First, it needs to consider that Autonomous Driving is a term that describes the Systems used in Automotive Field. This system allows the possibility to control the car’s movement without interaction with humans. As a result, these systems are very complex. They involve many subsystems, logical and physical layers, and different kinds of information acquisition, decision, and embedded systems. It uses sensors, actuators, machine learning systems, and complex and powerful algorithms to execute software and travel between destinations without a human operator. “The sensors gather real–time data of the surrounding environment including geographical coordinates, speed and direction of the car, its acceleration and the obstacles which the vehicle can encounter.”  

Now two significant technical challenges are present: perception and decision-making. The company uses machine learning algorithms to surround problems solved by using deep networks. As a result, this caused a significant change in the testing of these systems but offered major possibilities to the machine vision system for detecting and perceiving a scene. As a rule, supervised learning techniques are used to train these neural networks. Deep learning approaches for intelligent computer vision perception systems consist of three major building blocks: data preparation, model generation, and model deployment.  

The first stage, data preparation, focuses on preparing data for further training and testing neuronal networks. This stage requires a large amount of data, such as images of situations on the road, persons, cars, etc.  

The model generation stage involves the development of network architecture.  

The last stage is model deployment; in this stage, some tests are provided on neuronal networks. To have a functional deep-learning trained model, it is necessary to have a dataset that can cover 100 percent of real-world scenes. It is impossible now because such a dataset will have a thousand gigabytes. Another problem is that real traffic situations can contain some anomalies. The main question is how to teach intelligent computer vision systems to detect and generate some decisions according to them.  

Modern companies study these problems. The most relevant answer to this question is to use synthetic data and simulation environments, which can simulate and test in conditions close to reality.  

The growing interest in autonomous cars has also led to the developing of several testing platforms, such as SimLab. SimLab was set up at fkaSV (a subsidiary of German company fka) and provides access to new start-ups for autonomous car technologies to test their solutions that include algorithms for autonomous cars, behavioral testing, application testing, and communication testing. Testing and validating trained models and developed systems require innovative testing environments and techniques. Knowing and testing the vehicles under different traffic and environmental conditions is necessary.  

The development of autonomous driving car systems in recent years has generated several problems in international law and the regulation of autonomous driving car behavior, leading to the creation of an international standard for car automation called SAE J3016. During the existence of the SAE J3016 standard, it has firmly entered everyday life and spread among representatives of the automotive sector. In this regard, it is imperative to periodically and timely make changes, corrections, and supplement the document to contain up-to-date information and correspond to the actual situation in industries. The current version of the standard provides a more descriptive visualization of SAE automation levels. Neural networks have enormous possibilities in autonomous driving, they can solve the most complex tasks, but they require as input information a set of photos. This information must correspond to specifications such as size and color. The images must be processed so that the neural network can then be able to extract the necessary information. Based on the studied articles, all photos undergo a processing process, such as binarization, formatting, and segmentation. These steps are required to prepare a clear image for detection, so these steps are significant. Often in real life, the neural network interprets the information in an image that can be changed by up to 80% compared to the actual situation acquired through the dashboard. 

An eloquent example is rain, or flakes of snow, various areas, and optical errors, all of which must be detected and removed from the original image. Knowledge and use of these digital image processing technologies are the basis of every intelligent scene perception visual system. Suppose the computing power of HW equipment allows using several neural networks, for instance, for detecting cars and traffic lights. In that case, this approach is welcome because it offers a high detection rate. Still, it is worth mentioning that such an approach requires enormous financial resources.  

Description of the designed system

The elaborated project represents an experimental system (Figure 4) in artificial intelligence and machine vision to study modern techniques and technologies for information processing, such as machine vision and neuronal networks. The primary source of information is the video sequence that contains one or more real situations recorded from the car’s dashboard. The developed system aims to process and extract information that would help the driver while driving. As mentioned earlier, the system can only generate some warning signals, but with the possibility of integrating this visual information processing system with other methods such as lidar, ultrasonic sensor, etc.