Browse Topic: Visibility

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Research on Ultrasonic Measurement Technology for Submarine Pipeline Deformation2026-99-0223To be published on 07/31/2026
Aiming at the measurement of buckling deformation defects of submarine pipelines in turbid waters, a precise measurement method for submarine pipeline deformation was proposed based on ultrasonic ranging technology. A unified underwater coordinate system for submarine pipelines and measurement sensors is established, and a three-dimensional model of the pipeline outer surface is constructed on this basis to provide a basis for calculating pipeline deformation elements. On the basis of underwater ultrasonic velocity correction, measurement accuracy control measures were proposed. Two types of ultrasonic measurement transducers and measurement systems were designed, and engineering applications were carried out to measure the deformation of submarine pipelines in the project. The measurement results indicate that the ultrasonic measurement system operates well under harsh sea conditions such as high turbidity, low visibility, and high flow velocity in the construction sea area, with high measurement accuracy. The three-dimensional model of the deformed pipeline is constructed accurately, and the deformation characteristics of the pipeline can be accurately calculated, meeting the requirements of engineering applications and providing effective data support for submarine pipeline maintenance.
Wang, Kekuan, He, Yazhang, Wang, Hong, Zhang, Tao, Cheng, Peiliang, Sun, Xinyan, Bai, Qian
North American CAV Performance Data StandardWP-00157/22/2026
As the deployment of connected and automated vehicles (CAVs) expands, the need for a consistent, cross-industry approach to performance relevant CAV data exchange is becoming more pressing. Vehicle developers, infrastructure owners and operators (IOOs), and technology providers generate and consume data that support safety, mobility, and operational efficiency, yet much of the data remains fragmented, inconsistently formatted, and difficult to reuse across systems. To address these gaps, the Society of Automotive Engineers (SAE) and the Canadian Standards Association (CSA) convened a multi-stakeholder workshop on November 3, 2025, with participants representing original equipment manufacturers (OEMs), automated driving system (ADS) developers, state and local agencies, standards bodies, and technology partners. The workshop focused on identifying challenges, clarifying needs, and outlining a path toward a North American CAV Performance Data Standard. Key themes from the workshop included: -The need for a shared data language to support safe and interoperable CAV operations. -The lack of consistent formatting, labeling and visibility regarding who produces and consumes data. -A “start small, iterate and scale” approach beginning with well-defined use cases. -The need for technical harmonization and governance structures that build trust and support sustained coordination. This white paper summarizes the key findings and outlines a practical approach to developing a Version 0.1 base-layer data standard that can support measurable progress in 2026 and beyond.
Nesheli, Mahmood
Privacy-Preserving UAV-Based Traffic Data Acquisition Platform2026-99-05957/10/2026
This paper puts forward a Privacy-Preserving UAV-Based Traffic Data Acquisition Platform to address 1) privacy leakage, 2) limited scenario coverage, and 3) low traffic data utilization efficiency in urban traffic monitoring environments. Our system integrates three innovations: 1) Dynamic Privacy Masking (DPM) and Dual-Track acquisition (DTC), which hides sensitive information (e.g., faces, license plates or LPL) in real-time while preserving critical traffic data (e.g., vehicle density, speed), 2) traffic data Localization (DL) and Privacy-Enhanced Federated Learning (FEFL), enabling cross-regional collaboration without raw traffic data sharing by perturbing neural network updates with differential privacy (DP), and 3) Ground-Air Collaboration (GAC) and VPF (VPF), combining UAVs with ground sensors and digital twins (DTs) to cover blind spots (e.g., tunnels, extreme weather). Experimented on UA-DETRAC and CitySim traffic data-sets, the platform achieves 92% privacy compliance (GDPR/PIPL), 87.5% mAP accuracy, and 85% road network coverage, outperforming other methods (e.g., FedUAV, static blurring). It supports applications such as traffic flow optimization, accident prevention, and regulatory alignment.
Zhang, Shilin, Yan, Ming
Estimating Driver Field of View in Active Safety Systems2026-01-07757/1/2026
Driver monitoring systems are an important component of active safety systems, continuously evaluating the driver’s state and issuing real-time warnings. As defined by the SAE Levels of Automation, driving tasks are increasingly transferred from the driver to the vehicle from Level 0 to Level 2, however, the driver remains fully responsible for monitoring the driving environment. Current implementations, such as driver drowsiness and attention warning, assess driver alertness, while advanced driver distraction warning ensures that the driver maintains visual focus. Nevertheless, these systems do not identify the specific objects or regions the driver is observing. This limitation motivates the presented research question: can an in-car monitoring system be integrated with external environment perception sensors to infer the driver’s field of view (FoV)? This paper presents a system consisting of a driver-facing camera and a front-view camera. Facial features, including gaze direction, head pose, and iris offset are extracted using computer vision techniques. These features, together with cropped eye images, are used as inputs to a multi-modal network. Training labels were generated using a driving simulator study with 16 participants who sequentially fixated on visual targets displayed on a front screen. Experimental results show that the proposed system can predict driver visual attention and approximate FoV with a mean pixel error of 35.40 px, enabling identification of the regions of the road scene observed by the driver in real time. This work provides a foundation for explicitly modeling driver perception and its correspondence with vehicle perception systems.
Ji, Dejie, Lausch, Hendryk, Flormann, Maximilian, Henze, Roman
Mind the Gap: Quantifying Behavioral Fidelity in CARLA Using Naturalistic Drone Data2026-01-07717/1/2026
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.
Rebling, Patrick, Alphan, Metehan, Nenninger, Philipp
Route-Based Sensor-Aware Speed Planning and Support for Safety Validation for Level-3 Automated Driving2026-01-07727/1/2026
Level-3 and higher automated driving systems require longitudinal speed strategies that remain consistent with both physical stopping feasibility and realistic sensing constraints. This paper presents a route-based, sensor-aware speed planning method that supports safety validation and explicitly couples longitudinal driving strategy with sensor field-of-view coverage. Based on a concrete route extracted from digital maps and enriched with fleet data, point-wise maximum speeds are computed considering road curvature, speed limits, and comfort constraints. From the resulting drivable speed profile, physically consistent stopping paths and their endpoints are calculated for each route position, accounting for friction limits, scenario-dependent deceleration capabilities, and system delays between perception and braking. The set of stopping paths is aggregated into a region of interest (ROI) representing the spatial area that must be reliably perceived to guarantee safe stopping. This ROI is overlaid with the geometric fields of view of camera, radar, and lidar sensors, enabling the definition of a compact and interpretable key performance indicator (KPI) based on the number of sensor modalities covering critical regions. Rather than evaluating a specific sensor configuration, the proposed KPI establishes a geometric interface between braking-based perception requirements and multi-modal sensing coverage. The approach reveals the structural sensitivity of perception demands to route geometry and braking assumptions and provides a systematic basis for perception-aware speed release decisions. The method is applicable to highways, interchanges, and other route types, and contributes a modular geometric framework for sensor-aware safety analysis in Level-3 and higher automated driving systems.
Kohler, Paul Leonhard, Resch, Michael
Ergonomic Assessment of Glove-Box Design in Passenger Vehicles for the Indian Scenario15-19-02-00096/10/2026
Passenger vehicles experience severe packaging constraints around the instrument panel, rendering glove-box operation a critical yet ergonomically underexplored interaction. Although glove-box interaction occurs frequently during routine vehicle use, its potential implications for ergonomic risk remain largely unexamined in existing automotive research. To isolate the influence of driver-side packaging constraints from component-level design effects, this study adopts a comparative evaluation of driver and co-driver glove-box interaction as a built-in control condition. This study introduces a discomfort-based evaluation framework that integrates Digital Human Modeling with India-specific anthropometric datasets. A composite loss-function scoring model is developed to quantify functional usability differences across four glove-box configurations, defined by variations in latch placement (center or side) and storage-bin mechanisms (fixed or rotating). Indians are utilized to assess reachability and visibility during glove-box interaction. Ergonomic performance is analyzed through reach and visibility metrics for both latch actuation and storage-access tasks. For the co-driver, all configurations exhibit 0% loss, confirming that usability remains unaffected. In contrast, the driver assessment reveals pronounced limitations. Center-mounted latches prove inaccessible from a neutral seated posture, reflecting an approximate loss function of 55%. Among the side-latch alternatives, the rotating-bin configuration achieves the lowest discomfort score (41%), supported by more favorable access posture and smoother hand-entry alignment. The findings specify that ergonomic limitations stem primarily from driver-side packaging constraints rather than inherent flaws in the glove box unit. Based on the reach and visibility loss values obtained through the developed framework, the Side-Latch + Rotating-Bin configuration emerges as the most suitable design option for passenger-vehicle layout. The proposed methodology offers a practical decision-support tool for early stage ergonomic evaluation of glove-box configurations in passenger vehicles.
