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Intensive learning based urban intersection passing method for driverless vehicle

An unmanned vehicle and reinforcement learning technology, applied in the field of unmanned vehicle urban intersection traffic based on reinforcement learning, can solve problems such as safety and efficiency, uncomfortable driving experience, etc.

Active Publication Date: 2018-12-04
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0011] The present invention proposes an unmanned vehicle urban intersection traffic method based on reinforcement learning, which solves the uncomfortable driving experience caused by factors that do not consider safety and efficiency in the existing unmanned driving technology

Method used

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  • Intensive learning based urban intersection passing method for driverless vehicle
  • Intensive learning based urban intersection passing method for driverless vehicle
  • Intensive learning based urban intersection passing method for driverless vehicle

Examples

Experimental program
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Embodiment 1

[0072] 1.1. Scene construction Prescan simulation scene construction

[0073] The construction of the virtual scene is completed in the GUI module, which includes the road and infrastructure part, the traffic participant part, the sensor module, the weather setting and the light source setting. In the road database, you can set straight roads, arc (or multiple bends) roads, intersections, Y-shaped (or T-shaped) roads, and roundabout roads, etc., and you can also set various marking lines, such as zebra crossings, straight and left-turn markings, speed limit and height limit signs, road signs, etc.; the infrastructure database can set the surrounding environment required for simulation, including different types of trees, vegetation, roadside buildings and traffic lights; traffic participant database Different types of vehicles can be set, such as trucks, cars, e-bike and other vehicle models, and different types of pedestrians can be set, including adults, elderly and children...

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Abstract

The invention discloses an intensive learning based urban intersection passing method for a driverless vehicle. The method includes a step 1 of collecting vehicle continuous running state informationand position information through a photographing method, the vehicle continuous running state information and position information including speed, lateral speed and acceleration value, longitudinal speed and acceleration value, traveling track curvature value, accelerator opening degree and brake pedal pressure; a second step of obtaining characteristic motion track and the velocity quantity of actual data through clustering; a step 3 of processing original data by an exponential weighting moving average method; a step 4 of realizing the interaction passing method by utilizing an NQL algorithm. The NQL algorithm of the invention is obviously superior to a Q learning algorithm in learning ability when handling complex intersection scenes and a better training effect can be achieved in shorter training time with less training data.

Description

technical field [0001] The invention belongs to the field of unmanned driving, and more specifically relates to a method for passing unmanned vehicles at urban intersections based on reinforcement learning. Background technique [0002] As a sharp tool to improve the efficiency of urban road traffic and reduce road safety accidents in the future, unmanned vehicles have been greatly developed in recent years, and many scientific research institutions and universities at home and abroad are still increasing their research and development efforts. However, in order for unmanned vehicles to achieve complete autonomous driving in the mixed actual road environment, it is necessary to allow the "control brain" of unmanned vehicles to have the same ability to learn and adapt to changes as human drivers. However, the vehicle intelligent driving system based on traditional rules is only suitable for specific driving scenarios, and cannot realize the environmental adaptability and robu...

Claims

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Application Information

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IPC IPC(8): G08G1/01G08G1/017G08G1/052G08G1/08
CPCG08G1/0125G08G1/0175G08G1/052G08G1/08
Inventor 陈雪梅
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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