Future Intelligent Transportation Systems Research Articles

Mobile Collaborative Learning Over Opportunistic Internet of Vehicles

Machine learning models are widely applied for vehicular applications, which are essential to future intelligent transportation system (ITS). Traditional model training methods commonly employ a client-server architecture to perform local training and global iterative aggregations, which can consume significant bandwidth resources that are often absent in vehicular networks, especially in high vehicle density scenarios. Modern vehicle users naturally can collaboratively train machine learning models as they are the data owner and have strong local computing power from the onboard units (OBU). In this paper, we propose a novel collaborative learning scheme for mobile vehicles that can utilize the opportunistic vehicle-to-roadside (V2R) communication to exploit the common priors of vehicular data without interaction with a centralized coordinator. Specifically, vehicles perform local training during the driving journey, and simply upload its local model to roadside unit (RSU) encountered on the way. RSU's model will be updated accordingly and sent back to the vehicle via the V2R communication. We have theoretically shown that RSUs' models can eventually converge without a backhaul connection. Extensive experiments upon various road configurations demonstrate that the proposed scheme can efficiently train models among vehicles without dedicated Internet access and scale well with both the road range and vehicle density.

Highly efficient photonic radar by incorporating MDM-WDM and machine learning classifiers under adverse weather conditions.

Photonic radar, a cornerstone in the innovative applications of microwave photonics, emerges as a pivotal technology for future Intelligent Transportation Systems (ITS). Offering enhanced accuracy and reliability, it stands at the forefront of target detection and recognition across varying weather conditions. Recent advancements have concentrated on augmenting radar performance through high-speed, wide-band signal processing-a direct benefit of modern photonics' attributes such as EMI immunity, minimal transmission loss, and wide bandwidth. Our work introduces a cutting-edge photonic radar system that employs Frequency Modulated Continuous Wave (FMCW) signals, synergized with Mode Division and Wavelength Division Multiplexing (MDM-WDM). This fusion not only enhances target detection and recognition capabilities across diverse weather scenarios, including various intensities of fog and solar scintillations, but also demonstrates substantial resilience against solar noise. Furthermore, we have integrated machine learning techniques, including Decision Tree, Extremely Randomized Trees (ERT), and Random Forest classifiers, to substantially enhance target recognition accuracy. The results are telling: an accuracy of 91.51%, high sensitivity (91.47%), specificity (97.17%), and an F1 Score of 91.46%. These metrics underscore the efficacy of our approach in refining ITS radar systems, illustrating how advancements in microwave photonics can revolutionize traditional methodologies and systems.

Integrating deep reinforcement learning and social-behavioral cues: A new human-centric cyber-physical approach in automated vehicle decision-making

This paper proposes a novel human-centric approach to enhance decision-making for autonomous vehicles in complex urban driving situations by integrating Deep Q-Network (DQN) reinforcement learning and social value orientation. In the proposed method, a deep neural network (DNN) is employed to approximate the optimal Q-values for various states and actions in the space of possible actions and reachable states. In order to improve the optimization convergence, an Adam optimization is proposed by combining the advantages of adaptive learning rates and momentum methods. The proposed framework also incorporates a collision avoidance component that allows vehicles to navigate safely through pedestrian crossings. The proposed method is validated through simulation experiments, which show that the proposed approach outperforms traditional decision-making and RL methods in terms of safety and efficiency. Finally, the results demonstrate that integrating social value orientation and DQN-RL can lead to more human-like and socially compliant decision-making frameworks for automated vehicles. This research contributes to developing a new human-centric cyber-physical approach for automated vehicle decision-making and has significant implications for designing future intelligent transportation systems.

Intelligent Data-Enabled Task Offloading for Vehicular Fog Computing

Fog computing is a key component of future intelligent transportation systems (ITSs) that can support the high computation and large storage requirements needed for autonomous driving applications. A major challenge in such fog-enabled ITS networks is the design of algorithms that can reduce the computation times of different tasks by efficiently utilizing available computational resources. In this paper, we propose a data-enabled cooperative technique that offloads some parts of a task to the nearest fog roadside unit (RSU), depending on the current channel quality indicator (CQI). The rest of the task is offloaded to a nearby cooperative computing vehicle with available computing resources. We developed a cooperative computing vehicle selection technique using an artificial neural network (ANN)-based prediction model that predicts both the computing availability once the task is offloaded to the potential computing vehicle and the link connectivity when the task result is to be transmitted back to the source vehicle. Using detailed simulation results in MATLAB 2020a software, we show the accuracy of our proposed prediction model. Furthermore, we also show that the proposed technique reduces total task delay by 37% compared to other techniques reported in the literature.

