Search Results (40)

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

For the commercialization of connected vehicles and smart cities, extensive research is carried out on autonomous driving, Vehicle-to-Everything (V2X) communication, and platooning. However, limitations remain, such as restrictions to highway environments, and studies are conducted separately due to challenges in ensuring reliability and real-time performance under external influences. This paper proposes a cooperative autonomous driving system based on V2X network implemented in the CARLA simulator, which simulates an urban environment to optimize vehicle-embedded systems and ensure safety and real-time performance. First, the proposed Throttle–Steer–Brake (TSB) driving technique reduces the computational overhead for following vehicles by utilizing the control commands of a leading vehicle. Second, a V2X network is designed to support object perception, cluster escape, and joining. Third, an urban perception system is developed and validated for safety. Finally, pseudonymized vehicle identifiers, Advanced Encryption Standard (AES), and the Edwards-curve Digital Signature Algorithm (EdDSA) are employed for data reliability and security. The system is validated in processing time and accuracy, confirming feasibility for real-world application. TSB driving demonstrates a computation speed approximately 466 times faster than conventional waypoints-based driving. Accurate urban perception and V2X communication enable safe cluster escape and joining, establishing a foundation for cooperative autonomous driving with improved safety and real-time capabilities. Full article

As transportation systems evolve, ensuring safe and efficient mobility in Intelligent Transportation Systems remains a priority. Work zones, in particular, pose significant safety challenges due to lane closures, which can lead to abrupt braking and sudden lane changes. Most previous research on Connected and Autonomous Vehicles (CAVs) assumes ideal communication conditions, overlooking the effects of message loss and network unreliability. This study presents a comprehensive smart work zone (SWZ) framework that enhances lane-change safety by the integration of both Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. Sensor-equipped SWZ barrels and Roadside Units (RSUs) collect and transmit real-time hazard alerts to approaching CAVs, ensuring coverage of critical roadway segments. In this study, a co-simulation framework combining VEINS, OMNeT++, and SUMO is implemented to assess lane-change safety and communication performance under realistic network conditions. Findings indicate that higher Market Penetration Rates (MPRs) of CAVs can lead to improved lane-change safety, with time-to-collision (TTC) values shifting toward safer time ranges. While lower transmission thresholds allow more frequent communication, they contribute to earlier network congestion, whereas higher thresholds maintain efficiency despite increased packet loss at high MPRs. These insights highlight the importance of incorporating realistic communication models when evaluating traffic safety in connected vehicle environments. Full article

The advancement of intelligent road transport represents a promising direction in the evolution of transportation systems, aimed at improving road safety and reducing traffic accidents. The integration of artificial intelligence, sensors, and machine vision systems enables autonomous vehicles (AVs) to rapidly adapt to changes in the road environment, minimizing human error and significantly reducing collision risks. These technologies provide continuous and highly precise control, including adaptive acceleration, braking, and maneuvering, thereby enhancing overall road safety. Connected vehicles utilizing C-V2X (Cellular Vehicle-to-Everything) communication primarily feature real-time operation, safety, and stability. However, communication flaws, such as signal fading, time delays, packet loss, and malicious network attacks, can affect vehicle-to-vehicle interactions in cooperative intelligent transport systems (C-ITSs). This study explores how C-V2X technology, compared to traditional DSRC, improves communication latency and enhances vehicle communication efficiency. Using SUMO simulations, various traffic scenarios were modeled with different autonomous vehicle penetration rates and communication technologies, focusing on traffic conflict rates, travel time, and communication performance. The results demonstrated that C-V2X reduced latency by over 99% compared to DSRC, facilitating faster communication between vehicles and contributing to a 38% reduction in traffic conflicts at 60% AV penetration. Traffic flow and safety improved with increased AV penetration, particularly in congested conditions. While C-V2X offers substantial benefits, challenges such as data packet loss, communication delays, and security vulnerabilities must be addressed to fully realize its potential. Future advancements in 5G and subsequent wireless communication technologies are expected to further reduce latency and enhance the effectiveness of C-ITSs. This study underscores the potential of C-V2X to enhance collision avoidance, alleviate congestion, and improve traffic management, while also contributing to the development of more reliable and efficient transportation systems. The continued refinement of simulation models and collaboration among stakeholders will be crucial to addressing the challenges in CAV integration and realizing the full benefits of connected transportation systems in smart cities. Full article

