Pedestrian Perception Tracking in Complex Environment of Unmanned Vehicles Based on Deep Neural Networks

Q3 Engineering

EAI Endorsed Transactions on Energy Web Pub Date : 2024-04-15 DOI:10.4108/ew.5793

Ruru Liu, Feng Hong, Zuo Sun

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引用次数: 0

Abstract

INTRODUCTION: In recent years, machine learning and deep learning have emerged as pivotal technologies with transformative potential across various industries. Among these, the automobile industry stands out as a significant arena for the application of these technologies, particularly in the development of smart cars with unmanned driving systems. This article delves into the extensive research conducted on the detection technology employed by autonomous vehicles to navigate road conditions, a critical aspect of driverless car technology. OBJECTIVES: The primary aim of this research is to explore and highlight the intricacies of road condition detection for autonomous vehicles. Emphasizing the importance of this key component in the development of driverless cars, we aim to provide insights into cutting-edge algorithms that enhance the capabilities of these vehicles, ultimately contributing to their widespread adoption. METHODS: In addressing the challenge of road condition detection, we introduce the TidyYOLOv4 algorithm. This algorithm, deemed more advantageous than YOLOv4, particularly excels in pedestrian recognition within urban traffic environments. Its real-time capabilities make it a suitable choice for detecting pedestrians on the road under dynamic conditions. RESULTS: The application of the TidyYOLOv4 algorithm in autonomous vehicles has yielded promising results, especially in enhancing pedestrian recognition in urban traffic settings. The algorithm's real-time functionality proves crucial in ensuring the timely detection of pedestrians on the road, thereby improving the overall safety and efficiency of autonomous vehicles. CONCLUSION: In conclusion, the detection of road conditions is a critical aspect of autonomous vehicle technology, with implications for safety and efficiency. The TidyYOLOv4 algorithm emerges as a noteworthy advancement, outperforming its predecessor YOLOv4 in pedestrian recognition within urban traffic environments. As companies continue to invest in driverless technology, leveraging such advanced algorithms becomes imperative for the successful deployment of autonomous vehicles in real-world scenarios.

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基于深度神经网络的无人车复杂环境下的行人感知跟踪

简介：近年来，机器学习和深度学习已成为各行各业具有变革潜力的关键技术。其中，汽车行业是应用这些技术的重要领域，尤其是在开发配备无人驾驶系统的智能汽车方面。本文深入探讨了对自动驾驶汽车导航路况所采用的检测技术进行的广泛研究，这是无人驾驶汽车技术的一个重要方面。目标：本研究的主要目的是探索和强调自动驾驶车辆路况检测的复杂性。我们强调无人驾驶汽车开发过程中这一关键组成部分的重要性，旨在深入探讨可增强这些车辆能力的尖端算法，最终促进无人驾驶汽车的广泛采用。方法：在应对路况检测这一挑战时，我们引入了 TidyYOLOv4 算法。该算法被认为比 YOLOv4 更具优势，尤其擅长在城市交通环境中识别行人。它的实时性使其成为在动态条件下检测路上行人的合适选择。结果：TidyYOLOv4 算法在自动驾驶汽车中的应用取得了可喜的成果，尤其是在提高城市交通环境中的行人识别能力方面。事实证明，该算法的实时功能对于确保及时发现路上行人至关重要，从而提高了自动驾驶汽车的整体安全性和效率。结论：总之，路况检测是自动驾驶汽车技术的一个关键方面，对安全和效率都有影响。TidyYOLOv4 算法在城市交通环境中的行人识别能力优于其前身 YOLOv4，是一项值得关注的进步。随着各公司继续投资无人驾驶技术，利用这种先进的算法已成为在实际场景中成功部署自动驾驶汽车的当务之急。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EAI Endorsed Transactions on Energy Web Energy-Energy Engineering and Power Technology

CiteScore

2.60

自引率

0.00%

发文量

审稿时长

10 weeks

期刊介绍： With ICT pervading everyday objects and infrastructures, the ‘Future Internet’ is envisioned to undergo a radical transformation from how we know it today (a mere communication highway) into a vast hybrid network seamlessly integrating knowledge, people and machines into techno-social ecosystems whose behaviour transcends the boundaries of today’s engineering science. As the internet of things continues to grow, billions and trillions of data bytes need to be moved, stored and shared. The energy thus consumed and the climate impact of data centers are increasing dramatically, thereby becoming significant contributors to global warming and climate change. As reported recently, the combined electricity consumption of the world’s data centers has already exceeded that of some of the world''s top ten economies. In the ensuing process of integrating traditional and renewable energy, monitoring and managing various energy sources, and processing and transferring technological information through various channels, IT will undoubtedly play an ever-increasing and central role. Several technologies are currently racing to production to meet this challenge, from ‘smart dust’ to hybrid networks capable of controlling the emergence of dependable and reliable green and energy-efficient ecosystems – which we generically term the ‘energy web’ – calling for major paradigm shifts highly disruptive of the ways the energy sector functions today. The EAI Transactions on Energy Web are positioned at the forefront of these efforts and provide a forum for the most forward-looking, state-of-the-art research bringing together the cross section of IT and Energy communities. The journal will publish original works reporting on prominent advances that challenge traditional thinking.