Autonomous Traffic System Management based on YOLOR

Traffic signals used today work by controlling the signals using a timer. However, this is primitive as it causes delays on certain lanes. For example, due to red signal on high traffic lane and green signal on low traffic lane can causing traffic jams. To create a successful traffic control system and eliminate human supervision across massive traffic regions, we provide a practical model of autonomous traffic system management based on YOLOR. This idea is being implemented via object identification and violation detection, which combine an RFID sensor and the YOLOR algorithm to recognise vehicles and track violations. The YOLO model has been successfully trained to capture vehicle number plates. This is an improved approach to controlling traffic during peaks. We can use YOLOR to determine the number of vehicles on a particular lane to decide which lane gets a green/red signal.