Modeling a cognitive–behavioral pathway to drivers’ behavioral intention in response to tunnel auditory warnings under acoustic fluctuations

Tunnel roadside auditory warning systems are designed to deliver critical safety messages. In-cabin acoustic fluctuations, such as those caused by vehicle speed and window state, are considered important factors that may impair perceptual and cognitive processing. This study examines how these acoustic variations influence drivers’ detection, comprehension, and behavioral intention regarding auditory warnings.
The study recreated ten distinct in-cabin acoustic environments by combining five vehicle speeds (0, 20, 40, 60, and 80 km/h) with two window states (closed/open). Real-world tunnel auditory warning recordings under these conditions were integrated into an audio-visual experimental platform. Thirty-six licensed drivers participated in this controlled experiment, during which multiple cognitive processing indicators were collected: auditory perception reaction time, speech intelligibility, listening effort, semantic comprehension, heart rate variability, and behavioral intention. A two-stage analytical approach was implemented: generalized estimating equations (GEE) were first used to assess how speed and window state affected warning detection, followed by structural equation modeling (SEM) to evaluate the relationships among the cognitive processing indicators.
Vehicle speed and window state significantly affected warning detection, with performance declining notably above 60 km/h, especially under open-window conditions. Both auditory perception reaction time and speech intelligibility were substantially impaired as speed increased. Listening effort rose with increased speech intelligibility, though this relationship demonstrated a non-linear pattern. Semantic comprehension was strongly predicted by speech intelligibility and showed significant variation across different levels of listening effort. Crucially, key information comprehension emerged as the strongest direct predictor of behavioral intention, while listening effort exhibited both direct and indirect effects on intention. The structural equation model confirmed these relationships, revealing significant mediation pathways through listening effort and comprehension in the connection between perceptual processing and behavioral outcomes.
This study shows that drivers’ responses to tunnel roadside auditory warnings cannot be explained by detectability alone. Post-detection cognitive factors, particularly speech intelligibility and key information comprehension, play a critical role in shaping drivers’ behavioral intention following successful perception of the warning. The findings underscore the relevance of incorporating drivers’ perceptual and cognitive responses when assessing roadside auditory warnings in complex tunnel environments.

隧道路边听觉警示系统（Tunnel roadside auditory warning systems）旨在传递关键安全警示讯息。由车辆行驶速度与车窗状态变化引发的车内声学波动，被视为可能损害感知与认知加工过程的重要影响因素。本研究旨在探究此类声学变化如何影响驾驶员对听觉警示的察觉、理解与行为意向。

本研究通过组合5种车辆行驶速度（0、20、40、60、80 km/h）与2种车窗状态（关闭/开启），复刻出10种不同的车内声学环境。本研究将此类工况下采集的真实隧道听觉警示录音整合至视听实验平台中。36名持有合法驾照的驾驶员参与了此项控制实验，实验期间采集了多项认知加工指标：听觉感知反应时、言语可懂度、聆听努力、语义理解能力、心率变异性以及行为意向。本研究采用两阶段分析方法：首先通过广义估计方程（generalized estimating equations, GEE）分析车速与车窗状态对警示察觉的影响，随后借助结构方程模型（structural equation modeling, SEM）评估各项认知加工指标间的关联关系。

车速与车窗状态对警示察觉具有显著影响：当车速超过60 km/h时，驾驶员的警示察觉性能显著下降，尤其在车窗开启工况下更为明显。随着车速提升，驾驶员的听觉感知反应时与言语可懂度均出现显著下降。聆听努力随言语可懂度提升而升高，但二者间的关系呈现非线性特征。语义理解能力可通过言语可懂度进行有效预测，且在不同聆听努力水平下呈现显著差异。尤为关键的是，关键信息理解能力是行为意向最强的直接预测因子，而聆听努力对行为意向同时存在直接与间接影响。结构方程模型验证了上述关联关系，揭示出在感知加工与行为结果之间，存在通过聆听努力与理解能力实现的显著中介通路。

本研究表明，驾驶员对隧道路边听觉警示的响应，无法仅通过察觉性能进行解释。警示被成功感知后的认知加工因素——尤其是言语可懂度与关键信息理解能力——在塑造驾驶员的行为意向方面发挥着关键作用。本研究结果凸显了在复杂隧道环境中评估路边听觉警示系统时，纳入驾驶员的感知与认知响应指标的必要性。