Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic Trajectory Prediction via Energy Plan Denoising
<|reference_start|>Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic Trajectory Prediction via Energy Plan Denoising: Pedestrian trajectory prediction plays a pivotal role in the realms of autonomous driving and smart cities. Despite extensive prior research employing sequence and generative models, the unpredictable nature of pedestrians, influenced by their social interactions and individual preferences, presents challenges marked by uncertainty and multimodality. In response, we propose the Energy Plan Denoising (EPD) model for stochastic trajectory prediction. EPD initially provides a coarse estimation of the distribution of future trajectories, termed the Plan, utilizing the Langevin Energy Model. Subsequently, it refines this estimation through denoising via the Probabilistic Diffusion Model. By initiating denoising with the Plan, EPD effectively reduces the need for iterative steps, thereby enhancing efficiency. Furthermore, EPD differs from conventional approaches by modeling the distribution of trajectories instead of individual trajectories. This allows for the explicit modeling of pedestrian intrinsic uncertainties and eliminates the need for multiple denoising operations. A single denoising operation produces a distribution from which multiple samples can be drawn, significantly enhancing efficiency. Moreover, EPD's fine-tuning of the Plan contributes to improved model performance. We validate EPD on two publicly available datasets, where it achieves state-of-the-art results. Additionally, ablation experiments underscore the contributions of individual modules, affirming the efficacy of the proposed approach.<|reference_end|>
@article{liu2024modeling,
    title={Modeling Pedestrian Intrinsic Uncertainty for Multimodal Stochastic
  Trajectory Prediction via Energy Plan Denoising},
    author={Yao Liu, Quan Z. Sheng, Lina Yao},
    journal={arXiv preprint arXiv:2405.07164},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.07164},
    primaryClass={cs.CV}
}