# Road Traffic Gesture Autonomous Integrity Monitoring Using Fuzzy Logic
## 1 Introduction
In the automotive environment, a gesture is used to either manipulate a system or convey a message. These upper body movements usually originate from the head and/or hand(s). A gesture is called static if the processing of a single image is sufficient for its interpretation. This is loosely known as a posture. If such a gesture is cast over a timespan, the message will be same at the beginning and end of its propagation. Dynamic gestures, on the other hand, involve the processing of a sequence of postures. A gesture that is acknowledged to trigger an event or carry out a command is recognized as an offline gesture. Traffic scene-understanding technology uses sensory data collection, analyses, and interpretation. The cost in hardware, software, and energy demands towards correctly identifying gestures under all weather, though not fully accomplished, is increasing. A number of approaches used to detect and recognize gestures are presented in [9; 10; 11; 12; 13]. Traditionally, Light Detection and Ranging (LiDAR), Radio Detection and Ranging (Radar), Ultrasonic, and Vision (camera) systems with supplemental sensors have been employed for the mitigation of traffic scene contingencies [14; 15; 16]. In general, inertial measuring units may help cars to detect their physical movements and stabilize themselves for safety purposes while Global Positioning System (GPS) units help in obtaining place and time information on local maps. Imaging sensors have been used to identify road obstacles, read road signs, and recognize other moving vehicles and pedestrians. Lidar sensors have been more accurate in determining vehicle positions, whereas radar sensors have been good at estimating speeds of moving objects. By fusing data from GPS, lidar, radar, video cameras, and other sources, efficient algorithms have been implemented to detect poses of drivers, riders, police officers and traffic marshals under varying climatic conditions [17, 18, 19] in answer to some road safety recommendations. Junctions without traffic lights outnumber those with traffic lights [20]. When yielding the right of way at crossroads without traffic lights, exaggerated hand gestures are used by human drivers. The autonomous car by nature is devoid of this ability even when fully occupied and is more disadvantaged under bad weather when on-board cameras and sensors cannot capture an approaching driver's hand signals accurately. The driverless-car-to-human-driver gesture communication gap in gridlock situations at intersections needs addressing. Bad weather and its associated power failure compounds the problem of intersection communication between classic vehicles and autonomous vehicles that rely on camera feed, even in the presence of roadside traffic lights. Prevailing sensor and algorithmic limitations also make gridlock conflict resolution at crossroads a difficult task among manned and unmanned vehicles. Fuzzy logic is widely used in the automobile industry, having the advantage of using simple rules for the optimization of control systems that incorporate engineering expertise. It is also code-space-efficient and can be used for a Gesture Autonomous Integrity Monitoring (GAIM) in-vehicle traffic light solution to gridlock situations that bypasses most of the sensor and heavy algorithmic limitations in the literature. This could be accomplished by creating a vehicular network with microcontrollers that run the same program and communicate traffic priorities wirelessly at road intersections. Such a traffic control technology, requiring no large-scale or long-term storage such as the driving cloud platform and being decoupled from roadside infrastructure, will be cheap to acquire, easy to install, and shareable among autonomous and regular vehicles. The authors of [21, 22] concluded that an in-vehicle traffic light technology could properly manage traffic flow at unsignalized intersections.