Fast Semi-Supervised Anomaly Detection of Drivers' Behavior using Online Sequential Extreme Learning Machine

With the wide spread of artificial intelligence (AI) technologies, many applications using AI are increasingly deployed in many fields. Specially anomaly detection is one of the key applications of AI. Among several targets, detecting anomaly behavior of drivers or vehicles has been attracting due to the growing demand of safety driving. It is crucial to study and evaluate techniques for anomaly driving detection with AI technologies. The Online Sequential Extreme Learning Machine (OS-ELM) is a recently attracting neural network model that has high memory efficiency and can perform highspeed sequential learning with streaming data. Though OSELM is known to be effective for anomaly detection, it has not yet been verified for non-stationary time series data such as driving sensor data. In this paper, we study the effectiveness of OS-ELM based anomaly driving behavior detector using sensor data of vehicles and compared the performance of it with a Hidden Markov Model (HMM) based and traditional Long Short-Term Memory (LSTM) based methods. Since the existing driving behavior benchmark data is not enough for evaluating anomaly driving, we also create a new dataset with a powered wheelchair. Throughout the evaluation, we show that the OS-ELM based anomaly driving detector has almost the same or even better accuracy in anomaly driving detection with much faster sequential learning speed compared with the HMM or LSTM based detector.