Measurement-based fading characterization of multi-link UAV to mobile vehicle channel

To establish a stable, effective and reliable communication systems between unmanned aerial vehicle (UAV) and mobile vehicles, the propagation characteristics of air-ground (AG) wireless channels need to be better understand. In this paper, a narrowband multi-link channel measurement for UAV to mobile vehicles at 2.4 and 5.9 GHz is presented. Based on the measured data, some essential channel characteristics such as large-scale fading (LSF) including path loss, shadow fading (SF), and small-scale fading (SSF) including fading depth (FD), magnitude distribution and Rician distribution K-factor are analyzed. Then the cross-correlation characteristics of different channel parameters and receiving antenna numbers at 2.4 GHz and 5.9 GHz are systematically investigated. These researches provide some references to optimize antenna design and multi-link channel modeling of UAV to mobile vehicle communication systems in future intelligent transportation system (ITS).

RTK ramp faults detection and exclusion by the hybrid control chart

For real-time kinematic (RTK) positioning in intelligent transportation systems (ITS), the slow-growing faults detection and exclusion (FDE) in carrier phase measurements with time-correlated satellite navigation data poses a significant challenge. In response, we propose the short-baseline RTK Receiver Autonomous Integrity Monitoring (RTK-RAIM), which utilizes a hybrid control chart with a colored Kalman filter for detecting small yet persistent faults. The hybrid control chart incorporates the windowed multivariate homogeneously weighted moving average and dual multivariate cumulative sum charts to accelerate fault detection. The fault exclusion is conducted using the solution separation. Experimental validation using static data and dynamic data in suburban and urban areas demonstrates the efficacy of RTK-RAIM in detecting ramp faults with time-correlated noise. Results show that Colored KF can attenuate the detection metrics in the fault-free case when measurements are time-correlated. The hybrid control chart, operating in an open environment, has the ability to detect carrier phase slope faults with a high probability, even when the slope is as small as 0.01cycle/s. It can also detect carrier-phase ramp faults whose gradient are 0.1cycle/s in an urban area. By enhancing the detection ability of FDE in RTK, this hybrid-control-chart-based RAIM promotes safer and more dependable performance in future ITS applications.

Live Traffic Video Multicasting Services in UAV-Assisted Intelligent Transport Systems: A Multiactor Attention Critic Approach

Live traffic video is vitally important for vehicles in future intelligent transport systems (ITS). Due to the limitation of onboard sensors, vehicles may not be able to obtain a full view of the traffic situations which endangers safety for autonomous driving vehicles. In this paper, we propose a traffic video multicasting scheme by using video splitting and group splitting techniques for unmanned aerial vehicles (UAVs) assisted ITS, in which UAVs are considered as the eyes in the sky to capture real-time traffic videos. We aim to maximize the long-term video quality received by vehicles by jointly optimizing vehicle grouping and spectrum allocation. Considering the interactions among UAVs, the above optimization problem is formulated as a multi-agent coordination problem in the form of a Markov game (MG). The MG is subsequently solved by leveraging a state-of-the-art multi-agent deep reinforcement learning (MADRL) algorithm, namely, multi-actor attention critic (MAAC), in which an attention mechanism is utilized to pay attention to other agents to make the learning process more effective and scalable. Extensive simulation results show that the MAAC-based algorithm has better performance in terms of video quality and spectrum efficiency compared with the baseline methods.

Edge computing for Vehicle to Everything: a short review.

Vehicle to Everything (V2X) communications and services have sparked considerable interest as a potential component of future Intelligent Transportation Systems. V2X serves to organise communication and interaction between vehicle to vehicle (V2V), vehicle to infrastructure (V2I), vehicle to pedestrians (V2P), and vehicle to networks (V2N). However, having multiple communication channels can generate a vast amount of data for processing and distribution. In addition, V2X services may be subject to performance requirements relating to dynamic handover and low latency communication channels. Good throughput, lower delay, and reliable packet delivery are the core requirements for V2X services. Edge Computing (EC) may be a feasible option to address the challenge of dynamic handover and low latency to allow V2X information to be transmitted across vehicles. Currently, existing comparative studies do not cover the applicability of EC for V2X. This review explores EC approaches to determine the relevance for V2X communication and services. EC allows devices to carry out part or all of the data processing at the point where data is collected. The emphasis of this review is on several methods identified in the literature for implementing effective EC. We describe each method individually and compare them according to their applicability. The findings of this work indicate that most methods can simulate the EC positioning under predefined scenarios. These include the use of Mobile Edge Computing, Cloudlet, and Fog Computing. However, since most studies are carried out using simulation tools, there is a potential limitation in that crucial data in the search for EC positioning may be overlooked and ignored for bandwidth reduction. The EC approaches considered in this work are limited to the literature on the successful implementation of V2X communication and services. The outcome of this work could considerably help other researchers better characterise EC applicability for V2X communications and services.

