Automatic driving local path planning method and system

Abstract

The invention discloses an automatic driving local path planning method, which comprises the following steps of: (1) receiving map data and vehicle positioning data of an area to be driven by a vehicle so as to generate a local map of a drivable area of the vehicle; (2) performing time sampling, speed sampling and angle sampling from the local map; (3) generating a plurality of predicted driving tracks based on time sampling, speed sampling and angle sampling; (4) traversing all the predicted driving tracks, carrying out collision detection on each predicted driving track, if a detection result shows that collision occurs, rejecting the predicted driving track, and if the detection result shows that collision does not occur, putting the predicted driving track into a predicted driving track database; and (5) selecting an optimal track from the predicted driving track database, and outputting the optimal track to the vehicle. Correspondingly, the invention further discloses an automaticdriving local path planning system.

Description

technical field [0001] The present invention relates to a control method and system, in particular to a path planning method and system. Background technique [0002] In recent years, with the rapid development of unmanned driving technology, it is more and more possible for unmanned vehicles to be used in daily life. The safety, reliability and comfort of unmanned vehicles are particularly important, which puts forward higher requirements for the optimization of unmanned vehicle movement routes. [0003] Path planning has always been a hot spot in the research of unmanned vehicles. At present, many researchers have conducted a lot of research on path planning methods. [0004] Existing path planning methods mainly include: Voronoi diagram method based on sampling, fast search random tree and other methods; methods such as node-based Dijkstra and Dynamic Window Approach (DWA). Among them, the DWA algorithm is easy to understand and implement, and is one of the most widely ...

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Problems solved by technology

[0005] However, due to some defects in the DWA algorithm itself in the prior art, the algorithm itself is widely used in the field of robotics, but relatively less in the field of unmanned driving.

Defects of the DWA algorithm include: speed sampling will cause the robot / unmanned vehicle to approach obstacles wirelessly, which does not conform to the unmanned driving strategy; in addition, due to the lack of time sampling, it cannot make planning behavior similar to human drivers (planning for the future path within a certain period of time)

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