Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints
This addresses the problem of trajectory forecasting for automated driving systems, though it is incremental as it builds on existing generative models.
The paper tackles the lack of diversity in multiple-trajectory forecasting for road users by proposing a method that combines a pretrained generative model with a determinantal point process and knowledge-based constraints, resulting in significant improvements in diversity and quality on the nuScenes dataset.
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.