Intelligent Transportation Research Articles

Mitigating Intermittent Connectivity Problems in Vehicle-to-Vehicle Communication (V2VC): A Sparse Network Computational Model (SNCM)

INTRODUCTION: Wireless communication has made remarkable progress, by the rapid development of wireless technology in Artificial Intelligence (AI). Intelligent Transportation Systems (ITS), and Vehicular Ad Hoc Networks (VANETs) have received significant attention to ensure safety. However, V2V communication in VANETs faces uncontrollable challenges due to frequent intermittent connectivity issues in infrastructure-less networks. Addressing these problems in both safety and non-safety applications is a complex task.OBJECTIVES: To mitigate the intermittent connectivity problems, a novel Sparse Network Computational Model (SNCM) was proposed.METHODS: Extensive simulations using MATLAB to analyze the impact of spatial-temporal variations under different traffic flow densities. We varied the sensitivity factor (λ) at different time intervals while maintaining a constant traffic density. RESULTS: The findings indicate that there is no need to increase λ beyond certain thresholds for each level of service. The simulation results provide valuable guidelines for designing sparse networks, effectively mitigating frequent intermittent disconnections. Simulation experiments revealed an optimal threshold for the sensitivity factor λ for each level of service. Increasing λ beyond certain thresholds did not yield significant improvements in mitigating disconnections in V2V communication.CONCLUSION: The results provide valuable insights and guidelines for designing sparse networks to enhance connectivity and address intermittent disconnection issues. This paper presents a groundbreaking endeavor, and therefore, direct comparisons with existing protocols to evaluate its overall performance are beyond the scope of this paper. Instead, the SNCM protocol is intended to set a standard for future researchers to benchmark their research contributions against.

Video Content Analysis for Detection of Road Traffic Congestion Pattern: An Optimized YOLOv8 Based Approach

This paper presents a new approach to Video Content Analysis (VCA) with a specific focus on detecting and analyzing road traffic congestion patterns. The implemented methodology deploys the YOLOv8 (You Only Look Once) object detection framework, which has been optimized to accurately and efficiently identify road traffic objects within video streams. The primary goal is to improve traditional VCA systems by capturing and understanding complex road congestion scenarios. This research develops a customized YOLO model optimized specifically for road traffic analysis. The optimization process addresses the challenges inherent in real-world traffic scenarios including variations in lighting conditions, diverse vehicle types and dynamic traffic flow patterns. The developed model seeks to enable robust and real-time detection of congestion-related events such as heavy congestion and normal congestion thereby contributing to improved traffic management and overall road safety. Moreover, the paper explores the integration of advanced techniques for congestion pattern analysis including vehicle detection, tracking, and counting. The combination of these features facilitates a comprehensive understanding of traffic dynamics and congestion evolution over time. The proposed approach is trained and evaluated on benchmark datasets and real-world video footage to assess its performance. Experimental results demonstrate the effectiveness of the optimized YOLOv8-based approach in accurately detecting and analyzing road traffic congestion patterns. The system exhibits promising capabilities in handling diverse and challenging scenarios thereby serving as a valuable tool for intelligent transportation systems, urban planning and traffic management. This research significantly contributes to the advancement of video-based traffic analysis, providing a robust solution for monitoring and mitigating congestion-related challenges on road networks.

Attention-Enhanced Defensive Distillation Network for Channel Estimation in V2X mm-Wave Secure Communication

Millimeter-wave (mm-wave) technology, crucial for future networks and vehicle-to-everything (V2X) communication in intelligent transportation, offers high data rates and bandwidth but is vulnerable to adversarial attacks, like interference and eavesdropping. It is crucial to protect V2X mm-wave communication from cybersecurity attacks, as traditional security measures often fail to counter sophisticated threats and complex attacks. To tackle these difficulties, the current study introduces an attention-enhanced defensive distillation network (AEDDN) to improve robustness and accuracy in V2X mm-wave communication under adversarial attacks. The AEDDN model combines the transformer algorithm with defensive distillation, leveraging the transformer’s attention mechanism to focus on critical channel features and adapt to complex conditions. This helps mitigate adversarial examples by filtering misleading data. Defensive distillation further strengthens the model by smoothing decision boundaries, making it less sensitive to small perturbations. To evaluate and validate the AEDDN model, this study uses a publicly available dataset called 6g-channel-estimation and a proprietary dataset named MMMC, comparing the simulation results with the convolutional neural network (CNN) model. The findings from the experiments indicate that the AEDDN, especially in the complex V2X mm-wave environment, demonstrates enhanced performance.

