Riders Dataset for Behavioural Analytics

Artificial Intelligence based Smart Driver Assistant for Reduction of Road Fatality Reference No.: IIIT-H/IHub/Project/Mobility/2023-24/M2-018?The Dataset presents rider behavior profiles that were collected for 5 riders to study and analyze different driving patterns: sudden acceleration, sleepy driving, normal driving, and haphazard driving. Sudden acceleration involves rapid increases in speed, simulating aggressive driving. Sleepy driving, which was further divided into sleepy left deviation and sleepy right deviation, replicates the behavior of a drowsy rider veering off course. Normal driving represents consistent, stable riding patterns, while haphazard driving includes any erratic maneuvers not covered by the other profiles. This data was used to identify patterns and triggers associated with each profile, calibrating and testing the effectiveness of the model in real-world scenarios. The Dataset was collected for a 4km Test route