Related papers: PiP: Planning-informed Trajectory Prediction for Autonomous Driving

PiP: Planning-informed Trajectory Prediction for Autonomous Driving

URL: http://arxiv.org/abs/2003.11476v2
Date: Mon, 18 Jan 2021 06:14:56 GMT
Title: PiP: Planning-informed Trajectory Prediction for Autonomous Driving
Authors: Haoran Song, Wenchao Ding, Yuxuan Chen, Shaojie Shen, Michael Yu Wang, Qifeng Chen
Abstract summary: We propose planning-informed trajectory prediction (PiP) to tackle the prediction problem in the multi-agent setting. 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.
Score: 69.41885900996589
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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