Evaluation of Different Approaches to Address Safety Validation of Automated Driving

Junietz, Philipp; Wachenfeld, Walther; Klonecki, Kamil; Winner, Hermann

doi:10.1109/itsc.2018.8569959

Cited by 39 publications

(27 citation statements)

References 14 publications

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“…In contrast, it is essential to mention the self-certification procedure as well, where it is the vehicle manufacturer itself who performs vehicle inspections and issues a certification of conformity (e.g., in the USA), validating that its product does fulfil the requirements of the given market [22]. The future challenge is especially the successful integration of new aspects into these processes, like safe driving without a human driver in the control loop [23], [24], automotive cybersecurity [25]- [27] and artificial intelligence based system control [28], to mention a few.…”

Section: Background Of Automotive Testing and Validationmentioning

confidence: 99%

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Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems

Szalay

2021

IEEE Access

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Testing self-driving vehicles is still a new and immature process; the globally harmonised procedure expected much later. The resource-demanding nature of real-world tests makes it indispensable to develop and improve the efficiency of virtual environment based testing methods. Accordingly, a novel Xin-the-Loop framework is proposed to fully exploit the recent advances in info-communication technologies, vehicle automation, and testing and validation requirements. This methodology real-time connects physical and virtual testing with high correlation while completely blurs the sharp boundaries between them. Measurement results confirm the superior performance of the 5G communication link in providing a stable, real-time connection between the real world and its virtual representation. The live demonstration proved the presented concept at the newly constructed Hungarian proving ground for automated driving. The performed investigation also includes comprehensive benchmarking, focusing on the most up-to-date automotive testing frameworks. The analysis considers the methodologies and techniques applied by the most relevant actors in the automotive testing sector worldwide. Accordingly, the newly developed testing framework is evaluated and validated in light of the state-of-the-art methods used by the automotive industry.INDEX TERMS Automotive proving ground, automated vehicle control, autonomous vehicle, co-simulation, digital twin, mixed-reality testing, scenario-based testing, testing and validation, virtual and mixed reality, V-model, X-in-the-loop testing.

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Section: Background Of Automotive Testing and Validationmentioning

confidence: 99%

“…Vehicle systems became increasingly complex, and as a result, testing and validation tasks went far beyond the vehicle itself, reflecting the complexity of the entire transport systems [23], [33]- [35].…”

Section: Related Workmentioning

confidence: 99%

Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems

Szalay

2021

IEEE Access

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“…As early as 2016, Huang et al [11] wrote a short overview of AV test methods, the extent of which was deemed to be unsatisfactory, and which, due to the rapid development in this field, no longer reflected the current state of the art. An overview of the properties of different safety validation methods was published by Junietz et al [12] in 2018. There, the evaluation of the properties is strongly emphasized.…”

Section: Introductionmentioning

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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“…Ulbrich [4] elaborated the basic terms of scenario, situation and scenario. It is already a well-known and established test approach to the automotive industry for V.V&T [5]. The DARPA Grand Challenge is a representative.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Urban Test Scenario Generation Using Microscopic Traffic Simulation

et al. 2020

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Scenario-based testing is already a well-known test approach to the automotive industry for the Validation, Verification and Testing of Connected and Automated Vehicles. How to construct the scenarios of complex traffic environment is a major challenge to this method. A common approach for generating scenarios is collecting and post-processing natural traffic data with a lot of time and money costs. In order to reduce the cost of scenario collection, we propose a low-cost method based on Microscopic Traffic Simulation to obtain a large number of urban traffic scenarios. Based on public data, we establish a microscopic traffic model for a specific area within the Shenzhen urban. Through a simulation, the 24-hour traffic behavior of vehicles for this area is simulated. About 189,752 scenarios covering the entire travel process are generated, which is equivalent to the data collected by a well-equipped car traveling 254,480 kms or 8,288 h. Our scenarios include not only typical urban scenarios such as U-turn and Parallel-driving (two vehicles driving in parallel on a single lane), but also collision accident scenarios of various forms. In addition, in order to evaluate the risks faced by Connected and Automated Vehicles in different scenarios, we design a new criticality metric, Scenario Risk Index, based on the risk assessment principle. The Scenario Risk Index has nothing to do with a certain function, and can quantitatively comprehensively evaluate the criticality and loss of potential accidents. INDEX TERMS Test scenario, microscopic traffic simulation, traffic conflict technique, risk assessment.

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Evaluation of Different Approaches to Address Safety Validation of Automated Driving

Cited by 39 publications

References 14 publications

Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems

Next Generation X-in-the-Loop Validation Methodology for Automated Vehicle Systems

Survey on Scenario-Based Safety Assessment of Automated Vehicles

Low-Cost Urban Test Scenario Generation Using Microscopic Traffic Simulation

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