Jujjavarapu, Sreeram, Kota, Srinivas, Kotkunde, Nitin, Jasti, Naga Vamsi Krishna
Autonomous Aircraft Docking: A Vision-Based Approach to Aircraft Parking Systems2026-26-07866/1/2026
Automated aircraft parking systems enhance airport ground operations by enabling precise and autonomous docking of aircraft at gates. These systems reduce turnaround time, minimize human error, and optimize apron space through real-time object detection, obstacle avoidance, and dynamic path planning. Unlike fixed guided-path methods, the proposed system adapts to congestion and environmental conditions such as low visibility, ensuring safety and efficient maneuvering. Validation through simulation demonstrates the system’s potential to improve operational resilience and support scalable automation in future airport infrastructure.
Penugonda, Navya Sunaina, Ediga, Venkatadiwakar Goud
Accelerating Aircraft Verification and Certification through an Integrated Virtual and Physical Testing Approach2026-26-07106/1/2026
Aircraft verification and certification entail a variety of testing tasks and require coordination among numerous stakeholders across different disciplines to ensure alignment on requirements. Historically, certification strategies have relied on both physical testing and high-fidelity simulation. The integration of these complementary approaches is essential to address their respective blind spots and to support credible certification evidence. A key challenge lies in the rigorous correlation of simulation models with physical test data. Flutter verification, for instance, is a critical component in defining the aircraft’s flight envelope and plays a foundational role in certifying safe operational boundaries. In this work, the process of freedom from flutter verification is demonstrated. This work introduces a novel approach to combining simulation and test data with the aim to accelerate and streamline the verification process leading to more efficient and cost-effective aircraft development. In addition, it is shown how the flutter verification process can be deployed using a simulation process and data management (SPDM) tool from which tasks are assigned and results are collected allowing transparency about the status of the workflow and providing stakeholders access to the data they need when they need it. The workflow is demonstrated using ground vibration test measurement performed on a full-scale F16 aircraft. Throughout the process, simulation data, test results, requirements, and supporting documentation are systematically managed within the SPDM framework. This enables effective cross domain collaboration between simulation and test engineers while also maintaining a single source of truth for proof of compliance and progressively building a robust digital thread throughout the development lifecycle.
Hallez, Raphael, Yadabettu, Dayanand Kumar, de Boer, Jens, Aspasiou, Vicky
Machine Learning–Based Detection of External Gunshot Sound Sources in Propeller Acoustic Near FieldF-0082-2026-01405/5/2026
This paper presents a study of gunshot acoustic signal detectability in the near field of propeller noise, with a focus on the isolation of external gunshot signatures masked by propeller-induced noise. Controlled measurements were conducted in a Recirculation Delayed Anechoic Chamber (RDAC), where acoustic data were collected across varying rotor speeds, source locations, and propagation distances. Propeller noise characteristics were verified using UCD-QuietFly. The recorded signals were analyzed for the acoustic pressure, sound pressure level, and overall sound pressure level directivity to quantify masking effects. Results show that RPM is the dominant factor governing signal detectability. At 3000 RPM, the gunshot signal remains clearly identifiable within the low frequency range of 200–2000 Hz. At 4000 RPM, the signal becomes partially masked, while at 5000 RPM, propeller noise fully dominates and the gunshot signal becomes undetectable. Detectability is further reduced with increasing propagation distance. In-plane microphone locations provide improved detectability. A machine learning-based spectral separation framework was developed to suppress propeller noise and enhance the visibility of impulsive gunshot signatures in multichannel spectrograms. Experimental results show that learning-based denoising is effective at lower RPMs where the signal-to-noise ratio remains favorable, but performance degrades as broadband masking intensifies at higher rotor speeds.
Sian-Bates, Grace, Li, Sicheng Kevin, Jiang, Peng, Chowdhury, Kowshik
MAVEN: A Multimodal Deep Learning Framework for In-Flight Long-Range Visibility EstimationF-0082-2026-03035/5/2026
Deep learning (DL) models have attained state-of-the-art performance in numerous fields. Nevertheless, for certain real-world applications, existing models encounter diverse challenges, ranging from a lack of generability to new data to issues of scalability and overfitting. In this context, integrating information extracted from different modalities holds promise as a potential solution to alleviate these challenges. This paper introduces MAVEN, a multimodal deep-learning framework for long-range atmospheric visibility estimation. Using multimodal deep learning, MAVEN fuses various modalities to estimate long-range atmospheric visibility. These modalities include RGB imagery, Edge Map, Entropy Map, Depth Map, and Normal Surface Map. Results show that in contrast to single-modality RGB, which achieves only 87.92% accuracy, multimodal deep learning models achieve an accuracy of over 96%. This significant improvement highlights the potential of multimodal approaches to enhance the accuracy and reliability of atmospheric visibility estimation, which is crucial for improving safety in applications such as aviation, maritime navigation, and autonomous vehicles. By addressing challenges such as data variability, environmental factors, and the inherent complexity of atmospheric conditions, MAVEN contributes to more reliable and robust visibility estimation systems, thereby enhancing safety and operational efficiency in critical environments.
Khelifi, Amine, Johnson, Charles, Bouaynaya, Nidhal, Carannante, Giuseppina, Bouhsine, Taha
Forvia Hella ready to light the night with ADB, but NHTSA test stands in the way26AUTD05_015/1/2026
A demonstration ride shows the glare-free, game-changing power of adaptive driving beams, already available in Europe. An approval test from NHTSA is proving difficult for OEMs to pass. I'm riding in the second row of a Lincoln Navigator fitted with Forvia Hella's adaptive driving beam (ADB) headlight system. The low- and high-beams are on, blasting everything in front of us for between 350 and 500 feet (122 and 152 m) with a bright, daylight-temperature LED light. Even traffic and street signs at the edges of the road, which normally aren't as well illuminated, are bathed in brightness. A car pulls out in front of us, and the system instantly adjusts, creating a tunnel of unlit space on and just next to the vehicle ahead. So even though we still have high beams on the rest of the road, that driver isn't facing the harsh glare that is the No. 1 complaint about today's high-intensity headlight systems.
Clonts, Chris
Camera Monitor Systems Test Protocols and Performance RequirementsJ3155_202604 (Current)4/13/2026
This SAE Recommended Practice provides test protocols with performance requirements for camera monitor systems (CMS) to replace existing statutorily required inside and outside rearview mirrors for U.S. market road vehicles. This practice expands specific technical content while retaining harmonization with the FMVSS 111 rear visibility standard and other international standards. This is accomplished by defining required roadway fields of view as specific fields of view (FOV) displayed inside the vehicle. Specific testing protocols and/or specifications are added to enhance ease of use using straightforward language, and any specifications are intended to be independent of different camera and display technologies unless otherwise explicitly stated.
Driver Vision Standards Committee
Method of Measuring and Reporting the Pressure Differential Flow Characteristics of a Hydraulic Fluid Power ValveJ1117_202604 (Current)4/12/2026
Applies to hydraulic fluid power valves as applied to Off-Road Self-Propelled Work Machines defined in SAE J1116.
