Systematic approach towards safety of the intended functionality
| dc.contributor.advisor | Howar, Falk | |
| dc.contributor.author | Zhu, Zhijing | |
| dc.contributor.referee | Törngren, Martin | |
| dc.date.accepted | 2025-08-20 | |
| dc.date.accessioned | 2025-10-31T06:49:51Z | |
| dc.date.available | 2025-10-31T06:49:51Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | With the transition from advanced driver assistance systems to automated vehicles, safety is becoming a key goal for broad market introduction. Functional safety for low-level automated driving (L0-L2 by SAE standard) is well-measurable and manageable based on the methods described by the standard ISO 26262. However, since the fallback of the human driver is gradually taken out of the loop for automated driving systems (ADS), ISO 26262 is insufficient to cover the analysis of certain critical situations. In these situations, failures are not only due to the vehicle’s E/E system, but will also be in addition due to difficult environmental situations. They are deemed difficult for ADS, as they could potentially be improperly handled due to certain specifications or design insufficiencies. Such conditions are crucial to safety verification: organizing scenario-based testing based on them is more efficient and feasible than exhaustively exploring the scenario space. Meanwhile, this requires systematically identifying these conditions within a given Operational Design Domain (ODD) and developing a corresponding test strategy. Thus, this thesis elaborates on a systematic approach to tackle the challenges around difficult environmental conditions for ADS. Firstly, we interpret the nature of difficult conditions based on the state-of-the-art literature. Next, we summarize three types of difficult conditions, namely the presence/absence of specific environmental factors within the given ODD, specific behaviors of environmental factors, and specific interactions among environmental factors. Correspondingly, we propose formal, machine-readable formulations for each type. Consequently, the difficult conditions can be described uniformly, in favor of evaluating these conditions against certain criteria, creating test cases, and tracing test results. After that, we design both analytical and data-driven approaches to systematically identify difficult environmental conditions from the given ODD: On the one hand, we design an analytical method called Scenario-based Hazard and Fault Analysis (SHFA), which supports domain experts to elicit difficult environmental conditions by analyzing potential hazards in driving scenarios with their domain experience. On the other hand, we aim at finding critical scenarios containing difficult environmental conditions from driving data. To that end, we develop a fully automatic pipeline for reconstructing automated vehicle disengagement scenarios from real test drives. Finally, we present an overall test strategy and a test case generation method to integrate difficult conditions into scenario-based testing. This thesis has been developed in close collaboration with industrial automated vehicle production, and therefore, the presented concept and methods target conformance and compliance with the state-of-the-art automotive safety standards like ISO 21448 and regulations like EU 2022/1426. To the best of our knowledge, this thesis provides the first coherent framework for identifying, managing, and testing difficult environmental conditions for verifying ADS on the system level. The empirical findings suggest that concepts and methods around difficult environmental conditions can significantly contribute to identifying and constructing critical test cases, thereby advancing scenario-based verification for automated vehicles. | en |
| dc.identifier.uri | http://hdl.handle.net/2003/44064 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-25831 | |
| dc.language.iso | en | |
| dc.subject | Automated driving systems | en |
| dc.subject | Verification & validation | en |
| dc.subject | Scenario-based testing | en |
| dc.subject.ddc | 004 | |
| dc.subject.rswk | Autonomes Fahrzeug | de |
| dc.subject.rswk | Verifikation | de |
| dc.subject.rswk | Validierung | de |
| dc.title | Systematic approach towards safety of the intended functionality | en |
| dc.title.alternative | Eliciting and evaluating difficult environmental conditions for automated driving systems | en |
| dc.type | Text | |
| dc.type.publicationtype | PhDThesis | |
| dcterms.accessRights | open access | |
| eldorado.secondarypublication | false |