Abstract

ABSTRACT In this paper, an integrated control method is proposed which is based on a planning of vehicle’s path and speed with respect to obstacles and a model predictive control for tracking this path. The planning layer builds a model predictive control framework based on the vehicle kinematics model; based on the potential field theory, comprehensively considers the vehicle’s state information and the relative position and velocity information of the obstacles, establishes the potential field function, introduces the optimization objective function, and optimizes vehicle’s path and speed. The tracking layer builds a model predictive control framework based on the vehicle dynamics model, establishes an optimized objective function that takes the optimal front wheel rotation angle and optimal longitudinal acceleration as inputs, and constrains the lateral acceleration and yaw angular velocity to achieve the vehicle’s obstacle avoidance path track. A co-simulation platform of CarSim and Matlab/Simulink was built to analyse the performance of the vehicle under static and dynamic obstacles under different initial speed conditions. The results show that the vehicle can track the reference path and reference speed smoothly, realize the horizontal and vertical comprehensive control of active obstacle avoidance, and verify the effectiveness of the proposed control method.

Highlights

  • Active obstacle avoidance has always been a hot issue in autonomous driving, and its reliability is directly related to users’ recognition of autonomous driving

  • The results show that the vehicle can track the reference path and reference speed smoothly, realize the horizontal and vertical comprehensive control of active obstacle avoidance, and verify the effectiveness of the proposed control method

  • Zhu et al [15] proposed an intelligent vehicle speed tracking control method based on model predictive control (MPC) framework, and realized driving or braking control through a noncalibrated switching algorithm, which was verified by simulation and real vehicle test

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Summary

Introduction

Active obstacle avoidance has always been a hot issue in autonomous driving, and its reliability is directly related to users’ recognition of autonomous driving. Zhu et al [15] proposed an intelligent vehicle speed tracking control method based on model predictive control (MPC) framework, and realized driving or braking control through a noncalibrated switching algorithm, which was verified by simulation and real vehicle test. In this paper, considering the horizontal and vertical control of the vehicle and the relative state information of the vehicle and the obstacle, a comprehensive control method based on the model predictive control theory for integrated path and speed integrated planning and tracking is proposed. The tracking layer tracks the reference path and reference speed planned by the planning layer based on the nonlinear vehicle dynamics model, and outputs the optimal front wheel angle and longitudinal acceleration to the execution layer vehicle model, thereby achieving smooth active obstacle avoidance

Planning controller design
Vehicle kinematics model
Obstacle avoidance objective function
Local planning path
Local speed planning
Vehicle model
Model predictive controller design
Simulation conditions and major parameters
Conclusions
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