Search Results (62)

Emergency maneuvers can drive vehicles into severe instability regimes within sub-second time scales, motivating last-moment warning interfaces with auditable false-alarm budgets. We study a proxy-triggered imminent-recognition setting: given a 0.1 s past-only slice of onboard signals, decide whether a conservative physics-defined instability proxy will trigger within the next $\tau =0.2$ s. The contribution is, therefore, a calibrated warning for a safety-relevant surrogate event, not a claim of predicting crashes or true instability outcomes directly. Because the corpus is terminal-phase aligned, the default causal monitor ($w=d=0.1$ s, $k=2$) is warnable on only $18.3\%$ of event runs; we, therefore, report run-level effectiveness both overall and conditional on warnability. We learn a lightweight hazard scorer and convert its scores into an operator-facing alarm rule via split-conformal calibration on held-out negative slices, exposing a slice-level false-alarm budget $\alpha$ with finite-sample, one-sided control of the marginal slice-level false positive rate (FPR) on exchangeable negatives. To address fleet heterogeneity, we additionally calibrate vehicle-conditioned (Mondrian) thresholds, enabling per-vehicle risk budgeting without retraining separate models. On the held-out test split at $\tau =0.2$ s, the scorer achieves AUPRC $\approx 0.251$ against a base rate of $0.638\%$, AUROC $\approx 0.986$, and ECE $\approx 0.034$. After calibration at $\alpha =5\%$, realized slice-level FPR concentrates near the prescribed budget while slice-level TPR on imminent positives remains high (≈0.982). We explicitly separate this slice-level guarantee from empirical run-level metrics such as FAR_run, EWR on warnable runs, and lead time, and we report dependence and shift diagnostics to delineate where the guarantee may degrade. The reported $\mu$-sensitivity analyses concern run-level descriptor perturbation and omission rather than validation of a within-run friction estimator with temporal lag. The result is a transparent, risk-budgeted monitoring primitive for last-moment vehicle-stability warning under clearly stated exchangeability assumptions. 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

This paper analyzes the dynamic behavior of a passenger bus running on a raised crosswalk. The main objective was to evaluate the vertical displacements and accelerations caused by the change in elevation, and to determine the potential for suspension damage. The study involved a numerical approach to the examination of a vehicle’s displacement related to the profile pavement by the implementation of a single body finite element module with suspension subjected to the effect of road unevenness. The so-obtained dynamic behavior with this model was implemented in MATLAB software, and the results were compared with the corresponding real-world accident data record and with an experimental study carried out with a bus running on a raised crosswalk at prescribed velocities. The velocity on the day of the accident was then calculated by computational simulations using the software PC-Crash^®. The results show that the vertical displacement caused by the raised crosswalk can vary according to the bus velocity and the raised crosswalk height. Moreover, the results show that reducing the height of the raised crosswalk and redesigning it for a smoother transition with the pavement can help in minimizing the negative effects from impacts on the bus body. The findings of this study provide valuable insights for engineers and transportation planners, and can be used to improve the design and placement of raised crosswalks in the future. Full article

This paper evaluates Advanced Driver Assistance Systems (ADASs) in test scenarios derived from naturalistic driving and crash data, mapped to ISO 26262, ISO/PAS 21448 (SOTIF), and ISO 34502. From eight high-risk scenarios, it is validated for left turns across oncoming traffic on a proving ground using a Škoda Superb iV against a soft Global Vehicle Target. ODD and spatiotemporal thresholds are parameterized and speed/acceleration profiles from GNSS/IMU data are analyzed. AEB and FCW performance varies across nominally identical runs, driven by human-in-the-loop variability and target detectability. In successful interventions, peak deceleration reached −0.64 g, meeting UNECE R152 criteria; in other runs, late detection narrowed TTC below intervention thresholds, leading to contact. Limitations in current protocols are identified and argue for scenario catalogs with realistic context (weather, surface, masking) and latency-aware metrics. The results motivate extending validation beyond standard tracks toward mixed methods linking simulation, scenario databases, and instrumented field trials. Full article

In a few years, most investment firms will deploy Generative AI (GenAI) and large language models (LLMs) for reduced-cost stock trading decisions. If GenAI-run investment decisions from most firms are heavily coordinated, they could all give a “sell” signal simultaneously, triggering market crashes. Likewise, simultaneous “buy” signals from GenAI-run investment decisions could cause market bubbles with algorithmically inflated prices. In this way, coordinated actions from LLMs introduce systemic risk into the global financial system. Existing risk analysis for GenAI focuses on endogenous risk from model performance. In comparison, exogenous risk from external factors like macroeconomic changes, natural disasters, or sudden regulatory changes, is understudied. This research fills the gap by creating a framework for measuring exogenous (systemic) risk from LLMs acting in the stock trading system. This research develops a concrete, quantitative framework to understand the systemic risk brought by using GenAI in stock investment by measuring the covariance between LLM stock price predictions across three industries (technology, automobiles, and communications) produced by eight large language models developed across the United States, Europe, and China. This paper also identifies potential data-driven technical, cultural, and regulatory mechanisms for governing AI to prevent negative financial and societal consequences. Full article

