Driver-initiated takeover in highway tunnels: effects of non-driving-related tasks on driving workload and takeover...

Advances in automated driving technology have facilitated the commercial deployment of Level 3 systems. Complex traffic scenarios still require drivers to resume vehicle control before full automation is realized. Assessment of driver takeover performance and associated risks is essential for enhancing automated driving safety. Current literature focuses primarily on system-initiated takeovers. Research on driver-initiated takeovers remains limited. This study addressed this gap by investigating driver-initiated takeovers triggered by traffic pressure within highway tunnels. The experimental design included four non-driving-related tasks (NDRTs) and a no-task condition. Visual, physiological, and behavioral data were collected from 28 drivers across five experimental conditions using a high-fidelity driving simulator. An evaluation index system was established based on the takeover behavior from three dimensions: takeover performance, driving workload, and risk level. A quantitative grading model for driving workload level (DWL) was developed using kernel principal component analysis and fuzzy C-means clustering. Additionally, a cloud entropy-optimized extension cloud model was used to comprehensively evaluate the risk of driver-initiated takeovers. The results indicated differences in takeover performance, driving workload, and model-derived risk assessment results across the tested task conditions. Significant differences in driver-initiated takeover time were observed across task conditions, reflecting variations in the timing of driver-initiated intervention. Short-term speed compensation was generally observed after takeover and was more pronounced under the visual and multimodal task conditions. DWL values were higher under all NDRTs than under the no-task condition, with the highest value observed under the multimodal task condition. DWL was classified into three levels: low [0, 0.537), medium [0.537, 0.816), and high [0.816, 1]. Within the relative risk classification for the current sample and experimental scenario, the no-task condition was classified as low risk; the auditory, visual, and social task conditions were classified as medium risk; and the multimodal task condition was classified as high risk. The THW results provided preliminary convergent evidence for these risk classifications. These findings provide a scientific basis for takeover warnings, driver state assessment, and human-machine co-driving safety interventions in highway tunnel scenarios.