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Map path planning method based on combination of improved Astar algorithm and grey wolf algorithm

A path planning and map technology, applied in calculation, calculation model, road network navigator, etc., can solve the problems that the path does not have connectivity and is not suitable for driverless cars

Pending Publication Date: 2020-12-15
上海智驾汽车科技有限公司
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  • Claims
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AI Technical Summary

Problems solved by technology

However, the path searched by the Astar algorithm does not have connectivity and is not suitable for driverless cars, so further improvement is needed when it is applied to the field of driverless cars

Method used

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  • Map path planning method based on combination of improved Astar algorithm and grey wolf algorithm
  • Map path planning method based on combination of improved Astar algorithm and grey wolf algorithm
  • Map path planning method based on combination of improved Astar algorithm and grey wolf algorithm

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Embodiment

[0066] Such as figure 1 The map path planning method based on the combination of the improved Astar and gray wolf algorithms shown in the figure specifically includes the following steps:

[0067] Step 1, build high-precision map based on data collection equipment, described data collection equipment includes the data collection vehicle of Ladybag3.5 panorama camera and GPS combined inertial navigation, and concrete construction method is:

[0068] (1) Use the collection car to stop every 10 meters to collect a picture and the GPS data of the vehicle corresponding to the picture at the moment;

[0069] (2) Change the perspective of the picture and stitch it into a whole map using PS, etc.;

[0070] (3) Since the absolute coordinates of the vehicle are known and the position of each marker relative to the vehicle is known, the absolute coordinates of the markers are known, and arcgis is used to mark the entire map;

[0071] (4) Save the map data and use arcgis to output the p...

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Abstract

The invention discloses a map path planning method based on combination of an improved Astar algorithm and a grey wolf algorithm. The map path planning method specifically comprises the following steps: constructing a high-precision map based on data acquisition equipment; introducing a vehicle kinematics model into the Astar algorithm, and importing the vehicle kinematics model into an xml file of the OpenDRIVE; searching an optimal path point in the arcgis derived map data by using the Atar algorithm, and generating a traceable motion track of the vehicle by combining an RS curve with the optimal path point searched by the Atar algorithm; matching the point, line and plane data of the arcgis with thexml file of the OpenDRIVE; performing safety and comfort optimization on a matched resultby using a GWO algorithm, and selecting an optimal path; after the optimal path is selected from the OpenDRIVE file, the color of a root and the like on the corresponding optimal path can be changed,and performing visualization by using an LGSVL simulator and the like.

Description

technical field [0001] The invention relates to the technical field of map path planning, in particular to a map path planning method based on the combination of improved Astar and gray wolf algorithms. Background technique [0002] Self-driving cars mainly provide map, positioning, perception, navigation and control functions. The central element of a map scene representation is the alignment of a lane, and the main goal of map scene modeling is to efficiently represent road geometry while maintaining a certain degree of precision. [0003] Currently, there are various methods to obtain accurate road geometry data. For example, in traditional GIS, high-resolution aerial camera images are obtained from airplanes, and road geometry is extracted by image processing devices. Many studies use vehicle-based detection methods to obtain more accurate road geometry. In this method, a rover equipped with various sensors explores the road and collects sensor data to obtain road geo...

Claims

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Application Information

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IPC IPC(8): G01C21/34G01C21/30G06N3/00G06Q10/04
CPCG01C21/3446G01C21/30G06Q10/047G06N3/006Y02T10/40
Inventor 林太东张辉刘淼
Owner 上海智驾汽车科技有限公司
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