Search Results (495)

High photovoltaic (PV) penetration in distribution networks (DNs) often causes network congestion, which in turn leads to renewable curtailment. Existing studies on battery swapping stations (BSSs) mainly focus on energy management of established stations, rather than system-level planning and coordination. To address these challenges, this study proposes a coordinated planning method for electric vehicle (EV) BSSs to improve PV utilization. The method integrates BSS siting, capacity sizing, and price-subsidy strategies into a unified mixed-integer linear programming (MILP) model. The model is developed to integrate road networks (RNs) and DNs, capturing the interaction between EV battery swapping behavior and DN operation. By guiding swapping behavior through price-subsidy strategies to align with local PV output, the method enables more flexible energy utilization and mitigates network congestion. Case studies are conducted on a combined IEEE 33-bus DN system and Sioux Falls RN. Results show that the proposed method can effectively improve local PV utilization and reduce curtailment without violating DN operational constraints. Overall, the proposed method provides an efficient and practical decision-support tool for the integrated planning of BSSs and renewable-rich DNs. Full article

As the deployment of Automated Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS-L2) expands, understanding their real-world safety performance becomes essential. This study examines the severity and contributing factors of crashes involving vehicles equipped with ADS and ADAS-L2 technologies using NHTSA data. Using machine learning models on crash datasets from 2021 to 2024, this research identifies patterns and risk factors influencing injury outcomes. After data preprocessing and handling missing values for severity classification, four models were trained: logistic regression, random forest, SVM, and XGBoost. XGBoost outperformed the others for both ADS and ADAS-L2, achieving the highest accuracy and recall. Variable importance analysis showed that for ADS crashes, interactions with other road users and poor lighting were the strongest predictors of injury severity, while for ADAS-L2 crashes, fixed object collisions and low light conditions were most influential. From a policy and engineering perspective, this study highlights the need for standardized crash reporting and improved ADS object detection and pedestrian response. It also emphasizes effective human–machine interface design and driver training for partial automation. Unlike previous research, this study conducts comparative model-based evaluations of both ADS and ADAS-L2 using recent crash reports to inform safety standards and policy frameworks. Full article

The comprehensive evaluation of factors that increase the difficulty of autonomous driving in various complex traffic situations and diverse roadway geometries within living lab environments is of great interest, particularly in developing sustainable urban mobility systems. This study introduces a novel methodology for assessing autonomous driving vulnerabilities and identifying urban traffic segments susceptible to autonomous driving risks in mixed traffic situations where autonomous and manual vehicles coexist. A microscopic traffic simulation network that realistically represents conditions in a living lab demonstration area was used, and twelve safety indicators capturing longitudinal safety and vehicle interaction dynamics were employed to compute an integrated risk score (IRS). The promising weighting of each indicator was derived through decision tree method calibrated with real-world traffic accident data, allowing precise localization of vulnerability hotspots for autonomous driving. The analysis results indicate that an IRS-based hotspot was identified at an unsignalized intersection, with an IRS value of 0.845. In addition, analytical results were examined comprehensively from multiple perspectives to develop actionable improvement strategies that contribute to long-term sustainability, encompassing roadway and traffic facility enhancements, provision of infrastructure guidance information, autonomous vehicle route planning, and enforcement measures. Furthermore, this study categorized and analyzed the characteristics of high-risk road sections with similar geometric features to systematically derive effective traffic safety countermeasures. This research offers a systematic, practical framework for safety evaluation in autonomous driving living labs, delivering actionable guidelines to support infrastructure planning and validate sustainable autonomous mobility. Full article

This systematic literature review explores the impacts of autonomous and connected mobility systems on sustainable road transportation. The evaluation process involves a multifaceted analysis, encompassing the assessment of their capacity to mitigate accidents, energy consumption, emissions, and urban traffic congestion. As a novel approach, this paper analyses the parameters of user acceptance of technology and how these are reflected in the overall impacts of automated and connected driving. Thus, based on a behavioral intention to use the new technology model, we aim to analyze the state of the art of the overall impacts that may be correlated with individual interests. To this end, a multi-factor approach is applied and potential interactions between factors that may arise are studied in a holistic and quantitative assessment of their combined effects on transportation systems. This impact assessment is a significant challenge, as numerous factors come into play, leading to conflicting effects. Since there is no significant penetration of vehicles with medium or high levels of automation, conclusions are often obtained through simulations or estimates based on hypotheses that must be considered when analyzing the results and can lead to significant dispersion. The results confirm that these technologies can substantially improve road safety, traffic efficiency, and environmental performance. However, their large-scale deployment will critically depend on the establishment of coherent regulatory frameworks, infrastructural readiness, and societal acceptance. Comprehensive stakeholder collaboration, incorporating industry, regulatory authorities, and society, is essential to successfully address existing concerns, facilitate technological integration, and maximize the societal benefits of these transformative mobility systems. Full article

