Edge Computing Assisted Autonomous Driving Using Artificial Intelligence

Ibn‐Khedher, Hatem; Laroui, Mohammed; Mabrouk, M.; Moungla, Hassine; Afifi, Hossam; Oleari, Alberto Nai; Kamal, Ahmed E.

doi:10.1109/iwcmc51323.2021.9498627

Cited by 9 publications

(11 citation statements)

References 11 publications

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“…Therefore, 5G radio remote heads (RRHs) antennas may provide real‐time data, which reduce the latency. Furthermore, through caching and processing massive vehicle data at distributed MEC and UAV nodes, autonomous driving quality can be extensively improved 99 . Moreover, high cybersecurity mechanisms and blockchain techniques increase communication reliability and data integrity.…”

Section: G Support For Ioavmentioning

confidence: 99%

“…It is envisioned that MEC will play a vital role in 6G mobile networks 7,8 . It can be applied in different IoAV applications such as edge assisted autonomous driving, 99 AI models training, 111 resources allocation, 112 and so on. It is worth mentioning that ETSI standards introduced multi‐access MEC support in different V2X use cases, as they require ultra‐low latency, ultra‐high reliability, and availability 101,113 .…”

Section: G Support For Ioavmentioning

confidence: 99%

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6G‐edge support of Internet of Autonomous Vehicles: A survey

Ibn‐Khedher,

Laroui,

Alfaqawi

et al. 2023

Trans Emerging Tel Tech

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With the commercial deployment of 5G mobile communication systems, 6G is proposed to meet the needs of new use cases including connected and autonomous vehicles (CAVs). In this article, we surveyed different 6G embracing technologies such as mobile edge computing, unmanned aerial vehicles, network function virtualization, software defined networking, and artificial intelligence that can enhance the Internet of Autonomous Vehicles (IoAV) network performance comparing to existing 5G enabled solutions. Then, we highlight the convergence path of 6G and IoAV to satisfy the next generation of CAV applications. Further, we propose 6G cellular communication to support the quality of next IoAV applications. Furthermore, the article attempts to study the serious challenges in the 6G‐IoAV research field and propose potential future directions.

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Section: G Support For Ioavmentioning

confidence: 99%

Section: G Support For Ioavmentioning

confidence: 99%

6G‐edge support of Internet of Autonomous Vehicles: A survey

Ibn‐Khedher,

Laroui,

Alfaqawi

et al. 2023

Trans Emerging Tel Tech

Self Cite

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“…Communication within vehicles and their ecosystem has been identified as a key enabler for deploying level 6 autonomy [116]. An example of connected vehicles, base stations, roadside units, edge-servers, infrastructure and remote cloud is shown in Figure 3.…”

Section: E Communications In Autonomous Vehiclesmentioning

confidence: 99%

“…3) Edge AI and CAV: Initially, cloud computing was proposed to facilitate computation, and decision-making for the connected vehicles [73], [301], [116]. However, the cloud computing approach had several challenges in transmitting high volume or flood of data from the vehicle to the cloud, data privacy and leakage, adversarial and poisoning attacks on the ground truth data, and algorithms present in the cloud [116]. Therefore, an approach to bring computation near the data source to tackle surplus data transmission to the cloud has been proposed in the form of edge computing.…”

Section: F Taxonomy Of Edge Ai Technologies For Cavmentioning

confidence: 99%

A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

Katare

Perino

Nurmi

et al. 2023

IEEE Commun. Surv. Tutorials

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Autonomous driving services depends on active sensing from modules such as camera, LiDAR, radar, and communication units. Traditionally, these modules process the sensed data on high-performance computing units inside the vehicle, which can deploy intelligent algorithms and AI models. The sensors mentioned above can produce large volumes of data, potentially reaching up to 20 Terabytes. This data size is influenced by factors such as the duration of driving, the data rate, and the sensor specifications. Consequently, this substantial amount of data can lead to significant power consumption on the vehicle. Similarly, a substantial amount of data will be exchanged between infrastructure sensors and vehicles for collaborative vehicle applications or fully connected autonomous vehicles. This communication process generates an additional surge of energy consumption. Although the autonomous vehicle domain has seen advancements in sensory technologies, wireless communication, computing and AI/ML algorithms, the challenge still exists in how to apply and integrate these technology innovations to achieve energy efficiency. This survey reviews and compares the connected vehicular applications, vehicular communications, approximation and Edge AI techniques. The focus is on energy efficiency by covering newly proposed approximation and enabling frameworks. To the best of our knowledge, this survey is the first to review the latest approximate Edge AI frameworks and publicly available datasets in energy-efficient autonomous driving. The insights from this survey can benefit the collaborative driving service development on low-power and memory-constrained systems and the energy optimization of autonomous vehicles.

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“…Tang et al [102] developed LoPECS, the first complete edge computing system for producing self-driving cars, which leverages the runtime layer of heterogeneous computing resources of low-power edge computing systems to meet the real-time requirements of self-driving applications. Ibn-Khedher et al [103] proposed an end-to-end architecture for edge-assisted autonomous driving that allows the rationing of computationally intensive autonomous driving services to shared resources on edge servers, improving the performance level of autonomous vehicles.…”

Section: Application and Challenges Of Iot Edge Computing In Internet...mentioning

confidence: 99%

Application and Research of IoT Architecture for End-Net-Cloud Edge Computing

Zhang

Zhou

et al. 2022

Electronics

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At the edge of the network close to the source of the data, edge computing deploys computing, storage and other capabilities to provide intelligent services in close proximity and offers low bandwidth consumption, low latency and high security. It satisfies the requirements of transmission bandwidth, real-time and security for Internet of Things (IoT) application scenarios. Based on the IoT architecture, an IoT edge computing (EC-IoT) reference architecture is proposed, which contained three layers: The end edge, the network edge and the cloud edge. Furthermore, the key technologies of the application of artificial intelligence (AI) technology in the EC-IoT reference architecture is analyzed. Platforms for different EC-IoT reference architecture edge locations are classified by comparing IoT edge computing platforms. On the basis of EC-IoT reference architecture, an industrial Internet of Things (IIoT) edge computing solution, an Internet of Vehicles (IoV) edge computing architecture and a reference architecture of the IoT edge gateway-based smart home are proposed. Finally, the trends and challenges of EC-IoT are examined, and the EC-IoT architecture will have very promising applications.

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Edge Computing Assisted Autonomous Driving Using Artificial Intelligence

Cited by 9 publications

References 11 publications

6G‐edge support of Internet of Autonomous Vehicles: A survey

6G‐edge support of Internet of Autonomous Vehicles: A survey

A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services

Application and Research of IoT Architecture for End-Net-Cloud Edge Computing

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