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High fidelity behavior prediction of human drivers is crucial for efficientand safe deployment of autonomous vehicles, which is challenging due to thestochasticity, heterogeneity, and time-varying nature of human behaviors. Onone hand, the trained prediction model can only capture the motion pattern inan average sense, while the nuances among individuals can hardly be reflected.On the other hand, the prediction model trained on the training set may notgeneralize to the testing set which may be in a different scenario or datadistribution, resulting in low transferability and generalizability. In thispaper, we applied a $\tau$-step modified Extended Kalman Filter parameteradaptation algorithm (MEKF$_\lambda$) to the driving behavior prediction task,which has not been studied before in literature. With the feedback of theobserved trajectory, the algorithm is applied to neural-network-based models toimprove the performance of driving behavior predictions across different humansubjects and scenarios. A new set of metrics is proposed for systematicevaluation of online adaptation performance in reducing the prediction errorfor different individuals and scenarios. Empirical studies on the best layer inthe model and steps of observation to adapt are also provided.

Online Adaptation of Neural Network Models by Modified Extended Kalman Filter for Customizable and Transferable Driving Behavior Prediction