Method for shortening intelligent car path planning and searching time based on heuristic search algorithm

Abstract

The invention discloses a method for shortening an intelligent car path planning and searching time based on a heuristic search algorithm. The method can be applied to the situation that an intelligent car deviates from an initially-set optimum path due to emergency. In view of the situation, the present invention provides a path searching algorithm having a dynamic heuristic function. The method comprises the following steps: extracting environmental information in real time by virtue of a global positioning system and a laser radar of a differential technology, and establishing an environmental map containing a barrier zone and a free zone by virtue of uniform rasterization of the environmental information; and triggering a deviation mechanism in the case that the intelligent car deviates from the original path, and calling the dynamic searching algorithm, and rapidly searching a feasible path in real time. By adopting the method, the contradiction between the optimism and searching efficiency of the global path planning and searching can be effectively processed, and technical support can be provided for the path planning of unmanned driving.

Description

technical field [0001] The invention relates to the field of self-driving cars, in particular to a method for shortening the search time for path planning of smart cars. Background technique [0002] The automatic driving system occupies an important position in the field of intelligent transportation, and is the product of the intersection of multiple technical disciplines such as computer artificial intelligence, robotics, control theory and electronic technology. It makes analysis and judgment based on the information obtained by each sensor, liberates people from single and persistent driving activities, reduces the impact of differences in driving behavior on the stability of traffic flow, and is conducive to improving the vehicle traffic rate of the existing road network and alleviating traffic congestion. Congestion, on the other hand, can improve automobile driving safety, reduce traffic accident rate, improve traffic safety, reduce energy consumption and environmenta...

AI Technical Summary

Problems solved by technology

However, these intelligent algorithms are not perfect at present, and there are some shortcomings. For example, GA has problems such as a large range of code length changes, low solution efficiency, and small solution scale; Dijkstra algorithm directly searches the global space without considering the target information, resulting in time-consuming path solving. Long, it is difficult to meet the needs of fast path planning

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