This study examines the safety and cost-effectiveness of implementing shoulder rumble strips (SRS) and road lighting on Saudi Arabian freeways, providing insights into their roles in fostering sustainable transport systems. By leveraging the Highway Safety Manual (HSM) framework, this research develops localized Crash Modification Factors (CMFs) for these interventions, ensuring evidence-based and context-specific evaluations. Data were collected for two periods—pre-pandemic (2017–2019) and post-pandemic (2021–2022). For each period, we obtained traffic crash records from the Saudi Highway Patrol database, traffic volume data from the Ministry of Transport and Logistic Services’ automated count stations, and roadway characteristics and pavement-condition metrics from the National Road Safety Center. The findings reveal that SRS reduces fatal and injury run-off-road crashes by 52.7% (CMF = 0.473) with a benefit–cost ratio of 14.12, highlighting their high cost-effectiveness. Road lighting, focused on nighttime crash reduction, decreases such crashes by 24% (CMF = 0.760), with a benefit–cost ratio of 1.25, although the adoption of solar-powered lighting systems offers potential for greater sustainability gains and a higher benefit–cost ratio. These interventions align with global sustainability goals by enhancing road safety, reducing the socio-economic burden of crashes, and promoting the integration of green technologies. This study not only provides actionable insights for achieving KSA Vision 2030’s target of improved road safety but also demonstrates how engineering solutions can be harmonized with sustainability objectives to advance equitable, efficient, and environmentally responsible transportation systems. Full article

Autonomous vehicles (AVs) are expected to eventually replace traditional vehicles that require human drivers. In recent years, several AV manufacturers have begun on-road testing to validate the safety of these vehicles. California is one of the few states to permit such testing, regulating it through a permit system. To ensure transparency and public awareness, the state mandates that any licensed AV manufacturer conducting on-road tests report crashes involving AVs. This must be conducted using a standardized format known as OL316, a requirement that has been in place since late 2014. While previous research has explored AV crash data, most studies have focused on specific timeframes without covering the entire period since 2014. Moreover, converting the data from PDFs to machine-readable formats has often been a manual process, and the description text field in reports has rarely been fully analyzed. This article presents a comprehensive, machine-readable dataset of AV crashes from 2014 to September 2024, along with publicly available parsing code to streamline future data analysis. Additionally, we provide an updated statistical analysis of AV crashes during this period. Furthermore, we leverage Generative AI (GenAI) to analyze the description text field of the OL316 reports. This analysis identifies common crash scenarios, contributing factors, and additional insights into moderate and major incidents. The final dataset comprises 728 crash entries. Notably, only 2% of the crashes were categorized as major, while 14% were classified as moderate. Furthermore, 43% of the crashes occurred while the AV was stationary, whereas 55% took place while the AV was in motion. Our GenAI analysis indicates that, in many instances, human drivers of non-autonomous vehicles were at fault. Common causes include rear-end collisions due to insufficient following distances, traffic violations such as running red lights or stop signs, and reckless behaviors like lane boundary violations or speeding. Full article

In deep learning (DL) frameworks, a checkpoint operation is widely used to store intermediate variable values (e.g., weights, biases, and gradients) on storage media. This operation helps to reduce the recovery time of running a machine learning (ML) model after sudden power failures or random crashes. However, the checkpoint operation can stall the overall training step of the running model and waste expensive hardware resources by leaving the GPU in idle sleep during the checkpoint operation. In addition, the completion time of the checkpoint operation is unpredictable in cloud server environments (e.g., AWS and Azure) because excessive I/O operations issued by other running applications interfere with the checkpoint operations in the storage stacks. To efficiently address the above two problems, we carefully designed Check-QZP, which reduces the amount of data required for checkpoint operations and parallelizes executions on the CPU and GPU by understanding the internal behaviors of the training step. For the evaluation, we implemented Check-QZP and compared it with the traditional approach in real-world multi-tenant scenarios. In the evaluation, Check-QZP outperformed the baseline in all cases in terms of the overall checkpoint time and the amount of data generated by the checkpoint operations, reducing them by up to 87.5% and 99.8%, respectively. In addition, Check-QZP achieved superior training speeds compared to the baseline. Full article

