Browse Topic: Cameras

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Next-Gen Driver Health and Wellness Platform for Fatigue Detection and Emotional Wellbeing Monitoring2026-26-0639To be published on 01/16/2026
Driver fatigue and emotional distress are major contributors to traffic accidents, especially among long-haul and professional drivers, making their detection a critical area in road safety and public health. While existing research has explored various sensor-based and wearable solutions to monitor driver states, these approaches often suffer from high cost, intrusiveness, or limited scalability. Camera-based systems have recently gained attention for being less invasive and more practical for real-world deployment; however, many still depend on multiple sensors or cloud-based processing, which raise concerns around energy consumption, privacy, and integration complexity. To address these gaps, we present a sustainable, camera-only Driver Health and Wellness Platform for real-time fatigue detection and emotional wellbeing monitoring. The system leverages a single in-cabin camera and applies lightweight, edge-optimized computer vision models to analyze facial features, eye dynamics
Iqbal, Shoaib, Imteyaz, Shahma
Adaptability Survey and analysis of Camera Monitoring Systems (CMS) on Various Driving Population for Passenger Vehicles in Indian Driving Scenario2026-26-0601To be published on 01/16/2026
This paper presents a comprehensive survey and data collection study on the adaptability of Camera Monitoring Systems (CMS) for passenger vehicles. With the growing demand for enhanced safety, automation, and driver assistance technologies, Camera Monitoring Systems (CMS) has emerged as a key component in modern automotive design. This study aims to explore the current state of camera-based monitoring in passenger vehicles, focusing on their adaptability through survey data collection of various driving population and analysis. This paper evaluates the acceptance of CMS configurations in replacement to conventional rear view mirrors through Position of Monitor, Clarity, CMS Adaptiveness to eyes, Comfort while turning, Merging into moving traffic, Monitoring Rear Traffic, while Getting Out of Car, while Overtaking, Coverage Area and Overall Acceptance. The findings offer valuable insights for manufacturers, engineers, and researchers working toward the evolution of intelligent vehicle
Sinha, Ankit, Tambolkar, Sonali Ameya, Belavadi Venkataramaiah, Shamsundara, Kauffmann, Maximilian
Mapless Yet Accurate: Trajectory Prediction for traffic agents using online HD Map Reconstruction for Autonomous Driving2026-26-0039To be published on 01/16/2026
Accurate trajectory prediction of traffic agents is vital for enabling safer and more reliable autonomous driving. High-definition (HD) and standard-definition (SD) maps play a critical role in this process by providing detailed lane topology and directional cues essential for forecasting the movement of surrounding traffic agents. However, creating HD maps is both expensive and resource-intensive, often relying on complex SLAM systems and specialized sensors, while SD maps, though more readily available, lack the precision needed for accurate navigation in autonomous driving. In this work, we present a novel framework for trajectory prediction that uses online reconstruction of HD maps using images from vehicle-mounted cameras, offering a more scalable and cost-effective solution. Our method achieves notable improvements in prediction accuracy, especially in scenarios lacking access to pre-built maps. Additionally, we propose a new evaluation metric that emphasizes safety by
Upreti, Minali, Girijal, Rahul, B A, NaveenKumar, Thontepu, Phani, Ghosh, Shankhanil, Chakraborty, Bodhisattwa
Automated Offboard System to Measure the Volume of Harvested Material2025-28-033111/06/2025
Measuring the volume of harvested material behind the machine can be beneficial for various agricultural operations, such as baling, dropping, material decomposition, cultivation, and seeding. This paper aims to investigate and determine the volume of material for use in various agricultural operations. This proposed methodology can help to predict the amount of residue available in the field, assess field readiness for the next production cycle, measure residue distribution, determine hay readiness for baling, and evaluate the quantity of hay present in the field, among other applications which would benefit the customer. Efficient post-harvest residue management is essential for sustainable agriculture. This paper presents an Automated Offboard System that leverages Remote Sensing, IoT, Image Processing, and Machine Learning/Deep Learning (ML/DL) to measure the volume of harvested material in real-time. The system integrates onboard cameras and satellite imagery to analyze the field
Singh, Rana Shakti, Stallin, Saravanan
Eyes of Robot Operating System for Future Hands2025-28-024611/06/2025
This paper presents a novel approach to automated robot programming and robot integration in manufacturing domain and minimizing the dependency on manual online/offline programming. Traditional industrial robots programming is typically done by online programing via teach pendants or by offline programming tools. This presents a major challenge as it requires skilled professionals and is a time-consuming process. In today’s competitive market, factories need to harness their full potential through smart and adaptive thinking to keep pace with evolving technology, customer demand, and manufacturing processes. This requires ability to manufacture multiple products on the same production line, minimum time for changeovers and implement robotic automation for efficiency enhancement. But each custom automation piece also demands significant human efforts for development and maintenance. By integrating the Robot Operating System (ROS) with vision-based 3D model generation systems, we address
