Search Results (1,513)

Current passive anti-rollover systems exhibit inadequate adaptability to complex operational environments. Additionally, due to unidentified critical factors driving rollover incidents during landing, the design of active anti-tipping systems for airdrop remains constrained. Given the foregoing circumstances, this paper divides the landing impact process of the vehicle into the airbag cushioning stage and the rigid collision stage. In the airbag cushioning stage, a vertical impact test bench and a fluid–structure interaction (FSI) model is built up to obtain the terminal impact velocity when the airbag’s touching down speed is set as around 8 m/s. An oblique impact test bench and a dynamic model are proposed to investigate the influence of terminal sideslip angles and impact velocities on the vehicle’s roll/pitch stability during the rigid collision phase. Experimental and numerical analyses reveal that the peak overload during the airbag cushioning stage reaches approximately 11 g while the terminal impact velocity in this stage is around 2 m/s. In the rigid collision stage, higher initial descent velocities amplify the peak roll angles and significantly compromise the roll stability. Notably, adjusting the terminal sideslip angle from 90° to 0°/180° triples the critical horizontal velocity threshold from 5.3 m/s to 14.7 m/s which markedly enhances the vehicle’s stability. To address this, an active sideslip angle control system activated at a 250 m altitude is developed to align the vehicle’s horizontal velocity vector with its longitudinal axis to nearly 0°/180° and thus improves the roll/pitch stability. This study establishes a technical foundation for the design of a highly reliable anti-rollover device for the airdrop vehicle. Full article

Unmanned surface vehicles (USVs) rely on multi-sensor perception, such as radar, LiDAR, GPS, and vision, to ensure safe and efficient navigation in complex maritime environments. Traditional ant colony optimization (ACO) for path planning, however, suffers from premature convergence, slow adaptation, and poor smoothness in cluttered waters, while the dynamic window approach (DWA) without global guidance can become trapped in local obstacle configurations. This paper presents a sensor-oriented hybrid method that couples an improved ACO for global route planning with an enhanced DWA for local, real-time obstacle avoidance. In the global stage, the ACO state–transition rule integrates path length, obstacle clearance, and trajectory smoothness heuristics, while a cosine-annealed schedule adaptively balances exploration and exploitation. Pheromone updating combines local and global mechanisms under bounded limits, with a stagnation detector to restore diversity. In the local stage, the DWA cost function is redesigned under USV kinematics to integrate velocity adaptability, trajectory smoothness, and goal-deviation, using obstacle data that would typically originate from onboard sensors. Simulation studies, where obstacle maps emulate sensor-detected environments, show that the proposed method achieves shorter paths, faster convergence, smoother trajectories, larger safety margins, and higher success rates against dynamic obstacles compared with standalone ACO or DWA. These results demonstrate the method’s potential for sensor-based, real-time USV navigation and collision avoidance in complex maritime scenarios. Full article

Blasts, vehicle collisions, and other unexpected incidents may cause lateral impacts on building structures, which threaten their safety. This paper investigates the impact resistance of cold-formed steel (CFS) L-shaped built-up columns (LBC). Firstly, a finite element model (FEM) was established and validated through experiments conducted by the authors. Then, a parametric analysis was conducted to quantify the effects of axial compression ratio, impact velocity, and dimensions on the impact response. The results indicated that: (1) The peak lateral impact force of the specimens presented a significant nonlinear trend with increasing axial compression ratio, and an optimal axial compression ratio was found as about 0.3. (2) Higher impact velocity intensified both force and displacement responses of the specimens, and both lateral impact peak force and maximum displacement increased significantly with the impact velocity. When the impact velocity rose from 3.13 m/s to 6.26 m/s, the peak force and maximum displacement increased by an average of 38.2% and 96.5%, respectively. (3) Increasing the cross-sectional dimensions and steel thickness, and reducing screw spacing, could significantly enhance the impact resistance and deformation capacity of the specimens. This study reveals the failure mechanism of such members and the laws of parameter influence, which can be used for impact design of CFS-LBC. Full article

