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Interaction of road, traffic and environment in the operational design domain of lane support systems: hybrid factorial-observational design and causal analysis.

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Title: Interaction of road, traffic and environment in the operational design domain of lane support systems: hybrid factorial-observational design and causal analysis.
Authors: Ghaderi O; Department of Civil Engineering and Architecture, University of Catania, Catania, Italy. Electronic address: omid.ghaderi@unict.it., Pappalardo G; Department of Civil Engineering and Architecture, University of Catania, Catania, Italy. Electronic address: giuseppina.pappalardo1@unict.it., Di Graziano A; Department of Civil Engineering and Architecture, University of Catania, Catania, Italy. Electronic address: adigraziano@unict.it., Dimauro GA; Department of Civil Engineering and Architecture, University of Catania, Catania, Italy. Electronic address: giovanni.dimauro@phd.unict.it., Wang X; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China; College of Transportation, Tongji University, Shanghai 201804, China. Electronic address: wangxs@tongji.edu.cn., Farah H; Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands. Electronic address: H.Farah@tudelft.nl., Cafiso S; Department of Civil Engineering and Architecture, University of Catania, Catania, Italy. Electronic address: dcafiso@unict.it.
Source: Accident; analysis and prevention [Accid Anal Prev] 2026 Oct; Vol. 236, pp. 108691. Date of Electronic Publication: 2026 Jul 28.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: Pergamon Press Country of Publication: England NLM ID: 1254476 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2057 (Electronic) Linking ISSN: 00014575 NLM ISO Abbreviation: Accid Anal Prev Subsets: MEDLINE
Imprint Name(s): Publication: Oxford : Pergamon Press
Original Publication: [New York, Pergamon Press]
MeSH Terms: Accidents, Traffic*/prevention & control , Environment Design* , Automobile Driving* , Automobiles*, Humans ; Machine Learning ; Weather
Abstract: 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.
(Copyright © 2026 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Advanced Driver Assistance Systems (ADAS); Lane Support System (LSS); Lane marking; Machine Learning (ML); Operational Design Domain (ODD); Road safety
Entry Date(s): Date Created: 20260728 Date Completed: 20260814 Latest Revision: 20260814
Update Code: 20260815
DOI: 10.1016/j.aap.2026.108691
PMID: 42520667
Database: MEDLINE
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  Data: Interaction of road, traffic and environment in the operational design domain of lane support systems: hybrid factorial-observational design and causal analysis.
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  Data: <searchLink fieldCode="AU" term="%22Ghaderi+O%22">Ghaderi O</searchLink>; Department of Civil Engineering and Architecture, University of Catania, Catania, Italy. Electronic address: omid.ghaderi@unict.it.<br /><searchLink fieldCode="AU" term="%22Pappalardo+G%22">Pappalardo G</searchLink>; Department of Civil Engineering and Architecture, University of Catania, Catania, Italy. Electronic address: giuseppina.pappalardo1@unict.it.<br /><searchLink fieldCode="AU" term="%22Di+Graziano+A%22">Di Graziano A</searchLink>; Department of Civil Engineering and Architecture, University of Catania, Catania, Italy. Electronic address: adigraziano@unict.it.<br /><searchLink fieldCode="AU" term="%22Dimauro+GA%22">Dimauro GA</searchLink>; Department of Civil Engineering and Architecture, University of Catania, Catania, Italy. Electronic address: giovanni.dimauro@phd.unict.it.<br /><searchLink fieldCode="AU" term="%22Wang+X%22">Wang X</searchLink>; The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Shanghai 201804, China; College of Transportation, Tongji University, Shanghai 201804, China. Electronic address: wangxs@tongji.edu.cn.<br /><searchLink fieldCode="AU" term="%22Farah+H%22">Farah H</searchLink>; Department of Transport and Planning, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands. Electronic address: H.Farah@tudelft.nl.<br /><searchLink fieldCode="AU" term="%22Cafiso+S%22">Cafiso S</searchLink>; Department of Civil Engineering and Architecture, University of Catania, Catania, Italy. Electronic address: dcafiso@unict.it.
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  Data: <searchLink fieldCode="MM" term="%22Accidents%2C+Traffic%22">Accidents, Traffic*</searchLink>/<searchLink fieldCode="MM" term="%22Accidents%2C+Traffic+prevention+%26+control%22">prevention & control</searchLink> <br /><searchLink fieldCode="MM" term="%22Environment+Design%22">Environment Design*</searchLink> <br /><searchLink fieldCode="MM" term="%22Automobile+Driving%22">Automobile Driving*</searchLink> <br /><searchLink fieldCode="MM" term="%22Automobiles%22">Automobiles*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Machine+Learning%22">Machine Learning</searchLink> ; <searchLink fieldCode="MH" term="%22Weather%22">Weather</searchLink>
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  Data: 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/(m<superscript>2</superscript>·lx) across the majority of environmental and physical conditions, although this transition is less evident under sharp curvature or rain.<br /> (Copyright © 2026 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
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  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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  Data: <i>Keywords: </i>Advanced Driver Assistance Systems (ADAS); Lane Support System (LSS); Lane marking; Machine Learning (ML); Operational Design Domain (ODD); Road safety
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        Text: English
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