Rapid Management of Unexpected Events in Urban V2I Communications Systems

Lucic, Michael C.; Ghazzai, Hakim; Khattab, Ahmed; Massoud, Yehia

doi:10.1109/icves.2019.8906309

Cited by 4 publications

(5 citation statements)

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“…In [22], we developed a smart placement of RSUs assisting crowdsourcing ITS infrastructure in a way to maximize the effective coverage of transmission architecture, while considering solar-powered RSUs and an amortized budget. We also developed heuristics designed to find a suboptimal configuration faster than solving a mixed integer programming problem [23] and to adjust RSU coverage to unexpected events [24]. Figure 5 shows RSU placement through our optimization framework.…”

Section: Its Technologies Enabling Crowdsourcingmentioning

confidence: 99%

“…The summary of our findings are shown in Table 1. Provides a trust management mechanism as a framework to secure vehicular social data, a cloud-based architecture for VSNs X [14] Advanced integrated sensing for autonomous vehicles X [2,4] Survey of VSN X [28,29] Distributed architecture for mobile crowdsourcing management X [3] UAVs in the ITS: their role in increasing network connectivity X [22][23][24] RSU placement: optimal, heuristic, and adjustment to unexpected events X X X [35] Proposal of automated elevated LiDAR to reduce autonomous vehicle computational overhead X X Applications [15] Crowdsourced monitoring and reporting of available street parking X X X [20] Crowdsourced data collection of road lane data X [21] Convolutional neural networks applied for lane detection X [8,9] Social media-based traffic prediction with NLP X [6] Social media data mining applied to detect roadway incidents X [7] Development and testing of a last-mile navigation system to add to existing mapping programs X [19] Infrastructure health monitoring X X [18] Advanced tracking of public transportation vehicles X [16,17] Generating a 3D map of surroundings based on crowdsourced data X [4] Utilization of mobile devices on fixed bus routes to measure and visualize traffic congestion X X Table 1. Cont.…”

Section: Overview Of the State Of The Artmentioning

confidence: 99%

“…In addition, we considered VSN components. We looked at the planning of RSU installation [22][23][24], along with UAVs as flying RSUs [3], trusted cloud computing systems [32], automated sensing systems [14,35], and decentralized control/access systems [28,29].…”

Section: Concept Reference(s) Contribution Crowdsourcing Variantmentioning

confidence: 99%

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Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview

et al. 2020

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The current and expected future proliferation of mobile and embedded technology provides unique opportunities for crowdsourcing platforms to gather more user data for making data-driven decisions at the system level. Intelligent Transportation Systems (ITS) and Vehicular Social Networks (VSN) can be leveraged by mobile, spatial, and passive sensing crowdsourcing techniques due to improved connectivity, higher throughput, smart vehicles containing many embedded systems and sensors, and novel distributed processing techniques. These crowdsourcing systems have the capability of profoundly transforming transportation systems for the better by providing more data regarding (but not limited to) infrastructure health, navigation pathways, and congestion management. In this paper, we review and discuss the architecture and types of ITS crowdsourcing. Then, we delve into the techniques and technologies that serve as the foundation for these systems to function while providing some simulation results to show benefits from the implementation of these techniques and technologies on specific crowdsourcing-based ITS systems. Afterward, we provide an overview of cutting edge work associated with ITS crowdsourcing challenges. Finally, we propose various use-cases and applications for ITS crowdsourcing, and suggest some open research directions.

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Section: Its Technologies Enabling Crowdsourcingmentioning

confidence: 99%

Section: Overview Of the State Of The Artmentioning

confidence: 99%

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Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview

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“…The idea is to move the LiDARs to elevated positions (either lamp-posts or sides of buildings), and the GPUs to the cloud or on the edge. The ELiDs are connected to roadside units (RSUs) [10], [11] that may dynamically shift coverage [12] to transfer ELiD's data to/from the AVs. In addition to the primary use for AV SLAM, the elevated perspective of the LiDARs provides the capability for the ELiD system to perform mapping tasks for urban municipal authorities, such as infrastructure monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Latency-Aware Task Offloading in Mobile Edge Computing Network for Distributed Elevated LiDAR

