# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 2 State of the Art
### Vehicular Communication  
Vehicular communication, along with digital maps, is another component of the digitalisation of road networks. It is generally described as vehicle-to-everything (V2X), though that is a collection of more specific communication types such as vehicle-to-pedestrian (V2P), vehicle-to-network (V2N), vehicle-to-vehicle (V2V), vehicle-2-infrastructure (V2I) and more, see Figure 6.  
From the perspective of the road infrastructure, V2N that sends and streams information from the cloud and V2I for direct communication with infrastructure are of high importance. The first commercial service of V2I was traffic light information, offered by Audi in 2016 in Las Vegas [46]. This service provides timely information about traffic light state via 4G/LTE, which is then used to support drivers in adapting their speed such that they can catch a green light. Such services can help in optimising traffic flow and fuel consumption by providing information to driver or road authorities such that they can \begin{table}
\begin{tabular}{c c} \hline \hline
**Application** & **Description** \\ \hline \multirow{7}{*}{Localisation} & An automated vehicle must be able to precisely determine its position \\  & in regard to other static and dynamic objects. A HD map \\  & in combination with sensing can help in centimetre-precision \\  & longitudinal and lateral position predictions. Therefore, vehicles still \\  & need their own perception sensors to scan the environment, global \\  & navigation satellite system (GNSS) and real-time kinematic (RTK) \\  & for centimetre precision, and IMU and wheel odometry to assess \\  & vehicle dynamics and support positioning when GNSS is not \\  & available. However, those types of information are compared with \\  & digital maps, which then support the ADAS to define and ensure \\  & precise localisation. \\ \hline \multirow{7}{*}{Environment perception} & The vehicle perceives its environment through sensing, such as via \\  & camera or LiDAR. Due to view blockage, harsh weather or light \\  & conditions; however, it is hard to detect objects and, moreover, \\  & identify the environment in its right context. In that case, HD maps \\  & bring detailed information about road geometry regardless \\  & of environmental conditions and beyond the view range of sensors. \\ \hline \multirow{7}{*}{Path planning} & An HD map can provide real-time information about traffic \\  & congestion, objects on the road, oil or ice ahead, which provides \\ \cline{1-1}  & a longer horizon and information far beyond the vehicle’s sensor \\ \cline{1-1}  & range. Such information can support navigation and path planning \\ \cline{1-1}  & tasks. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Main fields of the digital map applications. Figure 6: Vehicle communication types. optimise the duration of traffic light phases to achieve the best performance. Currently, many V2N and V2I applications are in consideration, such as in road works or warning of road obstacles ahead, congestion information, tollgate information, merging support, left turn assist, spot weather impact, queue warning and more [19]. However, higher automation levels place higher demands on data precision, latency and transfer data rate. Commonly, communication is divided into short-range and long-range broadcasts. Short-range communication is handled with an intelligent transportation system (ITS-G5) and dedicated short-range communication (DSRC), which are two vehicular communication protocols that use the allocated 5.9 GHz spectrum band. Both are based on the IEEE 802.11p physical layer that adds wireless access in vehicular environments (WAVE) to the IEEE 802.11 standard. Those protocols show sufficient latency for safety applications of 10 ms at 300 m range with no more than 100 users/km2[47]. Higher ranges and vehicle density are possible but at the expense of performance.