Browsing by Author "Marai, Oussama El"

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    AR-based Remote Command and Control Service: Self-driving Vehicles Use Case
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022-10-04) Marai, Oussama El; Taleb, Tarik; Song, Jae Seung; Department of Communications and Networking; Mobile Network Softwarization and Service Customization
    The recent technological advances in many fields have significantly contributed to the development of the Advanced Driver Assistance System (ADAS), which in turn will greatly contribute to the flourishing of self-driving vehicles that can operate autonomously in all road scenarios. Until then, keeping the human input in the loop remains vital to either make decisions in unseen situations or approve vehicles' proposed decisions. In this paper, we leverage VR technology to provide remote assistance for self-driving in critical situations. Specifically, we study the delivery of a 360° live stream at high resolution (4K) to a remote operation center for supporting self-driving vehicles' decisions when, for example, merging onto the highway. The 360° video stream will be consumed by a human operator wearing a head-mounted display for increased flexibility, faster control, and an immersive experience. In addition, the 360° stream is augmented with relevant context data, such as the vehicle's speed and distance to other road objects, in order to increase the human operator's awareness of the vehicle and its surroundings. Depending on the human operator's proximity to the source, the video stream can either be viewed through the cloud or the edge, which further reduces the glass-to-glass latency. Experimental results demonstrate the effectiveness of employing VR technology to remotely command and control self-driving vehicles in critical situations. The results show that a 360° stream at 4K resolution can be delivered in sub-second glass-to-glass latency, which allows the operator to make timely decisions.
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    Feature-based Vehicle Identification Framework for Optimization of Collective Perception Messages in Vehicular Networks
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2023-02-01) Masuda, Hidetaka; Marai, Oussama El; Tsukada, Manabu; Taleb, Tarik; Esaki, Hiroshi; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; University of Tokyo; University of Oulu
    The world is moving towards a fully connected digital world, where objects produce and consume data, at a sultry pace. Autonomous vehicles will play a key role in bolstering the digitization of the world. These connected vehicles must communicate timely data with their surrounding objects and road participants to fully and accurately understand their environments and eventually operate smoothly. As a result, the hugely exchanged data would scramble the network traffic that, at a given point, would no longer increase the awareness level of the vehicle. In this paper, we propose a vision-based approach to identify connected vehicles and use it to optimize the exchange of collective perception messages (CPMs), in terms of both the CPM generation frequency and the number of generated CPMs. To validate our proposed approach, we created a Cartery framework that integrates SUMO, Carla, and OMNeT++. We also compared our solution with both baselines and European Telecommunications Standards Institute solutions, considering three main KPIs: the channel busy ratio, environmental awareness, and the CPM generation frequency. Simulation results show that our proposed solution exhibits the best trade-off between the network load and situational awareness.
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    Roads Infrastructure Digital Twin: A Step Toward Smarter Cities Realization
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-03) Marai, Oussama El; Taleb, Tarik; Song, Jaeseung; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Sejong University
    Digital Twin is a new concept that consists of creating an up-to-date virtual asset in cyberspace which mimics the original physical asset in most of its aspects, ultimately to monitor, analyze, test, and optimize the physical asset. In this article, we investigate and discuss the use of the digital twin concept of the roads as a step toward realizing the dream of smart cities. To this end, we propose the deployment of a Digital Twin Box to the roads that is composed of a 360° camera and a set of IoT devices connected to a Single Onboard Computer. The Digital Twin Box creates a digital twin of the physical road asset by constantly sending real-time data to the edge/cloud, including the 360° live stream, GPS location, and measurements of the temperature and humidity. This data will be used for realtime monitoring and other purposes by displaying the live stream via head-mounted devices or using a 360° web-based player. Additionally, we perform an object detection process to extract all possible objects from the captured stream. For some specific objects (person and vehicle), an identification module and a tracking module are employed to identify the corresponding objects and keep track of all video frames where these objects appeared. The outcome of the latter step would be of utmost importance to many other services and domains such as national security. To show the viability of the proposed solution, we have implemented and conducted real-world experiments where we focus more on the detection and recognition processes. The achieved results show the effectiveness of the proposed solution in creating a digital twin of the roads, a step forward to enable self-driving vehicles as a crucial component of smart mobility, using the Digital Twin Box.
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    Smooth and Low Latency Video Streaming for Autonomous Cars during Handover
    (IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020-11-01) Marai, Oussama El; Taleb, Tarik; Department of Communications and Networking; Mobile Network Softwarization and Service Customization
    Self-driving vehicles are expected to bring many benefits among which are enhancing traffic efficiency and reliability, and reducing fuel consumption which would have a great economic and environmental impact. The success of this technology heavily relies on the full situational awareness of its surrounding entities. This is achievable only when everything is networked, including vehicles, users and infrastructure, and exchange the sensed data among the nearby objects to increase their awareness. Nevertheless, human intervention is still needed in the loop anyway to deal with unseen situations or compensate for inaccurate or improper vehicle's decisions. For such cases, video feed, in addition to other data such as LiDAR, is considered essential to provide humans with the real picture of what is happening to eventually take the right decision. However, if the video is not delivered in a timely fashion, it becomes useless or likely produces catastrophic outcomes. Additionally, any disruption in the streamed video, for instance during handover operation while traversing inter-countries cross borders, is very annoying to the user and possibly cause damage as well. in this article, we start by describing two important use cases, namely Remote Driving and Platooning, where the timely delivery of video is of extreme importance [1]. Thereafter, we detail our implemented solution to accommodate the aforementioned use cases for self-driving vehicles. Through extensive experiments in local and LTE networks, we show that our solution ensures a very low endto- end latency. Also, we show that our solution keeps the video outage as low as possible during handover operation.