Browsing by Author "Adnan, Ashik"

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    A deep learning based autonomous electric vehicle on unstructured road conditions
    (BRAC University, 2021-09) Adnan, Ashik; Rahman, G M Mahbubur; Hossain, Md. Mahafuj; Mim, Mahfuza Sultana; Rahman, Md. Khalilur
    Autonomous driving vehicles are too known as driver-less cars which is one of the foremost astounding advances of the twenty-first century, anticipated to be driverless, effective, and crash dodging ideal urban cars of the future. Autonomous cars actually sense the environment, navigate and fulfill human transportation capabilities without any human inclusion. Cameras, radar, lidar, GPS, and navigational pathways help this type of vehicle detect its surroundings[6]. Even when the conditions alter, advanced control systems interpret sensory data to maintain their locations. Autonomous vehicles are on their way to completely replacing the world’s transportation system. To reach this goal automobile industries have begun working in this zone to realize the potential and unravel the challenges as of now. A few companies have also started their trail. It will aid in reducing traffic, reducing pollution, avoiding maximum accidents, saving time, conserving energy, and improving human safety. As a result, with the aim and vision of eradicating these challenges from our country, we are focusing on an independent car that will assist us in saving ourselves from the daily revelations we generally confront on the road. Besides, it is high time we began working in Bangladesh on a driver-less vehicle.
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    Intelligent lane detection and path prediction for autonomous driving in varied weather
    (BRAC University, 2025-01) Adnan, Ashik; Rahman, Md. Khalilur
    Rapidly advancing intelligent and autonomous driving systems demand reliable computer vision-based perception technology, particularly for safe path detection in various weather and road conditions, essential for efficient vehicle navigation. This paper proposes a novel lane detection technique utilizing a pre-trained Keras-based CNN model capable of identifying the path ahead under challenging lighting and weather situations, such as nighttime and heavy rain, using videos acquired with a monocular camera. Furthermore, we address the issue of lane detection when lines are illuminated by vehicle headlights or streetlights, even under severely reduced visibility conditions caused by heavy rainfall, using a color filtering technique. We propose a path projection technique that integrates the widely used slope calculation method with convolutional neural networks (CNN). The Keras model facilitates the detection of lane lines, enabling the calculation of the center trajectory based on the identified road lanes. The projection technique demonstrates effective performance in low visibility and adverse weather conditions. The experimental results show that the presented algorithms effectively detect road lanes and predict paths in multiple weather conditions.