# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 3 System Architecture for Autonomous Vehicles
### Camens  
The camera in AVs can be classified as either visible-light based or infrared-based depending upon the wavelength of the device. The camera uses image sensors built with two technologies that are charge-coupled device (CCD) and a complementary metal-oxide-semiconductor (CMOS) [43]. The maximum range of the camera is around 250 mdepending on the quality of the lens [15]. The visible cameras use the same wavelength as the human eye i.e., 400-780 nm, and is divided into three bands: Red, Green and Blue (RGB). To obtain the stereoscopic vision, two VIS cameras are combined with known focal length to generate the new channel with the depth (D) information. Such a feature allows the camera (RGBD) to obtain a 3D image of the scene around the vehicle [67]. The infrared (IR) camera uses passive sensors with a wavelength between 780 nm and 1 mm. The IR sensors in AVs provide vision control in peak illumination. This camera assists AVs in BSD, side view control, accident recording and object identification [68]. Nevertheless, the performance of the camera changes in bad weather conditions such as snow, fog and moment-of-light variation [15]. The main advantages of a camera are that it can gather and record the texture, color distribution and contour of the surroundings accurately [15]. However, the angle of observation is limited due to narrow view of the camera lens [69]. Therefore, multiple cameras have been adopted in AVs to monitor the surrounding environment [70; 71]. A three-stage RGBD architecture using deep learning and convolutional neural networks was proposed by [PERSON] et al. for vehicle and pedestrian detection [72]. However, this requires the AV to process huge amount of data [15]. Currently, AVs do not possess such computational resources; therefore, computational offloading may be an appropriate solution [73]. Table 4 summarises the challenges of the discussed sensor technologies. It can be observed in Table 4 that the detection capability and reliability of the various sensors is limited in different environments. This limitation can be overcome and the accuracy of target detection along with the reliability can be improved through multi-sensor fusion. Radar-camera (RC) [56; 74], Camera-LiDAR (CL) [60; 75], Radar-LiDAR (RL) [76] and Radar-Camera-LiDAR (RCL) [77; 78] have been proposed where different sensors are combined together to improve the perception of the environment. Furthermore, in [14], three different sensor plans are developed based on range, cost and balance function. In this study, several different sensors are combined. In Plan \(A\), four cameras, a mm-Wave RADAR, 32- and 4-layer LiDAR and a GPS+IMU are employed. In Plan \(B\), four cameras, three mm-Wave RADAR, a four-layer LiDAR and a GPS+IMU are utilised. Finally, in Plan \(C\), two regular cameras, three mm-Wave RADARs, a surrounding camera and a twelve-unit ultrasonic sensor are utilised. \begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Sensor** & **Challenges** \\ \hline Ultrasonic Sensors & 1. Cannot be used at high speed \\  & 2. Maximum range is 2 m [15] \\  & 3. Very low resolution as compared to RADAR \\ \hline RADAR & 1. Generate a large number of false alarms due to surroundings metal objects \\  & 2. The range is between 5 m and 200 m \\  & 3. Generated images are of low resolution as compared to Cameras and LiDARs \\ \hline LiDAR & 1. Suffers extremely from the weather \\  & 2. Maximum range is 200 m [15] \\  & 3. Very expensive due to high price. \\ \hline Cameras & 1. Computation overheads which increases the time-critical applications [49] \\  & 2. The maximum range is 250 m depending upon the lens [15] \\  & 3. A big range resolution gap exists between the Cameras and RADARs or LiDARs. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of sensor and their challenges. GNSS and GPS, IMU: Global Navigation Satellite System and Global Positioning System, Inertial Measurement Unit  
This technology can determine the exact position of the AV and helps it navigate [79].