Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Sensing Technologies for Autonomous Driving and Intelligent Transportation Systems
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

Sensing Technologies for Autonomous Driving and Intelligent Transportation Systems

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

Deadline for manuscript submissions: 25 August 2026 | Viewed by 3144

Special Issue Editors

Dr. Yizhou Wang

E-Mail Website
Guest Editor
NVIDIA, Santa Clara, CA, USA
Interests: computer vision; autonomous driving; deep learning; object tracking
Dr. Hung-Min Hsu

E-Mail Website
Guest Editor
AIWaysion Inc., Seattle, WA, USA
Interests: computer vision; autonomous driving; deep learning; object tracking
Prof. Dr. Ikhlas Abdel-Qader

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Western Michigan University, 1903 W Michigan Avenue, Kalamazoo, MI 49008-5329, USA
Interests: signal processing; machine learning; artificial intelligence; data fusion; autonomous vehicles; localization and object tracking; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue focuses on state-of-the-art sensing technologies driving progress in autonomous vehicles and intelligent transportation systems (ITSs). Autonomous driving relies heavily on advanced onboard sensors such as cameras, LiDAR, radar, and ultrasonic devices to perceive the environment, detect obstacles, predict behaviors, and make real-time driving decisions. These sensing systems provide the foundation for safe, efficient, and reliable self-driving capabilities.

In parallel, ITS utilizes sensors embedded in transportation infrastructure—including traffic signals, roadways, and connected devices—to address traffic-related challenges like congestion, accidents, and emissions. By collecting and analyzing real-time data, ITS technologies enable dynamic traffic management, incident response, and optimized routing.

Another key focus of this Special Issue is the collaboration between autonomous vehicles and transportation infrastructure through cooperative driving automation (CDA). Using vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, vehicles and systems can share data, extend situational awareness, and coordinate maneuvers. This cooperative approach enhances safety, improves traffic flow, and supports the transition toward fully connected and automated mobility ecosystems.

The articles in this issue explore novel sensing methods, data fusion techniques, and communication frameworks essential to enabling seamless integration between vehicles and smart infrastructure for the future of intelligent, autonomous transportation.

Dr. Yizhou Wang
Dr. Hung-Min Hsu
Prof. Dr. Ikhlas Abdel-Qader
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

  • autonomous vehicle
  • sensor fusion
  • 3D object detection and tracking
  • prediction and planning
  • intelligent transportation systems
  • cooperative driving automation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

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

16 pages, 3103 KB  
Article
EdgeDenseCalib: Targetless Camera–LiDAR Calibration via Enhanced Edge Feature Densification
by Zhiyu He, Zhiwei Cao, Ning Xu, Zhipeng Zhao, Junyi Zhao, Zhao Sheng and Xiaoyu Zhao
Sensors 2026, 26(9), 2690; https://doi.org/10.3390/s26092690 - 26 Apr 2026
Viewed by 1117
Abstract
Accurate camera–LiDAR calibration is a fundamental prerequisite for reliable perception in autonomous systems. However, traditional methods typically rely on manual intervention or specific calibration targets, which restrict their flexibility and scalability in dynamic, real-world environments. To address the challenge of targetless calibration, we [...] Read more.
Accurate camera–LiDAR calibration is a fundamental prerequisite for reliable perception in autonomous systems. However, traditional methods typically rely on manual intervention or specific calibration targets, which restrict their flexibility and scalability in dynamic, real-world environments. To address the challenge of targetless calibration, we propose EdgeDenseCalib, a novel approach driven by enhanced edge feature densification. A key innovation lies in a two-stage process designed to densify the inherently sparse edge features in LiDAR data, thereby making them highly comparable to the fine-grained edges present in images. Consequently, this facilitates more reliable feature matching between the two cross-modal data sources. An optimization algorithm is subsequently employed to refine the alignment and minimize the reprojection error. Experiments on the KITTI dataset show our method achieves accurate calibration results of 0.105° in mean rotation error and 0.903 cm in mean translation error. Compared to state-of-the-art edge-based methods, our approach significantly improves the rotation accuracy by 33.1% to 89.9%. This work provides a practical and automatic calibration solution, contributing to the development of more robust perception systems for autonomous applications. Full article
(This article belongs to the Special Issue Sensing Technologies for Autonomous Driving and Intelligent Transportation Systems)
►▼ Show Figures

