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Applied Sciences
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Application of AI Technology in Intelligent Vehicles and Driving
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Application of AI Technology in Intelligent Vehicles and Driving

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 October 2024) | Viewed by 12982

Special Issue Editors

Dr. Ciprian Orhei

E-Mail Website1 Website2
Guest Editor
Department of Communications, Faculty of Electronics, Telecommunications and Information Technologies, Politehnica University of Timișoara, Bd. Vasile Parvan, Nr. 2, 300223 Timisoara, Romania
Interests: computer vision; image processing; signal, image, and video processing; pattern recognition; feature extraction; algorithms
Dr. Radu Vasiu

E-Mail Website1 Website2
Guest Editor
Institute for Digital Transformation, Politehnica University of Timisoara, Piata Victoriei, Nr. 2, 300006 Timișoara, Romania
Interests: multimedia technologies; audio–video compression; enhanced learning technologies; blockchain technologies; AR/VR technologies; smart city

Special Issue Information

Dear Colleagues,

In the rapidly evolving landscape of advanced driver-assistance systems (ADAS) and intelligent vehicles, artificial intelligence (AI) has become a tangible and integral component of modern vehicle systems. This Special Issue focuses on the transformative role of AI in redefining the fields of intelligent vehicles and driving, exploring both groundbreaking advancements and the challenges that lie ahead.

AI plays a crucial role in modern ADAS. Features like predictive maintenance and collision avoidance systems are not just innovative additions, but are becoming industry standards. These systems leverage AI to analyze vast amounts of data in real-time, providing critical insights and proactive solutions that significantly reduce the risk of accidents.

In addition to enhancing safety, AI-driven technologies are reshaping the efficiency of vehicles and traffic systems. From optimizing fuel consumption and reducing emissions to managing traffic flow in urban environments, AI plays an undisputable role in promoting sustainability and reducing the carbon footprint of transportation.

This Special Issue aims to provide a comprehensive overview of the current state of and future prospects for AI in intelligent vehicles and driving. Through a series of articles, research papers, and case studies, we invite readers to explore the innovative applications of AI, understand the challenges it faces and opportunities it provides, and envision the future of transportation shaped by this remarkable technology.

The topics of this Special Issue include, but are not limited to:

  • Sensor architectures for automotive perception;
  • Sensor calibration and multimodal sensor fusion;
  • Automotive multi-sensor fusion;
  • Automotive sensor datasets and field results;
  • Validation and evaluation methods for data-driven sensor systems;
  • Scene understanding;
  • Software architectures for autonomous vehicles;
  • Heads-up Display for Vehicle-to-Driver Communication;
  • Augmented Reality in automotive applications;
  • Comfort in automated driving;
  • Usability of automated driving Human–Machine Interfaces (HMIs) and external HMIs (eHMIs);
  • Usability of systems used in automotive software;
  • Interaction with other road users.

We look forward to receiving your contributions with insights into this exciting and dynamic field of research.

Dr. Ciprian Orhei
Dr. Radu Vasiu
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. Applied Sciences 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

  • driver-assistance systems
  • sensors
  • sensor fusion
  • computer vision
  • autonomous vehicles
  • HMI design strategies
  • interaction with other road users

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Published Papers (3 papers)

