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Sensors and Sensory Algorithms for Intelligent Transportation Systems

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1712

Special Issue Editors


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Guest Editor
Department of Mechanical Engineering, University of Waterloo, Waterloo, ON, Canada
Interests: sensor arrays; adaptive estimation; sensor fusion; mechatronic systems; vehicle systems; sensor networks; sensor localization
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Mechanical Engineering Department, University of Alberta, Edmonton, AB T6G 1H9, Canada
Interests: mobile robots' control; navigation; distributed algorithms in multi-robot settings
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to providing a comprehensive overview of different perspectives on the state-of-the-art sensor technologies, sensor network architectures, sensory algorithms, and remote sensing frameworks developed for intelligent transportation systems (ITS), including state estimation, motion planning, and controls for automated driving systems and autonomous navigation.

We are inviting you to contribute high-quality novel research and comprehensive survey articles providing consolidated, up-to-date perspectives in the field of ITS.

Topics of interest include, but are not limited to, the following:

  • Inertial measurement units (IMUs) and inertial sensors for ITS: IMU-based navigation;
  • Visual sensors and vison-based sensory architectures for ITS: vision-based navigation;
  • Sensor fusion and sensor networks/architectures for ITS: navigation, localization, mapping;
  • Sensor fusion and sensor networks/architectures for ITS: vehicular state estimation;
  • Sensor fusion and sensor networks/architectures for ITS: power train monitoring and control;
  • State estimation using on-board multimodal sensory data for autonomous navigation;
  • Distributed sensor networks for cooperative intelligent transportation systems (C-ITS);
  • Fault diagnosis and isolation in ITS sensor networks;
  • Machine learning approaches for reliable multimodal perception in ITS;
  • System-theoretic approaches to sensory algorithm design and sensor fusion in ITS;
  • Sensor design, sensory algorithms, and sensor networks/architectures for articulated ITS;
  • Sensor design, sensory algorithms, and sensor architectures for railway transportation systems.

Prof. Dr. Baris Fidan
Dr. Ehsan Hashemi
Guest Editors

Manuscript Submission Information

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Keywords

  • inertial sensors
  • visual sensors
  • navigation, localization, and mapping
  • sensor networks and sensor fusion
  • autonomous navigation
  • remote sensors

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Published Papers (2 papers)

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Research

14 pages, 949 KiB  
Article
A New Approach to ORB Acceleration Using a Modern Low-Power Microcontroller
by Jorge Aráez, Santiago Real and Alvaro Araujo
Sensors 2025, 25(12), 3796; https://doi.org/10.3390/s25123796 - 18 Jun 2025
Viewed by 203
Abstract
A key component in visual Simultaneous Location And Mapping (SLAM) systems is feature extraction and description. One common algorithm that accomplishes this purpose is Oriented FAST and Rotated BRIEF (ORB), which is used in state-of-the-art SLAM systems like ORB-SLAM. While it is faster [...] Read more.
A key component in visual Simultaneous Location And Mapping (SLAM) systems is feature extraction and description. One common algorithm that accomplishes this purpose is Oriented FAST and Rotated BRIEF (ORB), which is used in state-of-the-art SLAM systems like ORB-SLAM. While it is faster than other feature detectors like SIFT (340 times faster) or SURF (15 times faster), it is one of the most computationally expensive algorithms in these types of systems. This problem has commonly been solved by delegating this task to hardware-accelerated solutions like FPGAs or ASICs. While this solution is useful, it incurs a greater economical cost. This work proposes a solution for feature extraction and description based on a modern low-power mainstream microcontroller. The execution time of ORB, along with power consumption, are analyzed in relation to the number of feature points and internal variables. The results show a maximum of 0.6 s for ORB execution in 1241 × 376 resolution images, which is significantly slower than other hardware-accelerated solutions but remains viable for certain applications. Additionally, the power consumption ranges between 30 and 40 milliwatts, which is lower than FPGA solutions. This work also allows for future optimizations that will improve the results of this paper. Full article
(This article belongs to the Special Issue Sensors and Sensory Algorithms for Intelligent Transportation Systems)
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21 pages, 9314 KiB  
Article
Game-Theoretic Motion Planning with Perception Uncertainty and Right-of-Way Constraints
by Pouya Panahandeh, Ahmad Reza Alghooneh, Mohammad Pirani, Baris Fidan and Amir Khajepour
Sensors 2024, 24(24), 8177; https://doi.org/10.3390/s24248177 - 21 Dec 2024
Viewed by 810
Abstract
This paper addresses two challenges in AV motion planning: adherence to right-of-way and handling uncertainties, using two game-theoretic frameworks, namely Stackelberg and Nash Bayesian (Bayesian). By modeling the interactions between road users as a hierarchical relationship, the proposed approach enables the AV to [...] Read more.
This paper addresses two challenges in AV motion planning: adherence to right-of-way and handling uncertainties, using two game-theoretic frameworks, namely Stackelberg and Nash Bayesian (Bayesian). By modeling the interactions between road users as a hierarchical relationship, the proposed approach enables the AV to strategically optimize its trajectory while considering the actions and priorities of other road users. Additionally, the Bayesian equilibrium aspect of the framework incorporates probabilistic beliefs and updates them based on sensor measurements, allowing the AV to make informed decisions in the presence of uncertainty in the sensory system. Experimental assessments demonstrate the efficacy of the approach, emphasizing its ability to improve the reliability and adaptability of AV motion planning. Full article
(This article belongs to the Special Issue Sensors and Sensory Algorithms for Intelligent Transportation Systems)
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