Hepat, Abhijeet
Analysis of Flame Propagation Characteristics in a Hydrogen Engine using an Optically Accessible Engine2025-32-006111/03/2025
This study focused on the effects of hydrogen on the flame propagation characteristics and combustion characteristics of a small spark-ignition engine. The combustion flame in the cylinder was observed using a side-valve engine that allowed optical access. The fundamental characteristics of hydrogen combustion were investigated based on combustion images photographed in the cylinder with a high-speed camera and measured cylinder pressure waveforms. Experiments were conducted under various ignition timings and equivalence ratios and comparisons were made with the characteristics of an existing hydrocarbon liquid fuel. The hydrogen flame was successfully photographed, although it has been regarded as being difficult to visualize, thus enabling calculation of the flame propagation speed. As a result, it was found that the flame propagation speed of hydrogen was much faster than that of the existing hydrocarbon fuel. On the other hand, it was difficult to photograph the hydrogen flame
Arai, Yuto, Ueno, Takamori, Suda, Ryosuke, Sato, Ryoichi, Nakao, Yoshinori, Ninomiya, Yoshinari, Matsushita, Koichiro, Kamio, Tomohiko, Iijima, Akira
Enhanced Object Matching and Identification for Multi-Robot Systems in Complex Environments2025-01-043809/16/2025
Our research focuses on developing a novel loss function that significantly improves object matching accuracy in multi-robot systems, a critical capability for Safety, Security, and Rescue Robotics (SSRR) applications. By enhancing the consistency and reliability of object identification across multiple viewpoints, our approach ensures a comprehensive understanding of environments with complex layouts and interlinked infrastructure components. We utilize ZED 2i cameras to capture diverse scenarios, demonstrating that our proposed loss function, inspired by the DETR framework, outperforms traditional methods in both accuracy and efficiency. The function’s ability to adapt to dynamic and high-risk environments, such as disaster response and critical infrastructure inspection, is further validated through extensive experiments, showing superior performance in real-time decision-making and operational effectiveness. This work not only advances the state of the art in SSRR but also
Brown, Taylor J., Vincent, Grace, Nakamoto, Kyle, Bhattacharya, Sambit
Uncrewed Aerial System Detection and Tracking with Event Cameras2025-01-044009/16/2025
The emergence of SUAS as a threat vector introduces significant challenges in surveillance and defense due to their potential for low cross section and high speeds, defeating or evading many existing detection and tracking capabilities. This paper presents two algorithms—one for detection and one for tracking—developed for event cameras, which offer substantial improvements in temporal resolution, dynamic range, and low-light performance compared to traditional imaging systems, all of which are critical for effective UAS defense. These advancements address current limitations in using event cameras and pave the way for a new generation of robust robotic vision based on event cameras.
Anthony, David, Chambers, David, Towler, Jerry
Heat Flux Temporal Variation and Turbulent Structures on Diesel Flame-Impinged Wall by Comparing High-Speed Infrared Thermography and MEMS Sensor Measurements2025-24-003909/07/2025
For further elucidation of the extremely complex mechanism of wall heat transfer during diesel flame impingement, heat flux measurement results based on two different relatively new approaches, high-speed infrared thermography and Micro Electro- Mechanical Systems (MEMS) heat flux sensor, were compared. Both measurements were conducted on the chamber wall impinged by a diesel flame achieved in constant volume combustion vessels under similar experimental conditions. Infrared thermography was conducted using a high-speed infrared camera (TELOPS M3k, 13,000 fps, 128×128 pixels), allowing the capture of time-series temperature and heat flux distributions on the wall surface with a spatial resolution of 70 μm (9 mm / 128 pixels). This high-resolution imaging also enables detailed estimation of near-wall turbulent structures, which are considered to significantly influence the heat flux distributions. The MEMS sensor is composed of closely aligned (520 microns separated) multiple highly
Shimizu, Fumika, Morooka, Masato, Aizawa, Tetsuya, Dejima, Kazuhito, Nakabeppu, Osamu
Smart Capsule Designed to Study GI TractTBMG-5377309/01/2025
Engineers have developed a smart capsule called PillTrek that can measure pH, temperature, and a variety of different biomarkers. It incorporates simple, inexpensive sensors into a miniature wireless electrochemical workstation that relies on low-power electronics. PillTrek measures 7 mm in diameter and 25 mm in length, making it smaller than commercially available capsule cameras used for endoscopy but capable of executing a range of electrochemical measurements.
