Autonomous vehicle (AV) technology can provide a safe and convenient transportation solution for the public, but the complex and various environments in the real world make it difficult to operate safely and reliably. A connected autonomous vehicle (CAV) is an AV with vehicle connectivity capability, which enhances the situational awareness of the AV and enables the cooperation between AVs. Hence, CAV technology can enhance the capabilities and robustness of AV to be a promising transportation solution in the future. This paper introduces a representative architecture of CAVs and surveys the latest research advances, methods, and algorithms for sensing, perception, planning, and control of CAVs. It reviews the state-of-the-art and state-of-the-practice (when applicable) of a multi-layer Perception-Planning-Control architecture including on-board sensors and vehicular communications, the methods of sensor fusion and localization and mapping in the perception layer, the algorithms of decision making and trajectory planning in the planning layer, and the control strategies of trajectory tracking in the control layer. Furthermore, the implementations and impact of vehicle connectivity and the corresponding consequential challenges of cooperative perception, complex connected decision making, and multi-vehicle controls are summarized and their significant research issues enumerated. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems of CAVs in each Section which would be helpful to researchers in the field. The comprehensive coverage of this paper makes it particularly useful to academic researchers, practitioners, and students alike.
Federated learning (FL) plays an important role in the development of smart cities. With the evolution of big data and artificial intelligence, issues related to data privacy and protection have emerged, which can be solved by FL. In this paper, the current developments in FL and its applications in various fields are reviewed. With a comprehensive investigation, the latest research on the application of FL is discussed for various fields in smart cities. We explain the current developments in FL in fields, such as the Internet of Things (IoT), transportation, communications, finance, and medicine. First, we introduce the background, definition, and key technologies of FL. Then, we review key applications and the latest results. Finally, we discuss the future applications and research directions of FL in smart cities.
The transition to electric buses involves a shift in public transport systems, requiring changes to the role of public transport authorities (PTAs). This study analyses how PTAs' role as planners and procurers of public transport is impacted by electrification, employing a sustainability transition perspective. Through thematic analysis of semi-structured interviews with 25 interviewees involved in eight public procurements, both qualitative and quantitative changes were identified. The findings show that the organisational model influences how electrification changes the role and responsibilities of PTAs, as new technology alters established procurement logics and the reinterpretation of procurement principles. Additionally, this transition necessitates changes in long-term strategic planning, including adjustments in ownership structures and the distribution of responsibilities. The emergence and assessment of new questions and phenomena, for example, regarding battery production and sustainability, further change the role of the PTA. The rapid pace of this technological change challenges PTAs' ability to proactively manage developments, creating a dynamic where PTAs both take on new responsibilities and react to market changes. This study highlights the emerging tensions between PTAs' procurement principles and long-term planning objectives, calling attention to the need for a balanced approach to manage their evolving assignments effectively in the face of new technology.
Taking advantage of both vehicle-to-everything (V2X) communication and automated driving technology, connected and automated vehicles are quickly becoming one of the transformative solutions to many transportation problems. However, in a mixed traffic environment at signalized intersections, it is still a challenging task to improve overall throughput and energy efficiency considering the complexity and uncertainty in the traffic system. In this study, we proposed a hybrid reinforcement learning (HRL) framework which combines the rule-based strategy and the deep reinforcement learning (deep RL) to support connected eco-driving at signalized intersections in mixed traffic. Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I) communications to achieve higher mobility and energy efficiency in mixed connected traffic. The HRL framework has three components: a rule-based driving manager that operates the collaboration between the rule-based policies and the RL policy; a multi-stream neural network that extracts the hidden features of vision and V2I information; and a deep RL-based policy network that generate both longitudinal and lateral eco-driving actions. In order to evaluate our approach, we developed a Unity-based simulator and designed a mixed-traffic intersection scenario. Moreover, several baselines were implemented to compare with our new design, and numerical experiments were conducted to test the performance of the HRL model. The experiments show that our HRL method can reduce energy consumption by 12.70% and save 11.75% travel time when compared with a state-of-the-art model-based Eco-Driving approach.
