Intelligent vehicle lane change path planning method based on polynomial and radial basis function (RBF) neural network

Abstract

The invention relates to an intelligent vehicle lane change path planning method based on a polynomial and radial basis function (RBF) neural network. The intelligent vehicle lane change path planning method comprises the following steps that: the state information of obstacles and lane change vehicles in lanes are detected and determined according to a vehicle-mounted sensor, and the state information comprises positions, speed, acceleration and shapes; the lane change vehicles and the obstacles are geometrically covered, and in addition, a lane change path model using the time as the independent variable is built; boundary conditions of the lane change vehicles are obtained by the dynamic RBF neural network; the lane change path parameter is subjected to traversing in a certain range according to a certain step length, and the calculation of a polynomial method is combined to obtain the lane change path set under the specific boundary conditions; index functions for evaluating the merits of the lane change patch performance are defined, the optimal path in generated lance change paths is screened according to the index functions and is applied to the practical lane change process of vehicles; and whether the RBF neural network is updated or not is determined according to the merits of the boundary conditions of the generated lane change paths. The neural network has good self-adaption capability, so that the problem that the RBF neural network structure is oversize or undersize is solved.

Description

technical field [0001] The invention belongs to the field of computer application technology, and relates to intelligent vehicle lane-changing path planning under structured road conditions, specifically a method for vehicle path planning through computer programs, which is used for intelligent vehicle lane-changing on structured roads and complex road conditions. road path planning. Background technique [0002] Intelligent vehicle is a comprehensive system integrating environmental perception, planning and decision-making, multi-level assisted driving and other functions, and it is the carrier of many high-tech comprehensive integration. Intelligent vehicles are committed to improving the safety and comfort of vehicles and providing excellent human-vehicle interaction interfaces. It is an important part of the intelligent transportation system that is currently being developed in various countries, and it is also a research hotspot in the field of vehicle engineering in th...

AI Technical Summary

Problems solved by technology

Although the planning methods mentioned above can meet the expected goals to a certain extent, they all have defects in different aspects. For example, the path planning using the field method usually needs to regard the vehicle as a particle, which is not in line with the actual situation, so it cannot It is directly applied to the vehicle system with kinematic constraints. Although there are research results to solve this problem by adding constraints to the generated path, this greatly increases the amount of computation.

The difficulty in path planning using spline curves, Bezier curves, etc. lies in the selection of control points. How to make the path under the action of control points meet the vehicle kinematics constraints, comfort requirements and be able to perform collision detection on obstacles has not yet been solved. the solution

The key to using polynomials for path planning is the setting of boundary conditions. A set of suitable boundary conditions can make the planned path reasonable, otherwise the generated path is difficult to meet the design requirements.

Images