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Automatic driving system based on enhanced learning and multi-sensor fusion

A multi-sensor fusion and enhanced learning technology, applied in control/regulation systems, instruments, non-electric variable control, etc., can solve the problems of few breakthrough achievements and late start.

Active Publication Date: 2018-06-22
清华大学苏州汽车研究院(吴江)
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the country started relatively late in the field of autonomous driving, and there are few breakthrough achievements. It needs continuous innovation, combined with new technologies, and strives to make breakthroughs in the field of autonomous driving.

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  • Automatic driving system based on enhanced learning and multi-sensor fusion

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Embodiment

[0023] The preferred embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0024] Such as figure 1 As shown, the automatic driving system of the present invention includes three subsystems, specifically: a perception system, a control system and an execution system. Using deep learning and enhanced learning technology, build an automatic driving system that can interact with the environment, independently judge the external environment and make driving decisions. It will continue to evolve in the process of exploring the dynamics of vehicle driving behavior, and improve the vehicle's environmental perception and decision-making capabilities.

[0025] The perception system processes camera, lidar and GPS information through deep learning network models to obtain real-time vehicle and pedestrian information and 3D street view maps.

[0026] The control system, through the enhanced learning network model, processes ...

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Abstract

The invention discloses an automatic driving system based on enhanced learning and multi-sensor fusion. The system comprises a perception system, a control system and an execution system. The perception system high-efficiently processes a laser radar, a camera and a GPS navigator through a deep learning network so as to realize real time identification and understanding of vehicles, pedestrians, lane lines, traffic signs and signal lamps surrounding a running vehicle. Through an enhanced learning technology, the laser radar and a panorama image are matched and fused so as to form a real-time three-dimensional streetscape map and determination of a driving area. The GPS navigator is combined to realize real-time navigation. The control system adopts an enhanced learning network to process information collected by the perception system, and the people, vehicles and objects of the surrounding vehicles are predicted. According to vehicle body state data, the records of driver actions are paired, a current optimal action selection is made, and the execution system is used to complete execution motion. In the invention, laser radar data and a video are fused, and driving area identification and destination path optimal programming are performed.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and automatic driving, and in particular relates to an automatic driving system based on reinforcement learning and multi-sensor fusion. Background technique [0002] Autonomous driving technology has been valued by the world's auto companies, Internet companies, and research institutions in various universities, and all parties are actively promoting the development of autonomous driving. Automobile companies represented by Mercedes-Benz and Audi realize human-vehicle interaction, vehicle-vehicle interaction, and vehicle-road collaboration through the application of advanced technologies such as ultrasonic waves, radar, night vision devices, stereo cameras, and LEDs. However, China started relatively late in the field of autonomous driving, and there are few breakthrough achievements. It needs continuous innovation, combined with new technologies, and strives to make breakthroughs...

Claims

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Application Information

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IPC IPC(8): G05D1/02
CPCG05D1/0221G05D1/0231G05D1/0257G05D1/0278
Inventor 陈小琴王猛孙辉张伟
Owner 清华大学苏州汽车研究院(吴江)
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