SINADRA: Towards a Framework for Assurable Situation-Aware Dynamic Risk Assessment of Autonomous Vehicles

Reich, Jan; Trapp, Mario

doi:10.1109/edcc51268.2020.00017

Cited by 24 publications

(15 citation statements)

References 11 publications

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“…Monitoring the automated vehicle's state during run-time enables a dynamical assessment of the situational risk the vehicle is currently in [50], also known under the term dynamic risk assessment [80]. Based on these monitoring results, the system is able to then execute appropriate reactions, e.g.…”

Section: Run-time Monitoring (A2)mentioning

confidence: 99%

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

Westhofen

Neurohr

Koopmann

et al. 2022

Arch Computat Methods Eng

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The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today’s society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a well-established concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically choose adequate criticality metrics for such applications, we extensively review the state of the art of criticality metrics, their properties, and their applications in the context of automated driving. Based on this review, we propose a suitability analysis as a methodical tool to be used by practitioners. Both the proposed method and the state of the art review can then be harnessed to select well-suited measurement tools that cover an application’s requirements, as demonstrated by an exemplary execution of the analysis. Ultimately, efficient, valid, and reliable measurements of an automated vehicle’s safety performance are a key requirement for demonstrating its trustworthiness.

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Section: Run-time Monitoring (A2)mentioning

confidence: 99%

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

Westhofen

Neurohr

Koopmann

et al. 2022

Arch Computat Methods Eng

View full text Add to dashboard Cite

show abstract

“…Dynamic Risk Assessment (DRA) relies on situation awareness to support run-time decision-making of an ADS, e.g. [31,124,127,128]. In addition to situation awareness, DRA capabilities need to connect a given situation to the safety requirements of the system, or at least to some kind of (quantitative) risk measurement.…”

Section: Dynamic Risk Assessmentmentioning

confidence: 99%

“…IV-E and the TA metrics discussed in Sec. VI-A, Reich and Trapp [31] and Feth et al [128] suggest using risk metrics as a proxy for deducing the current (dynamic) risk.…”

Section: Dynamic Risk Assessmentmentioning

confidence: 99%

“…to cope with changes in the operational environment of autonomous systems and as a means to reduce the residual risk of operating the system through monitoring; • shifting portions of the assurance task from designtime to run-time, addressing (ii) and coping with (a) as well as (b), has been proposed as an effective means to maintain assurance of the system while allowing for improved performance. Supportive concepts including ConSerts [25,26], and Digital Dependability Identities (DDIs) [27,28,29]; and • Dynamic Safety Management (DSM) and Dynamic Risk Assessment (DRA), allowing the system to dynamically address (a) -(c) and support solving (i) and (ii), have been suggested as a means to allow the tactical decisions of the ADS to be made using appropriate runtime measures of risk [26,30,31].…”

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confidence: 99%

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Holistic Perspectives on Safety of Automated Driving Systems - Methods for Provision of Evidence

Gyllenhammar¹,

Campos²,

Törngren³

2022

Preprint

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<p>In recent years, the enormous investments in Automated Driving Systems (ADSs) have distinctly advanced ADS technologies. Despite promises made by several high profile auto-makers, it has however become clear that the challenges involved for deploying ADS have been drastically underestimated. This paper focuses on the challenge of providing sufficient evidence to support the safety claims of ADSs. The provisioning of such evidence clearly relates both to technical maturity of ADS systems (including actual experiences from deploying such systems), and on the development of methodologies for reasoning about ADS safety claims. Contrary to previous generations of automotive systems, common design, development, verification and validation methods for safety critical systems do not suffice to cope with the increased complexity and operational uncertainties of an ADS. Therefore, the aim of this paper is to provide an understanding of existing methods focusing on the development of a safe ADS and, most importantly, identifying the associated challenges and gaps. </p> <p>We present eight challenges, collectively distinguishing ADSs from safety critical systems in general, and discuss the existing methods in the light of these eight challenges. Based on this discussion, a set of research gaps are identified.</p>

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“…Monitoring the automated vehicle's state during run-time enables a dynamical assessment of the situational risk the vehicle is currently in [51], also known under the term dynamic risk assessment [81]. Based on these monitoring results, the system is able to then execute appropriate reactions, e.g.…”

Section: Run-time Monitoring (A2)mentioning

confidence: 99%

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

Westhofen¹,

Neurohr²,

Koopmann³

et al. 2021

Preprint

View full text Add to dashboard Cite

The large-scale deployment of automated vehicles on public roads has the potential to vastly change the transportation modalities of today's society. Although this pursuit has been initiated decades ago, there still exist open challenges in reliably ensuring that such vehicles operate safely in open contexts. While functional safety is a wellestablished concept, the question of measuring the behavioral safety of a vehicle remains subject to research. One way to both objectively and computationally analyze traffic conflicts is the development and utilization of so-called criticality metrics. Contemporary approaches have leveraged the potential of criticality metrics in various applications related to automated driving, e.g. for computationally assessing the dynamic risk or filtering large data sets to build scenario catalogs. As a prerequisite to systematically chooseThe research leading to these results is funded by the German Federal Ministry for Economic Affairs and Energy within the projects 'VVM -Verification & Validation Methods for Automated Vehicles Level 4 and 5' and 'SET Level -Simulation-based Development and Testing of Automated Driving', based on a decision by the Parliament of the Federal Republic of Germany.

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SINADRA: Towards a Framework for Assurable Situation-Aware Dynamic Risk Assessment of Autonomous Vehicles

Cited by 24 publications

References 11 publications

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

Holistic Perspectives on Safety of Automated Driving Systems - Methods for Provision of Evidence

Criticality Metrics for Automated Driving: A Review and Suitability Analysis of the State of the Art

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