# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 2 State of the Art
### Sensors
#### 2.1.4 Sensor Fusion  
Sensor fusion brings together inputs from multiple radars, LiDAR and cameras to balance the strengths of different sensors such that vehicle systems can use this information to support more complex ADAS.  
The basic principle of multi-sensor information fusion technology is similar to integrated information processing by the human brain. The multi-level and multi-spatial information of various sensors is processed in a complementary and optimal combination to eventually produce a consistent interpretation of the observed environment.  
This not only takes advantage of the synergistic operation of multiple sensors but also allows synthesising data from other information sources to improve the intelligence of the entire sensor system.  
The fusion technology trend currently has two mainstream solutions: early and late fusion. Figure 3 illustrates a typical late fusion architecture, where each sensor independently processes the generated raw data and has its own independent perception algorithm. When all the sensors have generated target data, the central processor then performs data fusion.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**LiDAR Type** & **Description** \\ \hline Microelectromechanical systems (MEMS) & MEMS mirrors rotate by balancing between two opposing forces: the electromagnetic force (Lorentz force) generated by a conductive coil around the mirror and the elastic force generated by a torsion bar that acts as the axis of rotation. MEMS mirrors can move in one dimension with a single axis or in two dimensions with two axes. \\ Micro-Scanning & Macron-Scanning \\  & operating frequency. Although MEMS LiDAR still contains moving parts, this near-solid-state technology is still promising because it is a proven technology in the integrated circuit industry that can satisfy stringent cost requirements. \\ \hline Flash LiDAR & The rotating parts of the scanning system are completely eliminated. As a result, it is truly solid-state. A single laser, propagated by an optical diffuser, illuminates the entire scene at once. It uses a two-dimensional photodiode array (similar to a CMOS/CCD in a camera) to capture the laser echoes, which are finally processed to form a three-dimensional point cloud. The true solid-state LiDAR can be positioned in places such as behind windscreens or integrated into headlights, which allows use of the already available cleaning systems. However, the key problem with 3D flash LiDAR is its limited detection range (typically \(<\) 100 m) and there are concerns for eye safety. Therefore, multiple true solid-state LiDARs would need to be used to cover the same angles as spinning LiDAR. \\ \hline Optical phased array (OPA) LiDAR & Does not comprise moving components. OPA LiDAR can drive a laser beam with various phase modulators, which can change the light speed through the lens. Despite high hopes for OPA as a promising technology, there is not yet a commercial product on the market. \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._Furthermore, early fusion (low level) was difficult to implement until a few years ago because the amount of processing required was huge. With the development of automotive electronic and electrical architecture and the popularity of domain controllers; however, this approach is expected to have significant growth in the future, as shown in Figure 4. There is only one algorithm for the perception of fused multi-dimensional integrated data in this architecture. The fused data are similar to a super sensor in the original layer that fuses all the data. This sensor can sense infrared light and has the ability to sense RGB and detect LiDAR 3D information. Finally, a result layer will be output in the detection results.