All-In-One Optoelectronic Transistors for Bio-Inspired Visual System.

Visual perception has profound effects on human decision-making and emotional responses. Replicating the functions of the human visual system through device development has been a constant pursuit in recent years. However, to fully simulate the various functions of the human visual system, it is often necessary to integrate multiple devices with different functions, resulting in complex, large-volume device structures and increased power consumption. Here, an optoelectronic transistor with comprehensive visual functions is introduced. By coupling diverse photoreceptive properties of the channel and electrical regulation through charge injection/ferroelectric switching from the hafnium-based gate, the devices can simulate functions of both photoreceptors in the retina and synapses in the visual cortex. A device array is constructed to confirm the perceptual functions of cone and rod cells. Subsequently, color discrimination and recognition for color images are achieved by combining the tunable perception and synapse functions. Then an intelligent traffic judgment system with this all-in-one device is developed, which is capable of making judgments and decisions regarding traffic signals and pedestrian movements. This work provides a potential solution for developing compact and efficient devices for the next-generation bio-inspired visual system.

A Hybrid Method Based on Haris Corner and Maximally Stable Extremal Regions for Vehicle Plate Number Detection

An intelligent transportation system (ITS) is a crucial element of a smart city, and it includes the capability to identify vehicle license plates. Utilizing digital image processing is a cost-effective method for identification. The tiny size of the number plate is just one of the many unfortunate difficulties with this approach. Hence, this research is crucial, particularly in enhancing the precision of detection. The Harris Corner approach is one way to locate the motor vehicle number plate location. However, the Harris corner method could be more optimal for analyzing moving vehicle videos as input. Since frame-by-frame variations in the video input's lighting, accuracy cannot be maximized. Furthermore, the vehicle and license plate appear significantly smaller due to the camera's distant positioning. Consequently, the authors suggest a hybrid approach using the Maximally Stable Extremal Regions (MSER) method. The Harris Corner and MSER methods will concurrently identify the initial position of the vehicle number plate. Moreover, the initial detection outcomes of the two techniques are compared and adjusted to achieve a more precise determination of the placement of the number plate. The results show that integrating the MSER method into the Harris Corner method yields a hybrid approach that enhances accuracy by 13%. Furthermore, it visually represents the selected number plate with greater accuracy.

An Improved and Secured 3-Factor Authentication Scheme for WSN in IoT Based Applications

Wireless Sensor Networks (WSN) has top-notch capacity for numerous domains of utility due to the potential to sense and apprehend unattended environments. IoT is playing an increasingly more essential position in clever healthcare, smart transportation and elegant grid using WSN. Therefore, challenges like privacy and data security associated with IoT wireless sensor networks because of sensor node being captured easily, consume less computing energy and low storage. Designing a secure authentication scheme will enable secure communication in WSN. Here, we proposed a improved three-factor authentication scheme for WSN utilizing biometric, password and smart card withstand against security attacks. Subsequently, by comparing the security, functional and performance parameters of our proposed scheme to the other schemes, our scheme provides better and efficient one. Therefore, our scheme is fairly workout in real time IoT applications involved in WSN scenario.

Deep learning-powered visual place recognition for enhanced mobile multimedia communication in autonomous transport systems

