A Method for the Estimation of Coexisting Risk-Inducing Factors in Traffic Scenarios
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
The purpose of this paper is to analyze naturalistic driving data and crash data in the United States of America concerning the multiple risk-inducing factors which exist in real traffic. The derived method allows to identify neutral characteristics occurring in many situations and extract risk-inducing attributes from real data by conducting the Successive Odds Ratio Analysis (SORA). The SORA algorithm uses two different types of data, e.g., baseline and crash data, calculates the criticality of each attribute, and evaluates combinations whereby the total criticality is affected positively or negatively. This paper focuses on the exemplary environment-related variables which are provided by the considered databases. Based on identified risk-inducing attributes, their associated characteristics will be investigated by using three measures, i.e., Support, Confidence, and Lift. The method has the potential to generate a scenario catalog consisting of critical test cases for the development of advanced driver assistance systems.
Details
Original language | English |
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Title of host publication | 2019 IEEE Intelligent Vehicles Symposium (IV) |
Publisher | IEEE Xplore |
Pages | 2243-2250 |
Number of pages | 8 |
ISBN (electronic) | 978-1-7281-0560-4, 978-1-7281-0559-8 |
ISBN (print) | 978-1-7281-0561-1 |
Publication status | Published - 12 Jun 2019 |
Peer-reviewed | Yes |
Publication series
Series | IEEE Intelligent Vehicles Symposium (IV) |
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ISSN | 1931-0587 |
Conference
Title | 2019 IEEE Intelligent Vehicles Symposium |
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Abbreviated title | IV 2019 |
Conference number | 30 |
Duration | 9 - 12 June 2019 |
City | Paris |
Country | France |
External IDs
Scopus | 85072292336 |
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ORCID | /0000-0002-0679-0766/work/141544983 |
Keywords
Keywords
- Accidents, Databases, Injuries, Computer crashes, Data mining, Vehicles, Estimation