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 language | English |
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Title of host publication | Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings |
Editors | Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm |
Publisher | Springer |
Pages | 598-614 |
Number of pages | 17 |
ISBN (Print) | 9783030585884 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | European Conference on Computer Vision 2020 - Glasgow, United Kingdom Duration: 23 Aug 2020 → 28 Aug 2020 Conference number: 16th https://link.springer.com/book/10.1007/978-3-030-58452-8 (Proceedings) https://eccv2020.eu (Website) |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12366 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | European Conference on Computer Vision 2020 |
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Abbreviated title | ECCV 2020 |
Country/Territory | United Kingdom |
City | Glasgow |
Period | 23/08/20 → 28/08/20 |
Internet address |
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