TU Darmstadt / ULB / TUbiblio

Acceleration-Based Collision Criticality Metric for Holistic Online Safety Assessment in Automated Driving

Wang, Cheng ; Popp, Christoph ; Winner, Hermann (2024)
Acceleration-Based Collision Criticality Metric for Holistic Online Safety Assessment in Automated Driving.
In: IEEE Access, 2022, 10
doi: 10.26083/tuprints-00026574
Article, Secondary publication, Publisher's Version

WarningThere is a more recent version of this item available.

Abstract

Criticality metrics are not only essential for collision avoidance systems but also play a vital role for verification and validation of automated vehicles. With respect to the first application, criticality metrics should be real-time capable and applicable in various traffic situations. For the second application, holistic safety evaluation by criticality metrics is desired. However, existing criticality metrics hardly meet these two requirements. They are either only applicable in post-processing or only assess the safety of maneuvers in longitudinal direction. Therefore, we propose a new acceleration-based criticality metric, which is real-time capable and applicable in both longitudinal and lateral directions. The theory of the proposed criticality metric is introduced and the definition is explained according to different scenarios. A simulation platform is established to validate the criticality metric. The simulation results demonstrate that the proposed criticality metric takes all possible maneuvers into account when meeting a critical situation. Apart from the longitudinal behavior, the lateral behavior of automated vehicles can also be evaluated in real-time. Consequently, it has a wider application scope than other criticality metrics. To demonstrate its contribution to verification and validation of automated vehicles, we apply the criticality metric to a naturalistic driving dataset. The results prove that our criticality metric has a higher precision and recall than Time to Collision. Additionally, it combines the abilities of Time to Collision and Time Head Way to assess the safety of automated vehicles in the longitudinal direction. The proposed criticality metric is real-time capable and is suitable for different situations.

Item Type: Article
Erschienen: 2024
Creators: Wang, Cheng ; Popp, Christoph ; Winner, Hermann
Type of entry: Secondary publication
Title: Acceleration-Based Collision Criticality Metric for Holistic Online Safety Assessment in Automated Driving
Language: English
Date: 29 January 2024
Place of Publication: Darmstadt
Year of primary publication: 2022
Place of primary publication: New York, NY
Publisher: IEEE
Journal or Publication Title: IEEE Access
Volume of the journal: 10
Collation: 13 Seiten
DOI: 10.26083/tuprints-00026574
URL / URN: https://tuprints.ulb.tu-darmstadt.de/26574
Corresponding Links:
Origin: Secondary publication service
Abstract:

Criticality metrics are not only essential for collision avoidance systems but also play a vital role for verification and validation of automated vehicles. With respect to the first application, criticality metrics should be real-time capable and applicable in various traffic situations. For the second application, holistic safety evaluation by criticality metrics is desired. However, existing criticality metrics hardly meet these two requirements. They are either only applicable in post-processing or only assess the safety of maneuvers in longitudinal direction. Therefore, we propose a new acceleration-based criticality metric, which is real-time capable and applicable in both longitudinal and lateral directions. The theory of the proposed criticality metric is introduced and the definition is explained according to different scenarios. A simulation platform is established to validate the criticality metric. The simulation results demonstrate that the proposed criticality metric takes all possible maneuvers into account when meeting a critical situation. Apart from the longitudinal behavior, the lateral behavior of automated vehicles can also be evaluated in real-time. Consequently, it has a wider application scope than other criticality metrics. To demonstrate its contribution to verification and validation of automated vehicles, we apply the criticality metric to a naturalistic driving dataset. The results prove that our criticality metric has a higher precision and recall than Time to Collision. Additionally, it combines the abilities of Time to Collision and Time Head Way to assess the safety of automated vehicles in the longitudinal direction. The proposed criticality metric is real-time capable and is suitable for different situations.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-265748
Additional Information:

This work was supported in part by the Deutsche Forschungsgemeinschaft (DFG—German Research Foundation), and in part by the Open Access Publishing Fund of Technical University of Darmstadt.

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics
Divisions: 16 Department of Mechanical Engineering
16 Department of Mechanical Engineering > Institute of Automotive Engineering (FZD)
Date Deposited: 29 Jan 2024 10:38
Last Modified: 30 Jan 2024 06:32
PPN:
Corresponding Links:
Export:
Suche nach Titel in: TUfind oder in Google

Available Versions of this Item

Send an inquiry Send an inquiry

Options (only for editors)
Show editorial Details Show editorial Details