Sensor-rich Automated Guided Vehicles (AGVs) are increasingly deployed in logistics, yet large fleets relying on fixed tracks face high maintenance costs and frequent route conflicts. This study targets rail-based material handling and proposes an end-to-end multi-AGV navigation pipeline under realistic operational constraints. A conflict-aware global planner, extended from the A* algorithm, generates feasible routes, while a multi-sensor perception stack integrates LiDAR and camera data to distinguish moving AGVs, static obstacles, and task targets. Based on this perception, a Deep Q-Network (DQN) policy with a tailored reward function enables real-time dynamic obstacle avoidance in complex traffic. Simulation results demonstrate that, compared with the Artificial Potential Field (APF) baseline, the proposed GG-DRL approach reduces collisions by ~70%, lowers planning time by 25–30%, shortens paths by 10–15%, and improves smoothness by 20–25%. On the Maze Benchmark Map, GG-DRL surpasses classical planners (e.g., RRT) and deep RL baselines (e.g., DDPG) in path quality, computation, and avoidance behavior, achieving an average path length of 81.12, computation time of 11.94 s, 5.2 avoidance maneuvers, and smoothness of 0.86. Robustness is maintained as a dynamic obstacles scale up to 30. These findings confirm that combining multi-sensor fusion with deep reinforcement learning enhances AGV safety, efficiency, and reliability, with broad potential for intelligent railway logistics. Full article

A wide range of research has been conducted on path planning and collision avoidance to enhance the operational efficiency of unmanned aerial vehicles (UAVs). The existing works have mainly assumed an environment with static obstacles and global positioning system (GPS) signals. However, practical environments have often been involved with dynamic obstacles, dense areas with numerous obstacles in confined spaces, and blocked GPS signals. In order to consider these issues for practical implementation, a deep reinforcement learning (DRL)-based method is proposed for path planning and collision avoidance in GPS-denied and dense environments. In the proposed method, robust path planning and collision avoidance can be conducted by using the received signal strength (RSS) value with the extended Kalman filter (EKF). Additionally, the attitude of the UAV is adopted as part of the action space to enable the generation of smooth trajectories. Performance was evaluated under single- and multi-target scenarios with numerous dynamic obstacles. Simulation results demonstrated that the proposed method can generate smoother trajectories and shorter path lengths while consistently maintaining a lower collision rate compared to conventional methods. Full article

Background/Objectives: This case represents the successful treatment of cervical spine injury from high-speed rear-impact motor vehicle collision and abnormal cervical kyphosis with left arm radiculopathy, utilizing conservative spine care rehabilitation methods. This patient was treated with a multimodal treatment approach integrating a cervical spine extension traction protocol. Subject and Methods: A 50-year-old male with a history of motor vehicle collision presented with left arm radiculopathy, as well as cervical and upper thoracic spine pain. Notably the cervical spine presented with kyphotic deformity. The patient presented, after a being struck during a rear-end motor vehicle collision, with neck, upper back, and left arm radiculopathy. Prescription medication and traditional chiropractic care proved ineffective for substantive symptom and quality-of-life improvement. Treatment frequency was three times per week for eight weeks using the Chiropractic Biophysics^® protocol of mirror image (MI^®) postural exercise, spinal adjustment, and cervical spinal traction. On completion of in-office care, the patient was treated monthly, performed home care at least three times per week, and was re-examined at one year. Results: Final examination after eight weeks of care showed significant improvement in cervical lordosis (21.8 degrees), resulting in reduced cervical kyphosis. The patient completed outcome indices before, during, and 12 months after cessation of active care, all indicating improvement. Conclusions: This case report demonstrates both subjective and objective improvement in cervical spine kyphosis and attendant symptoms. The successful treatment of chronic pain, peripheral weakness, and radiculopathy with long-term follow-up using CBP care is documented as well. The treatment was designed to improve sagittal balance and reduce radiographic abnormalities evincing spinal misalignment. Administration of subjective, objective, and health-related quality-of-life outcome indices during, following, and 12 months post-treatment are suggestive of long-term efficacy of Chiropractic BioPhysics^® (CBP) treatment methods. Larger studies are needed to substantiate this given the limitations of a case report. Full article

