  Highway driving invariably combines high speeds with the need to interact
closely with other drivers. Prediction methods enable autonomous vehicles (AVs)
to anticipate drivers' future trajectories and plan accordingly. Kinematic
methods for prediction have traditionally ignored the presence of other
drivers, or made predictions only for a limited set of scenarios. Data-driven
approaches fill this gap by learning from large datasets to predict
trajectories in general scenarios. While they achieve high accuracy, they also
lose the interpretability and tools for model validation enjoyed by kinematic
methods. This letter proposes a novel kinematic model to describe car-following
and lane change behavior, and extends it to predict trajectories in general
scenarios. Experiments on highway datasets under varied sensing conditions
demonstrate that the proposed method outperforms state-of-the-art methods.
