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To enhance the safety and stability of autonomous vehicles, we present a deep learning platooning-based video information-sharing Internet of Things framework in this study. The proposed Internet of Things framework incorporates concepts and mechanisms from several domains of computer science, such as computer vision, artificial intelligence , sens...
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... cameras, a 32-layer lidar, a 4-layer lidar, a millimeter-wave radar, and a GPS+ inertial sensor are utilized. 14 The distribution method is similar to that of Google's autonomous driving framework, and the statistics are shown in Figure 3. The main advantage of this distribution is that it can cover most of the areas surrounding the vehicle and adapt to various traffic situations and weather conditions. ...
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Autonomous Vehicles (AVs) being developed these days rely on various sensor technologies to sense and perceive the world around them. The sensor outputs are subsequently used by the Automated Driving System (ADS) onboard the vehicle to make decisions that affect its trajectory and how it interacts with the physical world. The main sensor technologi...
Citations
... Previous works on lane assistance [88][89][90][91][92], pedestrian detection [93][94][95][96][97][98], vehicle detection [99][100][101], object detection [102], traffic sign recognition [103][104][105], self-driving [106][107][108], determination of turning radius, and lateral acceleration in cargo [109] has shown great success in autonomous cars. Platooning-based video information sharing the Internet of Things framework has been proposed to enhance the safety and stability of autonomous vehicles [110]. However, autonomous trucks are still a challenge. ...
Due to the increasing use of automobiles, the transportation industry is facing challenges of increased emissions, driver safety concerns, travel demand, etc. Hence, automotive industries are manufacturing vehicles that produce fewer emissions, are fuel-efficient, and provide safety for drivers. Artificial intelligence has taken a major leap recently and provides unprecedented opportunities to enhance performance, including in the automotive and transportation sectors. Artificial intelligence shows promising results in the trucking industry for increasing productivity, sustainability, reliability, and safety. Compared to passenger vehicles, heavy-duty vehicles present challenges due to their larger dimensions/weight and require attention to dynamics during operation. Data collected from vehicles can be used for emission and fuel consumption testing, as the drive cycle data represent real-world operating characteristics based on heavy-duty vehicles and their vocational use. Understanding the activity profiles of heavy-duty vehicles is important for freight companies to meet fuel consumption and emission standards, prevent unwanted downtime, and ensure the safety of drivers. Utilizing the large amount of data being collected these days and advanced computational methods such as artificial intelligence can help obtain insights in less time without on-road testing. However, the availability of data and the ability to apply data analysis/machine learning methods on heavy-duty vehicles have room for improvement in areas such as autonomous trucks, connected vehicles, predictive maintenance, fault diagnosis, etc. This paper presents a review of work on artificial intelligence, recent advancements, and research challenges in the trucking industry. Different applications of artificial intelligence in heavy-duty trucks, such as fuel consumption prediction, emissions estimation, self-driving technology, and predictive maintenance using various machine learning and deep learning methods, are discussed.
... Zhou, Zishuo, et al [1], to strengthen the safety and stability of autonomous vehicles, a Deep learning approach that introduces the use of cameras and sensors selected for Baidu's Apollo autonomous vehicle and in this highlighted data is seen by AI. Sensors are used to capture directional information and the range of barriers and communication technology used to share information. ...
Internet of things (IoT) is the network of the devices includes the updating in technology, various devices are using sensors, actuators, embedded computing and cloud computing. This type of system leads to smart architecture in the home, cities and smart world. IoT plays an important role in traffic controlling and managing. In this paper, we give an overview of the various methods of traffic control management. With the help of this IOT kit, which includes different sensors to collect the data and process it accordingly with the help of big data analysis and deep learning algorithms, most accurate and efficient results are obtained for traffic management.
... • Distributed learning: local gradient computation [64][65][66][67][68][69] , over-the-air computing [70][71][72][73] , importance-aware RRM [74][75][76][77] , differential privacy [78,79] • Split inference: feature extraction [80][81][82][83][84] , importance-aware RRM [81,[85][86][87][88][89] , SplitNet approach [90][91][92][93] • Distributed consensus: local-sate estimation and prediction [94][95][96] , SDT [97] , PBFT consensus [98] , vehicle platooning [99][100][101] , Blockchain [97,102] • Machine-vision cameras: ROI-based effectiveness encoding [103,104] , camera-side feature extraction [105] , production-line inspection [106] , surveillance [103,107] , aerial and space sensing [108] KG-based SemCom ...
