Signal control intersection emptying method and system based on laser radar and trajectory prediction

Abstract

The invention provides a signal control intersection emptying method and system based on laser radar and trajectory prediction, and belongs to the field of intelligent traffic perception. Vehicle trajectory data at an entrance lane of an intersection is obtained through 3D laser radar detection, whether a motor vehicle intrudes into a signal lamp control intersection during signal lamp phase conversion is predicted, 3D laser radar installed at an entrance lane of an urban intersection is utilized to collect trajectory data of the vehicle, and whether the vehicle intrudes into the intersection after a yellow light ends and a red light is turned on or not is judged; and the running time of the intruding vehicle in the intersection is predicted, and the full red time of the intersection is adjusted based on the running time, so that the purposes of timely clearing the vehicles in the range of the intersection and reducing traffic conflicts are achieved. The method can provide a complete solution for actively identifying, preventing and controlling motor vehicle intrusion behaviors during the phase change period of signal lamps at the urban road intersection, and has the advantages of being independent of moving target feature information, accurate, stable and efficient in detection, low in cost, good in adaptability and the like.

Description

technical field [0001] The invention belongs to the field of intelligent traffic perception, and in particular relates to a method and system for clearing signal-controlled intersections based on laser radar and trajectory prediction. Background technique [0002] The level intersection of urban roads is the "throat" of the traffic capacity of the urban road network, and it is also a frequent area of ​​urban traffic accidents. According to the statistics of the traffic police department, more than 60% of the traffic accidents on urban roads are within the range of level intersections, and most of the accidents occur during the interval between green lights, that is, from the end of the green light of the previous phase to the next phase. During the period when the green light starts. Because at the end of the green phase, long phase transition times lead to heterogeneous decisions. Therefore, dangerous driving behaviors are more likely to occur at these intersections, such...

AI Technical Summary

Problems solved by technology

However, these existing methods, one is only to prevent the occurrence of dangerous behaviors, and the other is to require sufficient support from roadside and vehicle-side smart devices, which do not have sufficient adaptability and safety prevention and control efficiency

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