2023 IEEE Intelligent Vehicles Symposium (IV) 2023
DOI: 10.1109/iv55152.2023.10186595
|View full text |Cite
|
Sign up to set email alerts
|

Towards Realistic, Safety-Critical and Complete Test Case Catalogs for Safe Automated Driving in Urban Scenarios

Silvia Thal,
Philip Wallis,
Roman Henze
et al.
Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2
2

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(3 citation statements)
references
References 14 publications
0
3
0
Order By: Relevance
“…As described in Section 2.2, this system is designed to avoid collisions with vehicles darting out at the determined virtual velocity vir V or under. On the other hand, a study of naturalistic driving data at intersections in Japan and Germany by Thal et al shows that darting-out vehicles exceed the speed limit at a certain rate [31]. Therefore, we simulated the case of darting out at a higher velocity than the assumed virtual velocity vir V .…”
Section: Resultsmentioning
confidence: 99%
“…As described in Section 2.2, this system is designed to avoid collisions with vehicles darting out at the determined virtual velocity vir V or under. On the other hand, a study of naturalistic driving data at intersections in Japan and Germany by Thal et al shows that darting-out vehicles exceed the speed limit at a certain rate [31]. Therefore, we simulated the case of darting out at a higher velocity than the assumed virtual velocity vir V .…”
Section: Resultsmentioning
confidence: 99%
“…However, this is insufficient as, e.g., CF 1 could be true if the vehicle V1 is detected only for a single time frame. Hence, we define complementary constraints, i.e., CF (11)(12)(13)(14), to "ensure" continuous success of the intelligence functions from the moment they become active to the appropriate end boundaries, e.g., end of the scenario. For example, CF 1, CF 2, CF 11 and CF 12 together "ensure" the vehicle V1 is detected and recognized correctly throughout the scenario.…”
Section: Key Observationsmentioning
confidence: 99%
“…Background Scenario-based verification and validation (V&V) has emerged as the predominant approach for the performance evaluation of automated driving systems (ADSs) [1]- [3]. Compared to the distance-based approach where the ADSs are required to drive millions of miles [4], [5] to cover sufficient diversity of driving situations due to the long-tail effect, scenario-based methods aim to eliminate the redundancy and distil critical scenarios of interest directly from various data sources, e.g., domain expert knowledge [6]- [12] or naturalist driving data [12]- [24] by diverse types of scenario generation (aka, parameter sampling) algorithms. The generated scenarios are commonly evaluated against two metrics, i.e., criticality (e.g., distance-to-collision [22], [23] and time-to-collision [10], [12], [21], [25]), and coverage (e.g., parameter value combination [11], [12]).…”
Section: Introductionmentioning
confidence: 99%