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To significantly reduce injury and fatal accidents smart intersections equipped with sensors and communication infrastructure have been proposed. In this publication a novel multi sensor network to perceive the intersection environment is presented. Based on an intensive analysis of accident scenarios in Germany the system was designed to address 75 % of all severe and lethal accidents. 14 laserscanners, 10 cameras, signal phase tapping and an I2V communication unit have been installed at a public intersection in Aschaffenburg, Germany. By using computer based field of view modelling the sensor positions are carefully selected to avoid occlusions. Thus, the infrastructure perception system provides a bird's eye view. Our experiments show that spatial and temporal alignment of sensor data is achieved. We also demonstrate that a part of the sensor network, a calibrated stereo system, allows 3D coordinates in the field of view region of the cameras to be determined with an accuracy of 30 mm.

Abstract-This paper focuses on monocular-video-based stationary detection of the pedestrian's intention to enter the traffic lane. We propose a motion contour image based HOG-like descriptor, MCHOG, and a machine learning algorithm that reaches the decision at an accuracy of 99 % within the initial step at the curb of smart infrastructure. MCHOG implicitly comprises the body language of gait initiation, especially the body bending and the spread of legs. In a case study at laboratory conditions we present ROC performance data and an evaluation of the span of time necessary for recognition. While MCHOG in special cases indicates detection of the intention before the whole body moves, on average it allows for detection of the movement within 6 frames at a frame rate of 50 Hz and an accuracy of 80 %. Feasibility of the method in a real world intersection scenario is demonstrated.