The Operational Design Domain (ODD) defines the conditions under which automated driving and driver-assistance systems are expected to operate. This study evaluates the ODD of a camera-based Lane Support System (LSS) using direct Mobileye 6.0 lane-detection quality outputs. A large-scale hybrid factorial–observational field design covered 6 different Light × Weather combinations across 9,351 road sections on two-lane rural roads with wide variability in lane-marking retroreflectivity (RL) and road horizontal alignment and cross section characteristics.Statistical and machine-learning classification models were calibrated and compared to analyze the relationships between lane-marking quality and environmental, road, and traffic features. An AutoML-LightGBM pipeline with SMOTE-based class-imbalance treatment achieved the best accuracy of 0.81. SHAP analysis identified low RL, rain, night conditions, narrow lanes, and high curvature as contributors to critical detection conditions. Because standard ML is optimized for prediction rather than causal inference, Double Machine Learning was added to estimate adjusted effects from observational data. Higher RL, higher speed, and wider lanes were associated with better expected Mobileye quality scores, whereas rain, night conditions, and higher curvature were associated with lower detection quality. SHAP dependence and conditional SHAP analyses supported the identification of maintenance-mitigable infrastructure constraints and harder environmental/geometric ODD limits. One practical result is that RL transitions from low-quality detection mainly occur within 120–150 mcd/(m²·lx) across the majority of environmental and physical conditions, although this transition is less evident under sharp curvature or rain.

The development and integration of automated driving systems in vehicles hold substantial promise for fostering enhanced efficiency, environmental sustainability, and safety in transportation. Notably, at the lower levels of automation (LI, L2), the lane-keeping system emerges as a widely adopted automated driving feature, ensuring the vehicle’s alignment within its designated lane. With the recent introduction of new European regulations mandating the inclusion of emergency lane-keeping systems in all new vehicles starting July 2022, a growing prevalence of such systems is anticipated in the forthcoming decades. The precision and reliability of these systems in accurately detecting road markings and their distinctive features are paramount for achieving safe and intelligent mobility solutions. To fully capitalize on the advantages these systems offer, they need to expand their operational design domain. This necessitates a comprehensive understanding of their performance across diverse road design and maintenance conditions, supporting road operators in updating standards and maintenance protocols. The primary objective of this study is to investigate how various road characteristics impact the performance of lane-keeping assistant systems. Within this framework, the paper presents an experimental evaluation of Lane-Keeping System (LSS) performance conducted on two-lane rural roads. Advanced technologies for road monitoring and LSS were employed under different road and driving conditions. Through rigorous data analysis and the application of statistical models, variables significant to the fault probability of LSS were identified, highlighting the role played by horizontal curvature and driving speed. Results underscore the relevance of horizontal curvature as a critical factor constraining the physical infrastructure, shaping the operational design domain of LSS. This research contributes valuable insights toward optimizing lane-keeping assistant systems, thereby advancing the development and deployment of safe and efficient automated driving systems in diverse road scenarios.