## 2 Background  
### Evolution of ADAS Applications  
From the very early ADAS applications, such as anti-lock braking systems (ABS) to the more complex ones using the entire repository of environment sensors, e.g., radars, lidars, ultrasonic sensors and cameras, etc., and advanced human-machine interface solutions, which are in wide use today, the technology has undergone significant development while shaping the whole vehicle industry. It proved capable of reducing associated fatalities on roads across the world and providing more comfort and assistance for vehicle users, which became a primary selling point in car markets. For a more comprehensive review and history of driving assistance systems, see, e.g., [1,12]. The human driver's limited decision-making and control capacity and the continual striving to reduce human errors ultimately ended with the conceptual removal, or at least the partial replacement, of the human driver. This idea substantially contributed to the confirmation of the concept of self-driving car in the past decade. Later, therefore, the initial aspects which formed the evolution of ADAS were complemented by the issues created by the new requirements of automated, partially, and fully automated driving. Dependency on perception data requires using automated sensing and control functions on an ever-increasing scale. Thus, the two driving factors of ADAS development characterized above (i.e., human driver-related issues and the integration of environment detection technologies in vehicle control) are now spiraling in the research and development of the technologies of autonomous vehicles. The newly emerging requirements of autonomous driving, however, not only foster ADAS development but also pose many new unpleasant questions. One of the most intriguing problems is that all of the driver assistance systems used and deployed recently can operate on a relatively short time horizon only and in an extremely limited scope and settings. Emergency braking kicks in at the last instant before a predictable crash, and its activation is based on local distance measurements. Lane detection comes on briefly when a car veers out of its lane, whose activation is based on imaging information taken from the next fifty meters of the road ahead. There can be ADAS operations on board that are frequently interrupted in operation and remain uncoordinated. In these situations, the human driver must be continuously alert and take over the control from the partially automated system. The more automated the vehicle functions are, the more critical the presence of the human driver. This is because, on the one hand, human supervision is not adequate for an extended period of time. On the other hand, the constant shift between automated and human-driven operations is fraught with dangers. Air accident statistics prove that the vast majority of accidents caused by human error occur during the takeover of the autopilot. Humans cannot safely hand off the control efficiently and abruptly and counteract the limitations and deficiencies of the driver-assistance system. This must be changed drastically once the car should drive itself continuously for minutes or hours. Critical issues in the development of the latest ADAS technologies are, therefore, how to increase their operational design domain (ODD) and how to extend the time horizon of their operation. Improving the performance and efficiency of environment sensing seems to be the right step toward finding a solution. It is important to note that the operation of recent ADAS technologies is based on the principle of ego-centric perception, meaning that the driving environment is perceived and evaluated based on local measurements implemented from the viewpoint of a particular vehicle called the ego-vehicle. Relying on this principle is a somewhat limiting factor regarding both the size of the operational domain and the time horizon of operation. Just consider the line of sight and distance limitations of conventional sensors.