AI 中文总结
该研究基于递归贝叶斯滤波策略构建换道意图预测模型，在highD数据集上验证了其处理缺失观测的能力及稳健性，对比基线方法后明确了模型的优劣势。
AI 中文摘要
本文讨论并分析了一种基于运动学特征与周围观测的高速公路驾驶员换道意图预测方法。该方法采用可解释为隐马尔可夫模型的递归贝叶斯滤波策略，提出了两种似然项模型，并展示了它们如何处理缺失观测。研究的另一重点是校准所得概率以获得可靠预测。所提方法在highD数据集上进行了实际评估，并与多个基线方法对比，特别注重评估所开发算法的稳健性、时间一致性及校准效果。大量数值实验可对性能进行细致评估，并详细讨论不同方法的优势与局限性。
英文摘要
In this paper we discuss and analyze a method for lane-change intention anticipation of drivers on highways based on kinematic features and surrounding observations. The approach makes use of a recursive Bayesian filtering strategy, which can be interpreted as a hidden Markov model. We present two models for the likelihood term and show how they can handle missing observations. An additional focus of the work lies on calibrating the obtained probabilities in order to obtain reliable predictions. The introduced approach is evaluated in practice on the highD dataset and compared to several other baseline methods. Particular emphasize is put on evaluating the robustness and temporal consistency as well as calibration of the developed algorithm. Extensive numerical experiments allow for a careful performance assessment and detailed discussion of advantages and limitations of the different methodologies.
