Estimating Relevance for the Emergency Electronic Brake Light Application

In this paper, we compare two methods of estimating relevance for the emergency electronic brake light application: One method uses an analytically derived formula based on the minimum safety gap that is required to avoid a collision, whereas the other method uses a machine learning approach. The application works by disseminating reports about vehicles that perform emergency deceleration in an effort to warn drivers about the need to perform emergency braking. Vehicles that receive such reports have to decide on whether the information contained in the report is relevant to the driver and warn the driver if that is the case. Common ways of determining relevance are based on the lane or direction information, but using only these attributes can lead to many false warnings, which can desensitize the driver. Desensitized drivers may ignore warnings or completely turn off the system, thus eliminating any safety benefits of the application. We show that the machine learning method, compared with the analytically derived formula, can significantly reduce the number of false warnings by learning from the actions that drivers take after receiving a report. The methods were compared using simulated experiments with a range of traffic and communication parameters.