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

Abstract

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. • Open on-road LSS test under various weather conditions such as dry, wet and rain. • Generalized estimating equation to highlight correlation among observations. • Average 3.9% fault probability of LSS with high variability in two-lane rural roads. • Comparatively rain increases the fault probability by a factor of 2.75 than dry weather condition. • Marking RLw and curvature 1/ R are the most relevant road factors.