Recognizing Driver Braking Intention with Vehicle Data Using Unsupervised Learning Methods

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared. Additionally, the importance evaluation of features from raw dataset respect to driving maneuvers clustering will be proposed. The experiment data are collected from a pure electric vehicle in real world. Final results show that the proposed method can detect driver’s braking intention in a very beginning moment with a high accuracy and the most important features for driving maneuver clustering are selected.