Intention-aware Denoising Diffusion Model for Trajectory Prediction

• URL: http://arxiv.org/abs/2403.09190v1
• Date: Thu, 14 Mar 2024 09:05:25 GMT
• Title: Intention-aware Denoising Diffusion Model for Trajectory Prediction
• Authors: Chen Liu, Shibo He, Haoyu Liu, Jiming Chen,
• Abstract summary: Trajectory prediction is an essential component in autonomous driving, particularly for collision avoidance systems. We propose utilizing the diffusion model to generate the distribution of future trajectories. We propose an Intention-aware denoising Diffusion Model (IDM) Our methods achieve state-of-the-art results, with an FDE of 13.83 pixels on the SDD dataset and 0.36 meters on the ETH/UCY dataset.
• Score: 14.524496560759555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory prediction is an essential component in autonomous driving, particularly for collision avoidance systems. Considering the inherent uncertainty of the task, numerous studies have utilized generative models to produce multiple plausible future trajectories for each agent. However, most of them suffer from restricted representation ability or unstable training issues. To overcome these limitations, we propose utilizing the diffusion model to generate the distribution of future trajectories. Two cruxes are to be settled to realize such an idea. First, the diversity of intention is intertwined with the uncertain surroundings, making the true distribution hard to parameterize. Second, the diffusion process is time-consuming during the inference phase, rendering it unrealistic to implement in a real-time driving system. We propose an Intention-aware denoising Diffusion Model (IDM), which tackles the above two problems. We decouple the original uncertainty into intention uncertainty and action uncertainty and model them with two dependent diffusion processes. To decrease the inference time, we reduce the variable dimensions in the intention-aware diffusion process and restrict the initial distribution of the action-aware diffusion process, which leads to fewer diffusion steps. To validate our approach, we conduct experiments on the Stanford Drone Dataset (SDD) and ETH/UCY dataset. Our methods achieve state-of-the-art results, with an FDE of 13.83 pixels on the SDD dataset and 0.36 meters on the ETH/UCY dataset. Compared with the original diffusion model, IDM reduces inference time by two-thirds. Interestingly, our experiments further reveal that introducing intention information is beneficial in modeling the diffusion process of fewer steps.

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