Observe-Driver-and-Learn Platform for Relevance Estimation in Safety Warning Applications from Vehicular Ad Hoc Network

Abstract

Safety warning applications from a vehicular ad hoc network aim to disseminate alerts about dangerous events on the road with wireless communication technology and, when necessary, warn drivers receiving such alerts. Examples include the emergency electronic brake light or the highway merge warning. A major issue with such applications is false warnings, which lessen any safety benefits the applications provide. A high number of false warnings will lead to driver desensitization and reduce any potential safety benefits. Therefore any received alert has to be evaluated in terms of its relevance for the given vehicle. However, the relevance depends on a combination of many factors and is specific to a given application, so defining an estimator is difficult. A machine-learning method based on the principle of observe-driver-and-learn is proposed for finding relevance estimators. This method is evaluated for its effectiveness with three safety applications: electronic emergency brake lights, the highway merge warning, and the control loss warning.