In order to be acceptable to drivers, the adaptive cruise control (ACC) systems need to be designed based on the analysis of human driver driving behavior. A new safe distance model is proposed in this paper based on the analysis of real-world driving test data. The goal of the control algorithm is to achieve naturalistic behavior of the vehicle that can comprehensively address the issue of driving safety, tracking performance, fuel economy and ride comfort. Firstly, a hierarchical control architecture is utilized which lower controller compensates for nonlinear vehicle dynamics and enables tracking of the desired acceleration. Then the acceleration rate of the following vehicle is introduced as state variables in the model of the car-following system for a more realistic and comprehensive description of dynamic evolution between the preceding car and the following car. Next, the control objectives above are formulated into a constrained quadratic programming problem under the framework of model predictive control (MPC). Finally, the simulation results show that the control strategy proposed in this paper can provide natural following performance that not only can satisfy driving safety, tracking performance but also can achieve fuel economy and ride comfort.


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    Title :

    A Multi-Objective Model Predictive Control for Vehicle Adaptive Cruise Control System Based on a New Safe Distance Model


    Additional title:

    Int.J Automot. Technol.


    Contributors:
    Liu, Zhenze (author) / Yuan, Qing (author) / Nie, Guangming (author) / Tian, Yantao (author)

    Published in:

    Publication date :

    2021-04-01


    Size :

    13 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English