Unmanned aerial vehicles (UAVs) operating in uncertain environments must plan safe and efficient trajectories while avoiding obstacles. This work addresses this challenge by formulating UAV path planning as a stochastic optimal control problem using covariance control. The objective is to generate a closed-loop guidance policy that steers both the mean and covariance of the UAV’s state toward a desired target distribution while ensuring probabilistic collision avoidance with ellipsoidal obstacles. The stochastic problem is convexified and reformulated as a sequence of deterministic optimization problems, enabling efficient computation even from coarse initial guesses. Simulation results demonstrate that the proposed method successfully produces robust trajectories and feedback policies that satisfy chance constraints on obstacle avoidance and reach the target with prescribed statistical characteristics. Full article

Under no-load conditions, the wet clutch of vehicles generates drag torque across a wide speed range, which increases power loss in the transmission system and significantly impacts its efficiency and reliability. To address the clutch drag issue over a wide speed range, this study first establishes a low-speed drag torque model that simultaneously considers the viscous friction effects in both the complete oil film region and the oil film rupture zone of the friction pair clearance. Subsequently, by solving the fluid-structure interaction dynamics model of the friction plates, the collision force between high-speed friction pairs and the resulting friction torque are determined, forming a method for calculating high-speed collision-induced drag torque. Building on this, a unified drag torque model for wet clutches across a wide speed range is developed, integrating both viscous and collision-induced drag torques. The validity of the wide-speed-range drag torque model is verified through experiments. The results indicate that as oil temperature and friction pair clearance increase, the drag torque decreases and the rotational speed corresponding to the peak drag torque is reduced, while the onset of collision phenomena occurs earlier. Conversely, with an increase in oil supply flow rate, the drag torque rises and the rotational speed corresponding to the peak drag torque increases, but the onset of collision phenomena is delayed. Finally, with the optimization objectives of minimizing the peak drag torque in the low-speed range and the total drag torque at the maximum speed in the high-speed range, an optimization design model for the surface grooves of the clutch friction plates is constructed. An optimized groove pattern is obtained, and its effectiveness in suppressing drag torque across a wide speed range is experimentally validated. Full article

Rear-end collisions at unsignalized intersections remain a persistent issue in urban traffic environments, particularly at stop-controlled junctions. This study develops a real-time predictive model aimed at identifying potential rear-end conflicts, employing Deep & Cross Network Version 2 (DCNV2) to improve prediction accuracy. The methodology comprises three main components: data acquisition, model development, and interpretability analysis. Real-time vehicle trajectory data such as speed, inter-vehicle distance, and interaction behavior are collected and preprocessed before being analyzed using the DCNV2 model to uncover patterns associated with conflict risk. The model integrates cross-feature interactions to enhance predictive performance. Evaluation metrics, including accuracy, recall, and area under the curve (AUC), demonstrate that DCNV2 outperforms conventional classifiers such as logistic regression and support vector machines. To further evaluate model interpretability, SHapley Additive exPlanations (SHAP) are applied, revealing that short following distances, large speed differentials, and high traffic volumes on major roads are primary contributors to rear-end conflict risk. The findings provide actionable insights to inform proactive traffic safety strategies, particularly in urban areas where limited signalization or manual control exposes drivers to increased uncertainty. This predictive framework supports the development of real-time safety interventions and contributes to more effective risk mitigation at critical locations within the traffic network. Full article

