Humans approach driving in a holistic fashion which entails, in particular, understanding road events and their evolution. Injecting these capabilities in an autonomous vehicle has thus the potential to take situational awareness and decision making closer to human-level performance. To this purpose, we introduce the ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge the first of its kind. ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by a moving agent, the action(s) it performs and the corresponding scene locations. ROAD comprises 22 videos, originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event. We also provide as baseline a new incremental algorithm for online road event awareness, based on inflating RetinaNet along time, which achieves a mean average precision of 16.8% and 6.1% for frame-level and video-level event detection, respectively, at 50% overlap. Though promising, these figures highlight the challenges faced by situation awareness in autonomous driving. Finally, ROAD allows scholars to investigate exciting tasks such as complex (road) activity detection, future road event anticipation and the modelling of sentient road agents in terms of mental states.

中文翻译：

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ROAD：用于自动驾驶的ROad事件意识数据集

人类以整体方式驾车行驶，这尤其需要了解道路事件及其演变。因此，将这些功能注入自动驾驶汽车有可能使态势感知和决策更加接近人类水平的表现。为此，我们首先了解了用于自动驾驶的ROad事件意识数据集（ROAD）。ROAD旨在测试自动驾驶汽车检测道路事件的能力，道路事件定义为由移动代理人组成的三胞胎，其执行的动作以及相应的场景位置。ROAD包含22个视频，这些视频最初来自牛津RobotCar数据集，并带有边框，这些边框显示了每个道路事件在图像平面中的位置。我们还基于RetinaNet随时间的膨胀提供了一种新的在线道路事件意识增量算法作为基线，该算法在帧级和视频级事件检测中的平均平均精度分别为16.8％和6.1％，分别为50％重叠。这些数字虽然很有希望，但突出了自动驾驶中态势感知所面临的挑战。最后，ROAD使学者能够研究激动人心的任务，例如复杂的（道路）活动检测，未来的道路事件预期以及根据心理状态对有感觉的道路代理进行建模。这些数字突显了自动驾驶中态势感知所面临的挑战。最后，ROAD使学者能够研究激动人心的任务，例如复杂的（道路）活动检测，未来的道路事件预期以及根据心理状态对有感觉的道路代理进行建模。这些数字突显了自动驾驶中态势感知所面临的挑战。最后，ROAD使学者能够研究激动人心的任务，例如复杂的（道路）活动检测，未来的道路事件预期以及根据心理状态对有感觉的道路代理进行建模。