Search Results (58)

Traffic accidents involving pedestrians during the hours of darkness pose a serious threat to road safety due to reduced visibility and drivers’ delayed perception of the traffic situation. Accurate estimation of the detection distance for pedestrians is essential in the reconstruction of traffic accidents. This study analyzes the relationship between driver visibility, environmental conditions, and the ability to detect pedestrians in a timely manner during nighttime driving. The study examines the main factors influencing the driver’s “perception–reaction” process, including the illumination provided by the vehicle’s headlights, the illumination of the road environment, the contrast and reflective properties of the pedestrian’s clothing, as well as the driver’s level of attention. Using a graph-analytical method, the detection distances for pedestrians under nighttime conditions are estimated. A real-life accident scenario was reconstructed to determine whether the driver had sufficient time and distance to perceive the danger and take action to avoid a collision. The results show that pedestrian visibility depends on lighting conditions, which directly affect the driver’s reaction time. These findings contribute to the refinement of the methodological approach to reconstructing traffic accidents and can assist experts in conducting automotive technical examinations. Full article

Urban traffic accidents emerge from complex interactions among traffic instability, roadway structure, environmental disturbance, and temporal dynamics, yet many existing prediction approaches still treat accident risk as a discrete classification problem over isolated observations. This study proposes a Continuous Hazard Field Intelligence Framework for Traffic Accident Emergence and Urban Safety Prediction, which models hidden urban danger as a topology-aware spatio-temporal hazard field that evolves continuously across connected transportation infrastructure. The framework integrates heterogeneous urban traffic observations, including incident records, crash data, roadway attributes, temporal cues, and contextual risk factors, into a unified hazard-aware learning pipeline. A dedicated preprocessing strategy combines topology-constrained spatial alignment, temporal hazard window embedding, risk-diffusion feature lifting, hazard-sensitive normalization, and continuous hazard surface initialization to convert fragmented event-centered observations into a smooth and learning-ready hazard representation. A structured deep learning architecture is then developed to perform spatial hazard encoding, temporal hazard evolution, continuous hazard reconstruction, and localized accident emergence prediction. Experimental evaluation was conducted on two large-scale real-world traffic safety datasets, namely the XTraffic Incident Dataset (2022–2024) with 1,441,904 records and the Motor Vehicle Collisions–Crashes Dataset with 2,026,647 records. All model configurations were evaluated under the same experimental setting, using the same dataset-specific preprocessing protocol, a 70/30 train–test split, and identical evaluation metrics. The final CHFI configuration achieves 99.12% accuracy, 98.94% precision, 98.76% recall, 98.85% F1-score, and 0.998 AUC on Dataset 1, and 98.63% accuracy, 98.41% precision, 98.16% recall, 98.28% F1-score, and 0.997 AUC on Dataset 2. Compared with the initial non-hazard-aware baseline configuration evaluated under the same data split and evaluation protocol, the final CHFI model improves the F1-score by 7.91 percentage points on Dataset 1 and 8.26 percentage points on Dataset 2. These results indicate that the proposed hazard-field formulation can improve accident-emergence prediction within the controlled experimental setting, while the reported gains should be interpreted relative to the specified baseline and evaluation design. Full article

Distracted and fatigue-related driving behaviors are major causes of road traffic accidents, creating an urgent need for reliable driver monitoring systems. Vision-based detection methods have garnered widespread attention due to their low cost of deployment and practical applicability. However, existing lightweight models often suffer from limited global contextual perception and insufficient preservation of fine details. Motivated by these challenges, this study introduces an improved distracted and fatigue-related driving behavior detection model, YOLOv12-LAD, built on the YOLOv12 architecture. The proposed framework integrates a Large Separable Kernel Attention module (LSKA) to enhance global contextual perception, an Adaptive Downsampling module (ADown) to mitigate information loss during feature compression, and a Dynamic Sampling module (DySample) to enable content-adaptive feature reconstruction and improve multi-scale behavior representation. Experimental results show that YOLOv12-LAD achieved 97.5% precision, 96.3% recall, and 98.4% mAP@50 with only 2.5 million parameters, 6.2 GFLOPs, and an inference speed of 249 FPS. Ablation studies, comparisons with representative models, cross-dataset evaluation, and real-vehicle tests further verify the effectiveness and robustness of the proposed method. The proposed method demonstrates strong performance while maintaining computational efficiency, making it suitable for real-time vision-based driver monitoring applications. Full article

