Autonomous driving has become one of the most popular research topics within Artificial Intelligence. An autonomous vehicle is understood as a system that combines perception, decision-making, planning, and control. All of those tasks require that the vehicle collects surrounding data in order to make a good decision and action. In particular, the overtaking maneuver is one of the most critical actions of driving. The process involves lane changes, acceleration and deceleration actions, and estimation of the speed and distance of the vehicle in front or in the lane in which it is moving. Despite the amount of work available in the literature, just a few handle overtaking maneuvers and, because overtaking can be risky, no real-world dataset is available. This work contributes in this area by presenting a new synthetic dataset whose focus is the overtaking maneuver. We start by performing a thorough review of the state of the art in autonomous driving and then explore the main datasets found in the literature (public and private, synthetic and real), highlighting their limitations, and suggesting a new set of features whose focus is the overtaking maneuver.

翻译：自动驾驶已成为人工智能领域最热门的研究课题之一。自动驾驶车辆被理解为整合感知、决策、规划与控制的系统。所有这些任务要求车辆收集周围数据以做出正确决策和行动。其中，超车操作是最关键的驾驶行为之一。该过程涉及车道变换、加速与减速动作，以及估计前方车辆或目标车道内车辆的速度与距离。尽管文献中已有大量相关研究，但仅有少数涉及超车操作，且由于超车具有风险性，目前尚无真实世界数据集可用。本研究通过提出一个专注于超车操作的新型合成数据集，在该领域做出贡献。我们首先对自动驾驶领域的研究现状进行全面综述，继而探究文献中的主要数据集（包括公开与私有、合成与真实），揭示其局限性，并提出一组以超车操作为核心的新特征集合。