Rear-end collisions remain a significant category of road accidents, despite widespread passive safety systems. Although modern seats are designed to reduce injury risk, the influence of accessory lumbar supports on passenger safety is still insufficiently investigated. This study analyzes the biomechanical response of a Hybrid III 50th percentile dummy on a vehicle seat fitted with various lumbar support types, compared to a reference configuration. Tests were conducted on a sled bench, simulating impacts of varying energy using crash pulses of 10 g, 15 g, and 20 g, for each tested lumbar support configuration in carefully controlled laboratory conditions. A key element of the procedure was analyzing changes in head and chest acceleration waveforms relative to results obtained for the reference seat. To quantitatively assess discrepancies between signals, the Root Mean Square Error (RMSE) and the CORA (CORrelation and Analysis) objective rating method were applied. These tools enabled precise separation of amplitude changes from phase shifts arising from different system dynamics. The results show that additional equipment elements modify dummy–seat interaction, with the extent of biomechanical response changes also depending on crash pulse value. This indicates that ergonomic supports are not biomechanically neutral and should be considered in comprehensive safety analyses. Full article

Vehicle trajectory prediction is a pivotal technology in intelligent transportation systems. Existing methods encounter challenges in effectively modeling lane topology and dynamic interaction relationships in complex traffic scenarios, limiting prediction accuracy and reliability. This paper presents Lane Interaction Transformer (LITransformer), a lane-informed trajectory prediction framework that builds on spatio–temporal graph attention networks and Transformer-based global aggregation. Rather than introducing entirely new network primitives, LITransformer focuses on two design aspects: (i) a lane topology encoder that fuses geometric and semantic lane features via direction-sensitive, multi-scale dilated graph convolutions, converting vectorized lane data into rich topology-aware representations; and (ii) an Interaction-Aware Graph Attention mechanism (IAGAT) that explicitly models four types of interactions between vehicles and lane infrastructure (V2V, V2N, N2V, N2N), with gating-based fusion of structured road constraints and dynamic spatio–temporal features. The overall architecture employs a Transformer module to aggregate global scene context and a multi-modal decoding head to generate diverse trajectory hypotheses with confidence estimation. Extensive experiments on the Argoverse dataset show that LITransformer achieves a minADE of 0.76 and a minFDE of 1.20, and significantly outperforms representative baselines such as LaneGCN and HiVT. These results demonstrate that explicitly incorporating lane topology and interaction-aware spatio-temporal modeling can significantly improve the accuracy and reliability of vehicle trajectory prediction in complex real-world traffic scenarios. Full article

We developed a robust tri-platform co-simulation framework that integrates Simulink, AMESim, and RecurDyn to address the dynamic inconsistencies observed in traditional tensioning models for tracked vehicles. The proposed framework synchronizes nonlinear hydraulic dynamics, closed-loop control, and track–ground interactions within a unified time step, thereby ensuring causal consistency along the pressure–flow–force–displacement power chain. Five representative operating conditions—including steady tension tracking, random road excitation, steering/braking pulses, supply-pressure drops, and parameter perturbations—were analyzed. The results show that the tri-platform model reduces tracking error by up to 60%, shortens recovery time by 35%, and decreases energy consumption by 12–17% compared with dual-platform models. Both simulations and full-scale experiments confirm that strong cross-domain coupling enhances system stability, robustness, and energy consistency under variable supply pressure and parameter uncertainties. The framework provides a high-fidelity validation tool and a transferable modeling paradigm for electro-hydraulic actuation systems in tracked vehicles and other multi-domain machinery. Full article