With the development of communication and networking technologies, the Internet of Vehicles (IoV) has become the foundation of smart transportation. The development of blockchain and Machine Learning (ML) has contributed to the pervasiveness of the IoV, and they can effectively address the current issues of decentralisation, cyber security and data privacy in the IoV. In this article, blockchain and ML in the IoV are both reviewed, and corresponding technologies to support blockchain intelligence in the IoV are summarized. Importantly, blockchain intelligence is proposed as a key to integrate blockchain and ML, combining the advantages of both to drive the rapid development of the IoV. We discuss general frameworks, issuses, requirements and advantages for the implementation of blockchain intelligence in the IoV. Driven by its advantages, we summarize solutions of blockchain intelligence in the IoV from four aspects, including reliable interaction, network security and data privacy, trustworthy environment and scalability. Finally, a summary of current unresolved issues and challenges of blockchain intelligence in the IoV is presented, which provides guidelines for the future development of the IoV.
The automotive domain has realized amazing advancements in communication, connectivity, and automation—and at a breakneck pace. Such advancements come with ample benefits, such as the reduction of traffic accidents and the refinement of transit efficiency. However, these new developments were not necessarily made with security in mind. Researchers have unearthed a number of security vulnerabilities in paradigms such as in-vehicle networks (IVNs), the Internet of Vehicles (IoV), and intelligent transportation systems (ITSs). As automotive technologies continue to evolve, it would be realistic to expect new vulnerabilities to arise—both vulnerabilities that are identified and vulnerabilities that are not. If—or more pragmatically, when—these vulnerabilities are exploited, intrusion detection will be paramount. Therefore, we find it prudent to review intrusion detection in the automotive domain. We explore a myriad of threats and intrusion detection techniques—from the boundaries of the vehicle’s own network to the wider Internet of Vehicles (IoV). Intrusion detection, while not a panacea, can be a cost-effective solution to many automotive security issues. Generally, such intrusion detection systems (IDSs) do not disrupt existing hardware, infrastructure, or communications; rather, they merely tap into the network and monitor for suspicious traffic. Given the very reasonable price tag, the implementation of intrusion detection systems would be an auspicious step by the automotive industry to assure the security—and safety—of the modern automobile. This paper volunteers a comprehensive review of intrusion detection technologies in the automotive domain.
The Internet of Vehicles (IoV) is expected to become the central infrastructure to provide advanced services to connected vehicles and users for higher transportation efficiency and security. A variety of emerging applications/services bring explosively growing demands for mobile data traffic between connected vehicles and roadside units (RSUs), imposing the significant challenge of spectrum scarcity in the IoV. In this article, we propose a cooperative semantic-aware architecture to convey essential semantics from collaborated users to servers for lessening data traffic. In contrast to current solutions that are mainly based on piling up highly complex signal processing techniques and multiple access capabilities in terms of syntactic communications, this article puts forth the idea of semantic-aware content delivery in the IoV. Specifically, the successful transmission of essential semantics of the source data is pursued rather than the accurate reception of symbols regardless of their meaning, as in conventional syntactic communications. To assess the benefits of the proposed architecture, we provide a case study of an image retrieval task for vehicles in intelligent transportation systems (ITSs). Simulation results demonstrate that the proposed architecture outperforms existing solutions, with fewer radio resources, especially in a low-signal-to-noise-ratio (SNR) regime, which can shed light on the potential of the proposed architecture in extending applications in extreme environments.
Advanced digital data-driven applications have evolved and significantly impacted the transportation sector in recent years. This systematic review examines natural language processing (NLP) approaches applied to aviation safety-related domains. The authors use Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to conduct this review, and three databases (Web of Science, Scopus, and Transportation Research International Documentation) are screened. Academic articles from the period 2010–2022 are reviewed after applying two rounds of filtering criteria. The sub-domains, including aviation incident/accident reports analysis and air traffic control (ATC) communications, are investigated. The specific NLP approaches, related machine learning algorithms, additional causality models, and the corresponding performance are identified and summarized. In addition, the challenges and limitations of current NLP applications in aviation, such as ambiguity, limited training data, lack of multilingual support, are discussed. Finally, this review uncovers future opportunities to leverage NLP models to facilitate the safety and efficiency of the aviation system.
