Predicting Risky Driving in a Connected Vehicle Environment

Abstract

In this paper we propose an unsupervised learning framework to predict risky driving at intersections in a connected vehicle environment. The proposed framework uses time series k-means to categorize multi-dimensional time series trajectories into several context-aware driving patterns. Dynamic time warping (DTW) is implemented within the time series k-means algorithm for measuring the similarity between trajectories. DTW is adopted to make the framework robust to temporal distortions and missing data points. We train an isolation forest (iForest) model on the trajectory dataset to identify anomalous trajectories, and apply this model to clusters to provide Risky Driving Prediction (RDP) scores for each driving pattern. We provide a real-time online assessment approach to predict the risk score of driving trajectories that travel toward a signalized intersection. We use real-world connected vehicle trajectories collected by a road-side unit (RSU) in Ann Arbor, Michigan to implement our framework. We use several quantitative measures as well as illustrations to validate our model. We further discuss how the RDP framework can be used to develop network-level and individual vehicle-level insurance and safety focused applications.