Search Results (30)

This paper presents a novel method for locally estimating vehicle density on highways based on vehicle-to-vehicle (V2V) communication, a communication mode within intelligent transport systems (ITSs), enabled via IEEE 802.11p and 3GPP C-V2X technologies. Awareness messages (AMs), such as basic safety messages (BSMs, SAE J2735) and cooperative awareness messages (CAMs, ETSI EN 302 637-2), are periodically broadcast by vehicles and can be leveraged to sense the presence of nearby vehicles. Unlike existing approaches that directly combine the number of sensed vehicles with measured packet reception ratio (PRR) of the AM, our method accounts for the deviations in PRR caused by imperfect channel conditions. To address this, we estimate the actual packet reception probability (PRP)–distance curve by exploiting its inherent downward trend along with multiple measured PRR points. From this curve, two metrics are introduced: node awareness probability (NAP) and average awareness ratio (AAR), the latter representing the ratio of sensed vehicles to the total number of vehicles. The real density is then estimated using the number of sensed vehicles and AAR, mitigating the underestimation issues common in V2V-based methods. Simulation results across densities ranging from 0.02 vehs/m to 0.28 vehs/m demonstrate that our method improves estimation accuracy by up to 37% at an actual density of 0.28 vehs/m, compared with methods relying solely on received AMs, without introducing additional communication overhead. Additionally, we demonstrate a practical application where the basic safety message (BSM) transmission rate is dynamically adjusted based on the estimated density, thereby improving traffic management efficiency. Full article

With the rapid development of intelligent connected vehicle technology, false data injection (FDI) attacks have become a major challenge in the Internet of Vehicles (IoV). While deep learning methods can effectively identify such attacks, the dynamic, distributed architecture of the IoV and limited computing resources hinder both privacy protection and lightweight computation. To address this, we propose FedIFD, a federated learning (FL)-based detection method for false data injection attacks. The lightweight threat detection model utilizes basic safety messages (BSM) for local incremental training, and the Q-FedCG algorithm compresses gradients for global aggregation. Original features are reshaped using a time window. To ensure temporal and spatial consistency, a sliding average strategy aligns samples before spatial feature extraction. A dual-branch architecture enables parallel extraction of spatiotemporal features: a three-layer stacked Bidirectional Long Short-Term Memory (BiLSTM) captures temporal dependencies, and a lightweight Transformer models spatial relationships. A dynamic feature fusion weight matrix calculates attention scores for adaptive feature weighting. Finally, a differentiated pooling strategy is applied to emphasize critical features. Experiments on the VeReMi dataset show that the accuracy reaches 97.8%. Full article

This study investigates the use of Autonomous Sensing Infrastructure and Connected and Autonomous Vehicles (CAV) technologies to support infrastructure-to-vehicle (I2V) and infrastructure-to-everything (I2X) communications, including the alerting of drivers and pedestrians. It describes research findings in the following CAV functionalities: (1) Intersection-based object detection and tracking; (2) Basic Safety Message (BSM) generation and transmission; and (3) In-Vehicle BSM receipt and display, including handheld (smartphone) application BSM receipt and user presentation. The study summarizes the various software and hardware components used to create the I2V and I2X prototype solutions, which include open-source and commercial software as well as industry-standard transportation infrastructure hardware, e.g., Signal Controllers. Results from in-lab testing demonstrate effective object detection (e.g., pedestrians, bicycles) based on sample traffic camera video feeds as well as successful BSM message generation and receipt using the leveraged software and hardware components. The I2V and I2X solutions created as part of this research are scheduled to be deployed in a real-world intersection in coordination with state and local transportation agencies. Full article

Drone-assisted radio communication is revolutionizing future wireless networks, including sixth-generation (6G) and beyond, by providing unobstructed, line-of-sight links from air to terrestrial vehicles, enabling robust cellular cehicle-to-everything (C-V2X) communication networks. However, addressing communication latency is imperative, especially when considering autonomous vehicles. In this study, we analyze different types of delay and the factors impacting them in drone-assisted C-V2X networks. We specifically investigate C-V2X Mode 4, where multiple vehicles utilize available transmission windows to communicate the frequently collected sensor data with an embedded drone server. Through a discrete-time Markov model, we assess the medium access control (MAC) layer performance, analyzing the trade-off between data rates and communication latency. Furthermore, we compare the delay between cooperative perception messages (CPMs) and periodically transmitted basic safety messages (BSMs). Our simulation results emphasize the significance of optimizing BSM and CPM transmission intervals to achieve lower average delay as well as utilization of drones’ battery power to serve the maximum number of vehicles in a transmission time interval (TTI). The results also reveal that the average delay heavily depends on the packet arrival rate while the processing delay varies with the drone occupancy and state-transition rates for both BSM and CPM packets. Furthermore, an optimal policy approximates a threshold-based policy in which the threshold depends on the drone utilization and energy availability. Full article

