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Intelligent Transportation Systems: When is Safety Information Relevant?
| Citations: | 4 - 4 self |
BibTeX
@MISC{Szczurek_intelligenttransportation,
author = {Piotr Szczurek and Bo Xu and Ouri Wolfson and Jie Lin},
title = {Intelligent Transportation Systems: When is Safety Information Relevant?},
year = {}
}
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Abstract
Abstract — In this paper, we compare two methods of estimating relevance for the emergency electronic brake light application. One uses an analytically derived formula based on the minimal safety gap required to avoid a collision. The other method uses a machine learning approach. The application works by disseminating reports about vehicles that are performing emergency deceleration in effort to warn drivers about the need to perform emergency braking. Vehicles which receive such reports have to decide whether the information contained in the report is relevant to the driver, and warn the driver if that is the case. Common ways to determine relevance are based on the lane or direction information, but using only these attributes can still lead to many false warnings, which can desensitize the driver. Desensitized drivers may ignore warnings or turn off the system completely, thus eliminating any safety benefits of the application. We show that the machine learning method, in comparison to the analytically derived formula, is able to significantly reduce the number of false warnings by learning from the actions drivers take after receiving a report. The methods were compared using simulated experiments with a range of traffic and communication parameters. Keywords- machine learning, vehicle safety
Keyphrases
safety information relevant intelligent transportation system safety benefit common way minimal safety gap vehicle safety many false warning emergency electronic brake light application emergency deceleration keywords machine learning direction information false warning action driver communication parameter simulated experiment