This paper introduces a new concept of a smart and sustainable tire based on a removable tread band: ECOTIRE. Current tires, though crucial for road information and vehicle control, such as braking, traction, and turning, remain disconnected from Advanced Driver Assistance Systems (ADAS). Additionally, their production, use, and recycling pose significant environmental challenges, requiring sustainable materials and lifecycle improvements. The ECOTIRE concept makes it possible to separate the part of the tire subject to wear and apply new materials with reduced environmental impact. At the same time, the service life of the casing is extended, facilitating the introduction of sensors that improve vehicle safety. This study explores the purely mechanical connection between the casing and tread, demonstrating the feasibility of this innovative tire structure while eliminating the need for rubber matrix-based materials for a proper bond between the two components. Experimental tests using a rubber sample to simulate the tire–road contact patch validate the effectiveness of the mechanical link under varying normal loads. Grip test results, measuring longitudinal and lateral forces, show promising performance. This advancement in tire technology marks a first step toward sustainability, tire performance, and smart integration, ultimately reducing environmental impact. Full article

Sound-based early fault detection for vehicles is a critical yet underexplored area, particularly within Intelligent Transportation Systems (ITSs) for smart cities. Despite the clear necessity for sound-based diagnostic systems, the scarcity of specialized publicly available datasets presents a major challenge. This study addresses this gap by contributing in multiple dimensions. Firstly, it emphasizes the significance of sound-based diagnostics for real-time detection of faults through analyzing sounds directly generated by vehicles, such as engine or brake noises, and the classification of external emergency sounds, like sirens, relevant to vehicle safety. Secondly, this paper introduces a novel dataset encompassing vehicle fault sounds, emergency sirens, and environmental noises specifically curated to address the absence of such specialized datasets. A comprehensive framework is proposed, combining audio preprocessing, feature extraction (via Mel Spectrograms, MFCCs, and Chromatograms), and classification using 11 models. Evaluations using both compact (52 features) and expanded (126 features) representations show that several classes (e.g., Engine Misfire, Fuel Pump Cartridge Fault, Radiator Fan Failure) achieve near-perfect accuracy, though acoustically similar classes like Universal Joint Failure, Knocking, and Pre-ignition Problem remain challenging. Logistic Regression yielded the highest accuracy of 86.5% for the vehicle fault dataset (DB1) using compact features, while neural networks performed best for datasets DB2 and DB3, achieving 88.4% and 85.5%, respectively. In the second scenario, a Bayesian-Optimized Weighted Soft Voting with Feature Selection (BOWSVFS) approach is proposed, significantly enhancing accuracy to 91.04% for DB1, 88.85% for DB2, and 86.85% for DB3. These results highlight the effectiveness of the proposed methods in addressing key ITS limitations and enhancing accessibility for individuals with disabilities through auditory-based vehicle diagnostics and emergency recognition systems. Full article

Ensuring comfort in light mobility is a crucial aspect for supporting individuals’ well-being and safety while driving scooters, riding bicycles, etc. In fact, factors such as the hand grip on the handlebar, positions of the wrist and arm, overall body posture, and affecting vibrations play key roles. Wearable systems offer the ability to noninvasively monitor physiological parameters, such as body temperature and heart rate, aiding in personalized comfort assessment. In this context, user positions while driving or riding are, on the other hand, more challenging to monitor ecologically. Developing effective smart gloves as a support for comfort and movement monitoring introduces technical complexities, particularly in sensor selection and integration. Light and flexible sensors can help in this regard by ensuring reliable sensing and thus addressing the optimization of the comfort for the driver. In this work, a novel wireless smart glove is proposed, integrating four bend sensors, four force-sensitive sensors, and one inertial measurement unit for measuring the finger movements, hand orientation, and the contact force exerted by the hand while grasping the handlebar during driving or riding. The smart glove has been proven to be repeatable (1.7%) and effective, distinguishing between different grasped objects, such as a flask, a handlebar, a tennis ball, and a small box. Additionally, it proved to be a valuable tool for monitoring specific actions while riding bicycles, such as braking, and for optimizing the posture during the ride. Full article