CTTC C1, Hydraulic Systems
Real-Time End-to-End Stop Sign and Traffic Light Detection Development and Vehicle Testing2026-01-00144/7/2026
With the rise of end-to-end autonomous driving, visual perception for environmental understanding has become a key research topic in advanced driver assistance system (ADAS) development. Most existing end-to-end models generate only executable control commands or planned trajectories, making the prediction process difficult to interpret. In this study, we present an end-to-end approach for traffic-light recognition and stop-sign detection built on top of the open-source openpilot framework. Instead of deploying separate object detection networks, we extend the existing backbone with two lightweight multi-task heads: a traffic-light detection and classification head, and a stop-sign detection head with confidence estimation. The modified architecture preserves openpilot’s core driving functionality by reusing shared features and incorporating compact residual and feed-forward layers. The additional perception outputs are appended to the original outputs, ensuring that the model’s performance on other driving tasks remains unaffected. The proposed model is trained under diverse scenes and lighting conditions and demonstrates high accuracy in traffic-light classification and stop-sign detection, maintaining stable and consistent behavior during on-road evaluation. Furthermore, the enhanced model is fully compatible with Comma 3X hardware and has been successfully deployed and validated on a 2025 Nissan Leaf test vehicle. This work demonstrates the feasibility of developing a compact, lightweight, and deployable perception module that integrates traffic-signal understanding directly into an end-to-end driving model with minimal architectural modification.
Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin
Radar-Based Dynamic Pixel Activation in Camera Sensors for Optimized Image Processing, Power Efficiency, and Thermal Management2026-01-00474/7/2026
The increased integration of radar and vision sensors in modern vehicles has significantly improved environmental perception, safety, and automation. Nevertheless, conventional camera modules capture images in fixed, continuous frames, leading to unnecessary data processing, power consumption, and heat generation in the limited space of small sensors. The paper discusses the technology of Radar Based Dynamic Pixel Activation (RDPA); whereby radar data can be used to dynamically activate specific pixels on the camera sensor, optimizing image capture and processing. Through a systematic literature review of peer-reviewed articles published between 2021 and 2025, we examined the literature on radar-camera fusion, adaptive imaging, and sensor design that is efficient in power consumption. The review indicates a research gap that there is no current paradigm that dynamically activates sensor pixels at the hardware level using radar data. We aggregated ten topical studies and proposed a conceptual model where radar-determined Regions of Interest (ROIs) trigger localized pixel activation. The framework reduces the computational load, improves power efficiency and enhances thermal performance without affecting image fidelity. The paper also explains how RDPA may affect the Driver Monitoring Systems (DMS), Occupant Monitoring Systems (OMS), and Advanced Driver Assistance Systems (ADAS), and how it is more beneficial than traditional full frame imaging. Difficulties with synchronization, hardware interpenetration, and algorithmic synchronization are discussed. Altogether, RDPA is an excellent prospect to intelligent, energy-saving, and thermally stable vehicle perception systems of the next generation.
Kasarla, Nagender Reddy
Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis2026-01-01704/7/2026
Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporting more interpretable decision-making, yet their robustness to physical adversarial attacks, especially whether such attacks transfer across different VLM architectures, is not well understood and poses a practical risk when attackers do not know which model a vehicle uses. We address this gap with a systematic cross-architecture study of adversarial transferability in VLM-based driving, evaluating three representative architectures (Dolphins, OmniDrive, and LeapVAD) using physically realizable patches placed on roadside infrastructure in both crosswalk and highway scenarios. Our transfer-matrix evaluation shows high cross-architecture effectiveness, with transfer rates of 73–91% (mean TR = 0.815 for crosswalk and 0.833 for highway) and sustained frame-level manipulation over 64.7–79.4% of the critical decision window even when patches are not optimized for the target model. We further find asymmetric architecture-level risk, with Dolphins most vulnerable to incoming transfer attacks (VS = 0.82) and LeapVAD producing the most transferable patches (TO = 0.882), while models sharing CLIP-based vision encoders exhibit stronger bidirectional transfer. Overall, these results indicate that current VLM-based autonomous driving systems share systematic cross-architecture weaknesses that architectural diversity alone does not resolve, underscoring the need for defenses and design principles that explicitly account for transferability in safety-critical deployment.
Fernandez, David, MohajerAnsari, Pedram, Salarpour, Amir, Pese, Mert D.
Reconstruction of Pedestrian-Vehicle Collisions Using 3D Skeletal Estimation from Vehicle-Mounted Camera Video2026-01-05754/7/2026
Pedestrian fatalities in traffic accidents continue to rise, with severe injuries often resulting from both vehicle impact and subsequent ground contact, frequently occurring outside the field of view of vehicle-mounted cameras. This study presents a proof-of-concept (PoC) approach for reconstructing three-dimensional pedestrian motion—including occluded regions—using dashcam video. The method integrates 2D human pose estimation (MMPose) and monocular depth estimation (Depth Anything V2),the latter was fine-tuned on a custom dataset, to generate 3D skeletal coordinates.To evaluate motion matching, the reconstructed pedestrian poses were quantitatively compared with a database of vehicle collision simulations using the THUMS human body model and skeletal data representing real-world crash scenarios generated in PC-Crash. Composite similarity indices based on thoracic center of gravity trajectory and torso orientation vectors were employed for this comparison. Preliminary results indicate that the fine-tuned system achieves an average RMSE of approximately 0.1 m for key skeletal points, enabling accurate depth estimation for 3D pose reconstruction. Matching experiments with 11 PC-Crash cases demonstrated high similarity scores, and reconstructed sequences successfully identified critical injury events such as head-to-ground contact in occluded regions, confirming the feasibility of this approach for accident reconstruction and injury risk assessment. However, this study remains preliminary, limited to controlled indoor experiments with a single vehicle type and few subjects. Real-world crash footage and diverse vehicle geometries were not considered, and skeletal reconstruction from actual accident videos has not yet been implemented. Future work will expand the simulation dataset, refine similarity weighting, and validate the approach using real crash video. Ultimately, this technology may support forensic analysis and emergency response, but further validation is required before real-world application.
Onishi, Koji, Wang, Kewang, Uno, Eriko, Ichikawa, Koji, Tanase, Noboru, Ando, Takahiro
Miner’s Safety Bot: An ESP32-Based Autonomous Mobile Mine Safety Monitoring System2026-28-01162/12/2026
Mining operations are important to industrial growth, but they expose the mining workers to risk including hazardous gases, elevated ambient temperatures, and dynamic structural instabilities within underground environments. Safety systems in the past, typically based on fixed sensor networks or manual patrols, fall short in accurate hazard detection amidst shifting mine conditions. The proposed project Miner's Safety Bot advanced this paradigm by leveraging an ESP 32 microcontroller as a mobile platform that integrates gas sensing, thermal monitoring, visual inspection and autonomous obstacle avoidance. The system incorporates MQ7 semiconductor gas sensor to monitor real time carbon monoxide (CO), offering detection range from 5 to 2000 ppm with accuracy of 5 ppm. Temperature and humidity are monitored through DHT11 digital sensor, calibrated to ensure reliability across the harsh microclimates in mines. Navigation and autonomous movement are enabled by Ultrasonic Sensor (HC-SR04) with 3 mm accuracy level for obstacle detection, that is integrated into mobile chassis which is driven by L298N dual H-bridge motor drivers. The bot's orientation and sensor field of view are controlled by a servo motor. For visual inspection, ESP32-CAM module streams real time visuals from mine. Wireless data transmission uses the ESP32's inbuilt Wi-Fi to link sensor outputs to the Blynk IoT platform, that enables to monitor data remotely.
D, Suchitra, D, Anitha, Muthukumaran, Balasubramaniam, Mohanraj, Siddharth, Subash Chandra Bose, Rohan
Rear Wiper Motor with Nozzle Integration2026-26-06001/16/2026
In today's dynamic driving environments, reliable rear wiping functionality is essential for maintaining safe rearward visibility. This study sharing the next-generation rear wiper motor assembly that seamlessly integrates the washer nozzle, delivering improved performance alongside key benefits such as better Buzz, Squeak, and Rattle (BSR) characteristics, reduced system complexity, cost savings, and enhanced perceived quality. This integrated design simplifies the hose routing which improves the compactness and the efficiency of the design. This also enhances the spray coverage and minimizes the dry wiping unlike the traditional systems that position the washer nozzle separately. A non-return valve (NRV) is incorporated to eliminate spray delays ass it maintains consistent water flow giving cleaning effectiveness. Since this makes the nonfunctional parts completely leak proof due to the advanced sealing, it increases the durability and reliability in long run. As this proposal offers a sustainable solution, it can be considered as the new benchmark in rear wiper technology.
