PiP: Planning-informed Trajectory Prediction for Autonomous Driving

Abstract

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.

European Conference on Computer Vision (ECCV) 2020; Project page at http://haoran-song.github.io/planning-informed-prediction

References in corpus (1)

• TraPHic: Trajectory Prediction in Dense and Heterogeneous Traffic Using Weighted Interactions

Cited by in corpus (5)

• A Novel Graph based Trajectory Predictor with Pseudo Oracle
• Efficient Speed Planning for Autonomous Driving in Dynamic Environment with Interaction Point Model
• DiPA: Probabilistic Multi-Modal Interactive Prediction for Autonomous Driving
• Grounding Linguistic Commands to Navigable Regions
• Co-MTP: A Cooperative Trajectory Prediction Framework with Multi-Temporal Fusion for Autonomous Driving