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Sensors and Sensor Fusion for Decision Making in Autonomous Driving and Vehicles

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

Deadline for manuscript submissions: closed (30 May 2024) | Viewed by 2623

Special Issue Editor


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Guest Editor
School of Engineering and Computing, University of Central Lancashire, Preston PR1 2HE, UK
Interests: mobile robots; multi-agent systems; Internet; artificial intelligence; remotely operated vehicles; road safety; road vehicles; traffic engineering computing; Internet of Things; automobiles; autonomous aerial vehicles
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Special Issue Information

Dear Colleagues,

Vehicles are becoming increasingly automated by taking on more and more tasks under improving intelligent control systems equipped with enhanced sensor technologies and artificial intelligence (AI) techniques from the prior automation level to the next automation level—targeting the autonomy of level 5 with no steering wheel, no pedals, no breaks, and even no windshield. Sensors are the main components of autonomous vehicles (AVs), i.e., self-driving vehicles (SDVs), which are paving the way for autonomous driving by providing AVs with the ability to perceive the environment through continuous vehicle–environmental interaction. Vehicle sensors, with multiple sensor data fusions, feed the main phases of self-driving, i.e., vehicle learning and decision-making, which are instilled with advanced artificial intelligence (AI). No efficient self-driving is possible without an accurate perception of the environment, leading to poor decision-making in AVs. In this Special Issue, we are keen to process the most recent sensor technologies, either already developed or being developed, for AVs to establish the most experienced (self-) driver. More explicitly, we would like to analyze the role of sensors in increasing the efficacy of vehicles’ autonomous decision-making. In this direction, we would like to invite the academic and industrial research communities to submit original research as well as review articles to this Special Issue.

Dr. Kaya Kuru
Guest Editor

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Keywords

  • autonomous vehicles
  • self-driving vehicles
  • sensor fusion
  • autonomous driving
  • driverless vehicles
  • vehicle automation

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Published Papers (1 paper)

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Research

28 pages, 8798 KiB  
Article
Forward Collision Warning Strategy Based on Millimeter-Wave Radar and Visual Fusion
by Chenxu Sun, Yongtao Li, Hanyan Li, Enyong Xu, Yufang Li and Wei Li
Sensors 2023, 23(23), 9295; https://doi.org/10.3390/s23239295 - 21 Nov 2023
Cited by 3 | Viewed by 2138
Abstract
Forward collision warning (FCW) is a critical technology to improve road safety and reduce traffic accidents. However, the existing multi-sensor fusion methods for FCW suffer from a high false alarm rate and missed alarm rate in complex weather and road environments. For these [...] Read more.
Forward collision warning (FCW) is a critical technology to improve road safety and reduce traffic accidents. However, the existing multi-sensor fusion methods for FCW suffer from a high false alarm rate and missed alarm rate in complex weather and road environments. For these issues, this paper proposes a decision-level fusion collision warning strategy. The vision algorithm and radar tracking algorithm are improved in order to reduce the false alarm rate and omission rate of forward collision warning. Firstly, this paper proposes an information entropy-based memory index for an adaptive Kalman filter for radar target tracking that can adaptively adjust the noise model in a variety of complex environments. Then, for visual detection, the YOLOv5s model is enhanced in conjunction with the SKBAM (Selective Kernel and Bottleneck Attention Mechanism) designed in this paper to improve the accuracy of vehicle target detection. Finally, a decision-level fusion warning fusion strategy for millimeter-wave radar and vision fusion is proposed. The strategy effectively fuses the detection results of radar and vision and employs a minimum safe distance model to determine the potential danger ahead. Experiments are conducted under various weather and road conditions, and the experimental results show that the proposed algorithm reduces the false alarm rate by 11.619% and the missed alarm rate by 15.672% compared with the traditional algorithm. Full article
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