Lane changing model parameter optimization method based on mixed Gaussian-hidden Markov model

An optimization method and model parameter technology, applied in two-dimensional position/course control, vehicle position/route/height control, non-electric variable control, etc., can solve problems such as not being the best and unreasonable model structure, and achieve good results Accuracy, the effect of improving accuracy

Inactive Publication Date: 2019-03-15
UNIV OF SHANGHAI FOR SCI & TECH
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Problems solved by technology

[0006] The present invention is aimed at the problem that the HMM model structure parameters in the driver's lane-changing intention recognition algorithm based on the hidden Markov model are set by experience, and the unreasonable setting makes the final model structure not the best. A lane-changing model parameter optimization method based on a mixed Gaussian-hidden Markov model, using particle swarm optimization to optimize the number of mixed Gaussian models and the number of states, so as to improve the accuracy of driver's lane-changing intention recognition

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  • Lane changing model parameter optimization method based on mixed Gaussian-hidden Markov model
  • Lane changing model parameter optimization method based on mixed Gaussian-hidden Markov model
  • Lane changing model parameter optimization method based on mixed Gaussian-hidden Markov model

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Embodiment Construction

[0034] 1. Establishment of lane-changing intention recognition model

[0035] In order to identify the current driving behavior, by analyzing the characteristics and rules of the vehicle in the lane-changing phase, the speed of the lane-changing vehicle, the inter-vehicle distance and speed difference between the lane-changing vehicle and the front and rear vehicles in the original lane, and the target vehicle and the front and rear vehicles are selected as Observing the state parameters, and selecting lane change and going straight as the implicit state parameters, a recognition model of the driver's lane change intention is established by using the observed state to obtain the implicit state. as follows figure 1 A schematic diagram of a lane-changing vehicle is shown. where G fo is the straight-line distance between the lane-changing vehicle S and the vehicle in front of the lane-changing vehicle; G ro is the straight-line distance between the lane-changing vehicle S and ...

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Abstract

The invention relates to a lane changing model parameter optimization method based on a mixed Gaussian-hidden Markov model. Operation states of a vehicle and surrounding vehicles are analyzed, and a driving lane changing intention recognition method based on the hidden Markov model is established, that is, prediction of an implicit driver lane changing intention is achieved by using lane changingcharacterization parameter observation states capable of being observed; a highway vehicle database with concentrated NGSIM data serves as the basis, a lane changing characterization parameter sampleis extracted, training and verification are carried out on the hidden Markov model by using an HMM tool box programming algorithm in MATLAB, and the number of mixed Gaussian models and the number of states are optimized by using a particle swarm algorithm. The number of the mixed Gaussian models and the number of the states are optimized by using the particle swarm algorithm, so that the accuracyof driver lane changing intention recognition is improved; better accuracy is finally obtained for the prediction result of the driver lane change intention.

Description

technical field [0001] The invention relates to an unmanned driving technology, in particular to a method for optimizing parameters of a lane-changing model based on a mixed Gaussian-hidden Markov model. Background technique [0002] Unmanned driving is one of the main development directions of future automobiles. Research on unmanned vehicles is of great significance to ensure road traffic safety, improve road traffic capacity, and protect people's property safety. [0003] Unmanned driving is a comprehensive technology integrating environmental perception and cognition, dynamic planning and decision-making, behavior control and execution, among which perception and cognition are one of the most critical links in unmanned driving. Prerequisites for planning and decision-making. However, in practical applications, it is difficult to establish an accurate driver cognitive behavior model, especially the lane-changing behavior model, which is a commonly used driving behavior t...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D1/02
CPCG05D1/0221G05D1/0223G05D1/0253G05D1/0276G05D2201/0212
Inventor 孙涛李洁
Owner UNIV OF SHANGHAI FOR SCI & TECH
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