... Most recently, deep learning has been adopted to empower platooning. Essentially, CNN-based effective encoders are designed to intelligently extract information from real-time videos captured by on board cameras, such as traffic lights, lanes, and obstacles [96] . Exchanging such sensing data and using them for consensus on complex manoeuvres give the platoon collective intelligence for auto-driving. ...
In the 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this fundamental work, the main theme of wireless system design up until the fifth generation (5G) was the data rate maximization. In Shannon's theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between Internet-of-things (IoT) and artificial intelligence (AI). By broadening the scope of the classic communication-theoretic framework, in this article, we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities: human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M) communications. The latter two represent the paradigm shift in communication and computing, and define the main theme of this article. H2M SemCom refers to semantic techniques for conveying meanings understandable not only by humans but also by machines so that they can have interaction and "dialogue". On the other hand, M2M SemCom refers to effective techniques for efficiently connecting multiple machines such that they can effectively execute a specific computation task in a wireless network. The first part of this article focuses on introducing the SemCom principles including encoding, layered system architecture, and two design approaches: 1) layer-coupling design; and 2) end-to-end design using a neural network. The second part focuses on the discussion of specific techniques for different application areas of H2M SemCom [including human and AI symbiosis, recommendation, human sensing and care, and virtual reality (VR)/augmented reality (AR)] and M2M SemCom (including distributed learning, split inference, distributed consensus, and machine-vision cameras). Finally, we discuss the approach for designing SemCom systems based on knowledge graphs. We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in sixth generation (6G) featuring connected intelligence and integrated sensing, computing, communication, and control.
... Thus, to adapt to the dynamic AV environment, intelligent techniques were introduced [27]. The Convolutional Neural Networks in [12] collects and shares the videos of extensive infrastructure features captured by the sensors of the AV with the other AVs in the platoon. The Q-network reinforcement learning technique in [28] finds the optimal locations at which the base stations can be fixed to provide better platoon features to the AVs. ...
... acc max (20) However by (12), acc x (t) = 0, ΔV x (t) = 0, and V x (t) = V b at b. Hence, the substitution of the values of (12) in (20) produces the balanced IVD between two AVs in the Smart-Platoon. The balanced IVD (IV D b ) is as follows, ...
... acc max (20) However by (12), acc x (t) = 0, ΔV x (t) = 0, and V x (t) = V b at b. Hence, the substitution of the values of (12) in (20) produces the balanced IVD between two AVs in the Smart-Platoon. The balanced IVD (IV D b ) is as follows, ...
Cooperative driving of AVs, a ground-breaking initiative of vehicle platooning, epitomizes the next wave in the vehicular technology. The development of Autonomous Vehicles (AVs) envisions the promising technology of future Intelligent Transportation Systems (ITS). However, complex road structures and increased vehicles cause traffic congestion and lack in road safety which eventually leads to horrible accidents. This paper proposes a hybrid Deep Reinforcement and Genetic algorithm for Smart-Platooning (DRG-SP) the AVs. The leverage of deep reinforcement learning mechanism addresses the computational complexity and accommodates the high dynamic platoon environments. The adoption of the Genetic Algorithm in Deep Reinforcement learning overcomes the slow convergence problem, which arises from the high-dimensional AV environment. The simulation results reveal that the Smart-Platooning effectively forms and maintains the platoons, minimizing traffic congestion and fuel consumption with effective infrastructure utilization.
... • Distributed consensus: Local-sate estimation and prediction [93][94][95], SDT [96], PBFT consensus [97], Vehicle platooning [98][99][100], Blockchain [96,101]. ...
... Most recently, deep learning have been adopted to empower platooning. Essentially, CNN-based effectiveness encoders are designed to intelligently extract information from real-time videos captured by on-board cameras, such as traffic lights, lanes and obstacles [95]. Exchanging such sensing data and use them for consensus on complex manoeuvres give the platoon collective intelligence for autodriving. ...
In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this, the main theme of wireless system design up until 5G was the data rate maximization. In his theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between IoT and AI. By broadening the scope of the classic framework, in this article we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities, human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M) communications. The latter two, the main theme of the article, represent the paradigm shift in communication and computing. H2M SemCom refers to semantic techniques for conveying meanings understandable by both humans and machines so that they can interact. M2M SemCom refers to effectiveness techniques for efficiently connecting machines such that they can effectively execute a specific computation task in a wireless network. The first part of the article introduces SemCom principles including encoding, system architecture, and layer-coupling and end-to-end design approaches. The second part focuses on specific techniques for application areas of H2M (human and AI symbiosis, recommendation, etc.) and M2M SemCom (distributed learning, split inference, etc.) Finally, we discuss the knowledge graphs approach for designing SemCom systems. We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in 6G featuring connected intelligence and integrated sensing, computing, communication, and control.