Lucic¹,

Ghazzai²,

Alsharoa³

et al. 2020

Preprint

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Recently, elevated LiDAR (ELiD) has been proposed as an alternative to local LiDAR sensors in autonomous vehicles (AV) because of the ability to reduce costs and computational requirements of AVs, reduce the number of overlapping sensors mapping an area, and to allow for a multiplicity of LiDAR sensing applications with the same shared LiDAR map data. Since ELiDs have been removed from the vehicle, their data must be processed externally in the cloud or on the edge, necessitating an optimized backhaul system that allocates data efficiently to compute servers. In this paper, we address this need for an optimized backhaul system by formulating a mixed-integer programming problem that minimizes the average latency of the uplink and downlink hop-by-hop transmission plus computation time for each ELiD while considering different bandwidth allocation schemes. We show that our model is capable of allocating resources for differing topologies, and we perform a sensitivity analysis that demonstrates the robustness of our problem formulation under different circumstances.

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“…A variety of applications and use cases have been discussed in [4][5][6][7]. VSN enables interactions between different participants, including human-to-vehicle and vehicle-to-vehicle interactions [8,9]. As an example, the connected vehicle technology in NYC is developed to leverage the safety of road users.…”

mentioning

confidence: 99%

A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

2020

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Modern taxi services are usually classified into two major categories: traditional taxicabs and ride-hailing services. For both services, it is required to design highly efficient recommendation systems to satisfy passengers' quality of experience and drivers' benefits. Customers desire to minimize their waiting time before rides, while drivers aim to speed up their customer hunting. In this paper, we propose to leverage taxi service efficiency by designing a generic and smart recommendation system that exploits the benefits of Vehicular Social Networks (VSNs). Aiming at optimizing three key performance metrics, number of pick-ups, customer waiting time, and vacant traveled distance for both taxi services, the proposed recommendation system starts by efficiently estimating the future customer demands in different clusters of the area of interest. Then, it proposes an optimal taxi-to-region matching according to the location of each taxi and the future requested demand of each region. Finally, an optimized geo-routing algorithm is developed to minimize the navigation time spent by drivers. Our simulation model is applied to the borough of Manhattan and is validated with realistic data. Selected results show that significant performance gains are achieved thanks to the additional cooperation among taxi drivers enabled by VSN, as compared to traditional cases.Electronics 2020, 9, 648 2 of 24 a means of transportation and enhance the efficiency of both services for the benefits of both customers and drivers.In regular taxi services, traditional ways for taxi drivers to find potential customers include driving around the city and waiting at some 'hot spots', e.g., taxicab stands. For the first option, taxi drivers usually follow an intuition-based trajectory hoping to find customers as soon as possible, while for the second option, most of the drivers will target the same hot spots since based on their personnel experience, they know when and where customers will be gathered. In the latter case, regular taxi drivers may be subject to an unfair competition since the number of taxis is higher than the demand or vice versa. Hence, traditional solutions for customer hunting are usually exhaustive and inaccurate. On the other hand, for the ride-hailing taxi services, although a central server is dedicated to manage the requests of customers and allocate them to drivers, similar problems that face regular taxi services still exist. Customers' requests might still be raised far away from drivers' locations and high vacant distances are accumulated, resulting in huge and redundant fuel consumption. In Portland, the average waiting times are estimated to be around six and ten minutes for regular and ride-hailing taxi services, respectively, according KGW News [2]. Therefore, it is recommended to enhance the efficiency of such transportation services by tackling the offer/demand problem in both taxi categories.Thanks to the spread of on-board and infrastructure-based sensors [3], collecting and sharing data have become very com...

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Rapid Management of Unexpected Events in Urban V2I Communications Systems

Cited by 4 publications

References 10 publications

Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview

Leveraging Intelligent Transportation Systems and Smart Vehicles Using Crowdsourcing: An Overview

A Latency-Aware Task Offloading in Mobile Edge Computing Network for Distributed Elevated LiDAR

A Generic Data-Driven Recommendation System for Large-Scale Regular and Ride-Hailing Taxi Services

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