Dhage, Prashant, K, Nagarajan, G, Sabari Rajan
Factory Digitalization Efficiency Improvement through Mechanization of Point Cloud to 3D Modelling2026-26-03071/16/2026
The growing environmental, economic, and social challenges have spurred a demand for cleaner mobility solutions. In response to the transformative changes in the automotive sector, manufacturers must prioritize digital validation of products, manufacturing processes, and tools prior to mass production. This ensures efficiency, accuracy, and cost-effectiveness. By utilizing 3D modelling of factory layouts, factory planners can digitally validate production line changes, substantially reducing costs when introducing new products. One key innovation involves creating 3D models using point cloud data from factory scans. Traditional factory scanning processes face limitations like blind spots and periodic scanning intervals. This research proposes using drones equipped with LiDAR (Light Detection and Ranging) technology for 3D scanning, enabling real-time mapping, autonomous operation, and efficient data collection. Drones can navigate complex areas, access small spaces, and optimize factory planning with precise point cloud data. This enables planners to maintain updated layouts and implement necessary modifications for future projects. However, the current manual process of converting point cloud data into 3D models is time-intensive, causing delays in meeting market demand. To address this, point cloud data is segmented into two categories: (a) standard 3D components from libraries and (b) non-standard components like machines and air ducts. Automating the point cloud-to-3D modelling process yields significant improvements in conversion results. While automation enhances the placement of standard objects with geometric precision, smoother surface finishing is still required for non-standard components.
Narad, Akshay Maruti, C H, Ajheyasimha, Vijayasekaran, Vinothkumar, Fasge, Abhishek
Experimental Study to Investigate and Control Image Blurriness due to IRVM Vibration2026-26-03211/16/2026
Vibration is one of the prominent factors that determine the quality & comfort level of a vehicle. Moreover, if vibration occurs in areas that are almost entirely within customer touchpoints, it could become a critical factor behind vehicle comfort and affects the brand image within the market negatively. The interior rear-view mirror (IRVM) is one of the important components inside passenger cabin, providing drivers with a clear view of the rear traffic. However, vibrations induced by engine operation, road irregularities, and aerodynamic forces can cause the IRVM to oscillate, leading to image blurriness and compromised visibility and safety. This paper investigates the underlying causes of IRVM vibration and its impact on rear visibility. Through experimental analysis we identify key factors contributing to mirror instability. The findings indicate the specific frequencies of vibration, particularly those resonating with the mirror's natural frequency, significantly exacerbating image blurriness. This paper presents the design modifications and damping solutions to mitigate these vibrations, enhancing the overall safety and driving experience. The study results provide valuable insights for automotive engineers and designers aiming to reduce IRVM vibration and enhance the driver/ passenger safety.
Khan, Aamir Naved, Saraswat, Vivek, Jha, Kartik, Singh, Hemendra, Seenivasan, Gokulram, Khan, Nafees
Comprehnsive Measurement and Evidential Evaluation of Driver Drowsiness and Alertness Warning System2026-26-05201/16/2026
The paper aimed to improve the accurate quantification of driver drowsiness and to provide comprehensive, evidence-based validation for a Vision-Based Driver Drowsiness and Alertness Warning System. Advanced quantification of driver drowsiness is designed to enhance distinction of true positive events from False Positive and False Negative events. Methodology to pursue this included assessing inputs such as facial features, driver visibility, dynamic driving tasks, driving patterns, driving course time and vehicle speed. The system is programmed to actively learn Eye Aspect Ratio (EAR) reference and adapt personalised EAR threshold value to process EAR frames against the learnt threshold value. This method optimized the data frames to enhance the evaluation and processing of essential frames, thereby reducing delays in the processor and the Human-Machine Interface (HMI) warning module. Comprehensive validation is systematically conducted within a controlled test track environment to ensure precise execution of protocols, maintaining inputs closely aligned with real-time scenarios. The test methodology comprised the execution of pre-defined protocols that is steering robot and a technology-neutral procedure. Pre-defined protocols are scenarios created using the aforementioned assessing inputs. Cartesian coordinates of the system’s camera and driver eye point relative to the seating reference point (SgRP) are identified using a coordinate measurement machine (CMM) to measure the driver's position within the camera's field of view and mark the visibility zones. The protocols are executed with precision using a global navigation satellite system (GNSS), visual sensor, audio sensor and data logger. Subsequently, the system is tested with number of drivers trained on the Karolinska Sleepiness Scale (KSS) to conduct technology-neutral method for statistical analysis. Detailed analysis of the tested data, concluded with results and explored future prospects for quantifying driver drowsiness are discussed. The paper also discussed observations and challenges associated with the functionality of conventional systems and protocols currently deployed in the market.
Balasubrahmanyan, Chappagadda, Akbar Badusha, A
Method and System of Detecting Obstacle around Vehicle Door2026-26-00261/16/2026
Vehicle door-related accidents, especially in urban environments, pose a significant safety risk to pedestrians, infrastructure and vehicle occupants. Conventional rear view systems fails to detect obstacles in blind spots directly below the Outside Rear View Mirror (ORVM), leading to unintended collisions during door opening. This paper presents a novel vision-based obstacle detection system integrated into the ORVM assembly. It utilizes the monocular camera and a projection-based reference image technique. The system captures real-time images of the ground surface near the door and compares them with calibrated reference projections to detect deviations caused by obstacles such as pavements, potholes or curbs. Once such an obstacle is detected the vehicle user is alerted in the form of a chime.
Bhuyan, Anurag, Khandekar, Dhiraj, Jahagirdar, Shweta
Effect of Correlated Colour Temperature of Automotive LED Headlamps on Visibility and Safety2026-26-01301/16/2026
The objective of this study was to examine the effect of Correlated Colour Temperature (CCT) of automotive LED headlamps on driver’s visibility and comfort during night driving. The experiment was conducted on different headlamps having different correlated colour temperatures ranging from 5000K to 6500K in laboratory. Further study was conducted involving participants of different age group and genders for understanding their perception to identify objects when observed in light of different LED headlamps with different CCTs. Studies have shown that both Correlated Colour Temperature and illumination level affect driver’s alertness and performance. Further study required on headlamps with automatically varying CCT to get better solution on driver’s visibility and safety.
Patil, Mahendra G., Kirve, Jyoti, Parlikar, Padmakumar
Development of a New Methodology for Measuring the Dimensions of Lighting Components in Construction Equipment Vehicles (CEVs) as per IS/ISO 12509:2004 using 3-Dimensional Planar Laser2026-26-01451/16/2026
The light and light signaling devices installation test as per as per IS/ ISO 12509:2004 & IS/ISO 12509:2023 for Earth Moving Machinery / Construction Equipment Vehicles is a mandatory test to ensure the safety and comfort of both road users and operators. Considering the shape and size of construction equipment vehicles, accurate measurement of lighting installation requirements is crucial for ensuring safety and regulatory compliance. The international standard IS/ISO 12509:2004 & IS/ISO 12509:2023 outlines specific criteria for these installation requirements of lighting components, including the precise measurement of various dimensions to ensure optimal visibility and safety. Among these dimensional requirements, the dimension 'E' i.e., the “distance between the outer edges of the machine and the illuminating surface of the lighting device” plays a critical role in the performance of vehicle lighting systems. Traditional methods of measuring this dimension, such as using a measuring tape and long straight rod, in another method Using Rope, Plumb and Measuring tape have limitations in terms of precision and consistency due to machine size and shape. This paper presents a method development approach utilizing a 3-Dimensional planar laser for measuring dimension 'E' in Construction Equipment Vehicles (CEVs). Measurement through the planar laser method is found to offer significantly higher accuracy compared to conventional measuring techniques, particularly when applied to the complex shapes and sizes of CEV’s such as Motor Graders, Wheel Loaders and Backhoe Loaders. This approach not only enhances the measurement accuracy but also improves the efficiency of the testing process. The paper discusses the methodologies, results, comparison of 3 measuring methods and potential applications of Planar laser in the context of IS/ISO 12509:2004 & IS/ISO 12509:2023, offering a promising alternative method for future testing and certification of Construction Equipment Vehicle’s lighting systems.
