多层视线诱导下隧道入口驾驶行为调控与安全增益评价OA

[Objective]To improve the smoothness of driving behavior and traffic safety levels at tun-nel entrances,an improved solution was developed based on a multilevel visual guidance concept.Field experiments with real vehicles were conducted to evaluate its effectiveness and quantify its comprehensive safety benefits during the day and night.[Method]Field experiments were per-formed both during the day and at night in two tunnels.One tunnel was equipped with the proposed solution,whereas the other used the solution specified in current standards and served as the control group.A driving behavior evaluation system for the tunnel entrance zone was developed by statisti-cally analyzing the regulatory effects of the solution on behavior and exploring its regulatory mecha-nisms.Subsequently,the comprehensive safety benefits during the day and night were evaluated us-ing factor analysis and the entropy weight method.[Result]The scheme significantly improved driv-ing behavior by inducing 62.98%and 55.57%earlier tunnel detection in the day and night,respec-tively,while significantly reducing lateral avoidance maneuvers(83.27%/79.95%reduction in portal avoidance and 41.38%/37.94%reduction in sidewall avoidance during daytime/nighttime,respective-ly).This intervention significantly increased trajectory gradient by 4.02 times(daytime)and 3.68 times(nighttime),with deceleration initiated 54.67%(daytime)and 45.41%(nighttime)earlier with smoother execution.Comprehensive safety evaluation scores demonstrated the hierarchy:0.96(post-implementation nighttime)>0.12(post-implementation daytime)>-0.28(baseline night-time)>-0.80(baseline daytime).[Conclusion]Regardless of the time,the tunnel entrance improve-ment solution based on the multi-level visual guidance concept prompted earlier driver perception and reaction,promoted smoother driving behavior,and significantly improved safety benefits.[Ap-plication]This study provides a paradigm for the design and evaluation of the effectiveness of visual guidance schemes in similar high-risk traffic scenarios.