Prior Knowledge Input to Improve LSTM Auto-encoder-based Characterization of Vehicular Sensing Data

Precision in event characterization in connected vehicles has become increasingly important with the responsive connectivity that is available to modern vehicles. Event characterization via vehicular sensors is utilized in safety and autonomous driving applications in vehicles. While characterization systems are capable of predicting risky driving patterns, the precision of such systems 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 modeling 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 a 14.7% accuracy boost over the baseline by achieving an accuracy of 0.96.