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Recent Advances in Intelligent Vehicles and Intelligent Transportation Systems
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Recent Advances in Intelligent Vehicles and Intelligent Transportation Systems

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

Deadline for manuscript submissions: 15 February 2026 | Viewed by 5369

Special Issue Editors

Dr. Jinghua Guo

E-Mail Website
Guest Editor
Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen 384002, China
Interests: multi-agent system; distributed control; intelligent driving; advanced sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Jingyao Wang

E-Mail Website
Guest Editor
Department of Automation, Xiamen University, Xiamen 384002,China
Interests: intelligent electric vehicles; vehicle dynamics and control; vision system
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent vehicles are an integral part of intelligent transportation systems, which utilize a combination of sensors, cameras, radar, and advanced algorithms to navigate roads safely without human intervention. Ongoing research in this area focuses on several key areas, including environmental perception, decision-making, path planning, and vehicle control.

Environmental Perception: Intelligent vehicles rely on a variety of sensors, such as lidars, radars, and cameras, to perceive their environment. These sensors provide data about the surrounding objects, including their position, speed, and direction. Research in this area aims to improve the accuracy and reliability of these sensors, as well as develop algorithms to fuse data from multiple sensors for a more comprehensive understanding of the vehicle's surroundings.

Decision-Making and Path Planning: Once the vehicle has perceived its environment, it must make decisions about how to navigate safely and efficiently. This involves selecting the best path to reach the destination while avoiding obstacles and adhering to traffic rules. Research in this area focuses on developing advanced algorithms that can handle complex scenarios, such as intersections, pedestrian crossings, and merging into traffic.

Vehicle Control: Intelligent vehicles require precise control algorithms to ensure smooth and safe driving. These algorithms must take into account various factors, such as road conditions, weather, and the behavior of other road users. Research in this area aims to develop robust control systems that can adapt to changing conditions and respond appropriately in emergency situations.

This Special Issue addresses new environmental perception, decision-making, path planning, and vehicle control techniques used in intelligent vehicles and intelligent transportation systems. Survey papers are also welcome.

Dr. Jinghua Guo
Dr. Jingyao Wang
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

  • intelligent vehicles
  • advanced sensors
  • environmental perception
  • intelligent decision
  • information safety
  • vehicle dynamics and control

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Published Papers (4 papers)