The accurate simulative reconstruction of blind spot accidents requires innovative simulation methods. The objective of this paper is to analyze the avoidability of a specific blind spot accident and assess the impact of various parameters as if an active turning assistant had been installed in the truck. Additionally, it proposes a novel adaptation of the turning assistant system, along with an adapted simulation model tailored for drawbar trailers. The analyses presented in this paper were performed using PC-Crash accident simulation software, applying the “Active Safety” module. After performing a simulation of an accident involving a right-turning truck with a center axle trailer and a pedestrian, the avoidability of the accident was examined by simulating the scenario as if the truck involved in the accident had been equipped with an active turning assistant system. Subsequently, a parameter analysis was conducted to analyze the effect of changes in the active turning assistant’s parameters and changes in the pedestrian’s direction of entry on the avoidability of the accident. In doing so, we determined the parameters for the worst-case (collision) and the best-case (no collision) scenarios. Finally, an adaptation and further development of the active turning assistant, along with a corresponding simulation method for drawbar trailers, are proposed. Full article

The Automatic Identification System (AIS) frequently suffers from data loss and irregular report intervals in real maritime environments, compromising the reliability of downstream navigation, monitoring, and trajectory reconstruction tasks. To address these challenges, we propose BAARTR (Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel Trajectory Reconstruction), a novel kinematically consistent interpolation framework. Operating solely on time, latitude, and longitude inputs, BAARTR explicitly enforces boundary velocities derived from raw AIS data. The framework adaptively selects a velocity-estimation strategy based on the AIS reporting gap: central differencing is applied for short intervals, while a hierarchical cubic velocity regression with a quadratic acceleration constraint is employed for long or irregular gaps to iteratively refine endpoint slopes. These boundary slopes are subsequently incorporated into a clamped quartic interpolation at a 1 s resolution, effectively suppressing overshoots and ensuring velocity continuity across segments. We evaluated BAARTR against Linear, Spline, Hermite, Bezier, Piecewise cubic hermite interpolating polynomial (PCHIP) and Modified akima (Makima) methods using real-world AIS data collected from the Mokpo Port channel, Republic of Korea (2023–2024), across three representative vessels. The experimental results demonstrate that BAARTR achieves superior reconstruction accuracy while maintaining strictly linear time complexity ($\mathit{O}\left(\mathit{N}\right)$). BAARTR consistently achieved the lowest median Root Mean Square Error (RMSE) and the narrowest Interquartile Ranges (IQR), producing visibly smoother and more kinematically plausible paths-especially in high-curvature turns where standard geometric interpolations tend to oscillate. Furthermore, sensitivity analysis shows stable performance with a modest training window (n ≈ 16) and minimal regression iterations (m = 2–3). By reducing reliance on large training datasets, BAARTR offers a lightweight, extensible foundation for post-processing in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic Service (VTS), as well as for accident reconstruction and multi-sensor fusion. Full article

With the aging of China’s expressway network, the number of maintenance projects continues to increase, and issues such as construction safety, driving risk, and traffic efficiency have become increasingly prominent. This paper systematically reviews relevant research progress from four aspects: safety characteristics, traffic capacity, work-zone layout, and speed limit management. The review indicates that Western scholars have made extensive use of rich data resources—such as traffic parameters and accident records from expressway maintenance road sections—and have developed relatively systematic and well-established research frameworks in theoretical analysis, practical application, and evaluation methods. In contrast, Chinese studies have mainly relied on specific maintenance projects, commonly employing on-site investigations and traffic simulations to address particular problems, with limited systematization and generalization. Looking forward, it is essential to further strengthen the standardized collection and statistical analysis of traffic data (including accident data) for expressway maintenance road sections. Meanwhile, for complex scenarios such as multi-lane segments, special road sections, reconstruction and expansion sections, as well as extreme climatic conditions and nighttime operations, comprehensive research should be conducted by leveraging new-generation driving simulation, big data analytics, and artificial intelligence technologies, thereby providing scientific support and methodological foundations for building a systematic theoretical framework for traffic safety in expressway maintenance road sections. Full article

