Abstract
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.
Highlights
It is an important working condition for driverless vehicles to complete safe parallel parking in narrow parking spaces, where it is more difficult to park than in ordinary parking conditions [1]
The main methods of path planning in parallel parking are based on geometry, sampling, and numerical optimization
The path the condition of curvature continuity, thenetwork initial path is fitted by the Bézier curve and optimized by the radial basis function neural network method
Summary
It is an important working condition for driverless vehicles to complete safe parallel parking in narrow parking spaces, where it is more difficult to park than in ordinary parking conditions [1]. The main methods of path planning in parallel parking are based on geometry, sampling, and numerical optimization. The constrained optimization problem of arc-line-cyclotron curve is solved to establish the entry section path, the adjustment section path is planned through the combination of arc-line, guiding the car to complete parking task safely and comfortably [7]. In the path planning method based on the improved quintic polynomial and designed penalty function, the genetic algorithm is used to calculate the optimum parking path and minimum parking space to complete fast and effective parking with less vehicle damage and low space requirements [9]. To solve the multi-vehicle cooperative autonomous parking trajectory planning problem uniformly and effectively with a faster convergence speed, a vehicle kinematics model with dynamic constraints, namely endpoint and collision avoidance constraints, is established. Conclusions are drawn in etc., are output and transmitted to the actuators to realize steering, braking, etc
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