In highly interactive driving scenarios, accurate prediction of other road participants is critical for safe and efficient navigation of autonomous cars. Prediction is challenging due to the difficulty in modeling various driving behavior, or learning such a model. The model should be interactive and reflect individual differences. Imitation learning methods are able to learn interactive models.

However, the learned models usually average out individual differences. When used to predict trajectories of individual vehicles, these models are biased. This project investigates adaptable prediction frameworks, which performs online adaptation of the offline learned models to recover individual differences and time-varying behaviors for better prediction. In particular, we combine a family of recursive least square parameter adaptation algorithms (RLS-PAA) with various offline learned models. RLS-PAA has analytical solutions and is able to adapt the model for every single vehicle efficiently online.

Sponsor: Holomatic

Period of Performance: 2019 ~ 2019

Point of Contact: Tianhao Wei