# Road Infrastructure Challenges Faced by Automated Driving: A Review
## 2 State of the Art
### Sensors
#### 2.1.1 Camera  
The camera is the technological counterpart to human vision. Therefore, there is a logical and evolutionary explanation for why cameras are the most popular vision system used today. A camera is the most advanced vision technology when it comes to providing the highest resolution and amount of details of the scenery. However, its performance highly depends on the light passing through the lenses to the light-sensitive sensor. A lack or excess of light leads to overexposed or underexposed images from which, sometimes, it is impossible to retrieve image information. A similar effect is caused by abrupt changes in lighting conditions, when the camera needs some time to regain the contrast despite their high dynamic range. Today, in ADAS, different camera types are used to achieve intended functionality, such as mono and stereo camera systems, infrared and movable cameras for parking assistance-related applications (see Table 1). Camera-based ADAS for detection and recognition of traffic signs and lane markings use the traditional and deep learning approaches. Traditional image processing techniques are usually connected with lane detection tasks and have been slowly replaced by deep learning methods in recent years. This approach can be described in the following structured chain of operations:  
1. Image pre-processing: Firstly, unfavourable illumination effects and noise are reduced in images. Furthermore, colour-space transformation is performed in which RGB images are transformed to other formats such as HSV, HSL, LAB and YCbCr in order  
Figure 1: Types of sensors and features for vehicle perception [23]. to enhance image contrast. Finally, pre-processing is concluded with the identification of relevant image parts or so-called region of interest (ROI). 2. Feature extraction: After the image is uncluttered and ROI is defined, relevant features are extracted. In this phase, the aim is to provide information to fit edge and lane models. To achieve this, different actions such as image perspective transformation and intensity histograms are used. 3. Edge and lane modelling: Based on the extracted features, the geometric elements are fitted. A popular method is the Hough transform. 4. Time integration: This phase aims to achieve temporal and positional consistency by integrating frames using lane tracking. For lane tracking algorithms, Kalman filters and particle filters are widely used due to their ability to predict an object's future location. Finally, the vehicle motion, captured with odometry, GPU and IMU, is integrated with the geometrical models to identify previously detected lanes within the current frame. Unlike the traditional methods, deep learning lane detection methods are learnable and gaining more popularity. This approach is usually based on segmentation by labelling image pixels, and the most commonly used algorithm is the convolution neural network (CNN). For a more detailed explanation of lane detection methods, the reader is referred to existing surveying literature [24; 25; 26; 27], in which, both traditional and deep learning lane detection methods have been reviewed. In contrast to lane marking, algorithms for traffic signs can be divided into two phases: detection and recognition. Detection is responsible for identifying the presence and precisely locating a traffic sign on the image or, in other words, extracting the ROI. This phase achieves high performance as it uses information about the specific colours and shapes of traffic signs. Accordingly, detection algorithms can be grouped into five categories: colour-based, shape-based, colour- and shape-based, machine-learning-based and LiDAR-based methods [28]. After the sign is detected, recognition algorithms are applied to classify the meaning and category of traffic signs. For that purpose, the deep learning algorithms show the best performance, where CNN is again the most popular algorithm. Along with those two phases, the tracking phase can also be added [29].