The progress of autonomous transport systems (ATS) involves efficient multimedia communication for real-time data tradeoffs and environmental issues. Deep learning (DL) powered visual place recognition (VPR) was developed as an effective tool to improve mobile multimedia communication in ATS. VPR relates to the capability of a method or device to recognize and identify particular places or locations from the visual scene. This procedure involves inspecting visual data, like images or video frames, to control the unique features or features connected with diverse locations. By leveraging camera sensors, VPR allows vehicles to detect their surroundings, enabling context-aware communication and enhancing the entire system's performance. DL-empowered VPR offers a transformative manner to improve mobile multimedia communication in ATS. By identifying and understanding their situation, autonomous vehicles can communicate most effectively and operate reliably and safely, paving the way for a future characterized by seamless and intelligent transportation. This article develops a novel Deep Learning-Powered Visual Place Recognition for Enhanced Multimedia Communication in Autonomous Transport Systems (DLVPR-MCATS) methodology. The main aim of the DLVPR-MCATS methodology is to recognize visual places or not utilize optimal DL approaches. For this purpose, the DLVPR-MCATS approach utilizes a bilateral filtering (BF) based preprocessing model. For the feature fusion model, the DLVPR-MCATS approach follows three models: residual network (ResNet), EfficientNet, and MobileNetv2. Moreover, the hyperparameter tuning method uses the Harris Hawks Optimization (HHO) model. Finally, the bidirectional long short-term memory (BiLSTM) technique is implemented to recognize visual places. A wide range of simulations is executed to validate the solution of the DLVPR-MCATS method. The experimental validation of the DLVPR-MCATS method portrayed a superior performance over other models concerning various aspects.

Optimization of the age of correlated information in V2X networks with edge computing

Vehicle-to-Everything (V2X) communication has the potential to revolutionize the travel experience in intelligent transportation, which has received the great attention recently. However, ensuring the freshness of information from multiple sources is critical for the real-time and reliable communication in vehicular networks, especially for timely updates of service centers. To address this issue, we use a promising metric called Age of Correlated Information (AoCI), which can characterize the freshness of multi-source information. Therefore, we propose a novel model that can dynamically regulate the channel activation matching and edge computing collaboration strategy to minimize AoCI in V2X vehicular networks. Firstly, we describe the system model of a V2X network with edge computing, including definitions and assumptions for freshness of information, edge co-computing, etc. Secondly, we formulate the joint optimization problem as a source-related age minimization (SRAM) problem, which is NP-complete. A heuristic algorithm is proposed to solve it under fast-fading channel. Finally, since traditional graph models cannot capture the changing correlation between nodes in dynamic networks, we use graph convolutional networks(GCN) to extract the features of multi-source correlation. The features extracted by GCN include relevant attributes of the sources and its communication links. The features are provided as input to a double deep Q network (DDQN) for training the model that can adapt to a dynamic network environment. Extensive simulation experiments in different network scenarios validate that our proposed method can effectively and efficiently reduce the average AoCI and the computational resources.

Multimodal Dataset Construction and Validation for Driving-Related Anger: A Wearable Physiological Conduction and Vehicle Driving Data Approach

Anger impairs a driver’s control and risk assessment abilities, heightening traffic accident risks. Constructing a multimodal dataset during driving tasks is crucial for accurate anger recognition. This study developed a multimodal physiological -vehicle driving dataset (DPV-MFD) based on drivers’ self-reported anger during simulated driving tasks. In Experiment 1, responses from 624 participants to anger-inducing videos and driving scenarios were collected via questionnaires to select appropriate materials. In Experiments 2 and 3, multimodal dynamic data and self-reported SAM emotion ratings were collected during simulated and real-vehicle tasks, capturing physiological and vehicle responses in neutral and anger states. Spearman’s correlation coefficient analysis validated the DPV-MFD’s effectiveness and explored the relationships between multimodal data and emotional dimensions. The CNN-LSTM deep learning network was used to assess the emotion recognition performance of the DPV-MFD across different time windows, and its applicability in real-world driving scenarios was validated. Compared to using EEG data alone, integrating multimodal data significantly improved anger recognition accuracy, with accuracy and F1 scores rising by 4.49% and 9.14%, respectively. Additionally, real-vehicle data closely matched simulated data, confirming the dataset’s effectiveness for real-world applications. This research is pivotal for advancing emotion-aware human–machine- interaction and intelligent transportation systems.

