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Sustainability
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Collaborative Technology for a Sustainable Transition to Automated Driving
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Collaborative Technology for a Sustainable Transition to Automated Driving

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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 23491

Special Issue Editors

Dr. António Lobo

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Guest Editor
Faculty of Engineering of the University of Porto, Research Centre for Territory, Transports and Environment, 4200-465 Porto, Portugal
Interests: automated vehicles; road safety; road operations
Dr. Sara Ferreira

E-Mail Website
Guest Editor
Research Centre for Territory, Transports and Environment (CITTA), Faculty of Engineering of the University of Porto, Rua Dr. Roberto Frias s/n, 4200-465 Porto, Portugal
Interests: accident modeling; road safety; road users’ behavior; technological sciences; engineering; civil engineering; infrastructures engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. António Couto

E-Mail Website
Guest Editor
Faculty of Engineering of the University of Porto, Research Centre for Territory, Transports and Environment, 4200-465 Porto, Portugal
Interests: transport and health; road safety; econometrics; transport engineering and management; transport analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advanced driver assistance systems have experienced a significant technological boost during the last decade, being present on almost every new vehicle sold today. The integration of these systems will progressively pave the way for the introduction of vehicles with increased automation capabilities. However, a large-scale deployment of automated vehicles on public roads will only be successful if technology is proved safe and convenient to all road users.

In addition, ambitious road safety targets, such as the European Commission’s "Vision Zero" strategy, aimed at eliminating road crashes caused by human errors by 2050, heavily rely on vehicle automation, which puts an additional pressure on manufacturers and regulatory bodies to ensure a smooth transition to fully-automated driving.

With this Special Issue, we are looking for new concepts and applications to feed the development of collaborative driving systems for different levels of automation. We are especially interested in naturalistic or driving simulation studies on one or more of the following topics:

  • Interactions between the automated vehicle and its driver/occupants, including requested or unrequested takeover manoeuvres, and perceived levels of safety and comfort during automated driving;
  • Interactions between the automated vehicle and other road users, including communication and actions towards pedestrians, cyclists and drivers of non-automated vehicles;
  • Behavioural modelling for different types of drivers and driving modes that mimic human behaviour;
  • New concepts of human-machine interfaces to improve safety and user experience.

Submitted papers should clearly contribute to bridge the gap between automated driving technology and different types of road users, presenting tools to address their specific needs and requirements and, ultimately, to promote a widespread acceptance and sustainable rollout of automated vehicles.

Dr. António Lobo
Prof. Dr. Sara Ferreira
Prof. Dr. António Couto
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

  • automated vehicles
  • collaborative technology
  • naturalistic driving
  • driving simulation
  • behavioral modeling

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Published Papers (6 papers)

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Editorial

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6 pages, 489 KiB  
Editorial
Driving as a Service: Promoting a Sustainable Transition to Automated Driving
by Sérgio Pedro Duarte, António Lobo, Sara Ferreira and António Couto
Sustainability 2024, 16(7), 2809; https://doi.org/10.3390/su16072809 - 28 Mar 2024
Cited by 2 | Viewed by 1013
Abstract
Automated vehicles (AVs) promise to make a revolution in the mobility paradigm and to bring benefits for traffic management and environmental quality, improving, in general, the quality of life in society [...] Full article
(This article belongs to the Special Issue Collaborative Technology for a Sustainable Transition to Automated Driving)
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Research