Road accidents cause 1.3 million deaths annually, motivating the United Nations (UN) to develop a strategy seeking to halve this number by 2030. Portugal, with 60 deaths per million inhabitants in 2022, ranks sixth in European Union (EU) road fatalities, although these numbers have been decreasing since 2010. Rollover accidents account for 33% of traffic fatalities in the U.S.; yet, only 3% of crashes involve rollover accidents. These are particularly dangerous and mainly involve medium-to-large-sized vehicles having high centers of gravity (CoG), such as SUVs and heavy vehicles. On the other hand, bus accidents make up only 2% of EU road deaths, often involving vulnerable road users. Road forensic investigations rely on CoG positioning data for accurate accident reconstructions, using key equations for calculating skid and overturning speed limits. To complement the already existing equations, and by using a rigid body system, an equation for the evaluation of the overturning velocity in a curved trajectory is developed and proposed, considering the suspension stiffness properties of a vehicle. Finally, a real-world accident investigation involving a bus overturning is presented, and the method that was developed is applied. The developed formulation showed good results compared to the ones that were obtained during the forensic investigation and reduced the error from 5% to 2% compared to the existing equations. Full article

This article presents a comprehensive forensic tool for crime scene and traffic accident investigations, integrating advanced 3D reconstruction and semantic and dynamic analyses; the tool facilitates the accurate documentation and preservation of crime scenes through photogrammetric techniques, producing detailed 3D models based on images or video captured under specified protocols. The system includes modules for semantic analysis, enabling object detection and classification in 3D point clouds and 2D images. By employing machine learning methods such as the Random Forest model for point cloud classification and the YOLOv8 architecture for object detection, the tool enhances the accuracy and reliability of forensic analysis. Furthermore, a dynamic analysis module supports ballistic trajectory calculations for crime scene investigations and the vehicle impact speed estimation using the Equivalent Barrier Speed (EBS) model for traffic accidents. These capabilities are integrated into a single, user-friendly platform offering significant improvements over existing forensic tools, which often focus on singular tasks and require expertise. This tool provides a robust, accessible solution for law enforcement agencies, enabling more efficient and precise forensic investigations across different scenarios. Full article

Traumatic brain injury (TBI) is a significant public health concern and its rising prevalence in road traffic accidents underscores the need for deeper understanding and tailored investigation. This study explores the feasibility of employing the female finite element head model (FeFEHM) to analyse biomechanical responses in two distinct road traffic accident scenarios, focusing on strain and stress distribution in critical brain structures. Two collision scenarios from the German In-Depth Accident Study (GIDAS) were reconstructed using validated Total Human Model for Safety (THUMS) simulations. The extracted skull kinematics were applied to the FeFEHM in ABAQUS to compute maximum principal strain, von Mises stress, and intracranial pressure across key brain regions, including the corpus callosum and pituitary gland. Simulations revealed strain concentrations in the parietal and temporal lobes, while the mid-body region was the most affected in the corpus callosum. Pituitary gland deformation was minimal under both loading conditions. Our findings align qualitatively with reported injury sites and injury risk was consistent with those observed in the real-world crashes. The findings highlight the potential of integrating sex-specific biomechanical models into crash biomechanics workflows. Future work should extend this approach across larger datasets and impact scenarios to support its implementation in regulatory and engineering contexts, since the actual sample size prevents conclusions regarding sex-specific biomechanics. Full article

