Machine Learning Technologies in Internet of Vehicles

Ali, Elmustafa Sayed; Hassan, Mona Bakri; Saeed, Rashid A.

doi:10.1007/978-3-030-76493-7_7

Cited by 23 publications

(10 citation statements)

References 48 publications

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“…Once the training was completed, the model was tested with complete new sets of images it has not seen before in the test set [23]. Figures 12 and 13 show that the model is able to detect cars and motorcycles to a high degree of certainty without mistaking it for other vehicles that have not been labelled in the datasets such as Lorries and trucks [24]. However, as the two classes move further and appear smaller, that certainty of prediction decreases significantly indicating that the model struggles to correctly differentiate vehicles with large scale of variance (see Figure 14) [24].…”

Section: Performance Of Proposed Systemmentioning

confidence: 99%

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Khalifa

Wajdi

Saeed

et al. 2022

Journal of Advanced Transportation

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Vehicle detection in Intelligent Transportation Systems (ITS) is a key factor ensuring road safety, as it is necessary for the monitoring of vehicle flow, illegal vehicle type detection, incident detection, and vehicle speed estimation. Despite the growing popularity in research, it remains a challenging problem that must be solved. Hardware-based solutions such as radars and LIDAR are been proposed but are too expensive to be maintained and produce little valuable information to human operators at traffic monitoring systems. Software based solutions using traditional algorithms such as Histogram of Gradients (HOG) and Gaussian Mixed Model (GMM) are computationally slow and not suitable for real-time traffic detection. Therefore, the paper will review and evaluate different vehicle detection methods. In addition, a method of utilizing Convolutional Neural Network (CNN) is used for the detection of vehicles from roadway camera outputs to apply video processing techniques and extract the desired information. Specifically, the paper utilized the YOLOv5s architecture coupled with k-means algorithm to perform anchor box optimization under different illumination levels. Results from the simulated and evaluated algorithm showed that the proposed model was able to achieve a mAP of 97.8 in the daytime dataset and 95.1 in the nighttime dataset.

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Section: Performance Of Proposed Systemmentioning

confidence: 99%

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Khalifa

Wajdi

Saeed

et al. 2022

Journal of Advanced Transportation

Self Cite

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“…Deep learning technologies for Internet of vehicle (IoV) networks have been studied previously [ 25 , 26 , 27 , 28 , 29 ]. The authors of [ 25 ] discussed deep learning applications for security and collision prediction in the internet of vehicle (IoV) networks, and they proposed a DRL-based resource allocation method to enhance multiple QoS requirements, such as latency and suitable data rate requirements.…”

Section: Related Workmentioning

confidence: 99%

“…They addressed various learning networks, e.g., CNN, recurrent neural networks, DRL, classification, clustering, and regression. The authors of [ 27 ] presented a comprehensive review and analysis of machine learning technologies for IoV applications, e.g., energy- and buffer-aware optimization, edge caching, intelligent decisions for network scheduling and adaptation, intelligent autonomous driving, etc. The authors of [ 28 ] presented a comprehensive review of resource allocation and management for the IoV over 5G radio access networks.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications

Han

2023

Sensors

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Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links.

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“…Today's growth in every aspect of life dragged attention to the need for better decision-making processes when addressing e-services to improve the decision-making for customers. These systems use personalized e-services which employ the techniques and mechanisms with an artificial intelligence background, by means of user profiling and preference discovery [29]. The use of various machine learning techniques increased the level of quality of recommendations and improved user satisfaction.…”

Section: In Robotics Recommendation Systemsmentioning

confidence: 99%

Robotics architectures based machine learning and deep learning approaches

Alatabani¹,

Ali²,

Mokhtar³

et al. 2022

IET Conf. Proc.

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Robotics has been playing a vital role in our daily lives with a wide range of applications to improve the quality of life. With a variety of usable applications in the medical, manufacturing, and transportation industries, there is a continuous need for improving the performance of robotics for the importance of precision in executing commands and tasks. The implementation of precise commands has led to intense research on approaches to improve the performance of robotics. Machine Learning (ML) and Deep Learning (DL) have been drawing attention to applying architectures and algorithms to robotics which imposed a positive impact on the field of robotics. ML and DL applications in robotics include areas of computer vision, imitation learning, self-supervised learning, assistive and medical technologies, multi-agent learning, and manufacturing. This paper provides a comprehensive review of autonomous vs automatic robotics, robotic applications, extreme learning machine methods, and ML for soft robotics applications, in addition, to discussing the challenges, and future trends for AI applications in robotics applications.

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Machine Learning Technologies in Internet of Vehicles

Cited by 23 publications

References 48 publications

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications

Robotics architectures based machine learning and deep learning approaches

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