The application of advanced computational algorithms used for cooperative communication transmission of vehicular networks: a proposed method

Telematics will be one of the critical technologies in the future intelligent transportation system and establish communication between vehicles and vehicles, vehicles and networks, and vehicles and people. Thus, vehicles can sense mobile environments and make rational driving decisions. Therefore, the safety and efficiency of traffic flow would be enhanced. However, due to the unknown nature and higher complexity of the connected network environments of vehicles, the utilization of conventional optimization theory fails to generate satisfying results. To address the problem, this article proposes a methodology for collaborative transmission for communication regarding the Internet of Vehicles (IoV) with the help of advanced computational algorithms. The article employs a multi-intelligence advanced computational algorithm to construct a collaborative communication transmission mechanism in the telematics communication system model. The proposed algorithm fully considers the vehicle mobility and quality-of-service (QoS) of telematics services within the network slice. It adjusts the slice’s radio resource allocation and parameter settings on an expanded time scale to improve the QoS of telematics services and increase the system’s long-term revenue. The simulation results show that the proposed algorithm has a more significant performance improvement than conventional algorithms using telematics information transmission. For example, when the same load conditions are under consideration, the total capacity of the vehicle-to-infrastructure (V2I) link optimized by the proposed algorithm is still higher than that of the other three baseline strategies.

An insight into digital twin behavior of vehicular ad hoc network for real-time cloud security and monitoring

Vehicular Ad-Hoc Network (VANET) Technology is advancing due to the convergence of VANET and cloud computing technologies, Vehicular Ad-Hoc Network (VANET) entities can benefit from the cloud service provider’s favourable storage and computing capabilities. Cloud computing, the processing and storage capabilities provided by various cloud service providers, would be available to all VANET enterprises. Digital Twin helps in creating a digital view of the Vehicle. It focuses on the physical behaviour of the Vehicle as well as the software it alerts when it finds issues with the performance. The representation of the Vehicle is created using intelligent sensors, which are in OBU of VANET that help collect info from the product. The author introduces the Cloud-based three-layer key management for VANET in this study. Because VANET connections can abruptly change, critical negotiation verification must be completed quickly and with minimal bandwidth. When the Vehicles are in movement, we confront the difficulty in timely methods, network stability, and routing concerns like reliability and scalability. We must additionally address issues such as fair network access, inappropriate behaviour identification, cancellation, the authentication process, confidentiality, and vehicle trustworthiness verification. The proposed All-Wheel Control (AWC) method in this study may improve the safety and efficiency of VANETs. This technology would also benefit future intelligent transportation systems. The Rivest–Shamir–Adleman (RSA) algorithm and Chinese Remainder Theorem algorithms generate keys at the group, subgroup, and node levels. The proposed method produces better results than the previous methods.

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends.

The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaborative decision-making technology for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) methods have become an efficient way in solving decision-making problems. However, with the development of computing technology, graph reinforcement learning (GRL) methods have gradually demonstrated the large potential to further improve the decision-making performance of CAVs, especially in the area of accurately representing the mutual effects of vehicles and modeling dynamic traffic environments. To facilitate the development of GRL-based methods for autonomous driving, this paper proposes a review of GRL-based methods for the decision-making technologies of CAVs. Firstly, a generic GRL framework is proposed in the beginning to gain an overall understanding of the decision-making technology. Then, the GRL-based decision-making technologies are reviewed from the perspective of the construction methods of mixed autonomy traffic, methods for graph representation of the driving environment, and related works about graph neural networks (GNN) and DRL in the field of decision-making for autonomous driving. Moreover, validation methods are summarized to provide an efficient way to verify the performance of decision-making methods. Finally, challenges and future research directions of GRL-based decision-making methods are summarized.

A non‐linear fractional‐order type‐3 fuzzy control for enhanced path‐tracking performance of autonomous cars

AbstractPath‐tracking and lane‐keeping efficiency of driverless cars remain critical characteristics of the efficient and safe deployment of such vehicles in future intelligent transportation systems. This study introduces a robust type‐3 (T3) fuzzy controller implementation for the path‐tracking task of driverless cars during critical driving conditions and subject to exogenous disturbances. Unlike many existing control paradigms, the proposed scheme is independent of the parameter information and assumes the system dynamics are unknown and non‐linear. Control inputs are constructed to improve robustness by eliminating the error bounds while ensuring stability by leveraging the Lyapunov stability theorem and Barbalat's lemma. Also, a predicate scheme based on non‐linear predictive control technique is introduced to enhance the lateral displacement. Based on the obtained results, the schemed controller exhibits competitive effectiveness in path‐tracking tasks, and strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances.

