Search Results (68)

Road traffic accidents represent a critical road safety issue, the severity of which depends on the complex interplay of multiple factors. This issue directly impacts Target 3.6 of Sustainable Development Goal (SDG) 3, which aims to halve global deaths and injuries by 2030, and SDG 11, which focuses on safe and sustainable transport systems. The study of these factors and their interrelationships is important in the scientific literature. The objective of this study is to analyze the factors that determine the severity of road traffic accidents, identifying the most important ones and their correlations. A dataset containing variables such as infrastructure, location, time, and vehicle type, among others, was used to predict severity, applying Association Rules to identify latent correlations and the Classification and Regression Tree for hierarchical risk classification. The results reveal that the type of collision is the primary predictor of severity; the highest severity is associated with heavy traffic and head-on or side-impact collisions, involving critical scenarios, in the early morning hours and in rural areas, linked to trucks. The combined use of both tools provides a scientific basis for designing interventions on highly vulnerable road segments, contributing to the fulfillment of the 2030 Agenda for safe mobility. Full article

In this study, the transition of intersection coordination models was explored using a Dynamic Game framework. The performance limitations of traditional static models, which define payoffs based on fixed geometric conflicts, were also investigated to propose a novel dynamic utility function and evaluate it at each simulation step. Its important function is a continuous dynamic risk penalty derived from the immediate traffic state, allowing adaptive, risk-aware decisions to be made by vehicles. Based on the assumption of complete information, all vehicles have full knowledge of the characteristics of their rival vehicles, such as driving styles, as well as emergency vehicles like fire trucks and ambulances. Emergency vehicle priority is ensured through a high-cost penalty structure. The Pure Strategy Nash Equilibrium is solved for using the Iterated Best Response (IBR) algorithm. Through the MATLAB simulation of urban mobility, the dynamic, risk-aware framework was found to significantly improve safety metrics (e.g., near-collision events) compared to its static counterpart. Finally, the stability of the decision is analyzed by evaluating the IBR convergence rates across various driver-type compositions. Full article

To address the high sensing cost, uneven material distribution, and safety–efficiency trade-off in close-range wheel loader–dump truck collaborative unloading, this study proposes a perception–task–control framework for autonomous unloading. A complementary front–rear vision configuration is used to perceive the dump-truck bed under varying relative viewpoints, and the estimated bed pose is further transformed into executable unloading targets. To improve load distribution, a partition-aware task-generation strategy is developed, by which the unloading objective is extended from a single target point to sequential zone-level targets. An event-triggered two-stage reinforcement learning controller is then designed to organize the unloading process. The first stage guides the loader toward a perception-enabled region, while the second stage performs vision-guided precision alignment and coordinated lifting according to the current zone-level target. A closed-loop co-simulation environment is constructed using MATLAB/Simscape R2025b and Unreal Engine, and field-test data are used for simulation–field response comparison. The simulation results under representative operating conditions show that the proposed framework can complete sequential zone-level unloading without collision under the tested conditions. The quantitative results support the effectiveness of the method in terms of target completion, completion time, terminal positioning accuracy, lifting completion, and collision avoidance. The field-test comparison further indicates that the developed simulation model can reproduce the main trajectory, articulation-angle, and lifting-cylinder displacement responses of the wheel loader during unloading. These results demonstrate the feasibility of integrating low-cost visual perception, partition-aware task generation, and two-stage learning-based control for autonomous wheel-loader unloading. Full article