... For Autonomous cars, the Internet Of Things (IOT) framework is used , which combines the ideas and devices from numerous domains of computer science like sensor technology, computer vision, artificial intelligence, and communication technology (Zhou, Akhtar, Man, & Siddique, 2019). ...
Research in Autonomous Driving is taking momentum due to the inherent advantages of autonomous driving systems. The main advantage being the disassociation of the driver from the vehicle reducing the human intervention. However, the Autonomous Driving System involves many subsystems which need to be integrated as a whole system. Some of the tasks include Motion Planning, Vehicle Localization, Pedestrian Detection, Traffic Sign Detection, Road-marking Detection, Automated Parking, Vehicle Cybersecurity, and System Fault Diagnosis.
This paper aims to the overview of various Machine Learning and Deep Learning Algorithms used in Autonomous Driving Architectures for different tasks like Motion Planning, Vehicle Localization, Pedestrian Detection, Traffic Sign Detection, Road-marking Detection, Automated Parking, Vehicle Cybersecurity and Fault Diagnosis. This paper surveys the technical aspects of Machine Learning and Deep Learning Algorithms used for Autonomous Driving Systems. Comparison of these algorithms is done based on the metrics like mean Intersect in over Union (mIoU), Average Precision (AP)missed detection rate, miss rate False Positives Per Image (FPPI), and average number for false frame detection.
This study contributes to picture a review of the Machine Learning and Deep Learning Algorithms used for Autonomous Driving Systems and is organized based on the different tasks of the system.
... In the field of image detection, many relevant practitioners or researchers have conducted research. In [5], to enhance the safety and stability of self-driving cars, the author proposes a deep learning-based video information sharing IoT framework that uses computer vision to highlight information captured by cameras, such as road edges, traffic lights, and zebra crossings. The semantics of the highlighted information is recognized by artificial intelligence. ...
Images are the most intuitive way for humans to perceive and obtain information, and they are one of the most important sources of information. With the development of information technology, the use of digital image processing methods to locate and identify targets is widely used, so it is particularly important to detect the targets of interest quickly and accurately in the image. The traditional image detection system has the problems of low detection accuracy, long time consumption, and poor stability. Therefore, this paper proposes the design and research of artificial intelligence image detection system based on Internet of Things and cloud computing. The system designed in this article mainly includes three links, namely: image processing analysis design link in cloud computing environment, image feature collection module design link, and image integration detection link. The main technologies used in image processing and analysis in the cloud computing environment are virtualization technology, distributed massive data storage, and distributed computing. In the image feature collection module, before the image is input to the neural network, it is necessary to perform preprocessing operations on the distorted image and perform perspective correction; then use the deep residual network in deep learning to extract features. Finally, there is the image integration detection link. First, the target category judgment and position correction are performed on the regions generated by the candidate region generation network, and then the integrated image detection is performed through the improved target detection method based on the frame difference method. Through simulation experiments, compared with the traditional image detection system, the speed advantage of the artificial intelligence image detection system designed in this paper is obvious in the case of a large increase in the number of images. On images at different pixel levels, the accuracy of the image detection system proposed in this paper is always higher than that of traditional image detection systems, and the CPU usage and memory usage are at a lower level. In addition, within three months, the stability is also at a relatively high level of 0.9.
... In the field of image detection, many relevant practitioners or researchers have conducted research. In [5], to enhance the safety and stability of self-driving cars, the author proposes a deep learning-based video information sharing IoT framework that uses computer vision to highlight information captured by cameras, such as road edges, traffic lights, and zebra crossings. The semantics of the highlighted information is recognized by artificial intelligence. ...
Images are the most intuitive way for humans to perceive and obtain information, and they are one of the most important sources of information. With the development of information technology, the use of digital image processing methods to locate and identify targets is widely used, so it is particularly important to detect the targets of interest quickly and accurately in the image. The traditional image detection system has the problems of low detection accuracy, long time consumption, and poor stability. Therefore, this paper proposes the design and research of artificial intelligence image detection system based on Internet of Things and cloud computing. The system designed in this article mainly includes three links, namely: image processing analysis design link in cloud computing environment, image feature collection module design link, and image integration detection link. The main technologies used in image processing and analysis in the cloud computing environment are virtualization technology, distributed massive data storage, and distributed computing. In the image feature collection module, before the image is input to the neural network, it is necessary to perform preprocessing operations on the distorted image and perform perspective correction; then use the deep residual network in deep learning to extract features. Finally, there is the image integration detection link. First, the target category judgment and position correction are performed on the regions generated by the candidate region generation network, and then the integrated image detection is performed through the improved target detection method based on the frame difference method. Through simulation experiments, compared with the traditional image detection system, the speed advantage of the artificial intelligence image detection system designed in this paper is obvious in the case of a large increase in the number of images. On images at different pixel levels, the accuracy of the image detection system proposed in this paper is always higher than that of traditional image detection systems, and the CPU usage and memory usage are at a lower level. In addition, within three months, the stability is also at a relatively high level of 0.9.
