Intersection turning overflow detection method based on vehicle track and long-short memory neural network

A vehicle trajectory, neural network technology, applied in the field of steering overflow detection, can solve the problem that the detection method cannot meet the real-time performance

Active Publication Date: 2020-01-31
ZHEJIANG UNIV
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Problems solved by technology

[0003] In order to overcome the problem that the existing detection methods cannot meet real-time performance, the present invention proposes a method for steering overflow detection based on long short memory neural network and using vehicle trajectory

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  • Intersection turning overflow detection method based on vehicle track and long-short memory neural network
  • Intersection turning overflow detection method based on vehicle track and long-short memory neural network
  • Intersection turning overflow detection method based on vehicle track and long-short memory neural network

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Embodiment Construction

[0024] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0025] figure 1 Schematic representation of vehicle steering overflow. Among them, the queue of left-turn traffic is too long, which blocks the entrance of the widening section. Therefore, the through-traffic flow is blocked upstream and cannot enter the widening section. When the straight green light starts, because the upstream straight vehicle cannot enter the widening section, the straight green light has a lot of waste.

[0026] figure 2 It is a schematic diagram of an example vehicle trajectory and cutting, space-time mapping. Among them, TR 1 and TR 2 for two tracks. The figure only shows the parameter length r of the left turn signal LT,k (the left turn red light time of the kth cycle). The processing flow of the straight signal parameters is similar.

[0027] For the k-th left-turn red light signal, its start time and end time are obtained...

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Abstract

The invention discloses an intersection turning overflow detection method based on a vehicle track and a long-short memory neural network. The method comprises steps of cutting a track of a vehicle sample according to a starting time point and an ending time point, and extracting a track of a corresponding signal period; then calculating speed of the vehicle at each track point according to the track; then mapping the speed to a time axis and a space axis separately, so as to obtain a speed curve of the vehicle on the time axis in the period, and then sampling to obtain a comprehensive time-space speed vector. On the other hand, a detector distributed at the inlet of a stretching section acquires speed, and then turning overflow duration in a signal period is acquired by setting a speed threshold; the comprehensive time-space speed vector and the turning overflow duration are used as parameters so as to train the long-short memory neural network; and the trained neural network can be used for detecting turning overflow.

Description

technical field [0001] The invention relates to a steering overflow detection method used in urban traffic control. Specifically, it relates to a method for detecting steering overflow at an intersection approach based on vehicle trajectory and long-short memory neural network. Background technique [0002] Traffic control uses signal lights to allocate time to traffic flows in different directions at the intersection approach. During peak times, however, steering spillover events often occur. The steering overflow event refers to the phenomenon that during peak hours, some vehicles queue up too long, spread from the widening section to the upstream, and block other turning traffic flows. There are many negative effects of turning overflow: reducing the traffic capacity of the intersection, increasing the delay of turning, and reducing the reliability of traffic operation. Detection of overturning using various traffic data is a prerequisite for effective intersection con...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G06N3/04G06N3/08
CPCG08G1/0125G06N3/08G06N3/044G06N3/045
Inventor 祁宏生戴茹梦
Owner ZHEJIANG UNIV
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