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Intelligent roadside multi-source data fusion method based on Bayesian tensor decomposition

A multi-source data and fusion method technology, applied in the field of intelligent transportation, can solve the problems of data quality differences of collection equipment

Pending Publication Date: 2021-09-10
BEIHANG UNIV
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Problems solved by technology

[0004] In order to make full use of intelligent roadside heterogeneous data and solve the problem of data quality differences between different acquisition devices, a method for intelligent roadside multi-source data fusion based on Bayesian tensor decomposition is provided

Method used

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  • Intelligent roadside multi-source data fusion method based on Bayesian tensor decomposition
  • Intelligent roadside multi-source data fusion method based on Bayesian tensor decomposition
  • Intelligent roadside multi-source data fusion method based on Bayesian tensor decomposition

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Embodiment 1

[0086] This specific embodiment relates to an intelligent roadside multi-source data fusion method based on Bayesian tensor decomposition, specifically taking the speed data extracted by video detectors and radar detectors as an example, involving two aspects: data preprocessing and data fusion. part;

[0087] Data preprocessing module:

[0088] Use video detector and radar detector to detect data, collect speed information every 60s, sort according to time window and lane, get the speed of all passing vehicles that have been divided into lanes on this road section for a month, and store it in the database;

[0089] If the data of this lane is missing during the collection period, it is recorded as w 1 , assigned a value of 0;

[0090] If the speed value detected by the two sensors exceeds 90km / h, it is regarded as abnormal data, which is recorded as w 1 , assigned a value of 0;

[0091] If the speed is complete, it is recorded as w 2 , the raw data of the sensor is w=w ...

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Abstract

The invention relates to an intelligent roadside multi-source data fusion method based on Bayesian tensor decomposition. The method comprises the following steps: (1) data preprocessing: collecting traffic flow information at regular intervals through a video and radar detector, marking time labels on lanes, and carrying out the effective recognition and processing of missing and abnormal data to obtain an original data set; and (2) data fusion: constructing a speed tensor, and solving related parameters by using the Bayesian thought to obtain a fused speed tensor. The method is based on intelligent road side heterogeneous data, meanwhile, the Bayesian statistical idea is utilized, the tensor decomposition theory is introduced, the characteristics of high-order tensor mining data characteristics are fully considered, effective fusion of data between different data sources is achieved, and therefore the precision and quality of the data are improved.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, and relates to an intelligent roadside multi-source data fusion method in an intelligent transportation scene. Background technique [0002] With the rapid development of intelligent transportation, intelligent roadside traffic flow detection equipment is widely used, and a large number of video detectors, radars, and laser sensors are applied to roadside traffic flow detection. In the context of the coexistence of multiple sensors on the intelligent roadside, how to comprehensively and effectively utilize multi-source data to obtain more accurate road information is crucial to comprehensively and accurately grasping the operating status of road traffic. Therefore, there is an urgent need for an effective method that can realize multi-source heterogeneous data fusion under intelligent roadside. [0003] Current data fusion can be divided into three methods: stage-based data fusion, feat...

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Application Information

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IPC IPC(8): G06K9/62G06N7/00
CPCG06N7/01G06F18/251G06F18/253
Inventor 任毅龙兰征兴于海洋赵亚楠
Owner BEIHANG UNIV
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