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Unmanned and Autonomous Surface Vehicles: Localization, Control, and Navigation

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

Deadline for manuscript submissions: 30 March 2025 | Viewed by 956

Special Issue Editor


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Guest Editor
Department of Civil and Environmental Engineering, Carleton University, Ottawa, ON, Canada
Interests: road safety analysis; highway design

Special Issue Information

Dear Colleagues,

We are seeing rapid advances in vehicle control and autonomous surface systems. Artificial-intelligence-powered systems are gaining mainstream acceptance due to the unique capabilities that they offer. Surface vehicles can be utilized in a wide range of contexts, including, but not limited to, transportation, industry, and warehouses. The unmanned or autonomous operation of such systems can offer tremendous functional benefits, such as higher safety and efficiency expectations in the deployment of connected and autonomous vehicles and improved traffic flow.

This Special Issue will cover topics including autonomous and unmanned vehicle control, safety countermeasures, safe interactions with objects and other road users, navigation, spatial awareness of surrounding environments, and key advancements in the sensors used in the control and navigation of unmanned and autonomous vehicles.

This Special Issue aims to disseminate cutting-edge research on several topics related to unmanned and autonomous surface vehicles, with a particular focus on localization, control, and navigation.

Dr. Karim Ismail
Guest Editor

Manuscript Submission Information

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Keywords

  • unmanned surface vehicles
  • autonomous surface vehicles
  • safe interactions
  • outdoor navigation
  • localization
  • control

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Published Papers (1 paper)

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Research

29 pages, 234471 KiB  
Article
Optimizing Camera Exposure Time for Automotive Applications
by Hao Lin, Darragh Mullins, Dara Molloy, Enda Ward, Fiachra Collins, Patrick Denny, Martin Glavin, Brian Deegan and Edward Jones
Sensors 2024, 24(16), 5135; https://doi.org/10.3390/s24165135 - 8 Aug 2024
Viewed by 785
Abstract
Camera-based object detection is integral to advanced driver assistance systems (ADAS) and autonomous vehicle research, and RGB cameras remain indispensable for their spatial resolution and color information. This study investigates exposure time optimization for such cameras, considering image quality in dynamic ADAS scenarios. [...] Read more.
Camera-based object detection is integral to advanced driver assistance systems (ADAS) and autonomous vehicle research, and RGB cameras remain indispensable for their spatial resolution and color information. This study investigates exposure time optimization for such cameras, considering image quality in dynamic ADAS scenarios. Exposure time, the period during which the camera sensor is exposed to light, directly influences the amount of information captured. In dynamic scenarios, such as those encountered in typical driving scenarios, optimizing exposure time becomes challenging due to the inherent trade-off between Signal-to-Noise Ratio (SNR) and motion blur, i.e., extending exposure time to maximize information capture increases SNR, but also increases the risk of motion blur and overexposure, particularly in low-light conditions where objects may not be fully illuminated. The study introduces a comprehensive methodology for exposure time optimization under various lighting conditions, examining its impact on image quality and computer vision performance. Traditional image quality metrics show a poor correlation with computer vision performance, highlighting the need for newer metrics that demonstrate improved correlation. The research presented in this paper offers guidance into the enhancement of single-exposure camera-based systems for automotive applications. By addressing the balance between exposure time, image quality, and computer vision performance, the findings provide a road map for optimizing camera settings for ADAS and autonomous driving technologies, contributing to safety and performance advancements in the automotive landscape. Full article
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