Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Artificial Intelligence and Internet of Things in Autonomous Vehicles -...
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Artificial Intelligence and Internet of Things in Autonomous Vehicles - 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 3937

Special Issue Editors

Dr. Saber Fallah

E-Mail Website
Guest Editor
Department of Mechanical Engineering Sciences, Connected Autonomous Vehicle Lab, University of Surrey, Guildford GU2 7XH, UK
Interests: AI and deep reinforcement learning; advanced control systems; optimization and prediction
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dimitrie Popescu

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Old Dominion University, 231 Kaufman Hall, Norfolk, VA, USA
Interests: wireless communications and networking (software defined radio platforms for implementing versatile communication systems, spectrum sensing and spectral shaping for cognitive radio, interference avoidance/suppression, transmitter/receiver optimization to support quality of service); cyber-physical systems (vehicular networking and vehicle-to-infrastructure communications and information exchange for intelligent transportation systems, integration of cyber- and physical components for small size spacecraft, CubeSats, sounding rocket payloads)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are happy to launch this follow-up Sensors Special Issue titled "Artificial Intelligence and Internet of Things in Autonomous Vehicles (Volume II)". Thanks to the many valuable submissions to our previous Special Issue (“Artificial Intelligence and Internet of Things in Autonomous Vehicles”; link here: https://www.mdpi.com/journal/sensors/special_issues/AIIoTAV), we have already presented some recent developments in the field of autonomous vehicles. The continuous progress of automated vehicle driving functions as well as the global deployment of cooperative intelligent transport systems (C-ITS) both motivate the research, innovation, and development of new traffic control strategies and ITS services. These activities will truly enable the transition from fully conventional traffic flows towards fully automated vehicle traffic. However, fully automated operation remains a challenge due to the inherent difficulties and safety concerns of real-world driving, especially in an urban setting, which is characterized by uncertainty over the intentions of other road users. The integration of V2X communication has the potential to reduce uncertainty by sharing information between road users and government-owned road infrastructure. However, traditional manual vehicles will dominate road use in the short-to-medium-term, and unconnected road users like pedestrians and cyclists will always remain a challenge for V2X solutions.

The safety impacts of automated vehicle technology occur primarily due to the fundamental change in the driving principle, that is, a shift of perception and decision-making from the human to the machine. Further, the connectivity among vehicles and between vehicle infrastructure will influence the performance of the technology as well as its user acceptance.

This Special Issue encourages authors from academia and industry to submit new research results about technological innovations and novel ideas for connected automated vehicles (CAVs), with special interest in artificial intelligence and Internet of Things and their safety impacts.

Dr. Saber Fallah
Prof. Dr. Dimitrie Popescu
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. Sensors 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 2600 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

  • vehicular networks
  • V2X communications and networks
  • wireless communication technologies for autonomous vehicles
  • AI and deep learning
  • reinforcement learning
  • control, optimization, and prediction
  • vision and environment perception
  • sensors’ data dissemination in the vehicular system
  • big data and data analysis
  • AI transparency
  • vehicle localization and autonomous navigation
  • sensor-based object detection and/or tracking in vehicular scenarios
  • sensor-based data compression in vehicular networks
  • edge-assisted sensor processing in vehicular networks
  • 5G/B5G-based sensing solutions for autonomous cars
  • human factors and HMI
  • security, privacy, and safety systems
  • sensors and detectors

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 669 KiB  
Article
Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles
by Jonathan Giron, Yaron Sela, Leonid Barenboim, Gail Gilboa-Freedman and Yair Amichai-Hamburger
Sensors 2024, 24(6), 1977; https://doi.org/10.3390/s24061977 - 20 Mar 2024
Viewed by 456
Abstract
The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional “one [...] Read more.
The emergence of autonomous vehicles (AVs) marks a transformative leap in transportation technology. Central to the success of AVs is ensuring user safety, but this endeavor is accompanied by the challenge of establishing trust and acceptance of this novel technology. The traditional “one size fits all” approach to AVs may limit their broader societal, economic, and cultural impact. Here, we introduce the Persona-PhysioSync AV (PPS-AV). It adopts a comprehensive approach by combining personality traits with physiological and emotional indicators to personalize the AV experience to enhance trust and comfort. A significant aspect of the PPS-AV framework is its real-time monitoring of passenger engagement and comfort levels within AVs. It considers a passenger’s personality traits and their interaction with physiological and emotional responses. The framework can alert passengers when their engagement drops to critical levels or when they exhibit low situational awareness, ensuring they regain attentiveness promptly, especially during Take-Over Request (TOR) events. This approach fosters a heightened sense of Human–Vehicle Interaction (HVI), thereby building trust in AV technology. While the PPS-AV framework currently provides a foundational level of state diagnosis, future developments are expected to include interaction protocols that utilize interfaces like haptic alerts, visual cues, and auditory signals. In summary, the PPS-AV framework is a pivotal tool for the future of autonomous transportation. By prioritizing safety, comfort, and trust, it aims to make AVs not just a mode of transport but a personalized and trusted experience for passengers, accelerating the adoption and societal integration of autonomous vehicles. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles - 2nd Edition)
►▼ Show Figures