Understanding vehicle acceleration behavior during intersection departures is critical for advancing traffic safety, sustainable mobility, and intelligent transport systems. This study presents a high-resolution kinematic analysis of 714 vehicle departures from signalized intersections, encompassing straight crossings, left turns, and right turns, and involving a diverse sample of internal combustion engine (ICE), hybrid electric (HEV), and battery electric vehicles (BEV). Using synchronized Micro Electro-Mechanical Systems (MEMS) accelerometers and Real-Time Kinematic (RTK)-GPS systems, the study captures longitudinal acceleration and velocity profiles over fixed distances. Results indicate that BEVs exhibit significantly higher acceleration and final speeds than ICE and HEV vehicles, particularly during straight crossings and longer left-turn maneuvers. Several mathematical models—including polynomial, arctangent, and Akçelik functions—were calibrated to describe acceleration and velocity dynamics. Findings contribute by modeling jerk and delay propagation, supporting better calibration of AV acceleration profiles and the optimization of intersection control strategies. Moreover, the study provides validated acceleration benchmarks that enhance the accuracy of forensic engineering and road accident reconstruction, particularly in scenarios involving intersection dynamics, and demonstrates that BEVs accelerate more rapidly than ICE and HEV vehicles, especially in straight crossings, with direct implications for traffic simulation, ADAS calibration, and urban crash analysis. Full article

Background: Reconstruction of distal forearm and hand soft tissue defects remains a complex surgical challenge due to the functional and aesthetic significance of the region. Several flap options have been established such as the posterior interosseous artery flap (PIA) or temporalis fascia flap (TFF), yet the anterolateral thigh flap (ALT) has gained increasing attention for its versatility and favorable risk profile. Methods: We retrospectively analyzed 12 patients (7 males, 5 females; mean age 51.8 years) who underwent free microvascular ALT reconstruction for distal forearm and hand defects between May 2020 and May 2025. Etiologies included infection, chemical burns, explosion injuries, and traffic accidents. The mean defect size was 75.4 cm², and the average operative time was 217 min. Secondary flap thinning was performed in eight cases. In one patient without available recipient veins, a pedicle vein was anastomosed using a coupler device anchored into a cortical window of the distal radius to establish venous outflow via the bone marrow. Results: All flaps demonstrated complete survival with successful integration. Minor complications included transient venous congestion in one case and superficial wound dehiscence in four cases. Functional outcomes were favorable, with postoperative hand function rated as very good in 10 of 12 patients at follow-up. The bone-anchored venous anastomosis provided effective venous drainage in the salvage case. Conclusions: The free microvascular ALT is a reliable and highly adaptable method for distal forearm and hand reconstruction. It provides excellent soft tissue coverage, allows for secondary contouring, and achieves both functional and aesthetic goals. Furthermore, intraosseous venous anastomosis using a coupler device might represent a novel adjunct that may expand reconstructive options in cases with absent or unusable recipient veins. Full article

Purpose: To evaluate the prevalence of undiagnosed ligamentous knee injuries in patients surgically treated for high-energy pelvic ring or acetabular fractures and propose a mechanism to diagnose these briefly post-hospital discharge. Methods: A retrospective case series (level of evidence IV) was conducted at Italy’s largest trauma center. Medical records from 2018 to 2023 were reviewed to identify patients who underwent surgical treatment for pelvic or acetabular fractures. Eligible patients were contacted for a structured telephone interview, which included a questionnaire on knee symptoms and the International Knee Documentation Committee (IKDC) score. Associations between demographic factors, trauma mechanism, and knee outcomes were statistically analyzed. Results: Fifty-nine patients (mean age 55 years, 72.9% male) were enrolled. Undiagnosed knee ligament injuries were present in 11.9%, with an additional 8.5% reporting persistent knee symptoms. The average time to diagnosis was 6.4 months post-discharge. Patients involved in road traffic accidents showed a significantly higher incidence of knee injuries (34.8%) compared to those who fell from a height (3.9%) (p = 0.049). Patients who had undergone ligament reconstruction had significantly lower IKDC scores (62.0 ± 8.2) than non-surgical cases (82.4 ± 12.1, p = 0.0002). No association was found with age or sex. Conclusions: Ligamentous knee injuries are frequently overlooked in the acute management of high-energy pelvic fractures, particularly in road traffic accidents. A systematic knee assessment before discharge or early outpatient imaging should be considered to improve detection and outcomes. Full article

