Details
| Originalsprache | Englisch |
|---|---|
| Seitenumfang | 7 |
| ISBN (elektronisch) | 9781728103235 |
| Publikationsstatus | Veröffentlicht - Nov. 2018 |
Publikationsreihe
| Name | IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC |
|---|---|
| Band | 2018-November |
Abstract
Understanding the formation of accidents is of major importance to the automotive industry, its related businesses and policymakers. This is not a trivial task considering the current stream of innovations driven by the development of autonomous vehicles. Historical accident data are inadequate for gauging the safety of future traffic systems. To cope with this challenge, we propose a microscopic traffic model that introduces small errors due to random misperception as an omnipresent cause for accidents - an issue affecting both human drivers and control systems of autonomous vehicles. We model errors dynamically by stochastic processes and investigate their impact on the safety and the efficiency of traffic systems by Monte Carlo simulations. We focus on two case studies: a simple one-lane road segment and a t-junction with turning vehicles.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Maschinenbau
- Ingenieurwesen (insg.)
- Fahrzeugbau
- Informatik (insg.)
- Angewandte Informatik
Ziele für nachhaltige Entwicklung
Zitieren
- Standard
- Harvard
- Apa
- Vancouver
- BibTex
- RIS
7 S. 2018. (IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC; Band 2018-November).
Publikation: Sonstige Publikation › Forschung › Peer-Review
}
TY - GEN
T1 - Modeling Traffic Accidents Caused by Random Misperception
AU - Berkhahn, Volker
AU - Kleiber, Marcel
AU - Schiermeyer, Chris
AU - Weber, Stefan
N1 - Funding information: The implementation of the simulation models is partially based on results of the MODIS research project that was financially supported by the German Research Foundation (DFG). Marcel Kleiber gratefully acknowledges financial support by Hannover Insurance School.
PY - 2018/11
Y1 - 2018/11
N2 - Understanding the formation of accidents is of major importance to the automotive industry, its related businesses and policymakers. This is not a trivial task considering the current stream of innovations driven by the development of autonomous vehicles. Historical accident data are inadequate for gauging the safety of future traffic systems. To cope with this challenge, we propose a microscopic traffic model that introduces small errors due to random misperception as an omnipresent cause for accidents - an issue affecting both human drivers and control systems of autonomous vehicles. We model errors dynamically by stochastic processes and investigate their impact on the safety and the efficiency of traffic systems by Monte Carlo simulations. We focus on two case studies: a simple one-lane road segment and a t-junction with turning vehicles.
AB - Understanding the formation of accidents is of major importance to the automotive industry, its related businesses and policymakers. This is not a trivial task considering the current stream of innovations driven by the development of autonomous vehicles. Historical accident data are inadequate for gauging the safety of future traffic systems. To cope with this challenge, we propose a microscopic traffic model that introduces small errors due to random misperception as an omnipresent cause for accidents - an issue affecting both human drivers and control systems of autonomous vehicles. We model errors dynamically by stochastic processes and investigate their impact on the safety and the efficiency of traffic systems by Monte Carlo simulations. We focus on two case studies: a simple one-lane road segment and a t-junction with turning vehicles.
UR - http://www.scopus.com/inward/record.url?scp=85060441146&partnerID=8YFLogxK
U2 - 10.1109/itsc.2018.8569483
DO - 10.1109/itsc.2018.8569483
M3 - Other publication
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
ER -