Jump to: Editorial

30 pages, 4623 KiB  
Article
Collective Driving to Mitigate Climate Change: Collective-Adaptive Cruise Control
by Saeed Vasebi and Yeganeh M. Hayeri
Sustainability 2021, 13(16), 8943; https://doi.org/10.3390/su13168943 - 10 Aug 2021
Cited by 4 | Viewed by 2719
Abstract
The transportation sector is the largest producer of greenhouse gas (GHG) emissions in the United States. Energy-optimal algorithms are proposed to reduce the transportation sector’s fuel consumption and emissions. These algorithms optimize vehicles’ speed to lower energy consumption and emissions. However, recent studies [...] Read more.
The transportation sector is the largest producer of greenhouse gas (GHG) emissions in the United States. Energy-optimal algorithms are proposed to reduce the transportation sector’s fuel consumption and emissions. These algorithms optimize vehicles’ speed to lower energy consumption and emissions. However, recent studies argued that these algorithms could negatively impact traffic flow, create traffic congestions, and increase fuel consumption on the network-level. To overcome this problem, we propose a collective-energy-optimal adaptive cruise control (collective-ACC). Collective-ACC reduces fuel consumption and emissions by directly optimizing vehicles’ trajectories and indirectly by improving traffic flow. Collective-ACC is a bi-objective non-linear integer optimization. This optimization was solved by the Non-dominated Sorting Genetic Algorithm (NSGA-II). Collective-ACC was compared with manual driving and self-centered adaptive cruise control (i.e., conventional energy-optimal adaptive cruise controls (self-centered-ACC)) in a traffic simulation. We found that collective-ACC reduced fuel consumption by up to 49% and 42% compared with manual driving and self-centered-ACC, respectively. Collective-ACC also lowered CO2, CO, NOX, and PMX by up to 54%, 70%, 58%, and 64% from manual driving, respectively. Game theory analyses were conducted to investigate how adopting collective-ACC could impact automakers, consumers, and government agencies. We propose policy and business recommendations to accelerate adopting collective-ACC and maximize its environmental benefits. Full article
(This article belongs to the Special Issue Collaborative Technology for a Sustainable Transition to Automated Driving)
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18 pages, 7013 KiB  
Article
Impact of External Human–Machine Interface Communication Strategies of Automated Vehicles on Pedestrians’ Crossing Decisions and Behaviors in an Urban Environment
by Marc Wilbrink, Merle Lau, Johannes Illgner, Anna Schieben and Michael Oehl
Sustainability 2021, 13(15), 8396; https://doi.org/10.3390/su13158396 - 27 Jul 2021
Cited by 36 | Viewed by 4782
Abstract
The development of automated vehicles (AVs) and their integration into traffic are seen by many vehicle manufacturers and stakeholders such as cities or transportation companies as a revolution in mobility. In future urban traffic, it is more likely that AVs will operate not [...] Read more.
The development of automated vehicles (AVs) and their integration into traffic are seen by many vehicle manufacturers and stakeholders such as cities or transportation companies as a revolution in mobility. In future urban traffic, it is more likely that AVs will operate not in separated traffic spaces but in so-called mixed traffic environments where different types of traffic participants interact. Therefore, AVs must be able to communicate with other traffic participants, e.g., pedestrians as vulnerable road users (VRUs), to solve ambiguous traffic situations. To achieve well-working communication and thereby safe interaction between AVs and other traffic participants, the latest research discusses external human–machine interfaces (eHMIs) as promising communication tools. Therefore, this study examines the potential positive and negative effects of AVs equipped with static (only displaying the current vehicle automation status (VAS)) and dynamic (communicating an AV’s perception and intention) eHMIs on the interaction with pedestrians by taking subjective and objective measurements into account. In a Virtual Reality (VR) simulator study, 62 participants were instructed to cross a street while interacting with non-automated (without eHMI) and automated vehicles (equipped with static eHMI or dynamic eHMI). The results reveal that a static eHMI had no effect on pedestrians’ crossing decisions and behaviors compared to a non-automated vehicle without any eHMI. However, participants benefit from the additional information of a dynamic eHMI by making earlier decisions to cross the street and higher certainties regarding their decisions when interacting with an AV with a dynamic eHMI compared to an AV with a static eHMI or a non-automated vehicle. Implications for a holistic evaluation of eHMIs as AV communication tools and their safe introduction into traffic are discussed based on the results. Full article
(This article belongs to the Special Issue Collaborative Technology for a Sustainable Transition to Automated Driving)
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20 pages, 943 KiB  
Article
Distractive Tasks and the Influence of Driver Attributes
by Sónia Soares, Carlos Campos, João Miguel Leitão, António Lobo, António Couto and Sara Ferreira
Sustainability 2021, 13(9), 5094; https://doi.org/10.3390/su13095094 - 1 May 2021
Cited by 5 | Viewed by 2736
Abstract
Driver distraction is a major problem nowadays, contributing to many deaths, injuries, and economic losses. Despite the effort that has been made to minimize these impacts, considering the technological evolution, distraction at the wheel has tended to increase. Not only tech-related tasks but [...] Read more.
