This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Hazard Warning Performance in Light of Vehicle Positioning Accuracy and Map-Less Approach Path Matching
- Journal Article
- DOI: https://doi.org/10.4271/2017-01-0073
ISSN: 1946-4614, e-ISSN: 1946-4622
Published March 28, 2017 by SAE International in United States
Citation: Barthels, A., Ress, C., Wiecker, M., and Müller, M., "Hazard Warning Performance in Light of Vehicle Positioning Accuracy and Map-Less Approach Path Matching," SAE Int. J. Passeng. Cars – Electron. Electr. Syst. 10(2):290-300, 2017, https://doi.org/10.4271/2017-01-0073.
Vehicle to Vehicle Communication use case performance heavily relies on market penetration rate. The more vehicles support a use case, the better the customer experience. Enabling these use cases with acceptable quality on vehicles without built-in navigation systems, elaborate map matching and highly accurate sensors is challenging. This paper introduces a simulation framework to assess system performance in dependency of vehicle positioning accuracy for matching approach path traces in Decentralized Environmental Notification Messages (DENMs) in absence of navigation systems supporting map matching. DENMs are used for distributing information about hazards on the road network. A vehicle without navigation system and street map can only match its position trajectory with the trajectory carried in the DENM. The framework supports simulating random and systematic positioning error influences, network effects, and different algorithms for approach path trace matching and DENM approach path trace generation. The tool allows assessing the observable system performance in terms of correct and incorrect matching of approach path traces resulting in true and false, positive and negative hazard warnings provided to the user. We provide an exemplary sensitivity analysis with a simple approach path trace matching implementation depending on varying positioning error influences and develop error bounds for acceptable system performance depending on typical street network geometry in the USA and Germany. The presented framework supports both a frontend for visualization, as well as headless operation for conducting large scale Monte-Carlo simulation runs.