PiP: Planning-informed Trajectory Prediction for Autonomous Driving
This addresses the challenge of socially compliant motion prediction in multi-agent settings for autonomous driving systems, though it is incremental in combining prediction and planning.
The paper tackles the problem of predicting surrounding vehicle trajectories for autonomous driving by integrating ego-vehicle planning into the prediction process, achieving state-of-the-art performance on highway datasets.
It is critical to predict the motion of surrounding vehicles for self-driving planning, especially in a socially compliant and flexible way. However, future prediction is challenging due to the interaction and uncertainty in driving behaviors. We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. Our approach is differentiated from the traditional manner of prediction, which is only based on historical information and decoupled with planning. By informing the prediction process with the planning of ego vehicle, our method achieves the state-of-the-art performance of multi-agent forecasting on highway datasets. Moreover, our approach enables a novel pipeline which couples the prediction and planning, by conditioning PiP on multiple candidate trajectories of the ego vehicle, which is highly beneficial for autonomous driving in interactive scenarios.