The safety of lithium-ion batteries is critical to the safety of battery electric vehicles (BEVs). The purpose of this work is to develop a method to predict battery thermal runaway in full electric vehicle crash simulation. The thermal–electrical–mechanical-coupled finite element analysis is used to model an individual lithium-ion battery cell, a battery module, a battery pack, and a battery electric vehicle with 24 battery modules in a live circuit connection. The lithium-ion battery is modeled using a representative approach, with each internal battery component individually modeled to represent its geometric shape and realistic thermal, mechanical, and electrical properties. A resistance heating solver and Randles circuit model built with a generalized voltage source are used to simulate the electrical behavior of the battery. The thermal simulation of the battery considers the heat capacity and thermal conductivity of different cell components, as well as heat conduction, radiation, and convection at their interfaces. The mechanical property of battery cell and battery module models is validated using spherical punch tests. The electrical property of the battery cell and battery module models is verified against CircuitLab simulation in an external short-circuit test. The simulation results for the battery module’s internal resistance are consistent with both experimental data and literature values. The multi-physics coupling phenomenon is demonstrated with a cylindrical compression simulation on the battery module. The multi-physics BEV model with 24 live battery modules is used to simulate the external short-circuit test and the side pole impact test. The simulation run time is less than 24 h. The results demonstrated the feasibility of using a representative battery model and multi-physics analysis to predict battery thermal runaway in full electric vehicle crash analysis. Full article

The use of smart indoor robotics services is gradually increasing in real-time scenarios. This paper presents a versatile approach to multi-robot backing crash prevention in indoor environments, using hardware schemes to achieve greater competence. Here, sensor fusion was initially used to analyze the state of multi-robots and their orientation within a static or dynamic scenario. The proposed novel hardware scheme-based framework integrates both static and dynamic scenarios for the execution of backing crash prevention. A round-robin (RR) scheduling algorithm was composed for the static scenario. Dynamic backing crash prevention was deployed by embedding a first come, first served (FCFS) scheduling algorithm. The behavioral control mechanism of the distributed multi-robots was integrated with FCFS and adaptive cruise control (ACC) scheduling algorithms. The integration of multiple algorithms is a challenging task for smarter indoor robotics, and the Xilinx-based partial reconfiguration method was deployed to avoid computational issues with multiple algorithms during the run-time. These methods were coded with Verilog HDL and validated using an FPGA (Zynq)-based multi-robot system. Full article

Machine learning currently holds a vital position in predicting collision severity. Identifying factors associated with heightened risks of injury and fatalities aids in enhancing road safety measures and management. Presently, Thailand faces considerable challenges with respect to road traffic accidents. These challenges are particularly acute in industrial zones, where they contribute to a rise in injuries and fatalities. The mixture of heavy traffic, comprising both trucks and non-trucks, significantly amplifies the risk of accidents. This situation, hence, generates profound concerns for road safety in Thailand. Consequently, discerning the factors that influence the severity of injuries and fatalities becomes pivotal for formulating effective road safety policies and measures. This study is specifically aimed at predicting the factors contributing to the severity of accidents involving truck and non-truck collisions in industrial zones. It considers a variety of aspects, including roadway characteristics, underlying assumptions of cause, crash characteristics, and weather conditions. Due to the fact that accident data is big data with specific characteristics and complexity, with the employment of machine learning in tandem with the Multi-variate Adaptive Regression Splines technique, we can make precise predictions to identify the factors influencing the severity of collision outcomes. The analysis demonstrates that various factors augment the severity of accidents involving trucks. These include darting in front of a vehicle, head-on collisions, and pedestrian collisions. Conversely, for non-truck related collisions, the significant factors that heighten severity are tailgating, running signs/signals, angle collisions, head-on collisions, overtaking collisions, pedestrian collisions, obstruction collisions, and collisions during overcast conditions. These findings illuminate the significant factors influencing the severity of accidents involving trucks and non-trucks. Such insights provide invaluable information for developing targeted road safety measures and policies, thereby contributing to the mitigation of injuries and fatalities. Full article

Among several approaches to analyzing crash research, the use of machine learning and econometric analysis has found potential in the analysis. This study aims to empirically examine factors influencing the single-vehicle crash for personal cars and trucks using decision trees (DT) and mixed binary logit with heterogeneity in means and variances (RPBLHMV) and compare model accuracy. The data in this study were obtained from the Department of Highway during 2011–2017, and the results indicated that the RPBLHMV was superior due to its higher overall prediction accuracy, sensitivity, and specificity values when compared to the DT model. According to the RPBLHMV results, car models showed that injury severity was associated with driver gender, seat belt, mount the island, defect equipment, and safety equipment. For the truck model, it was found that crashes located at intersections or medians, mounts on the island, and safety equipment have a significant influence on injury severity. DT results also showed that running off-road and hitting safety equipment can reduce the risk of death for car and truck drivers. This finding can illustrate the difference causing the dependent variable in each model. The RPBLHMV showed the ability to capture random parameters and unobserved heterogeneity. But DT can be easily used to provide variable importance and show which factor has the most significance by sequencing. Each model has advantages and disadvantages. The study findings can give relevant authorities choices for measures and policy improvement based on two analysis methods in accordance with their policy design. Therefore, whether advocating road safety or improving policy measures, the use of appropriate methods can increase operational efficiency. Full article