In Situ Observation of Different Soot Layers in a Model Filter Channel during Its Regeneration2025-01-031207/02/2025
In order to comply with increasingly stringent emission regulations and ensure clean air, wall-flow particulate filters are predominantly used in exhaust gas aftertreatment systems of combustion engines to remove reactive soot and inert ash particles from exhaust gases. These filters consist of parallel porous channels with alternately closed ends, effectively separating particles by forming a layer on the filter surface. However, the accumulated particulate layer increases the pressure drop across the filter, requiring periodic filter regeneration. During regeneration, soot oxidation breaks up the particulate layer, while resuspension and transport of individual agglomerates can occur. These phenomena are influenced by gas temperature and velocity, as well as by the dispersity and reactivity of the soot particles. Renewable and biomass based fuels can produce different types of soot with different reactivities and dispersities. Therefore, this study focuses on the influences of soot
Desens, Ole, Hagen, Fabian P., Meyer, Jörg, Dittler, Achim
U-Shift IV: Innovations and Challenges in Autonomous Urban Mobility2025-01-028407/02/2025
The U-Shift IV represents the latest evolution in modular urban mobility solutions, offering significant advancements over its predecessors. This innovative vehicle concept introduces a distinct separation between the drive module, known as the driveboard, and the transport capsules. The driveboard contains all the necessary components for autonomous driving, allowing it to operate independently. This separation not only enables versatile applications - such as easily swapping capsules for passenger or goods transportation - but also significantly improves the utilization of the driveboard. By allowing a single driveboard to be paired with different capsules, operational efficiency is maximized, enabling continuous deployment of driveboards while the individual capsules are in use. The primary focus of U-Shift IV was to obtain a permit for operating at the Federal Garden Show 2023. To achieve this goal, we built the vehicle around the specific requirements for semi-public road
Pohl, Eric, Scheibe, Sebastian, Münster, Marco, Osebek, Manuel, Kopp, Gerhard, Siefkes, Tjark
In-Orbit Testing of Autonomous ‘Swarm’ Satellites25AERP06_1006/01/2025
With 2D cameras and space robotics algorithms, astronautics engineers at Stanford have created a navigation system able to manage multiple satellites using visual data only. They recently tested it in space for the first time. Stanford University, Stanford, CA Someday, instead of large, expensive individual space satellites, teams of smaller satellites - known by scientists as a “swarm” - will work in collaboration, enabling greater accuracy, agility, and autonomy. Among the scientists working to make these teams a reality are researchers at Stanford University's Space Rendezvous Lab, who recently completed the first-ever in-orbit test of a prototype system able to navigate a swarm of satellites using only visual information shared through a wireless network. “It's a milestone paper and the culmination of 11 years of effort by my lab, which was founded with this goal of surpassing the current state of the art and practice in distributed autonomy in space,” said Simone D'Amico
NanoAvionics Satellite Suffers Space Debris Impact, Selfie Image Shows Damage25AERP06_1106/01/2025
In October 2024, Kongsberg NanoAvionics discovered damage to their MP42 satellite, and used the discovery as an opportunity to raise awareness on the need to reduce space debris generated by satellites. Kongsberg NanoAvionics, Vilnius, Lithuania Our MP42 satellite, which launched into low Earth orbit (LEO) two and a half years ago aboard the SpaceX Transporter-4 mission, recently took an unexpected hit from a small piece of space debris or micrometeoroid. The impact created a 6 mm hole, roughly the size of a chickpea, in one of its solar panels. Despite this damage, the satellite continued performing its mission without interruption, and we only discovered the impact thanks to an image taken by its onboard selfie camera in October of 2024. It is challenging to pinpoint exactly when the impact occurred because MP42's last selfie was taken a year and a half ago, in April of 2023.
Active Noise Control with Head Tracking System2025-01-001205/05/2025
In active noise control, the control region size (same meaning as zone of control) decreases as the frequency increases, so that even a small moving of the passenger's head causes the ear position to go out of the control region. To increase the size of the control region, many speakers and microphones are generally required, but it is difficult to apply it in a vehicle cabin due to space and cost constraints. In this study, we propose moving zone of quiet active noise control technique. A 2D image-based head tracking system captured by a camera to generate the passenger's 0head coordinates in real time with deep learning algorithm. In the controller, the control position is moved to the ear position using a multi-point virtual microphone algorithm according to the generated ear position. After that, the multi-point adaptive filter training system applies the optimal control filter to the current position and maintains the control performance. Through this study, it is possible to
Oh, ChiSung, Kang, Jonggyu, Kim, Joong-Kwan
Hybrid 3D Sound Intensity Measurements and Simulations Applied to a Truck Rear Axle2025-01-010505/05/2025
This study presents a novel methodology for optimizing the acoustic performance of rotating machinery by combining scattered 3D sound intensity data with numerical simulations. The method is demonstrated on the rear axle of a truck. Using Scan&Paint 3D, sound intensity data is rapidly acquired over a large spatial area with the assistance of a 3D sound intensity probe and infrared stereo camera. The experimental data is then integrated into far-field radiation simulations, enabling detailed analysis of the acoustic behavior and accurate predictions of far-field sound radiation. This hybrid approach offers a significant advantage for assessing complex acoustic sources, allowing for quick and reliable evaluation of noise mitigation solutions.
Fernandez Comesana, Daniel, Vael, Georges, Robin, Xavier, Orselli, Joseph, Schmal, Jared
Localisation of Ultra-Short Sound Emissions from Automotive Components by Using the Sound Field Scanning Method2025-01-009205/05/2025
Design verification and quality control of automotive components require the analysis of the source location of ultra-short sound events, for instance the engaging event of an electromechanical clutch or the clicking noise of the aluminium frame of a passenger car seat under vibration. State-of-the-art acoustic cameras allow for a frame rate of about 100 acoustic images per second. Considering that most of the sound events introduced above can be far less than 10ms, an acoustic image generated at this rate resembles an hard-to-interpret overlay of multiple sources on the structure under test along with reflections from the surrounding test environment. This contribution introduces a novel method for visualizing impulse-like sound emissions from automotive components at 10x the frame rate of traditional acoustic cameras. A time resolution of less than 1ms eventually allows for the true localization of the initial and subsequent sound events as well as a clear separation of direct from
Rittenschober, Thomas
Machine Learning-Based Vision Mapping and Planning for Flexibility in High-Mix Robotic Manufacturing2025-01-015705/02/2025
Industries that require high-accuracy automation in the creation of high-mix/low-volume parts, such as aerospace, often face cost constraints with traditional robotics and machine tools due to the need for many pre-programmed tool paths, dedicated part fixtures, and rigid production flow. This paper presents a new machine learning (ML) based vision mapping and planning technique, created to enhance flexibility and efficiency in robotic operations, while reducing overall costs. The system is capable of mapping discrete process targets in the robot work envelope that the ML algorithms have been trained to identify, without requiring knowledge of the overall assembly. Using a 2D camera, images are taken from multiple robot positions across the work area and are used in the ML algorithm to detect, identify, and predict the 6D pose of each target. The algorithm uses the poses and target identifications to automatically develop a part program with efficient tool paths, including
Langan, Daniel, Hall, Michael, Goldberg, Emily, Schrandt, Sasha
Segment Manipulator Retrofit2025-01-015905/02/2025
The segment manipulator machine, a large custom-built apparatus, is used for assembling and disassembling heavy tooling, specifically carbon fiber forms. This complex yet slow-moving machine had been in service for nineteen years, with many control components becoming obsolete and difficult to replace. The customer engaged Electroimpact to upgrade the machine using the latest state-of-the-art controls, aiming to extend the system's operational life by at least another two decades. The program from the previous control system could not be reused, necessitating a complete overhaul.