Ghodke, Dhananjay Sunil, Belavadi Venkataramaiah, Shamsundara, Tambolkar, Sonali Ameya
Single Radar: A Robust and Cost-Effective Solution for ADAS and Parking Functionality2026-26-00491/16/2026
With rapid advancements in Autonomous Driving (AD) & Advanced Driver Assistance Systems (ADAS), numerous sensors are integrated in vehicles to achieve higher and reliable level of autonomy. Due to the growing number of sensors and its fusion creates complex architecture which causes challenges in calibration, cost, and system reliability. Considering the need for further ADAS advancements and addressing the challenges, this paper evaluates a novel solution called One Radar - a single radar system with a wide field of view enabled by advanced antenna design. Placing the single radar at the rear of the vehicle eliminates the need for corner radars and ultrasonic sensors used for parking assistance. With rigorous real-world testing in different urban and low-speed scenarios, the single radar solution showed comparable accuracy in object detection with warning and parking assistance to the conventional combination of corner radars and ultrasonic sensors. The simple single sensor-based architecture not only reduces signal processing complexity and development time but also minimizes interference risks that comes with multiple sensor setup. This innovation results in a significant reduction in the Bill of Materials (BOM) for manufacturers by up to 40% for rear/side sensing modules, lowering production costs and enabling more affordable ADAS-equipped vehicles for end customers. Additionally, the simplified design enhances scalability for mass market adoption. The research paper talks about the single radar performance in various use cases to validate the features such as Rear parking alert system (RPAS), Door open warning (DOW), Blind spot detection (BSD), Lane change warning (LCW) and Rear collision warning (RCW) functionalities, highlighting its versatility as a standalone sensor module for future autonomous systems.
Anandan, Ram, Sharma, Akash
Ambient Light Reflection Analysis Using Dimensional Modeling Tools to Predict Windshield View Obscuration and Improve Safety2026-26-04761/16/2026
Ambient light reflecting off internal components of the car, specifically the Head-Up Display (HUD), creates unwanted reflections on the Windshield. These reflections can obscure the driver's field of view, potentially compromising safety and reducing visual comfort. The extent of this obscuration is influenced by geometrical factors such as the angle of the HUD and the curvature of the Windshield, which need to be analyzed and managed. The primary motivation is to improve driver safety and visual comfort. This is driven by the need to address the negative impact of ambient light reflecting off Head-Up Displays (HUDs), which can impair visibility through the Windshield. There is a need for tools and methods to address this issue proactively during the vehicle design phase. This study employs a tool-based modeling method to trace the pathways of ambient light from its source, reflecting off the HUD, and onto the Windshield using a dimensional modeling tool. It focuses on: Geometrical surfaces (specifically HUD angle and Windshield curvature) Modeling the pathways of ambient light from its source, reflecting off the HUD, and onto the Windshield using the dimensional modeling tool Measuring and analyzing the resulting areas of reflection caused within the driver's field of view. The method aims to evaluate the extent of disruption or obscuration within the driver's field of view caused by the reflections in millions of vehicles.
Muchchandi, Vinod, Akula, Satya Jayanth, Mahindrakar, Pramod, G S, Sharath
Microcontroller-Based Modular Mirror for Heavy and Mid-Commercial Vehicles2026-26-00181/16/2026
Existing ICE Mid and Heavy commercial vehicles in the Indian and international market are recording a large number of mishaps due to blind spots and non-accessibility of the driver to the opposite side mirror in real-time driving. Non-driver side rear view mirror adjustment creates the need for the driver to get down and adjust the mirror manually/get support from the co-passenger. The paper proposes a solution for a Microcontroller-based compact mirror adjustment system, which will run with minimal economy and highest efficiency. This will assist drivers in aesthetically and safely monitoring of mirror to check on specific blind spots in day conditions This will reduce the prone accidents due to non-visibility by approximately 30%, ensuring enhanced road safety and driver comfort. The Indian commercial vehicle segment needs this solution to be implemented when we look at the rate of increasing demand and also accident rates.
Jambagi, Vaibhavi Vyankatesh, Gangvekar, Onkar, Bhandari, Kiran Kamlakar
Vehicle Self-Soiling from Exterior Cleaning Mechanisms2026-26-04251/16/2026
The automotive industry is rapidly advancing towards autonomous vehicles, making sensors such as Cameras, LiDAR, and RADAR critical components for ensuring constant information exchange between the vehicle and its surrounding environment. However, these sensors are vulnerable to harsh environmental conditions like rain, dirt, snow, and bird droppings, which can impair their functionality and disrupt accurate vehicle maneuvers. To ensure all sensors operate effectively, dedicated cleaning is implemented, particularly for Level 3 and higher autonomous vehicles. It is important to test sensor cleaning mechanisms across different weather conditions and vehicle operating scenarios to ensure reliability and performance. One crucial aspect of testing is tracking the trajectory of the cleaning fluid to ensure it does not cause self-soiling of vehicles and affects the field of view or visibility zones of other components like the windshield. While wind tunnel tests are valuable, digitalizing this process is vital for making design decisions early in vehicle development. This work presents a digital methodology to test the self-soiling of a vehicle due to the cleaning systems present on vehicle exterior components, e.g. during mud cleaning at different vehicle speeds. The cleaning mechanism involves multiple water nozzles positioned above, below, or on the sides of these components, which spray water jets to remove dirt or mud deposits. The developed numerical method models the motion of cleaning fluid and contaminants after component cleaning. Steady-state aerodynamic simulations using the Finite Volume Method (FVM) are used to capture airflow, while the interaction of air with cleaning fluid and components is analyzed using a Smoothed Particle Hydrodynamics (SPH) solver. Correlations from this study and wind tunnel tests reveal potential optimization opportunities for existing cleaning systems by inspecting surrounding airflows at various vehicle speeds. Preliminary design evaluations indicate a specific vehicle speed range where self-soiling of vehicle components such as the windshield occurs due to mud cleaning. The proposed numerical method provides the capability to evaluate and qualitatively compare vehicle self-soiling due to various cleaning system designs of exterior components, offering valuable insights for optimizing cleaning mechanisms in autonomous vehicles.
Mane, Suvidya, Makam, Sri Lalith Madhav, Varghese, Rixson, Desu, Harsha
MAF-NET: A Multi-Scale Feature Fusion Network for Low-Light Driving Image Enhancement2025-01-730412/31/2025
In low-light driving scenarios, in-vehicle camera images encounter technical challenges, including severe brightness degradation and short exposure times. Conventional driving image enhancement algorithms are susceptible to issues such as the loss of image features and significant color distortion. The proposed solution to this problem is a multi-scale attention fusion network (MAF-NET) for the enhancement of images captured during low-light driving conditions. The network’s structural design is uncomplicated. The model incorporates a meticulously designed multi-scale attention fusion module (MAFB), along with all essential components for network connectivity. The MAF is predicated on a heavy parameter residual feature block design and incorporates a multi-scale channel attention mechanism to capture richer global/local features. A substantial body of experimental evidence has demonstrated that, in comparison with prevailing algorithms, MAF-NET exhibits superior performance in low-light enhancement, detail retention, and color reproduction. Moreover, it attains commendable results in both subjective visibility assessments of nighttime driving scenarios and objective image quality metric tests, such as PSNR and SSIM.
Pan, Deng, Chen, Yuhan, Shi, Yicui, Li, Jie, Li, Guofa
Research on the Visibility Attenuation Model of Pavement Markings for Older Drivers Based on Retroreflective Luminance Coefficient2025-99-022012/23/2025
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Cheng, Lizhi, Guan, Yanyan, Cheng, Xinyu, Hu, Jiangbi, Fu, Youlei, Yang, Biyu, Song, Shousong
Semiautomatic Headlamp Beam Switching DevicesJ565_202511 (Current)11/13/2025
This SAE Standard provides test procedures, performance requirements, and guidelines for semiautomatic headlamp beam switching (SHBSD) devices.
