Traffic light lane level control relationship extraction method and device

Abstract

The invention relates to a traffic light lane level control relationship extraction method and device. The method comprises the steps that laser point cloud data and high definition real image data ofa road intersection in the forward direction are collected, and on the basis of a data fusion technology, two kinds of data are matched; by means of an image semantic segmentation technology, boundary contour coordinates of the traffic lights are extracted from a laser point cloud; characteristic information of traffic light images is extracted from the high definition real image data, and the types of the traffic lights are judged; the traffic lights and the corresponding lanes are matched according to the types, and the traffic light lane level control relationship is obtained. A self-driving car runs according to the lane level path, when the self-driving car passes through the intersection, position and attribute information of controlled signal lights is obtained from high definitionmap data, environmental perception and vehicle body positioning are conducted together with other types of sensors in a matched mode, and then traffic signals are interpreted in a targeted mode; theprocessing efficiency and accuracy degree of self-driving cars on the traffic signals are greatly improved.

Description

technical field [0001] The invention relates to the technical field of automatic driving, in particular to a method and device for extracting lane-level control relations of traffic lights. Background technique [0002] High-precision map is a kind of map making technology and method dedicated to serving the automatic driving of cars. The characteristics of precision such as "over-the-horizon", "imperceptible" and "prior knowledge" make it irreplaceable in the field of automatic driving. It is often used to assist self-driving vehicles in environmental perception, body positioning and decision-making, and realize lane-level path planning and navigation. [0003] In the complex intersection environment, vehicles and pedestrians from multiple directions converge here, and the right of way conflicts, so it is necessary to redistribute the right of way based on the phase change of traffic lights. The self-driving car drives according to the planned lane-level path. When passin...

AI Technical Summary

Problems solved by technology

Limited by the perception range of on-board sensors and complex traffic conditions at intersections, this process is not easy, and the reliability of decision-making predictions is greatly reduced

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