Luker, Zachary, Donahue, Michael
High-Speed Optical Diagnostics of Misfire Limits in a Spark-Ignited Heavy-Duty Hydrogen Engine2025-01-840104/01/2025
This study investigates the ignitability of hydrogen in an optical heavy-duty SI engine. While the ignition energy of hydrogen is exceptionally low, the high load and lean mixtures used in heavy-duty hydrogen engines lead to a high gas density, resulting in a much higher breakdown voltage than in light-duty SI engines. Spark plug wear is a concern, so there is a need to minimise the spark energy while maintaining combustion stability, even at challenging conditions for ignition. This work consists of a two-stage experimental study performed in an optical engine. In the first part, we mapped the combustion stability and frequency of misfires with two different ignition systems: a DC inductive discharge ignition system, and a closed-loop controlled capacitive AC system. The equivalence ratio and dwell time were varied for the inductive system while the capacitive system instead varied spark duration and spark current in addition to equivalence ratio. A key finding was that spark energy
Hallstadius, Peter, Saha, Anupam, Sridhara, Aravind, Andersson, Öivind
A Review of Off-Road Datasets, Sensor Technologies and Terrain Traversability Analysis2025-01-833904/01/2025
Autonomous ground navigation has advanced significantly in urban and structured environments, supported by the availability of comprehensive datasets. However, navigating complex and off-road terrains remains challenging due to limited datasets, diverse terrain types, adverse environmental conditions, and sensor limitations affecting vehicle perception. This study presents a comprehensive review of off-road datasets, integrating their applications with sensor technologies and terrain traversability analysis methods. It identifies critical gaps, including class imbalances, sensor performance under adverse conditions, and limitations in existing traversability estimation approaches. Key contributions include a novel classification of off-road datasets based on annotation methods, providing insights into scalability and applicability across diverse terrains. The study also evaluates sensor technologies under adverse conditions and proposes strategies for incorporating event-based and
Musau, Hannah, Ruganuza, Denis, Indah, Debbie, Mukwaya, Arthur, Gyimah, Nana Kankam, Patil, Ashish, Bhosale, Mayuresh, Gupta, Prakhar, Mwakalonge, Judith, Jia, Yunyi, Mikulski, Dariusz, Grabowsky, David, Hong, Jae Dong, Siuhi, Saidi
Validation of SynthEyes for Use in Collision Reconstruction2025-01-868804/01/2025
Accurate reconstruction of vehicle collisions is essential for understanding incident dynamics and informing safety improvements. Traditionally, vehicle speed from dashcam footage has been approximated by estimating the time duration and distance traveled as the vehicle passes between reference objects. This method limits the resolution of the speed profile to an average speed over given intervals and reduces the ability to determine moments of acceleration or deceleration. A more detailed speed profile can be calculated by solving for the vehicle’s position in each video frame; however, this method is time-consuming and can introduce spatial and temporal error and is often constrained by the availability of external trackable features in the surrounding environment. Motion tracking software, widely used in the visual effects industry to track camera positions, has been adopted by some collision reconstructionists for determining vehicle speed from video. This study examines the
Perera, Nishan, Griffiths, Harrison, Prentice, Greg
Empirical Analysis on Machine Vision Recognition of Green Bike Lanes for Vulnerable Road Users Safety2025-01-801704/01/2025
Deliberate modifications to infrastructure can significantly enhance machine vision recognition of road sections designed for Vulnerable Road Users, such as green bike lanes. This study evaluates how green bike lanes, compared to unpainted lanes, enhance machine vision recognition and vulnerable road users safety by keeping vehicles at a safe distance and preventing encroachment into designated bike lanes. Conducted at the American Center for Mobility, this study utilizes a vehicle equipped with a front-facing camera to assess green bike lane recognition capabilities across various environmental conditions including dry daytime, dry nighttime, rain, fog, and snow. Data collection involved gathering a comprehensive dataset under diverse conditions and generating masks for lane markings to perform comparative analysis for training Advanced Driver Assistance Systems. Quality measurement and statistical analysis are used to evaluate the effectiveness of machine vision recognition using
Ponnuru, Venkata Naga Rithika, Das, Sushanta, Grant, Joseph, Naber, Jeffrey, Bahramgiri, Mojtaba
RGB2BEV-Net: A PyTorch-Based End-to-End Pipeline for RGB to BEV Segmentation Using an Extended Dataset for Autonomous Driving2025-01-802304/01/2025
In this study, we introduce RGB2BEV-Net, an end-to-end pipeline that extends traditional BEV segmentation models by utilizing raw RGB images with Bird’s Eye View (BEV) generation. While previous work primarily focused on pre-segmented images to generate corresponding BEV maps, our approach expands this by collecting RGB images alongside their affiliated segmentation masks and BEV representations. This enables direct input of RGB camera sensors into the pipeline, reflecting real-world autonomous driving scenarios where RGB cameras are commonly used as sensors, rather than relying on pre-segmented images. Our model processes four RGB images through a segmentation layer before converting them into a segmented BEV, implemented in the PyTorch framework after being adapted from an original implementation that utilized a different framework. This adaptation was necessary to improve compatibility and ensure better integration of the entire system within autonomous vehicle applications. We
Hossain, Sabir, Lin, Xianke
