Drivers’ visual interaction performance of on-board computer under different heat conditions: Based on ELM and entropy weight

Abstract

• This paper reveals the impact of heat challenges on driving behaviours. • A laboratory experiment among 53 subjects was conducted to investigate driving behaviours. • Driving behaviours considered in aspects of eye movement characteristics and completion time of operation tasks. • Extreme learning machine model and entropy weight method were used. • The most suitable temperatures for navigation and music control were 22 and 24 ℃. In-vehicle thermal environment is important to driving behaviours. The behaviours are always related to thermal comfort, fatigue and information reading, which are sensitive to the interior design. However, previous studies have merely analysed the impact of in-vehicle temperature and in-vehicle design on driving behaviours separately. There is still a gap to understand how the two affect driving behaviours simultaneously. Therefore, this study aims to investigate the driving behaviours (e.g., eye movement characteristics and completion time of operation tasks) subject to different combinations of temperature and interface layouts. The study was conducted based on a laboratory experiment among 53 subjects, extreme learning machine (ELM) model and entropy weight method. The subjects were inquired to conduct two tasks including navigation and music control for analysing the variation of fixation duration, fixation times and completion time. The temperature conditions consisted of six levels ranging from 22 to 32 °C and there were two types of interface layouts (checkerboard type and linear type). The results show a non-linear relationship between completion time and the factors of temperature, interface layout and operation task, and the co-impacts of these factors were more complex. Furthermore, the analysis indicates that the most suitable temperatures for navigation and music control were 22 and 24 °C. Moreover, the ELM model outperformed the back propagation neural network (BPNN) model in predicting the relationship between visual ergonomics and operation efficiency with the intervention of ambient temperature. In addition, entropy weights imply that both fixation duration and completion time were more sensitive to in-vehicle temperature than fixation times. Overall, beyond previous studies on the impact of in-vehicle design on driving behaviours, this study considers the additional heat-induced impacts on driving behaviours, which is meaningful to provide a reference for in-vehicle environment design.