Study on the Controlled Field Test Scenarios of Automated Vehicles

Xie, Fei; Chen, Tao; Xia, Qing; Huang, Li; Shu, Hong

doi:10.4271/2018-01-1633

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…Xie et al [86] present a similar approach, which is implemented in three scenarios (curve, following and lane-change). Zhou and Re [87] also focus on surrounding traffic participants and create a structured test catalogue for an Adaptive Cruise Control (ACC) based on the number of participants and their abstract behavior, which according to the authors is sufficient to obtain a sufficient coverage of critical scenarios from real driving data.…”

Section: A Sampling Within Parameter Rangesmentioning

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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Section: A Sampling Within Parameter Rangesmentioning

confidence: 99%

Survey on Scenario-Based Safety Assessment of Automated Vehicles

et al. 2020

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“…3, each hazardous event is a combination of a hazard and an operational condition. With the methods proposed in [30], [61], [62], [63], the identified pre-crash functional scenarios can be used as the set of all the operational conditions for HARA as described in [64].…”

Section: Requirement Analysismentioning

confidence: 99%

“…However, none of the primary studies provide an explicit ODD definition. Some of the primary studies explicitly or implicitly provide the scope of the ODD, e.g., driving on a highway [30], [61] and driving on a structured road [62], [63], [75], [76]. Methods in these studies make assumptions about the environment (e.g., the behavior of other vehicles) within the ODD, and propose systematics to reason about critical functional scenarios based on these assumptions.…”

Section: Targeted Oddmentioning

confidence: 99%

Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study

Zhang¹,

Tao²,

Tan

et al. 2023

IIEEE Trans. Software Eng.

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Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

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“…However, none of the primary studies provide an explicit ODD definition. Some of the primary studies explicitly or implicitly provide the scope of the ODD, e.g., driving on a highway [30], [61] and driving on a structured road [62], [63], [73], [74]. Methods in these studies make assumptions about the environment (e.g., the behavior of other vehicles) within the ODD, and propose systematics to reason about critical functional scenarios based on these assumptions.…”

Section: Targeted Oddmentioning

confidence: 99%

Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

Zhang¹,

Tao²,

Tan³

et al. 2021

Preprint

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Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic literature review in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

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Study on the Controlled Field Test Scenarios of Automated Vehicles

Cited by 10 publications

References 6 publications

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Finding Critical Scenarios for Automated Driving Systems: A Systematic Mapping Study

Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

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