Decentralized Perception System with Multiple Viewpoints2025-01-809704/01/2025
Vehicle-to-Infrastructure (V2I) cooperation has emerged as a fundamental technology to overcome the limitations of the individual ego-vehicle perception. Onboard perception is limited by the lack of information for understanding the environment, the lack of anticipation, the drop of performance due to occlusions and the physical limitations of embedded sensors. The perception of V2I in a cooperative manner improves the perception range of the ego vehicle by receiving information from the infrastructure that has another point of view, mounted with sensors, such as camera and LiDAR. This technical paper presents a perception pipeline developed for the infrastructure based on images with multiple viewpoints. It is designed to be scalable and has five main components: the image acquisition for the modification of camera settings and to get the pixel data, the object detection for fast and accurate detection of four wheels, two wheels and pedestrians, the data fusion module for robust
Picard, Quentin, Morice, Malo, Fadili, Maryem, Pechberti, Steve
Object Detection for City and Highway Driving Scenario with YOLOX and Mask RCNN2025-01-801504/01/2025
This paper explores the integration of two deep learning models that are currently being used for object detection, specifically Mask R-CNN and YOLOX, for two distinct driving environments: urban cityscapes and highway settings. The hypothesis underlying this work is that different methods of object detection will work best in different driving environments, due to the differences in their unique strengths as well as the key differences in those driving environments. Some of these differences in the driving environment include varying traffic densities, diverse object classes, and differing scene complexities, including specific differences such as the types of signs present, the presence or absence of stoplights, and the limited-access nature of highways as compared to city streets. As part of this work, a scene classifier has also been developed to categorize the driving context into the two categories of highway and urban driving, in order to allow the overall object detection
Patel, Krunal, Peters, Diane
A Proactive System to Anticipate and Store the Damage Information on a Parked Car Using Machine Learning Algorithm2025-01-821104/01/2025
Vehicle ADAS Systems majorly comprises of two functions: Driving and Parking. The most common form of damage to the vehicle which goes unnoticed with unidentified cause are parking damages. A vehicle once parked at a certain location may get damaged without knowledge of the user. In this work developed a solution that not only pre-warns the driver but also prepares the vehicle beforehand if it suspects a damage may occur. This eliminates the latency between damage and information capture, detects small damages such as scratches, classifies the type of damage and informs the user beforehand. This is solution is different from our competitors as the existing solutions informs the user about the scratches/damages, but these solutions are expensive, have high response time, and the damage information is captured after the damage has occurred. The solution consists of the following check blocks: Precondition, Sensor Control and Action Module. The Precondition Module observes the vehicle
Debnath, Sarnab, Patil, Prasad, Belur Subramanya, Sheshagiri, Govinda, Shiva Prasad
Evaluation of Several Tesla Dashcam Angles for Model 3 and Y via Reverse Project Photogrammetry2025-01-870004/01/2025
Tesla Model 3 and Model Y vehicles come equipped with a standard dashcam feature with the ability to record video in multiple directions. Front, side, and rear views were readily available via direct USB download. Additional types of front and side views were indirectly available via privacy requests with Tesla. Prior research neither fully explored the four most readily available camera views across multiple vehicles nor field camera calibration techniques particularly useful for future software and hardware changes. Moving GPS instrumented vehicles were captured traveling approximately 7.2 kph to 20.4 kph across the front, side, and rear views available via direct USB download. Reverse project photogrammetry projects and video timing data successfully measured vehicle speeds with an average error of 2.45% across 25 tests. Previously researched front and rear camera calibration parameters were reaffirmed despite software changes, and additional parameters for the side cameras
Jorgensen, Michael, Swinford, Scott, Imada, Kevin, Farhat, Ali
Discrepancies between Metadata and Actual Camera Field of View: A Photogrammetric Approach2025-01-868604/01/2025
Camera matching photogrammetry is widely used in the field of accident reconstruction for mapping accident scenes, modeling vehicle damage from post collision photographs, analyzing sight lines, and video tracking. A critical aspect of camera matching photogrammetry is determining the focal length and Field of View (FOV) of the photograph being analyzed. The intent of this research is to analyze the accuracy of the metadata reported focal length and FOV. The FOV from photographs captured by over 20 different cameras of various makes, models, sensor sizes, and focal lengths will be measured using a controlled and repeatable testing methodology. The difference in measured FOV versus reported FOV will be presented and analyzed. This research will provide analysts with a dataset showing the possible error in metadata reported FOV. Analysts should consider the metadata reported FOV as a starting point for photogrammetric analysis and understand that the FOV calculated from the image
Smith, Connor A., Erickson, Michael, Hashemian, Alireza
Developing a Camera-Based Perspective Transformation Method for Quantifying Driver Direct Visibility for Passenger Vehicles2025-01-866704/01/2025
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
Mueller, Becky, Bragg, Haden, Bird, Teddy
A Method for Lens Distortion Correction of Algorithmically Altered Images2025-01-868004/01/2025