Low-Altitude UAV Air-to-Ground Multilink Channel Modeling and Analysis at 2.4 and 5.9 GHz

Due to low cost and high operability, unmanned aerial vehicle(UAV) becomes a key component in future intelligent transportation system(ITS) for various use cases. Air-to-ground(A2G) communication is a fundamental technology for integration of UAV in the ITS, where accurate analysis and modeling of UAV A2G wireless channel are of great importance for communication algorithm development. In this paper, we present a comprehensive analysis and modeling of low altitude UAV A2G channel at 2.4 and 5.9 GHz based on multi-link channel measurement campaign. Based on the measured data, the value of path loss as a function of the elevation angle and distance is proposed. Further, the shadow fading(SF) is analyzed for different frequency bands and the spatial correlation is evaluated. Particularly, it is found that the influence of UAV altitude on shadowing is significant and should be considered when developing A2G communication system.

Vehicle Clustering and Resource Allocation Algorithm Based on Cellular Network

<p>As a special Mobile Ad-hoc Network (MANET), Vehicular Ad-hoc Network (VANET) plays a very important role in the future intelligent transportation system. In order to solve the problems of unstable communication connection, fast network topology change and low communication resource utilization caused by high vehicle mobility in VANET, a low-complexity resource allocation algorithm based on vehicle cluster is proposed. Firstly, considering the speed, position and moving direction of the vehicles, a vehicle clustering algorithm based on movement consistency is proposed to cluster the vehicles and keep the vehicle cluster stable. Secondly, a low-complexity resource allocation algorithm is proposed to improve the utilization rate of communication resources, which is constrained by the interference caused by the vehicle clusters to the cellular users. Simulation results show that the proposed algorithm has low complexity and can better maintain the stability of vehicle clusters and improve the system capacity in the common complex Internet of Vehicles (IoV) scenarios in cities.</p> <p> </p>

Robust Beamforming Design for RIS-Aided Integrated Sensing and Communication System

It is expected that the future intelligent transportation system will be endowed with the sensing ability to cope with the complex road environment. Therefore, the integrated sensing and communications (ISAC) system can complement the development of intelligent transportation. In this work, a novel reconfigurable intelligent surface (RIS)-aided ISAC system is investigated, in which an RIS reflects signals to the vehicle target and user by creating a directional path to enhance sensing and communication performance. We are interested in the joint robust design of transmitted beamformer at the dual-functional radar-communication (DFRC) base station and phase-shift at the RIS to maximize the radar mutual information subject to user achievable rate constraint under imperfect angles knowledge and channel state information (CSI). Specifically, two CSI error models, namely, the bounded and the mixed bounded-moment error models, are considered. Then, a worst-case robust (WCR) beamforming problem, as well as a mixed chance-constrained and worst-case robust (MCWR) beamforming problem, are separately formulated. Furthermore, we develop two efficient methods to convert the formulated semi-infinite constraint problems into feasibility ones, and an alternate optimization framework is proposed to obtain stationary points of the original problems. Simulation results are provided to validate the effectiveness of the proposed transformation methods and solution.

Visible light communication for vehicular applications: A novel architecture with proof-of-concept prototype

When there is an increasing interest in visible light communication (VLC), outdoor vehicle VLC has emerged as a promising candidate technology for future intelligent transportation systems. However, in VLC based vehicular applications, several challenges impede successful commercial application of VLC based products. This article first provides a thorough overview of the existing challenges. To overcome these challenges, we propose a novel architecture with tracking and environment sensing ability for practical vehicular applications. Moreover, aproof-of-concept prototype is implemented to validate the feasibility of the proposed system. Experimental and simulation results show that the proposed VLC system can provide reliable communications with a bit-error rate less than 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−4</sup> for vehicles under strong interference lights. Finally, based on the evaluations, we propose some key design issues for future studies in this research area.

