Multi-agent Traffic Prediction via Denoised Endpoint Distribution

• URL: http://arxiv.org/abs/2405.07041v1
• Date: Sat, 11 May 2024 15:41:32 GMT
• Title: Multi-agent Traffic Prediction via Denoised Endpoint Distribution
• Authors: Yao Liu, Ruoyu Wang, Yuanjiang Cao, Quan Z. Sheng, Lina Yao,
• Abstract summary: Trajectory prediction at high speeds requires historical features and interactions with surrounding entities. We present the Denoised Distribution model for trajectory prediction. Our approach significantly reduces model complexity and performance through endpoint information.
• Score: 23.767783008524678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The exploration of high-speed movement by robots or road traffic agents is crucial for autonomous driving and navigation. Trajectory prediction at high speeds requires considering historical features and interactions with surrounding entities, a complexity not as pronounced in lower-speed environments. Prior methods have assessed the spatio-temporal dynamics of agents but often neglected intrinsic intent and uncertainty, thereby limiting their effectiveness. We present the Denoised Endpoint Distribution model for trajectory prediction, which distinctively models agents' spatio-temporal features alongside their intrinsic intentions and uncertainties. By employing Diffusion and Transformer models to focus on agent endpoints rather than entire trajectories, our approach significantly reduces model complexity and enhances performance through endpoint information. Our experiments on open datasets, coupled with comparison and ablation studies, demonstrate our model's efficacy and the importance of its components. This approach advances trajectory prediction in high-speed scenarios and lays groundwork for future developments.

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