Method and system for predicting the behavior of motor vehicles running yellow lights based on 3D lidar

Abstract

The present invention relates to a method and system for predicting the behavior of a motor vehicle running a yellow light based on 3D laser radar, including: using 3D laser radar to detect and obtain vehicle track data; mapping the vehicle track data into a three-dimensional coordinate system within the range of an entrance road, according to Classify the vehicle in the lane where the vehicle is located; after receiving the yellow light start signal, input the vehicle trajectory data acquired within the first 1.5s of the yellow light time into the trajectory prediction model, and use the trajectory prediction model to predict the vehicle’s trajectory within the first 1.5s of the yellow light time The trajectory data is judged, and the vehicle trajectory data 1.5s after the yellow light time is predicted according to the judgment result; the vehicle traffic trend after the yellow light time is judged according to the vehicle trajectory data 1.5s after the yellow light time. The invention can provide a good foundation for solving the traffic safety problem of vehicle intrusion during the phase change of the signal lights at the city signal control intersection, and has the advantages of not relying on the characteristic information of the moving target, accurate, stable and efficient detection, low cost, good adaptability, etc. .

Description

technical field [0001] The invention relates to the field of intelligent traffic perception, in particular to a method and system for predicting the behavior of motor vehicles running yellow lights based on 3D laser radar. Background technique [0002] The ultra-high population density and increasing car ownership in Chinese cities have contributed to the high density of arterial road network in urban road construction. At intersections in Chinese cities with traffic light control, a longer phase transition time is generally used at the end of the green phase, i.e., 3 seconds for a flashing green indication followed by 3 seconds for a yellow indication. Such a long phase transition time leads to heterogeneous decision making. Therefore, dangerous driving behaviors are more likely to occur at these intersections, such as red light driving, sudden stop, aggressive passing, and inconsistent decision-making of the vehicle in front and behind, which may lead to right-angle and r...

AI Technical Summary

Problems solved by technology

Such a long phase transition time leads to heterogeneous decision making

Therefore, dangerous driving behaviors are more likely to occur at these intersections, such as red light driving, sudden stops, aggressive passing, and inconsistent decision-making of the vehicle in front and behind, which may lead to right-angle and rear-end collision accidents

[0003] For the above-mentioned intersection traffic safety problems, the traditional solution is to promulgate corresponding traffic safety laws and regulations and strengthen law enforcement, severely punish illegal acts such as running red lights, in order to achieve the purpose of reducing the occurrence of such incidents, but this kind of after-the-fact Punishment usually treats the symptoms but not the root cause, and it is difficult to achieve good results; in recent years, because of the interest in the concept of vehicle-road coordination, some scholars have begun to consider the coordinated control between intelligent road facilities and intelligent vehicles to control the dangerous behavior of such intersections. Forecasting and early warning, however, this requires sufficient equipment base, and at this stage, my country's traffic system and road facilities have not yet met the requirements

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