Automatic driving automobile path planning method and system based on offline incremental learning

Abstract

The invention provides an automatic driving automobile path planning method and system based on off-line incremental learning. The method comprises the following steps: determining a current state variable and target information of an automobile; utilizing a probability path prediction model based on vehicle kinematics to predict a state variable of the vehicle at the next moment in real time so as to plan a short-term path of the vehicle; constructing an artificial potential field based on the two-dimensional map information, and distributing different potential functions for different types of obstacles and road structures on the two-dimensional map; acquiring offline data of an automobile driving path through a linear quadratic adjustment strategy, and training the offline data by using a radial basis function neural network so as to predict the driving distance of the automobile driving path; planning a long-term driving path of the automobile based on a fast expansion random path planning algorithm of off-line learning, and determining an optimal obstacle avoidance path of automatic driving of the automobile in combination with a prediction result of the driving distance; according to the invention, vehicle trajectory prediction is efficiently and accurately realized.

Description

technical field [0001] The present invention belongs to the field of path planning of automatic driving vehicles, and more specifically relates to a method and system for path planning of automatic driving vehicles based on off-line incremental learning. Background technique [0002] Despite decades of evolution in transportation systems, traffic accidents remain the leading cause of death worldwide. Self-driving technology promises to reduce accidents, as self-driving systems can replace human drivers and autonomously control movement based on road conditions and vehicle state. A fundamental task in autonomous driving is planning collision-free motion in the presence of numerous static and moving obstacles. The main techniques of obstacle avoidance include path planning, decision-making and path following. The path planning model aims to generate reference paths, avoid obstacles, and satisfy the requirements of road safety rules and vehicle kinematic constraints. [0003...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the defects of the prior art, the purpose of the present invention is to provide a method and system for autonomous vehicle path planning based on offline incremental learning, aiming at solving the problem that the prior art cannot effectively and quickly obtain the best vehicle according to the specified standard. The problem with trajectory planning schemes

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