Abstract:The increased mobility requirements of modern lifestyles put more stress on existing traffic infrastructure, which causes reduced traffic flow, especially in peak traffic hours. This calls for new and advanced solutions in traffic flow regulation and management. One approach towards optimisation is a transition from static to dynamic traffic light intervals, especially in spots where pedestrian crossing cause stops in road traffic flow. In this paper, we propose a smart pedestrian traffic light triggering mech… Show more
“…In each scenario, the targets that carry a certain speed (radial with respect to the radar) have been analyzed separately to improve target separation and minimize background noise. That is, the speed estimation, obtained thanks to the Doppler shift of the signals, is carried out with the sole purpose of selecting particular targets, pedestrians [19,20] or motorcycles in our case. This way of proceeding is the natural one after performing a two-dimensional FFT of the received signals.…”
Among the sensors necessary to equip vehicles with an autonomous driving system, there is a tacit agreement that cameras and some type of radar would be essential. The ability of radar to spatially locate objects (pedestrians, other vehicles, trees, street furniture, and traffic signs) makes it the most economical complement to the cameras in the visible spectrum in order to give the correct depth to scenes. From the echoes obtained by the radar, some data fusion algorithms will try to locate each object in its correct place within the space surrounding the vehicle. In any case, the usefulness of the radar will be determined by several performance parameters, such as its average error in distance, the maximum errors, and the number of echoes per second it can provide. In this work, we have tested experimentally the AWR1843 MIMO radar from Texas Instruments to measure those parameters.
“…In each scenario, the targets that carry a certain speed (radial with respect to the radar) have been analyzed separately to improve target separation and minimize background noise. That is, the speed estimation, obtained thanks to the Doppler shift of the signals, is carried out with the sole purpose of selecting particular targets, pedestrians [19,20] or motorcycles in our case. This way of proceeding is the natural one after performing a two-dimensional FFT of the received signals.…”
Among the sensors necessary to equip vehicles with an autonomous driving system, there is a tacit agreement that cameras and some type of radar would be essential. The ability of radar to spatially locate objects (pedestrians, other vehicles, trees, street furniture, and traffic signs) makes it the most economical complement to the cameras in the visible spectrum in order to give the correct depth to scenes. From the echoes obtained by the radar, some data fusion algorithms will try to locate each object in its correct place within the space surrounding the vehicle. In any case, the usefulness of the radar will be determined by several performance parameters, such as its average error in distance, the maximum errors, and the number of echoes per second it can provide. In this work, we have tested experimentally the AWR1843 MIMO radar from Texas Instruments to measure those parameters.
“…Artega et al [33] proposed a fuzzy logic method to control traffic lights based on the flow rate of vehicles. Nimac et al [34] presented pedestrian detection and a traffic light control scheme using radars, but this technique is focused on the traffic light trigger mechanism and is not optimizing the pedestrian traffic lights.…”
In recent years, researchers have proposed smart traffic light control systems to improve traffic flow at intersections, but there is less focus on reducing vehicle and pedestrian delays simultaneously. This research proposes a cyber-physical system for smart traffic light control utilizing traffic detection cameras, machine learning algorithms, and a ladder logic program. The proposed method employs a dynamic traffic interval technique that categorizes traffic into low, medium, high, and very high volumes. It adjusts traffic light intervals based on real-time traffic data, including pedestrian and vehicle information. Machine learning algorithms, including convolutional neural network (CNN), artificial neural network (ANN), and support vector machine (SVM), are demonstrated to predict traffic conditions and traffic light timings. To validate the proposed method, the Simulation of Urban Mobility (SUMO) platform was used to simulate the real-world intersection working. The simulation result indicates the dynamic traffic interval technique is more efficient and showcases a 12% to 27% reduction in the waiting time of vehicles and a 9% to 23% reduction in the waiting time of pedestrians at an intersection when compared to the fixed time and semi-dynamic traffic light control methods.
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