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

Gelder, Erwin de; Paardekooper, Jan‐Pieter; Camp, Olaf Op den; Schutter, Bart De

doi:10.1080/15389588.2019.1602727

Cited by 27 publications

(15 citation statements)

References 36 publications

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“…On the other hand, the fidelity of the simulation results may be compromised by the simplicity of the simulations. When using the proposed method to assess the risk of deploying an ADS in the real world, evidence 4 To determine whether enough data have been collected to estimate the pdf accurately, the metric proposed in [65] can be used. is needed to justify the fidelity of the simulation results.…”

Section: Discussionmentioning

confidence: 99%

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Risk Quantification for Automated Driving Systems in Real-World Driving Scenarios

Gelder¹,

Elrofai²,

Saberi

et al. 2021

IEEE Access

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The development of safety validation methods is essential for the safe deployment and operation of Automated Driving Systems (ADSs). One of the goals of safety validation is to prospectively evaluate the risk of an ADS dealing with real-world traffic. ISO 26262 and ISO/DIS 21448, the leading standards in automotive safety, provide an approach to estimate the risk where the former focuses on risks due to potential malfunctioning of components and the latter focuses on risks due to possible functional insufficiencies. The main shortcomings of the approach provided in ISO 26262 are that it depends on subjective judgments of safety experts and that only a qualitative risk estimation is performed. ISO/DIS 21448 addresses these shortcomings partially by providing statistical methods to guide the safety validation, but no complete method is provided to quantify the risk. The first objective of this article is to propose a method to estimate the risk of an ADS in a more quantitative and objective manner. A data-driven approach is used to rely less on subjective judgments of safety experts. The output of the method is the expected number of injuries in a potential collision. Thus, the method is quantitative, the result is easily interpretable, and the result can be compared with road crash statistics. The second objective is to provide a method that supports the risk assessment as stipulated by the ISO 26262 and ISO/DIS 21448 standards by decomposing the quantified risk into the 3 aspects of risk as mentioned in these standards: exposure, severity, and controllability. The proposed methods are illustrated by means of a case study in which the risk is quantified for a longitudinal controller in 3 different types of scenarios. The code of the case study is publicly available.

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Section: Discussionmentioning

confidence: 99%

“…The advantage of the Gaussian kernel is that it gives the possibility to calculate a metric that quantifies the completeness of the data[65] and to apply conditional sampling when generating scenario parameters[66]. Both these topics are out of scope of this article.6 VOLUME 4, 2016…”

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confidence: 99%

Risk Quantification for Automated Driving Systems in Real-World Driving Scenarios

Gelder¹,

Elrofai²,

Saberi

et al. 2021

IEEE Access

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“…• Scenarios that have something in common can be grouped together, which enables characterization of types of scenarios and facilitates discussion of scenarios. • The completeness of a set of scenarios can be assessed by considering the completeness of scenario categories (see, e.g., [71]) and the completeness of scenarios in each category (see, e.g., [72]). We describe the formal relation between a scenario and a scenario category with the verb "to comprise", denoted by .…”

Section: Scenario Categorymentioning

confidence: 99%

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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“…de Gelder et al [77] examine how many scenarios from real data are needed to completely describe the parameter ranges of the activity ''braking''. For this purpose, the probability density function (PDF) of the parameters of the activities is determined by kernel density estimation (KDE).…”

Section: Scenario Parameterizationmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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When will automated vehicles come onto the market? This question has puzzled the automotive industry and society for years. The technology and its implementation have made rapid progress over the last decade, but the challenge of how to prove the safety of these systems has not yet been solved. Since a market launch without proof of safety would neither be accepted by society nor by legislators, much time and many resources have been invested into safety assessment in recent years in order to develop new approaches for an efficient assessment. This paper therefore provides an overview of various approaches, and gives a comprehensive survey of the so-called scenario-based approach. The scenario-based approach is a promising method, in which individual traffic situations are typically tested by means of virtual simulation. Since an infinite number of different scenarios can theoretically occur in real-world traffic, even the scenario-based approach leaves the question unanswered as to how to break these down into a finite set of scenarios, and find those which are representative in order to render testing more manageable. This paper provides a comprehensive literature review of related safety-assessment publications that deal precisely with this question. Therefore, this paper develops a novel taxonomy for the scenario-based approach, and classifies all literature sources. Based on this, the existing methods will be compared with each other and, as one conclusion, the alternative concept of formal verification will be combined with the scenario-based approach. Finally, future research priorities are derived.

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

Cited by 27 publications

References 36 publications

Risk Quantification for Automated Driving Systems in Real-World Driving Scenarios

Risk Quantification for Automated Driving Systems in Real-World Driving Scenarios

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

Survey on Scenario-Based Safety Assessment of Automated Vehicles

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