DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction

doi:doi:10.57702/j9s0io3z

DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction

Road user trajectory prediction in dynamic environments is a challenging but crucial task for various applications, such as autonomous driving. One of the main challenges in this domain is the multimodal nature of future trajectories stemming from the unknown yet diverse intentions of the agents. Diffusion models have shown to be very effective in capturing such stochasticity in prediction tasks.

BibTex:

@dataset{Younwoo_Choi_and_Ray_Coden_Mercurius_and_Soheil_Mohamad_Alizadeh_Shabestary_and_Amir_Rasouli_2024,
    abstract = {Road user trajectory prediction in dynamic environments is a challenging but crucial task for various applications, such as autonomous driving. One of the main challenges in this domain is the multimodal nature of future trajectories stemming from the unknown yet diverse intentions of the agents. Diffusion models have shown to be very effective in capturing such stochasticity in prediction tasks.},
    author = {Younwoo Choi and Ray Coden Mercurius and Soheil Mohamad Alizadeh Shabestary and Amir Rasouli},
    doi = {10.57702/j9s0io3z},
    institution = {No Organization},
    keyword = {'Autonomous driving', 'Diffusion models', 'Pedestrian prediction', 'Trajectory prediction'},
    month = {dec},
    publisher = {TIB},
    title = {DICE: Diverse Diffusion Model with Scoring for Trajectory Prediction},
    url = {https://service.tib.eu/ldmservice/dataset/dice--diverse-diffusion-model-with-scoring-for-trajectory-prediction},
    year = {2024}
}