Around-View-Monitoring-Based Automatic Parking System Using Parking Line Detection

This paper introduces an automatic parking method using an around view monitoring system. In this method, parking lines are extracted from the camera images, and a route to a targeted parking slot is created. The vehicle then tracks this route to park. The proposed method extracts lines from images using a line filter and a Hough transform, and it uses a convolutional neural network to robustly extract parking lines from the environment. In addition, a parking path consisting of curved and straight sections is created and used to control the vehicle. Perpendicular, angle, and parallel parking paths can be created; however, parking control is applied according to the shape of each parking slot. The results of our experiments confirm that the proposed method has an average offset of 10.3 cm and an average heading angle error of 0.94°.

Clothoid curve optimization for parallel parking path planning and tracking control

In order to solve the problem of abrupt curvature change at the connection between arcs and straight lines in circle-line-circle (C-L-C) combined parallel parking paths, curvature optimization was carried out by using a cycloid curve, and the trajectory curvature and heading Angle were made to meet the pose requirements of parallel parking. By establishing the kinematics model state equation and taking the optimized C-L-C trajectory as the reference trajectory, the error model was obtained. The model predictive controller based on the error model was designed, and the effectiveness of the path planning and model predictive controller was verified on Carsim and Matlab/Simulink co-simulation platform.

Path Planning Optimization for Driverless Vehicle in Parallel Parking Integrating Radial Basis Function Neural Network

To optimize performances such as continuous curvature, safety, and satisfying curvature constraints of the initial planning path for driverless vehicles in parallel parking, a novel method is proposed to train control points of the Bézier curve using the radial basis function neural network method. Firstly, the composition and working process of an autonomous parking system are analyzed. An experiment concerning parking space detection is conducted using an Arduino intelligent minicar with ultrasonic sensor. Based on the analysis of the parallel parking process of experienced drivers and the idea of simulating a human driver, the initial path is planned via an arc-line-arc three segment composite curve and fitted by a quintic Bézier curve to make up for the discontinuity of curvature. Then, the radial basis function neural network is established, and slopes of points of the initial path are used as input to train and obtain horizontal ordinates of four control points in the middle of the Bézier curve. Finally, simulation experiments are carried out by MATLAB, whereby parallel parking of driverless vehicle is simulated, and the effects of the proposed method are verified. Results show the trained and optimized Bézier curve as a planning path meets the requirements of continuous curvature, safety, and curvature constraints, thus improving the abilities for parallel parking in small parking spaces.