Understanding Driver Acceptance of Advanced Driver Assistance Systems in Indian Heterogeneous Traffic: A Structural Equation Modeling Approach

Traffic accidents remain a crucial challenge in India where heterogeneous traffic conditions and human negligence contribute to majority of fatalities. Though Advance driver assistance technology is found to be effective globally, its successful deployment in India needs understanding local driver readiness and its automation pertaining regional traffic patterns. This study aims to evaluate the primary factors that influence the driver behaviour and its level of Advanced Driver Assistance System (ADAS) automation of required for mitigating human error. Perception of drivers were analysed through multistage analytical framework involving Exploratory Factor Analysis (EFA) to identify latent factors. Subsequently Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) (through SPSS-AMOS) were performed to confirm the validity of measurement and structural models. EFA identified four latent constructs namely, Display, Automation Warning Skills and Driving Behavior with satisfactory reliability with Cronbach's α ranging from 0.720 to 0.779.SEM modelling showed a positive correlation between driving ability and accepting ADAS. Inexperienced drivers preferred automation greatly (β = 0.94) when compared to experienced drivers (β = 0.33). The highly rated advanced driving features are automation warnings, pedestrian protection, adaptive speed control, lane-change assistance, and real-time display functions. These findings highlight an urgent need for localized, driver-centric ADAS tailored to Indian roadways. The study offers vital support for urban planning, and gradual policy implementation aligning with SDGs 3,9 & 11supporting the shift towards safer and sustainable road transportation.