Search Results (160)

Sensor fusion in intelligent suspension systems constitutes a fundamental technology for optimizing vehicle dynamic stability, ride comfort, and occupant safety. By integrating data from multiple sensor modalities, this study proposes a hierarchical multi-sensor fusion framework for active suspension control, aiming to enhance control precision. Initially, a binocular vision system is employed for target detection, enabling the identification of lane curvature initiation points and speed bumps, with real-time distance measurements. Subsequently, the integration of Global Positioning System (GPS) and inertial measurement unit (IMU) data facilitates the extraction of road elevation profiles ahead of the vehicle. A BP-PID control strategy is implemented to formulate mode-switching rules for the active suspension under three distinct road conditions: flat road, curved road, and obstacle road. Additionally, an ant colony optimization algorithm is utilized to fine-tune four suspension parameters. Utilizing the hardware-in-the-loop (HIL) simulation platform, the observed reductions in vertical, pitch, and roll accelerations were 5.37%, 9.63%, and 11.58%, respectively, thereby substantiating the efficacy and robustness of this approach. Full article

At the Politecnica Salesiana University (UPS) in Guayaquil, Ecuador, urban mobility challenges were addressed with the aim of improving students’ quality of life and promoting sustainability. This study evaluated the technical, economic, and social feasibility of implementing a shared transportation (carpooling) system using a quantitative-descriptive approach. Surveys were applied to a stratified sample of 256 students to analyze transportation habits. Route planning was performed using ArcGIS software, and costs were calculated with Microsoft Excel. Social impact assessment involved focus groups and analysis of variables such as changes in mobility patterns, system acceptance, and perceived safety, comfort, and accessibility. Key indicators included the percentage of students willing to participate in the pilot (82.7%), satisfaction with travel time savings (85.7% fully satisfied), and positive perceptions of safety and comfort. The results suggest that the proposed system is not only economically viable but also widely accepted by students, contributing to reduced stress, travel time, and single-occupancy vehicle use. This study demonstrates the feasibility of shared transport in urban universities and provides a replicable model to guide sustainable mobility policies that improve safety, comfort, and efficiency in student commuting. Full article

Passenger safety remains a primary goal in vehicle engineering, requiring the development of advanced passive safety systems to reduce injuries during collisions. Impact attenuators (particularly for race cars) are a crucial component for the safety of the driver. The impact of the impact attenuator (IA) is demonstrated by the behavior of a seat-belted dummy in a frontal collision with a rigid wall. The aim of this paper is to confirm the qualities of water as a damping agent in the manufacturing of the IA. To reach a conclusion, a theoretical model is used and experimental tests are performed. Once the loads operating on the dummy have been identified, it is confirmed that they fall within the range that the existing requirements recommend. The car is viewed as a structure with a seat-belt-fastened dummy and an impact attenuator. Research is being conducted on a new water-based impact attenuator technology. A frontal collision of the car–dummy assembly was taken into consideration when analyzing the dummy’s behavior in accordance with the criteria. A simulation program was used to calculate the accelerations at various points on the mannequin’s body as well as the force that manifested on the seat belts. So, the good qualities of IAs using water are revealed and support designers in their efforts to obtain better shock behavior. In the simulation, the variation of internal energy accumulated by the vehicle, displacements and velocities of various points on the chassis, as well as the accelerations of the vehicle and the occupant were determined. In the experiment, the vehicle velocities for both test cases were established and used in the simulation, and the accelerations of the vehicle and dummy were measured. The assessment was carried out by comparing experimental and simulation data, focusing on acceleration values recorded on both the dummy and the vehicle. Evaluation criteria such as HIC and ThAC were applied to determine the severity of the impact and the effectiveness of the proposed water-based attenuator. Full article

