Fast and complete automatic driving trajectory planning method

Abstract

The invention relates to a fast and complete automatic driving trajectory planning method. The method comprises the steps that 1) a basic path is planned; 2) collision detection is carried out on thebasic driving path, and step 3) is carried out; if collision does not exist, whether the basic driving path reaches a target point is determined; if so, step 4) is carried out; if not, step 3) is carried out; 3) after a sampling / search path planning method is used to acquire a smooth effective path, step 4) is carried out; 4) after a curvature extreme value corresponding to the path is acquired, segmentation velocity is planned to acquire a trajectory; and 5) collision detection is carried out on the trajectory in time domain and spatial domain; if collision exists, whether the distance between a vehicle and an obstacle is greater than 2 times the minimum braking distance; if so, velocity re-planning and returning to step 4) are carried out; if not, returning to step 1) is carried out; finally the path and velocity are combined to generate a trajectory and output the trajectory. Compared with the prior art, the method provided by the invention has the advantages of trajectory planninginstantaneity improvement, stability, reliability and the like.

Description

technical field [0001] The invention relates to the field of trajectory planning of unmanned vehicles under urban structured roads, in particular to a fast and complete automatic driving trajectory planning method. Background technique [0002] With the development of society and the continuous improvement of people's living standards, the number of domestic passenger cars has increased by 13.5 times in the 17 years from 2000 to 2017, and the number of passenger cars in the future is estimated to be 400 million. However, the increasingly severe energy crisis, high-load traffic pressure, and increasing social requirements for driving safety have accelerated the pace of unmanned driving technology. As one of the cores of unmanned driving technology, the trajectory planning strategy must improve the real-time performance of the algorithm to respond to changes in the environment, avoid collisions, and cause unnecessary personal and property losses. At the same time, the strategy...

AI Technical Summary

Problems solved by technology

[0003] Traditional autonomous driving trajectory planning strategies generally consider using sampling methods (such as A*) or search methods (such as RRT) and other algorithms with analytical completeness or probabilistic completeness, but such algorithms have a blind search process and a solution process. It is extremely time-consuming and the generated trajectory is a section of polyline segment, the curvature is not continuous, and it is difficult for the vehicle to execute; there are also fast planning methods such as Dubins curve, Reeds-Shepp curve, spline curve, etc., but these algorithms It is not complete. When the environment is relatively complex, the ability to obtain a feasible solution drops sharply, and the time-consuming is also rising.

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