Improving environmental awareness for autonomous vehicles

Peixoto, Maria J. P.; Azim, Akramul

doi:10.1007/s10489-022-03468-6

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…The architectural design should be based on the local environment and social status. At the same time, attention should be paid to protecting the surrounding ecological environment [12]. During construction, it is necessary to consider whether the existing forms of various natural resources and the use methods meet the national standards.…”

Section: Green Ecological Buildingmentioning

confidence: 99%

Particle Swarm Optimization Algorithm to Correlation Analysis of Green Ecological Buildings and Natural Environment Protection

2023

NEP

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With the acceleration of urbanization, urban environmental problems are becoming more and more serious. Particle swarm optimization algorithm has unique advantages in solving the problems of green ecological buildings and urban environmental development. In order to clarify the relationship between green ecological buildings and environmental protection, this paper uses particle swarm optimization algorithm to explore the relationship between the two. In this paper, the investigation and comparison methods are mainly used to study the correlation between green ecological buildings and natural environment protection. The survey results show that the incremental cost of water saving accounts for 5.8% of the total cost. Therefore, in green buildings, we need to focus on the construction of water to reduce water pollution and waste.

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Section: Green Ecological Buildingmentioning

confidence: 99%

Particle Swarm Optimization Algorithm to Correlation Analysis of Green Ecological Buildings and Natural Environment Protection

2023

NEP

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“…The exploration of reinforcement learning (RL) in autonomous driving has opened new avenues for addressing these complex urban challenges [3][4][5]. However, scalability remains a concern in multi-AV lane-changing scenarios within mixed traffic.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Smart Cities through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles

Louati,

Kariri

et al. 2024

Sustainability

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As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to significantly improve efficiency and safety, and reduce environmental impacts. This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. By incorporating a local reward system that values efficiency, safety, and passenger comfort, and a parameter-sharing scheme that encourages inter-agent collaboration, our MA2C algorithm presents a comprehensive approach to urban traffic management. The MA2C algorithm leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing both environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces.

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