  Representing diverse and plausible future trajectories of actors is crucial
for motion forecasting in autonomous driving. However, efficiently capturing
the true trajectory distribution with a compact set is challenging. In this
work, we propose a novel approach for generating scene-specific trajectory sets
that better represent the diversity and admissibility of future actor behavior.
Our method constructs multiple trajectory sets tailored to different scene
contexts, such as intersections and non-intersections, by leveraging map
information and actor dynamics. We introduce a deterministic goal sampling
algorithm that identifies relevant map regions and generates trajectories
conditioned on the scene layout. Furthermore, we empirically investigate
various sampling strategies and set sizes to optimize the trade-off between
coverage and diversity. Experiments on the Argoverse 2 dataset demonstrate that
our scene-specific sets achieve higher plausibility while maintaining diversity
compared to traditional single-set approaches. The proposed Recursive
In-Distribution Subsampling (RIDS) method effectively condenses the
representation space and outperforms metric-driven sampling in terms of
trajectory admissibility. Our work highlights the benefits of scene-aware
trajectory set generation for capturing the complex and heterogeneous nature of
actor behavior in real-world driving scenarios.
