# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 2 State of the Art
### Sensors
#### 2.1.3 LiDAR  
LiDAR was introduced in 1930 by the Irish physicist [PERSON]. Since then, it has been used in a wide range of applications for geology, geography, archaeology, aircraft industries and more. With the development of automated driving, it has imposed itself as a sensory system for scenery perception. LiDAR works on the time-of-flight principle shown in Figure 2, in which infrared laser pulses are emitted at a high frequency \begin{table}
\begin{tabular}{c c} \hline \hline
**Radar Type** & **Description** \\ \hline Long-range radar (LRR) & LRR is typically limited by a narrow field of view (FOV) and is often used to detect objects at long distances (e.g., up to 300 m) to aid in emergency braking, collision warning and adaptive cruise control. \\ \hline Mid-range radar (MRR) & MRR has a broader FOV, typically detecting objects up to 150 m, and can detect objects approaching laterally at intersections, with the result that it can often be used for cross-traffic alert functions. \\ \hline Short-range radar (SRR) & SRR has the widest FOV and can detect objects in a wide-angle area at short distances and is typically used for parking assistance, cross-traffic alerting and rear collision alerting. \\ \hline Frequency modulated continuous wave (FMCW) radar & In FMCW radar, the signal’s frequency increases linearly with time. This type of signal is also known as a chirp. The chirp emitted by the transmitting antenna is reflected by an object and captured by the receiving antenna. A mixer combines the transmission and receiving signals to produce an intermediate frequency (IF) signal. The distance, velocity and angle information can be determined by calculating the IF phase, Doppler effect and the different distances from the object to each antenna [32]. \\ \hline Unmodulated continuous wave (CW) radar & This is a simple CW radar device lacking frequency modulation that can provide Doppler information for vehicle detection and speed measurement [33]. However, the disadvantage of this type of radar is that the target distance cannot be determined because the necessary time markers are lacking. Hence, the radar system is not able to accurately determine the timing of the transmitting and receiving cycles and convert them into ranges. Additionally, such radars are widely used in various speed measurement systems, such as traffic speed radars. \\ \hline \hline
4D radar can measure the three spatial directions (x, y and z) of an object and their associated radial velocity components [32]. In contrast to FMCW, digital code modulation technology has also been transformed from military applications to automotive-grade products and was introduced by [34; 35]. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Radar types used in ADAS. and reflected by the environment. The reflected optical signals are received by photodiodes. The time that passes between emitting and receiving is directly proportional to distance based on the speed of light. Therefore, the distances from surrounding objects are easily collected using LiDAR. LiDAR outputs a point cloud (PC) as a collection of points that hold information such as x, y and z coordinates, intensity, time and much more [36]. Intensity is the reflectivity of the object from which the laser beam bounces and is affected by scan angle, range, surface roughness, surface compound and moisture content [37]. LiDAR demonstrates superiority over cameras in 3D data collection and less sensitivity to lighting conditions but still suffers under adverse weather conditions. Before deep learning became popular, traditional machine learning methods were mainly used for PC classification and detection. The idea of many detection algorithms is to conduct ground segmentation and then clustering. Finally, the clustered PC is identified. Moreover, many algorithms firstly map the point cloud data into a two-dimensional plane before recognition to improve calculation efficiency.