# A Systematic Review of Perception System and Simulators for Autonomous Vehicles Research
## 2 Environment Perception System
### Cameras  
In the perception system of autonomous vehicles and from a point of view of the wavelength received by the device, cameras can be classified as visible (VIS) or infrared (IR). The element used by the camera to capture a scene is known as an imaging sensor and has traditionally been implemented with two technologies: Charge-coupled device (CCD) and complementary metal oxide semiconductor (CMOS). CCD image sensors are manufactured by an expensive manufacturing process that confers them unique properties such as high quantification efficiency and low noise. CMOS was developed to reduce the cost of manufacturing at the expense of reducing its performance. The design of the extraction architecture of the luminosity values allows the selection and processing of regions of interest (ROI); furthermore, the CMOS device has a lower consumption than CCDs. These characteristics make them the most used technology for mobile devices. On the other hand, CCD technology has a high dynamic range and higher image quality in low light environments. The differences of both technologies begin to overlap and it is expected that in the future, CMOS technology will replace CCD [12; 13]. VIS cameras capture wavelengths between 400 nm to 780 nm (see Figure 2), same as the human eye can. The visible spectrum is divided into three bands or channels: R, G and B, which will be coded separately. These devices are the most commonly used in AV perception systems to obtain information about the surroundings of the vehicle due to their low cost, high quality colour information, and high resolution. The huge volume of data generated by means of the device supposes a further problem for the processing system. The most common applications are BSD, side view control, accident recording, object identification, LCA, and signs detection. VIS cameras are highly affected by variations in lighting conditions, rain, snow or fog conditions and for this reason are combined with RADAR and LiDAR technologies to increase its robustness. The combinations of two VIS cameras with a known focal distance allows stereoscopy vision to be performed, which adds a new channel called depth information. Cameras with these features are known as RGBD. These devices supply a 3D representation of the scene around the vehicle. IR cameras are passive sensors that work in infrared (IR) wavelengths ranges between 780 nm to 1 mm. There are many devices which work in this spectrum because fewer light interferences exist (e.g., LiDARs). Perception systems that includes IR cameras [14; 15] work in near-infrared (NIR: 780 nm-3 mm) or mid-infrared (MIR: 3 mm-50 mm, known as thermal cameras) ranges. The uses of NIR usually replace or complement VIS cameras. IR cameras are used: (1) In situations where there are peaks of illumination; for example, at the exit of a tunnel, when driving in front of the sun or when long light crosses the car; and (2) in hot body detection, such as pedestrians [16; 17; 18], animals [19] or other vehicles [20]. In these cases, the thermal cameras allow the segmentation process to be simplified to fewer operations based on thresholds and they are not affected by weather or lighting conditions. On the other hand, they supply a grey scale image and the bigger cell size of the image sensor notably reduces its resolution. ToF cameras are active sensors that use the time of flight principle to obtain a 3D representation of the objects in the scene. ToF cameras emit NIR light pulses of 850 nm with an LED (Light EmittingDiodes) array and they measure the difference in phase \(\Delta\varphi\) between the modulated signal emitted (\(\mathrm{s_{E}}\)) and the signal received (\(\mathrm{s_{R}}\)) to compute the distance, as shown in Equation 1 and Figure 6[21]. \[d=\frac{c}{2}\times\frac{\Delta\varphi}{2\pi f_{mod}} \tag{2}\]  
The distance ranges from 10 s for indoor scenes and about 4 s for outdoor scenes, depending on the number of LEDs in the matrix.