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PID-like IT2FLC-Based Autonomous Vehicle Control in Urban Areas

  • Research Article-Systems Engineering
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Abstract

Reliable autonomous navigation requires a seamless framework that blends perception, localization, planning, and control. Therefore, this research sought to optimize the accuracy of the steering angle, braking, and throttle control as well as the precise localization of self-driving cars within the complicated urban environment. To provide reliable AV control, cutting-edge technology was used: a proportional–integral–derivative-like interval type 2 fuzzy logic controller (PID-like IT2FLC). This advanced controller improved the AV motion control stability, precision, and efficiency. Multiple technologies worked simultaneously to build perception, path planning, and localization. A minimal convolutional neural network (CNN) trained on red–green–blue (RGB) images precisely localized the vehicle’s position. The A* algorithm, essential for AV path-planning software, determined the optimal trajectories to navigate complex urban areas by avoiding obstructions and obeying traffic laws. Control performance improved by reducing errors using the sophisticated Car Learning to Act (CARLA) simulator for validation. You Only Look Once version 3 (YOLOv3) was 98.87% accurate for object perception in empirical tests. The simulation results confirmed the effectiveness of the suggested approach with mean squared error (MSE) values of 0.039, 0.0099, and 0.0047 to predict the position (x, y) and the orientation, respectively, based on the CNN. With an MSE of 0.0244 and 0.077 for the steering angle and speed, respectively, the simulation results showed that the suggested technique performed well under various weather conditions and when compared to prior research. Specifically, there was a 15.28% enhancement in the MSE for the steering angle and an impressive 88.15% enhancement for speed.

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Abbreviations

AV(s):

Autonomous vehicle(s)

AD:

Autonomous driving

A*:

A- “star”

CARLA:

Car learning to act

CNN:

Convolutional neural network

COCO:

Common objects in context

DL:

Deep learning

FOU:

Footprint of uncertainty

GNSS:

Global navigation satellite system

GPS:

Global Positioning System

IMU:

Inertial measurement unit

IT2FLC:

Interval type 2 fuzzy logic controller

KM:

Karnik–Mendel

LiDAR:

Light detection and ranging

LMF:

Lower membership function

LSTM:

Long short-term memory

Map:

Mean average precision

MPCs:

Model predictive controllers

MSE:

Mean squared error

PD:

Proportional–derivative

PI:

Proportional–integral

PID-like IT2FLC:

Proportional–integral–derivative-like interval type 2 fuzzy logic controller

RADAR:

Radio detection and ranging

RGB:

Red–green–blue

RL:

Reinforcement learning

RNN:

Recurrent neural networks

SLAM:

Simultaneous localization and mapping

UMF:

Upper membership function

YOLO:

You only look once

YOLOv3:

You only look once version 3

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Authors and Affiliations

  1. University of Technology-Iraq, Baghdad, Iraq

    Shahad S. Ghintab & Mohammed Y. Hassan

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  1. Shahad S. Ghintab
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Correspondence to Shahad S. Ghintab.

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Ghintab, S.S., Hassan, M.Y. PID-like IT2FLC-Based Autonomous Vehicle Control in Urban Areas. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-09104-4

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