A Nonlinear Rebalanced Control Compensation Model for Visual Information of Drivers in the Foggy Section of Expressways
Source abstract
Abstract
To obtain the optimal driving visual guidance methods in sudden low-visibility fog environments, it is crucial to analyze the changes in visual characteristics and information demand under low-visibility foggy conditions. The paper constructs a driving visual information demand model for foggy environments based on visual information input and output, using Shannon’s theory and feedback control theory. Two types of foggy road sections with the same visibility, one with guidance lights and one without, were selected for real-vehicle experiments based on the driver’s blood pressure, heart rate, and driving gaze domain tests. The study found the following: (1) In sudden foggy environments, the amount of driving information obtained by drivers decreases instantly with a sudden drop in visibility, failing to meet the information demand for driving cognition, thereby disrupting the dynamic balance state of driving based on speed, visibility, and other road environment factors. The experiment also found that in low-visibility environments, the radius of the human eye’s visual gaze domain becomes smaller, with the gaze range mainly concentrated directly in front of the vehicle, and the lower the visibility, the smaller the gaze domain range; (2) Foggy conditions affect changes in drivers’ blood pressure and heart rate. Installing guidance lights with sufficient illumination at foggy sections to compensate for drivers’ visual information can effectively supplement the visual information required for safe driving; (3) The experiment indicates that the guidance effect of the lights is most pronounced when visibility is within the range of [50 m, 150 m]; however, when visibility is above 500 m, the presence of guidance lights can, to some extent, affect driving safety and increase the risk of accidents.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Impact of Light on Environment and Health · Traffic and Road Safety · Evacuation and Crowd Dynamics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Traffic & Transportation, Chongqing Jiaotong University, Chongqing 400074, China
Chongqing Key Laboratory of Geomechanics & Geoenvironment Protection, Army Logistics Academy, Chongqing 401331, China
Funding
National Natural Science Foundation of China · Science and Technology Research Program of Chongqing Education Commission of China · Chongqing Municipal Education Commission of Science and Technology · Changsha University of Science and Technology Transportation Engineering and Surveying Science and Technology Research Base · Higher Education Teaching Reform of Chongqing Education Commission
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