This paper describes main challenges encountered during data enrichment phase of connected vehicle experiments. It also compares data imputation approaches for data coming from actual driving scenarios and obtained using in-vehicle data acquisition devices. Three distinct window-based approaches were used for cleaning and imputing the missing values in different CAN-bus (Controller Area Network) signals. Lengths of windows used for data imputation for the three approaches were: 1) entire time-course, 2) day, and 3) trip (defined as duration between vehicle engine ON to OFF). An algorithm for identification of engine ON and OFF events will also be presented, in case this signal is not explicitly captured during the data acquisition phase. As a case study, these imputation techniques were applied to the data from vehicle’s CAN information in a driver behavior classification experiment. Forty four connected vehicles were used to provide data on GPS location, engine speed, vehicle speed, engine torque, brake, clutch, acceleration pedal, direction, shift indicator, fuel level, and gear. Comparison of key statistical parameters and insights obtained will be presented and discussed to demonstrate the importance of a robust and accurate data imputation technique. Performance on modeling of driving behavior using these imputation techniques, by leveraging classification models, will also be reported.