Intelligent vehicles and Advanced Driver Assistance Systems (ADAS) are being developed rapidly over the past few years. Many applications such as vehicle localization, environment perception, and path planning have shown promising potentialities. While there is great interest in migrating from complete human-controlled vehicles towards fully autonomous vehicles, it is natural that researchers spending more effort trying to understand the interaction between vehicles with various levels of automation in large-scale traffic scenarios. Next-generation vehicles are expected to have the capacity of evaluating driver conditions, vehicle capabilities, surrounding traffic contexts, and take advantage of such knowledge to ensure safe and efficient driving. Three general research questions are raised to achieve this goal, which are (i) how can we acquire sufficient data, (ii) how to evaluate and understand driving behavior, and (iii) how to deliver information effectively to drivers. In this article, we present a review of previous studies from the UTDrive project attempts to answer above questions.

中文翻译：

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UTDrive 研究回顾：从自然驾驶数据中学习驾驶员行为

智能汽车和高级驾驶辅助系统（ADAS）在过去几年发展迅速。许多应用，如车辆定位、环境感知和路径规划，都显示出有前景的潜力。虽然人们对从完整的人工控制车辆迁移到全自动车辆非常感兴趣，但研究人员很自然地会花费更多的精力来尝试了解大规模交通场景中具有不同自动化水平的车辆之间的相互作用。下一代车辆有望能够评估驾驶员状况、车辆能力、周围交通环境，并利用这些知识来确保安全高效的驾驶。为了实现这一目标，提出了三个一般性研究问题，它们是（i）我们如何获取足够的数据，（ii）如何评估和理解驾驶行为，以及（iii）如何有效地向驾驶员传递信息。在本文中，我们回顾了 UTDrive 项目之前的研究，试图回答上述问题。