Figure 1

34 pages, 3357 KB  
Article
Sequence-Preserving Dual-FoV Defense for Traffic Sign and Light Recognition in Autonomous Vehicles
by Abhishek Joshi, Janhavi Krishna Koda and Abhishek Phadke
Sensors 2026, 26(5), 1737; https://doi.org/10.3390/s26051737 - 9 Mar 2026
Viewed by 614
Abstract
For Autonomous Vehicles (AVs), recognizing traffic lights and signs is critical for safety because perception errors directly affect navigation decisions. Real-world disturbances such as glare, rain, dirt, and graffiti, as well as digital adversarial attacks, can lead to dangerous misclassifications. Current research lacks [...] Read more.
For Autonomous Vehicles (AVs), recognizing traffic lights and signs is critical for safety because perception errors directly affect navigation decisions. Real-world disturbances such as glare, rain, dirt, and graffiti, as well as digital adversarial attacks, can lead to dangerous misclassifications. Current research lacks (i) temporal continuity (stable detection across consecutive frames to prevent flickering misclassifications), (ii) multi-field-of-view (FoV) sensing, and (iii) integrated defenses against both digital and natural degradation. This paper presents two principal contributions: (1) a three-layer defense framework integrating feature squeezing, inference-time temperature scaling (softmax τ = 3 without distillation training), and entropy-based anomaly detection with sequence-level temporal voting; (2) a 500 sequence dual-FoV benchmark (30k base frames, 150k with perturbations) from aiMotive, Waymo, Udacity, and Texas sources across four operational design domains. The unified defense stack achieves 79.8% mAP on a 100-sequence test set (6k base frames, 30k with perturbations), reducing attack success rate from 37.4% to 18.2% (51% reduction) and high-risk misclassifications by 32%. Cross-FoV validation and temporal voting enhance stability under lighting changes (+3.5% mAP) and occlusions (+2.7% mAP). Defense improvements (+9.5–9.6% mAP) remain consistent across native 3D (aiMotive, Waymo) and projected 2D (Udacity, Texas) annotations. Preliminary recapture experiments (n = 15 scenarios) show 2.5% synthetic–physical ASR gap (p = 0.18), though larger validation is needed. Code, models, and dataset reconstruction tools are publicly available. Full article
(This article belongs to the Special Issue Sensing Technologies for Autonomous Driving and Intelligent Transportation Systems)
►▼ Show Figures

Figure 1

15 pages, 6426 KB  
Article
Adaptive Multiple-Attribute Scenario LoRA Merge for Robust Perception in Autonomous Driving
by Ryosuke Kawata, Joonho Lee, Yanlei Gu and Shunsuke Kamijo
Sensors 2026, 26(4), 1336; https://doi.org/10.3390/s26041336 - 19 Feb 2026
Viewed by 792
Abstract
Perception models for autonomous driving are predominantly trained on clear, daytime data, leaving their performance under rare conditions—particularly in multiple-attribute (joint weather–lighting) conditions such as night × rainy or night × snowy—an open challenge. To address this, we propose a parameter-efficient fine-tuning (PEFT) [...] Read more.
Perception models for autonomous driving are predominantly trained on clear, daytime data, leaving their performance under rare conditions—particularly in multiple-attribute (joint weather–lighting) conditions such as night × rainy or night × snowy—an open challenge. To address this, we propose a parameter-efficient fine-tuning (PEFT) framework that dynamically applies lightweight, scenario-specific Low-Rank Adaptation (LoRA) experts. At its core, our method features an adaptive pipeline that dynamically determines the LoRA experts to apply based on the encountered environmental conditions. We validate our framework on a unified semantic segmentation benchmark (MUSES, BDD100K, and Cityscapes) covering six scenarios (day/night × weather). Our approach improves the mIoU by up to 3.23 points over a strong baseline in single-attribute settings, and in data-scarce multiple-attribute cases, merged LoRA experts outperform the baseline expert by up to 5.99 points, demonstrating effective generalization across compounded conditions. Full article
(This article belongs to the Special Issue Sensing Technologies for Autonomous Driving and Intelligent Transportation Systems)
►▼ 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