In the analysis of electric vehicle (EV) energy consumption, three main approaches are commonly used: physics-based models, artificial intelligence (AI) models, and hybrid frameworks that combine both. This combination enables more accurate estimations of EV energy consumption under diverse operating conditions, while also supporting applications in eco-driving, route planning, and urban energy management. Accurate analysis and prediction of EV energy consumption are critical for vehicle design, route planning, grid integration, and range anxiety. Recent advances in AI, notably machine learning (ML) and deep learning (DL), enable data-driven models that capture complex interactions among driving behavior, vehicle characteristics, road topology, traffic, and environmental conditions. This paper reviews the state of the art and presents a structured methodology for building, validating, and deploying AI models for EV energy consumption and efficiency analysis. Features, model architectures, performance metrics, explainability techniques, and system-level applications are discussed. Full article

Off-road vehicles, including those intended for mountain tourism, are also designed for people with special needs. These designs primarily concern the design of the drive of the vehicle, which can be manual, foot-powered, electric or a combination of these. Unusual forms of controlling these vehicles are also used, which use various parts of the body for this purpose, including the torso. In addition to using specific parts of the body to control the vehicle, an alternative is to use vision for this purpose, such as through eye tracking and similar techniques. The problem with these applications is the high prices of the devices and software used. They are mainly implemented in military solutions. The cost of these forms of vehicle control is too high; often higher than the price of the vehicle. This article presents an overview of the broad concept and technical solutions used to control various vehicles using hardware that can interact with the organ of vision. An extremely cheap prototype of this type of solution for several dozen EUR is also proposed in this article. The device uses methods based on vision techniques using the OpenCV 4_11 library. The research results in this area are presented, stating that such control is efficient. Full article

Autonomous vehicles (AVs) at SAE Levels 3 require a take-over request to switch from autonomous to manual mode when leaving the operational design domain (ODD). An appropriate take-over request lead time (TORlt) is necessary for safe interaction between AVs and non-AVs. This study developed a methodology to derive the optimal TORlt for AVs entering the area out of the ODD using a multi-agent driving simulator experiment. The multi-criteria decision-making method was adopted to integrate evaluation indicators to derive an optimal TORlt. The TORlt was defined as 3, 6, 9, 12, and 15 s in the driving simulation experiment scenario. The driving simulation experiment was conducted with a total of 60 participants. The simulation network was a two-lane urban road in each direction with a total length of 1.7 km, including a school zone where the autonomous driving mode is prohibited. Three requirements were established to determine the optimal TORlt: minimizing the take-over time, maximizing the success rate of take-over, and minimizing the potential of rear-end collisions due to vehicle interactions. After conducting comparative analyses of individual evaluation indicators for each scenario, a multi-criteria decision-making method was used for integrated evaluation to determine the optimal TORlt. It was found that the optimal TORlt for AVs on urban roads is 9 s. The results of this study can be used as valuable fundamentals in determining take-over requests for AVs toward safer vehicle interactions in the traffic stream. Full article

The increasing adoption of electric bicycles (e-bikes) has led to a growing need for advanced active safety systems, such as anti-lock braking systems (ABSs), to enhance rider safety. In recent years, both hydraulic and electromechanical ABSs were researched. To support the development and validation of these systems, this paper presents a longitudinal vehicle dynamics model of an electric bicycle. The model captures key physical interactions, including drivetrain, transmission, braking, and tire–road contact, to accurately simulate longitudinal motion. By leveraging this model, future studies can perform virtual validation of active safety components in a controlled and repeatable environment, reducing the dependency on costly and time-intensive physical testing. The proposed model lays the foundation for a model-based design approach, enabling early-stage performance assessment and optimization of safety-critical functions in electric bicycles. Full article

As the development of autonomous vehicles (AVs) progresses, new seating arrangements are emerging. Face-to-face seating is common in SAE L4 AV concepts and is intended to facilitate social interaction during autonomous driving, enabling previously unfeasible non-driving related tasks (NDRTs). However, this is countered by the unpopularity of rearward seating orientations, which is particularly pronounced in cars. In order to develop countermeasures to address this unpopularity, a deeper understanding of the underlying mechanisms is required. This study validates a model that predicts the acceptance of AVs and takes seating orientation into account. To this end, a study with N = 46 participants was conducted to investigate the influence of seating orientation on AV acceptance and related factors such as transparency, trust, and motion sickness. Additionally, internal human–machine interfaces (iHMIs) were evaluated in regard to their ability to compensate for the disadvantages of a rearward seating orientation. To achieve a realistic implementation of a fully functional SAE L4 AV, an experimental vehicle was equipped with a steering and pedal robot, performing self-driven journeys on a test track. The iHMIs provided information about upcoming maneuvers and detected road users. While engaged in a social NDRT, participants experienced a total of six journeys. Seating orientation and iHMI visualization were manipulated between journeys. Rearward-facing passengers showed lower levels of trust and higher levels of motion sickness than forward-facing passengers. However, the iHMIs had no effect on acceptance or related factors. Based on these findings, an updated version of the model is proposed, showing that rearward-facing passengers in autonomous vehicles pose a particular challenge for trust calibration and motion sickness mitigation. During NDRTs, iHMIs which depend on the attention of AV occupants for information transfer appear to be ineffective. Implications for future research and design of iHMIs to address this challenge are discussed. Full article