With the rapid advancement of autonomous vehicles reshaping urban transportation, the importance of innovative traffic management solutions has escalated. This research addresses these challenges through the deployment of roadside units (RSUs), aimed at enhancing traffic flow and safety within the autonomous driving era. Our research, conducted in diverse road settings such as straight and traffic circle roads, delves into the RSUs’ capacity to diminish traffic density and alleviate congestion. Employing vehicle-to-infrastructure communication, we can scrutinize its essential role in navigating autonomous vehicles, incorporating basic safety messages (BSMs) and probe vehicle data (PVD) to accurately monitor vehicle presence and status. This paper presupposes the connectivity of all vehicles, contemplating the integration of on-board units or on-board diagnostics in legacy vehicles to extend connectivity, albeit this aspect falls beyond the work’s current ambit. Our detailed experiments on two types of roads demonstrate that vehicle behavior is significantly impacted when density reaches critical thresholds of 3.57% on straight roads and 34.41% on traffic circle roads. However, it is important to note that the identified threshold values are not absolute. In our experiments, these thresholds represent points at which the behavior of one vehicle begins to significantly impact the flow of two or more vehicles. At these levels, we propose that RSUs intervene to mitigate traffic issues by implementing measures such as prohibiting lane changes or restricting entry to traffic circles. We propose a new message set in PVD for RSUs: road balance. Using this message, RSUs can negotiate between vehicles. This approach underscores the RSUs’ capability to actively manage traffic flow and prevent congestion, highlighting their critical role in maintaining optimal traffic conditions and enhancing road safety. Full article

This paper presents our research on a traffic signal control system (TSCS) at V2X intersections. The overall objective of the study is to create an implementable TSCS. The specific objective of this paper is to investigate a distributed system towards implementation. The objective function of minimizing queue delay is formulated as the integral of queue lengths. The discrete queueing estimation is mixed with macro and micro traffic flow models. The novel proposed architecture alleviates the communication network bandwidth constraint by processing BSMs and computing queue lengths at the local intersection. In addition, a two-stage distributed system is designed to optimize offsets, splits, and cycle length simultaneously and in real time. The paper advances TSCS theories by contributing a novel analytic formulation of delay functions and their first degree of derivatives for a two-stage optimization model. The open-source traffic simulation engine Enhanced Transportation Flow Open-Source Microscopic Model (ETFOMM version 1.2) was selected as a simulation environment to develop, debug, and evaluate the models and the system. The control delay of the major direction, minor direction, and the total network were collected to assess the system performance. Compared with the optimized TSCS timing plan by the Virginia Department of Transportation, the system generated a 21% control delay reduction in the major direction and a 7% control delay reduction in the minor direction at just a 10% penetration rate of connected vehicles. Finally, the proposed distributed and centralized systems present similar performances in the case study. Full article

With the development and popularization of Intelligent Transportation Systems (ITS), Vehicle Ad-Hoc Networks (VANETs) have attracted extensive attention as a key technology. In order to achieve real-time monitoring, VANET technology enables vehicles to collect real-time traffic updates through information collection devices and transmit this information to Roadside Units (RSUs), which are processed and integrated by an information processing center. However, high vehicle density leads to a conflict between minimizing the interval for vehicles to send Basic Safety Messages (BSMs) to RSUs and the limited communication resources of VANETs. To address this issue, we propose a MAC protocol based on the 802.11 CSMA/CA mechanism with the Preemptive-Resume Priority scheme. The arbitration device provides preemptive service to data packets with higher priority levels, thereby reducing data transmission delay. Moreover, queuing theory is employed to calculate the total delay for vehicles to send BSMs to a drone receiver, minimizing the BSM transmission interval and achieving minimal delay to meet safety driving requirements. The effectiveness and superiority of this mechanism and algorithm are demonstrated through simulation experiments. Full article