... In order to address the risk of collision at such small inter-vehicle distances, Murthy and Masrur [39] present an experimentally validated approach to optimize the braking system of autonomous vehicle platoon considering the vehicle loading condition for executing a collision free braking maneuver. Moreover, the close/narrow inter-vehicle distance, used in vehicle platoon studies [28], [38] and in this study, have been substantiated by the recent advancements in autonomous vehicle ecological cooperative control [40], [41] coupled with IoT [42], [43] to ensure passenger safety. Models deployed for drag coefficient prediction include Artificial Neural Networks, Regression models and Support Vector Regression. ...
Machine learning is used for extraction of valuable information from data thus helping in exploration of hidden patterns, leading to learning models that can be used for prediction. In the domain of autonomous vehicles machine learning techniques have been applied in several areas, vehicle platooning being one of them. Vehicle platooning is a vital feature of automated highways which provides the key benefits of fuel economy, road safety and environmental protection coupled with safe road transportation. However, high computational cost associated with the numerical simulation of vehicle aerodynamics makes the Computational Fluid Dynamics (CFD) study of vehicle platoon prohibitively expensive and complex. Machine learning, with its high predictive power, has emerged as a promising compliment to CFD studies of external aerodynamics. This paper presents estimation error based performance comparison of five different supervised learning algorithms: Support Vector Regression, Polynomial Regression, Linear Regression and two different models of Neural Networks for prediction of aerodynamic drag coefficient corresponding to each vehicle in a two, three and four vehicle platoon configurations based on the drag coefficients provided by experimental study at different inter-vehicle distances. Predicted drag coefficients are then juxtaposed with CFD data from numerical simulations to evaluate closeness to experimental drag coefficients. Results reveal that polynomial regression model best fits the aerodynamics with 0.0223 estimation error. To the best of our knowledge no machine learning based methods have been applied before for modeling aerodynamic drag on vehicle platoon.
... Literature [15] designed an analysis framework for automatic driving target detection based on visual analysis technology, which effectively assists and optimizes the research work of assisted driving. Literature [16] developed a video information-sharing IoT model with a deep learning queue as the core logic, which promotes the reduction of collision probability in the process of automatic driving and improves the safety and stability of automatic driving. Literature [17] aims to solve the problem of lane identification and tracking in autonomous driving by conceptualizing a lane detection framework with computer vision technology as the underlying architecture, which is examined in practical performance tests to confirm the feasibility of the proposed framework. ...
The rapid evolution of autonomous driving technology has spurred deeper exploration into its multifaceted domains. This paper focuses on enhancing the intelligence and safety of autonomous driving systems through the application of computer vision. Specifically, the integration of EIoU loss and Varifocal Loss functions within the YOLOv5 algorithm facilitates the training of higher-quality samples at reduced costs. This enhanced YOLOv5 algorithm is adept at detecting traffic entities and incorporates the Matrix NMS algorithm to further refine the loss function adjustments. Moreover, this study employs the proximal policy optimization algorithm to construct a vehicular behavioral decision model for autonomous driving. This model utilizes cubic polynomial equations to depict lane-changing trajectories and introduces a safety distance rule to mitigate collision risks during autonomous maneuvers. Comparative analyses reveal that the modified YOLOv5 algorithm surpasses its predecessors—YOLOv3 and YOLOv4—in terms of detection accuracy and processing speed. Notably, the improved YOLOv5 algorithm exhibits lower network loss values than its original version, achieving faster and more stable convergence. In practical applications, the algorithm successfully identified and labeled 20,025 intelligent vehicular targets, with initial testing accuracy reaching 96.12%. This accuracy improved to 96.36% following the application of EIoU adaptive tuning to reduce risk-free class loss, and further fine-tuning elevated the accuracy to 96.54%. The integration of computer vision into autonomous driving technology thus holds significant promise for advancing the field.
