A Visual-Based Approach for Driver’s Environment Perception and Quantification in Different Weather Conditions

• Published in: Applied sciences Vol. 13; no. 22; p. 12176
• Main Authors: Luo, Longxi, Liu, Minghao, Mei, Jiahao, Chen, Yu, Bi, Luzheng
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2023
• Subjects: Analysis; Automobile drivers; Behavior; Bridges; Driverless cars; driving behavior modeling; driving simulation; environmental quantification; Experiments; Fog; Machine vision; Roads & highways; Sensors; Street signs; visual perception; Weather
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app132212176

The decision-making behavior of drivers during the driving process is influenced by various factors, including road conditions, traffic situations, weather conditions, and so on. However, our understanding and quantification of the driving environment are still very limited, which not only increases the risk of driving but also hinders the deployment of autonomous vehicles. To address this issue, this study attempts to transform drivers’ visual perception into machine vision perception. Specifically, the study provides a detailed decomposition of the elements constituting weather and proposes three environmental quantification indicators: visibility brightness, visibility clarity, and visibility obstruction rate. These indicators help us to describe and quantify the driving environment more accurately. Based on these indicators, a visual-based environmental quantification method is further proposed to better understand and interpret the driving environment. Additionally, based on drivers’ visual perception, this study extensively analyzes the impact of environmental factors on driver behavior. A cognitive assessment model is established to evaluate drivers’ cognitive abilities in different environments. The effectiveness and accuracy of the model are validated through driver simulation experiments, thereby establishing a communication bridge between the driving environment and driver behavior. This research achievement enables us to better understand the decision-making behavior of drivers in specific environments and provides some references for the development of intelligent driving technology.