Urban traffic congestion poses persistent challenges to mobility, public safety, and governance efficiency in metropolitan areas. This study proposes an intelligent traffic flow forecasting framework based on an extended Long Short-Term Memory (xLSTM) model, specifically designed for real-time congestion prediction and proactive police dispatch support. Utilizing a real-world dataset collected from over 300 vehicle detector (VD) sensors, the proposed model integrates vehicle volume, speed, and lane occupancy data at five-minute intervals. Methodologically, the xLSTM model incorporates matrix-based memory cells and exponential gating mechanisms to enhance spatio-temporal learning capabilities. Model performance is evaluated using multiple metrics, including congestion classification accuracy, F1-score, MAE, RMSE, and inference latency. The xLSTM model achieves a congestion prediction accuracy of 87.3%, an F1-score of 0.882, and an average inference latency of 41.2 milliseconds—outperforming baseline LSTM, GRU, and Transformer-based models in both accuracy and speed. These results validate the system’s suitability for real-time deployment in police control centers, where timely prediction of traffic congestion enables anticipatory patrol allocation and dynamic signal adjustment. By bridging AI-driven forecasting with public safety operations, this research contributes a validated and scalable approach to intelligent transportation governance, enhancing the responsiveness of urban mobility systems and advancing smart city initiatives. Full article

Rear-end collisions represent a major concern in automotive safety, particularly due to the risk of whiplash injuries among vehicle occupants. The accurate simulation of occupant kinematics during such impacts is critical for the development of advanced safety systems. This paper presents an enhanced multibody simulation model specifically designed for rear-end crash scenarios, incorporating integrated active headrest mechanisms and sensor-based activation logic. The model combines detailed representations of vehicle structures, suspension systems, restraint systems, and occupant biomechanics, allowing for the precise prediction of crash dynamics and occupant responses. The system was developed using Simscape Multibody, with CAD-derived components interconnected through physical joints and validated using controlled experimental crash tests. Special attention was given to modelling contact forces, suspension behaviour, and actuator response times for the active headrest system. The model achieved a root mean square error (RMSE) of 4.19 m/s² and a mean absolute percentage error (MAPE) of 0.71% when comparing head acceleration in frontal collision tests, confirming its high accuracy. Validation results demonstrate that the model accurately reproduces occupant kinematics and head acceleration profiles, confirming its reliability and effectiveness as a predictive tool. This research highlights the critical role of integrated sensor-actuator systems in improving occupant safety and provides a flexible platform for future studies on intelligent vehicle safety technologies. Full article

In recent decades, the automotive industry has faced challenges around improving energy efficiency, reducing pollutant emissions, increasing occupant safety, and reducing production costs. To solve these challenges, it is necessary to reduce the weight of vehicle bodies. In this way, the steel industry has developed more efficient metal alloys. To combine vehicle mass reduction with improved performance in deformations in cases of impact, a new family of advanced steels is present, AHSS (Advanced High-Strength Steels). However, this family of steels has lower formability and greater springback compared to conventional steels; if it is not properly controlled, it will directly affect the accuracy of the product and its quality. Different regions of a stamped component, such as the flange, the body wall, and the punch pole, are subjected to different states of stress and deformation, determined by numerous process variables, such as friction/lubrication and tool geometry, in addition to blank holder force and drawbead geometry, which induce the material to different deformation modes. Thus, it is understood that the degree of work hardening in each of these regions can be evaluated by grain morphology and material hardening, defining critical regions of embrittlement that, consequently, will affect the material’s stampability. This work aims to study the formability of the cold-formed DP600 steel sheets in the die radius region using a Modified Nakazima test, varying drawbead geometry, followed by a nanohardness evaluation and material characterization through the electron backscatter diffraction (EBSD). The main objective is to analyze the work hardening in the critical blank regions by applying these techniques. The nanoindentation evaluations were consistent in die radius and demonstrated the hardening influence, proving that the circular drawbead presented the most uniform hardness variation along the profile of the stamped blank and presented lower hardness values in relation to the other geometries, concluding that the drawbead attenuates this variation, contributing to better sheet formability, which corroborates the Forming Limit Curve results. Full article

