Improving Vehicle Trajectory Prediction with Online Learning.

In autonomous driving systems, predicting the trajectory of surrounding vehicles facilitates the decision-making and trajectory planning of ego cars. Most previous works train the trajectory predictor based on the offline dataset. While the offline general predictors capture the average distribution in the dataset, they commonly neglect the low-probability corner cases that outside the general distribution of dataset. However, the corner cases might cause inevitable displacement errors and goals missing in the evaluation. Offline data augmentation and model refinement could essentially improve the accuracy while we can leverage the historical trajectories to specialize the predictor by self-supervision. In this paper, we propose an online learning framework that updates the general trajectory predictor with sequential history data at corner cases. We design the temporal data-generation method and training scheme for online learning, which can generally accommodate learning-based predictors. Since online learning costs expensive online computation resources, we devise a switching discriminator to distinguish the corner cases by evaluating the metric improvement of online learning over the offline general predictor. We validate that the online learning method can effectively reduce the displacement errors and miss rate caused by vehicle velocity variation and promote the intention convergence at multi-modal intersections. Experiments also show that the switching discriminator limits the switching-on rate of online learning cases to 2.45% while reducing the miss rate by 33.4%. The code for generating sequentially arranged online learning dataset(Argoverse) is in https://gitee.com/mindspore/models/tree/ master/research/cv/TraPred OnlineLearning.