Driver distraction is a major problem nowadays, contributing to many deaths, injuries, and economic losses. Despite the effort that has been made to minimize these impacts, considering the technological evolution, distraction at the wheel has tended to increase. Not only tech-related tasks but every task that captures a driver’s attention has impacts on road safety. Moreover, driver behavior and characteristics are known to be heterogeneous, leading to a distinct driving performance, which is a challenge in the road safety perspective. This study aimed to capture the effects of drivers’ personal aspects and habits on their distraction behavior. Following a within-subjects approach, a convenience sample of 50 drivers was exposed to three unexpected events reproduced in a driving simulator. Drivers’ reactions were evaluated through three distinct models: a Lognormal Model to make analyze the visual distraction, a Binary Logit Model to explore the adopted type of reaction, and a Parametric Survival Model to study the reaction times. The research outcomes revealed that drivers’ behavior and perceived workload were distinct when they were engaged in specific secondary tasks and for distinct drivers’ personal attributes and habits. Age and type of distraction showed statistical significance regarding the visual behavior. Moreover, reaction times were consistently related to gender, BMI, sleep patterns, speed, habits while driving, and type of distraction. The habit of engaging in secondary tasks while driving resulted in a cumulative better performance. Full article
(This article belongs to the Special Issue Collaborative Technology for a Sustainable Transition to Automated Driving)
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19 pages, 4438 KiB  
Article
Drivers’ Age and Automated Vehicle Explanations
by Qiaoning Zhang, Xi Jessie Yang and Lionel P. Robert, Jr.
Sustainability 2021, 13(4), 1948; https://doi.org/10.3390/su13041948 - 11 Feb 2021
Cited by 12 | Viewed by 3021
Abstract
Automated vehicles (AV) have the potential to benefit our society. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about automated decision-making. However, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of [...] Read more.
Automated vehicles (AV) have the potential to benefit our society. Providing explanations is one approach to facilitating AV trust by decreasing uncertainty about automated decision-making. However, it is not clear whether explanations are equally beneficial for drivers across age groups in terms of trust and anxiety. To examine this, we conducted a mixed-design experiment with 40 participants divided into three age groups (i.e., younger, middle-age, and older). Participants were presented with: (1) no explanation, or (2) explanation given before or (3) after the AV took action, or (4) explanation along with a request for permission to take action. Results highlight both commonalities and differences between age groups. These results have important implications in designing AV explanations and promoting trust. Full article
(This article belongs to the Special Issue Collaborative Technology for a Sustainable Transition to Automated Driving)
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25 pages, 8233 KiB  
Article
Joint Optimization of Intersection Control and Trajectory Planning Accounting for Pedestrians in a Connected and Automated Vehicle Environment
by Biao Yin, Monica Menendez and Kaidi Yang
Sustainability 2021, 13(3), 1135; https://doi.org/10.3390/su13031135 - 22 Jan 2021
Cited by 13 | Viewed by 7915
Abstract
Connected and automated vehicle (CAV) technology makes it possible to track and control the movement of vehicles, thus providing enormous potential to improve intersection operations. In this paper, we study the traffic signal control problem at an isolated intersection in a CAV environment, [...] Read more.
Connected and automated vehicle (CAV) technology makes it possible to track and control the movement of vehicles, thus providing enormous potential to improve intersection operations. In this paper, we study the traffic signal control problem at an isolated intersection in a CAV environment, considering mixed traffic including various types of vehicles and pedestrians. Both the vehicle delay and the pedestrian delay are incorporated into the model formulation. This introduces some additional complexity, as any benefits to pedestrians will come at the expense of higher delays for the vehicles. Thus, some valid questions we answer in this paper are as follows: Under which circumstances could we provide priority to pedestrians without over penalizing the vehicles at the intersection? How important are the connectivity and autonomy associated with CAV technology in this context? What type of signal control algorithm could be used to minimize person delay accounting for both vehicles and pedestrians? How could it be solved efficiently? To address these questions, we present a model that optimizes signal control (i.e., vehicle departure sequence), automated vehicle trajectories, and the treatment of pedestrian crossing. In each decision step, the weighted sum of the vehicle delay and the pedestrian delay (e.g., the total person delay) is minimized by the joint optimization on the basis of the predicted departure sequences of vehicles and pedestrians. Moreover, a near-optimal solution of the integrated problem is obtained with an ant colony system algorithm, which is computationally very efficient. Simulations are conducted for different demand scenarios and different CAV penetration rates. The performance of the proposed algorithm in terms of the average person delay is investigated. The simulation results show that the proposed algorithm has potential to reduce the delay compared to an actuated signal control method. Moreover, in comparison to a CAV-based signal control that does not account for the pedestrian delay, the joint optimization proposed here can achieve improvement in the low- and moderate-vehicle-demand scenarios. Full article
(This article belongs to the Special Issue Collaborative Technology for a Sustainable Transition to Automated Driving)
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