Predicting road scenes from brief views of driving video
Predicting road scenes from brief views of driving video
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DOI：
10.1167/19.5.8
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发表时间：
2019-05-01
影响因子：
1.8
通讯作者：
Rosenholtz, Ruth
中科院分区：
文献类型：
--
作者：
Wolfe, Benjamin;Fridman, Lex;Rosenholtz, Ruth
如果一辆车正在自动驾驶，并要求司机接管，司机需要多少时间来理解场景并做出适当的反应？先前对自然场景感知的研究表明，观察者很快就能获得要点，但要点水平的理解可能不足以使行动成为可能。移动的道路环境不能仅用静态图像来研究，安全驾驶需要预测未来的事件。我们进行了两个实验，以检查如何快速的主题可以感知他们看到的道路场景，并根据他们的心理表征的场景作出预测。在这两个实验中，受试者都进行了时间顺序预测任务，在这个任务中，他们观看了道路视频的简短片段，并指出两个静止帧中的哪一个将在视频结束后出现。通过改变预览视频剪辑的持续时间，我们确定了准确预测记录的道路场景所需的观看持续时间。我们在Mechanical Turk上进行了初步实验以探索空间，并在实验室中进行了后续实验以解决道路类型和刺激辨别力的问题。我们的研究结果表明，能够进行预测的表示可以从道路场景的简要视图中开发出来，并且不同的道路环境（例如，城市与高速公路驾驶）对驾驶员对即将到来的场景进行准确预测所需的观看持续时间具有显著影响。
If a vehicle is driving itself and asks the driver to take over, how much time does the driver need to comprehend the scene and respond appropriately? Previous work on natural-scene perception suggests that observers quickly acquire the gist, but gist-level understanding may not be sufficient to enable action. The moving road environment cannot be studied with static images alone, and safe driving requires anticipating future events. We performed two experiments to examine how quickly subjects could perceive the road scenes they viewed and make predictions based on their mental representations of the scenes. In both experiments, subjects performed a temporal-order prediction task, in which they viewed brief segments of road video and indicated which of two still frames would come next after the end of the video. By varying the duration of the previewed video clip, we determined the viewing duration required for accurate prediction of recorded road scenes. We performed an initial experiment on Mechanical Turk to explore the space, and a follow-up experiment in the lab to address questions of road type and stimulus discriminability. Our results suggest that representations which enable prediction can be developed from brief views of a road scene, and that different road environments (e.g., city versus highway driving) have a significant impact on the viewing durations drivers require to make accurate predictions of upcoming scenes.