PiP: Planning-informed trajectory prediction for autonomous driving

Haoran Song, Wenchao Ding, Yuxuan Chen, Shaojie Shen, Michael Yu Wang, Qifeng Chen

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

21 Citations (Scopus)

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 the 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.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings
EditorsAndrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm
PublisherSpringer
Pages598-614
Number of pages17
ISBN (Print)9783030585884
DOIs
Publication statusPublished - 2020
Externally publishedYes
EventEuropean Conference on Computer Vision 2020 - Glasgow, United Kingdom
Duration: 23 Aug 202028 Aug 2020
Conference number: 16th
https://link.springer.com/book/10.1007/978-3-030-58452-8 (Proceedings)
https://eccv2020.eu (Website)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12366 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Computer Vision 2020
Abbreviated titleECCV 2020
Country/TerritoryUnited Kingdom
CityGlasgow
Period23/08/2028/08/20
Internet address

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