Defining Pass-/Fail-Criteria for Particular Tests of Automated Driving Functions

Klamann, Björn; Lippert, Moritz; Amersbach, Christian; Winner, Hermann

doi:10.1109/itsc.2019.8917483

Cited by 20 publications

(15 citation statements)

References 15 publications

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“…In the domain of automated vehicles, they are called critical scenarios as described by Junietz [22]. In a previous publication [23], we already showed how hazards, that may i.a. be derived by the consideration of violated safety goals, can be broken down to the module level.…”

Section: Resultsmentioning

confidence: 99%

Comparing Different Levels of Technical Systems for a Modular Safety Approval—Why the State of the Art Does Not Dispense with System Tests Yet

Klamann

Winner

2021

Energies

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While systems in the automotive industry have become increasingly complex, the related processes require comprehensive testing to be carried out at lower levels of a system. Nevertheless, the final safety validation is still required to be carried out at the system level by automotive standards like ISO 26262. Using its guidelines for the development of automated vehicles and applying them for field operation tests has been proven to be economically unfeasible. The concept of a modular safety approval provides the opportunity to reduce the testing effort after updates and for a broader set of vehicle variants. In this paper, we present insufficiencies that occur on lower levels of hierarchy compared to the system level. Using a completely new approach, we show that errors arise due to faulty decomposition processes wherein, e.g., functions, test scenarios, risks, or requirements of a system are decomposed to the module level. Thus, we identify three main categories of errors: insufficiently functional architectures, performing the wrong tests, and performing the right tests wrongly. We provide more detailed errors and present examples from the research project UNICARagil. Finally, these findings are taken to define rules for the development and testing of modules to dispense with system tests.

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Section: Resultsmentioning

confidence: 99%

Comparing Different Levels of Technical Systems for a Modular Safety Approval—Why the State of the Art Does Not Dispense with System Tests Yet

Klamann

Winner

2021

Energies

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“…Table 6 summarizes the experiments' key information. A method developed within the project UNICARagil [46] is utilized to identify potential causal factors for integrity failure of the VDSE by using Fault Tree Analysis (FTA) and System Theoretic Process Analysis (STPA) [47]. The selection of measurement experiments is made such that the selected scenarios include as many of the identified potential causal factors as possible in real measurements.…”

Section: Methodsmentioning

confidence: 99%

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

Gottschalg

Leinen

2021

Sensors

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High-integrity information about the vehicle’s dynamic state, including position and heading (yaw angle), is required in order to implement automated driving functions. In this work, a comparison of three integrity algorithms for the vehicle dynamic state estimation of a research vehicle for an application in automated driving is presented. Requirements for this application are derived from the literature. All implemented integrity algorithms output a protection level for the position and heading solution. In the comparison, four measurement data sets obtained for the vehicle dynamic state estimation, which is based on a Global Navigation Satellite Signal receiver, inertial measurement units and odometry information (wheel speeds and steering angles), are used. The data sets represent four driving scenarios with different environmental conditions, especially regarding the satellite signal reception. All in all, the Kalman Integrated Protection Level demonstrated the best performance out of the three implemented integrity algorithms. Its protection level bounds the position error within the specified integrity risk in all four chosen scenarios. For the heading error, this also holds true, with a slight exception in the very challenging urban scenario.

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“…Instead of using an environmental concept like a pedestrian as the center of investigation, the methodology of Ref. [67] outlines how particular test criteria for AV subsystems can be defined in a top-down way, starting from overall safety goals. The methodology is, however, not yet applied specifically to the perception subsystem.…”

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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Defining Pass-/Fail-Criteria for Particular Tests of Automated Driving Functions

Cited by 20 publications

References 15 publications

Comparing Different Levels of Technical Systems for a Modular Safety Approval—Why the State of the Art Does Not Dispense with System Tests Yet

Comparing Different Levels of Technical Systems for a Modular Safety Approval—Why the State of the Art Does Not Dispense with System Tests Yet

Comparison and Evaluation of Integrity Algorithms for Vehicle Dynamic State Estimation in Different Scenarios for an Application in Automated Driving

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

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