Adaptive Cruise Control using Fuzzy Logic (original) (raw)
In recent years many studies on intelligent vehicles have been devoted to solve problem such as accident prevention, traffic flow smoothing. Adaptive Cruise Control (ACC) is used to maintain a constant safe distance between the host vehicle and the leading vehicle to avoid rear end collisions. It is an automotive feature that allows the speed of the vehicle to adapt to the traffic environment. ACC operates in distance control mode and velocity control mode. The method by which the ACC vehicle’s speed is controlled is via engine throttle control and limited brake operation. The inter-vehicular distance between the vehicles is measured. Desired speed is obtained from the distance measured. Neural Network and fuzzy logic Controller is trained to produce the desired acceleration and braking. In this research, ACC is implemented using the comparative analysis of Neural Network and Fuzzy Algorithm. The results demonstrate that for every parameter the proposed architecture outrages the conventional Neural Networks. The model is developed on MATLAB platform and comparisons were made based on evaluation parameters.
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— This paper presents an Adaptive Fuzzy Cruise Control (AFCC) system. The AFCC is modeled, designed and tested using MATLAB/SIMULINK. The presented AFCC gives numerous important points to the driver. It gives the driver the controllability of speeding intelligently. The proposed AFCC is simpler for a driver to drive in various driving conditions. It gives the driver a commanding on safety as well in various driving conditions.
Jordan Journal of Electrical Engineering, 2024
Adaptive cruise control (ACC), cruise control (CC), and automatic emergency braking (AEB) serve as the basis of longitudinal automated driving, and as such have been the subject of much research. Model predictive control (MPC) and fuzzy logic are often considered to be the next steps in improving the capability of these systems, but the two control strategies have not been compared to each other in the ACC, CC and AEB applications. Also, the three features (ACC, CC and AEB) have never been compiled into a single fuzzy logic controller. The purpose of this paper is to design a fuzzy logic-based ACC, CC, and AEB controller and compare it to an equivalent MPC controller. All three controllers control the desired longitudinal acceleration, and their functionality is tested using Matlab's Fuzzy Logic Designer and other Simulink toolboxes. Ultimately, the results of the analysis demonstrate that the proposed fuzzy controller operates just as well if not better than the MPC controller and that the fuzzy controller is able to operate well in all tested scenarios.
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