This paper examines the energy efficiency of smart electric vehicles equipped with regenerative braking systems under challenging weather conditions. While Advanced Driver Assistance Systems (ADAS) are primarily designed to enhance driving safety, they often overlook energy efficiency. This study proposes a Weather-Adaptive Regenerative Braking Strategy (WARBS) system, which leverages onboard sensors and data processing capabilities to enhance the energy efficiency of regenerative braking across diverse weather conditions while minimizing unnecessary alerts. To achieve this, we develop driving style recognition models that integrate road conditions, such as weather and road friction, with different driving styles. Next, we propose an adaptive deceleration plan that aims to maximize the conversion of kinetic energy into electrical energy for the vehicle’s battery under varying weather conditions, considering vehicle dynamics and speed constraints. Given that the potential for energy recovery through regenerative braking is diminished on icy and snowy roads compared to dry ones, our approach introduces a driving context recognition system to facilitate effective speed planning. Both simulation and experimental validation indicate that this approach can significantly enhance overall energy efficiency. Full article

Addressing the traffic safety issues caused by pedestrian–vehicle conflicts during street crossing, this study proposes optimization strategies from both theoretical and technical perspectives. A safety braking distance model is introduced, taking into account pedestrians’ psychological safety and vehicle braking processes. Additionally, an active safety warning system for crosswalks has been designed. This system features a modular design, including detection, control, alarm, and wireless communication modules. It can monitor, in real-time, the positions and speeds of pedestrians and vehicles, assess potential conflicts between them under various scenarios, and implement different warning strategies accordingly. Compared to mainstream variable message sign (VMS) warning systems, this proposed system shows significant advantages in terms of section-weighted total delay metrics. Through simulations involving 3000 pedestrian crossings and comparative analyses of vehicle speed, pedestrian speed, vehicle deceleration rate, and accident numbers before and after the application of the active safety warning system, it was found that the critical accident rate indicator decreased from 0.27% to 0.06%. The results demonstrate that the system effectively provides bidirectional warnings to pedestrians and vehicles, significantly enhancing the safety of pedestrian street crossings. This research offers new insights into addressing pedestrian crossing safety issues. Full article

A smart regenerative braking system for EVs can reduce unnecessary brake operations by assisting in the braking of a vehicle according to the driving situation, road slope, and driver’s preference. Since the strength of regenerative braking is generally determined based on calibration data determined during the vehicle development process, some drivers could encounter inconveniences when the regenerative braking is activated differently from their driving habits. In order to solve this problem, various deep learning-based algorithms have been developed to provide driving stability by learning the driving data. Among those artificial intelligence algorithms, anomaly detection algorithms can successfully separate the deceleration data in abnormal driving situations, and the resulting refined deceleration data can be used to train the regression model to achieve better driving stability. This study evaluates the performance of a personalized driving assistance system by applying driver characteristic data, obtained through an anomaly detection algorithm, to vehicle control. Full article

Magnetorheological (MR) brakes are flourishing in low-torque applications due to their dynamic controllability nature. Researchers have introduced multi-layer and multipole concepts to increase the torque–volume ratio (TVR) of the MR brake. However, the combination of these two ideas did not exist due to the design limitations. Therefore, this study aims to design a brake that combines the multipole magnetic field and multi-layered structure concepts. The axial slots were introduced on the brake rotor and the stator drum axial surfaces to achieve a high TVR. These slots stop the flux bypass in the inner layers; therefore, the magnetic flux can also reach the brake’s outer layers. This brake was designed with multiple stator and rotor drums and MR fluid layers. The number of poles was placed so that the magnetic field from these poles traveled in a closed loop via the stator, rotor, and MR layers. A 3D model of the brake was prepared for the virtual study. Electromagnetic simulations were conducted to analyze the effect of axial slots’ and other design parameters of the brake. According to those simulation results, the axial slots’ width and position significantly affect the brake output torque. The maximum torque obtained from the brake is 38 Nm, and the TVR value of the brake is 41 Nm/dm³. Additionally, multiphysics simulations were performed to understand the Joule-heating effect of the magnetic coil and the frictional heating in MR fluid. Results showed that the maximum possible temperature in the brake is under the MR fluid temperature limits. Therefore, this multipole multi-layered (MPML) MR brake with axial slots idea is very useful for high-torque MR brake growth. Full article

