elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

A collaborative framework for semi-automatic scenario-based mining of big road user data

Irizar Da Silva, Imanol and Zhang, Meng and Gimm, Kay (2023) A collaborative framework for semi-automatic scenario-based mining of big road user data. IEEE-ITSC 2023 Bilbao, 2023-09-24 - 2023-09-28, Bilbao, Spanien. (In Press)

[img] PDF
5MB

Abstract

Traffic research has benefited from a significant expansion in the amount of available data. Consequently, the need arises for an automatic and efficient method to extract and analyze relevant traffic situations instead of a more traditional and manual approach like manual video annotation. This paper presents a framework to create such a data pipeline. The user must define the target scenarios and the pipeline will abstract the available trajectory data into candidate scenes (groups of interacting trajectories) and select the matches for the target scenarios. These scenes will be mined and modelled automatically for new valuable information. Furthermore, Surrogate Measures of Safety (SMoS) are applied to identify the critical and atypical scenes of the target scenarios. A set of eight scenarios containing interactions between bicycles and MRUs (Motorized Road Users) at the AIM (Application Platform for Intelligent Mobility) Research Intersection in the city of Braunschweig, Germany, was mined by a team of three researchers using the presented framework to validate it with positive results.

Item URL in elib:https://elib.dlr.de/200723/
Document Type:Conference or Workshop Item (Speech)
Title:A collaborative framework for semi-automatic scenario-based mining of big road user data
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Irizar Da Silva, ImanolUNSPECIFIEDhttps://orcid.org/0009-0002-2375-8079UNSPECIFIED
Zhang, MengUNSPECIFIEDhttps://orcid.org/0000-0003-1655-764XUNSPECIFIED
Gimm, KayUNSPECIFIEDhttps://orcid.org/0000-0002-5136-685XUNSPECIFIED
Date:28 May 2023
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:In Press
Keywords:Scenario Mining, Naturalistic Driving Data, Collaborative Data Mining, Map Matching, Interactions, Modelling, Criticality Detection, Anomaly
Event Title:IEEE-ITSC 2023 Bilbao
Event Location:Bilbao, Spanien
Event Type:international Conference
Event Start Date:24 September 2023
Event End Date:28 September 2023
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Road Transport
DLR - Research area:Transport
DLR - Program:V ST Straßenverkehr
DLR - Research theme (Project):V - KoKoVI - Koordinierter kooperativer Verkehr mit verteilter, lernender Intelligenz
Location: Berlin-Adlershof , Braunschweig
Institutes and Institutions:Institute of Transportation Systems > Information Gathering and Modelling, BS
Institute of Transportation Systems > Information Gathering and Modelling, BA
Deposited By: Irizar Da Silva, Imanol
Deposited On:11 Dec 2023 12:35
Last Modified:24 Apr 2024 21:01

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Website and database design: Copyright © German Aerospace Center (DLR). All rights reserved.