# Road Traffic Gesture Autonomous Integrity Monitoring Using Fuzzy Logic
## 7 Conclusions  
The Road Traffic Gesture Autonomous Integrity Monitoring in-vehicle traffic control system has been implemented for resolving gridlock conflicts involving autonomous and classical vehicles at four-legged intersections. After delineating the mutual characteristics between pairs of vehicle location coordinates, a fuzzy logic classification model was implemented that has a 100% accuracy in identifying the relative positions of vehicles ahead, on the right side, on the left side, and on the back side of each vehicle. Eight ESP32 WROOM DA microcontrollers embedded with the ESP-Now feature were chosen for forming the intended vehicular network. Based on their assigned registration and MAC addresses, the eight boards running the same program were able to communicate and vote for vehicles in the northern lane to disperse. Two cars in the northern lane were dispersed under thirty-six possible arrival patterns with no extra node turning green from the remaining six nodes running the same program. When the coordinates were reshuffled to simulate the order and direction of arrival, the system performed as expected. The scalable, self-organizing network is able to admit new boards and disconnect them safely without degrading performance.  
The scalable system employs a single sensor whose functionality does not degrade under bad weather and requires only the addition of a MAC address and vehicle registration number when admitting new cars. The recommendation for a more effective centimeter-precision GPS input to the system cannot be overemphasized. Perhaps a military-grade precision sensor with such capabilities can be made available to evaluate the performance of the Road Traffic Gesture Autonomous Integrity Monitoring system. While a GPS sensor may receive updates on the health status of satellites in orbit, the sensor itself does not give updates on its health status. It is recommended that the health status of sensors is further researched for the long-term impact on operation of the in-vehicle traffic light system. This will serve as a warning and help in preventive maintenance. Priority is indicated by three LED lights. The serial monitor outputs texts that indicate results of relative vehicle location classification. An on-screen dial indicator on a tablet could be introduced to show the locations of nearby vehicles ahead, on the right, and on the left side to enhance the system output. This will aid in blind-spot detection at intersections, as well. In-vehicle displays can convey more information to the intimate space of the driver than a glance at roadside traffic lights. Integrating the system with existing in-vehicle networks could make diagnostic fault codes easily available for system improvement and maintenance.  
Successful simulations point to the fact that the cheap, infrastructure-less in-vehicle traffic control technology would lead to lightweight algorithms for autonomous vehicles to communicate with human drivers and be able to turn all four-legged road intersections into signalized ones at zero cost to governments.  
**Author Contributions:** Conceptualization, K.O.A. and M.H.; methodology, K.O.A.; software, K.O.A.; validation, K.O.A. and M.H.; investigation, K.O.A. and M.H.; resources, K.O.A. and M.H.; writing--original draft preparation, K.O.A.; writing--review and editing, K.O.A. and M.H.; visualization, K.O.A.; supervision, M.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.