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Cutting-Edge Sensor and Fusion Approaches for Robust and Reliable Autonomous Driving

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 317

Special Issue Editors


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Guest Editor
Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: systems and tools for CAD and VLSI; formal validation methods for hardware and software systems; embedded systems; autonomous driving; sensor networks; developing parallel algorithms and optimization techniques to achieve practical solutions with limited resources
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Institute of Safety Science and Cybersecurity, Óbuda University, Budapest, Hungary
Interests: security issues in the world of autonomous vehicles; information security awareness of students
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous driving technology relies heavily on the accurate perception of a vehicle’s surroundings, which is achieved through an integrated use of diverse sensors and advanced sensor fusion algorithms. This Special Issue invites the submission of original research articles, reviews, and technical notes focusing on the latest advancements in sensor technology and multi-sensor fusion techniques tailored for autonomous driving applications. Topics of interest include sensor calibration, data synchronization, environmental perception, obstacle detection, and decision-making algorithms that leverage data from cameras, LiDAR, radar, and other sensor modalities. Innovative sensor fusion frameworks (spanning data-level, feature-level, and decision-level fusion) that improve robustness and safety under varying environmental conditions are a key theme. Contributions addressing challenges such as sensor noise, computational complexity, real-time processing, and deep learning-based sensor fusion are highly encouraged. This issue aims to provide a comprehensive overview of state-of-the-art sensor systems and fusion algorithms, highlight technological breakthroughs, and foster interdisciplinary dialogue to accelerate the development of safer and more reliable autonomous vehicles. Researchers, engineers, and practitioners working at the intersection of sensor technology and autonomous mobility will find this issue valuable for advancing the development of intelligent transportation systems.

Dr. Stefano Quer
Dr. Gábor Kiss
Guest Editors

Manuscript Submission Information

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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.

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Keywords

  • autonomous driving
  • sensor fusion
  • multi-sensor integration
  • environmental perception
  • data-level fusion
  • intelligent transportation systems
  • real-time processing
  • sensor networks
  • perception algorithms
  • safety in autonomous driving

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Published Papers (1 paper)

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Research

32 pages, 2129 KB  
Article
Artificial Intelligence-Based Depression Detection
by Gabor Kiss and Patrik Viktor
Sensors 2026, 26(2), 748; https://doi.org/10.3390/s26020748 - 22 Jan 2026
Viewed by 161
Abstract
Decisions made by pilots and drivers suffering from depression can endanger the lives of hundreds of people, as demonstrated by the tragedies of Germanwings flight 9525 and Air India flight 171. Since the detection of depression is currently based largely on subjective self-reporting, [...] Read more.
Decisions made by pilots and drivers suffering from depression can endanger the lives of hundreds of people, as demonstrated by the tragedies of Germanwings flight 9525 and Air India flight 171. Since the detection of depression is currently based largely on subjective self-reporting, there is an urgent need for fast, objective, and reliable detection methods. In our study, we present an artificial intelligence-based system that combines iris-based identification with the analysis of pupillometric and eye movement biomarkers, enabling the real-time detection of physiological signs of depression before driving or flying. The two-module model was evaluated based on data from 242 participants: the iris identification module operated with an Equal Error Rate of less than 0.5%, while the depression-detecting CNN-LSTM network achieved 89% accuracy and an AUC value of 0.94. Compared to the neutral state, depressed individuals responded to negative news with significantly greater pupil dilation (+27.9% vs. +18.4%), while showing a reduced or minimal response to positive stimuli (−1.3% vs. +6.2%). This was complemented by slower saccadic movement and longer fixation time, which is consistent with the cognitive distortions characteristic of depression. Our results indicate that pupillometric deviations relative to individual baselines can be reliably detected and used with high accuracy for depression screening. The presented system offers a preventive safety solution that could reduce the number of accidents caused by human error related to depression in road and air traffic in the future. Full article
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