Accidents in highway tunnels involving trucks carrying flammable cargoes can be dangerous, needing immediate confrontation to detect and safely evacuate the trapped people to lead them to the safety exits. Unfortunately, existing sensing technologies fail to detect and track trapped persons or moving vehicles inside tunnels in such an environment. This paper presents a distributed Bluetooth system architecture that uses detection equipment following a MIMO approach. The proposed equipment uses two long-range Bluetooth and one BLE transponder to locate vehicles and trapped people in motorway tunnels. Moreover, the detector’s parts and distributed architecture are analytically described, along with interfacing with the authors’ resources management system implementation. Furthermore, the authors also propose a speed detection process, based on classifier training, using RSSI input and speed calculations from the tunnel inductive loops as output, instead of the Friis equation with Kalman filtering steps. The proposed detector was experimentally placed at the Votonosi tunnel of the EGNATIA motorway in Greece, and its detection functionality was validated. Finally, the detector classification process accuracy is evaluated using feedback from the existing tunnel inductive loop detectors. According to the evaluation process, classifiers based on decision trees or random forests achieve the highest accuracy.
Real Time Speed Estimation of Moving Vehicles from Side View Images from an Uncalibrated Video Camera