A smart city (SC) includes different systems that are highly interconnected. Transportation and energy systems are two of the most important ones that must be operated and planned in a coordinated framework. In this paper, with the complete implementation of the SC, the performance of each of the network elements has been fully analyzed; hence, a nonlinear model has been presented to solve the operation and planning of the SC model. In the literature, water treatment issues, as well as energy hubs, subway systems (SWSs), and transportation systems have been investigated independently and separately. A new method of subway and electric vehicle (EV) interaction has resulted from stored energy obtained from subway braking and EV parking. Hence, considering an SC that simultaneously includes renewable energy, transportation systems such as the subway and EVs, as well as the energy required for water purification and energy hubs, is a new and unsolved challenge. In order to solve the problem, in this paper, by presenting a new system of the SC, the necessary planning to minimize the cost of the system is presented. This model includes an SWS along with plug-in EVs (PEVs) and different distributed energy resources (DERs) such as Photovoltaics (PVs), Heat Pumps (HPs), and stationary batteries. An improved grey wolf optimizer has been utilized to solve the nonlinear optimization problem. Moreover, four scenarios have been evaluated to assess the impact of the interconnection between SWSs and PEVs and the presence of DER technologies in the system. Finally, results were obtained and analyzed to determine the benefits of the proposed model and the solution algorithm. Full article

Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance. Full article

The need for certification standards for new convergence products, such as a smart LED streetlight system, has been identified as a critical issue. This study proposes simulation modeling for smart LED streetlight systems and suggests three certification standards: the minimum time to initiate dimming-up, the duration of the dimming-up period, and the number of concurrently controlled streetlights. We utilized Relux to model streetlights and roads in terms of luminance levels, and used analytical formulas to compute the braking distances of oncoming vehicles. The two models were integrated into a smart LED streetlight system model using Simio. Simulation experiments were conducted with two objectives: to provide certification standards, and to apply and verify them in real-world cases. We experimented with 630 scenarios, modeling various dynamic situations involving roads and vehicles, and applied the model to two actual roads in the Republic of Korea to test its validity. The model was subsequently applied to roads for which traffic-volume data were available, to determine potential energy savings. The proposed simulation method can be applied to a smart LED streetlight system and to new products that lack certification standards. Furthermore, the proposed certification standards offer alternative approaches to operating streetlight systems more efficiently. Full article

As urban centers evolve into smart cities, sustainable mobility emerges as a cornerstone for ensuring environmental integrity and enhancing quality of life. Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to significantly improve efficiency and safety, and reduce environmental impacts. This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV lane-changing in mixed-traffic scenarios, a critical component of intelligent transportation systems in smart cities. By incorporating a local reward system that values efficiency, safety, and passenger comfort, and a parameter-sharing scheme that encourages inter-agent collaboration, our MA2C algorithm presents a comprehensive approach to urban traffic management. The MA2C algorithm leverages reinforcement learning to optimize lane-changing decisions, ensuring optimal traffic flow and enhancing both environmental sustainability and urban living standards. The actor–critic architecture is refined to minimize variances in urban traffic conditions, enhancing predictability and safety. The study extends to simulating realistic human-driven vehicle (HDV) behavior using the Intelligent Driver Model (IDM) and the model of Minimizing Overall Braking Induced by Lane changes (MOBIL), contributing to more accurate and effective traffic management strategies. Empirical results indicate that the MA2C algorithm outperforms existing state-of-the-art models in managing lane changes, passenger comfort, and inter-vehicle cooperation, essential for the dynamic environment of smart cities. The success of the MA2C algorithm in facilitating seamless interaction between AVs and HDVs holds promise for more fluid urban traffic conditions, reduced congestion, and lower emissions. This research contributes to the growing body of knowledge on autonomous driving within the framework of sustainable smart cities, focusing on the integration of AVs into the urban fabric. It underscores the potential of machine learning and artificial intelligence in developing transportation systems that are not only efficient and safe but also sustainable, supporting the broader goals of creating resilient, adaptive, and environmentally friendly urban spaces. Full article