Jesus M. Barajas, Sarah Grajdura, Alex Karner
et al.
In early 2025, the US Department of Transportation (DOT) presented new criteria for discretionary funding prioritization based on marriage rates, birth rates, and compliance with Federal immigration policy. This policy diverges from the previous equity and justice-focused prioritization. We analyze how the new DOT policy will affect discretionary transportation spending priorities across geography, sociodemographics, transportation burdens and barriers, and voting lines. The new 2025 DOT policy shifts funding priorities towards white, Trump-voting areas and away from Black, Latino, and lower-resourced populations and those experiencing higher travel burdens and barriers.
Transportation and communications, Urban groups. The city. Urban sociology
Ladan Gholami, Pietro Ducange, Alberto Gotta
et al.
Accurate prediction of receiver state is vital for optimizing network performance in urban settings, where rapid spatial variations in channel conditions pose significant challenges to communication quality. This paper presents a Machine Learning-based framework for predicting channel states in Unmanned Aerial Vehicle-assisted mmWave communication networks. Given that mmWave signals are susceptible to blockage by buildings and other urban structures, predicting receiver conditions at a specific location can be determined by directly deploying the geometric features describing the built-up environment surrounding the receiver. A set of geometrical features is extracted and used as input to train the adopted learning models, namely Decision Tree, Linear Decision Tree (LDT), Random Forest, Support Vector Machine, and Deep Neural Network (DNN), to estimate the probability of three distinct receiver states: Line-of-Sight, Non-Line-of-Sight, and Blocked. Experimental results indicate that the DNN-based model achieves the highest prediction accuracy and robustness, while the LDT provides computational efficiency and straightforward explainability. To improve the interpretability of the black-box DNN model, we employ the SHapley Additive exPlanations (SHAP) method, which identifies the most influential environmental features in state probability prediction. Furthermore, we enrich the standard 3GPP model by incorporating the top SHAP-ranked features, leading to notable performance improvements.
Telecommunication, Transportation and communications
In this paper, we study the downlink precoding of spatial non-stationary (SnS) extremely large-scale multiple-input multiple-output (XL-MIMO) in the near-field channel with uniform spherical wave (USW) and mixed line-of-sight and non-line-of-sight environments. First, we present a novel precoding scheme by replacing the digital beamforming (BF) in the conventional hybrid BF with user-subarray pairing network. The presented scheme utilizes the SnS channel characteristic efficiently, and can facilitate the high-speed implementation and the deployment of low-resolution digital-to-analog converters. Next, we study the optimizations to maximize both the sum-rate and min-rate by designing the power allocation, user-subarray pairing network, and analog BF jointly. For the sum-rate maximization, we first reformulate the corresponding nonconvex problem by the quadratic transformation to facilitate the further processing. Then, the alternating optimization framework is utilized to optimize the variables alternately. Concretely, the Riemannian conjugate gradient method, projected gradient ascent method, and mathematical programming with equilibrium constraints alternating direction method are employed to optimize the analog BF, power allocation, and user-subarray pairing network, respectively. For the min-rate maximization, we extend the aforementioned solutions by proper modifications. Finally, the effectiveness of the proposed optimization algorithms is verified through computer simulations.
