Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach

• URL: http://arxiv.org/abs/2407.20732v2
• Date: Mon, 09 Dec 2024 09:50:05 GMT
• Title: Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach
• Authors: Abhishek Vivekanandan, J. Marius Zöllner,
• Abstract summary: This study introduces a novel approach for generating scene-specific trajectory sets tailored to different contexts.<n>A deterministic goal sampling algorithm identifies relevant map regions, while our Recursive In-Distribution Subsampling (RIDS) method enhances trajectory plausibility.<n>Experiments on the Argoverse 2 dataset demonstrate that our method achieves up to a 10% improvement in Driving Area Compliance.
• Score: 12.335528093380631
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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