Characterizing risky driving behavior is crucial in a connected vehicle environment, particularly to improve driving experience through enhanced safety features. Artificial intelligence-backed solutions are vital components of the modern transportation. However, such systems require significant volume of driving event data for an acceptable level of performance. To address the issue, this study proposes a novel framework for precise risky driving behavior detection that takes advantage of an attention-based neural network model. The proposed framework aims to recognize five driving events including harsh brake, aggressive acceleration, harsh left turn and harsh right turn alongside the normal driving behavior. Through numerical results, it is shown that the proposed model outperforms the stateof-the-art solutions by reaching an average accuracy of 0.96 and F1-score of 0.92 for all classes of driving events. Thus, it reduces the false positive instances compared to the previous models. Furthermore, through extensive experiments, structural details of the attention-based neural network is investigated to provide the most viable configuration for the analysis of the vehicular sensory data.
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 knowledgebased 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.
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