T09:21:00Z)
• 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)
• Conservative Estimation of Perception Relevance of Dynamic Objects for Safe Trajectories in Automotive Scenarios [0.0]
  The concept of relevance currently remains insufficiently defined and specified. We propose a novel methodology to overcome this challenge by exemplary application to collision safety in the highway domain. We present a conservative estimation which dynamic objects are relevant for perception and need to be considered for a complete evaluation.
  arXiv  Detail & Related papers  (2023-07-20T13:43:48Z)
• A Requirements-Driven Platform for Validating Field Operations of Small Uncrewed Aerial Vehicles [48.67061953896227]
  DroneReqValidator (DRV) allows sUAS developers to define the operating context, configure multi-sUAS mission requirements, specify safety properties, and deploy their own custom sUAS applications in a high-fidelity 3D environment. The DRV Monitoring system collects runtime data from sUAS and the environment, analyzes compliance with safety properties, and captures violations.
  arXiv  Detail & Related papers  (2023-07-01T02:03:49Z)
• Explainable Abuse Detection as Intent Classification and Slot Filling [66.80201541759409]
  We introduce the concept of policy-aware abuse detection, abandoning the unrealistic expectation that systems can reliably learn which phenomena constitute abuse from inspecting the data alone. We show how architectures for intent classification and slot filling can be used for abuse detection, while providing a rationale for model decisions.
  arXiv  Detail & Related papers  (2022-10-06T03:33:30Z)
• Control-Aware Prediction Objectives for Autonomous Driving [78.19515972466063]
  We present control-aware prediction objectives (CAPOs) to evaluate the downstream effect of predictions on control without requiring the planner be differentiable. We propose two types of importance weights that weight the predictive likelihood: one using an attention model between agents, and another based on control variation when exchanging predicted trajectories for ground truth trajectories.
  arXiv  Detail & Related papers  (2022-04-28T07:37:21Z)
• Towards formalization and monitoring of microscopic traffic parameters using temporal logic [1.3706331473063877]
  We develop specification-based monitoring for the analysis of traffic networks using the formal language Signal Temporal Logic. We develop monitors that identify safety-related behavior such as conforming to speed limits and maintaining appropriate headway. This work can be utilized by traffic management centers to study the traffic stream properties, identify possible hazards, and provide valuable feedback for automating the traffic monitoring systems.
  arXiv  Detail & Related papers  (2021-10-12T17:59:26Z)
• CausalCity: Complex Simulations with Agency for Causal Discovery and Reasoning [68.74447489372037]
  We present a high-fidelity simulation environment that is designed for developing algorithms for causal discovery and counterfactual reasoning. A core component of our work is to introduce textitagency, such that it is simple to define and create complex scenarios. We perform experiments with three state-of-the-art methods to create baselines and highlight the affordances of this environment.
  arXiv  Detail & Related papers  (2021-06-25T00:21:41Z)
• Safety of the Intended Driving Behavior Using Rulebooks [0.5898893619901381]
  The ISO/PAS 21448 guidance recommends a process to ensure the Safety of the Intended Functionality (SOTIF) for road vehicles. For the path planning function, defining the correct sequence of control actions for each vehicle in all potential driving situations is intractable. We establish that Rulebooks provide a functional description of the path planning task in an AV and discuss the potential usage of the method for verification and validation.
  arXiv  Detail & Related papers  (2021-05-10T16:11:15Z)
• Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework [0.0]
  We develop a comprehensive and operable definition of the notion of scenario. We propose an object-oriented framework in which scenarios and their building blocks are defined as classes of objects. We provide definitions and justifications of each of the terms.
  arXiv  Detail & Related papers  (2020-01-30T08:14:26Z)
• Counter-example Guided Learning of Bounds on Environment Behavior [11.357397596759172]
  We present a data-driven solution that allows for a system to be evaluated for specification conformance without an accurate model of the environment. Our approach involves learning a conservative reactive bound of the environment's behavior using data and specification of the system's desired behavior.
  arXiv  Detail & Related papers  (2020-01-20T19:58:24Z)

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.