This article presents the design, implementation, and validation of a Remotely Piloted Safety Cone (RPSC), an autonomous robotic system developed to enhance safety and operational efficiency in road maintenance. The RPSC addresses challenges associated with road works, including workers’ exposure to traffic hazards and inefficiencies of traditional traffic cones, such as manual placement and retrieval, limited visibility in low-light conditions, and inability to adapt to dynamic changes in work zones. In contrast, the RPSC offers autonomous mobility, advanced visual signalling, and real-time communication capabilities, significantly improving safety and operational flexibility during maintenance tasks. The RPSC integrates sensor fusion, combining Global Navigation Satellite System (GNSS) with Real-Time Kinematic (RTK) for precise positioning, Inertial Measurement Unit (IMU) and encoders for accurate odometry, and obstacle detection sensors within an optimised navigation framework using Robot Operating System (ROS2) and Micro Air Vehicle Link (MAVLink) protocols. Complying with European regulations, the RPSC ensures structural integrity, visibility, stability, and regulatory compliance. Safety features include emergency stop capabilities, visual alarms, autonomous safety routines, and edge computing for rapid responsiveness. Field tests validated positioning accuracy below 30 cm, route deviations under 15 cm, and obstacle detection up to 4 m, significantly improved by Kalman filtering, aligning with digitalisation, sustainability, and occupational risk prevention objectives. Full article

This paper presents a review of explosion mitigation techniques for road tunnels, with a focus on scenarios involving high-pressure hydrogen tank rupture under fire conditions. Both passive and active strategies are considered—including structural configurations (e.g., tunnel branching, vent openings, right-angle bends) and protective systems (e.g., drop-down perforated plates, high-performance fibre-reinforced cementitious composite (HPFRCC) panels)—to reduce blast impact on tunnel occupants and structures. The review highlights that while measures such as blast walls or energy-absorbing barriers can significantly attenuate blast pressures, an integrated approach addressing both blast load reduction and structural resilience is essential. This paper outlines how coupled computational fluid dynamics–finite element method (CFD–FEM) simulations can evaluate these mitigation methods, and we discuss design considerations (e.g., optimising barrier placement and tunnel geometry) for enhanced safety. The findings provide guidance for designing safer hydrogen vehicle tunnels, and they identify gaps for future research, including the need for experimental validation of combined CFD–FEM models in hydrogen fire–explosion scenarios. Full article

Crash boxes play a vital role in improving vehicle safety by absorbing collision energy and reducing the forces transmitted to occupants. Additive manufacturing (AM) has become a powerful method for developing advanced crash boxes by enabling complex geometries. This review provides a comprehensive examination of recent progress in AM crash boxes, with a focus on three key aspects: geometric design innovations, material behavior, and manufacturing techniques. The review investigates the influence of various AM-enabled structural configurations, including tubular, origami-inspired, lattice, and bio-inspired designs, on crashworthiness performance. Among these, bio-inspired structures exhibit superior energy absorption characteristics, achieving a mean specific energy absorption $\left(SEA\right)$ of 21.51 J/g. Material selection is also explored, covering polymers, fiber-reinforced polymers, metals, and multi-material structures. Metallic AM crash boxes demonstrate the highest energy absorption capacity, with a mean $SEA$ of 28.65 J/g. In addition, the performance of different AM technologies is evaluated, including Stereolithography (SLA), Material Jetting (MJT), Selective Laser Melting (SLM), Selective Laser Sintering (SLS), Fused Deposition Modeling (FDM), and hybrid manufacturing techniques. Among these, crash boxes produced by SLM show the most favorable energy absorption performance, with a mean $SEA$ of 16.50 J/g. The findings presented in this review offer critical insights to guide future research and development in the design and manufacturing of next-generation AM crash boxes intended to enhance vehicle safety. Full article

