论文标题
通过本地加权学习在城市环境中的道路用户位置预测
Road User Position Prediction in Urban Environments via Locally Weighted Learning
论文作者
论文摘要
本文着重于预测目标道路用户的未来位置的问题，鉴于其当前状态，包括位置和速度。采用了加权平均方法，其中权重是根据包含先前观察到的道路使用者的状态轨迹的数据确定的。特别是，引入了一个相似性功能，以从与目标最相似的道路用户状态的数据中提取。该公式会产生一个易于解释的模型，几乎没有参数可以校准。这种加权平均模型（WAM）的性能在与最先进方法的相同现实数据上进行了评估，显示出令人鼓舞的结果。 WAM在更长的预测范围内优于基线常数速度模型，使WAM适合运动计划应用。 WAM和基线神经网络模型的性能相当。尽管如此，WAM仍然只有三个易于解释的参数，而复杂的神经网络模型具有数千个很难分析的参数。
This paper focuses on the problem of predicting the future position of a target road user given its current state, consisting of position and velocity. A weighted average approach is adopted, where the weights are determined from data containing the state trajectories of previously observed road users. In particular, a similarity function is introduced to extract from data those previously observed road users' states that are most similar to the target's one. This formulation results in an easily interpretable model with few parameters to calibrate. The performance of this weighted average model(WAM) is evaluated on the same real-world data as state-of-the-art methods, showing promising results. WAM outperforms the baseline constant velocity model at longer prediction horizons, making WAM suitable for motion planning applications. WAM and a baseline neural network model performs comparably. Still, WAM has only three parameters which are easily interpretable, while the complex neural network model has thousands of parameters which are difficult to analyze.
