# Road Traffic Gesture Autonomous Integrity Monitoring Using Fuzzy Logic
## 1 Introduction  
Traffic engineering addresses the functional aspects of transportation infrastructure by developing increasingly intelligent systems to ensure the safe and efficient movement of people and goods by road, rail, water, or air. Road traffic research can be credited with the development of road surface markings, traffic signs, traffic lights, and various rules and regulations that are used to calm traffic in residential and restricted areas. The successful development of intelligent systems has been achieved mainly through collaboration with electrical and computer engineering counterparts. A conventional traffic light configuration consists of a detector loop, a lighting/display device, local controllers, a master controller, communication lines, and a traffic control center, made possible by the convergence of traffic engineering, electrical engineering, and computer engineering disciplines. Similar collaborations have led to the evolution of various driver assistance systems on the automobile. Intelligent vehicles are becoming more common as cognitive car research gains momentum in transferring mental abilities into technological systems [1, 2]. Hardware can now learn through software just like humans, giving birth to various context-aware Advanced Driver Assistance Systems (ADASs) on the automobile. The car of the future isautonomous [3; 4]. Drunkenness, distraction, inattentiveness, carelessness, tiredness, and fatigue lead to handling errors that do not plague the autonomous vehicle. Improving road traffic safety and eliminating automobile handling errors requires a concerted effort from Original Equipment Manufacturers (OEMs), industrial practitioners, and researchers, as well as policy makers. The annual budget at state and local levels in the United States of America (U.S.A) for operating and maintaining signalized intersections is in excess of $1.23 billion, as indicated by the Traffic Signal Benchmarking and State of the Practice Report, 2019. To this end, self-correcting road traffic technologies and policies are being progressively designed and refined, leading to Artificial Intelligence (AI) applications for monitoring road traffic situations that have made auto-piloting and navigation more reliable [5; 6]. Road intersections serve a useful purpose in changing the direction of travel by guiding vehicles from one lane into the other. However, they present opportunities for conflicts [7; 8]. All manners of road users would want to cross the intersection first and in the shortest possible time. This leads to a situation where all road crossing participants want to be at the same place at the same time. The controller of an approaching vehicle must therefore be apt and very alert in entering and leaving an intersection, as a digression of thought can lead to catastrophic and unintended consequences. Conventionally, there have been three forms of conflict control at intersections. Passive control implements basic rules to ensure the free flow of low-volume traffic from one lane into the next. The dispersion of low-volume traffic is mainly dependent on road traffic regulations, traffic signs, and road markings. In semi or partial control, physical barriers are used to channel traffic flow from conflicting points into milder areas. These physical channels may include islands or traffic rotaries. Active conflict control involves the imposition of time and direction constraints on arriving and departing vehicles in heavy-traffic environments. Here, traffic control signals may be used to impose time constraints whereas grade- or level-separated roads may be used to activate direction constraints. The latter has an added advantage of high-speed crossing, especially on highways. Gesture detection and recognition on a traffic scene presents a way for humans to interact with the automobile using body language. The possibility is attained through computer vision by the use of processing systems running neural networks or rule-based algorithms.