Road Illumination Devices Standards Committee
Visibility Study for Tractor with Rear Implement2025-28-032011/6/2025
This paper details a process involving digital content creation tools to conduct visibility studies for machinery that utilize vision-based perception system. In this paper we go through the process of preparing over 50 unique tillage implements with Light geometry attached to assess areas of potential sensor occlusion. We optimized the CAD Geometry of the model by removing Duplicate parts, Wiring, Floating geometry. Also, optimization of heavier parts was required and ensuring of high-altitude parts (i.e. SMV, Starfire, Lights, brackets) was important to ensure proper visibility studies. Making sure that the setup of the implement matched that of its corresponding vehicle was also Pertinent. The developed high-fidelity models that are used to Conduct perception occlusion analysis, develop simulations, quickly verify component geometries. Occlusion analyses facilitate cross-team discussions of the perception system coverage and expedite product development. Moreover, the generated high-fidelity small-sized digital assets can be used to generate synthetic scenarios in simulation and thus facilitate software qualification. This ability to execute simulations leveraging high quality and controlled inputs, can help reduce the burden of in field-testing this limiting the exposure to uncontrolled variables and difficult to reproduce scenarios. The ability to quickly verify component geometries and simulate various scenarios and machine configurations allows for a more agile development process. Teams can iterate faster, respond to feedback more effectively, and bring innovations to market more rapidly. The insights gained from Camera visibility studies can be integrated with emerging technologies such as artificial intelligence and machine learning. This integration can lead to the development of smarter perception systems that continuously learn and adapt, enhancing overall safety and performance.
Kumar, Pravin, GUMASTE, Amey, Rode, Aboli, Goč, Matej
Design and Implementation of a Portable Setup for Operator’s Field of Vision Testing on Construction Equipment Vehicles in accordance with IS/ISO 5006:20172025-28-027011/6/2025
The Operator’s Field of Vision (FOV) test, conducted in accordance with IS/ISO 5006:2017, is a vital assessment to ensure the safety and operational comfort of personnel operating Construction Equipment Vehicles (CEVs) / Earth-Moving Machinery. IS/ ISO 5006:2017 defines rigorous guidelines for evaluating the operator’s visibility from the driver's seat, with particular emphasis on the Filament Position Centre Point (FPCP), determined from the Seat Index Point (SIP) coordinates. The test includes assessment of masking areas, focusing on the Visibility Test Circle (a 24-meter diameter ground-level circle around the machine), and on the Rectangular Boundary on which a vertical test object is placed at a height specific to the machine type and its operating mass. These parameters are designed to simulate real-world operating conditions. This paper introduces a portable testing setup developed specifically for conducting the Operator’s FOV test as per IS/ISO 5006:2017. The setup facilitates include accurate verification of X and Y coordinates of the SIP, Integration of a high-intensity light system to project and assess masking areas, and Quick & repeatable deployment in field conditions, enhancing usability across various types of CEVs and earth moving machinery. The portable design ensures adaptability, reduces overall testing time, and upholds the accuracy requirements stipulated by IS/ISO 5006:2017. This solution not only enables more efficient visibility assessments but also supports enhanced safety compliance for manufacturers and operators. It presents a cost-effective, field-deployable solution for ensuring visibility requirements in construction equipment vehicles / earth-moving equipment.
Ghodke, Dhananjay Sunil, Tambolkar, Sonali Ameya, Belavadi Venkataramaiah, Shamsundara
A Strategic TQM Approach to Enhance Passenger Cabin Comfort Using T-Matrix2025-28-042310/30/2025
This study demonstrates the application of the T-Matrix, a Total Quality Management (TQM) tool to improve thermal comfort in automotive climate control systems. Focusing on the commonly reported customer issue of insufficient cabin cooling, particularly relevant in hot and congested Indian driving conditions, the research systematically investigates 36 failure modes identified across the product lifecycle, from early design through production and post-sale customer usage. Root causes are first categorized using an Ishikawa diagram and then mapped using the T-Matrix across three critical stages: problem creation, expected detection, and actual detection. This integrated approach reveals process blind spots where existing validation and inspection systems fail to catch known risks, particularly in rear-seat airflow performance and component variability from suppliers. By applying this TQM methodology, the study identifies targeted improvement actions such as improved thermal targets, component tuning, and supplier-level quality controls. The outcomes demonstrate measurable enhancements in air velocity, cabin cooldown time, and noise levels, contributing to increased customer satisfaction and reduced warranty incidence. This work underscores the importance of proactive, cross-functional quality management and supports the evolving role of structured TQM tools such as the T-Matrix, in addressing modern automotive quality challenges.
Jaiswara, Prashant, Kulkarni, Shridhar, Deshmukh, Ganesh, Nayakawadi, Uttam, Joshi, Gaurav, Shah, Geet, Jaybhay, Sambhaji
Enhanced Cooperative Perception through Asynchronous Vehicle-to-Infrastructure Framework with Delay Mitigation for Connected and Automated Vehicles12-09-01-00088/8/2025
Perception is a key component of automated vehicles (AVs). However, sensors mounted to the AVs often encounter blind spots due to obstructions from other vehicles, infrastructure, or objects in the surrounding area. While recent advancements in planning and control algorithms help AVs react to sudden object appearances from blind spots at low speeds and less complex scenarios, challenges remain at high speeds and complex intersections. Vehicle-to-infrastructure (V2I) technology promises to enhance scene representation for connected and automated vehicles (CAVs) in complex intersections, providing sufficient time and distance to react to adversary vehicles violating traffic rules. Most existing methods for infrastructure-based vehicle detection and tracking rely on LIDAR, RADAR, or sensor fusion methods, such as LIDAR–camera and RADAR–camera. Although LIDAR and RADAR provide accurate spatial information, the sparsity of point cloud data limits their ability to capture detailed object contours of objects far away, resulting in inaccurate 3D object detection results. Furthermore, the absence of LIDAR or RADAR at every intersection increases the cost of implementing V2I technology. To address these challenges, this article proposes a V2I framework that utilizes monocular traffic cameras at road intersections to detect 3D objects. The results from the roadside unit (RSU) are then combined with the on-board system using an asynchronous late fusion method to enhance scene representation. Additionally, the proposed framework provides a time delay compensation module to compensate for the processing and transmission delay from the RSU. Lastly, the V2I framework is tested by simulating and validating a scenario similar to the one described in an industry report by Waymo. The results show that the proposed method improves the scene representation and the CAV’s perception range, giving it enough time and space to react to the adversary vehicles.
Saravanan, Nithish Kumar, Jammula, Varun Chandra, Yang, Yezhou, Wishart, Jeffrey, Zhao, Junfeng
Flight Compartment GlareARP6161A (Current)7/25/2025
This document is a tool for the certifying authority, flight deck crew station designers, instrument suppliers, lighting suppliers, and component suppliers. It is an aid to understanding and meeting relevant regulatory requirements, particularly those relating to pilot compartment view (refer to 14 CFR § 25.773[a][2]) and instrument lights (refer to 14 CFR § 25.1381[a][2]) for glare arising from visible electromagnetic radiation.
A-20A Crew Station Lighting
Composite and Bonded Structure Technician/Specialist Training DocumentAIR4938D (Current)7/25/2025
This document establishes the minimum curriculum requirements for training, practical assessments, and certifying composite structure repair personnel and metalbond repair personnel. It establishes criteria for the certification of personnel requiring appropriate knowledge of the technical principles underlying the composite structural repairs and/or metalbond they perform. Persons certified under this document may be eligible for licensing/certification/qualification by an appropriate authority, in addition to this industry-accepted technician certification. Teaching levels have been assigned to the curriculum to define the knowledge, skills, and abilities graduates will need to make repairs to composite or metalbond structure. Minimum hours of instruction have been provided to ensure adequate coverage of all subject matter, including lecture and laboratory. These minimums may be exceeded and may include an increase in the total number of training hours and/or increase in the teaching levels. AIR6291 (for metalbond repair) and AIR6292 (for fiber composite repair) provide repair process flows, cautionary explanation of critical process steps, and industry best practices obtained through service experience and lessons learned. Training organizations should incorporate these into their training course syllabi, as appropriate, to conform with the training standards specified by this report. Each curriculum is a subpart of this document. Part 1 is the General Composite Structure Bonded Repair curriculum, independent of the application. Part 2 is the Commercial Aircraft Composite Structure Bonded Repair curriculum. Part 3 is the Commercial Aircraft Composite Structure Bolted Repair curriculum. Part 4 is the Commercial Aircraft Structure Metalbond Repair curriculum.