Photogrammetry is a commonly used type of analysis in accident reconstruction. It allows the location of physical evidence, as shown in photographs and video, and the position and orientation of vehicles, other road users, and objects to be quantified. Lens distortion is an important consideration when using photogrammetry. Failure to account for lens distortion can result in inaccurate spatial measurements, particularly when elements of interest are located toward the edges and corners of images. Depending on whether the camera properties are known or unknown, various methods for removing lens distortion are commonly used in photogrammetric analysis. However, many of these methods assume that lens distortion is the result of a spherical lens or, more rarely, is solely due to distortion caused by other known lens types and has not been altered algorithmically by the camera. Today, several cameras on the market algorithmically alter images before saving them. These camera systems use
Pittman, Kathleen, Mockensturm, Eric, Buckman, Taylor, White, Kirsten
A Study on Reconstructing in-Cylinder Combustion Images Based on Local Images2025-01-838104/01/2025
The current leading experimental platform for engine visualization research is the optical engine, which features transparent window components classified into two types: partially visible windows and fully visible windows. Due to structural limitations, fully visible windows cannot be employed under certain complex or extreme operating conditions, leading to the acquisition of only local in-cylinder combustion images and resulting in information loss. This study introduces a method for reconstructing in-cylinder combustion images from local images using deep learning techniques. The experiments were conducted using an optical engine specifically designed for spark-ignition combustion modes, capturing in-cylinder flame images under various conditions with high-speed cameras. The primary focus was on reconstructing the flame edge, with in-cylinder combustion images categorized into three types: images where the flame edge is fully within the partially visible window, partly within the
Wang, Mianheng, Zhang, Yixiao, Du, Haoyu, Xiao, Ma, Mao, Jianshu, Fang, Yuwen
An Experimental Investigation of the Liquid Jet Breakup Characteristics in a Vaporizer under Equivalent Operating Conditions of a Microturbine Combustion Chamber2025-01-845804/01/2025
This study experimentally investigates the liquid jet breakup process in a vaporizer of a microturbine combustion chamber under equivalent operating conditions, including temperature and air mass flow rate. A high-speed camera experimental system, coupled with an image processing code, was developed to analyze the jet breakup length. The fuel jet is centrally positioned in a vaporizer with an inner diameter of 8mm. Airflow enters the vaporizer at controlled pressures, while thermal conditions are maintained between 298 K and 373 K using a PID-controlled heating system. The liquid is supplied through a jet with a 0.4 mm inner diameter, with a range of Reynolds numbers (Reliq = 2300÷3400), and aerodynamic Weber numbers (Weg = 4÷10), corresponding to the membrane and/or fiber breakup modes of the liquid jet. Based on the results of jet breakup length, a new model has been developed to complement flow regimes by low Weber and Reynolds numbers. The analysis of droplet size distribution
Ha, Nguyen, Quan, Nguyen, Manh, Vu, Pham, Phuong Xuan
Terrain Environment Estimating Method for off-Road Vehicle Anticipated Driving Area Based on Stereo Vision2025-01-827904/01/2025
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
Zhao, Jian, Zhang, Xutong, Hou, Jie, Chen, Zhigang, Zheng, Wenbo, Gao, Shang, Zhu, Bing, Chen, Zhicheng
Accuracy of Timestamps in Digital and Network Video Recorders2025-01-869004/01/2025
Video analysis plays a major role in many forensic fields. Many articles, publications, and presentations have covered the importance and difficulty in properly establishing frame timing. In many cases, the analyst is given video files that do not contain native metadata. In other cases, the files contain video recordings of the surveillance playback monitor which eliminates all original metadata from the video recording. These “video of video” recordings prevent an analyst from determining frame timing using metadata from the original file. However, within many of these video files, timestamp information is visually imprinted onto each frame. Analyses that rely on timing of events captured in video may benefit from these imprinted timestamps, but for forensic purposes, it is important to establish the accuracy and reliability of these timestamps. The purpose of this research is to examine the accuracy of these timestamps and to establish if they can be used to determine the timing
Molnar, Benjamin, Terpstra, Toby, Voitel, Tilo
The Effect of Tracker and Control Point Distributions on Vehicle Position and Speed Estimates from Dash Camera Video2025-01-869704/01/2025
Dash cameras (dashcams) can provide collision reconstructionists with quantifiable vehicle position and speed estimates. These estimates are achieved by tracking 2D video features with camera-tracking software to solve for the time history of camera position, and speed can then be calculated from the position-time history. Not all scenes have the same geometric features in quality or abundance. In this study, we compared the vehicle position and derived-speed estimates from dashcam video for different numbers and spatial distributions of tracked features that mimicked the continuum between barren environments and feature-rich environments. We used video from a dashcam mounted in a vehicle undergoing straight-line emergency braking. The surrounding environment had abundant trackable features on both sides of the road, including road markings, streetlights, signs, trees, and buildings. We first created a reference solution using SynthEyes, a 3D camera- and object-tracking program, and
Young, Cole, Ahrens, Matthew, Flynn, Thomas, Siegmund, Gunter P.