Figure 1

14 pages, 6512 KiB  
Article
Road Descriptors for Fast Global Localization on Rural Roads Using OpenStreetMap
by Stephen Ninan and Sivakumar Rathinam
Sensors 2023, 23(18), 7915; https://doi.org/10.3390/s23187915 - 15 Sep 2023
Viewed by 763
Abstract
Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot’s understanding of a given scene. For autonomous vehicles (AVs), detailed 3D maps [...] Read more.
Accurate pose estimation is a fundamental ability that all mobile robots must posses in order to navigate a given environment. Much like a human, this ability is dependent on the robot’s understanding of a given scene. For autonomous vehicles (AVs), detailed 3D maps created beforehand are widely used to augment the perceptive abilities and estimate pose based on current sensor measurements. This approach, however, is less suited for rural communities that are sparsely connected and cover large areas. Topological maps such as OpenStreetMap have proven to be a useful alternative in these situations. However, vehicle localization using these maps is non-trivial, particularly for the global localization task, where the map spans large areas. To deal with this challenge, we propose road descriptors along with an initialization technique for localization that allows for fast global pose estimation. We test our algorithms on (real world) maps and benchmark them against other map-based localization as well as SLAM algorithms. Our results show that the proposed method can narrow down the pose to within 50 cm of the ground truth significantly faster than the state-of-the-art methods. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles - 2nd Edition)
►▼ Show Figures

Figure 1

17 pages, 5391 KiB  
Article
How to Design the eHMI of AVs for Urgent Warning to Other Drivers with Limited Visibility?
by Dokshin Lim and Yongwhee Kwon
Sensors 2023, 23(7), 3721; https://doi.org/10.3390/s23073721 - 04 Apr 2023
Cited by 1 | Viewed by 1863
Abstract
The importance of an external interaction interface (eHMI) has grown in recent years. Most eHMI concepts focus on communicating autonomous vehicle (AV)’s yielding intention to pedestrians at a crossing. However, according to previous studies, pedestrians at a crossing rely mainly on the vehicle’s [...] Read more.
The importance of an external interaction interface (eHMI) has grown in recent years. Most eHMI concepts focus on communicating autonomous vehicle (AV)’s yielding intention to pedestrians at a crossing. However, according to previous studies, pedestrians at a crossing rely mainly on the vehicle’s movement information (implicit communication) rather than information from eHMIs (explicit communication). This paper has the purpose of proposing a specific use case in which the eHMI of future AVs could play an indispensable role in the safety of other road users (ORUs). Often VRUs cannot see the traffic flow due to a series of parked or stopped vehicles, which is a frequent cause of fatal traffic collision accidents. Drivers may also not be able to see approaching pedestrians or other cars from the side for the same reason. In this paper, the impact of an eHMI is tested from the perspective of drivers with limited visibility when a jaywalker steps into the road. A combination of colors, shapes, and information levels is presented on an eHMI. We show that our proposed eHMI design, in the deadlock scenario of a jaywalker and a driver who both lack visibility, significantly reduced the reaction time compared to when there was no eHMI. In the experiment, the willingness to stop, varying from 0 to 5, was measured from the driver’s perspective. The results showed that most users felt uncertainty and did not move quickly when seeing the light band color alone. Textual information on the eHMI was significantly more effective in providing an urgent warning of this specific scenario than vertical and horizontal light bands with color without text. In addition, red color, blinking rapidly above 3 Hz, and egocentric messages were also necessary to reduce the PRT(perception response time). By using text-added eHMI (Vertical + Text eHMI), the mean time to achieve a score above 4 for willingness to stop was 2.113 s faster than when there was no eHMI. It was 2.571 s faster than the time until the slider of the participants reached the maximum level for willingness to stop. This is a meaningful amount of difference when considering a PRT of 2.5 s, which is the Korean road design standard. As eHMIs tend to be applied for smarter mobility, it is expected that they will be more effective in preventing accidents if the eHMI is standardized in autonomous driving level 2 to 3 vehicles driven by humans before fully autonomous driving becomes a reality. Full article
(This article belongs to the Special Issue Artificial Intelligence and Internet of Things in Autonomous Vehicles - 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop