Special Issue "Driver-Vehicle Automation Collaboration"

A special issue of Vehicles (ISSN 2624-8921).

Deadline for manuscript submissions: 25 October 2021.

Special Issue Editors

Prof. Dr. Yahui Liu
E-Mail Website
Guest Editor
School of Vehicle and Mobility, Tsinghua University, 100084 Beijing, China
Interests: vehicle system dynamics; driver-vehicle automation collaboration
Dr. Chen Lv
E-Mail Website
Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: human-automation collaboration; automatic control; automated vehicles; cyber-physical systems; IoT; intelligent transportation systems; intelligent vehicles; electric vehicles
Special Issues and Collections in MDPI journals
Prof. Dr. Liting Sun
E-Mail
Guest Editor
Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA
Interests: autonomous driving; human-robot interaction; behavior prediction and motion planning
Prof. Dr. Jian Wu
E-Mail
Guest Editor
School of Mechanical and Automotive Engineering, Liaocheng University, Liaocheng 252000, China
Interests: driver-vehicle system dynamics; steering system and actuator design of ADAS

Special Issue Information

Dear Colleagues,

Before realizing fully autonomous driving, highly automated vehicles will play a significant role in the development of vehicle intelligence technologies. Highly automated driving presents an exciting new development in vehicle technology, however, in the meantime it poses a new challenge, namely how to ensure a safe, smart, and smooth interactions between human driver and automation functionality. Therefore, a better understanding of the interaction between human drivers and automation systems becomes a key issue to the realization of effective and efficient driver-automation collaboration for automated driving.

The special session aims to provide up-to-date research concepts, theoretical findings and practical solutions that could help implement the interaction between human driver and vehicle automation. Papers are invited in all these areas (but are not limited to them), as they are multidisciplinary topics involving economic and vehicle aspects as well. Both theoretical and experimental works are welcome, especially those including validation with real-world data or experiments. Recently, interest in information fusion, decision making, traffic optimization has been raised; therefore, papers exploring the utility of vehicle dynamic control in these topics are also encouraged.

Prof. Dr. Yahui Liu
Prof. Dr. Chen Lv
Prof. Dr. Liting Sun
Prof. Dr. Jian Wu
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 papers will be 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. Vehicles is an international peer-reviewed open access quarterly 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 1200 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

  • Vehicle System Dynamics
  • Driver-automation shared control
  • Drivers’ interaction with vehicle automation
  • Vehicle dynamics control strategies
  • Models, simulators, and test beds for Driver-vehicle System
  • Application of AI and machine learning for Driver-vehicle System
  • Driver cognitive behaviors: strategy, trust, learning and errors
  • Driver actuation behaviors: neuromuscular dynamics and delay
  • Driver perception behaviors: preview, haptic and vestibular sensing
  • Automated driving and autonomous vehicles

Published Papers (6 papers)

