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Transportation and Vehicle Automation
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Transportation and Vehicle Automation

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 3340

Special Issue Editors

Dr. Shuo Li

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Cassie Building, Claremont Road, Newcastle upon Tyne NE1 7RU, UK
Interests: Connected and Automated Vehicles (CAVs); Cooperative Intelligent Transport Systems (C-ITS); Advanced Driver-Assistance Systems (ADASs); human factors in ITS; human-machine interactions in ITS; older drivers and ITS
Prof. Dr. Phil Blythe

E-Mail Website
Guest Editor
Intelligent Transport Systems, Newcastle University, Newcastle upon Tyne, UK
Interests: electric vehicles; automation; future mobility; decarbonisation
Dr. Simon J. Edwards

E-Mail Website
Guest Editor
School of Engineering, Newcastle University, Newcastle, UK
Interests: connected and auotmated vehicles; cooperative ITS; urban mobility; electro-mobility; inclusive mobility
Dr. Yanghanzi Zhang

E-Mail Website
Guest Editor
School of Engineering, Cassie Building Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: Connected and Automated Vehicles (CAVs); clean and sustainable energy; transport and environment, vehicle and transport-related emissions; cooperative intelligent transport system (C-ITS); human-machine interface design

Special Issue Information

Dear Colleagues,

Technologies for road transport are developing and advances in vehicle technologies are making rapid progress. Automated vehicles, one of the most popular technologies in the transportation industry, have the potential to revolutionize transportation and shape the future of mobility. The forthcoming arrival of automated vehicles on public roads has the potential to provide significant economic, social and environmental benefits. Automated vehicles potentially improve road safety by removing human error from the crash equation. They are able to potentially drive faster, more smoothly and more safely than the conventional vehicles controlled by a human, which substantially reduces traffic congestion and improves road efficiency. By reducing energy consumption, they can also reduce pollution and minimize carbon footprint. The revolutionary driver-vehicle interactions they potentially introduce could extend drivers’ role from solely being active drivers to include passive monitoring and being a passenger, which potentially makes driving easier, more enjoyable and more productive. In addition, they could significantly improve social inclusion by providing greater mobility options for older people, people with disabilities as well as people without driving licenses. Although the promising benefits of vehicle automation have been widely acknowledged, significant challenges remain before automated vehicles become commonplace. Such challenges include ensuring safe and reliable automated driving in different weather and road conditions; safe and comfortable interaction with conventional vehicles operated by human, buses, cyclists and pedestrians; cyber security issues; public acceptance and perception; human-machine interactions, human factors; regulatory and liability challenges; as well as ethical implications. To contribute to addressing these challenges and providing new knowledge and innovations to facilitate the development and implementation of automated vehicles, Sustainability is launching a Special Issue on Transportation and Vehicle Automation. 

  • Human factors in vehicle automation.
  • Human-centred design in vehicle automation.
  • Artificial intelligence in vehicle automation.
  • The impact of vehicle automation on carbon emission.
  • End-users’ attitudes and acceptance of automated vehicles.
  • Behavioural intention to use automated vehicles.
  • Emerging mobility services with automated vehicles.
  • Cyber security for connected and autonomous vehicles.
  • Automated driving control algorithm.
  • Automated vehicles interaction with cyclists and pedestrians.
  • Policy-related implications of automated vehicles.
  • Ethical challenges of automated vehicles.
  • Infrastructure for connected and automated vehicles.

We look forward to receiving your contributions.

Dr. Shuo Li
Prof. Dr. Phil Blythe
Dr. Simon J. Edwards
Dr. Yanghanzi Zhang
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.

