Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu, Shaoshan; Liu, Liangkai; Tang, Jie; Bo, Yu; Wang, Yifan; Shi, Weisong

doi:10.1109/jproc.2019.2915983

Cited by 431 publications

(158 citation statements)

References 230 publications

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“…For example, the transmission latency is usually tens (or hundreds) of milliseconds between an end user and a cloud server, while this number is usually several milliseconds or even at microsecond level. The emerging 5G technology further enhances the advances of edge computing from the perspective of low latency transmission, which empowers a series of emerging applications, such as autonomous driving [24], virtual reality/augmented reality and healthcare-related applications.…”

Section: ) Low Latencymentioning

confidence: 99%

“…The coming era of 5G and mobile edge computing (MEC) has enabled vehicle information to be readily accessible anytime and anywhere with low latency, forming an Internet of Vehicle (IoV) [92]. Integrated with the latest advances in deep learning, IoV will enable more intelligent transportation management, such as autonomous driving [24], traffic prediction, traffic signal control, as summarized in Tab. 2.…”

Section: Smart Transportationmentioning

confidence: 99%

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Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

et al. 2020

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With the booming development of Internet-of-Things (IoT) and communication technologies such as 5G, our future world is envisioned as an interconnected entity where billions of devices will provide uninterrupted service to our daily lives and the industry. Meanwhile, these devices will generate massive amounts of valuable data at the network edge, calling for not only instant data processing but also intelligent data analysis in order to fully unleash the potential of the edge big data. Both the traditional cloud computing and on-device computing cannot sufficiently address this problem due to the high latency and the limited computation capacity, respectively. Fortunately, the emerging edge computing sheds a light on the issue by pushing the data processing from the remote network core to the local network edge, remarkably reducing the latency and improving the efficiency. Besides, the recent breakthroughs in deep learning have greatly facilitated the data processing capacity, enabling a thrilling development of novel applications, such as video surveillance and autonomous driving. The convergence of edge computing and deep learning is believed to bring new possibilities to both interdisciplinary researches and industrial applications. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. We also highlight the key research challenges and promising research directions therein. We believe this survey will inspire more researches and contributions in this promising field. INDEX TERMS Internet of Things, edge computing, deep learning, intelligent edge applications.

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Section: ) Low Latencymentioning

confidence: 99%

Section: Smart Transportationmentioning

confidence: 99%

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

et al. 2020

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“…The key enablers, possible applications, main technologies, and existing problems are also discussed. Liu et al [29] have conducted a review on the mobile edge computing for vehicular networks in terms of system architecture, interesting applications, and security issues. Khattak et al [30] have introduced an intelligent transport light control system where road-side-units are in charge of conducting computation and generating notifications based on edge computing.…”

Section: B Spatial Challenges For V2x Communicationsmentioning

confidence: 99%

Edge-Based V2X Communications With Big Data Intelligence

Guleng

Liu

et al. 2020

IEEE Access

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Vehicular Internet-of-Things applications require an efficient Vehicle-to-Everything (V2X) communication scheme. However, it is particularly challenging to achieve a high throughput and low latency with limited wireless resources in highly dynamic vehicular networks. In this article, we propose a scheme that enhances V2V communications through integration of vehicle edge-based forwarding and learning-based edge selection policy optimization. The proposed scheme has three main characteristics. First, the Hierarchical edge-based preemptive route creation is introduced to create hierarchical edges and conduct efficient packet forwarding as well as route aggregation. Second, Two-stage learning is introduced to select efficient edge nodes using big data driven traffic prediction and reinforcement learning-based edge node selection. Third, Context-aware edge selection is employed to improve the performance of edge-based forwarding in various contexts. We use real traffic big data and realistic vehicular network simulations to evaluate the performance of the proposed scheme and show the advantage over other baseline approaches. INDEX TERMS Edge computing, traffic big data, vehicular ad hoc networks, V2X communications.

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“…An autonomous vehicle edge computing system consists of three layers: vehicle, edge, and cloud [18]. Each autonomous vehicle is equipped with onboard edge device(s) that integrates the needed Car1 Car1 Car2 Figure 1: Occlusion and truncation situations naturally occur in point clouds data.…”

Section: Introductionmentioning

confidence: 99%

Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds

Chen

Tang

Yang

et al. 2019

2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

216

242

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Autonomous vehicles are heavily reliant upon their sensors to perfect the perception of surrounding environments, however, with the current state of technology, the data which a vehicle uses is confined to that from its own sensors. Data sharing between vehicles and/or edge servers is limited by the available network bandwidth and the stringent real-time constraints of autonomous driving applications. To address these issues, we propose a point cloud feature based cooperative perception framework (F-Cooper) for connected autonomous vehicles to achieve a better object detection precision. Not only will feature based data be sufficient for the training process, we also use the features' intrinsically small size to achieve real-time edge computing, without running the risk of congesting the network. Our experiment results show that by fusing features, we are able to achieve a better object detection result, around 10% improvement for detection within 20 meters and 30% for further distances, as well as achieve faster edge computing with a low communication delay, requiring 71 milliseconds in certain feature selections. To the best of our knowledge, we are the first to introduce feature-level data fusion to connected autonomous vehicles for the purpose of enhancing object detection and making realtime edge computing on inter-vehicle data feasible for autonomous vehicles.

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Edge Computing for Autonomous Driving: Opportunities and Challenges

Cited by 431 publications

References 230 publications

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

Deep Learning for Edge Computing Applications: A State-of-the-Art Survey

Edge-Based V2X Communications With Big Data Intelligence

Cooper: Cooperative Perception for Connected Autonomous Vehicles Based on 3D Point Clouds

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