PV-LaP: Multi-sensor fusion for 3D Scene Understanding in intelligent transportation systems

Intelligent transportation systems are pivotal in modern urban development, aiming to enhance traffic management efficiency, safety, and sustainability. However, existing 3D Visual Scene Understanding methods often face challenges of robustness and high computational complexity in complex traffic environments. This paper proposes a Multi-Sensor Signal Fusion method based on PV-RCNN and LapDepth (PV-LaP) to improve 3D Visual Scene Understanding. By integrating camera and LiDAR data, the PV-LaP method enhances environmental perception accuracy. Evaluated on the KITTI and WHU-TLS datasets, the PV-LaP framework demonstrated superior performance. On the KITTI dataset, our method achieved an Absolute Relative Error (Abs Rel) of 0.079 and a Root Mean Squared Error (RMSE) of 3.014, outperforming state-of-the-art methods. On the WHU-TLS dataset, the method improved 3D reconstruction precision with a PSNR of 19.15 and an LPIPS of 0.299. Despite its high computational demands, PV-LaP offers significant improvements in accuracy and robustness, providing valuable insights for the future development of intelligent transportation systems.

Local Differential Privacy for correlated location data release in ITS

The ubiquity of location positioning devices has facilitated the implementation of various Intelligent Transportation System (ITS) applications that generate an enormous volume of location data. Recently, Local Differential Privacy (LDP) has been proposed as a rigorous privacy framework that permits the continuous release of aggregate location statistics without relying on a trusted data curator. However, the conventional LDP was built upon the assumption of independent data, which may not be suitable for inherently correlated location data. This paper investigates the quantification of potential privacy leakage in a correlated location data release scenario under a local setting, which has not been addressed in the literature. Our analysis shows that the privacy guarantee of LDP could be degraded in the presence of spatial–temporal and user correlations, albeit the perturbation is performed locally and independently by the users. This privacy guarantee is bounded by a privacy barrier that is affected by the intensity of correlations. We derive several important closed-form expressions and design efficient algorithms to compute such privacy leakage in a correlated location data. We subsequently propose a Δ-CLDP model that enhances the conventional LDP by incorporating the data correlations, and design a generic LDP data release framework that renders adaptive personalization of privacy preservation. Extensive theoretical analyses and simulations on scalable real datasets validate the security and performance efficiency of our work.

Self-adaptive equation embedded neural networks for traffic flow state estimation with sparse data

The data-driven approach in intelligent traffic systems has achieved successive breakthroughs, thanks to the ever-increasing volume of traffic data. Nonetheless, in practical scenarios, the collected data often contain some issues, e.g., missing values, significantly impacting the accuracy and efficiency of the algorithms. To enhance the precision of traffic estimation utilizing the sparse data, we have developed a physics-informed neural network (PINN) based algorithm in the line with the traffic flow theory and deep learning principles. In contrast to the conventional PINNs, our approach uniquely incorporates a self-adaptive macro model for mixed flow into the network's architecture, serving as an embedded source of physics information. With this algorithm, we can capture the dynamic behavior of an entire traffic flow including its spatiotemporal evolution with sparse traffic data such as initial and boundary value information. To realize the model's adaptability, we have revised the macro model by inverting its parameters and incorporating a data-driven nonlinear element, which simplifies the intricate macro model structure. The network's effectiveness has been validated through the experiments conducted on a mixed traffic flow system experiencing local agglomeration and real-world data, demonstrating its capability for precise traffic simulation, efficient traffic flow prediction, and interpretability. Our study offers a novel insight for data-driven traffic flow state estimation.

Isolation enhancement of a capacitively-fed MIMO antenna using a quasi-fractal parasitic element and defected ground structure

Vehicular Internet of Things (IoT) is facilitated by efficient RF front ends with suppressed mutual coupling for enhanced spatial diversity and increased channel capacity. This paper presents a mutual coupling suppressed MIMO antenna with a hybrid decoupling technique for Vehicle-to-Everything (V2X) communications, enabling IoT in automotive systems. The single elements consist of a radiating patch with a cleaving circular slot to introduce a capacitive effect on the radiating structure. Afterwards, the single-unit design is further extrapolated to a 2 × 2 MIMO antenna. The mutual coupling is suppressed between antenna elements by introducing a quasi-fractal parasitic element and a defected ground structure (DGS). The MIMO antenna is designed to conform to the requirements posed by V2X systems in Dedicated Short-Range Communications (DSRC) and Intelligent transportation system (ITS) scenarios. The proposed MIMO antenna offers measured |S11| < −10 dB of 200 MHz, ranging from 5.77 GHz to 5.97 GHz, fully covering the spectrum guided by the IEEE 802.11p standard. A physical prototype is fabricated and placed on a car roof to assess the congruency between measured and simulated results. The MIMO antenna exhibits exceptional diversity properties, such as enhanced isolation (>28 dB) between its individual elements, a diversity gain (DG) close to the ideal value of 10 dB (9.99 dB), peak realized gain of 6.5 dBi, an ECC below 0.001, and a beam coverage area of 180° in azimuthal and elevation plane by dynamic port switching. Thus, the proposed MIMO antenna module is a considerable candidate for future V2X communication paradigms.