The study presents a comparative analysis of five distinct image processing methodologies for roadside wild animal detection using thermal imagery, aiming to identify an optimal approach for embedded system implementation to mitigate wildlife–vehicle collisions. The evaluated techniques included the following: bilateral filtering followed by thresholding and SIFT feature matching; Gaussian filtering combined with Canny edge detection and contour analysis; color quantization via the nearest average algorithm followed by contour identification; motion detection based on absolute inter-frame differencing, object dilation, thresholding, and contour comparison; and animal detection based on a YOLOv8n neural network. These algorithms were applied to sequential thermal images captured by a custom roadside surveillance system incorporating a thermal camera and a Raspberry Pi processing unit. Performance evaluation utilized a dataset of consecutive frames, assessing average execution time, sensitivity, specificity, and accuracy. The results revealed performance trade-offs: the motion detection method achieved the highest sensitivity (92.31%) and overall accuracy (87.50%), critical for minimizing missed detections, despite exhibiting the near lowest specificity (66.67%) and a moderate execution time (0.126 s) compared to the fastest bilateral filter approach (0.093 s) and the high-specificity Canny edge method (90.00%). Consequently, considering the paramount importance of detection reliability (sensitivity and accuracy) in this application, the motion-based methodology was selected for further development and implementation within the target embedded system framework. Subsequent testing on diverse datasets validated its general robustness while highlighting potential performance variations depending on dataset characteristics, particularly the duration of animal presence within the monitored frame. Full article

The development of efficient and reliable trajectory-planning strategies for swarms of unmanned aerial vehicles (UAVs) is an increasingly important area of research, with applications in surveillance, search and rescue, smart agriculture, defence operations, and communication networks. This article provides a comprehensive and critical review of the various techniques available for UAV swarm trajectory planning, which can be broadly categorised into three main groups: traditional algorithms, biologically inspired metaheuristics, and modern artificial intelligence (AI)-based methods. The study examines cutting-edge research, comparing key aspects of trajectory planning, including computational efficiency, scalability, inter-UAV coordination, energy consumption, and robustness in uncertain environments. The strengths and weaknesses of these algorithms are discussed in detail, particularly in the context of collision avoidance, adaptive decision making, and the balance between centralised and decentralised control. Additionally, the review highlights hybrid frameworks that combine the global optimisation power of bio-inspired algorithms with the real-time adaptability of AI-based approaches, aiming to achieve an effective exploration–exploitation trade-off in multi-agent environments. Lastly, the article addresses the major challenges in UAV swarm trajectory planning, including multidimensional trajectory spaces, nonlinear dynamics, and real-time adaptation. It also identifies promising directions for future research. This study serves as a valuable resource for researchers, engineers, and system designers working to develop UAV swarms for real-world, integrated, intelligent, and autonomous missions. Full article

Conflicts in freeway interchange diverging areas remain poorly understood, particularly their characteristics and severity determinants. To address this gap, we extracted over 20,000 vehicle trajectories from UAV footage at 16 interchange divergence zone across five multi-lane expressways using a YOLOX–DeepSORT method. From these trajectories, we identified longitudinal and lateral conflicts and classified their severity into minor, moderate, and severe levels using a two-dimensional extended time-to-collision metric. Subsequently, we incorporated 19 macroscopic traffic-flow and microscopic driver-behavior variables into four conflict-severity models–multivariate logistic regression, random forest, CatBoost, and XGBoost—and conducted to identify the key determinants of conflict severity based on the optimal models. The results indicate that lateral conflicts last longer and pose higher collision risks than longitudinal ones. Furthermore, moderate conflicts are most prevalent, whereas severe conflicts are concentrated within 300 m upstream of exit ramps. Specifically, for longitudinal conflicts, the most influential factors include speed difference, target-vehicle speed, truck involvement, traffic density, and exit behavior. In contrast, for lateral conflicts, the most critical factors include lane-change frequency, speed difference, target-vehicle speed, distance to the exit ramp, and truck proportion. Overall, these findings support the development of hazardous-driving warning systems and proactive safety management strategies in interchange diverging areas. Full article