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation. Full article

Unstable car-following behavior under truck-induced visual occlusion on mountainous two-lane roads significantly increases rear-end crash risk. However, compared with studies focusing on overtaking or curve risk prediction, the car-following-truck (CFT) risk and its influencing factors have received limited attention. Therefore, this study used unmanned aerial vehicles (UAVs) to collect high-resolution trajectory data of CFT scenarios on both straight and curved segments under truck-induced occlusion. First, the CFT risk was quantified based on an anticipated collision time (ACT) indicator, a two-dimensional surrogate safety measure that accounts for vehicle acceleration variations. Then, extreme value theory (EVT) was applied to calibrate alignment-specific risk thresholds. Finally, an XGBoost-based risk identification model was developed using vehicle dynamics-related features, and feature importance analysis combined with partial dependence interpretability was conducted to obtain key influencing factors. The results show that the calibrated ACT thresholds are approximately 3.838 s for straight segments and 4.385 s for curved segments, providing a reliable basis for risk classification. In addition, the XGBoost-based risk identification achieved accuracies of 90.63% and 95.87% for straight and curved segments, respectively. Further analysis indicates that CFT distance was the contributing factor. Moreover, risk increases markedly within a 10–20 m range on straight segments, while it rises rapidly once spacing falls below about 10 m on curved segments. Speed and acceleration differences exhibited stronger amplifying effects under short-spacing conditions. These findings provide a micro-behavioral basis for safety management and intelligent driving applications on mountainous roads with high truck mixing rates, supporting safer and more sustainable traffic operations. Full article

Miniature commercial trucks constitute a critical component of urban freight systems but face elevated crash risk due to distinctive driving patterns, frequent operation, and variable loads. This study quantifies how long-term and short-term driving behaviors jointly shape crash economic loss levels and identifies factors most strongly associated with severe claims. A driver-level dataset linking multi-source running behavior indicators, vehicle attributes, and insurance claims is constructed, and an enhanced Wasserstein generative adversarial network with Euclidean distance is employed to synthesize minority crash samples and alleviate class imbalance. Crash economic loss levels are modeled using a random-effects generalized ordinal logit specification, and model performance is compared with a generalized ordered logit benchmark. Marginal effects analysis is used to evaluate the influence of pre-collision driving states (straight, turning, reversing, rolling, following closely) and key behavioral indicators. Results indicate significant effects of inter-provincial duration and count ratios, morning and empty-trip frequencies, no-claim discount coefficients, and vehicle age on crash economic loss, with prolonged speeding duration and fatigued mileage associated with major losses, whereas frequent speeding and fatigue episodes are primarily linked to minor claims. These findings clarify causal patterns for miniature commercial truck crashes with different economic losses and provide an empirical basis for targeted safety interventions and refined insurance pricing. 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

This article presents an analysis of the effects of braking system damage on the course of the vehicle collision and driving safety. Research was conducted using simulation methods in the V-SIM 7.0 environment, analysing the collision between a car and a truck at three speeds—50, 60, and 70 km/h—under the assumption of a braking system malfunction in the car. The obtained results showed that as the speed of the truck increased, the total kinetic energy of the system nearly doubled, resulting in deformation of the vehicle’s body front of up to 0.6 m. The maximum force acting on the car decreased with increasing speed, which was due to the change in the point of impact. The recorded acceleration values of the car indicate a moderate level of overloads, which should not cause serious injuries to the passengers but do suggest significant stress on the vehicle’s load-bearing structure. The research may serve as a foundation for further work on braking system diagnostics, the development of friction materials, and the modelling of energy absorption processes in collisions involving vehicles of varying mass and geometry. Full article

Rear-end collisions involving maintenance vehicles remain a critical source of severe injuries and fatalities in highway work zones. Existing studies on Rear Impact Guards (RIGs) and Truck-Mounted Attenuators (TMAs) have primarily relied on vehicle-based acceleration metrics or low-speed tests, leaving uncertainty regarding their performance under high-energy impact conditions. This study investigates occupant injury risk and vehicle crash behavior through full-scale frontal impact tests conducted at 80 km/h using a 2002 Renault SM520 passenger car against (1) a truck equipped with a RIG and (2) the same truck equipped with a TMA. Hybrid III 50th percentile ATDs, high-speed imaging, and multi-axis accelerometers were employed to measure occupant kinematics and injury responses. Occupant Risk Indices (THIV (Theoretical Head Impact Velocity), ASI (Acceleration Severity Index), PHD (Post-impact Head Deceleration), and ORA (Occupant Ridedown Acceleration)) and the ATD-based HIC36 were evaluated to assess crash severity. The RIG test exhibited severe underride, resulting in an HIC36 value of 1810, far exceeding the FMVSS 208 limit. In contrast, the TMA significantly reduced occupant injury risk, lowering HIC36 by 83.5%, and maintained controlled vehicle deceleration without compartment intrusion. Comparisons between FSM-based indices and ATD-measured injury responses revealed discrepancies in impact timing and occupant motion, highlighting limitations of current evaluation methodologies. The findings demonstrate the necessity of high-speed testing and ATD-based injury assessment for accurately characterizing RIG/TMA performance and provide evidence supporting improvements to roadside safety hardware standards and work-zone protection strategies. Full article