Published Papers (2 papers)

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21 pages, 2824 KiB  
Article
Assessing the Effects of Modalities of Takeover Request, Lead Time of Takeover Request, and Traffic Conditions on Takeover Performance in Conditionally Automated Driving
by Weida Yang, Zhizhou Wu, Jinjun Tang and Yunyi Liang
Sustainability 2023, 15(9), 7270; https://doi.org/10.3390/su15097270 - 27 Apr 2023
Cited by 2 | Viewed by 1617
Abstract
When a conditionally automated vehicle controlled by the machine faces situations beyond the capability of the machine, the human driver is requested to take over the vehicle. This study aims to assess the short-term effects of three factors on the takeover performance: (1) [...] Read more.
When a conditionally automated vehicle controlled by the machine faces situations beyond the capability of the machine, the human driver is requested to take over the vehicle. This study aims to assess the short-term effects of three factors on the takeover performance: (1) traffic conditions (complex and simple); (2) modality of takeover request (auditory and auditory + visual); (3) lead time of takeover request (TORlt, 5 s and 7 s). The scenario is the obstacle ahead. Indicators include: (1) Take Over Reaction Time (TOrt); (2) approximate entropy (ApEn), operating order of steering wheel Angle and pedal torque; (3) the choice of target lane and speed of lane-changing; (4) mean and standard deviation of acceleration and velocity; (5) quantifiable lateral cross-border risk and longitudinal collision risk. A driving simulation experiment is conducted to collect data for analysis. The effects of the three factors on takeover performance are analyzed by analysis of variance (ANOVA) and non-parametric tests. The results show that when the traffic conditions are complex, drivers have a larger ApEn of the steering wheel angle and brake pedal torque, and a smaller ApEn of acceleration pedal torque. In the 5 s TORlt case, drivers have a smaller ApEn of brake pedal torque the interaction between TORlt, traffic conditions, and modality of TOR affects ApEn of accelerator pedal torque. 5 s TORlt/complex traffic condition makes the scene more urgent, which is easy to cause driver to make sudden and simultaneous turning and sudden braking dangerous behavior meanwhile. Compared with other combinations of modality and TORlt, the combination of 7 s and auditory + visual significantly reduces the lateral cross-border risk and longitudinal collision risk. Full article
(This article belongs to the Special Issue Transportation and Vehicle Automation)
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15 pages, 12596 KiB  
Essay
A Vehicle Detection Method Based on an Improved U-YOLO Network for High-Resolution Remote-Sensing Images
by Dudu Guo, Yang Wang, Shunying Zhu and Xin Li
Sustainability 2023, 15(13), 10397; https://doi.org/10.3390/su151310397 - 30 Jun 2023
Cited by 3 | Viewed by 1183
Abstract
The lack of vehicle feature information and the limited number of pixels in high-definition remote-sensing images causes difficulties in vehicle detection. This paper proposes U-YOLO, a vehicle detection method that integrates multi-scale features, attention mechanisms, and sub-pixel convolution. The adaptive fusion module (AF) [...] Read more.
The lack of vehicle feature information and the limited number of pixels in high-definition remote-sensing images causes difficulties in vehicle detection. This paper proposes U-YOLO, a vehicle detection method that integrates multi-scale features, attention mechanisms, and sub-pixel convolution. The adaptive fusion module (AF) is added to the backbone of the YOLO detection model to increase the underlying structural information of the feature map. Cross-scale channel attention (CSCA) is introduced to the feature fusion part to obtain the vehicle’s explicit semantic information and further refine the feature map. The sub-pixel convolution module (SC) is used to replace the linear interpolation up-sampling of the original model, and the vehicle target feature map is enlarged to further improve the vehicle detection accuracy. The detection accuracies on the open-source datasets NWPU VHR-10 and DOTA were 91.35% and 71.38%. Compared with the original network model, the detection accuracy on these two datasets was increased by 6.89% and 4.94%, respectively. Compared with the classic target detection networks commonly used in RFBnet, M2det, and SSD300, the average accuracy rate values increased by 6.84%, 6.38%, and 12.41%, respectively. The proposed method effectively solves the problem of low vehicle detection accuracy. It provides an effective basis for promoting the application of high-definition remote-sensing images in traffic target detection and traffic flow parameter detection. Full article
(This article belongs to the Special Issue Transportation and Vehicle Automation)
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