Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints - Cnam - Conservatoire national des arts et métiers Access content directly
Conference Papers Year : 2022

Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints

Abstract

Predicting multiple trajectories for road users is important for automated driving systems: ego-vehicle motion planning indeed requires a clear view of the possible motions of the surrounding agents. However, the generative models used for multiple-trajectory forecasting suffer from a lack of diversity in their proposals. To avoid this form of collapse, we propose a novel method for structured prediction of diverse trajectories. To this end, we complement an underlying pretrained generative model with a diversity component, based on a determinantal point process (DPP). We balance and structure this diversity with the inclusion of knowledge-based quality constraints, independent from the underlying generative model. We combine these two novel components with a gating operation, ensuring that the predictions are both diverse and within the drivable area. We demonstrate on the nuScenes driving dataset the relevance of our compound approach, which yields significant improvements in the diversity and the quality of the generated trajectories.

Dates and versions

hal-04071275 , version 1 (17-04-2023)

Licence

Copyright

Identifiers

Cite

Laura Calem, Hedi Ben-Younes, Patrick Perez, Nicolas Thome. Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints. 2022 26th International Conference on Pattern Recognition (ICPR), Aug 2022, Montreal, Canada. pp.3478-3484, ⟨10.1109/ICPR56361.2022.9956270⟩. ⟨hal-04071275⟩
20 View
0 Download

Altmetric

Share

Gmail Facebook X LinkedIn More