Post-Hoc Scenario-Based Testing of Automated Driving Systems: Classification of Driving Scenarios and Checking of Functional Requirements in Recorded Data

We present a post-hoc approach for scenario-based testing of automated driving systems, enabling the analysis of safety and correctness for (cooperative) automated driving systems in many scenarios without conducting tests for individual scenarios. The system under test is operated in its physical environment’ and data is recorded during operation. Then, driving scenarios are identified in this data and functional requirements are checked, yielding pass or fail verdicts for individual scenarios. We validate the envisioned post-hoc approach in a single-case mechanism experiment by the example of a platooning controller, identifying a previously unknown bug in the tested system, as well as a functional insufficiency concerning the intended operational design domain.

Authors

Institutions

Publication Details

Published
2025-06-22
DOI
https://doi.org/10.1109/iv64158.2025.11097740
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Post-Hoc Scenario-Based Testing of Automated Driving Systems: Classification of Driving Scenarios and Checking of Functional Requirements in Recorded Data

Falk Howar, Harun Teper, Stefan Naujokat, Till Schallau et al.
Autonomous Vehicle Technology and Safety
article

Post-Hoc Scenario-Based Testing of Automated Driving Systems: Classification of Driving Scenarios and Checking of Functional Requirements in Recorded Data

Falk Howar, Harun Teper, Stefan Naujokat, Till Schallau, Dominik Schmid, Jian-Jia Chen, Nick Pawlinorz
article en

Abstract

We present a post-hoc approach for scenario-based testing of automated driving systems, enabling the analysis of safety and correctness for (cooperative) automated driving systems in many scenarios without conducting tests for individual scenarios. The system under test is operated in its physical environment’ and data is recorded during operation. Then, driving scenarios are identified in this data and functional requirements are checked, yielding pass or fail verdicts for individual scenarios. We validate the envisioned post-hoc approach in a single-case mechanism experiment by the example of a platooning controller, identifying a previously unknown bug in the tested system, as well as a functional insufficiency concerning the intended operational design domain.

TU Dortmund University (DE), Machine Intelligence Research Institute (US), Fraunhofer Institute for Software and Systems Engineering (DE)
Openalex Percentile: Top 79%
Autonomous Vehicle Technology and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.