Artificial-Intelligence-Based Autonomous Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 2675

Special Issue Editors

Engineering Cluster, Singapore Institute of Technology, Singapore 138683, Singapore
Interests: UAS system engineering; UAV flight dynamics modelling & control; UAV flight testing, verification, and validation; resilience and reliability of UAV system; predictive maintenance of UAV system
Infocomm Technology Cluster, Singapore Institute of Technology (SIT), Singapore 138683, Singapore
Interests: commercial machine learning solutions; deep learning
IInfocomm Technology Cluster, Singapore Institute of Technology, Singapore 138683, Singapore
Interests: computer vision; image and video analytics; machine learning; deep learning
Infocomm Technology Cluster, Singapore Institute of Technology (SIT), Singapore 138683, Singapore
Interests: automotive electrical/electronic architectures; distributed systems; many-core systems

Special Issue Information

Dear Colleagues,

The rapid advancement and maturation of artificial intelligence (AI) algorithms and edge computing coupled with multimodal sensing are creating intelligent autonomous systems capable of operating in complex environments with high levels of independence and self-determination. Through AI, these autonomous systems can perceive, learn, reason and act with self-awareness and respond intelligently to unforeseen changes in the environment. The success of these intelligent systems will have the potential to greatly improve the quality of everyone’s daily lives in health care, transportation and workplaces.

This Special Issue focuses on the scientific novelties, practical use cases and best practices of AI-based autonomous systems from both research and industries.

The topics include, but are not limited to, the following:

  • AI algorithms for autonomous systems;
  • Intelligent cognition for autonomous systems;
  • Learning-enabled collaborative and swarm intelligence autonomous systems;
  • Robustness issues for AI-enabled autonomous systems.

Dr. Yew Chai Paw
Dr. Soh Donny
Dr. Indriyati Atmosukarto
Dr. Peter Waszecki
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • machine learning
  • autonomous systems

Published Papers (1 paper)

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Research

18 pages, 4067 KiB  
Article
An Efficient Adaptive Noise Removal Filter on Range Images for LiDAR Point Clouds
by Minh-Hai Le, Ching-Hwa Cheng and Don-Gey Liu
Electronics 2023, 12(9), 2150; https://doi.org/10.3390/electronics12092150 - 08 May 2023
Cited by 5 | Viewed by 2213
Abstract
Light Detection and Ranging (LiDAR) is a critical sensor for autonomous vehicle systems, providing high-resolution distance measurements in real-time. However, adverse weather conditions such as snow, rain, fog, and sun glare can affect LiDAR performance, requiring data preprocessing. This paper proposes a novel [...] Read more.
Light Detection and Ranging (LiDAR) is a critical sensor for autonomous vehicle systems, providing high-resolution distance measurements in real-time. However, adverse weather conditions such as snow, rain, fog, and sun glare can affect LiDAR performance, requiring data preprocessing. This paper proposes a novel approach, the Adaptive Outlier Removal filter on range Image (AORI), which combines a projection image from LiDAR point clouds with an adaptive outlier removal filter to remove snow particles. Our research aims to analyze the characteristics of LiDAR and propose an image-based approach derived from LiDAR data that addresses the limitations of previous studies, particularly in improving the efficiency of nearest neighbor point search. Our proposed method achieves outstanding performance in both accuracy (>96%) and processing speed (0.26 s per frame) for autonomous driving systems under harsh weather from raw LiDAR point clouds in the Winter Adverse Driving dataset (WADS). Notably, AORI outperforms state-of-the-art filters by achieving a 6.6% higher F1 score and 0.7% higher accuracy. Although our method has a lower recall than state-of-the-art methods, it achieves a good balance between retaining object points and filter noise points from LiDAR, indicating its promise for snow removal in adverse weather conditions. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Based Autonomous Systems)
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