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Sensors and Sensing Technologies for Traffic, Driving and Transportation
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Sensors and Sensing Technologies for Traffic, Driving and Transportation

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

Deadline for manuscript submissions: 10 June 2025 | Viewed by 620

Special Issue Editors

Dr. Duo Lu

E-Mail Website
Guest Editor
Department of Computer Science and Physics, Rider University, New Jersey, NJ 08648, USA
Interests: computer vision; deep learning; robotics perception; tracking; intelligent transportation systems (ITSs); sensors and embedded systems
Dr. Bharatesh Chakravarthi

E-Mail Website
Guest Editor
Computer Science and Engineering, School of Computing and Augmented Intelligence, Ira. A. Fulton Schools of Engineering, Arizona State University, Tempe, AZ 85281, USA
Interests: event-based vision (neuromorphic vision); artificial neural networks; sensors and multimodal sensing; intelligent transportation systems

Special Issue Information

Dear Colleagues,

You are invited to submit to this Special Issue, entitled “Sensors and Sensing Technologies for Traffic, Driving and Transportation”, of Sensors.

The advancement of sensors and sensing technologies for traffic, driving, and transportation has transformed the landscape of intelligent transportation systems (ITSs) and intelligent vehicles. With rapid developments in sensor technologies such as LiDAR, radar, cameras, and V2X communication, modern vehicles and infrastructure are becoming increasingly connected and autonomous. These innovations have enabled safer, more efficient, and smarter transportation systems, where real-time traffic monitoring, autonomous driving, and advanced driver-assistance systems (ADASs) are now integral components. As a result, sensors are playing a crucial role in enhancing road safety, reducing congestion, and improving the overall transportation experience.

This Special Issue aims to highlight innovative research on novel sensors, computer vision, artificial intelligence, sensor data processing, sensor data visualization, and the application of sensing technologies in intelligent transportation systems (ITSs) and intelligent vehicles (IVs). We welcome contributions from all fields related to sensors and sensing technologies applied in traffic, driving, and transportation, including, but not limited to, the following:

  • Novel sensor modalities (LiDAR, radar, cameras, etc.);
  • Data fusion for multiple sensors or multiple sensing modalities;
  • Collaborative sensing and V2X communication;
  • Sensing technologies for autonomous driving systems;
  • Sensing technologies for advanced driver-assistance systems (ADASs);
  • Environmental sensing for intelligent transportation;
  • Smart infrastructure with sensing capabilities;
  • Safety and security of sensing technologies;
  • Artificial intelligence in sensing technologies;
  • Datasets and data collection methods;
  • Applications of sensing technologies in ITSs and IVs. 

Dr. Duo Lu
Dr. Bharatesh Chakravarthi
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

  • sensors
  • sensing technologies
  • sensor fusion
  • intelligent transportation systems (ITSs)
  • intelligent vehicles (IVs)
  • artificial intelligence (AI)
  • datasets

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Published Papers (1 paper)

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Research

21 pages, 29624 KiB  
Article
Object Detection and Classification Framework for Analysis of Video Data Acquired from Indian Roads
by Aayushi Padia, Aryan T. N., Sharan Thummagunti, Vivaan Sharma, Manjunath K. Vanahalli, Prabhu Prasad B. M., Girish G. N., Yong-Guk Kim and Pavan Kumar B. N.
Sensors 2024, 24(19), 6319; https://doi.org/10.3390/s24196319 - 29 Sep 2024
Abstract
Object detection and classification in autonomous vehicles are crucial for ensuring safe and efficient navigation through complex environments. This paper addresses the need for robust detection and classification algorithms tailored specifically for Indian roads, which present unique challenges such as diverse traffic patterns, [...] Read more.
Object detection and classification in autonomous vehicles are crucial for ensuring safe and efficient navigation through complex environments. This paper addresses the need for robust detection and classification algorithms tailored specifically for Indian roads, which present unique challenges such as diverse traffic patterns, erratic driving behaviors, and varied weather conditions. Despite significant progress in object detection and classification for autonomous vehicles, existing methods often struggle to generalize effectively to the conditions encountered on Indian roads. This paper proposes a novel approach utilizing the YOLOv8 deep learning model, designed to be lightweight, scalable, and efficient for real-time implementation using onboard cameras. Experimental evaluations were conducted using real-life scenarios encompassing diverse weather and traffic conditions. Videos captured in various environments were utilized to assess the model’s performance, with particular emphasis on its accuracy and precision across 35 distinct object classes. The experiments demonstrate a precision of 0.65 for the detection of multiple classes, indicating the model’s efficacy in handling a wide range of objects. Moreover, real-time testing revealed an average accuracy exceeding 70% across all scenarios, with a peak accuracy of 95% achieved in optimal conditions. The parameters considered in the evaluation process encompassed not only traditional metrics but also factors pertinent to Indian road conditions, such as low lighting, occlusions, and unpredictable traffic patterns. The proposed method exhibits superiority over existing approaches by offering a balanced trade-off between model complexity and performance. By leveraging the YOLOv8 architecture, this solution achieved high accuracy while minimizing computational resources, making it well suited for deployment in autonomous vehicles operating on Indian roads. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Traffic, Driving and Transportation)
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