Machine Learning-Driven Event Characterization under Scarce Vehicular Sensing Data
Nima Taherifard, Murat Şimşek, Charles Lascelles, Burak Kantarcı · 2020
Connected vehicle networks and future autonomous driving systems call for characterization of risky behavior to improve safety models and autonomous driving features. While risky behavior patterns entail potential safety issues on road networks, the advent of vehicular sensing and vehicular networks cannot guarantee accurate characterization of driving/movement behavior of vehicles. One of the roadblocks against robust event characterization systems in connected vehicles is the scarcity of anomalous data to enable training of event classification models. With this in mind, the contribution of this paper is two-fold: 1) a reliable methodology to generate representative data under the scarcity of diverse anomalous sensory data, 2) classification of mobility/driving events of vehicles with high accuracy. To this end, as a baseline method, an optimized deep recurrent neural network-based encoding model is introduced to extract the precise feature representation of the anomalous data. In addition to this, an event characterization pipeline is introduced that uses the representation provided by the encoder to generate reliable data, then train and classify future events. To improve the classification performance of the baseline method, a long short-term memory (LSTM)-based auto-encoder network is proposed. Through experimental results, it is shown that the LSTM-based auto-encoder network can achieve over 0.93 accuracy, which outperforms the baseline recurrent neural network model by 12%.