Collaborative Autonomous Driving: Vision and Challenges

Zheng, Dong; Shi, Wei; Tong, Guangmo; Yang, Kecheng

doi:10.1109/metrocad48866.2020.00010

Cited by 20 publications

(4 citation statements)

References 71 publications

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“…A complete realization of autonomous vehicles not only require the real-time acquisition of multimodal visual data using various in-car sensors such as light detection and ranging (LiDAR) and cameras but also need an efficient collaborative communication mechanism to share such useful visual information with other autonomous or simple vehicles on the road as well as with RSU. Collaborative communication among autonomous vehicles and with the roadside edge units is a key enabler technology that may lead to an overall improvement in the flow of traffic and prevent fatal crashes [132], which may occur otherwise. The following example scenario discusses the real-life accident that occurred due to the absence of collaborative communication.…”

Section: E Vehicular Network and The Rise Of Autonomous Drivingmentioning

confidence: 99%

“…Therefore, employing collaborative communication in these scenarios may avoid fatal crashes. For example, if the Tesla Model S car in the previous accidental example collaborated with RSU and then RSU, in turn, collaborated with the truck on completely another side of the road, the accident could have been avoided [132].…”

Section: E Vehicular Network and The Rise Of Autonomous Drivingmentioning

confidence: 99%

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Information-Centric Mobile Networks: A Survey, Discussion, and Future Research Directions

et al. 2023

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Information-centric networking (ICN) and its fruition, the named data networking (NDN) is a paradigm shift from host-centric address-based communication architecture to the content-centric namebased one. ICN intends to resolve various major issues faced by today's internet architecture such as privacy, security, consistent routing, and mobility, to name a few. With the massive increase of mobile data traffic in today's era, mobility is one of the major concerns in networking. On the one hand, ICN realization i.e., the NDN follows a pull-based communication model and natively supports the consumer (end-user) mobility in wired networks by maintaining the forwarding states on intermediate nodes. Nevertheless, the mobile consumer nodes confront issues in wireless networking environments such as excessive energy consumption as a result of request flooding, content retrieval delays due to intermittent connectivity, and bandwidth consumption due to the broadcasting nature of the wireless medium, among others. The producer (contentgenerator) mobility, on the other hand, was not initially supported in the original architectural design of NDN for both wired and wireless networks. Therefore, to efficiently address the degradation issues incurred by mobile consumer/producer nodes, a plethora of mobility management schemes have been proposed over the recent few years. In this paper, we provided a detailed survey on the existing research efforts-in the context of producer as well as consumer mobility, that have been proposed in the literature. Moreover, we outlined various research directions considering the role of mobility in futuristic technologies such as artificial intelligence-enabled smart networks, edge computing, software-defined networking, vehicular-fog computing, autonomous driving, and resource-constrained Internet of Things.INDEX TERMS Named data networking, mobility management, producer mobility, wireless networking, artificial intelligence, edge computing, vehicular fog computing, autonomous driving. I. INTRODUCTIONT HE 21st century is experiencing immense growth in the production of data 1 as the result of the adaptation of the Internet of Things (IoT) vision in almost every walk of life [1]- [4]. The produced data is shared by enabling the communication between two parties: 1) the producer node, which could be a resource-rich server device on the Internet or resource-constrained sensor node in wireless sensor 1 We use Data with a uppercase "D" to refer to ICN/NDN Data packet whereas data with lowercase "d" refer to general data. networks (WSNs), among others, and 2) the consumer node, which could be an end-user using a mobile application on a smartphone, browsing a website on a laptop, or a base station in the WSNs environment. Due to the heavy data production in real-time scenarios, the current internet architecture and other existing wireless networking environments such as mobile ad hoc networks (MANETs), vehicular ad hoc networks (VANETs), and WSNs, are facing significant challenges specifically when the ...

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Section: E Vehicular Network and The Rise Of Autonomous Drivingmentioning

confidence: 99%

Section: E Vehicular Network and The Rise Of Autonomous Drivingmentioning

confidence: 99%

Information-Centric Mobile Networks: A Survey, Discussion, and Future Research Directions

et al. 2023

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“…Therefore, offloading heavy computation workloads to the infrastructure is proposed to accelerate the computation and save energy. However, to perform a feasible offloading, the offloading framework must offload computations to the infrastructure while ensuring timing predictability [224]. Therefore, how to schedule the order of offloading workloads is still a challenge to benefit from the smart infrastructure.…”

Section: H How To Benefit From Smart Infrastructure?mentioning

confidence: 99%

Edge Computing for Autonomous Driving: Opportunities and Challenges

Liu¹,

et al. 2019

Self Cite

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The recent proliferation of computing technologies, e.g., sensors, computer vision, machine learning, hardware acceleration, and the broad deployment of communication mechanisms, e.g., DSRC, C-V2X, 5G, have pushed the horizon of autonomous driving, which automates the decision and control of vehicles by leveraging the perception results based on multiple sensors. The key to the success of these autonomous systems is making a reliable decision in a real-time fashion. However, accidents and fatalities caused by early deployed autonomous vehicles arise from time to time. The real traffic environment is too complicated for the current autonomous driving computing systems to understand and handle. In this paper, we present the state-of-the-art computing systems for autonomous driving, including seven performance metrics and nine key technologies, followed by eleven challenges and opportunities to realize autonomous driving. We hope this paper will gain attention from both the computing and automotive communities and inspire more research in this direction.

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“…Another promising approach that could highly impact traffic inefficiencies, including dissipation of traffic instabilities is the collaborative driving behavior of the drivers. Collaborative driving (CD), also combined with autonomy [15,61], is an important emerging aspect of Intelligent Transportation Systems (ITS). CD is often times based on communication with various possible approaches proposed in the literature [26,32,40,44,47,49], and needs to take into account human behavior [34,56].…”

Section: Collaborative Drivingmentioning

confidence: 99%

A rigorous multi-population multi-lane hybrid traffic model and its mean-field limit for dissipation of waves via autonomous vehicles

Kardous,

Hayat,

McQuade

et al. 2022

Preprint

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In this paper, a multi-lane multi-population microscopic model, which presents stop and go waves, is proposed to simulate traffic on a ringroad. Vehicles are divided between human-driven and autonomous vehicles (AV). Control strategies are designed with the ultimate goal of using a small number of AVs (less than 5% penetration rate) to represent Lagrangian control actuators that can smooth the multilane traffic flow and dissipate the stop-and-go waves. This in turn may reduce fuel consumption and emissions.

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Collaborative Autonomous Driving: Vision and Challenges

Cited by 20 publications

References 71 publications

Information-Centric Mobile Networks: A Survey, Discussion, and Future Research Directions

Information-Centric Mobile Networks: A Survey, Discussion, and Future Research Directions

Edge Computing for Autonomous Driving: Opportunities and Challenges

A rigorous multi-population multi-lane hybrid traffic model and its mean-field limit for dissipation of waves via autonomous vehicles

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