🤖 AI Summary
This work addresses the representational limitations and modeling bias of linear trajectory models in autonomous driving motion prediction. We systematically evaluate their fitting performance across vehicle, cyclist, and pedestrian trajectories. To mitigate overfitting and improve generalization, we propose the first empirical Bayes-based joint estimation framework that simultaneously infers the observation noise distribution and model parameter priors from heterogeneous real-world trajectory data—enabling prior-driven, regularized modeling. Empirical results demonstrate that linear models achieve high fidelity (mean fitting error < 0.2 m over 2 s) despite minimal complexity, outperforming sophisticated nonlinear baselines. Cross-modal statistical analysis further reveals strong generalization robustness across traffic participants. Our findings provide theoretical foundations and methodological support for lightweight, interpretable, and production-deployable motion prediction systems.
📝 Abstract
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.
