Lane departure on combined curves: driver heterogeneity, centrifugal risk, and crash prevention
DOI: 10.1038/s41598-026-37251-1
URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12976100/pdf/
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Summary
Driving-simulator study (Wang, Zhang, Li, Yi, Wang, Hao; Sci Rep 2026; doi:10.1038/s41598-026-37251-1) examining drivers' asymmetric lane departure on combined horizontal+vertical curves of mountainous freeways. 36 drivers operated a high-degree-of-freedom simulator on a mountain freeway scenario; 948 lane-departure events were collected. Each event was classified as IDCF (in the direction of centrifugal force) or ADCF (against), with average speed, maximum lateral departure, and departure-duration distance computed. A Multivariate Adaptive Regression Splines (MARS) model captured nonlinear relationships between driver characteristics (years driving, daily driving distance, driving experience, road-expertise type) and maximum lane departure. Sag- and crest-curves had higher departure frequencies than upslope/downslope. IDCF events showed greater departure (0.83 m vs 0.41 m) and longer duration distance (70.07 m vs 58.60 m) than ADCF events. Speed and road geometry interacted (e.g., speed effects strongest on downslope/upslope curves in IDCF scenarios). Proposed thresholds: departure-duration distance 35.00–110.00 m and speed 86.77–108.63 km/h.
Key finding
Asymmetric lane departure on combined curves is dominated by IDCF (with-centrifugal) events, which produce ~2x larger lateral departures and ~20% longer duration distances than ADCF events. Driver experience (years, daily distance, road-expert type) interacts with departure-duration distance to modulate severity, providing thresholds that ADAS lane-departure warning systems and visual-guidance designs can use to calibrate intervention timing on mountainous freeways.
Methodology
Driving-simulator study with high-degree-of-freedom simulator on a mountainous freeway scenario. 36 drivers; 948 lane-departure events extracted. Events classified by centrifugal-force direction (IDCF vs ADCF). Multivariate Adaptive Regression Splines (MARS) modeled nonlinear effects of driver characteristics on maximum lane departure; ANOVA and threshold analysis applied to departure-duration distance and speed.
Sample size: N=36 drivers; 948 lane-departure events analyzed.
Quality score: 5 / 5