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A Crowd Sensing Approach to Video Classification of Traffic Accident Hotspots
Journal
Lecture Notes in Computer Science (LNCS)
ISSN
0302-9743
ISSN-Digital
1611-3349
ISBN
978-3-319-96133-0
Type
conference paper
Date Issued
2018-07
Author(s)
Abstract
Despite various initiatives over the recent years, the number of traffic accidents has been steadily increasing and has reached over 1.2 million fatalities per year world wide. Recent research has highlighted the positive effects that come from educating drivers about accident hotspots, for example, through in-vehicle warnings of upcoming dangerous areas. Further, it has been shown that there exists a spatial correlation between to locations of heavy braking events and historical accidents. This indicates that emerging accident hotspots can be identified from a high rate of heavy braking, and countermeasures deployed in order to prevent accidents before they appear. In order to contextualize and classify historic accident hotspots and locations of current dangerous driving maneuvers, the research at hand introduces a crowd sensing system collecting vehicle and video data. This system was tested in a naturalistic driving study of 40 vehicles for two months, collecting over 140,000km of driving data and 36,000 videos of various traffic situations. The exploratory results show that through applying data mining approaches it is possible to describe these situations and determine information regarding the involved traffic participants, main causes and location features. This enables accurate insights into the road network, and can help inform both drivers and authorities.
Project(s)
Language
English
HSG Classification
contribution to scientific community
Book title
14th International Conference, MLDM 2018, New York, NY, USA, July 15-19, 2018, Proceedings, Part II
Publisher
Springer
Volume
14
Start page
183
End page
197
Pages
15
Event Title
Machine Learning and Data Mining in Pattern Recognition Conference
Event Location
New Jersey, NJ, U.S.A.
Event Date
July 2018
Subject(s)
Division(s)
Contact Email Address
bernhard.gahr@unisg.ch
Eprints ID
255385