# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 1 Introduction
Austroads also conducted extensive studies regarding connected and automated vehicles (CAVs) and infrastructure changes to support automated vehicles (AVs). The studies were conducted through five modules in which the authors examined road standards and gaps in physical and digital road infrastructure mostly related to traffic signs and road markings [12; 13; 14; 15]. They concluded that, in Australia and New Zealand, only a few roads are fully suitable for automated driving over an extended distance. They also reported that the main reason why road authorities do not develop new standards is a lack of guidance and costs. In addition to those five modules, Austroads carried out a study on open data policies through literature research and interviews with stakeholders involved in the CAV data distribution chain [17]. The Permanent International Association of Road Congresses (PIARC) reported on challenges and opportunities for road operators and road authorities in the domain of CAVs [18; 19]. The European Commission (EC) is also very active with EU-funded research and directives to support future trends in mobility. One such example is the INFRAMIX project that introduced so-called infrastructure support levels for automated driving (ISAD) to categorise road infrastructure based on its capability to support automated driving [20]. The focus of this paper is to provide a clear and concise overview of the synergy and existing limitations between road infrastructure and ADS. This paper is considered as an addition to reports that identify challenges and opportunities on this particular topic. An extensive technology survey and cost-benefit analysis are not in the scope of this paper. Our target audience is researchers that are new to the field, road authorities and bodiesinvolved in international activities towards defining the requirements of road infrastructure for the successful adoption of ADS. Therefore, a basic overview of the state-of-the-art ADS would be a valuable source of information. That information could clarify the root cause of limits that ADS is facing. As a whole, the contributions of this paper are:  
1. Provision of a short overview and the basic work principles of the most used and promising state-of-the-art ADS technologies. 2. Determination of the limitations and advances of ADS concerning road infrastructure (particularly for road markings and traffic signs). 3. Categorisation of the gaps and limitations according to challenges that should be addressed to support the transition towards higher levels of automation. We approach the problem by first providing a short introduction of perception sensors and their basic work principles. This is the key to identifying the root cause of the limitations. In addition to this overview, sensor fusion, digital maps, and vehicular communication are introduced, which provide insight into emerging and promising technologies that aim to enhance ADS reliability. This state-of-the-art overview is given in Section 2. In Section 3, we introduce known limitations and advances in ADS technology from the perspective of road infrastructure. To address this topic properly, we selected traffic sign recognition systems (TSRS) and lane support systems (LSS) as reference advanced driver assistance systems (ADAS), which are essential and mature technologies for automated vehicles (AVs), and they are directly linked to the road infrastructure, which makes them an ideal candidate for this paper. Through the identified limitations, we identify gaps that could be addressed in future research activities and, moreover, those limitations are grouped into five challenges, and a summary of their mutual impacts and dependencies is included. The challenges are organised into the following categories: international harmonisation, revision in the maintenance of the road infrastructure, review of common design patterns, digitalisation of road networks, and interdisciplinarity. Discussion of the challenges is found in Section 4. The paper concludes with Section 6.