An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

Alheeti, Khattab M. Ali; Alaloosy, Abdulkareem; Khalaf, Haitham; Alzahrani, Abdulkareem; Al_Dosary, Duaa

doi:10.32604/cmc.2022.019773

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…The confusion matrix is defined by four measures, which comprise: True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN). The measures are calculated as follows [19]:…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

A New Classification Method for Drone-Based Crops in Smart Farming

Al-Rami

Alheeti²,

Aldosari

et al. 2022

Int. J. Interact. Mob. Technol.

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During the past decades, smart farming became one of the most important revolutions in the agriculture industry. Smart farming makes use of different communication technologies and modern information sciences for increasing the quality and quantity of the product. On the other hand, drones showed a major potential for enhancing imagery systems and remote sensing usage for many different applications such as crop classification, crop health monitoring, and weed management. In this paper, an intelligent method for classifying crops is proposed to use a transfer learning approach based on a number of drone images. Moreover, the Convolutional Neural Network (CNN) method is used as a classifier to improve efficiency for obtaining more accurate results in the training and testing phases. Various metrics are measured to evaluate the efficiency of the proposed model such as accuracy rate of detection, error rate and confusing matrix. It is found to be proven from the experimental results that the proposed method presents more efficient results with an accuracy detection rate of 92.93%.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

A New Classification Method for Drone-Based Crops in Smart Farming

Al-Rami

Alheeti²,

Aldosari

et al. 2022

Int. J. Interact. Mob. Technol.

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“…Meanwhile, the transmission reliability of the 5G-V2X network has reached 99.999%, also far above that of the LTE-V2X network. By incorporating vehicles into a connected automated transport system, 5G-V2X technology makes it possible to control ICV autonomous driving in a real-time manner by employing cloud computing and big data of traffic scenarios [3,4].…”

Section: Introductionmentioning

confidence: 99%

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

Fu¹,

Fu²

2023

Computers, Materials &Amp; Continua

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This paper is to explore the problems of intelligent connected vehicles (ICVs) autonomous driving decision-making under a 5G-V2X structured road environment. Through literature review and interviews with autonomous driving practitioners, this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system. Secondly, situated on this framework, it builds a hierarchical finite state machine (HFSM) model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method (EWM) and analytic hierarchy process method (AHP) and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution (TOPSIS) and grey relational analysis (GRA). The HFSM model is composed of two layers: the global FSM model and the local FSM model. The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module, meanwhile pulsating feedback to the former as real-time refresh data. To identify different traffic scenarios in a cerebrum-like way, the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events, and the local FSM model is designed as 16 states and 8 characteristic events. In respect to designing a cerebrum-like algorithm for state transition, this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event; then, it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition. To verify the feasibility, reliability, and safety of the HFSM model as well as its TOPSIS-GRA state transition algorithm, this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform. The results display that the accuracy of obstacle detection gets 98%, lane line prediction is beyond 70 m, the speed of collision avoidance is higher than 45 km/h, the distance of collision avoidance is less than 5 m, path planning time for obstacle avoidance is averagely less than 50 ms, and brake deceleration is controlled under 6 m/s 2 . These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may 1052 CMC, 2023, vol.75, no.1 bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

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“…Finding the optimal RSU deployment scheme is an NP-hard combinatorial optimization problem [9][10][11]. Existing studies on RSU deployment methods generally focus on determining the optimal locations, interval, minimum number of deployments, maximum coverage range, or maximum connectivity within cost constraints [12][13][14][15]. Lehsaini M. et al [16] proposed the use of metaheuristic methods, Guerna A. et al [17] proposed the use of a bio-inspired RSU placement system using ant colony optimization, Zhang L. et al [18] proposed an improved multi-objective quantum particle swarm optimization (MOQPSO) algorithm for RSU deployment, and Silva C. M. et al [19] presented an integer linear programming formulation and heuristic methods based on taboo search, all of which maximize network coverage with lower cost.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

Zhang

Lu²,

Chen

et al. 2023

Sustainability

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With the increasing number of Connected and Autonomous Vehicles (CAVs), the heterogeneous traffic flow on highways now consists of a mix of CAVs and Non-networked Autonomous Vehicles (NAVs). The current deployment of Roadside Units (RSUs) on highways is mostly based on uniform or hotspot locations. However, when the permeability of CAVs on the road varies, the communication network may face challenges such as excessive energy consumption due to closely spaced RSU deployments at high CAV permeability or communication interruptions due to widely spaced RSU deployments at low CAV permeability. To address this issue, this paper proposes an improved D-LEACH clustering algorithm based on vehicle clustering; analyzes the impact of RSU and vehicle communication radius, mixed traffic density, and different CAV permeabilities in the heterogeneous traffic flow on the RSU deployment interval; and calculates the rational and effective RSU deployment interval schemes under different CAV permeabilities on highways in the heterogeneous traffic flow. When the heterogeneous traffic flow density is stable and CAV continues to penetrate, the RSU communication radius and deployment interval can be adjusted to ensure that the network connectivity is maintained at a high level. When the RSU and vehicle communication radius are stable, the mixed traffic density is 0.05, and the CAV permeability is 0.2, the RSU deployment interval can be set to 1235 m; when the mixed traffic density is 0.08 and the CAV penetration rate is 0.7, the RSU deployment interval can be set to 1669 m to ensure that the network connectivity is maintained at a high level.

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An Optimal Distribution of RSU for Improving Self-Driving Vehicle Connectivity

Cited by 7 publications

References 12 publications

A New Classification Method for Drone-Based Crops in Smart Farming

A New Classification Method for Drone-Based Crops in Smart Farming

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

Optimization of Roadside Unit Deployment on Highways under the Evolution of Intelligent Connected-Vehicle Permeability

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