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Electronics
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Connected and Autonomous Vehicles in Mixed Traffic Systems
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Connected and Autonomous Vehicles in Mixed Traffic Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 1779

Special Issue Editors

Dr. Wenwei Yue

E-Mail Website
Guest Editor
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: vehicular networks; 6G; intelligent transportation systems; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Lina Zhu

E-Mail Website
Guest Editor
State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, China
Interests: routing protocols of IoVs; on-demand services in 6G; simulation platform and test system for 6G; heterogeneous data fusion
Special Issues, Collections and Topics in MDPI journals
Dr. Peibo Duan

E-Mail Website
Guest Editor
School of Software, Northeastern University, Shenyang 110819, China
Interests: machine learning and intelligent transportation systems

Special Issue Information

Dear Colleagues,

As connected and automated vehicles (CAVs) gradually enter transportation systems, a transition period arises where CAVs with varying levels of automation and connectivity will coexist with human-driven vehicles (HDVs) in road networks. This coexistence gives rise to new and potentially complex interactions between CAVs at different levels of automation and HDVs, significantly impacting traffic safety, efficiency, and road capacity. To address these challenges, it is crucial to conduct research focused on understanding the nature of these interactions, analyzing the impacts of CAVs on mixed traffic, examining human behavioral adaptations, and further leveraging automation and connectivity to enhance driving performance.

This Special Issue of Electronics aims to present state-of-the-art papers in the domain of connected and automated vehicles and their impacts in mixed traffic systems. We invite researchers to contribute with innovative and original research papers or insightful review papers. Topics include, but are not limited to, the following areas:

  • Connection and automation technologies for connected and automated vehicles;
  • Optimal control strategies of connected and automated vehicles for the driving performance of mixed traffic;
  • Impacts of connected and automated vehicles in safety, travel efficiency, road capacity and fuel consumption;
  • Theoretical and mathematical modelling of mixed traffic;
  • Human behavioral adaptations in mixed traffic;
  • Application of artificial intelligence in connected and automated vehicles;
  • Field tests, virtual reality, and simulation studies for autonomous driving in mixed traffic.

Dr. Wenwei Yue
Dr. Lina Zhu
Dr. Peibo Duan
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. Electronics 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

  • connected and automated vehicles
  • mixed traffic
  • human-driven vehicles
  • autonomous driving
  • artificial intelligence

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Published Papers (2 papers)

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Research

18 pages, 6309 KiB  
Article
Exploration of Traffic Accident-Based Pilot Zones for Autonomous Vehicle Safety Validation
by Siyoon Kim, Minje Cho and Yonggeol Lee
Electronics 2024, 13(17), 3390; https://doi.org/10.3390/electronics13173390 - 26 Aug 2024
Viewed by 500
Abstract
Recently, the commercialization of autonomous vehicles has increased the importance of verifying vehicle safety through autonomous trials. Autonomous driving trials are conducted in limited areas within artificially constructed test roads and pilot districts and directly explore road sections and areas with similar environments [...] Read more.
Recently, the commercialization of autonomous vehicles has increased the importance of verifying vehicle safety through autonomous trials. Autonomous driving trials are conducted in limited areas within artificially constructed test roads and pilot districts and directly explore road sections and areas with similar environments to ensure the safety of AVs driving on real roads. Many previous studies have evaluated the complex response potential of AVs by deriving edge scenarios to ensure their safety. However, difficulties arise in exploring real roads with traffic accident factors and configurations similar to those in edge scenarios, making validation on real roads uncertain. This paper proposes a novel method for exploring pilot zones using traffic accident data to verify the safety of autonomous vehicles (AVs). The method employs a CNN + BiGRU model trained on DMV dataset data to classify traffic accidents as AV- or human-caused. The model’s classification accuracy was evaluated using recall, precision, F1 score, and accuracy, achieving 100.0%, 97.8%, 98.9, and 99.5%, respectively. The trained model was applied to the KNPA dataset, identifying 562 out of 798 cases as AV-like, indicating potential areas of high accident density due to AV operation. Outlier detection and DBSCAN clustering were utilized to identify compact pilot zones, effectively reducing the area size compared to raw data clusters. This approach significantly lowers the cost and time associated with selecting test roads and provides a viable alternative for countries lacking real AV accident data. The proposed method’s effectiveness in identifying pilot zones demonstrates its potential for advancing AV safety validation. Full article
(This article belongs to the Special Issue Connected and Autonomous Vehicles in Mixed Traffic Systems)
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22 pages, 1055 KiB  
Article
A Distributed Scheme for the Taxi Cruising Route Recommendation Problem Using a Graph Neural Network
by Ying Li, Yongsheng Huang, Zhipeng Liu and Bin Zhang
Electronics 2024, 13(3), 574; https://doi.org/10.3390/electronics13030574 - 31 Jan 2024
Viewed by 684
Abstract
Despite considerable research efforts being devoted to the taxi cruising route recommendation (TCRR) problem, existing studies still have some shortcomings. To begin with, the competition and collaboration between taxis are not sufficiently taken into account. Furthermore, the TCRR is heavily reliant on potential [...] Read more.
Despite considerable research efforts being devoted to the taxi cruising route recommendation (TCRR) problem, existing studies still have some shortcomings. To begin with, the competition and collaboration between taxis are not sufficiently taken into account. Furthermore, the TCRR is heavily reliant on potential taxi demand, which is time-variant and difficult to accurately predict due to the underlying spatiotemporal correlation and dynamic traffic patterns. Moreover, the consideration of competition and cooperation among taxis increases the complexity of the TCRR problem, making conventional centralized algorithms computationally expensive. In this paper, we first formulate TCRR as a biobjective optimization problem to balance the collaboration and competition between taxis. Subsequently, we forecast short-term taxi demand using the proposed long-short-term-memory-based graph convolutional network (LSTM-GCN), which considers diverse factors such as road topology, points of interest (POIs), and multiple time-scale features. Lastly, we propose a distributed algorithm based on a Lagrange dual decomposition. The experimental and simulation results demonstrate that our TCRR scheme performs better than any other counterpart, (i) resulting in a 3% reduction in idle taxis per hour, (ii) performing four times faster than the centralized algorithms to obtain the optimal solution, and (iii) resulting in a 7% increase in average profit. Full article
(This article belongs to the Special Issue Connected and Autonomous Vehicles in Mixed Traffic Systems)
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