An efficient framework of developing video-based driving simulation for traffic sign evaluation. (June 2022)

• Record Type: Journal Article
• Authors: Zhang, Tingting; Zhou, Xiao; Wang, Pei; Chan, Ching-Yao
• Is Part Of: Journal of safety research. Volume 81(2022)
• Page Start: 101
• Page End: 109
• Publication Date: 2022-06
• Subjects: Methodological framework -- Driving simulation program -- Vehicle motion -- Traffic sign evaluation -- Changeable message signs -- Image processing; Industrial safety -- Periodicals; Accidents -- Prevention -- Periodicals; Safety -- Periodicals; Accidents, Occupational -- Periodicals; Sécurité du travail -- Périodiques; Accidents -- Prévention -- Périodiques; Accidents -- Prevention; Industrial safety; Periodicals; 363.1
• DOI: 10.1016/j.jsr.2022.02.001
• Languages: English
• ISSNs: 0022-4375

Abstract

Highlights: A video-based driving simulation is established with video and IMU data recorded during on-road driving. The signs of interest are integrated onto the video footage and adjusted to fit into the background. The motion data are incorporated into the video sequence to yield a on-road-like experience of car movements. The participants can operate throttle and brake to drive through the video sequence with control over the speed of video playback. The simulation can be quickly established for testing multiple signs with relatively low costs of time and devices. Abstract: Introduction: 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. Method : 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. Practical Applications: 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.