  We propose a method for automated synchronization of vehicle sensors useful
for the study of multi-modal driver behavior and for the design of advanced
driver assistance systems. Multi-sensor decision fusion relies on synchronized
data streams in (1) the offline supervised learning context and (2) the online
prediction context. In practice, such data streams are often out of sync due to
the absence of a real-time clock, use of multiple recording devices, or
improper thread scheduling and data buffer management. Cross-correlation of
accelerometer, telemetry, audio, and dense optical flow from three video
sensors is used to achieve an average synchronization error of 13 milliseconds.
The insight underlying the effectiveness of the proposed approach is that the
described sensors capture overlapping aspects of vehicle vibrations and vehicle
steering allowing the cross-correlation function to serve as a way to compute
the delay shift in each sensor. Furthermore, we show the decrease in
synchronization error as a function of the duration of the data stream.
