Real-time driving manoeuvre prediction using IO-HMM and driver cephalo-ocular behaviour

Driving Assistance Systems increase safety and provide a more enjoyable driving experience. Among the objectives motivating these technologies rests the idea of predicting driver intent within the next few seconds, in order to avoid potentially dangerous manoeuvres. In this work, we develop a model of driver behaviour for turn manoeuvres that we then apply to anticipate the most likely turn manoeuvre a driver will effect a few seconds ahead of time. We demonstrate that cephalo-ocular behaviour such as variations in gaze direction and head pose play an important role in the prediction of driver-initiated manoeuvres. We tested our approach on a diverse driving data set recorded with an instrumented vehicle in the urban area of London, ON, Canada. Experiments show that our approach predicts turn manoeuvres 3.8 seconds before they occur with an accuracy over 80% in real-time.