Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach
This work addresses the problem of compact trajectory representation for autonomous driving systems, offering an incremental improvement over existing methods.
The paper tackles the challenge of efficiently representing diverse future trajectories for motion forecasting in autonomous driving by introducing a scene-specific trajectory set approach, achieving up to a 10% improvement in Driving Area Compliance on the Argoverse 2 dataset while maintaining competitive displacement errors.
Representing diverse and plausible future trajectories is critical for motion forecasting in autonomous driving. However, efficiently capturing these trajectories in a compact set remains challenging. This study introduces a novel approach for generating scene-specific trajectory sets tailored to different contexts, such as intersections and straight roads, by leveraging map information and actor dynamics. A deterministic goal sampling algorithm identifies relevant map regions, while our Recursive In-Distribution Subsampling (RIDS) method enhances trajectory plausibility by condensing redundant representations. Experiments on the Argoverse 2 dataset demonstrate that our method achieves up to a 10% improvement in Driving Area Compliance (DAC) compared to baseline methods while maintaining competitive displacement errors. Our work highlights the benefits of mining such scene-aware trajectory sets and how they could capture the complex and heterogeneous nature of actor behavior in real-world driving scenarios.