A Hybrid Framework Combining Vehicle System Knowledge with Machine Learning Methods for Improved Highway Trajectory Prediction

Vehicle-to-vehicle communication is a solution to improve the quality of on-road traveling in terms of throughput, safety, efficiency and comfort. However, road users that do not communicate their planned activities can create dangerous situations, so prediction models are needed to foresee and anticipate their motions in the drivable space. Various prediction methods exist, either physics-based, data-based or hybrids, but they all make conservative assumptions about others’ intentions, or they are developed using unrealistic data, and it is unclear how they perform for trajectory prediction. In this work, we introduce and demonstrate an optimal hybrid framework that overcomes these limitations, by combining the predictions of several physics-based and data-based models. Using on-road measured data we show that this novel framework outperforms the individual models in both longitudinal and lateral position predictions. We also discuss the required prediction boundaries from a safety perspective and estimate the accuracies of the models in relation to automated vehicle functions. The results achieved by this method will enable increased safety, comfort and even more proactive reactions of the automated vehicles