Driverless vehicle local path planning method based on model prediction

Abstract

The invention discloses a driverless car local path planning method based on model prediction. The method comprises the steps: 1, establishing a driverless car kinematics model; 2, in the vehicle pathplanning process, achieving the approximation of a driving environment formed by a vehicle and an obstacle through multiple circles; 3, fitting a lane line and a road edge curve; 4, constructing a target function J, wherein a first item and a second item of the target function represent the deviation between the predicted value and the reference position of the center line of the main lane, the third item represents the deviation between the predicted output course angle and the main lane midline course angle so as to ensure that the vehicle course angle is the same as the road course angle and the front wheel turning angle does not change suddenly; 4, ensuring that the vehicle runs according to the reference speed; 5, in order to avoid obstacles existing on the current lane, avoiding thecollision. Compared with the prior art, the method guarantees the quick and efficient path planning, and facilitates the calculation of the shortest distance between the main vehicle and the surrounding vehicles and obstacles.

Description

technical field [0001] The present invention relates to the field of path planning of unmanned vehicles, in particular to a model prediction method for local path planning of unmanned vehicles with typical road scene decision-making. Background technique [0002] The local path planning of unmanned vehicles is based on the global path planning to find a real-time route for the vehicle that conforms to the principles of vehicle kinematics and dynamics, can avoid dynamic and static obstacles, and conforms to traffic rules. In view of the above constraints and the uncertainty brought about by real-time changes in the external environment, the local path planning of unmanned vehicles has become a complex decision-making and control problem. Researching efficient algorithms suitable for real-time path planning of unmanned vehicles has always been the goal of many unmanned vehicle researchers. [0003] Some traditional path planning algorithms, such as artificial potential field ...

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Problems solved by technology

However, the high-calculation sampling process has become the biggest obstacle in its practical application.

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