Validating the Sun System in 3ds Max for Recreating Shadows2025-01-868504/01/2025
Shadow positions can be useful in determining the time of day that a photograph was taken and determining the position, size, and orientation of an object casting a shadow in a scene. Astronomical equations can predict the location of the sun relative to the earth, and therefore the position of shadows cast by objects, based on the location’s latitude and longitude as well as the date and time. 3D computer software includes these calculations as a part of their built-in sun systems. In this paper, the authors examine the sun system in the 3D modeling software 3ds Max to determine its accuracy for use in accident reconstruction. A parking lot was scanned using a FARO LiDAR scanner to create a point cloud of the environment. A camera was then set up on a tripod at the environment, and photographs were taken at various times throughout the day from the same location. This environment was 3D modeled in 3ds Max based on the point cloud, and the sun system in 3ds Max was configured using the
Barreiro, Evan, Erickson, Michael, Smith, Connor, Carter, Neal, Hashemian, Alireza
The Accuracy of Vehicle Speeds, Decelerations, and Brake Onset Times Calculated from Onboard Dash Cameras2025-01-869104/01/2025
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
Flynn, Thomas, Ahrens, Matthew, Young, Cole, Siegmund, Gunter P.
Solving Vehicle Speed and Acceleration from Video Evidence Using Optimization2025-01-868304/01/2025
This paper introduces a method to solve the instantaneous speed and acceleration of a vehicle from one or more sources of video evidence by using optimization to determine the best fit speed profile that tracks the measured path of a vehicle through a scene. Mathematical optimization is the process of seeking the variables that drive an objective function to some optimal value, usually a minimum, subject to constraints on the variables. In the video analysis problem, the analyst is seeking a speed profile that tracks measured vehicle positions over time. Measured positions and observations in the video constrain the vehicle’s motion and can be used to determine the vehicle’s instantaneous speed and acceleration. The variables are the vehicle’s initial speed and an unknown number of periods of approximately constant acceleration. Optimization can be used to determine the speed profile that minimizes the total error between the vehicle’s calculated distance traveled at each measured
Snyder, Sean, Callahan, Michael, Wilhelm, Christopher, Johnk, Chris, Lowi, Alvin, Bretting, Gerald
Intelligent Detection Technologies for Pre-Paving Asphalt Pavement2025-01-717902/21/2025
This paper presents advanced intelligent monitoring methods aimed at enhancing the quality and durability of asphalt pavement construction. The study focuses on two critical tasks: foreign object detection and the uniform application of tack coat oil. For object recognition, the YOLOv5 algorithm is employed, which provides real-time detection capabilities essential for construction environments where timely decisions are crucial. A meticulously annotated dataset comprising 4,108 images, created with the LabelImg tool, ensures the accurate detection of foreign objects such as leaves and cigarette butts. By utilizing pre-trained weights during model training, the research achieved significant improvements in key performance metrics, including precision and recall rates. In addition to object detection, the study explores color space analysis through the HSV (Hue, Saturation, Value) model to effectively differentiate between coated and uncoated pavement areas following the application of
Hu, Yufan, Fan, Jianwei, Tang, Fanlong, Ma, Tao
Vehicle Positioning Technology Based on Stereo Vision2025-01-716302/21/2025
Vehicle localization in enclosed environments, such as indoor parking lots, tunnels, and confined areas, presents significant challenges and has garnered considerable research interest. This paper proposes a localization technique based on an onboard binocular camera system, utilizing binocular ranging and spatial intersection algorithms to achieve active localization. The method involves pre-deploying reference points with known coordinates within the experimental space, using binocular ranging to measure the distance between the camera and the reference points, and applying the spatial intersection algorithm to calculate the camera’s center coordinates, thereby completing the localization process. Experimental results demonstrate that the proposed algorithm achieves sub-meter level localization accuracy. Localization accuracy is significantly influenced by the calibration precision of the binocular camera and the number of reference points. Higher calibration precision and a greater
Feifei, Li, Haoping, Qi, Yi, Wei
Cryptographic Method for Secure Object Segmentation for Autonomous Driving Perception Systems12-08-01-000802/14/2025
The modern-day vehicle’s driverless or driver-assisted systems are developed by sensing the surroundings using a combination of camera, lidar, and other related sensors by forming an accurate perception of the driving environment. Machine learning algorithms help in forming perception and perform planning and control of the vehicle. The control of the vehicle which reflects safety depends on the accurate understanding of the surroundings by the trained machine learning models by subdividing a camera image fed into multiple segments or objects. The semantic segmentation system comes with the objective of assigning predefined class labels such as tree, road, and the like to each pixel of an image. Any security attacks on pixel classification nodes of the segmentation systems based on deep learning result in the failure of the driver assistance or autonomous vehicle safety functionalities due to a falsely formed perception. The security compromisations on the pixel classification head of
Prashanth, K.Y., Rohitha , U.M.
