Assessing the operational design domain of lane support system for automated vehicles in different weather and road conditions

With the growing rate of automated vehicles(AVs)at the lower level of automation,the experimental tests are also in progress with vehicles at higher levels.In the absence of extended digital infrastructures and deployment of level 5 full automated vehicles,the physical infrastructure is required to maintain a fundamental role to enable their introduction in public roads.This paper focuses on lane support system(LSS)whose operational design domain(ODD)is strongly connected to the road characteristics and conditions.An experimental test was carried out with a state of the art,and LSS and advanced technologies were used for road monitoring on different roads under various environmental conditions including dry,wet pavements and rain.We applied the generalized estimation equation for logistic regression to account within-cluster homogeneity which is induced by repeated measures on the same road sections.Statistical models allow the identification of variables that are significant for the LSS fault probability among various effects of road features including marking,pavement distress,weather conditions,horizontal curvature,and cross section.Results pointed out the relevance of the wet retro-reflection of marking(RLw)and the horizontal curvature in the definition of ODD for LSS.Threshold values have been proposed for the tested LSS.Wet pavement doesn’t affect the LSS performance when compared to the dry condition.Rain was shown to be critical even with very good road characteristics.

SOTIF risk mitigation based on unified ODD monitoring for autonomous vehicles

Purpose–The purpose of this paper is to design a unified operational design domain(ODD)monitoring framework for mitigating Safety of the Intended Functionality(SOTIF)risks triggered by vehicles exceeding ODD boundaries in complex traffic scenarios.Design/methodology/approach–A unified model of ODD monitoring is constructed,which consists of three modules:weather condition monitoring for unusual weather conditions,such as rain,snow and fog;vehicle behavior monitoring for abnormal vehicle behavior,such as traffic rule violations;and road condition monitoring for abnormal road conditions,such as road defects,unexpected obstacles and slippery roads.Additionally,the applications of the proposed unified ODD monitoring framework are demonstrated.The practicability and effectiveness of the proposed unified ODD monitoring framework for mitigating SOTIF risk are verified in the applications.Findings–First,the application of weather condition monitoring demonstrates that the autonomous vehicle can make a safe decision based on the performance degradation of Lidar on rainy days using the proposed monitoring framework.Second,the application of vehicle behavior monitoring demonstrates that the autonomous vehicle can properly adhere to traffic rules using the proposed monitoring framework.Third,the application of road condition monitoring demonstrates that the proposed unified ODD monitoring framework enables the ego vehicle to successfully monitor and avoid road defects.Originality/value–The value of this paper is that the proposed unified ODD monitoring framework establishes a new foundation for monitoring and mitigating SOTIF risks in complex traffic environments.