As an essential highway safety facility, roadside W-beam guardrails effectively prevent errant vehicles from entering hazardous zones or causing secondary collisions by blocking and redirecting them, thereby reducing accident severity. With the rapid development of the automotive industry, the front bumper height of small passenger cars generally ranges between 405 mm and 485 mm. However, the lower edge height of the current Chinese Class A W-beam guardrail is 444 mm above the ground, which leads to a high risk of “underride” during collisions, resulting in elevated occupant injury risks. To address this issue, this paper proposes an optimized guardrail structure composed of a double W-beam and a C-type beam, aiming to reduce the underride risk for small passenger cars while accommodating multi-vehicle protection needs. In this design, the double W-beam is installed at a height of 560 mm and the C-type beam at 850 mm, connected to circular posts using a regular hexagonal anti-obstruction block. The beam thickness is uniformly 3 mm, while the thickness of other components is 4 mm. To systematically evaluate the impact of material strength on both safety performance and cost, two material configurations are proposed: Scheme 1 uses Q235 carbon steel for all components; Scheme 2 reduces the thickness of the C-type beam to 2.5 mm and employs Q355 high-strength low-alloy steel, with the thickness of the connected anti-obstruction block reduced to 3.5 mm, while the other components retain Q235 steel and unchanged structural dimensions. Using finite element simulation, collisions involving small passenger cars, medium trucks, and buses are simulated, and performance comparisons are conducted based on vehicle trajectory and guardrail deformation. For the small passenger car scenario, risk quantification indicators—Acceleration Severity Index (ASI), Theoretical Head Impact Velocity (THIV), and Post-impact Head Deceleration (PHD)—are introduced to assess occupant injury. The results demonstrate that Scheme 2 not only meets the required protection level but also significantly reduces occupant risk for small passenger cars, lowering the injury rating from Class C to Class B. Moreover, the overall structural mass is reduced by approximately 1407 kg per kilometer, with material costs decreased by about RMB 10,129, demonstrating favorable economic efficiency. The proposed structural optimization not only effectively mitigates small car underride and improves multi-vehicle protection performance but also provides the industry with a novel guardrail geometric design directly applicable to engineering practice. The technical approach of enhancing material strength and reducing component thickness also offers a feasible reference for lightweight design, material savings, and cost optimization of guardrail systems, contributing significantly to improving the safety and sustainability of road transportation infrastructure. Full article