Path tracking is an essential task for autonomous driving (AD), for which controllers are designed to issue commands so that vehicles will follow the path of upper-level decision planning properly to ensure operational safety, comfort, and efficiency. Current path-tracking methods still face challenges in balancing tracking accuracy with computational overhead, and more critically, lack consideration for Motion Sickness (MS) mitigation. However, as AD applications divert occupants’ attention to non-driving activities at varying degrees, MS in self-driving vehicles has been significantly exacerbated. This study presents a novel framework, the Hybrid Supervised–Reinforcement Learning (HSRL), designed to reduce passenger discomfort while achieving high-precision tracking performance with computational efficiency. The proposed HSRL employs expert data-guided supervised learning to rapidly optimize the path-tracking model, effectively mitigating the sample efficiency bottleneck inherent in pure Reinforcement Learning (RL). Simultaneously, the RL architecture integrates a passenger MS mechanism into a multi-objective reward function. This design enhances model robustness and control performance, achieving both high-precision tracking and passenger comfort optimization. Simulation experiments demonstrate that the HSRL significantly outperforms Proportional–Integral–Derivative (PID) and Model Predictive Control (MPC), achieving improved tracking accuracy and significantly reducing passengers’ cumulative Motion Sickness Dose Value (MSDV) across several test scenarios. Full article

The objective of this study was to analyze the effects of variables such as pre-crash emergency braking and reclined posture on human injuries in autonomous vehicle collisions using an active human model and through crash analysis. To achieve this, the MADYMO (MAthematical DYnamic MOdels) active human model was validated for predicting occupant responses during pre-crash emergency braking. Its biofidelity during crash conditions was also validated. Additionally, the model was validated under component-level impact conditions to ensure its suitability for predicting occupant injuries. Two autonomous vehicle-relevant crash scenarios reconstructed based on actual accident conditions were selected. Variations in collision conditions, such as collision angles, overlaps, and relative collision speeds, were applied to selected crash scenarios. A finite element vehicle-to-vehicle crash analysis was performed to obtain the crash pulse. Using the validated crash analysis model, a parametric simulation study was conducted by applying variations to parameters such as emergency braking, seat-related parameters, and muscle activity. Finally, the impact of each variable on injury risk was analyzed using the Wilcoxon rank sum test. Analysis results showed that a reclined posture and a seat track position located 300 mm rearward from the baseline seat track position had a significant impact on injuries. Evaluation results on the effects of these variables can contribute to the development of safety evaluation standards for autonomous vehicles, such as crash safety regulations, by crash safety assessment organizations. Full article

Cruise construction involves a lengthy logistical cycle, complex processes, and large volumes of diverse materials, inevitably generating reverse flows. To mitigate risks such as stock congestion, production disruption, and occupational hazards, this study proposes a novel reverse logistics network optimization model that integrates cost, efficiency, and Health, Safety, Environment (HSE) risk factors. Realistic factors including vehicle load, transport cost, loading time, and risk weight were considered to improve model applicability. Fuzzy time windows quantify worker risk exposure and operational efficiency, adding decision-making complexity. A three-phase Levy mutation discrete crow search algorithm (DCSA) was developed, introducing the Levy flight strategy to replace random search and enhance the discretization and solution diversity. The comparative analysis shows that DCSA performs as well as NSGA-II, while outperforming DGWO, demonstrating both stability and efficiency. Comparative analysis with a cost-only scenario revealed that although short-term economic gains may be achieved under cost minimization, such approaches often overlook risks with potential long-term impacts. This highlights the necessity of integrating safety concerns into reverse logistics planning, and confirms the model’s robustness and practical value, thus supporting decision makers in aligning reverse logistics planning in shipyards with sustainability and operational efficiency goals. Full article

