Beyond-Design-Basis Evaluation of Advanced Driver Assistance Systems

Reiterer, Florian; Zhou, Jinwei; Kovanda, Jan; Rulc, Vojtech; Kemka, Vladislav; Re, Luigi del

doi:10.1109/ivs.2019.8813893

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…If the scenario database is available, the exhaustive search can be achieved through checking every test case derived from NDS/FOT data [53]. Otherwise, the exhaustive search can discretize continuous parameters and perform a full-scale grid search to examine every existing test case in the scenario space [43], [86], [104].…”

Section: Exploration Methodsmentioning

confidence: 99%

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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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Section: Exploration Methodsmentioning

confidence: 99%

“…• L3-: CSI methods will be classified into this category if their SoIs have only longitudinal control [42], [43], [44], [45], [46], [47] or a driver's operations [48] are required.…”

Section: Systems Of Interestmentioning

confidence: 99%

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

Zhang¹,

Tao²,

Tan³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…• L3+: CSI methods will be classified into this category if their SoIs have both longitudinal and lateral control, and they do not require a driver. 10 • L3-: CSI methods will be classified into this category if their SoIs have only longitudinal control [42], [43], [44], [45], [46], [47] or a driver's operations [48] are required.…”

Section: Systems Of Interestmentioning

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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“…Mathematical-model evaluation -based on a relatively simple mathematical description of an accident; as an example see [5,11] 3. Evaluation by finite-element simulation -based on a detailed simulation of an accident [13] The first methodology was firstly presented in [12] and is recommended for a simple and fast BDBSA evaluation of predefined specific crash configurations. The other two methodologies of evaluation are then described in [13], and can be used in more general cases than the first methodology.…”

Section: Safety Evaluationmentioning

confidence: 99%

“…The accident injury classification system design is not easy for the theoretical situation of the driver assistance system failure. The Beyond-Design-Basis Safety Assessment (BDBSA) methodology was designed to manage this most demanding part [22,23,12,14]. The scheme of the methodology is depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

Pre-crash control strategy of driver assistance system

Kovanda¹,

Rulc²

2021

NNW

Self Cite

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The aim of the article is the optimisation process of the ADAS (Advanced Driver Assistance Systems) control. The methodology is based on the classification of ADAS systems according to the situations of unavoidable accidents. The evaluation of expected consequences uses injury biomechanics, which represents the extended definition of HMI (Human-Machine Interaction). The evaluation of injury mechanism and the machine intervention enables to control this process with the target to minimise the consequent injuries. Then the decision making takes new inputs to the control process and it enriches the multiparametric control of the system with the target to minimise the consequences.

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Beyond-Design-Basis Evaluation of Advanced Driver Assistance Systems

Cited by 4 publications

References 13 publications

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

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

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

Pre-crash control strategy of driver assistance system

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