AGen: Adaptable generative prediction networks for autonomous driving

Abstract

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, such as parameter sharing generative adversarial imitation learning (PS-GAIL), are able to learn interactive models. However, the learned models average out individual differences. When used to predict trajectories of individual vehicles, these models are biased. This paper introduces an adaptable generative prediction framework (AGen), which performs online adaptation of the offline learned models to recover individual differences for better prediction. In particular, we combine the recursive least square parameter adaptation algorithm (RLS-PAA) with the offline learned model from PS-GAIL. RLS-PAA has analytical solutions and is able to adapt the model for every single vehicle efficiently online. The proposed method is able to reduce the root mean squared prediction error in a 2.5s time window by 60%, compared with PS-GAIL.