Identification and Quantification of Hazardous Scenarios for Automated Driving

Kramer, Birte; Neurohr, Christian; Büker, Matthias; Böde, Eckard; Fränzle, Martin; Damm, Werner

doi:10.1007/978-3-030-58920-2_11

Cited by 48 publications

(39 citation statements)

References 9 publications

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“…Menzel et al [8] suggest three abstraction levels for scenarios: functional, logical, and concrete scenarios. Scenarios are developed at an abstract level during the concept phase [15] and get detailed and concretized throughout the development and test process [8]. According to Menzel et al [8], the most abstract level of scenario representation is called functional and describes a scenario via linguistic notation using natural, non-structured language terminology.…”

Section: A Simulation-and Scenario-based Testingmentioning

confidence: 99%

A Taxonomy for Quality in Simulation-Based Development and Testing of Automated Driving Systems

et al. 2022

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Ensuring the quality of automated driving systems is a major challenge the automotive industry is facing. In this context, quality defines the degree to which an object meets expectations and requirements. Especially, highly automated and automated vehicles at SAE level 4 and 5 will be expected to operate safely in various contexts and complex situations without misconduct. Thus, a systematic approach is needed to show their safe operation. A way to address this challenge is simulation-based testing as pure physical testing is not feasible for automated driving at level 4 and 5 since several billion kilometers of driving are necessary. During simulation-based testing, the data used to evaluate the actual quality of an automated driving system are generated using a simulation. However, to rely on these simulation data, the overall simulation, which also includes its simulation models, must provide a certain quality level. This quality level depends on the intended purpose for which the generated simulation data should be used. Therefore, three categories of quality can be considered: simulation quality (e.g., reliable simulation tool), quality of the automated driving system (e.g., handling of dangerous situations), and scenario quality (e.g., scenario criticality). Quality must be determined and evaluated in various process steps in developing and testing automated driving systems, the overall simulation, and the simulation models used for the simulation. In this paper, we propose a conceptual taxonomy to serve a better understanding of quality in the development and testing process to have a clear separation and insight where further testing is neededboth in terms of automated driving systems and simulation, including their simulation models and scenarios used for testing.

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Section: A Simulation-and Scenario-based Testingmentioning

confidence: 99%

A Taxonomy for Quality in Simulation-Based Development and Testing of Automated Driving Systems

et al. 2022

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“…In [37]- [41], risk quantification is proposed for real-time use, e.g., to support the path planning of a self-driving vehicle, so this is not intended to be used for a prospective risk assessment. In [42], a method for quantifying the risk of scenarios is proposed based on the probability of occurrence of so-called environmental conditions and the probability that an error propagates in the fault tree given a specified environment condition. However, they [42] do not provide methods to estimate or specify these probabilities.…”

Section: A Risk Quantification Of Automated Driving Systemsmentioning

confidence: 99%

“…In [42], a method for quantifying the risk of scenarios is proposed based on the probability of occurrence of so-called environmental conditions and the probability that an error propagates in the fault tree given a specified environment condition. However, they [42] do not provide methods to estimate or specify these probabilities. Furthermore, the role of a back-up operator, e.g., a human driver that supervises the ADS, is not considered, whereas the influence of the backup operator is an important aspect that is considered in the ISO 26262 and ISO/DIS 21448 standards.…”

Section: A Risk Quantification Of Automated Driving Systemsmentioning

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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“…This approach aims to create a collection of critical or relevant scenarios, depending on the test object, its objectives, and requirements. These scenarios are developed at an abstract level during the concept phase [19] and get detailed and concretized throughout the development and test process.…”

Section: Introductionmentioning

confidence: 99%

A taxonomy for quality in simulation-based development and testing of automated driving systems

Schütt,

Steimle,

Kramer

et al. 2021

Preprint

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Ensuring the safety and performance requirements of automated driving systems is a major challenge for the automotive industry.One way to tackle this problem is a simulation-based approach. However, to be able to rely on results generated by this method, the simulation process needs to fulfill certain quality criteria depending on the intended usage. Hence, quality should be measured and determined at many different levels and areas of the testing and developing landscape, providing information with varying degrees of abstraction. Additionally, quality not only has to be assessed for a complete automated driving system but also for the simulation models that approximate the vehicles' components before they can be used for simulation. This taxonomy provides a better understanding of the concept of quality during the development and test process and introduces the possibility to systematically evaluate whether development steps in this process need to be repeated or further assessed.

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Identification and Quantification of Hazardous Scenarios for Automated Driving

Cited by 48 publications

References 9 publications

A Taxonomy for Quality in Simulation-Based Development and Testing of Automated Driving Systems

A Taxonomy for Quality in Simulation-Based Development and Testing of Automated Driving Systems

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

A taxonomy for quality in simulation-based development and testing of automated driving systems

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