AMS CACRC Commercial Aircraft Composite Repair Committee
A 3D Visibility Label for Mobility Coatings: Enhancing Traffic Safety Through Color and Sensor Perception12-09-02-00097/22/2025
The mobility industry is rapidly advancing towards more autonomous modes of transportation with the adoption of sophisticated self-driving technologies. However, a critical challenge, being the lack of standardized norms for defining, measuring, and ensuring vehicle visibility across various dynamic traffic environments, remains. This lack of awareness of visibility is hindering the development of new regulations for vehicle visibility and the controlled transition to a fully-integrated autonomous future. While current efforts focus on improving sensing technologies like computer vision, LiDAR systems, and sensor fusion development, two key issues remain unresolved: 1 The absence of a representative and realistic three-dimensional color visibility model for measuring and comparing the visibility of complex shapes with large but varying color coated three-dimensional surface areas. 2 The need for enhanced visibility solutions that improve visibility and vehicle detectability for all traffic participants while maintaining color styling freedom. This article presents a new visibility assessment model that measures the three-dimensional Point-of-View (PoV) Visibility of mobility coatings using a 5-parameter Visibility Label. It evaluates how visible a mobility coating color is on a complex three-dimensional vehicle shape from a separate observer’s perspective or an observing device. Key components of this three-dimensional Point-of-View Color Visibility Model include: Moving beyond flat panel color design and measurements to a comprehensive 3D Visibility Model Considering visibility for human vision, computer vision and LiDAR visibility modalities Establishing automotive glossy solid white as reference color The Visibility Label helps users understand how visible complex shapes with different colored coatings are. It quantifies visibility in real-world scenarios, enabling easy comparison between designs and finishes. This simplifies decisions on which coatings enhance visibility most. The article shows that similar colors can have a significant different 3D PoV Color Visibility. It explores ways to enhance this visibility with new color coating technologies, particularly through Crystal Glass Pigment (CGP) formulations that significantly improve 3D color visibility. The example in Appendix B demonstrates an increase in 3D Color Visibility of 200% for Human Visibility and 600% for Full Object Visibility for the addition of 5 weight-percent CGP, and 183% increase for LiDAR Visibility for the addition of 17 weight-percent CGP to an existing silvery metallic automotive refinish color. By emphasizing the importance of 3D PoV Color Visibility in traffic environments where human-controlled, semi- and fully autonomous vehicles coexist, and highlighting the influence of color and shape on vehicle visibility, this comprehensive approach aims to significantly impact road safety today while facilitating a controlled transition to an autonomous future.
Mijnen, Paul W., Moerenburg, Joost H.
Systematic Derivation of Requirements for the Perception Task of Free Space Driving Assistance Function Applied to Trucks2025-01-02797/2/2025
The larger size and expanded blind spots of heavy-duty trucks in comparison to passenger cars, create unique challenges for truck drivers navigating narrow roads, such as in urban scenarios. For this reason, the detection of free space around the vehicle is of critical importance, as it has the potential to save lives and reduce operating costs due to less maintenance and downtime. Despite the existence of numerous approaches to free space detection in the literature, few of these have been applied to the trucking sector, disregarding important aspects for these kinds of vehicles such as the altitude at which obstacles are located. This paper aims to present the initial results of our research, a “Not Free Space Warner”, a driving assistance function intended for implementation in series trucks. A methodology is followed to define the characteristics that the perception component of this function shall fulfill. To this end, an analysis of the most critical accidents and common driving situations that truck drivers encounter is conducted, with a particular focus on the potential contribution of free space detection to assist the driver. By deriving and analyzing multiple scenarios from the use cases, the requirements to be met by the perception pipeline of function are defined. To validate these requirements, a Mercedes Actros equipped with multiple ground truth sensors, utilized as a research vehicle, is presented. Finally, the limitations and challenges associated with its implementation in the context of trucks are discussed.
Martinez, Cristian, Peters, Steven
LightNetDehaze: Lightweight and Unsupervised Single-Image Dense Haze Removal12-09-01-00055/26/2025
Image dehazing techniques can play a vital role in object detection, surveillance, and accident prevention, especially in scenarios where visibility is compromised because of light scattering by atmospheric particles. To obtain a high-quality image or as an initial step in processing, it’s crucial to restore the scene’s information from a single image, given that this is an ill-posed inverse problem. The present approach utilized an unsupervised learning approach to predict the transmission map from a hazy image and used YOLOv8n to detect the car from a clear recovered image. The dehazing model utilized a lightweight parallel channel architecture to extract features from the input image and estimate the transmission map. The clear image is recovered using an atmospheric scattering model and given to the YOLOv8n for car detection. By incorporating dark channel prior loss during training, the model eliminates the need for a paired dataset. The proposed dehazing model with fewer parameters speeds up the dehazing process, which can detect the objects in less response time. The network follows unsupervised learning, which eliminates the need of ground truth image or transmission map of a clear image. The proposed method tried to solve the issue of high computational complexity and long latency when used as a preprocessing stage in computer vision applications. The proposed network ranks first in terms of parameters and FLOPs, which are lower by scale 102 and 103, respectively, compared to the method ranked second. The results highlight the effectiveness of the proposed method compared to other methods and ranked first in number of car detections using YOLOv8n. The inference time to dehaze the image is comparable to the method ranked first and 66% lower than the third rank.
Dave, Chintan, Patel, Hetal, Kumar, Ahlad
A Review of Proposed Enhancements to the Broken Wheel Vertiport Marking & SymbologyF-0081-2025-01575/20/2025
Electric Vertical Takeoff and Landing (eVTOL) aircraft present a series of challenges to traditional aviation infrastructure that was designed for conventional rotorcraft. Questions have arisen within the vertical flight community as to the validity and applicability of applying current heliport markings and symbology to vertiports. Several of these questions were addressed in a previous paper from VFS Forum 80: "A Comparison of Proposed Concepts for Vertiport Markings and Symbology" (Ref. 6). In contrast, this paper extends that work and presents the results of additional research to enhance the visibility of the Federal Aviation Administration’s (FAA) “Broken Wheel” symbology. These notional enhancements to the "Broken Wheel" symbology were evaluated over the course of an experimental study using helicopter-rated pilots in the FAA William J. Hughes Technical Center’s S76-D and Loft Dynamics H125 and R22 rotorcraft flight simulators.
Johnson, Charles, Thompson, Lacey, Morfitt, Grant
Abstraction of a Flight Test Maneuver to Evaluate Handling Qualities for Helicopter Air-to-Air RefuelingF-0081-2025-03195/20/2025
Several efforts have been made to develop Flight Test Maneuvers for Handling Qualities evaluations, aimed at quantifying the effects of vehicle characteristics and assistance systems on a Helicopter Air-to-Air Refueling mission profile. However, these Flight Test Maneuvers have not achieved widespread adoption, likely due to the substantial logistical challenges associated with tanker deployment. Depending on a tanker aircraft not only incurs significant costs but also requires extensive organizational effort and prior testing, before Handling Qualities can be evaluated for the aerial refueling capabilities of a new rotorcraft design. Additionally, these available Flight Test Maneuver setups are not standardized or widely applied to the same degree as Mission Task Elements of the Aeronautical Design Standard, which limits repeatability and comparability. A new approach is proposed to address these limitations by introducing a repeatable, standardized method to reveal Handling Qualities deficiencies considering a worst-case situation of Helicopter Air-to-Air Refueling. This approach involves analyzing drogue motion to create a synthetic, deterministic target forcing function, based on the summation of several sine waves. Resulting laws of motion are applied to a target tracking task replicating a drogue chasing scenario by projecting all required references into the pilots' field of view. Piloted simulator studies conducted at the Air Vehicle Simulator (AVES) of the German Aerospace Center (DLR) demonstrate a high degree of similarity in pilot control behavior between the proposed Flight Test Maneuver and actual simulated Helicopter Air-to-Air Refueling.
Schmidt, Sven, Jusko, Tim
Pilot Visibility from the Flight DeckARP4101/2A (Current)5/2/2025
This document recommends criteria to assure adequate visibility from the flight deck. The flight-deck windshield must provide sufficient external vision to permit the pilot to perform any maneuvers within the operating limits of the aircraft safely and, at the same time, afford an unobstructed internal view of the flight instruments and other critical components and displays from the same eye position.
