Connected vehicles ecological driving based on deep reinforce learning: Application of Web 3.0 technologies in traffic optimization

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Minghui Ma , Xu Han , Shidong Liang , Yansong Wang , Lan Jiang
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引用次数: 0

Abstract

With the fast development of Web3.0 technology, connected vehicles can now handle and communicate data more safely and effectively. When combined with 5G/6G communication technology, these vehicles can optimize emissions in transportation networks to a greater extent. This study proposed an ecologically car-following model from natural driving data by using deep reinforcement learning under the context of the rapid development of Web 3.0 technologies. Firstly, by utilizing naturalistic driving data, an environment for connected vehicle car-following is created. Secondly, this paper uses SAC (Soft Actor-Critic) deep reinforcement learning algorithm and designs novel reward function based on ecological driving principles and car-following characteristics to reduce fuel consumption and emissions while maintaining safe distance with leading vehicle. Subsequently, the established model is tested, and results indicate that model not only performs well in terms of collision occurrences, Time-to-Collision (TTC), and driving comfort on test set but also achieves reduction of 5.50% in fuel consumption and reductions of 15.04%, 5.63%, and 9.60% in pollutant emissions (NOx, CO, and HC) compared to naturalistic manually driven vehicles.
基于深度强化学习的车联网生态驾驶:Web 3.0 技术在交通优化中的应用
随着 Web3.0 技术的快速发展,互联车辆现在可以更安全、更有效地处理和交流数据。当与 5G/6G 通信技术相结合时,这些车辆可以在更大程度上优化交通网络中的排放。在 Web 3.0 技术飞速发展的背景下,本研究利用深度强化学习,从自然驾驶数据中提出了一种生态学汽车跟随模型。首先,通过利用自然驾驶数据,创建了车联网汽车跟车环境。其次,本文采用 SAC(Soft Actor-Critic)深度强化学习算法,并根据生态驾驶原则和跟车特性设计了新颖的奖励函数,以降低油耗和排放,同时与前车保持安全距离。随后,对建立的模型进行了测试,结果表明,与自然手动驾驶车辆相比,该模型不仅在碰撞发生率、碰撞时间(TTC)和驾驶舒适性方面表现出色,而且在油耗方面降低了 5.50%,在污染物排放(氮氧化物、一氧化碳和碳氢化合物)方面分别降低了 15.04%、5.63% 和 9.60%。
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
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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