Multi-Sensor Fusion in Safety Monitoring Systems at Intersections

As urbanization increases, urban traffic has become increasingly dense and dangerous. Motivated by the problem of traffic congestion, this work develops a safety monitoring systems for use at intelligent intersections that is based on a laser range finder and a camera. An artificial neural network (ANN) is used to execute an algorithm that classifies data from the laser range finder (concerning, for example, vehicles and bikes). To compensate for data loss and obstruction of the line-of-sight of the laser range finder, template matching is applied to classify the objects in the images of the camera. Also, the system transmits the image from the camera, compressed by vector quantization, as well as status in real time to remote control sites through a wireless network and a 3G mobile network for drivers and a control center. Finally, the laser range finder and the camera are integrated in a manner consistent with Bayesian theorem to improve the reliability of classification.