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Research

Article
Camera-Based Lane Detection—Can Yellow Road Markings Facilitate Automated Driving in Snow?
Vehicles 2021, 3(4), 661-690; https://doi.org/10.3390/vehicles3040040 - 13 Oct 2021
Viewed by 153
Abstract
Road markings are beneficial to human drivers, advanced driver assistance systems (ADAS), and automated driving systems (ADS); on the contrary, snow coverage on roads poses a challenge to all three of these groups with respect to lane detection, as white road markings are [...] Read more.
Road markings are beneficial to human drivers, advanced driver assistance systems (ADAS), and automated driving systems (ADS); on the contrary, snow coverage on roads poses a challenge to all three of these groups with respect to lane detection, as white road markings are difficult to distinguish from snow. Indeed, yellow road markings provide a visual contrast to snow that can increase a human drivers’ visibility. Yet, in spite of this fact, yellow road markings are becoming increasingly rare in Europe due to the high costs of painting and maintaining two road marking colors. More importantly, in conjunction with our increased reliance on automated driving, the question of whether yellow road markings are of value to automatic lane detection functions arises. To answer this question, images from snowy conditions are assessed to see how different representations of colors in images (color spaces) affect the visibility levels of white and yellow road markings. The results presented in this paper suggest that yellow markings provide a certain number of benefits for automated driving, offering recommendations as to what the most appropriate color spaces are for detecting lanes in snowy conditions. To obtain the safest and most cost-efficient roads in the future, both human and automated drivers’ actions must be considered. Road authorities and car manufacturers also have a shared interest in discovering how road infrastructure design, including road marking, can be adapted to support automated driving. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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Article
Development and Evaluation of a Threshold-Based Motion Cueing Algorithm
Vehicles 2021, 3(4), 636-645; https://doi.org/10.3390/vehicles3040038 - 02 Oct 2021
Viewed by 214
Abstract
In this paper, a motion cueing algorithm (MCA) without a frequency divider is proposed, which aims to reproduce the longitudinal reference acceleration as far as possible via tilt coordination. Using a second-order rate limit, the human perception thresholds can directly be taken into [...] Read more.
In this paper, a motion cueing algorithm (MCA) without a frequency divider is proposed, which aims to reproduce the longitudinal reference acceleration as far as possible via tilt coordination. Using a second-order rate limit, the human perception thresholds can directly be taken into account when parameterizing the MCA. The washout is compensated by tilt coordination and means of feedback from the translational acceleration. The proposed MCA is compared with the classical washout algorithm and the compensation MCA based on selected qualitative metrics and their workspace demand. In addition, a subjective study on the evaluation of the MCA was conducted. The results show that even high washout rates are not noticeable by the test subjects. Overall, the MCA was rated as very good. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
Article
Safety and Risk Analysis of Autonomous Vehicles Using Computer Vision and Neural Networks
Vehicles 2021, 3(3), 595-617; https://doi.org/10.3390/vehicles3030036 - 15 Sep 2021
Viewed by 806
Abstract
The autonomous vehicle (AVs) market is expanding at a rapid pace due to the advancement of information, communication, and sensor technology applications, offering a broad range of opportunities in terms of energy efficiency and addressing climate change concerns and safety. With regard to [...] Read more.
The autonomous vehicle (AVs) market is expanding at a rapid pace due to the advancement of information, communication, and sensor technology applications, offering a broad range of opportunities in terms of energy efficiency and addressing climate change concerns and safety. With regard to this last point, the rate of reduction in accidents is considerable when switching safety control tasks to machines from humans, which can be noted as having significantly slower response rates. This paper explores this thematic by focusing on the safety of AVs by thorough analysis of previously collected AV crash statistics and further discusses possible solutions for achieving increased autonomous vehicle safety. To achieve this, this technical paper develops a dynamic run-time safe assessment system, using the standard autonomous drive system (ADS), which is developed and simulated in case studies further in the paper. OpenCV methods for lane detection are developed and applied as robust control frameworks, which introduces the factor of vehicle crash predictability for the ego vehicle. The developed system is made to predict possible crashes by using a combination of machine learning and neural network methods, providing useful information for response mechanisms in risk scenarios. In addition, this paper explores the operational design domain (ODD) of the AV’s system and provides possible solutions to extend the domain in order to render vehicle operationality, even in safe mode. Additionally, three case studies are explored to supplement a discussion on the implementation of algorithms aimed at increasing curved lane detection ability and introducing trajectory predictability of neighbouring vehicles for an ego vehicle, resulting in lower collisions and increasing the safety of the AV overall. This paper thus explores the technical development of autonomous vehicles and is aimed at researchers and practitioners engaging in the conceptualisation, design, and implementation of safer AV systems focusing on lane detection and expanding AV safe state domains and vehicle trajectory predictability. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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Article
An Approach to the Definition of the Aerodynamic Comfort of Motorcycle Helmets
Vehicles 2021, 3(3), 545-556; https://doi.org/10.3390/vehicles3030033 - 23 Aug 2021
Viewed by 448
Abstract
The aim of this work is to obtain a reliable testing methodology for the characterization of the perceived aerodynamic comfort of motorcycle helmets. Attention was paid to the rider’s perception of annoying vibrations induced by wind. In this optic, an experimental comparative campaign [...] Read more.
The aim of this work is to obtain a reliable testing methodology for the characterization of the perceived aerodynamic comfort of motorcycle helmets. Attention was paid to the rider’s perception of annoying vibrations induced by wind. In this optic, an experimental comparative campaign was performed in the wind tunnel, testing 16 helmets in two different configurations of neck stiffness. The dataset was collected within a convolutional neural network (CNN or ConvNet) of images, creating a ranking by identifying the best and the worst helmets. The results revealed that each helmet has unique aerodynamic characteristics. Depending on the ranking scale previously created, the aerodynamic comfort of each helmets can be classified within the scale. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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Article
A Neural-Network-Based Methodology for the Evaluation of the Center of Gravity of a Motorcycle Rider
Vehicles 2021, 3(3), 377-389; https://doi.org/10.3390/vehicles3030023 - 15 Jul 2021
Viewed by 834
Abstract
A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of [...] Read more.
A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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Article
On the Tripped Rollovers and Lateral Skid in Three-Wheeled Vehicles and Their Mitigation
Vehicles 2021, 3(3), 357-376; https://doi.org/10.3390/vehicles3030022 - 11 Jul 2021
Viewed by 555
Abstract
Active safety systems for three-wheeled vehicles seem to be in premature development; in particular, delta types, also known as tuk-tuks or sidecars, are sold with minimal protection against accidents. Unfortunately, the risk of wheel lifting and lateral and/or longitudinal vehicle roll is high. [...] Read more.
Active safety systems for three-wheeled vehicles seem to be in premature development; in particular, delta types, also known as tuk-tuks or sidecars, are sold with minimal protection against accidents. Unfortunately, the risk of wheel lifting and lateral and/or longitudinal vehicle roll is high. For instance, a tripped rollover occurs when a vehicle slides sideways, digging its tires into soft soil or striking an object. Unfortunately, research is mostly aimed at un-tripped rollovers while most of the rollovers are tripped. In this paper, models for lateral skid tripped and un-tripped rollover risks are presented. Later, independent braking and accelerating control actions are used to develop a dynamic stability control (DSC) to assist the driver in mitigating such risks, including holes/bumps road-scenarios. A common Lyapunov function and an LMI problem resolution ensure robust stability while optimization allows tuning the controller. Numerical and HIL tests are presented. Implementation on a three-wheeled vehicle requires an inertial measurement unit, and independent ABS and propulsion control as main components. Full article
(This article belongs to the Special Issue Driver-Vehicle Automation Collaboration)
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