Unmanned vehicle control system based on deep learning

An unmanned vehicle and deep learning technology, applied in the field of unmanned driving, can solve the problems of complex vehicle environment and control methods that fail to achieve expected results.

Pending Publication Date: 2017-09-26
BEIJING UNION UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the complex road environment and vehicle environment in actual driving, the existing control methods cannot achieve the expected effect under different vehicle loads, different vehicle speeds, different road curvatures, and different road surfaces.

Method used

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  • Unmanned vehicle control system based on deep learning
  • Unmanned vehicle control system based on deep learning

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Embodiment Construction

[0016] The embodiment of the present invention provides an unmanned vehicle control system based on deep learning, which controls the steering, braking and acceleration of the vehicle through the CAN bus vehicle network, and realizes the automatic patrol driving of the vehicle.

[0017] like figure 1 , 2 As shown, the vehicle control system includes: a controller, a learning unit, wherein,

[0018] The controller is used to complete the control of the unmanned vehicle, and can work in the manual driving mode and the automatic driving mode, with the embedded DSP chip conforming to the car specification level as the embedded processor core, coupled with SDRAM (synchronous dynamic random access memory), ADC (analog-to-digital conversion) interface, DAC (digital-to-analog conversion) interface, CAN (controller area network) interface, network interface, serial interface and digital IO interface.

[0019] The controller includes: an acquisition unit and an update unit, wherein, ...

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PUM

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Abstract

The invention discloses an unmanned vehicle control system based on deep learning. The unmanned vehicle control system comprises a controller, a learning unit and a learning module, the controller comprises a collection unit and an update unit, the collection unit is used for collecting control action information of a driver in a manual driving mode, environment variable information of a vehicle and preset route traveling precollimation point and vehicle body direction included angle, the learning module is used for optimizing a vehicle control algorithm according to collected data by means of deep learning, and the update unit is used for loading the optimized control algorithm acquired by the deep learning into an embedded processor of the controller.

Description

technical field [0001] The invention belongs to the technical field of unmanned driving, and in particular relates to an unmanned vehicle control system based on deep learning. Background technique [0002] With the continuous development of driverless technology, more and more driverless cars are being tested. Unmanned driving technology can be divided into three levels: environment perception system, intelligent decision-making system and control execution system. Among them, the environment perception system is that unmanned vehicles rely on additional sensors such as precise navigation, image recognition and radar to collect and fuse data to complete the perception of their own position and attitude, surrounding environment and obstacles. The intelligent decision-making system intelligently makes path planning and decision-making for unmanned vehicles based on the results of the environmental perception system. The control execution system actually controls the steerin...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B60W30/14B60W30/182B60W40/105
CPCB60W30/14B60W30/182B60W40/105
Inventor 刘元盛杨建锁韩玺路铭张文娟郝天翔
Owner BEIJING UNION UNIVERSITY
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