  Precision in event characterization in connected vehicles has become
increasingly important with the responsive connectivity that is available to
the modern vehicles. Event characterization via vehicular sensors are utilized
in safety and autonomous driving applications in vehicles. While
characterization systems have been shown to be capable of predicting the risky
driving patterns, precision of such systems still remains an open issue. The
major issues against the driving event characterization systems need to be
addressed in connected vehicle settings, which are the heavy imbalance and the
event infrequency of the driving data and the existence of the time-series
detection systems that are optimized for vehicular settings. To overcome the
problems, we introduce the application of the prior-knowledge input method to
the characterization systems. Furthermore, we propose a recurrent-based
denoising auto-encoder network to populate the existing data for a more robust
training process. The results of the conducted experiments show that the
introduction of knowledge-based modelling enables the existing systems to reach
significantly higher accuracy and F1-score levels. Ultimately, the combination
of the two methods enables the proposed model to attain 14.7\% accuracy boost
over the baseline by achieving an accuracy of 0.96.
