Scenario-Based Functional Safety for Automated Driving on the Example of Valet Parking

Schönemann, Valerij; Winner, Hermann; Glock, Thomas; Otten, Stefan; Sax, Eric; Boeddeker, Bert; Verhaeg, Geert; Tronci, Fabrizio; Padilla, Gustavo Garcia

doi:10.1007/978-3-030-03402-3_5

Cited by 13 publications

(8 citation statements)

References 8 publications

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“…An example of how functional safety requirements for the perception subsystem can be derived based on a fault tree analysis (FTA) is given in Ref. [122] in the context of automated valet parking.…”

Section: Concrete Approaches Of Specifying Perceptionmentioning

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: Concrete Approaches Of Specifying Perceptionmentioning

confidence: 99%

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

2022

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“…Of particular importance for any safety-relevant application is a systematic safety process that includes identification of hazards and risks, derivation of safety goals and decomposition of the high-level safety goals into safety requirements on item and component level [30] [31]. For systems up to SAE Level 2, this is well covered by ISO 26262 and ISO/PAS 21448.…”

Section: Requirement Elicitationmentioning

confidence: 99%

Fundamental Considerations around Scenario-Based Testing for Automated Driving

Neurohr¹,

Westhofen²,

Henning³

et al. 2020

Preprint

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The homologation of automated vehicles, being safety-critical complex systems, requires sound evidence for their safe operability. Traditionally, verification and validation activities are guided by a combination of ISO 26262 and ISO/PAS 21448, together with distance-based testing. Starting at SAE Level 3, such approaches become infeasible, resulting in the need for novel methods. Scenario-based testing is regarded as a possible enabler for verification and validation of automated vehicles. Its effectiveness, however, rests on the consistency and substantiality of the arguments used in each step of the process. In this work, we sketch a generic framework around scenario-based testing and analyze contemporary approaches to the individual steps. For each step, we describe its function, discuss proposed approaches and solutions, and identify the underlying arguments, principles and assumptions. As a result, we present a list of fundamental considerations for which evidences need to be gathered in order for scenario-based testing to support the homologation of automated vehicles. * The research leading to these results is partly funded by the German Federal Ministry for Economic Affairs and Energy within the project "VVM -Verification & Validation Methods for Automated Vehicles Level 4 and 5" and by the German Federal Ministry of Education and Research within the project "TESTOMAT -The Next Level of Test Automation" under grant agreement No. 01IS17026H.

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“…The focus of our paper is on the simulation of the entire traffic in the parking environments. In (Schönemann et al, 2019) the authors study the parking maneuvers and derive analytically the minimum safety distance between traffic participants. Our framework can be used to derive empirically certain parameters important from the safety perspective but its main goal is in identifying malfunction and corner cases of the autonomous driving algorithms in complex traffic situations.…”

Section: Parking Sites Simulationmentioning

confidence: 99%

“…The scenarios are described in OpenSCENARIO format and can be played in various driving simulators. The toolchain was integrated into a broader test system comprising of autonomous vehicle control logic, vehicle dynamic, sensors, traffic observer, real data database (Schönemann et al, 2019;Esen et al, 2020).…”

Section: Margin Parking Slotmentioning

confidence: 99%

An Automatic Scenario Generator for Validation of Automated Valet Parking Systems

Tagliavini

Ferraro

Kloda

et al. 2020

Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems

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A primary goal of self-driving car manufacturers is to create an autonomous car system that is clearly and demonstrably safer than an average human-controlled car. The real-world tests are expensive, time-consuming and potentially dangerous. The virtual simulation is therefore required. The autonomous driving valet parking is expected to be the first commercially available automated driving function without a human driver at the wheel (SAE Level 4). Although many simulation solutions for the automotive market already exist, none of them features the parking environments. In this paper, we propose a new software virtual scenario generator for the parking sites. The tool populates the synthetics parking maps with objects and actions related to these environments: the cars driving from the drop-off point towards the vacant slots and the randomly placed parked cars, each with a given probability of exiting its slot. The generated scenarios are in the OpenSCENARIO format and are fully simulated in the Virtual Test Drive simulator.

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Scenario-Based Functional Safety for Automated Driving on the Example of Valet Parking

Cited by 13 publications

References 8 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

Fundamental Considerations around Scenario-Based Testing for Automated Driving

An Automatic Scenario Generator for Validation of Automated Valet Parking Systems

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