Integrated longitudinal and lateral coordinated control system for connected and automated vehicles driven by fuzzy control algorithm
TL;DR
This study provides technical support for trajectory planning and coordinated vehicle control of intelligent vehicles, contributing to high-precision obstacle avoidance and multi-objective coordinated control of connected and automated vehicles.
Abstract
Connected and Automated Vehicles face challenges of fixed parameters, poor dynamic adaptability, and the lack of longitudinal and lateral coordination, which affect safe and stable vehicle operations. To solve these problems, this study aims to develop an advanced coordinated control system for intelligent vehicles. This study proposes a dynamics modeling technique based on online calibration of connected parameters. This technique integrates the real-time data update patterns of vehicle-infrastructure cooperation to construct an accurate motion model, which combines dynamic parameter inputs to achieve precise evaluation of vehicle driving states. In addition, this study adopts a control technique based on Adaptive Fuzzy Sliding Mode (AFSM) and fuzzy Reinforcement Learning (RL) for coordinated vehicle management and control. This technique takes dynamic states as inputs, enhances the suppression of chattering interference by introducing a fuzzy inference layer, and calibrates the final control results through a dual strategy integrating spatial constraints and adaptive mechanisms. Experiments are conducted based on a commercial bus. In lateral target tracking control, the model in this study reaches a displacement of 3.78 m at 20 s, and the lateral tracking error drops to −0.02 m, outperforming similar models. In real-vehicle extreme lane-changing control experiments, the root-mean-square lateral error of the model in this study is 0.035 m, while the maximum control chattering rate is only 2.4%. Finally, in longitudinal and lateral coordinated performance analysis, the maximum speed error of the model in this study is 0.25 km/h, and the jerk is 0.12 m/s 3 , both of which are superior to similar models. The proposed technique demonstrates good application effects in addressing parameter ambiguity and dynamic control imbalance. This study provides technical support for trajectory planning and coordinated vehicle control of intelligent vehicles, contributing to high-precision obstacle avoidance and multi-objective coordinated control of connected and automated vehicles.