Linear trajectory models provide mathematical advantages to autonomous driving applications such as motion prediction. However, linear models' expressive power and bias for real-world trajectories have not been thoroughly analyzed. We present an in-depth empirical analysis of the trade-off between model complexity and fit error in modelling object trajectories. We analyze vehicle, cyclist, and pedestrian trajectories. Our methodology estimates observation noise and prior distributions over model parameters from several large-scale datasets. Incorporating these priors can then regularize prediction models. Our results show that linear models do represent real-world trajectories with high fidelity at very moderate model complexity. This suggests the feasibility of using linear trajectory models in future motion prediction systems with inherent mathematical advantages.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An Empirical Bayes Analysis of Object Trajectory Representation Models


    Contributors:


    Publication date :

    2023-09-24


    Size :

    6690174 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Improved Trip Distribution Models Using the Empirical Bayes Method

    Zhang, Qian / Ye, Xin / Wang, Ke et al. | Transportation Research Record | 2024


    Comparative Analysis of Empirical Bayes and Bayesian Hierarchical Models in Hotspot Identification

    Guo, Xiaoyu / Wu, Lingtao / Zou, Yajie et al. | Transportation Research Record | 2019


    Trajectory Prediction Algorithm Based on Variational Bayes

    Ma, Xiaolong / Liu, Gang / He, Bing et al. | IEEE | 2018



    Speech Trajectory Recognition in SOFM by Using Bayes Theorem

    He, J. / Leich, H. / IEEE; Hong Kong Chapter of Signal Processing | British Library Conference Proceedings | 1994