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Intelligent Sensors for Smart and Autonomous Vehicles: 2nd Edition
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Intelligent Sensors for Smart and Autonomous Vehicles: 2nd Edition

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1468

Special Issue Editors

Prof. Dr. István Barabás

E-Mail Website
Guest Editor
Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca, 400001 Cluj-Napoca, Romania
Interests: intelligent sensors; sensor fusion in automotive applications; automotive testing; powertrain concept; energy efficiency; autonomous vehicles; computer modeling; simulation in the automotive field
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Horia Beles

E-Mail Website
Guest Editor
Department of Mechanical Engineering and Automotive, University of Oradea, Universitatii St. 1, 410087 Oradea, Romania
Interests: propulsion systems for road vehicles; internal combustion engines; active and passive vehicle safety; road accident reconstruction; road safety; autonomous vehicles
Dr. Calin Iclodean

E-Mail Website
Guest Editor
Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca, 400641 Cluj-Napoca, Romania
Interests: electric vehicles; fuel cell vehicles; powertrain concept; electronic control unit; in-vehicle communication network; energy efficiency; autonomous vehicles; computer modeling and simulation in the automotive field
Special Issues, Collections and Topics in MDPI journals
Dr. Stefano Quer

E-Mail Website
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

Special Issue Information

Dear Colleagues,

Autonomous vehicles (AVs), particularly those targeted at SAE levels 4 and 5, rely on an exceptional level of intelligence to navigate and determine their next course of action in real-time. This capability is critically dependent on modern and intelligent sensing systems.

Data from an extensive number of sensors drives the core operations of AV, including localization, environmental awareness, behavior prediction and motion planning.

While the fundamental principles of AV sensing were established (many in the first edition of this Special Issue), the shift to pervasive, dependable and secure autonomous operation necessitates solutions that address emerging challenges, including:

  • Reliability and robustness in extreme environments;
  • The use of uncertainty-aware sensing to evaluate risk in safety-critical scenarios;
  • V2X (Vehicle-to-Everything) communication to enable cooperative perception;
  • Integration of next-generation sensor technology (e.g., event cameras and 4D radar).

Building on the success of the first edition, “Intelligent Sensors for Smart and Autonomous Vehicles”, we encourage contributions that aim to advance AV technologies by addressing these cutting-edge issues.  We are seeking novel studies that extend beyond conventional perception algorithms to focus on the underlying sensor technologies, thereby developing more reliable, safe and robust autonomous transportation systems.

We welcome papers that investigate the role of Artificial Intelligence (AI) and/or Machine Learning (ML) in improving sensory performance and data interpretation, particularly in the following non-exhaustive themes/subjects:

  • Robust and accurate perception systems for severe weather and lighting conditions;
  • Uncertainty quantification and risk assessment for sensor data;
  • V2X communication for cooperative perception and data fusion;
  • Next-generation sensor technologies, including event cameras, 4D radar and solid-state LiDAR;
  • Intelligent sensor integration and data fusion for environmental modeling, world construction and simulation;
  • AI/ML for sensor calibration, anomaly detection and self-correction;
  • Cybersecurity and data privacy in cooperative sensing systems;
  • Advanced sensor-based object classification and prediction;
  • Real-time data processing at the sensor level (edge) and fog computing architectures.

Prof. Dr. István Barabás
Prof. Dr. Horia Beles
Dr. Calin Iclodean
Dr. Stefano Quer
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 250 words) can be sent to the Editorial Office for assessment.

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 sensors
  • optimized sensors
  • sensors fusion
  • sensor integration
  • imaging sensors
  • range sensors
  • inertial sensors
  • autonomous driving
  • artificial intelligence
  • machine learning
  • deep learning
  • big data processing
  • virtual reality
  • cloud computing
  • edge computing
  • fog computing

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Related Special Issue

Published Papers (2 papers)