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Research

15 pages, 8113 KiB  
Article
Credibility Assessment for Digital Twins in Vehicle-in-the-Loop Test Based on Information Entropy
by Tianfang Gao, Liang Chen, Xinghui Zhang, Jinghua Guo and Dong Ni
Sensors 2025, 25(5), 1372; https://doi.org/10.3390/s25051372 - 24 Feb 2025
Viewed by 412
Abstract
Digital twins in vehicle-in-the-loop (VIL) test has great practical significance for the functional development, testing and evaluation of intelligent vehicle. The study about the credibility assessment of dynamically evolving models still lacks effective approaches. In addition, it has rarely been studied in automotive [...] Read more.
Digital twins in vehicle-in-the-loop (VIL) test has great practical significance for the functional development, testing and evaluation of intelligent vehicle. The study about the credibility assessment of dynamically evolving models still lacks effective approaches. In addition, it has rarely been studied in automotive tests. In this paper, a closed loop test of dynamic virtual and real-world interaction was built, and its characteristics are also analyzed. According to its characteristics and assessment methods, a credibility assessment methodology based on information entropy is proposed to reveal the degree of its own information confusion and structural relevance of different information, which involves ApEn and cross-ApEn. The algorithm has been successfully verified in experiments and it has been found that the inconsistent weight of the real and digital vehicle is an important factor on digital twins VIL tests. Furthermore, the effect of the length of series on the credibility assessment has been emphatically studied, and the results show that it has no more than 2% effect on the credibility assessment. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicles and Intelligent Transportation Systems)
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18 pages, 2652 KiB  
Article
EdgeNet: An End-to-End Deep Neural Network Pretrained with Synthetic Data for a Real-World Autonomous Driving Application
by Leanne Miller, Pedro J. Navarro and Francisca Rosique
Sensors 2025, 25(1), 89; https://doi.org/10.3390/s25010089 - 27 Dec 2024
Viewed by 938
Abstract
This paper presents a novel end-to-end architecture based on edge detection for autonomous driving. The architecture has been designed to bridge the domain gap between synthetic and real-world images for end-to-end autonomous driving applications and includes custom edge detection layers before the Efficient [...] Read more.
This paper presents a novel end-to-end architecture based on edge detection for autonomous driving. The architecture has been designed to bridge the domain gap between synthetic and real-world images for end-to-end autonomous driving applications and includes custom edge detection layers before the Efficient Net convolutional module. To train the architecture, RGB and depth images were used together with inertial data as inputs to predict the driving speed and steering wheel angle. To pretrain the architecture, a synthetic multimodal dataset for autonomous driving applications was created. The dataset includes driving data from 100 diverse weather and traffic scenarios, gathered from multiple sensors including cameras and an IMU as well as from vehicle control variables. The results show that including edge detection layers in the architecture improves performance for transfer learning when using synthetic and real-world data. In addition, pretraining with synthetic data reduces training time and enhances model performance when using real-world data. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicles and Intelligent Transportation Systems)
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15 pages, 8875 KiB  
Article
Smart Rumble Strip System to Prevent Over-Height Vehicle Collisions
by Ricky W. K. Chan
Sensors 2024, 24(19), 6191; https://doi.org/10.3390/s24196191 - 25 Sep 2024
Viewed by 1147
Abstract
Collisions of over-height vehicles with low clearance bridges is commonly encountered worldwide. They have caused damage to bridge structures, interruption to traffic, injuries or even fatalities to road users. To mitigate such risks, passive systems that involve warning gantries, flashing lights and illuminated [...] Read more.
Collisions of over-height vehicles with low clearance bridges is commonly encountered worldwide. They have caused damage to bridge structures, interruption to traffic, injuries or even fatalities to road users. To mitigate such risks, passive systems that involve warning gantries, flashing lights and illuminated signage are commonly installed. Semi-active systems using laser- or infrared-based detection systems in conjunction with visual warnings have been implemented. Nevertheless, some drivers ignore these visual warnings and collisions continue to occur. This paper presents a novel concept for a collision prevention system, which makes use of a series of sensor-activated, motorized rumble strips. These rumble strips span across a certain distance ahead of a low clearance bridge. When an over-height vehicle is detected, a mechanism is triggered which elevates the rumble strips. The noise and vibrations produce a vigorous alert to the offending driver. They also increase effective friction of the road surface, thus assisting to slow down the vehicle and shorten the stopping distance. The strips will be lowered after a certain time has elapsed, thus minimizing their effects on other vehicles. This article presents a conceptual framework and quantifies the vibration and noise caused by rumble strips in road tests. Road tests indicated that the vibration level typically exceeded 1 g and noise level reached approximately 90 dB in the cabin of a 3.5-ton truck. Fabrication of a proof-of-concept mechanized rumble strip model was presented and verified in an outdoor environment. The circuitry and mechanical design, and requirements in actual implementation, are discussed. The proposed event-triggered rumble strip system could significantly mitigate over-height vehicle collisions that cause major disruptions and injuries worldwide. Further works, including a comprehensive road test involving various types of vehicles, are envisaged. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicles and Intelligent Transportation Systems)
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14 pages, 1533 KiB  
Article
Driver Fatigue Detection Using Heart Rate Variability Features from 2-Minute Electrocardiogram Signals While Accounting for Sex Differences
by Chao Zeng, Jiliang Zhang, Yizi Su, Shuguang Li, Zhenyuan Wang, Qingkun Li and Wenjun Wang
Sensors 2024, 24(13), 4316; https://doi.org/10.3390/s24134316 - 3 Jul 2024
Cited by 4 | Viewed by 2240
Abstract
Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. [...] Read more.
Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann–Whitney U test, p < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers’ mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection. Full article
(This article belongs to the Special Issue Recent Advances in Intelligent Vehicles and Intelligent Transportation Systems)
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