Hardware Simulation of Active Lane Keeping Assist Based on Fuzzy Logic

Basjaruddin, Noor Cholis; Kuspriyanto, Kuspriyanto; Saefudin, Didin; Rachman, Alditama · 2017 · Crossref

DOI: 10.11591/ijeecs.v5.i2.pp321-326

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Summary

This paper addresses the development of an Active Lane Keeping Assist (ALKA) system, a subsystem of Advanced Driver Assistance Systems (ADAS) designed to prevent lane departure crashes, which account for 19% of all accidents. Motivated by the need to mitigate accidents caused by driver inattention or dozing, the authors propose a fuzzy logic-based algorithm to actively steer a vehicle back into its lane. The study aims to validate this algorithm through hardware simulation, distinguishing ALKA from simpler Lane Departure Warning systems by its ability to physically correct vehicle trajectory rather than merely alerting the driver. The research methodology involves designing a fuzzy logic controller with two primary inputs: vehicle speed and lateral deviation. Speed is categorized into three fuzzy sets (low, medium, high), while deviation is divided into seven sets ranging from small left to big right. The output variable is the steering angle, defined by small, medium, and big steering angles, with a range of 0–30 degrees. The system was implemented on a battery-operated toy car equipped with six infrared line-tracking sensors (TCRT5000L) to detect lane markers. The hardware architecture utilizes an Arduino Mega 2560 microcontroller to process sensor data and control an H-bridge for motor regulation, while an Arduino Uno 328 handles data logging to an SD card and display on an LCD. The simulation tested the algorithm at three distinct speed levels: slow, medium, and high. The experimental results demonstrated that the fuzzy logic algorithm successfully restored the toy car to the lane and prevented it from exiting the track in most trials. However, performance varied significantly by speed. The system achieved a safety index of 100% at medium speed, indicating perfect lane keeping across ten trials. At low speed, the safety index was 83.3%, with the car deviating more frequently than at higher speeds. Conversely, high-speed testing yielded the lowest safety index of 50%. The authors noted that the toy car exhibited a mechanical tendency to veer right, which influenced the deviation data, but confirmed that the algorithm correctly responded to these deviations by adjusting the steering angle and reducing speed as designed. The study concludes that the developed fuzzy logic ALKA algorithm functions as intended, effectively maintaining lane position under controlled conditions. The findings suggest that medium speeds offer the most stable performance for this specific implementation. The authors identify limitations in the current setup, particularly the reliance on infrared sensors and the mechanical biases of the toy car. For real-world vehicle implementation, they recommend improving the algorithm by adding more fuzzy sets for finer control and replacing infrared sensors with camera-based systems to enhance accuracy and ensure passenger comfort. This work contributes to the broader field of ADAS by providing a validated, low-cost hardware simulation framework for testing active lane-keeping strategies.

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• lane positioning

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• Theoretical Contribution: computational model