Proposed herein is a technique that introduces a novel mechanism to increase the effectiveness of new drivers by providing real-time alerts that can be generated by comparing a new driver’s attention level with an attention level inferred from a group of good drivers that have safely driven different segments of the new driver’s current journey path. This technique of using individual segments versus an entire path increases the availability of good driving data that can be applied to derive useful insights. In one implementation, this augmentation mechanism can be used within a network edge application that may be deployed within a Fog node inside the vehicle. The novelty of this technique is the use of a very recent Electroencephalography (EEG) dataset generated from good drivers who have driven individual segments within a same journey path.
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Arunachalam, Chidambaram and Dahir, Hazim, "REAL-TIME DRIVING INSIGHTS USING HISTORICAL "ATTENTION LEVEL" DATA FROM GOOD DRIVERS", Technical Disclosure Commons, (December 02, 2019)