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Research

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16 pages, 12399 KiB  
Article
Shadow Removal for Enhanced Nighttime Driving Scene Generation
by Heejun Yang, Oh-Hyeon Choung and Yuseok Ban
Appl. Sci. 2024, 14(23), 10999; https://doi.org/10.3390/app142310999 - 26 Nov 2024
Viewed by 778
Abstract
Autonomous vehicles depend on robust vision systems capable of performing under diverse lighting conditions, yet existing models often exhibit substantial performance degradation when applied to nighttime scenarios after being trained exclusively on daytime data. This discrepancy arises from the lack of fine-grained details [...] Read more.
Autonomous vehicles depend on robust vision systems capable of performing under diverse lighting conditions, yet existing models often exhibit substantial performance degradation when applied to nighttime scenarios after being trained exclusively on daytime data. This discrepancy arises from the lack of fine-grained details that characterize nighttime environments, such as shadows and varying light intensities. To address this gap, we introduce a targeted approach to shadow removal designed for driving scenes. By applying Partitioned Shadow Removal, an enhanced technique that refines shadow-affected areas, alongside image-to-image translation, we generate realistic nighttime scenes from daytime data. Experimental results indicate that our augmented nighttime scenes significantly enhance segmentation accuracy in shadow-impacted regions, thereby increasing model robustness under low-light conditions. Our findings highlight the value of Partitioned Shadow Removal as a practical data augmentation tool, adapted to address the unique challenges of applying shadow removal in driving scenes, thereby paving the way for improved nighttime performance in autonomous vehicle vision systems. Full article
(This article belongs to the Special Issue Application of AI Technology in Intelligent Vehicles and Driving)
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27 pages, 2179 KiB  
Article
A Review of Deep Learning Advancements in Road Analysis for Autonomous Driving
by Adrian-Paul Botezatu, Adrian Burlacu and Ciprian Orhei
Appl. Sci. 2024, 14(11), 4705; https://doi.org/10.3390/app14114705 - 30 May 2024
Cited by 10 | Viewed by 3156
Abstract
The rapid advancement of autonomous vehicle technology has brought into focus the critical need for enhanced road safety systems, particularly in the areas of road damage detection and surface classification. This paper explores these two essential components, highlighting their importance in autonomous driving. [...] Read more.
The rapid advancement of autonomous vehicle technology has brought into focus the critical need for enhanced road safety systems, particularly in the areas of road damage detection and surface classification. This paper explores these two essential components, highlighting their importance in autonomous driving. In the domain of road damage detection, this study explores a range of deep learning methods, particularly focusing on one-stage and two-stage detectors. These methodologies, including notable ones like YOLO and SSD for one-stage detection and Faster R-CNN for two-stage detection, are critically analyzed for their efficacy in identifying various road damages under diverse conditions. The review provides insights into their comparative advantages, balancing between real-time processing and accuracy in damage localization. For road surface classification, the paper investigates the classification techniques based on both environmental conditions and material road composition. It highlights the role of different convolutional neural network architectures and innovations at the neural level in enhancing classification accuracy under varying road and weather conditions. The main finding of this work is that it offers a comprehensive overview of the current state of the art, showcasing significant strides in utilizing deep learning for road analysis in autonomous vehicle systems. The study concludes by underscoring the importance of continued research in these areas to further refine and improve the safety and efficiency of autonomous driving. Full article
(This article belongs to the Special Issue Application of AI Technology in Intelligent Vehicles and Driving)
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Review

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20 pages, 3712 KiB  
Review
Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
by Shichen Fu, Zhenhua Yang, Yuan Ma, Zhenfeng Li, Le Xu and Huixing Zhou
Appl. Sci. 2024, 14(7), 3016; https://doi.org/10.3390/app14073016 - 3 Apr 2024
Cited by 10 | Viewed by 8277
Abstract
Detecting the factors affecting drivers’ safe driving and taking early warning measures can effectively reduce the probability of automobile safety accidents and improve vehicle driving safety. Considering the two factors of driver fatigue and distraction state, their influences on driver behavior are elaborated [...] Read more.
Detecting the factors affecting drivers’ safe driving and taking early warning measures can effectively reduce the probability of automobile safety accidents and improve vehicle driving safety. Considering the two factors of driver fatigue and distraction state, their influences on driver behavior are elaborated from both experimental data and an accident library analysis. Starting from three modes and six types, intelligent detection methods for driver fatigue and distraction detection from the past five years are reviewed in detail. Considering its wide range of applications, the research on machine vision detection based on facial features in the past five years is analyzed, and the methods are carefully classified and compared according to their innovation points. Further, three safety warning and response schemes are proposed in light of the development of autonomous driving and intelligent cockpit technology. Finally, the paper summarizes the current state of research in the field, presents five conclusions, and discusses future trends. Full article
(This article belongs to the Special Issue Application of AI Technology in Intelligent Vehicles and Driving)
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