Development of a Methodology for Estimating the Availability of ADAS-Dependent Road Infrastructure

Abstract

Advanced driver assist systems are being promoted with the expectation that enhanced driver support will mitigate road trauma. While these technologies are optimised for certain road and traffic conditions, not all roads across Australasia are equipped with ADAS-supportive infrastructure. This study developed a desk-top methodology for using road classes (disaggregated by remoteness levels) to estimate the presence of quality roads, road delineation and speed signage in Victoria, Australia. Aerial imagery and mapping data were used to assess a number of random locations based on a developed protocol. The methodology demonstrated that in Victoria, major and arterial roads across all remoteness levels had high-quality sealed surfaces but 42% of all remote roads were unsealed. Delineation (crucial for lane support systems) were absent across 73% of sub-arterial roads independent of remoteness, and absent across 96% of sub-arterial roads in regional and remote areas. Speed sign availability across remote and regional areas was sparse, with only 65% of all roads assessed having signage. Results are reflective of Victoria’s road funding model and consistent with on-road audits conducted by other researchers. This methodology enables the proportion ADAS-ready roads to be estimated so the benefits of ADAS technologies can be quantified and investments into ADAS-supportive infrastructure be readily allocated.

Quantifying the Lost Safety Benefits of ADAS Technologies Due to Inadequate Supporting Road Infrastructure

“…The previously identified methodology [24] was used to determine the availability of high-, medium-and low-quality bitumen as well as the availability of high-, medium-, adequate-and low-quality delineation across road classes across the three states. In brief, this involved identifying the distribution of FSI crashes across the road networks disaggregated by remoteness levels (as defined by the ABS 2016 [56]).…”

“…The desktop analysis used ArcGIS satellite imagery to determine how each road class ranked within each remoteness level in terms of bitumen quality and delineation availability. Bitumen quality and delineation availability was ranked according to the previously published protocol [24]. In brief, this involved ranking bitumen quality as high, medium or low, and delineation quality as high, medium, adequate or low as per the definitions and examples presented in Supplementary Table S1 [24].…”

“…Bitumen quality and delineation availability was ranked according to the previously published protocol [24]. In brief, this involved ranking bitumen quality as high, medium or low, and delineation quality as high, medium, adequate or low as per the definitions and examples presented in Supplementary Table S1 [24].…”

“…Yet, little is known about the ability of the existing road network to support these vehicle technologies. While previous research has demonstrated that not all roads are optimised for supporting advanced in-vehicle technologies [24], no study, to date, has quantified what proportion of technology-sensitive crashes are unlikely to be prevented due to the technologies failing as a result of poor road infrastructure. A study is needed to quantify how much of the assumed benefits of advanced driver assistance systems (ADAS) that have been derived in previous studies will not be realised because of suboptimal roads and driving conditions.…”

“…Studies that have previously estimated the benefits of advanced driver assist technologies have been conducted on the basis that road conditions are always adequate to support the technologies. The assumption that the entire road network is fully supportive of systems such as AEB, LKA and ISA, however, is severely flawed [24]. The objective of this study was to quantify what proportion of the crash population that is assumed to be mitigated by AEB and LKA, will not actually be mitigated due to absent road infrastructure.…”