S-7 Flight Deck Handling Qualities Stds for Trans Aircraft
Unmanned Aircraft Systems Help Responders in Urban Environments25AERP05_095/1/2025
The Science and Technology Directorate's (S&T) National Urban Security Technology Laboratory (NUSTL) recently brought together emergency responders from across the nation to test unmanned aircraft systems (UAS) from the Blue UAS Cleared List. By providing an aerial vantage point, and creating standoff distance between responders and potential threats, UAS can significantly mitigate safety risks to responders by allowing them to assess and monitor incidents remotely. U.S. Department of Homeland Security, Washington, D.C. In November 2024, the U.S. Department of Homeland Security's (DHS) National Urban Security Technology Laboratory (NUSTL) teamed up with Mississippi State University's (MSU) Raspet Flight Research Laboratory, and DAGER Technology LLC, to conduct an assessment on selected models of cybersecure “Blue UAS.” The drones, including models from Ascent AeroSystems, Freefly Systems, Parrot Drones, Skydio, and Teal Drones, are cybersecure and commercially available to assist emergency responders with their public safety operations. These evaluations were a continuation of previous tests held in rural Texas last June. The overall goal was to assess various capabilities (e.g., camera visual acuity, latency, and command and control link quality) in different geographic settings and terrain.
Terrain Environment Estimating Method for off-Road Vehicle Anticipated Driving Area Based on Stereo Vision2025-01-82794/1/2025
Off-road vehicles are required to traverse a variety of pavement environments, including asphalt roads, dirt roads, sandy terrains, snowy landscapes, rocky paths, brick roads, and gravel roads, over extended periods while maintaining stable motion. Consequently, the precise identification of pavement types, road unevenness, and other environmental information is crucial for intelligent decision-making and planning, as well as for assessing traversability risks in the autonomous driving functions of off-road vehicles. Compared to traditional perception solutions such as LiDAR and monocular cameras, stereo vision offers advantages like a simple structure, wide field of view, and robust spatial perception. However, its accuracy and computational cost in estimating complex off-road terrain environments still require further optimization. To address this challenge, this paper proposes a terrain environment estimating method for off-road vehicle anticipated driving area based on stereo vision. First, a real-vehicle data acquisition platform was constructed using data converters such as Kvaser and stereo vision cameras. Second, by integrating stereo vision perception boundary constraints, vehicle body axis expansion techniques, and the Constant Turn Rate and Velocity (CTRV) model, the method reliably extracts environmental information from the intended driving region, which could avoided the use of global information estimation and reducing computational costs. Building on this foundation, we developed a data delay timestamp synchronization mechanism and a circular buffer to enable the acquisition of vehicle dynamics signals, as well as road feature categories and point cloud data for 16 types of roads, including dirt roads, highways, rocky roads, and others. Thirdly, by establishing U-Net semantic segmentation model, Mean Intersection over Union and Crossentropy loss, pavement type identification is completed. Finally, road unevenness estimation was performed using a Random Sample Consensus (RANSAC) algorithm optimized with a Voxel Grid Filter. Experimental results demonstrate that the proposed estimation method enables off-road vehicles to accurately and robustly estimate environmental information such as road type and road unevenness with relatively low computational costs, achieving an average Intersection over Union of 85.96 and a loss function as low as 0.38.
Zhao, Jian, Zhang, Xutong, Hou, Jie, Chen, Zhigang, Zheng, Wenbo, Gao, Shang, Zhu, Bing, Chen, Zhicheng
Headliner Design Considerations to Meet Safety and Regulations2025-01-82454/1/2025
Headliners are one of the largest components inside an automobile, stretching from the front windshield to the rear windshield. Besides its aesthetic purpose, it contributes to multiple other purposes like housing different components, helps in NVH, defines the interior roominess, and plays a crucial role in defining the deployment of curtain airbag. The headliner also plays a role in meeting regulatory requirements like upward visibility and headroom requirements of the occupants. During the deployment of curtain airbag, it is important that the headliner-pillar interface aids in the easy opening of airbag, with the least hindrance. This is defined by multiple factors like the location of headliner-pillar interface, its distance from the airbag ramp bracket, the position of the inflator, the mountings of the headliner and pillar trims, to name a few. Also, during the deployment of the airbag, it is important that parts such as grabhandle, speaker grilles, etc which are fitted on the headliner does not get detached or break off, which in turn can be dangerous to the occupants. The design of pillar trims and the ramp bracket also plays a critical role in ensuring that the pillar trim edges are secure during the airbag deployment, and aid in the easy release of airbag into the cabin. Incorrect design of headliner or pillar trim, can result in different problems such as improper airbag deployment, airbag getting struck between pillar trim to body, fly-off of headliner child parts, etc. This would also result in several iterations of design which is a waste of time and resources. In this paper, we cover various design aspects of headliner assembly to meet the safety and regulations and have an improved deployment of curtain airbag. By considering the design aspects upfront, we were able to save at least two iterations of air bag deployment and quicken the development time by four months.
Sabesan, Arvind Kochi, D., Anantha, Kakani, Phani Kumar
The Accuracy of Vehicle Speeds, Decelerations, and Brake Onset Times Calculated from Onboard Dash Cameras2025-01-86914/1/2025
Videos from cameras onboard a moving vehicle are increasingly available to collision reconstructionists. The goal of this study was to evaluate the accuracy of speeds, decelerations, and brake onset times calculated from onboard dash cameras (“dashcams”) using a match-moving technique. We equipped a single test vehicle with 5 commercially available dashcams, a 5th wheel, and a brake pedal switch to synchronize the cameras and 5th wheel. The 5th wheel data served as the reference for the vehicle kinematics. We conducted 9 tests involving a constant-speed approach (mean ± standard deviation = 57.6 ± 2.0 km/h) followed by hard braking (0.989 g ± 0.021 g). For each camera and brake test, we extracted the video and calculated the camera’s position in each frame using SynthEyes, a 3D motion tracking and video analysis program. Scale and location for the analyses were based on a 3D laser scan of the test site. From each camera’s position data, we calculated its speed before braking and its average deceleration during braking, and we then compared these values to the reference speed and average deceleration, respectively, to estimate a bias and uncertainty for each camera and kinematic parameter. Across the 5 cameras tested here, speed estimates varied from an underestimate of 0.45 ± 0.05 km/h (bias ± uncertainty) to an overestimate of 0.80 ± 0.16 km/h, and average deceleration estimates varied from an underestimate of 0.012 ± 0.013 g to an overestimate of 0.049 ± 0.012 g. The video-based brake onset times lagged the actual brake onset times for four of the five cameras, varying from 14 ± 41 ms before brake onset to 94 ± 29 ms after brake onset. These data show that, for cameras of the quality tested here, dashcam video can be used to estimate vehicle speed, average deceleration, and the time of brake application with relatively small biases and uncertainties that vary between cameras.
Flynn, Thomas, Ahrens, Matthew, Young, Cole, Siegmund, Gunter P.
Developing a Camera-Based Perspective Transformation Method for Quantifying Driver Direct Visibility for Passenger Vehicles2025-01-86674/1/2025
This study outlines a camera-based perspective transformation method for measuring driver direct visibility, which produces 360-degree view maps of the nearest visible ground points. This method is ideal for field data collection due to its portability and minimal space requirements. Compared with ground truth assessments using a physical grid, this method was found to have a high level of accuracy, with all points in the vehicle front varying less than 0.30 m and varying less than 0.6 m for the A- and B-pillars. Points out of the rear window varied up to 2.4 m and were highly sensitive to differences in the chosen pixel due to their greater distance from the camera. Repeatability through trials of multiple measurements per vehicle and reproducibility through measures from multiple data collectors produced highly similar results, with the greatest variations ranging from 0.19 to 1.38 m. Additionally, three different camera lenses were evaluated, resulting in comparable results within 0.12–0.99 m. A parametric study looking at seat position and eye height suggests that assessing different eye heights may be the most insightful method for identifying the range of possible driver visibility. The visibility maps created using this method will allow researchers to assess driver blind zones and how they change with vehicle structural trends over time, assess the role of blind zones in crash scenarios with vulnerable road users, and provide consumers with information about comparative visibility for potential vehicle purchases.
Mueller, Becky, Bragg, Haden, Bird, Teddy
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