Over the past few decades, sensors have become more advanced and made impressive strides across an increasing number of sensing modalities. Despite the improved capabilities and breadth of available sensor systems, those used for intelligent transportation have remained relatively uniform across platforms; as a result, the algorithms and techniques for these platforms do not take full advantage of the rich information that modern sensors can provide. Since all tasks - including perception, localisation, decision-making, and learning - are built on top of sensing, exploring alternative approaches to sensing is a compelling research area that can render all subsequent tasks more robust and accurate.
The objective of this workshop is to explore unconventional sensing for intelligent transportation in three ways. Firstly, it will investigate sensor systems not typically applied to specific transportation tasks, such as radar for precise localisation, audio for failure detection, and RF sensing for road traffic estimation. Secondly, it will explore non-traditional sensor configurations and placements, such as ground-facing cameras using shadows to detect occluded moving objects. Lastly, it will look into sensing of commonly overlooked information, such as atmospheric sensors for gauging road surface traction or in-vehicle sensors for driving analysis. Via these three themes, this special session aims to stimulate discussion and research into non-traditional sensing to improve transportation systems' reliability and accuracy.
The topics that would suit the themes of this workshop include (but are not limited to):
Title: Sensing and big data analysis for Natural Disaster management
Abstract: Natural Disaster Management (NDM, e.g., for wildfires, floods) can be greatly improved by automating precise semantic 3D mapping and disaster evolution prediction to achieve NDM goals in near-real-time. To this end, many heterogeneous extreme data sources must be analyzed and fused: smart drone sensors (e.g., RGB, RGBD and thermal cameras, Lidars, motion sensors), emergency vehicle sensors (similar sensors but equipped with more computing power) and in-situ sensors (smoke sensors, moisture sensors, water flow meters), meteorological sensors, remote sensing data, topographical data, and geosocial media data (text, image and videos). The lecture focus is on a) how to efficiently manage such a heterogeneous and mobile sensor network, including intelligent sensor placement and b) how to analyze the resulting data having extreme nature, due to their varying resolution and quality, very large volume and update rate, different spatiotemporal resolutions and acquisition frequencies, real-time needs and multilingualism. Extreme data analytics can help developing an integrated, ground-breaking NDM platform, focusing on real-time semantic extraction from multiple heterogeneous data modalities and sources, on-the-fly construction of a meaningful semantically annotated 3D disaster area map, prediction of disaster evolution and improved communication between service providers and end-users, through automated process triggering and response recommendations. Semantic analysis computations will be distributed across the edge-to-cloud continuum, in a federated manner, to minimize latency. Extreme data analytics will be performed in a trustworthy and transparent way, by greatly advancing state-of-the-art AI and XAI approaches. The constantly updated 3D map and the disaster evolution predictions will form the basis for an advanced, interactive, Extended Reality (XR) interface, where the current situation will be visualized and different response strategies will be dynamically evaluated through simulation by NDM personnel. An innovative, scalable and efficient implementation platform will provide precise NDM support, based on extreme data analytics.
Biography: Prof. Ioannis Pitas (IEEE fellow, IEEE Distinguished Lecturer, EURASIP fellow) received the Diploma and PhD degree in Electrical Engineering, both from the Aristotle University of Thessaloniki (AUTH), Greece. Since 1994, he has been a Professor at the Department of Informatics of AUTH and Director of the Artificial Intelligence and Information Analysis (AIIA) lab. He served as a Visiting Professor at several Universities. His current interests are in the areas of computer vision, machine learning, autonomous systems, intelligent digital media, image/video processing, human-centred computing, affective computing, 3D imaging and biomedical imaging. He has published over 920 papers, contributed to 45 books in his areas of interest and edited or (co-)authored another 11 books. He has also been member of the program committee of many scientific conferences and workshops. In the past he served as Associate Editor or co-Editor of 13 international journals and General or Technical Chair of 5 international conferences. He delivered 98 keynote/invited speeches worldwide. He co-organized 33 conferences and participated in technical committees of 291 conferences. He participated in 75+ R&D projects, primarily funded by the European Union and is/was principal investigator in 47 such projects. He is the coordinator of the Horizon Europe R&D project TEMA, AUTH principal investigator in H2020 R&D projects Aerial Core, AI4Media (one of the 4 H2020 ICT48 AI flagship projects) and Horizon Europe R&D projects AI4Europe, SIMAR. He is chair of the International AI Doctoral Academy (AIDA). He was chair and initiator of the IEEE Autonomous Systems Initiative. Prof. Pitas led the big European H2020 R&D project MULTIDRONE. He has 35200+ citations to his work and h-index 88+. According to Research.com he is ranked first in Greece and 319 worldwide in the field of Computer Science (2022).
Title: Overcoming Industry Challenges: How Radar Localization is Solving Complex Problems
Abstract: This talk will cover the benefits to industry of radar localization for autonomous off-road vehicles. We will explore the history and evolution of Terran360 radar localization solution through the Oxa-Navtech partnership, and the journey of scaling the technology into a production-ready product. We will also discuss the various applications of the product and the industry challenges it solves. Finally, we will look into the future of the partnership and technology. Attendees will gain a deeper understanding of radar localization and how it unlocks the full potential of automation in harsh outdoor environments.
Biographies: Sam Wood is a product manager in the industrial automation sector for Navtech Radar in Oxford. The IA sector has been focussed on radar deployments in mission critical applications across multiple industries such as mining, marine, logistics, rail and robotics. With a track record of successful product development and launch, Sam has led two industrial radar product launches, the CIR Gen2 and Terran360. Sam been working with early adopters of the Terran360 solution working with Oxa to build out the products features and specifications.
Aamir Aziz is principal product engineer for radar localization and leads the localization and mapping teams, including radar, vision, lidar and GPS. He has led and delivered projects deploying Oxa's Universal Autonomy software platform across a range of diverse environments, vehicle platforms and use cases. Most recently, Aamir led Oxa's localization team in partnership with Navtech to launch Terran360, the world's first all-weather radar localization solution for industrial autonomous vehicles.
Please submit your papers on http://its.papercept.net/ using the code umt3u.
Formatting requirements, submissions instructions, and general information for authors are available here. Submitted papers shall not exceed six pages (two additional pages allowed with a fee) as a PDF file in IEEE two-column format.
All the accepted papers, if presented at IEEE ITSC 2023, will be published in IEEE Xplore. At least one co-author of each accepted paper must register for and attend the workshop.
IEEE ITSC 2023 will take place in Bilbao (Spain), 24-28 September 2023. Information about the conference venue is available here.
The Workshop on Beyond Traditional Sensing for Intelligent Transportation will be in Room 5H.
Information about the conference registration is available at the main website of IEEE ITSC 2023.
The members of the organisation committee are:
The workshop is co-organised and sponsored by the Nordic University Hub on Industrial IoT (funded by Nordforsk).