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Intelligent vehicle lane changing track prediction method suitable for man-machine hybrid driving environment

A trajectory prediction, intelligent vehicle technology, applied in prediction, road vehicle traffic control systems, computer parts and other directions, can solve the problems of complex models, inapplicable accuracy, insufficient accuracy, etc., to achieve great practical promotion value, high precision, good automatic The effect of adaptability and robustness

Active Publication Date: 2019-12-10
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the current lane-changing behavior model of unmanned vehicles still faces the problem that the model is too complex and inapplicable or the accuracy is insufficient

Method used

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  • Intelligent vehicle lane changing track prediction method suitable for man-machine hybrid driving environment
  • Intelligent vehicle lane changing track prediction method suitable for man-machine hybrid driving environment
  • Intelligent vehicle lane changing track prediction method suitable for man-machine hybrid driving environment

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Experimental program
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Embodiment

[0052] Such as Figure 1-Figure 3 As shown, a lane-changing trajectory prediction method for intelligent vehicles suitable for human-machine mixed driving environment includes the following steps:

[0053] S1 obtains the trajectory data of the driving driverless car on the highway section;

[0054] S2 processes the acquired driving trajectory data, obtains the vehicle's own spatial information, motion information, and data information of the relative state of adjacent vehicles, and screens out lane-changing data based on the data information to generate a sample database required for the model.

[0055] Firstly, the obtained data is preliminarily screened, vehicle data with missing information is proposed, and the vehicle’s own spatial information, motion information and relative state data of adjacent vehicles are obtained. Lane-changing data is screened out based on the above information, and a sample with a sequence length of N is generated. database.

[0056] The vehicle...

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Abstract

The invention discloses an intelligent vehicle lane changing track prediction method suitable for a man-machine hybrid driving environment. The method comprises the steps that S1,acquiring track dataof an unmanned vehicle traveling on a highway section; S2, processing the acquired driving track data, acquiring space information and motion information of the vehicle and data information of relative states of adjacent vehicles, screening out lane changing data according to the data information, and generating a sample database; S3, constructing a driverless vehicle lane change trajectory prediction model; S4, training a vehicle lane changing track prediction model to obtain an optimal lane changing track prediction model structure, an optimal training sample capacity and an optimal historical sequence length; and S5, verifying the vehicle lane changing track prediction model trained in the step S4.

Description

technical field [0001] The invention relates to the technical field of unmanned driving, in particular to a method for predicting lane-changing trajectories of intelligent vehicles in a human-machine mixed driving environment. Background technique [0002] Human-machine mixed driving traffic environment refers to the traffic environment in which human-driven vehicles and unmanned vehicles are mixed in the future road system. With the rapid development of autonomous driving technology, unmanned vehicles will enter the actual road traffic system in the foreseeable future, and will operate in a human-machine mixed traffic environment for a long time. Lane changing behavior is a basic traffic driving task, which is of great significance to driving safety and traffic flow stability. However, the accuracy of existing lane-changing models cannot meet practical use. At present, there is no literature on predicting lane-changing trajectories of unmanned vehicles based on deep learn...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/02G06Q10/04G08G1/01
CPCG06Q10/04G06N3/02G08G1/0125G06F18/214
Inventor 黄玲黄子虚吴泽荣郭亨聪游峰张荣辉
Owner SOUTH CHINA UNIV OF TECH
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