Harmonizing Maritime Innovation: Enhancing International and National Standardization in Intelligent Ship Transport Systems

Abstract The rapid advancement of maritime technologies, particularly in the domain of autonomous ships and maritime intelligent transport systems (MITS), necessitates a robust framework for digital interoperability at both national and international levels. This paper presents an in-depth analysis of the current state of standardization within MITS. It emphasizes the importance of harmonizing efforts to facilitate the integration of innovative technologies into the maritime sector, ensuring safety, efficiency, and environmental sustainability. To gain broader insights, standardization practices from other industries are examined. In conclusion, the paper argues that enhancing collaboration and standardization in MITS is essential for the successful implementation of maritime innovations. It calls for a collective effort from national and international stakeholders to develop a unified framework that supports the growth of autonomous ship technology and the evolution of intelligent maritime transport systems.

CLLS: Efficient certificateless lattice-based signature in VANETs

The rapid development of Vehicular Ad-hoc Network (VANETs) has improved road safety and traffic management, and brought great convenience to intelligent transportation system (ITS). However, the transmission of data over open channels caused many security issues. Certificateless cryptography solves the certificate management and key escrow problems, which makes it the primary method for message authentication in VANETs. However, with the emergence of quantum computing, traditional cryptography faces a significant challenge. Lattice-based cryptography are regarded as effective post-quantum ciphers. Nevertheless, nearly all existing lattice-based certificateless signature schemes rely on Gaussian sampling or trapdoor techniques, resulting in computational inefficiencies and large key and signature sizes that are impractical for VANETs. To address these issues, we proposed the first efficient algebraic lattice-based certificateless signature scheme in VANETs based on the Dilithium signature algorithm. The security of our certificateless lattice-based signature(CLLS) scheme is based on the MSIS and MLWE hardness assumption, which makes the scheme resistant to quantum attacks and easy to implement. Our scheme did not use Gaussian sampling or trapdoor techniques, which improve the computational and storage efficiency. As a result, the public key of our scheme is 1X smaller than the previous scheme and the size of signature is 2X smaller than the previous efficient algebraic lattice scheme. In addition, compared to the most efficient existing CLLS scheme, the signing and verification computation cost of our scheme are reduced by 20% and 55% respectively and our proposed CLLS scheme has low power consumption. Furthermore, the security of our scheme achieves strong unforgeability against chosen-message attacks(SUF-CMA) in the random oracle model(ROM), which surpasses that of existing lattice-based certificateless signature schemes.

SCVAN- BKM: Swarm clustering for vehicular ad hoc network with a secure blockchain-based key management scheme

The advancements in telematics and communication technology for automobiles have contributed to the development of Vehicular ad Hoc Networks (VANETs). These networks serve as the foundation for Intelligent Transportation Systems (ITS). Communication across various VANET entities is challenging due to the open communication environment and the highly dynamic nature of VANETs. One of the major challenges in VANETs is ensuring stable and secure transmission. To address these factors, several studies have been conducted. The article discusses a clustering algorithm for VANETs that leverages SCVAN-DPSO to create stable clusters and provide secure communication for mitigating malicious nodes. Additionally, Hyperelliptic Curve Cryptography (HECC) with signcryption in blockchain is proposed. This algorithm is designed to identify and mitigate malicious nodes, ensuring robust and secure communication within the network. To verify vehicles in VANETs, this study also suggests a safe and reliable key management method that generates and stores cryptographic keys using blockchain’s distributed ledger technology. By doing so, the risk of rogue nodes is reduced, and a safe, stable connection between vehicles is guaranteed. This work has been implemented using NS 3.34 simulation with the SUMO real-time traffic mobility simulator, and its performance has been evaluated. The proposed algorithm SCVAN-BKM provides a 99.6 % packet delivery ratio for 20 nodes, 0.4 % packet loss, end-to-end delay of 32.65 ms, throughput of 9.522 Mbps, and a cluster lifetime of 105 s. As the number of nodes increases in the communication zone, stable communication is maintained due to the clustering algorithm used in the proposed approach, as cluster formation supports stability in the high-mobility nature of vehicles, while secure communication is ensured using blockchain technology. The findings demonstrate that our approach is more effective and yields better outcomes than existing systems.