Telecommunication, Transportation and communications
Integrated sensing and communication (ISAC) systems have emerged as a promising solution to improve spectrum efficiency and enable functional convergence. However, ensuring secure information transmission while maintaining high-quality sensing performance remains a significant challenge. In this paper, we investigate an integrated sensing and covert communication (ISCC) system, in which a base station (BS) simultaneously serves multiple downlink users and senses malicious targets that may act as both potential eavesdroppers (Eves) and wardens. We propose a novel symbol-level precoding (SLP)-based waveform design for ISCC that achieves covert communication intrinsically, without requiring additional transmission resources such as artificial noise. The proposed design integrates symbol shaping to enhance reliability for legitimate users and noise shaping to obscure transmission activities from the targets. For imperfect channel state information (CSI), the framework incorporates bounded uncertainty models for user channels and target angles, yielding a more robust design. The resulting ISCC waveform optimization problem is non-convex; to address this, we develop a low-complexity proximal distance algorithm (PDA) with closed-form updates under both PSK and QAM modulations. Simulation results demonstrate that the proposed method achieves superior covertness and sensing-communication performance with negligible degradation compared to traditional beamforming and conventional SLP approaches without noise-shaping mechanisms.
This paper introduces a secure affine frequency division multiplexing (SE-AFDM) for wireless communication systems to enhance communication security. Besides configuring the parameter c1 to obtain communication reliability under doubly selective channels, we also utilize the time-varying parameter c2 to improve the security of the communications system. The derived input-output relation shows that the legitimate receiver can eliminate the nonlinear impact introduced by the time-varying c2 without losing the bit error rate (BER) performance. Moreover, it is theoretically proved that the eavesdropper cannot separate the time-varying c2 and random information symbols, such that the BER performance of the eavesdropper is severely deteriorated. Meanwhile, the analysis of the effective signal-to-interference-plus-noise ratio (SINR) of the eavesdropper illustrates that the SINR decreases as the value range of c2 expands. Numerical results verify that the proposed SE-AFDM waveform has significant security while maintaining good BER performance in high-mobility scenarios.
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.
Connected vehicles (CVs) utilizing cellular vehicle-to-everything (C-V2X) technology are increasingly coexisting on the road with regular vehicles (RVs). As these CVs interact with each other and with roadside infrastructure through vehicle-vehicle and vehicle-infrastructure cooperation, the characteristics of traffic flow are changing in significant ways. It is therefore crucial to understand how different parameters of CVs, roadside sensors, and V2X communications affect the stability of heterogeneous traffic flow. In this research, we investigate the impact of several transportation and infrastructure parameters on the stability of heterogeneous traffic flow. Specifically, we first examine the effects of traffic density, penetration rate of CVs, detection accuracy of roadside sensors, and time delays in V2X communications. We propose a novel C-V2X-based vehicle-vehicle/vehicle-infrastructure cooperation architecture and develop a car-following model based on it. Then, the theoretical stability condition for heterogeneous traffic flow is derived, which reveals the interdependence of transportation and infrastructure parameters. The numerical simulations show that the proposed C-V2X-based vehicle-vehicle/vehicle-infrastructure cooperation architecture achieves traffic flow stability at lower CV penetration rates compared to existing studies that only consider vehicle-to-vehicle communications. This finding highlights the importance of leveraging the full potential of C-V2X technology for improving traffic flow stability in real-world settings.
Sourav Banerjee, Debashis Das, Pushpita Chatterjee
et al.
The notion of an intelligent transportation system (ITS) aims to boost the performance of transportation networks, which has gained more and more traction in both academic and commercial circles. ITS is a constantly evolving vision that combines cutting-edge transportation approaches with new information, communication, computers, and other technology. ITS should discover consequence routes to enhance the sustainability, safety, and trustworthiness of the entire transportation system utilizing emerging technologies. In this paper, a sustainable safety management framework for connected vehicles is proposed by integrating blockchain. It introduces smart transportation equipment called an AI-enabled vehicle smart device (AVSD) for vehicular communications. AVSD can reduce energy consumption by decreasing the computational costs in vehicular communications. Smart contracts are used to identify vehicles automatically and establish secure communication among vehicles and emergency service stations (ESSs) like hospitals, police stations, and fire stations. The experiment results show that the proposed framework provides a communication environment for sustainable safety and security using the introduced smart transportation device. The proposed blockchain-enabled sustainable safety management framework has the potential to improve safety and sustainability in the transportation industry by creating a secure, decentralized, and transparent platform for managing safety data and promoting safe and sustainable driving behaviors.