Ensuring the safety of autonomous vehicles is becoming increasingly important with ongoing technological advancements. In this paper, we suggest a machine learning-based approach for detecting and responding to various abnormal behaviors within the V2X system, a system that mirrors real-world road conditions. Our system, including the RSU, is designed to identify vehicles exhibiting abnormal driving. Abnormal driving can arise from various causes, such as communication delays, sensor errors, navigation system malfunctions, environmental challenges, and cybersecurity threats. We simulated exploring three primary scenarios of abnormal driving: sensor errors, overlapping vehicles, and counterflow driving. The applicability of machine learning algorithms for detecting these anomalies was evaluated. The Minisom algorithm, in particular, demonstrated high accuracy, recall, and precision in identifying sensor errors, vehicle overlaps, and counterflow situations. Notably, changes in the vehicle’s direction and its characteristics proved to be significant indicators in the Basic Safety Messages (BSM). We propose adding a new element called linePosition to BSM Part 2, enhancing our ability to promptly detect and address vehicle abnormalities. This addition underpins the technical capabilities of RSU systems equipped with edge computing, enabling real-time analysis of vehicle data and appropriate responsive measures. In this paper, we emphasize the effectiveness of machine learning in identifying and responding to the abnormal behavior of autonomous vehicles, offering new ways to enhance vehicle safety and facilitate smoother road traffic flow. Full article

Occupational health and safety (OHS) is a largely technical field, still guided by a biomedical model of health that seeks to isolate factors that cause injury. Despite a growing literature on organisational and managerial factors influencing occupational health, their full integration into the OHS concept has been slow. A broader understanding is still needed to recognise the restructuring of work and the link between well-being at work and management style. In the context of a rapidly changing world of work, increasing workforce diversity, and inequality, OHS needs to take account of the social sciences and humanities to broaden its reductionist vision. Occupational illnesses, distress, and suffering, especially in relation to relational or organisational issues, have no initial cause or specific ontology; they result from a long-standing process or repetitive relational pattern that needs to be exposed and understood in greater depth, considering contextual factors and dynamics. Using the authors’ anthropological backgrounds and the basic principles of the double bind theory developed many decades ago by Gregory Bateson and his colleagues at the Palo Alto School of Communication, we propose a reflection on pragmatic paradoxes or double bind situations in the workplace (which can be briefly defined as the presence of contradictory or conflicting demands or messages), their potential impact on workers’ health and well-being, and how to resolve them. This paper sought to explore the world of pragmatic paradoxes and double binds by discussing different categories, types, or forms of paradoxes/double binds that occur in the context of occupational health and their underlying mechanisms. It also includes a discussion of the possible link to the concept of super-diversity, as it too is associated with migration channels, employment, gendered flows, and local systems. Finally, we discuss the practical implications of this understanding for health professionals, researchers, and policymakers, from a perspective of promoting more holistic and context-sensitive interactional approaches to occupational health. Full article

Detecting drivers’ cognitive states poses a substantial challenge. In this context, cognitive driving anomalies have generally been regarded as stochastic disturbances. To the best of the author’s knowledge, existing safety studies in the realm of human Driving Anomaly Detection (DAD) utilizing vehicle trajectories have predominantly been conducted at an aggregate level, relying on data aggregated from multiple drivers or vehicles. However, to gain a more nuanced understanding of driving behavior at the individual level, a more detailed and granular approach is essential. To bridge this gap, we developed a Data Anomaly Detection (DAD) model designed to assess a driver’s cognitive abnormal driving status at the individual level, relying solely on Basic Safety Message (BSM) data. Our DAD model comprises both online and offline components, each of which analyzes historical and real-time Basic Safety Messages (BSMs) sourced from connected vehicles (CVs). The training data for the DAD model consist of historical BSMs collected from a specific CV over the course of a month, while the testing data comprise real-time BSMs collected at the scene. By shifting our focus from aggregate-level analysis to individual-level analysis, we believe that the DAD model can significantly contribute to a more comprehensive comprehension of driving behavior. Furthermore, when combined with a Conflict Identification (CIM) model, the DAD model has the potential to enhance the effectiveness of Advanced Driver Assistance Systems (ADAS), particularly in terms of crash avoidance capabilities. It is important to note that this paper is part of our broader research initiative titled “Automatic Safety Diagnosis in the Connected Vehicle Environment”, which has received funding from the Southeastern Transportation Research, Innovation, Development, and Education Center. Full article

Vehicular ad hoc networks (VANETs) facilitate communication among vehicles and possess designated infrastructure nodes to improve road safety and traffic flow. As the number of vehicles increases, the limited bandwidth of the wireless channel used for vehicle-to-vehicle (V2V) communication can become congested, leading to packets being dropped or delayed. VANET congestion control techniques attempt to address this by adjusting different transmission parameters, including the data rate, message rate, and transmission power. In this paper, we propose a decentralized congestion control algorithm where each factor adjusts the data rate (bitrate) used to transmit its wireless packet congestion based on the current load on the channel. The channel load is estimated independently by each vehicle using the measured channel busy ratio (CBR). The simulation results demonstrate that the proposed approach outperforms existing data rate-based algorithms, in terms of both packet reception and overall channel load. Full article