Between 2019 and 2023, left-turn crashes accounted for 4.5% of traffic accidents in Japan, with 36% of injuries involving cyclists and 66% at signalized intersections. This study quantifies conflict situations between left-turning vehicles and straight-moving bicycles in real-world traffic environments and provides a foundation for determining appropriate timing of future in-vehicle early warning systems. Trajectories reconstructed from seven hours of camera footage yielded six spatio-temporal and behavioral indicators for 37 events with a post-encroachment time (PET) ≤ 3 s. Indicators—PET, time-to-crossing (TTC), right-of-way, urgent braking, deceleration to avoid a crash, and Kalman-based trajectory variance—were statistically related to a composite risk index, R. Approximately 80% of events fell within PETs of 2–3 s, while urgent braking occurred in 50% of cases with PETs of ≤2 s. Each 1 s reduction in PET increased R by 0.18 (R² = 0.55). PETs ≤ 2.5 s or TTCs ≤ 1.5 s flagged 95% of high-risk events 0.5 s in advance. Joint thresholds involving urgent braking and high variance raised coverage to 100%, with lead times of 0–1.4 s and a false alarm rate of 8%. These findings provide an innovative multi-indicator framework based on real-world trajectories, offering quantitative scenario-specific thresholds for effective in-vehicle warnings at urban intersections. Full article

In low-illumination environments, traditional traffic accident survey methods struggle to obtain high-quality data. This paper proposes a traffic accident reconstruction method utilizing an unmanned aerial vehicle (UAV) and auxiliary equipment. Firstly, a methodological framework for investigating traffic accidents under low-illumination conditions is developed. Accidents are classified based on the presence of obstructions, and corresponding investigation strategies are formulated. As for the unobstructed scene, a UAV-mounted LiDAR scans the accident site to generate a comprehensive point cloud model. In the partially obstructed scene, a ground-based mobile laser scanner complements the areas that are obscured or inaccessible to the UAV-mounted LiDAR. Subsequently, the collected point cloud data are processed with a multiscale voxel iteration method for down-sampling to determine optimal parameters. Then, the improved normal distributions transform (NDT) algorithm and different filtering algorithms are adopted to register the ground and air point clouds, and the optimal combination of algorithms is selected, thus, to reconstruct a high-precision 3D point cloud model of the accident scene. Finally, two nighttime traffic accident scenarios are conducted. DJI Zenmuse L1 UAV LiDAR system and EinScan Pro 2X mobile scanner are selected for survey reconstruction. In both experiments, the proposed method achieved RMSE values of 0.0427 m and 0.0451 m, outperforming traditional aerial photogrammetry-based modeling with RMSE values of 0.0466 m and 0.0581 m. The results demonstrate that this method can efficiently and accurately investigate low-illumination traffic accident scenes without being affected by obstructions, providing valuable technical support for refined traffic management and accident analysis. Moreover, the challenges and future research directions are discussed. Full article

In contemporary society, fatigue driving is a major cause of traffic accidents, making accurate and timely detection critical for improving driving safety. In this study, we propose a novel fatigue detection model, CSA-YOLO, designed to enhance the accuracy and efficiency of facial fatigue detection. The model is based on the YOLOv9s network model and introduces several key improvements to address the limitations of traditional methods, which often lose critical edge information. First, the Cross-Stage Partial Network (C3 module) replaces the RepNCSPELAN4 module to enhance the model’s ability to extract edge information effectively. Second, the incorporation of the SimAM attention mechanism improves feature selection, while the Content-Aware ReAssembly of Features (CARAFE) upsampling operator enhances the quality of reconstructed image details. Experimental results demonstrate that the proposed CSA-YOLO model achieves significant performance improvements, with a 2.24% increase in accuracy, a 2.58% improvement in recall, and a 2.44% boost in mAP compared to the original YOLOv9s model. These results highlight the model’s potential for practical application in reducing the risks of fatigue-related accidents. Full article