By observing their environment as well as other traffic participants, humans are enabled to drive road vehicles safely. Vehicle passengers, however, perceive a notable difference between non-experienced and experienced drivers. In particular, they may get the impression that the latter ones anticipate what will happen in the next few moments and consider these foresights in their driving behavior. To make the driving style of automated vehicles comparable to the one of human drivers with respect to comfort and perceived safety, the aforementioned anticipation skills need to become a built-in feature of self-driving vehicles. This article provides a systematic comparison of methods and strategies to generate this intention for self-driving cars using machine learning techniques. To implement and test these algorithms we use a large data set collected over more than 30000 km of highway driving and containing approximately 40000 real-world driving situations. We further show that it is possible to classify driving maneuvers upcoming within the next 5 s with an Area Under the ROC Curve (AUC) above 0.92 for all defined maneuver classes. This enables us to predict the lateral position with a prediction horizon of 5 s with a median lateral error of less than 0.21 m.

中文翻译：

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教学车辆预测：使用大数据集进行概率行为预测的系统研究

通过观察他们的环境以及其他交通参与者，人类能够安全地驾驶道路车辆。然而，车辆乘客认为没有经验的司机和有经验的司机之间存在显着差异。特别是，他们可能会得到这样的印象，即后者会预测接下来的几分钟会发生什么，并在他们的驾驶行为中考虑这些远见。为了使自动驾驶汽车的驾驶风格在舒适性和感知安全性方面与人类驾驶员相媲美，上述预测技能需要成为自动驾驶汽车的内置功能。本文提供了使用机器学习技术为自动驾驶汽车生成这种意图的方法和策略的系统比较。为了实现和测试这些算法，我们使用收集了超过 30000 公里的高速公路驾驶并包含大约 40000 个真实驾驶情况的大型数据集。我们进一步表明，对于所有定义的操作类别，有可能在接下来的 5 秒内对 ROC 曲线下面积 (AUC) 大于 0.92 的驾驶操作进行分类。这使我们能够以 5 s 的预测范围预测横向位置，其中位横向误差小于 0.21 m。