Autonomous vehicles, submersible robotic systems and drones, and other human-carrying equipment consistently adhere to a safety perimeter, ensuring collision-free navigation amidst surrounding objects. In contrast, roller coaster vehicles, despite being constrained to a predetermined track, necessitate frequent safety distance detection owing to the variability introduced by trees and decorative installations. Passengers’ limbs may protrude beyond vehicle boundaries, posing a collision hazard. The motion range of limbs, influenced by vehicle-specific conditions, mismatches standardized safety volumes (cylindrical, cubic, and rectangular) designed for mobile entities. The roller coaster industry’s current practice involves a moving safety frame, which visually inspects for collisions to assess safety distances, which is cumbersome and prone to oversight in intricate settings. Therefore, this study introduces a novel safety envelope detector (SE-detector). It creates a customer-defined virtual safety envelope around the roller coaster vehicle and measures the safety distance based on LiDAR (Light Detection and Ranging) to detect the intrusions of obstacles. Meanwhile, this SE-detector also innovatively integrated an accelerometer to synchronously measure the acceleration of the vehicle. The measured acceleration will be aligned with simulated sequences by dynamic time warping (DTW) algorithms to pinpoint intrusion location. Additionally, a wide-angle camera is also deployed to enhance perception of the surrounding environment. The SE-detector developed in this study has the capability to record inspection results. It is expected to enhance the inspection capabilities of the safety envelope for roller coasters, thereby improving the efficiency of safety distance inspection. Full article

The paper presents an algorithm for planning agricultural field surveying routes in the presence of obstacles, designed to address precision agriculture tasks. Unlike classical methods, which are typically limited to straightforward zigzag (Zamboni) traversal and basic perimeter-based obstacle avoidance, the proposed algorithm accounts for heterogeneous unmanned aerial vehicles (UAVs) of varying types, ranges, costs, and speeds, along with a mobile ground platform that enables drone takeoff and landing at multiple points along the road. The key innovation lies in a two-stage optimization procedure: initially, a random set of field partitions into multiple sub-polygons with predefined area proportions (considering internal obstacles) is generated. Subsequently, the optimal partitioning is selected, and based on this, a genetic algorithm is applied to optimize flight parameters, including flight angle, entry points, composition, and sequence of drone launches, and the ground platform route. This approach achieves more localized coverage of individual field segments, with each segment serviced by an appropriate drone type, while also enabling flexible movement of the ground platform, thereby reducing unnecessary flights. This brings down the price of the coverage by 10–30% in some cases. The concluding section discusses future directions, including the incorporation of three-dimensional terrain considerations, dynamic factors (such as changing weather conditions and drone stoppages due to technical issues), and automated collision avoidance in intersecting route segments. Full article

Objectives: Despite the implementation of additional safety measures, motor vehicle collisions (MVCs) still result in significant injuries and fatalities. This study aims to explore the severity of these injuries and the length of hospital stays (LOS) following MVCs. Furthermore, this study will assess how helmet use and alcohol influence trauma outcomes. Methods: This retrospective study from a single center includes 604 patients from 1 January 2016, to 31 December 2024. Patients were identified based on the Abbreviated Injury Scale (AIS) body regions. Descriptive statistics and ANOVA were performed on helmet use and blood alcohol concentration, with significance set at p < 0.01. Results: Mean LOS at the hospital (H) was 13 days, 10.53 h in the ED, and 113.32 h in the ICU. In total, 74.5% of patients were male and 25.5% were female. The mean injury severity score (ISS) was 22.58, with 99.83% representing blunt trauma. The majority of patients (94.21%) arrived with signs of life, with 50.99% patients discharged to home or self-care (routine discharge). A noticeable trend following 2020 showed an increase in ED discharges, and thus ED admissions, compared to years before 2020. Helmet use showed a non-significant trend toward reduced ISS and length of stay. ETOH level and primary payor source were not significantly associated with outcome variables in regression models, though patterns suggest a potential relationship between payor source and ED discharge disposition. Conclusions: This study identifies important clinical trends that merit further investigation. Helmet use may be associated with reduced injury severity and shorter hospital stays, while differences in primary payor source suggest disparities in ED discharge outcomes. These findings underscore the need for further research on payor disposition, helmet use, and ETOH level in MVCs. Full article