Criticality Metric for the Safety Validation of Automated Driving using Model Predictive Trajectory Optimization

Junietz, Philipp; Bonakdar, Farid; Klamann, Björn; Winner, Hermann

doi:10.1109/itsc.2018.8569326

Cited by 57 publications

(29 citation statements)

References 19 publications

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“…1 (microscopic metrics) or in relatively small amounts of test data with a research purpose. While microscopic metrics and criteria are necessary for a detailed analysis of the SUT, they alone are not sufficient for the overall safety assurance of AVs because their results need to be extrapolated to estimate macroscopic [127] metrics (definition in Sect. 1.3).…”

Section: Macroscopic Metrics Toward Approvalmentioning

confidence: 99%

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

2022

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Safety assurance of automated driving systems must consider uncertain environment perception. This paper reviews literature addressing how perception testing is realized as part of safety assurance. The paper focuses on testing for verification and validation purposes at the interface between perception and planning, and structures the analysis along the three axes (1) test criteria and metrics, (2) test scenarios, and (3) reference data. Furthermore, the analyzed literature includes related safety standards, safety-independent perception algorithm benchmarking, and sensor modeling. It is found that the realization of safety-oriented perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.

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Section: Macroscopic Metrics Toward Approvalmentioning

confidence: 99%

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

2022

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“…Currently, this is one of the major factors hindering the introduction of ADS in public roads. However, various safety assessment approaches for ADS [7][8][9] have emerged. Most prominent is the scenario-based approach, which uses the definition of a scenario that was originally introduced by [10].…”

Section: Safety Assessment For Adsmentioning

confidence: 99%

Virtual Validation of an Automated Lane-Keeping System with an Extended Operational Design Domain

et al. 2021

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Virtual testing using simulation will play a significant role in future safety validation procedures for automated driving systems, as it provides the needed scalability for executing a scenario-based assessment approach. This article combines multiple essential aspects that are necessary for the virtual validation of such systems. First, a general framework that contains the vital subsystems needed for virtual validation is introduced. Secondly, the interfaces between the subsystems are explored. Additionally, the concept of model fidelities is presented and extended towards all relevant subsystems. For an automated lane-keeping system with two different definitions of an operational design domain, all relevant subsystems are defined and integrated into an overall simulation framework. The resulting difference between both operational design domains is the occurrence of lateral manoeuvres, leading to greater demands of the fidelity of the vehicle dynamics model. The simulation results support the initial assumption that by extending the operation domain, the requirements for all subsystems are subject to adaption. As an essential aspect of harmonising virtual validation frameworks, the article identifies four separate layers and their corresponding parameters. In particular, the tool-specific co-simulation capability layer is critical, as it enables model exchange through consistently defined interfaces and reduces the integration effort. The introduction of this layered architecture for virtual validation frameworks enables further cross-domain collaboration.

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“…If an AV implementation proves unable to follow a Rulebook specification in many different situations, developers can identify the most difficult rules to follow. This can help tag scenarios encountered during validation testing with a level of criticality [40] based on the number and priority of violated rules. Tagging critical scenarios can guide on-road or virtual driving campaigns towards a more rapid reduction of the unknown unsafe scenarios.…”

Section: Validation Of the Sotifmentioning

confidence: 99%

Safety of the Intended Driving Behavior Using Rulebooks

Collin¹,

Bilka²,

Pendleton³

et al. 2020

2020 IEEE Intelligent Vehicles Symposium (IV)

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Autonomous Vehicles (AVs) are complex systems that drive in uncertain environments and potentially navigate unforeseeable situations. Safety of these systems requires not only an absence of malfunctions but also high performance of functions in many different scenarios. The ISO/PAS 21448 [1] guidance recommends a process to ensure the Safety of the Intended Functionality (SOTIF) for road vehicles. This process starts with a functional specification that fully describes the intended functionality and further includes the verification and validation that the AV meets this specification. For the path planning function, defining the correct sequence of control actions for each vehicle in all potential driving situations is intractable. In this paper, the authors provide a link between the Rulebooks framework, presented by [2], and the SOTIF process. We establish that Rulebooks provide a functional description of the path planning task in an AV and discuss the potential usage of the method for verification and validation.

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Criticality Metric for the Safety Validation of Automated Driving using Model Predictive Trajectory Optimization

Cited by 57 publications

References 19 publications

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

A Review of Testing Object-Based Environment Perception for Safe Automated Driving

Virtual Validation of an Automated Lane-Keeping System with an Extended Operational Design Domain

Safety of the Intended Driving Behavior Using Rulebooks

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