Current autonomous vehicles require human drivers to take over control during emergencies or in environments the system cannot handle. During other periods, drivers are permitted to engage in non-driving-related tasks. It is essential to investigate how the immersion in non-driving-related tasks affects drivers’ takeover performance under different scenarios. To address this, a mixed-design simulated driving experiment was conducted with 40 participants, incorporating three non-driving-related tasks (no task, watch video, play game), three takeover request lead times (3 s, 5 s, 7 s), and two obstacle types (dynamic, static). The takeover process was divided into three phases: preparation, obstacle avoidance, and recovery. Analysis of the areas of interest showed that engaging in non-driving-related tasks substantially reduced drivers’ visual attention tothe road ahead during the preparation phase. The Generalized Estimating Equations method was employed to investigate the effects of various factors on takeover performance. Model results showed that scenarios with static obstacles and longer takeover request times led to a significant reduction in mean lane deviation but a significant increase in the standard deviation of lane deviation, suggesting improved lateral control performance. A significant interaction was observed between the watch video task and static obstacles, which corresponded to a notable decrease in the mean vehicle speed during obstacle avoidance. Performance in the recovery phase was strongly predicted by that in the obstacle avoidance phase, indicating that the stability of the avoidance maneuver is a critical determinant of the subsequent recovery. These findings offer valuable insights for managing non-driving-related tasks and setting appropriate takeover request timings in automated driving systems. Full article

Electric vehicle charging stations (EVCSs) are critical interfaces between urban mobility and distribution grids and are increasingly exposed to false data that can mislead operations and degrade voltage quality. This study proposes a defense-planning framework that models how cyber manipulation propagates to physical impacts in a coupled transport–power system. The interaction is modeled as a tri-level defender–attacker–operator problem in which a defender hardens a subset of charging stations, an attacker forges measurements and demand, and an operator redispatches resources to keep the system secure. We solve this problem with a method that embeds corrective operation into the evaluation and uses improved implicit enumeration (IIE) with pruning to identify a small set of high-value stations to protect with far fewer trials than an exhaustive search. On a benchmark feeder coupled to a road network, protecting a few traffic-critical stations restores compliance with voltage limits under tested attack levels while requiring roughly an order of magnitude fewer evaluations than complete enumeration. Sensitivity analysis shows that the loss of reactive power from PV inverters (PV VARs) harms voltage profiles more than an equivalent reduction in distributed storage, indicating that maintaining local reactive capability reduces the number of stations that must be hardened to meet a given voltage target. These results guide utilities and city planners to prioritize protection at traffic-critical EVCSs and co-plan local Volt/VAR capability, achieving code-compliant voltage quality under adversarial conditions with markedly lower planning effort. Full article

Road safety has emerged as a global issue, driven by the rapid rise in vehicle ownership and traffic congestion. Human error, like distraction, drowsiness, and panic, is the leading cause of road accidents. Conventional driver monitoring systems (DMSs) frequently fail to detect these emotional and cognitive states, limiting their potential to prevent accidents. To overcome these challenges, this work proposes a robust deep learning-based DMS framework capable of real-time detection and response to emotion-driven driver behaviors that pose safety risks. The proposed system employs convolutional neural networks (CNNs), specifically the Inception module and a Caffe-based ResNet-10 with a Single Shot Detector (SSD), to achieve efficient, accurate facial detection and classification. The DMS is trained on a comprehensive and diverse dataset from various public and private sources, ensuring robustness across a wide range of emotions and real-world driving scenarios. This approach enables the model to achieve an overall accuracy of 98.6%, an F1 score of 0.979, a precision of 0.980, and a recall of 0.979 across the four emotional states. Compared with existing techniques, the proposed model strikes an effective balance between computational efficiency and complexity, enabling the precise recognition of driving-relevant emotions, making it a practical and high-performing solution for real-world in-car driver monitoring systems. Full article