The Internet of vehicles (IoVs) is an innovative paradigm which ensures a safe journey by communicating with other vehicles. It involves a basic safety message (BSM) that contains sensitive information in a plain text that can be subverted by an adversary. To reduce such attacks, a pool of pseudonyms is allotted which are changed regularly in different zones or contexts. In base schemes, the BSM is sent to neighbors just by considering their speed. However, this parameter is not enough because network topology is very dynamic and vehicles can change their route at any time. This problem increases pseudonym consumption which ultimately increases communication overhead, increases traceability and has high BSM loss. This paper presents an efficient pseudonym consumption protocol (EPCP) which considers the vehicles in the same direction, and similar estimated location. The BSM is shared only to these relevant vehicles. The performance of the purposed scheme in contrast to base schemes is validated via extensive simulations. The results prove that the proposed EPCP technique outperformed compared to its counterparts in terms of pseudonym consumption, BSM loss rate and achieved traceability. Full article

The development of quantum computing systems poses a great threat to the security of existing public key-based systems. As a result, the National Institute of Standards and Technology (NIST) started a Post-Quantum Cryptography (PQC) standardization project in 2015, and currently active research is being conducted to apply PQC to various cryptographic protocols. Unlike elliptic curve cryptography (ECC)-based schemes, PQC requires a large memory footprint and key/signature size. Therefore, when migrating PQC to a protocol, depending on the PQC and protocol specifications, it can be hard to migrate PQC. In the case of the WAVE protocol, it is difficult to satisfy the accuracy of a specific PQC algorithm because segmentation of the signature occurs during transmission due to the limitation of the maximum packet size. Therefore, in this paper, we present two methodologies that can apply PQC while complying with IEEE 1609.2 standards to the WAVE protocol in the V2V environment. Whereas previous migration studies have focused on designing a hybrid mode of protocols, this paper explores solutions more intuitively at the application layer of protocols. We analyzed two postquantum digital signature algorithms (Crystals-Dilithium and Falcon) and the structure of basic-safety messages (BSMs) of the V2V protocol on the size side. Through this, we propose methods that can perform an independent signature verification process without waiting for all divided signatures in the WAVE protocol. Our methodology overcomes the limitation that schemes with large signature sizes cannot be mounted into the WAVE protocol. We also note that the architecture used as an on-board unit (OBU) in an autonomous driving environment is mainly a microprocessor. We investigated an optimized PQC implementation in the OBU environment and simulated our methodology with the V2Verifier. Finally, we measured the accurate latency through simulation in Jetson Xavier, which is mainly used as an OBU in the V2V communication network. Full article

Vehicular ad hoc networks (VANETs) are crucial components of intelligent transportation systems (ITS) aimed at enhancing road safety and providing additional services to vehicles and their users. To achieve reliable delivery of periodic status information, referred to as basic safety messages (BSMs) and event-driven alerts, vehicles need to manage the conflicting requirements of situational awareness and congestion control in a dynamic environment. To address this challenge, this paper focuses on controlling the message transmission rate through a Markov decision process (MDP) and solves it using a novel reinforcement learning (RL) algorithm. The proposed RL approach selects the most suitable transmission rate based on the current channel conditions, resulting in a balanced performance in terms of packet delivery and channel congestion, as shown by simulation results for different traffic scenarios. Additionally, the proposed approach offers increased flexibility for adaptive congestion control through the design of an appropriate reward function. Full article

Terrorism is one of the most serious threats today. The perpetrators of the attacks use newer and newer tools and apply new methods of operation. Their goal is to cause fear; therefore, for the media message to be more and more attractive, the terrorists began to attack even more spectacularly. Considering the above, cities conducive to forming clusters of people are attractive places to carry out an attack. To meet the emerging challenges, cities increasingly use modern information and communication technologies, transforming into smart cities. One of the basic assumptions for this is to ensure high safety and public order. Antiterrorist protection is a particular challenge for city authorities. Considering the above, the aim of the research, the effects of which are presented in the article, was to identify and describe the basic features distinguishing selected categories of terrorist attacks carried out in cities. In the course of the research, the duration of the terrorist event was analyzed and the critical relationships between the time and the effectiveness of neutralization and rescue operations at the scene were identified. The research method used, in addition to the observation and analysis of the literature, was a descriptive case study. Full article