In the present study road safety impact analysis for certain advanced driver assistance systems (ADAS) was conducted. Based on a literature review, expert interviews and current adaptations in legislation, the most promising nine ADAS were selected. The impact was analysed based on statistical crash data from Austria. Factors such as infrastructure and weather conditions, market penetration, expected functionality of sensors, user acceptance and risk homeostasis were considered. A software tool was developed to calculate the crash reduction potential of the selected ADAS for the scenarios 2025, 2030 and 2040.The results show that the ADAS related to warning/braking have the greatest future reduction potential and could lead to a reduction of up to 8,700 crashes and 70 fatalities in Austria in 2040. In addition, the Intelligent Speed Assistance system would lead to an overall crash reduction of 8% compared to current crash numbers in Austria in 2040. The Turning Assistant for heavy goods vehicles shows the lowest reduction in crashes and casualties, but due to the highest severity per crash (93 fatalities per 1,000 crashes), it nevertheless provides an important contribution to the reduction of fatalities in road traffic.However, to benefit from the ADAS safety potential, it is highly relevant that these systems are used in a correct manner. In the future, it will be necessary to provide users with more information on the correct use, benefits and limitations of the respective ADAS and to integrate the use of these systems into driver education procedures and tests.

AbstractWithin the EU Horizon 2020 project SHOW (GA No 875530) auxiliary measures, via evaluating and adapting physical road infrastructure (for instance lane markings, traffic signs, sight distances), as well as via digital support were explored for their potential contribution to enabling automated shared mobility services on the road environment. This line of research was then followed up on in the EU Horizon Europe Project AUGMENTED CCAM (GA 101,069,717), tackling more specifically with PDI support for automated mobility. This chapter presents the activities and findings of these two projects in relation to physical infrastructure adaptations for automated vehicles for two pilot sites in Austria.

Automated vehicles (AVs) promise new opportunities for urban transport and potential benefits to road safety, transport efficiency, travel comfort and emission levels. However, substantial technical hurdles need to be overcome before AVs can operate regularly on urban streets and highways. Besides considerable infrastructure adaptations, one way to enable the operation of AVs are digital dynamic maps (DDM or DD-map), which combine a high-definition map of the physical road scape with dynamic road data obtained from digital sources. Here we present the results of the EU Project SHOW on digital dynamic maps and their relevance for city planning, in particular how cities can benefit from the interplay with map providers. We provide a workflow to set-up and operate a digital dynamic map and discuss how this might benefit transportation systems.

When discussing the implementation of automated driving systems, multiple factors need to be considered. Yet, a major factor is technical reliability which strongly depends on the consistent functionality of automated driving systems under varying road infrastructure. Most research (Galileo4Mobility (2018); ADAS&ME (2020); AUTOMATE (2020)) focuses on technical challenges and does not investigate if and to what extent physical road infrastructure (PI) contributes to safe automated driving.

collisions between vehicles leaving the road and unforgiving roadside objects such as trees, poles, road signs, etc. constitute a major road safety issue. On the Austrian road network, approximately 7.500 injury crashes occur every year due to run-off-road (rOr) manoeuvres (i.e. 20% of all injury crashes on public roads), contributing 35% to fatalities and 25% to serious injuries. Vehicle restraint systems (VrS) such as guardrails, concrete barriers, terminals or crash attenuators play a decisive role in mitigating the consequences of rOr crashes. unfortunately, most national road administrations (NrA) do not have a centralized data management, while geo-referenced information on VrS and their safety-related attributes are also not available as digitized data. researchers from the AIT have developed a novel approach to investigate, classify and evaluate VrS by means of image data processing, towards providing a comprehensive VrS inventory. The information obtained can be used for benefitcost-analyses, road safety inspections and the evaluation of the effectiveness of different vehicle restraint systems.