As a key roadside safety feature, longitudinal guardrail steel barriers are purposefully designed to contain and redirect errant vehicles to prevent roadway departure, dissipate impact energy through plastic deformation, and reduce the severity of vehicle crashes. Nevertheless, these systems should be carefully designed and assessed, as localized rupturing, especially near splice or impact locations, can lead to catastrophic failures, compromising vehicle containment, violating crash safety standards, and ultimately jeopardizing the safety of occupants and other road users. Before conducting full-scale crash testing, finite element analysis (FEA) tools are widely employed to evaluate the design efficiency, optimize system configurations, and preemptively identify potential failure modes prior to expensive physical crash testing. To accurately assess system behavior, calibrated material models and precise failure criteria must be utilized in these simulations. Despite the existence of numerous failure criteria and material models, the material characteristics of AASHTO M-180 guardrail steel have not been fully investigated. This paper significantly advances the FE modeling of ductile fracture in guardrail steel, addressing a critical need within the roadside safety community. This study formulates stress-state-dependent failure criteria and proposes advanced material modeling techniques. Extensive experimental testing was conducted on steel specimens having various triaxiality and Lode parameter values to reproduce a wide spectrum of complex, three-dimensional stress-state loading conditions. The test results were then used to identify material properties and construct a failure surface. Subsequent FEA, which incorporated the Generalized Incremental Stress-State-Dependent Damage Model (GISSMO) in conjunction with two LS-DYNA material models, illustrates the capability of the developed surface and material input parameters to predict material behavior under various stress states accurately. A parametric study was completed to further validate the proposed models, highlighting their robustness and reliability. Full article

Seatbelts are critical components of vehicle safety, continuously evolving through technological advancements and regulatory updates. Traditionally designed to secure occupants during collisions, seatbelt innovations, such as retractors, pretensioners, and load limiters, have significantly enhanced comfort and effectiveness. With the advent of autonomous vehicles, seatbelt systems must adapt to new safety challenges, including real-time tension adjustment through active seatbelt systems. These systems, integrated with active safety technologies like automatic emergency braking, offer a more comprehensive safety solution. Furthermore, seatbelt technology must address the diverse needs of different passenger categories. Quantitative data highlight the role of seatbelts for various passenger categories. Children are 55% more likely to be injured by rear structure intrusion and 27% more likely to suffer from compression into the front seat during rear impacts. Pregnant women generally experience milder injuries but are more prone to abdominal injuries. Older adults, who account for 17% of crash fatalities, are more likely to suffer thoracic injuries and fractures due to increased bone fragility. This review explores the integration of traditional and modern seatbelt systems, focusing on passenger-specific adaptations and the future role of seatbelts in autonomous vehicles. This study is based on a thorough literature review, analyzing data from the Web of Science, Scopus, and SAE databases, where available, to assess the contributions and impact of these innovations. Full article

Side impacts tend to produce more severe injuries than frontal collisions, particularly for vulnerable occupants such as children. Despite this, there is a limited number of studies and developments focused on side impact protection systems, and existing airbag evaluations often rely on destructive and high-cost test methods. This study introduces a novel, cost-effective, and nondestructive experimental testbed designed to evaluate curtain airbags for vehicles in segments B, C, D, and E. The main objective is to develop an adjustable mechanical structure that replicates the side frame geometry of multiple vehicles, allowing the mounting and evaluation of various curtain airbags under realistic conditions. The prototype, capable of withstanding deployment forces of up to 7000 N, was tested with a 3-year-old child dummy, recording a peak head acceleration of 136.17 g, corresponding to AIS level 2. Deployment speeds reached 7.77 m/s, with inflation times between 29 and 36 ms—values that fall within the range reported in previous experimental and numerical studies. The testbed demonstrated consistency in its performance metrics and offers a valuable tool for enhancing child occupant safety in side impacts. Furthermore, it provides a measurable Head Injury Criterion (HIC) range that can be used to interpret injury severity in child occupants. This work contributes significantly to the development of flexible and safe testing methodologies for side airbag systems, reducing the reliance on full-scale crash testing. Full article