Creative artificial intelligence and narrative transportation.

Creative artificial intelligence and narrative transportation.

Efficient license plate recognition in unconstrained scenarios

Automatic license plate recognition (ALPR) is a critical technology for intelligent transportation systems. Most existing ALPR methods are focused on specific application scenarios. Although there are a few methods that focus on unconstrained scenarios, they are very time-consuming. In this work, we propose an efficient ALPR (EALPR) framework, where we can handle distorted license plates (LP) caused by perspective problems with high efficiency. We design a light LPD structure based on efficient object detection methods and use anchor-free strategies for LPD to alleviate the problem of expensive costs. Benefitting from these optimizations and a united framework structure, the proposed EALPR has real-time efficiency. We evaluate our method on five datasets and the results show that our method achieves state-of-the-art accuracy: 98.15% on OpenALPR(EU), 95.61% on OpenALPR(BR), 99.51% on AOLP(RP), 88.81% on SSIG, 79.41% on CD-HARD. Additionally, our method achieves an impressive speed of 74.9 FPS (Frames Per Second), outperforming existing approaches and demonstrating its efficiency. Our source code can be accessed at https://github.com/wechao18/Efficient-alpr-unconstrained.

Intelligent Traffic Control Decision-Making Based on Type-2 Fuzzy and Reinforcement Learning

Intelligent traffic control decision-making has long been a crucial issue for improving the efficiency and safety of the intelligent transportation system. The deficiencies of the Type-1 fuzzy traffic control system in dealing with uncertainty have led to a reduced ability to address traffic congestion. Therefore, this paper proposes a Type-2 fuzzy controller for a single intersection. Based on real-time traffic flow information, the green timing of each phase is dynamically determined to achieve the minimum average vehicle delay. Additionally, in traffic light control, various factors (such as vehicle delay and queue length) need to be balanced to define the appropriate reward. Improper reward design may fail to guide the Deep Q-Network algorithm to learn the optimal strategy. To address these issues, this paper proposes a deep reinforcement learning traffic control strategy combined with Type-2 fuzzy control. The output action of the Type-2 fuzzy control system replaces the action of selecting the maximum output Q-value of the target network in the DQN algorithm, reducing the error caused by the use of the max operation of the target network. This approach improves the online learning rate of the agent and increases the reward value of the signal control action. The simulation results using the Simulation of Urban MObility platform show that the traffic signal optimization control proposed in this paper has achieved significant improvement in traffic flow optimization and congestion alleviation, which can effectively improve the traffic efficiency in front of the signal light and improve the overall operation level of traffic flow.

Disentangled traffic forecasting via efficient graph neural network

Abstract Traffic prediction is important to intelligent transportation systems, but it poses significant challenges due to the complex spatio-temporal correlation of traffic data. To address these challenges, we decompose complex traffic time series into events and trends using discrete wavelet transform. However, the high-frequency information decomposed by wavelet transform contains noise, so we designed a module to denoise the high-frequency events. We model the spatio-temporal correlations of trends and events separately using two mutually independent spatio-temporal networks. We use causal dilation convolution with local receptive fields and attention with global receptive fields to capture the correlation of fluctuating time series and the correlation of stable time series, respectively. We capture spatial correlation using a GNN optimized by an attention mechanism. Finally, we merge useful information from volatile events into predictable trends using adaptive event fusion. Our model significantly outperforms nine existing traffic prediction methods across four real datasets.