Dynamic driving risk in highway tunnel groups based on pupillary oscillations.

This study aims to understand the dynamic changes in driving risks in highway tunnel groups. Real-world driving experiments were conducted, collecting pupil area data to measure pupil size oscillations using the Percentage of Pupil Area Variable (PPAV) metric. The analysis focused on investigating relative pupil size fluctuations to explore trends in driving risk fluctuations within tunnel groups. The objective was to identify accident-prone areas and key factors influencing driving risks, providing insights for safety improvements. The findings revealed an overall "whipping effect" phenomenon in driving risk changes within tunnel groups. Differences were observed between interior tunnel areas and open sections, including adjacent, approach, and departure zones. Higher driving risks were associated with locations closer to the tail end of the tunnel group and shorter exit departure sections. Targeted safety improvement designs should consider fluctuation patterns in different directions, with attention to tunnels at the tail end. In open sections, increased travel distance and lengths of upstream and downstream tunnels raised driving risks, while longer open zones improved driving risks. Driving direction and sequence had minimal impact on risks. By integrating driver vision, tunnel characteristics, and the environment, this study identified high-risk areas and critical factors, providing guidance for monitoring and improving driving risks in tunnel groups. The findings have practical implications for the operation and safety management of tunnel groups.

Investigating the eye-catching effect in the entrance zone of highway tunnels through heart rate variability analysis.

OBJECTIVE: This study aimed to investigate the influence of visual attraction conditions on drivers' cognitive workload and physiological responses, assessed through heart rate variability (HRV) indicators. The study examined four experimental scenarios, including a baseline condition and three visual attractions (landscape-style architecture, tip slogan, and billboard) at tunnel entrances. METHODS: HRV indicators, including RMSSD, SDNN, LF, and LF/HF ratio, were analyzed to assess cognitive workload and physiological states. The study collected data from a sample of drivers under each experimental scenario and compared the HRV measures across conditions. RESULTS: The presence of visual attractions at tunnel entrances significantly impacted drivers' HRV. The introduction of visual attractions resulted in decreased RMSSD and SDNN values, indicating increased cognitive workload and reduced adaptability of the autonomic nervous system. Moreover, visual attractions led to increased LF values and LF/HF ratio, suggesting heightened sympathetic activation and potential cognitive engagement. CONCLUSIONS: Visual attractions have a significant impact on drivers' cognitive workload and physiological responses. Designing tunnel entrances with a balance between visual attractiveness and cognitive demands is essential to optimize drivers' cognitive performance and overall driving experience. These findings provide valuable insights for transportation authorities and designers to create safer and more user-friendly tunnel environments.

Analysis of Traffic Signs Information Volume Affecting Driver's Visual Characteristics and Driving Safety.

To study the influence of traffic signs information volume (TSIV) on drivers' visual characteristics and driving safety, the simulation scenarios of different levels of TSIV were established, and 30 participants were recruited for simulated driving tests. The eye tracker was used to collect eye movement data under three-speed conditions (60 km/h, 80 km/h, and 100 km/h) and different levels of TSIV (0 bits/km, 10 bits/km, 20 bits/km, 30 bits/km, 40 bits/km, and 50 bits/km). Principal component analysis (PCA) was used to select indicators sensitive to the influence of TSIV on the drivers' visual behavior and to analyze the influence of TSIV on the drivers' visual characteristics and visual workload intensity under different speed conditions. The results show that the fixation duration, saccade duration, and saccade amplitude are the three eye movement indicators that are most responsive to changes in the TSIV. The driver's visual characteristics perform best at the S3 TSIV level (30 bits/km), with the lowest visual workload intensity, indicating that drivers have the lowest psychological stress and lower driving workload when driving under this TSIV condition. Therefore, a density of 30 bits/km is suggested for the TSIV, in order to ensure the security and comfort of the drivers. The theoretical underpinnings for placing and optimizing traffic signs will be provided by this work.

Drivers' visual load at different time periods in entrance and exit zones of extra-long tunnel.

OBJECTIVE: As the number of tunnels and traffic accidents increase, it is necessary to study the drivers' visual characteristic in the tunnels. Considering that freeway tunnels have limited space and narrow sight zone, drivers usually have a short visual blind zone and visual shock when entering and exiting the tunnels. This study aims to investigate the characteristics of drivers' visual load in the entrance and exit zones of extra-long tunnels, and to provide a theoretical basis for the traffic safety prevention and control measures of the engineering design. METHODS: 20 drivers were enrolled to conduct real vehicle tests in the Guizhou Sifangdong Tunnel at different time periods (daytime, twilight, and nighttime). The drivers' pupil area was collected by an eye tracker. The maximum transient vibration value (MTVV) of the pupil area was selected as the index of visual load. In addition, the changing characteristics of visual load in the entrance and exit zones were examined. Using ANOVA, the significant difference of visual load in different zones and at different time periods were performed. Accordingly, the overall drivers' visual load in the entrance and the exit zones were compared. Exponential function models of the MTVV value and the speed of pupil area change were constructed, where the pattern of mutual influence was examined. RESULTS: The changing pattern of the drivers' visual load at different time periods in the entrance and exit zones were markedly different. The comparison of the overall visual load was as follows: exit zones at nighttime > entrance zones at nighttime > entrance zones at twilight > exit zones at twilight ≈ entrance zones at daytime ≈ exit zones at daytime. Moreover, the MTVV value positively correlated with the speed of the pupil area change. Finally, this study proposes an evaluation standard of visual comfort based on the speed of the pupil area change. CONCLUSION: This study highlights the driving risk in extra-long tunnel. These findings could provide a basis for studying the setting method of visual guidance facilities in entrance and exit zones of extra-long tunnel. Also, this study could provide a theoretical basis for the evaluation of drivers' visual load in the tunnel.