Scenario Identification for Validation of Automated Driving Functions

Elrofai, Hala; Worm, Daniël; Camp, Olaf Op den

doi:10.1007/978-3-319-44766-7_13

Cited by 43 publications

(41 citation statements)

References 3 publications

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“…A scenario should include the description of the static and dynamic environment. Although the description of the static environment is not a general prerequisite of a scenario, this is often included when speaking about traffic scenarios [13], [14], [19], [30], [38]. The static environment consists of all relevant 4 physical elements that do not undergo relevant changes with respect to the ego vehicle (s) within the time interval between the start and the end of the scenario.…”

Section: A Scenariomentioning

confidence: 99%

“…For example, the road may be part of the static environment, but if the change in the road temperature is relevant to the ego vehicle(s), the road is part of the dynamic environment. 6) A scenario includes at least one ego vehicle [14], [38].…”

Section: A Scenariomentioning

confidence: 99%

“…We base the definitions of each of the components on definitions that are commonly used in the field of the safety assessment of AVs [13], [14], [29], [35]- [37]. While being broadly consistent with existing definitions [13], [14], [38], this framework aims to be sufficiently explicit to enable the formalization of a scenario description. More specifically, because we give the characteristics of the concepts corresponding to scenarios and specify how those concepts interrelate, we can define the scenario components as objects of classes having attributes, methods, and relationships with objects that are members of other classes.…”

Section: Introductionmentioning

confidence: 99%

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Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework

Gelder¹,

Paardekooper²,

Saberi³

et al. 2024

IEEE Trans. Intell. Veh.

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The development of new assessment methods for the performance of automated vehicles is essential to enable the deployment of automated driving technologies, due to the complex operational domain of automated vehicles. One contributing method is scenario-based assessment in which test cases are derived from real-world road traffic scenarios obtained from driving data. Given the complexity of the reality that is being modeled in these scenarios, it is a challenge to define a structure for capturing these scenarios. An intensional definition that provides a set of characteristics that are deemed to be both necessary and sufficient to qualify as a scenario assures that the scenarios constructed are both complete and intercomparable.In this article, we develop a comprehensive and operable definition of the notion of scenario while considering existing definitions in the literature. This is achieved by proposing an object-oriented framework in which scenarios and their building blocks are defined as classes of objects having attributes, methods, and relationships with other objects. The object-oriented approach promotes clarity, modularity, reusability, and encapsulation of the objects. We provide definitions and justifications of each of the terms. Furthermore, the framework is used to translate the terms in a coding language that is publicly available.

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Section: A Scenariomentioning

confidence: 99%

Section: A Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework

Gelder¹,

Paardekooper²,

Saberi³

et al. 2024

IEEE Trans. Intell. Veh.

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“…The data are interpreted as an endless sequence of scenarios, where scenarios might overlap in time (Elrofai et al 2018). Several definitions of the term scenario in the context of traffic data have been proposed in the literature; for example, by Geyer et al (2014), Ulbrich et al (2015), and Elrofai et al (2016;. Because we want to differentiate between quantitative and qualitative descriptions, the definition of the term scenario is adopted from Elrofai et al (2018) because it explicitly defines a scenario as a quantitative description:…”

Section: Problem Definitionmentioning

confidence: 99%

“…(p. 9) Extracting scenarios from data received significant attention and the applied methods are very diverse. For example, Elrofai et al (2016) use a model-based approach to detect scenarios in which the ego vehicle is changing lanes, whereas Kasper et al (2012) Similar to Elrofai et al (2018, p. 9), we assume that a scenario consists of activities: "An activity is considered [to be] the smallest building block of the dynamic part of the scenario (maneuver of the ego vehicle and the dynamic environment)." An activity describes the time evolution of state variables.…”

Section: Problem Definitionmentioning

confidence: 99%

Safety assessment of automated vehicles: how to determine whether we have collected enough field data?

Gelder

Paardekooper

Camp³

et al. 2019

Traffic Injury Prevention

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Objective: The amount of collected field data from naturalistic driving studies is quickly increasing. The data are used for, among others, developing automated driving technologies (such as crash avoidance systems), studying driver interaction with such technologies, and gaining insights into the variety of scenarios in real-world traffic. Because data collection is time consuming and requires high investments and resources, questions like "Do we have enough data?," "How much more information can we gain when obtaining more data?," and "How far are we from obtaining completeness?" are highly relevant. In fact, deducing safety claims based on collected data-for example, through testing scenarios based on collected data-requires knowledge about the degree of completeness of the data used. We propose a method for quantifying the completeness of the so-called activities in a data set. This enables us to partly answer the aforementioned questions. Method: In this article, the (traffic) data are interpreted as a sequence of different so-called scenarios that can be grouped into a finite set of scenario classes. The building blocks of scenarios are the activities. For every activity, there exists a parameterization that encodes all information in the data of each recorded activity. For each type of activity, we estimate a probability density function (pdf) of the associated parameters. Our proposed method quantifies the degree of completeness of a data set using the estimated pdfs. Results: To illustrate the proposed method, 2 different case studies are presented. First, a case study with an artificial data set, of which the underlying pdfs are known, is carried out to illustrate that the proposed method correctly quantifies the completeness of the activities. Next, a case study with real-world data is performed to quantify the degree of completeness of the acquired data for which the true pdfs are unknown. Conclusion: The presented case studies illustrate that the proposed method is able to quantify the degree of completeness of a small set of field data and can be used to deduce whether sufficient data have been collected for the purpose of the field study. Future work will focus on applying the proposed method to larger data sets. The proposed method will be used to evaluate the level of completeness of the data collection on Singaporean roads, aimed at defining relevant test cases for the autonomous vehicle road approval procedure that is being developed in Singapore. ARTICLE HISTORY

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Statistical Modelling of Driving Scenarios in Road Traffic using Fleet Data of Production Vehicles

Reichenbächer,

Hipp,

Bringmann

2024

Proceedings

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Scenario Identification for Validation of Automated Driving Functions

Cited by 43 publications

References 3 publications

Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework

Towards an Ontology for Scenario Definition for the Assessment of Automated Vehicles: An Object-Oriented Framework

Safety assessment of automated vehicles: how to determine whether we have collected enough field data?

Statistical Modelling of Driving Scenarios in Road Traffic using Fleet Data of Production Vehicles

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