Comparison of parametric and non-parametric approaches for vehicle speed prediction

Predicting the future speed of the ego-vehicle is a necessary component of many Intelligent Transportation Systems (ITS) applications, in particular for safety and energy management systems. In the last four decades many parametric speed prediction models have been proposed, the most advanced ones being developed for use in traffic simulators. More recently non-parametric approaches have been applied to closely related problems in robotics. This paper presents a comparative evaluation of parametric and non-parametric approaches for speed prediction during highway driving. Real driving data is used for the evaluation, and both short-term and long-term predictions are tested. The results show that the relative performance of the different models vary strongly with the prediction horizon. This should be taken into account when selecting a prediction model for a given ITS application.