Browse Topic: Safety testing and procedures
In response to the current airworthiness regulations’ inability to cover the stall flight test requirements under icing conditions of civil aircraft with high-angle-of-attack restriction function and the lack of relevant flight test technologies in China, a study was conducted on the differences in airworthiness provisions for stall characteristics under icing conditions of such aircraft. Key technologies, including simulated ice accretion stall flight test methods, ice installation strategies, and data analysis techniques, are proposed and successfully applied to a specific civil aircraft. The results demonstrate that the methodologies proposed in this paper can effectively support simulated ice accretion stall tests, providing valuable insights for other similar aircraft.
This document is intended to establish a procedure to certify AD fallback test driver skill levels as an endorsement to SAE J3300 foundational level certification. The SAE J3300/3 endorsement can be used by the individual driver to qualify their skills as a test driver of vehicles with automated driving features. The SAE J3300/3 endorsement levels may also be used by test facilities or other organizations when seeking test or professional drivers with these skills. This document provides directions for obtaining the endorsement, including associated AD fallback test driving skill examination requirements, through SAE J3300-certified Examiners (refer to SAE J3300 for definition). Endorsement registration and associated records are administered through Probitas Authentication®. Probitas Authentication® is the current Independent Program Administrator for the SAE J3300 series. This document is a supplement to SAE J3300, providing information specific to the AD fallback test driver skill endorsement and clarifying the application of the rules set forth in SAE J3300 to the AD fallback test driver endorsement. While the references, definitions, rules, and guidelines presented in SAE J3300 Sections 1 through 5 apply to the AD fallback test driver endorsement, they are not repeated in this document.
Aiming at the problem of insufficient modeling of spatio-temporal heterogeneity in road traffic accident prediction, a dual task machine learning framework integrating geographical environment, location attributes and time periodicity is proposed. The dataset used in this study was derived from traffic accident records of Nanchang during 2019–2023. Firstly, geographical identifiers are generated by rounding and aggregating latitude and longitude coordinates. At the same time, the location type is processed by a one-hot encoding, so as to carry out spatial clustering analysis of accident hotspots. Compared with the North-South pattern, the contribution of geographical features shows a strong East-West trend. The kernel density heatmap identified Zone A and zone B as dual core high-risk areas. Secondly, the sinusoidal/cosine function is used to encode the time feature circularly, which effectively captures the daily change of the accident. The quantitative analysis of random forest regression model showed that time characteristics accounted for 89.2% of the variance of accident frequency interpretation, significantly exceeding the contribution of geographical factors (10.2%) and location attributes (0.6%). After hyperparameter optimization, the accuracy of XGBoost classifier in predicting serious accidents is 75.97%, and the AUC value is 0.8412, which has strong robustness, and provides reliable support for dynamic risk assessment of traffic management system.
Rigorous validation of SAE Levels 3 and 4 autonomous systems increasingly relies on simulation. However, the simulation-reality gap remains a challenge for human-in-the-loop assessments. This study empirically quantifies the behavioral fidelity of the Car-Learning-to-Act (CARLA) simulator by recreating specific real-world traffic scenarios using the high-precision exiD drone dataset. Twenty-five participants performed a series of maneuvers, including lane changes and time-critical cut-ins. Their performance was analyzed using Dynamic Time Warping (DTW), driver profiling, and Time-to-Collision (TTC) metrics. The findings reveal a clear distinction between relative and absolute behavioral validity. In strategic decision-making tasks, the simulation demonstrated remarkably high temporal fidelity. DTW analysis explained 94% of the trajectory variance. Participants initiated lane changes with an average lag of -9 frames (0.36 s) compared to naturalistic references. These results indicate that, despite the absence of peripheral optical flow, the simulator successfully elicits temporally correlated decision-making patterns suitable for assessing strategic driver intent. However, physical execution in reactive scenarios revealed significant absolute discrepancies. Although the high Pearson correlation (r ≈ 0.89) in velocity profiles proves that drivers recognize and react to hazards with realistic timing, their physical inputs were exaggerated. Participants displayed digital, over-modulated braking responses and maintained a negative safety bias of -11.26 m, a deviation attributed to the lack of vestibular g-force feedback and geometric minification. Furthermore, distinct driver profiles emerged. Risk-oriented participants exhibited a gaming effect by neglecting safety margins. In conclusion, while CARLA is highly valid for testing the temporal logic of driver interactions, absolute dynamics require calibration functions, such as force-feedback (pedal) tuning and visual deceleration cues like camera shake, to compensate for sensory limitations before it can be used for safety-critical validation.