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Research

28 pages, 8566 KB  
Article
Structural-Prior Deep Learning Network for Millimeter-Wave Radar Image Enhancement in Autonomous Driving Road Sensing
by Hongyan Chen, Tonghui Huang, Yuexia Wang, Jiajia Shi and Zhihuo Xu
Sensors 2026, 26(10), 2976; https://doi.org/10.3390/s26102976 - 9 May 2026
Viewed by 289
Abstract
Millimeter-wave radar imaging plays an increasingly important role in autonomous driving road perception due to its robustness under adverse weather conditions. However, radar images are inherently contaminated by multiplicative speckle noise, which severely degrades structural continuity, weakens target boundaries, and limits the perception [...] Read more.
Millimeter-wave radar imaging plays an increasingly important role in autonomous driving road perception due to its robustness under adverse weather conditions. However, radar images are inherently contaminated by multiplicative speckle noise, which severely degrades structural continuity, weakens target boundaries, and limits the perception of road scenes and surrounding objects. To address this problem, this paper proposes a structural-prior deep learning network for millimeter-wave radar image enhancement. The proposed framework first introduces an adaptive Otsu-based masking strategy to extract salient scattering structures and generate a coarse image structural prior for subsequent restoration. Guided by this prior, the network performs progressive feature enhancement through a continuous attention mechanism that integrates residual channel attention, context-aware feature extraction, and convolutional block attention, thereby enabling effective multi-scale representation learning while suppressing signal-dependent speckle interference. In addition, a composite loss function is designed by combining logarithmic denoising gain, total variation regularization, and a β-index edge-preservation term to jointly improve noise suppression, spatial smoothness, and structural fidelity. The proposed method is evaluated on the synthetic UC Merced dataset under different noise intensities and via cross-domain inference on the real-world RADIATE millimeter-wave radar dataset for autonomous driving scenarios. Experimental results demonstrate that the proposed network consistently outperforms conventional filtering methods and representative deep learning baselines in terms of PSNR, SSIM, β-index, and ENL while providing a superior preservation of road structures, target contours, and scene geometry. Ablation studies further confirm the effectiveness of the structural-prior guidance and continuous attention design. Furthermore, the network achieves a rapid inference latency of 12.35 milliseconds. These results indicate that the proposed method provides an effective and robust solution for millimeter-wave radar image enhancement and offers practical value for downstream road-scene perception in autonomous driving environments. Full article
(This article belongs to the Special Issue Intelligent Sensors for Smart and Autonomous Vehicles: 2nd Edition)
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21 pages, 8066 KB  
Article
Robust Localization and Tracking of VRUs with Radar and Ultra-Wideband Sensors for Traffic Safety
by Mouhamed Aghiad Raslan, Martin Schmidhammer, Ibrahim Rashdan, Fabian de Ponte Müller, Tobias Uhlich and Andreas Becker
Sensors 2026, 26(5), 1690; https://doi.org/10.3390/s26051690 - 7 Mar 2026
Viewed by 519
Abstract
The increasing risk to Vulnerable Road Users (VRUs) at urban intersections necessitates advanced safety mechanisms capable of operating effectively under diverse conditions, including adverse weather like heavy rain. While optical sensors such as cameras and LiDAR often degrade in poor visibility, Radio Frequency [...] Read more.
The increasing risk to Vulnerable Road Users (VRUs) at urban intersections necessitates advanced safety mechanisms capable of operating effectively under diverse conditions, including adverse weather like heavy rain. While optical sensors such as cameras and LiDAR often degrade in poor visibility, Radio Frequency (RF)-based systems offer resilient, all-weather tracking. This paper presents a novel approach to enhancing VRU protection by fusing two RF modalities: radar sensors and Ultra-Wideband (UWB) technology, a strong candidate for Joint Communication and Sensing (JCS). The research, conducted as part of the VIDETEC-2 project, addresses the limitations of existing vehicle-based and infrastructure-based systems, particularly in scenarios involving occlusions and blind spots. By leveraging radar’s environmental robustness alongside UWB’s precise, cost-effective short-range communication and localization, the proposed system delivers the framework for continuous vehicle and VRU tracking. The fusion of these sensor modalities, managed through a hybrid Kalman filter approach integrating an Unscented Kalman Filter (UKF) and an Extended Kalman Filter (EKF), allows reliable VRU tracking even in challenging urban scenarios. The experimental results demonstrate a reduction in tracking uncertainty and highlight the system’s potential to serve as a more accurate and responsive safety mechanism for VRUs at intersections. This work contributes to the development of intelligent road infrastructures, laying the foundation for future advancements in urban traffic safety. Full article
(This article belongs to the Special Issue Intelligent Sensors for Smart and Autonomous Vehicles: 2nd Edition)
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