A Systematic Approach to Analyzing Perception Architectures in Autonomous Vehicles

Kurzidem, Iwo; Ahmad, Sarosh; Schleiss, Philipp

doi:10.1007/978-3-030-58920-2_10

Cited by 11 publications

(8 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Ref. [145] identifies perception subsystem inputs that lead to highly uncertain world models for a given SUT by propagating uncertainties through the individual perception components.…”

Section: Test Scenarios Specific To the Sutmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

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.

show abstract

Section: Test Scenarios Specific To the Sutmentioning

confidence: 99%

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

2022

View full text Add to dashboard Cite

show abstract

“…Instead, the unexpected insufficiencies that the DNNs of the SUT have learned might lead to more critical behavior than what a usually suspected external influence could trigger [22,Safety Concern 4]. For example, [145] identifies perception subsystem inputs that lead to highly uncertain world models for a given SUT by propagating uncertainties through the individual perception components.…”

Section: Generating a Test Scenario Catalogmentioning

confidence: 99%

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

Hoss,

Scholtes,

Eckstein

2021

Preprint

View full text Add to dashboard Cite

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. We focus on testing for verification and validation purposes at the interface between perception and planning, and structure our 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, safetyindependent perception algorithm benchmarking, and sensor modeling. We find that the realization of safety-aware perception testing remains an open issue since challenges concerning the three testing axes and their interdependencies currently do not appear to be sufficiently solved.

show abstract

“…The granularity and definition of each property has been selected according to physical realisability, for instance, tyre sizes only within actual produced dimensions. The resulting domain model consists of all feasible combinations of these properties and can be used to identify known triggering events describing known performance limitations of the systems [9]. The domain model can also be used to determine coverage criteria for test cases.…”

Section: Domain Analysismentioning

confidence: 99%

Safety Assurance of Machine Learning for Chassis Control Functions

Burton

Kurzidem

Schwaiger

et al. 2021

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

This paper describes the application of machine learning techniques and an associated assurance case for a safety-relevant chassis control system. The method applied during the assurance process is described including the sources of evidence and deviations from previous ISO 26262 based approaches. The paper highlights how the choice of machine learning approach supports the assurance case, especially regarding the inherent explainability of the algorithm and its robustness to minor input changes. In addition, the challenges that arise if applying more complex machine learning technique, for example in the domain of automated driving, are also discussed. The main contribution of the paper is the demonstration of an assurance approach for machine learning for a comparatively simple function. This allowed the authors to develop a convincing assurance case, whilst identifying pragmatic considerations in the application of machine learning for safety-relevant functions.

show abstract

A Systematic Approach to Analyzing Perception Architectures in Autonomous Vehicles

Cited by 11 publications

References 25 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

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

Safety Assurance of Machine Learning for Chassis Control Functions

Contact Info

Product

Resources

About