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Sustainable Autonomous Driving Systems
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Sustainable Autonomous Driving Systems

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 4963

Special Issue Editors

Dr. Guangliang Cheng

E-Mail Website
Guest Editor
Department of Computer Science, University of Liverpool, Liverpool, UK
Interests: computer vision; remote sensing; change detection; hyperspectral image classification; road extraction
Special Issues, Collections and Topics in MDPI journals
Dr. Ting-Bing Xu

E-Mail Website
Guest Editor
School of Instrumentation Science and Optoelectronic Engineering, Beihang University, Beijing, China
Interests: computer vision; machine learning; model compression; model distillation
Dr. Xiangtai Li

E-Mail Website
Guest Editor
School of Computer Science and Engineering, Nanyang Technological University, Singapore
Interests: computer vision; light-weight scene understanding; vision transformer; segmentation and grouping; detection and tracking

Special Issue Information

Dear Colleagues,

The development of sustainable autonomous driving systems is a critical research area, given the increasing demand for efficient, eco-friendly, and safe transportation. Autonomous driving is an emerging field that requires complex and efficient algorithms to process sensor data and make decisions in real time. One of the main challenges in this field is designing sustainable and light-weight models that can run on resource-constrained devices, such as embedded systems or low-power processors, while still achieving high accuracy and robustness. This Special Issue on Sustainable Autonomous Driving Systems focuses on the latest developments, challenges, and opportunities in this field. The Issue features articles that cover various aspects of sustainable autonomous driving systems, including the latest computer vision and machine learning algorithms, such as network architecture design, network architecture search, light-weight model design on image/BEV/point-cloud perception algorithms, and other autonomous-driving-related topics. This Special Issue highlights the importance of sustainable technology in reducing the environmental impact of transportation and promoting sustainable development in the field of autonomous driving.

Dr. Guangliang Cheng
Dr. Ting-Bing Xu
Dr. Xiangtai Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainable model design
  • light-weight model design
  • network architecture search
  • model compression/distillation/quantization
  • transfer learning and domain adaptation
  • real-time image/point-cloud object detection
  • real-time image/point-cloud understanding
  • smart vehicle-to-vehicle communication system
  • other sustainable-autonomous-driving-related topics

Published Papers (2 papers)

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Research

18 pages, 725 KiB  
Article
Sustainable Smart Cities through Multi-Agent Reinforcement Learning-Based Cooperative Autonomous Vehicles
by Ali Louati, Hassen Louati, Elham Kariri, Wafa Neifar, Mohamed K. Hassan, Mutaz H. H. Khairi, Mohammed A. Farahat and Heba M. El-Hoseny
Sustainability 2024, 16(5), 1779; https://doi.org/10.3390/su16051779 - 21 Feb 2024
Cited by 3 | Viewed by 1024
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Sustainable Autonomous Driving Systems)
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18 pages, 4775 KiB  
Article
Improving the Performance of Autonomous Driving through Deep Reinforcement Learning
by Akshaj Tammewar, Nikita Chaudhari, Bunny Saini, Divya Venkatesh, Ganpathiraju Dharahas, Deepali Vora, Shruti Patil, Ketan Kotecha and Sultan Alfarhood
Sustainability 2023, 15(18), 13799; https://doi.org/10.3390/su151813799 - 15 Sep 2023
Viewed by 3460
Abstract
Reinforcement learning (RL) is revolutionizing the artificial intelligence (AI) domain and significantly aiding in building autonomous systems with a higher level comprehension of the world as we observe it. Deep learning (DL) facilitates RL to scale and resolve previously intractable problems, for instance, [...] Read more.
Reinforcement learning (RL) is revolutionizing the artificial intelligence (AI) domain and significantly aiding in building autonomous systems with a higher level comprehension of the world as we observe it. Deep learning (DL) facilitates RL to scale and resolve previously intractable problems, for instance, allowing supervision principles designed for robots to be acquired directly from visual data, developing video game proficiency from pixel-level information, etc. Recent research shows that RL algorithms help represent problems dealing with high-dimensional, unprocessed data input and can have successful applications in computer vision, pattern identification, natural language analysis, and speech parsing. This research paper focuses on training a simulation model of a car to navigate autonomously on a racetrack using RL. The study explores several fundamental algorithms in Deep RL, namely Proximal Policy Optimization (PPO), Deep Q-network (DQN), and Deep Deterministic Policy Gradient (DDPG). The paper documents a comparative analysis of these three prominent algorithms—based on their speed, accuracy, and overall performance. After a thorough evaluation, the research indicates that the DQN surpassed the other existing algorithms. This study further examined the performance of the DQN with and without ε-decay and observed that the DQN with ε-decay is better suited for our objective and is significantly more stable than its non ε-decay counterpart. The findings from this research could assist in improving the performance and stability of autonomous vehicles using the DQN with ε -decay. It concludes by discussing the fine-tuning of the model for future real-world applications and the potential research areas within the field of autonomous driving. Full article
(This article belongs to the Special Issue Sustainable Autonomous Driving Systems)
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