Studies have explored the factors influencing the safety of PTWs; however, very little has been carried out to comprehensively investigate the factors influencing fatal PTW crashes while considering the fault status of the rider in crash hotspot areas. This study employs spatio-temporal hotspot analysis and association rule mining techniques to discover hidden associations between crash risk factors that lead to fatal PTW crashes considering the fault status of the rider at statistically significant PTW crash hotspots in South Korea from 2012 to 2017. The results indicate the presence of consecutively fatal PTW crash hotspots concentrated within Korea’s densely populated capital, Seoul, and new hotspots near its periphery. According to the results, violations such as over-speeding and red-light running were critical contributory factors influencing PTW crashes at hotspots during summer and at intersections. Interestingly, while reckless riding was the main traffic violation leading to PTW rider at-fault crashes at hotspots, violations such as improper safety distance and red-light running were strongly associated with PTW rider not-at-fault crashes at hotspots. In addition, while PTW rider at-fault crashes are likely to occur during summer, PTW rider not-at-fault crashes mostly occur during spring. The findings could be used for developing targeted policies for improving PTW safety at hotspots. Full article

Some old-order Anabaptist communities rely on animal-drawn vehicles for transportation and farm work. This research examines reports involving horse-drawn vehicles found in the AgInjuryNews dataset, which provides a publicly accessible collection of agricultural injury reports primarily gathered from news media. The goals of this research are to characterize the reports and to compare results with previous research to assess the utility of using AgInjuryNews to examine horse-drawn vehicle incidents. A total of 38 reports representing 83 victims were identified. Chi-square tests comparing victim and incident traits for fatal and nonfatal injuries were significant for the victim’s role in the incident, vehicle type, presence of a motor vehicle, rear-ending by a motor vehicle, spooked horses, a victim being run over or struck by a vehicle, and a victim being ejected or falling from a vehicle. Additional analysis of incidents involving horse-drawn farm equipment showed that a significantly higher proportion of off-road incidents were fatal compared to on-road incidents. The proportion of fatal injuries in the AgInjuryNews dataset was approximately 10 times higher than observed in a study using Pennsylvania Department of Transportation (DOT) data. Compared to previous research, the AgInjuryNews reports contained a higher proportion of incidents where a motor vehicle rear-ended a horse-drawn vehicle, and fewer cases of horse-drawn vehicles being struck by motor vehicles while crossing or entering a main road and making left turns. Reports of buggy crashes found in AgInjuryNews differed from those found in a Nexis Uni search in that the bulk of the articles from Nexis Uni referred to cases involving criminal charges for impaired driving or hit-and-run crashes. While it is evident that the reports included in the sample are incidents that media sources find compelling rather than comprehensive injury surveillance, it is possible to gain new insights using the AgInjuryNews reports. Full article

During the development stage of a space vehicle, instrumented tests are carried out on the ground to prove the operational capacity of each liquid-propellant rocket engine, which is installed in this type of vehicle. The task of elaborating a Test Bench project for a propulsion unit with this application is complex and involves several steps, one of these steps being related to the analysis of this bench capacity to meet the algorithms for the liquid-propellant rocket-engine full run of tests, which is considered fundamental for this project’s operational success. Due to the high costs involved in this project’s elaboration and execution, it is strategic to use computational resources to evaluate, by simulation, the main operational functionalities that are previously established for this bench to perform. In this context, this work presents a model proposal through Petri Nets to evaluate, by computer simulation, an architecture capacity that was designed for the Test Bench to meet an algorithm dedicated to the liquid-propellant pipelines purge during the run of hot tests with the liquid-propellant rocket engine. The method used in this work to carry out the simulation shows the operational response of each module of this architecture, in accordance with the steps contained in the purge algorithm, which allows for analyzing, for each event of the process, the Petri Nets properties, mainly those related to the conservativeness, liveliness, deadlock-type, and confusion-type conflicts. The simulation carried out with the proposed model allows for the portrayal of the physical architecture and the operational states of the purge system according to the steps foreseen in the algorithm, showing that the conservation property is met because the number of marks remains constant, the vivacity property is also met since all positions have been reached, and there is no mortal-type conflict, as the simulation is not stopped; only confusion-type conflict is identified, which was solved with the strategic insertion of resources in the model in order to fix crashes related to the competition for tokens in the transition-enabled entries. The satisfactory results obtained in these simulations suggest that the modules provided for this architecture are sufficient and appropriate for carrying out all the steps contained in the purge algorithm, which will minimize or even eliminate the disorders that may be caused by the presence of foreign elements in the propellant supply lines during the tests with the rocket engine. Full article