Driving performance evaluations can contribute to fleet management and lead to safer and more economical driving conditions for manned or driverless fleet vehicles. One approach to driving performance evaluation involves quantitative mapping or categorical labeling of skill levels and categorizing of driving patterns from extraordinarily mild to the most aggressive. This paper presents a big data system for driving performance evaluations of drivers and trips using a probabilistic framework. The proposed framework combines a feature mixture model for scoring driving performance through defined objective comparison criteria and a latent style mixture model for classifying drivers by the main driving styles they exhibit. To demonstrate the effectiveness of the proposed models, we perform both quantitative and qualitative experiments. The results show that the former produces an interpretable and normal scorecard model, while the latter helps build an improved clustering model that represents enhanced driver behavior.

驾驶性能评估有助于车队管理，并为有人驾驶或无人驾驶车队提供更安全、更经济的驾驶条件。驾驶性能评估的一种方法涉及技能水平的定量映射或分类标记，以及将驾驶模式从非常温和到最激进的分类。本文提出了一个大数据系统，用于使用概率框架对驾驶员和出行进行驾驶绩效评估。所提出的框架结合了通过定义的客观比较标准对驾驶性能进行评分的特征混合模型和根据驾驶员表现出的主要驾驶风格对驾驶员进行分类的潜在风格混合模型。为了证明所提出模型的有效性，我们进行了定量和定性实验。