As a key member of the bridge substructure, the pier is always the most critical part under a variety of hazards, among which vehicle-induced impact is a rare but extreme load hazard that may result in significant structural damage and even the full failure of the bridge. Current design provisions, such as those in the AASHTO Load and Resistance Factor Design (LRFD) Bridge Design Specifications, adopt a constant equivalent static force (ESF) of 600 kips to represent vehicle impact loads. However, this simplified assumption neglects key parameters—such as vehicle speed, mass, and pier geometry—that significantly influence impact behavior. This study develops and evaluates two simplified impact load estimation models to improve the accuracy and practicality of design-level assessments: a reduced-order dynamic model and a response surface model. The reduced-order dynamic model captures vehicle–pier interaction through a simplified mass–spring system, while the response surface model uses regression-based relationships derived from extensive finite element (FE) simulations conducted in LS-DYNA. Sensitivity analyses identify the most influential parameters governing impact loads, including vehicle velocity, cargo mass, pier diameter, and impact height. The results show that both models can effectively predict peak dynamic and equivalent static impact loads, providing more accurate and physically interpretable results than the current constant-load approach. The proposed frameworks offer practical tools for bridge engineers to evaluate vehicle–pier collision scenarios and can be further extended for use in truck-to-pier and ship-to-pier impact analyses. 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 navigation in subterranean mines is hindered by deformable terrain, dust-laden visibility, and densely packed, safety-critical machinery. We propose a systems-oriented navigation framework that embeds semantic priorities into reactive planning for energy-aware autonomy in Robot Operating System (ROS). A lightweight Convolutional Neural Network (CNN) detector fuses RGB-D and LiDAR data to classify obstacles like humans, haul trucks, and debris, writing risk-weighted virtual LaserScans to the local planner so obstacles are evaluated by relevance rather than geometry. By integrating class-specific inflation layers in costmaps within a cyber–physical systems architecture, the system ensures ISO-compliant separation without sacrificing throughput. In Gazebo experiments with three obstacle classes and 60 runs, high-risk clearance increased by 34%, collisions dropped to zero, mission time remained statistically unchanged, and estimated kinematic effort increased by 6% relative to a geometry-only baseline. These results demonstrate effective systems integration and a favorable safety–efficiency trade-off in industrial cyber–physical environments, providing a reproducible reference for scalable deployment in real-world unstructured mining environments. Full article

The implementation of autonomous haulage trucks in open-pit mines represents a progressive advancement in the mining industry, but it poses potential safety risks that require thorough assessment. This study proposes an integrated model that combines discrete-event simulation (DES) with a risk matrix to assess collisions associated with three different operational scenarios, including non-autonomous, hybrid, and fully autonomous truck operations. To achieve these objectives, a comprehensive dataset was collected and analyzed using statistical models and natural language processing (NLP) techniques. Multiple scenarios were then developed and simulated to compare the risks of collision and evaluate the impact of eliminating human intervention in hauling operations. A risk matrix was designed to assess the collision likelihood and risk severity of collisions in each scenario, emphasizing the impact on both human safety and project operations. The results revealed an inverse relationship between the number of autonomous trucks and the frequency of collisions, underscoring the potential safety advantages of fully autonomous operations. The collision probabilities show an improvement of approximately 91.7% and 90.7% in the third scenario compared to the first and second scenarios, respectively. Furthermore, high-risk areas were identified at intersections with high traffic. These findings offer valuable insights into enhancing safety protocols and integrating advanced monitoring technologies in open-pit mining operations, particularly those utilizing autonomous haulage truck fleets. Full article

Pedestrian accidents involving heavy vehicles such as trucks and buses remain a critical safety issue, primarily due to structural blind spots. While existing systems like radar-based FCW and BSD have been adopted, they are not fully optimized for pedestrian detection, particularly under adverse weather conditions. This study focused on the empirical validation of a 360-degree pedestrian collision avoidance system using multichannel ultrasonic sensors specifically designed for heavy-duty vehicles. Eight sensors were strategically positioned to ensure full spatial coverage, and scenario-based field experiments were conducted under controlled rain (50 mm/h) and fog (visibility <30 m) conditions. Pedestrian detection performance was evaluated across six distance intervals (50–300 cm) using indicators such as mean absolute error (MAE), coefficient of variation (CV), and false-negative rate (FNR). The results demonstrated that the system maintained average accuracy of 97.5% even under adverse weather. Although rain affected near-range detection (FNR up to 17.5% at 100 cm), performance remained robust at mid-to-long ranges. Fog conditions led to lower variance and fewer detection failures. These empirical findings demonstrate the system’s effectiveness and robustness in real-world conditions and emphasize the importance of evaluating both distance accuracy and detection reliability in pedestrian safety applications. Full article