School of Mechanical and Electronic Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, Hubei 430070, China; California PATH, University of California, Berkeley, CA 94704, 1357 S.46th Street, Richmond, CA 94804, United States.
School of Mechanical and Electronic Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, Hubei 430070, China.
J Safety Res. 2022 Jun;81:101-109. doi: 10.1016/j.jsr.2022.02.001. Epub 2022 Feb 10.
The driving simulator is a widely adopted experimental platform for investigating human-factors questions related to traffic signs and other traffic control devices in a safe environment. This paper presents a methodological framework for developing a video-based simulation program for traffic-sign evaluation.
We firstly collected video data and vehicle movement data from on-road driving. Secondly, the signs on the collected video footage were detected and tracked automatically using image processing techniques. Images of newly designed signs were integrated onto the video footage and placed onto the real-world sign locations. The inserted image properties were fused to fit into the video background to yield a natural visual effect. Thirdly, the vehicle-movement data collected during the drive-through were incorporated into the video sequence as well as the motion of the driving simulator. Using throttle and brake pedals of the driving simulator, participants drove through the video sequence with control over the video's playback speed and the simulator's movement to achieve a comparable visualization and motion experience as real-world driving. Results Conclusions: This framework was used to investigate drivers' visual attention and understanding of various newly proposed changeable message signs (CMSs). The results prove that this framework effectively engaged drivers in the driving task in the realistic traffic scene and successfully evaluated drivers' perception and understanding of the traffic signs.
With this methodological framework, a driving simulation program based on real-world video data from specified road environment and vehicle-movement information can be quickly established and used for testing a variety of traffic control devices, especially traffic signs, in the study of human-machine interaction.
驾驶模拟器是一种广泛采用的实验平台，可用于在安全环境中研究与交通标志和其他交通控制设备相关的人为因素问题。本文提出了一种基于视频的交通标志评估仿真程序开发的方法框架。
我们首先从道路行驶中采集视频数据和车辆运动数据。其次，使用图像处理技术自动检测和跟踪所采集视频中的标志。将新设计的标志图像集成到视频中，并放置在真实世界的标志位置上。插入图像的属性与视频背景融合，以产生自然的视觉效果。然后，将行驶过程中采集的车辆运动数据纳入视频序列以及驾驶模拟器的运动中。参与者使用驾驶模拟器的油门和刹车踏板，以控制视频的播放速度和模拟器的运动，从而实现与真实世界驾驶相似的可视化和运动体验。结果结论：该框架用于研究驾驶员对各种新提出的可变信息标志 (CMS) 的视觉注意力和理解。结果证明，该框架有效地使驾驶员参与到真实交通场景中的驾驶任务中，并成功评估了驾驶员对交通标志的感知和理解。
通过这种方法框架，可以快速建立基于指定道路环境的真实世界视频数据和车辆运动信息的驾驶模拟程序，并用于测试各种交通控制设备，特别是交通标志，以研究人机交互。
