论文标题
对象轨迹表示模型的经验贝叶斯分析
An Empirical Bayes Analysis of Object Trajectory Representation Models
论文作者
论文摘要
线性轨迹模型为自动驾驶应用（例如运动预测）提供了数学优势。但是，尚未对真实世界轨迹的线性模型的表达能力和偏差进行彻底分析。我们对模型复杂性与拟合对象轨迹之间的折衷进行了深入的经验分析。我们分析了车辆，骑自行车的人和行人轨迹。我们的方法论估计了来自几个大型数据集的模型参数的观察噪声和先前的分布。结合这些先验可以使预测模型正规化。我们的结果表明，线性模型确实代表了具有高度忠诚度的现实世界轨迹。这表明在具有固有数学优势的未来运动预测系统中使用线性轨迹模型的可行性。
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.
