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Recognition Method of Driver's Lane Changing Intent Based on Gaussian Mixture Hidden Markov Model

A Gaussian mixture, driving intent technology, applied in character and pattern recognition, computational models, machine learning, etc., can solve problems such as distraction and driver's nervous attention, and achieve the effect of reducing costs, improving comfort, and high accuracy

Active Publication Date: 2022-05-17
JILIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Ignoring driving intentions so that the system may issue warnings or actions that are contrary to the driver's intentions, causing the driver to become nervous or distracted

Method used

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  • Recognition Method of Driver's Lane Changing Intent Based on Gaussian Mixture Hidden Markov Model
  • Recognition Method of Driver's Lane Changing Intent Based on Gaussian Mixture Hidden Markov Model
  • Recognition Method of Driver's Lane Changing Intent Based on Gaussian Mixture Hidden Markov Model

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Embodiment Construction

[0104] A driver's lane-changing intention recognition method based on a Gaussian mixture hidden Markov model includes the following steps:

[0105] Step 1, determine the frame where the face area is located and the coordinates corresponding to the horizontal direction of the left and right borders of the frame, and extract the driver's face area based on the YCbCr color space for skin color extraction, to obtain a rough positioning map of the face;

[0106] Step 2. Human eye positioning based on the ASEF (Average of Synthetic Exact Filters) filter. First, the filter needs to be trained using pictures, and the driver video data obtained through the driving recorder is disassembled into a sequence of pictures as training samples. Manually specify the center position of the left eye and right eye for each picture as the output, so that each sequence picture and output are a pair of training pairs, and the training pair is input into the ASEF filter for training, and the human eye ...

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Abstract

The invention belongs to the technical field of driver intention pattern recognition and machine learning, and specifically relates to a method for recognizing driver's lane-changing intention based on a Gaussian mixture hidden Markov model; it is a lane-changing driving intention recognition method based on data measured by on-board sensors. The method judges whether the driver has the intention to change lanes by observing the movement of the rearview mirror and the state of the steering wheel, and at the same time, judges whether there is a need to change lanes by monitoring the current road conditions. The method of the present invention estimates the most likely driving intention of the current driver based on the Gaussian mixture hidden Markov model, which can reduce the burden on the driver and improve the comfort of the driver on the premise of ensuring safety. If a dangerous situation occurs, it can be carried out earlier. Handle to avoid accidents.

Description

technical field [0001] The invention belongs to the technical field of driver intention pattern recognition and machine learning, and in particular relates to a driver lane-changing intention recognition method based on a Gaussian mixture hidden Markov model. Background technique [0002] In recent years, the level of automobile intelligence has been continuously improved. In the process of intelligence, the joint driving of the car by the driver and the intelligent control system is still a hot spot in current research. As the relatively most unstable main factor in the human-vehicle-road closed-loop system, the driver often exhibits different driving characteristics during the driving process. At present, when the automotive active safety assistance system represented by the advanced driver assistance system evaluates the traffic safety situation, it often perceives the surrounding environment information and the state of the vehicle through radar or vision, ignoring the d...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/59G06V40/16G06N20/00
CPCG06N20/00G06V40/161G06V40/171G06V20/597
Inventor 曲婷王一男曲文奇于树友
Owner JILIN UNIV