Modeling an ITS Management Solution for Mixed Highway Traffic With Eclipse MOSAIC

Mixed traffic of conventional and automated vehicles entails many challenges concerning the safe coexistence and the improvement of traffic efficiency. As a solution, a Traffic Management Center (TMC) combines information from physical sensors and communicated data to enable advanced traffic controllers. Since there is no sufficient penetration rate of automated vehicles nowadays, such a solution could be developed and tested with synthetically generated data using simulation, which models the future Intelligent Transportation System (ITS). This paper addresses the challenges for evaluation of such a system by the means of simulation. For this purpose, we present the Eclipse MOSAIC Multi-Domain Simulation Framework, which provides the possibility to couple best-in-class simulators of the domains traffic, application, and communication, as well as to include further models and external native code libraries. Moreover, we discuss modeling extensions for traffic control algorithms and our method to create a highway scenario based on real traffic data. As primary result, many research questions for mixed traffic and ITS could be investigated using the MOSAIC framework. In an exemplary study, we analyze the deployment effort for infrastructure and communication technologies in order to facilitate a Mainstream Traffic Flow Controller in a realistic scenario with mixed traffic. Simulations performed with MOSAIC confirmed that traffic efficiency improvement is feasible even with fewer infrastructure components, thus with less cost.

Edge computing for Vehicle to Everything: a short review

Vehicle to Everything (V2X) communications and services have sparked considerable interest as a potential component of future Intelligent Transportation Systems. V2X serves to organise communication and interaction between vehicle to vehicle (V2V), vehicle to infrastructure (V2I), vehicle to pedestrians (V2P), and vehicle to networks (V2N). However, having multiple communication channels can generate a vast amount of data for processing and distribution. In addition, V2X services may be subject to performance requirements relating to dynamic handover and low latency communication channels. Good throughput, lower delay, and reliable packet delivery are the core requirements for V2X services. Edge Computing (EC) may be a feasible option to address the challenge of dynamic handover and low latency to allow V2X information to be transmitted across vehicles. Currently, existing comparative studies do not cover the applicability of EC for V2X. This review explores EC approaches to determine the relevance for V2X communication and services. EC allows devices to carry out part or all of the data processing at the point where data is collected. The emphasis of this review is on several methods identified in the literature for implementing effective EC. We describe each method individually and compare them according to their applicability. The findings of this work indicate that most methods can simulate the EC positioning under predefined scenarios. These include the use of Mobile Edge Computing, Cloudlet, and Fog Computing. However, since most studies are carried out using simulation tools, there is a potential limitation in that crucial data in the search for EC positioning may be overlooked and ignored for bandwidth reduction. The EC approaches considered in this work are limited to the literature on the successful implementation of V2X communication and services. The outcome of this work could considerably help other researchers better characterise EC applicability for V2X communications and services.

How to protect smart and autonomous vehicles from stealth viruses and worms

Smart and autonomous vehicles are inseparable parts of the future Intelligent Transportation Systems (ITS). However, ITS components, and especially its vehicles, are prone to cyber threats. Interconnectivity of different parts, ranging from in-vehicle communication of different modules to vehicle and vehicle to infrastructure message exchanges open a window to the cyber attacks launched through these communication media. This paper introduces the concept of stealth virus or worm in smart and autonomous vehicles which can jeopardize the safety of passengers. Stealth attacks are designed to manipulate a system in a way that while the changes are not detectable by human, the system is negatively influenced over the time. A framework for Intrusion Detection System (IDS) is proposed afterward. The proposed IDS structure is scalable and easily deployable on current and future vehicles which are equipped with Controller Area Network (CAN) buses. Through a case study on car cruise control, a new stealth attack is presented. The attack is analytically discussed first. Then, it is shown how the proposed IDS can detect this kind of threats.

SeAC: SDN-Enabled Adaptive Clustering Technique for Social-Aware Internet of Vehicles

Since millions of smart vehicles in Internet-of-Vehicles (IoV) produce and relay data to analyze road conditions, creating social networks of vehicles in IoV is an important factor for the future Intelligent Transportation System (ITS). Likewise, the IoV architecture has seen vertical fragmentation of approaches used to meet the needs of different work domains. Therefore, IoV in combination with social networking, called Social IoV (SIoV), was created to address these alleged problems. However, one of the challenges in SIoV is that the social relations between vehicles grow and deplete very fast due to the extremely dynamic and unstable nature of the IoV. Therefore, a clustering-based scheme for SIoV, which is efficient in terms of stability can overcome this problem. We propose : an SDN-enabled adaptive clustering technique for SIoV. uses a 3D modeling approach to construct logical clusters that are based on factors such as physical location, social tie, and interest similarity among vehicles. Therefore, improves the stability of clusters and the efficiency of the underlying SIoV architecture. Additionally, by minimizing the trade-off between social and physical distances, lowers communication and computation costs. We evaluate, and the simulation results show that for two different topologies, the adaptive approach using can produce better results in terms of a stable cluster formation.