2024, Vol. 4, Issue 2, Part A
Deeplearning for enhancing autonomous vehicles perception and decision-making
Author(s): BDVS Naidu, Purvi Joshi, K Pawan Jayanth Reddy, G Dileep Kumar and Dr. Priyanka Kaushik
Abstract: Autonomous vehicles (AVs) have emerged as a game-changing technology capable of revolutionizing global transportation systems. However, their widespread adoption is dependent on their ability to accurately perceive and interpret complex real-world environments while making safe and efficient decisions in real time. Deep learning, a subset of artificial intelligence (AI) inspired by the structure and function of the human brain, has shown great promise in addressing these challenges by allowing AVs to learn from massive amounts of data and extract meaningful patterns for perception and decision-making tasks. This research paper investigates the use of deep learning techniques to improve autonomous vehicles' perception and decision-making capabilities. It delves into various neural network architectures, including convolutional neural networks (CNNs) for image recognition, recurrent neural networks (RNNs) for sequential data processing, and deep reinforcement learning (DRL) for uncertainty-based decision making. The paper investigates how these architectures can be tailored and optimised to meet the specific needs of autonomous driving scenarios, such as object detection, lane tracking, pedestrian recognition, and trajectory planning. The paper also discusses the challenges and limitations of deploying deep learning models in real-world AV systems, such as data quality, computational efficiency, and safety concerns. It also looks at current research projects and emerging trends aimed at addressing these challenges, such as the creation of novel architectures, data augmentation techniques, and simulation-based training methodologies.This paper, through a comprehensive review of existing literature and case studies, provides insights into the current state-of-the-art in deep learning for autonomous vehicles and identifies future research areas. The ultimate goal is to help advance AV technology by developing safer and more reliable autonomous driving systems that can navigate diverse and dynamic environments with human-like perception and decision-making abilities.
DOI: 10.22271/27084493.2024.v4.i2a.48
Pages: 01-18 | Views: 333 | Downloads: 132
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How to cite this article:
BDVS Naidu, Purvi Joshi, K Pawan Jayanth Reddy, G Dileep Kumar, Dr. Priyanka Kaushik. Deeplearning for enhancing autonomous vehicles perception and decision-making. Int J Electron Microcircuits 2024;4(2):01-18. DOI: 10.22271/27084493.2024.v4.i2a.48