Noise phenomena in automobiles caused by the stick-slip effect are increasingly among the most frequent reasons for customer complaints and therefore represent a critical vehicle quality attribute. To proactively address such issues, stick-slip testing of contacting material pairs is commonly applied during development. However, the predictive capability of current stick-slip test methods remains limited, particularly when highly flexible materials and realistic, stochastic excitation conditions are involved. The flexibility of sealing systems often allows the actual relative motion at the contact interface to be accommodated through adhesion and elastic deformation, thereby delaying or even preventing sliding. To date, this effect has not been represented by any characteristic parameter in conventional stick-slip testing. Instead, existing evaluations focus exclusively on the analysis of occurring stick-slip oscillations. For the initiation of stick-slip phenomena, however, not only the mean displacement between two stick-slip oscillations during the sliding phase is relevant, but also the relative displacement required to initiate the first slip event of the sealing contact. With the algorithm developed in this work, which reproducibly determines the distance to first slip based on changes in the friction force slope, this methodological gap is now closed. The displacement to first slip depends on numerous influencing factors, including profile geometry, normal load, sliding velocity, excitation profile, and environmental conditions, and was previously inaccessible by both experimental and numerical approaches. In particular, the onset of slip in sealing contacts can now be determined under stochastic excitation of the friction pairing, thereby closely reflecting real operating conditions. As a result, the prevention of noise phenomena can be significantly strengthened at an early stage of vehicle development.
1Systems level and integration testing are an integral part of the design and development of Automated Vehicles (AVs). Measurement science plays a pivotal role in testing to ensure the safe and efficient operation of AVs. This science establishes a common understanding of the units of measurement, crucial in linking human activities. This article describes the significance of measurement in studying interactions between key system technologies in AVs, including AI for perception, sensing, communications, and cybersecurity. To address the complexities of these interactions, a novel, adaptable, and interactive framework called the System Technology Interaction Model (STIM) is introduced. STIM considers both designed and emergent interactions between these system technologies, allowing AV developers to explore tailored experiments with the flexibility of filtering for focused testing. The framework currently models system interactions statically, not in real-time, to define potential relationships and influences during the design phase. The novelty of this framework comes from providing a holistic evaluation that captures testing of interactions between modules in addition to component-level testing, while other frameworks focus on testing individual component behaviors. It also assesses the equality of two interactions, meaning it ensures that two interactions behave the same way for consistent results. Moreover, the framework serves as a valuable tool for AV designers and safety regulators to aid in establishing robust design and assessment approaches. This work highlights the need for a common framework to thoroughly test AVs and gain a holistic understanding of system interactions. Finally, the framework aims to understand how to mitigate potential influences leading to AV malfunctions to advance the development and deployment of safe and reliable Automated Vehicles. The work focuses on level 1 and level 4 automated driving features to simplify the work, although it can be from level 1 to level 5. Although framework performance is inherently difficult to quantify, this framework’s performance can be reflected through its ability to accurately capture system interactions for improved AV design and support a broader usability among AV stakeholders. In the future, the framework can be expanded to include additional elements, such as infrastructure or other vehicles, to analyze information provided to AVs, allowing experts from various domains to collaborate, create similar models, integrate them when feasible, and model the interactions in real-time.
Air Traffic Management (ATM) must be familiar with the exact Aircraft Take-off Weights (ATOWs) of airplanes to make the most use of runways, maintain safety margins high, and keep utilization and resources in balance. This paper aims to present a dependable ATOW forecasting methodology that can assist the air transport industry in enhancing operational decision-making. This research used datasets acquired from the EUROCONTROL Performance Review Commission (PRC) 2024 Aircraft Take-Off Weight Estimation dataset featuring 527,000 flights over Europe containing aircraft details, air trips and flight conditions. Technique comprises structured data input, inspection of missing data, timestamp aggregation to identify demand cycles over time, and domain-specific feature engineering using distance_per_minute, block_minutes, taxiout_ratio, and a strong wake turbulence metric The two supervised learning models used were Linear Regression (LR) for understanding and XGBoost for performance prediction In comparison to LR's 4,409 kg MAE (mean absolute error), 7,061 kg RMSE (root mean square error), and 0.9825 R2 value, XGBoost significantly excelled with validation results showing an R2 value of 0.9992 and an RMSE of 1,514 kg In the absence of labelled test targets, cross-validation nevertheless showed a constant degree of generalizability The residual diagnostics showed that the model was reliable for practical execution with low-variance deviations that were unbiased An accurate ATOW estimate improves the demand-capacity balance and On-Time Performance (OTP) in ATM, which in turn affects the runway schedule, wake turbulence diversion, slot allocation, and fuel planning The results highlight the need to include ATOW predictions in both tactical and strategic planning to reduce delays, increase airspace usage, and promote sustainable aviation operation and possesses significant improvements will consist of weather and runway conditions, stochastic ambiguity computation, and drift monitoring to keep up with ever-changing operating variables while maintaining accurate forecasts.
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence modeling, model training, and interpretability analysis. The study demonstrates how this pipeline uncovers hidden relationships among operational errors, environmental influences, maintenance actions, design considerations, and crew behavior. The findings show that the integrated approach improves the accuracy and stability of risk prediction and highlights specific human factors patterns that consistently contribute to elevated risk levels. These insights support targeted mitigation strategies, inform design improvements, and help prioritize safety interventions. The work concludes that a combined human factors and predictive modeling framework enhances the ability of organizations to identify vulnerabilities earlier, allocate resources more effectively, and strengthen system resilience. This approach is adaptable to diverse aviation contexts and offers a practical path for transforming human factors data into actionable safety intelligence.
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