Classification of Driving Behavior Events Utilizing Kinematic Classification and Machine Learning for Down Sampled Time Series Data

摘要

The proliferation of connected cars globally has the potential to produce torrents of Big Data that will enable improvements in driver safety, new location based services, improvements in vehicle quality, and optimized vehicle designs. One aspect of connected car data involves driving behavior data and its use for Usage Based Insurance (UBI). UBI has become one of the most widely used applications of driving behavior data. Currently, the transmission and processing of high frequency driving behavior data from the connected car to the cloud is limited by wireless data costs and in-vehicle hardware complexity. To alleviate these issues, we detail the development of a machine learning framework utilizing a kinematic classification methodology applied to down sampled time series vehicle data sets for accurate imputation of driving behavior events in UBI applications. The down-sampled data, consisting of 5 second frames with data fields of timestamp, vehicle speed, and vehicle acceleration is classified into unique kinematic clusters to standardize any driving behavior data distributions. Subsequently, machine learning is used to impute harsh driving events for each 5 second frame in select kinematic clusters. This novel machine learning methodology reduced data set sizes by 75%, utilized a limited set of five attributes, and achieved an average precision and recall of 84.5% and 63.5% for two distinct connected car data sets with 1/5 Hz down-sampled data.