Autonomous Drone Solution for Human-Wildlife Conflict Management2025-28-019802/07/2025
Human-wildlife conflicts pose significant challenges to both conservation efforts and community well-being. As these conflicts escalate globally, innovative technologies become imperative for effective and humane management strategies. This paper presents an integrated autonomous drone solution designed to mitigate human-wildlife conflicts by leveraging technologies in drone surveillance and artificial intelligence. The proposed system consists of stationary IR cameras that are setup within the conflict prone areas, which utilizes machine learning to identify the presence of wild animals and to send the corresponding location to a drone docking station. An autonomous drone equipped with high-resolution IR cameras and sensors is deployed from the docking station to the provided location. The drone camera utilizes object detection technology to scan the specified zone to detect the animal and emit animal repelling ultrasonic sound from a device integrated to the drone to achieve non
Sadanandan, Vaishnav, Sadique, Anwar, George, Angeo Pradeep, Vinod, Vishal, Raveendran, Darshan Unni
SAE TOMORROW TODAY: How AI Alignment Makes AVs Safer1349501/10/2025
In order for AVs to perform safely and reliably, we need to teach them the language of human preference and expectations--and accelerating AI alignment can do just that. Enter Kognic, the industry-leading annotation platform for sensor-fusion datasets (e.g., camera, radar, and LIDAR data). By helping companies gather, organize, and refine massive datasets used for training AI models, Kognic is helping to ensure that AD/ADAS perform reliably and meet safety standards--all while minimizing costs and optimizing teams. To learn more, we sat down with Daniel Langkilde, Co-Founder and CEO, to discuss why the future of autonomous driving depends on effectively managing AI-driven datasets and how Kognic is leading dataset management for safety-critical AI. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in
Hineman, Marcie
Camera Inspired by the Human Eye Can Improve How Robots See, ReactTBMG-5233001/01/2025
A team led by University of Maryland computer scientists invented a camera mechanism that improves how robots see and react to the world around them. Inspired by how the human eye works, their innovative camera system mimics the tiny involuntary movements used by the eye to maintain clear and stable vision over time. The team’s prototyping and testing of the camera — called the Artificial Microsaccade-Enhanced Event Camera (AMI-EV) — was detailed in a paper published in the journal Science Robotics in May 2024.
Ultra-Compact Camera Technology Optimized for VR/AR DevicesTBMG-5230901/01/2025
Seoul National University College of Engineering announced that researchers from the Department of Electrical and Computer Engineering’s Optical Engineering and Quantum Electronics Laboratory have developed an optical design technology that dramatically reduces the volume of cameras with a folded lens system utilizing “metasurfaces,” a next-generation nano-optical device. By arranging metasurfaces on the glass substrate so that light can be reflected and moved around in the glass substrate in a folded manner, the researchers have realized a lens system with a thickness of 0.7 mm, which is much thinner than existing refractive lens systems. The research, which was supported by the Samsung Future Technology Development Program and the Institute of Information & Communications Technology Planning & Evaluation (IITP), was published on October 30 in the journal Science Advances. Traditional cameras are designed to stack multiple glass lenses to refract light when capturing images. While
A Methodology to Read QR Codes on Uneven SurfacesTBMG-5231101/01/2025
Sometimes, we try to capture a QR code with a good digital camera on a smartphone, but the reading eventually fails. This usually happens when the QR code itself is of poor image quality, or if it has been printed on surfaces that are not flat — deformed or with irregularities of unknown pattern — such as the wrapping of a courier package or a tray of prepared food. Now, a team from the University of Barcelona (UB) and the Universitat Oberta de Catalunya (UOC) has designed a methodology that facilitates the recognition of QR codes in these physical environments, where reading is more complicated.
Exploring Shock Wave–Boundary Layer Interaction Using Rainbow Schlieren Deflectometry01-18-01-000212/24/2024
The flow structure and unsteadiness of shock wave–boundary layer interaction (SWBLI) has been studied using rainbow schlieren deflectometry (RSD), ensemble averaging, fast Fourier transform (FFT), and snapshot proper orthogonal decomposition (POD) techniques. Shockwaves were generated in a test section by subjecting a Mach = 3.1 free-stream flow to a 12° isosceles triangular prism. The RSD pictures captured with a high-speed camera at 5000 frames/s rate were used to determine the transverse ray deflections at each pixel of the pictures. The interaction region structure is described statistically with the ensemble average and root mean square deflections. The FFT technique was used to determine the frequency content of the flow field. Results indicate that dominant frequencies were in the range of 400 Hz–900 Hz. The Strouhal numbers calculated using the RSD data were in the range of 0.025–0.07. The snapshot POD technique was employed to analyze flow structures and their associated
Datta, Narendra, Olcmen, Semih, Kolhe, Pankaj
Unified Multi-Modal Multi-Agent Cooperative Perception Framework for Intelligent Transportation Systems2024-01-702812/13/2024
Cooperative perception has attracted wide attention given its capability to leverage shared information across connected automated vehicles (CAVs) and smart infrastructure to address the occlusion and sensing range limitation issues. To date, existing research is mainly focused on prototyping cooperative perception using only one type of sensor such as LiDAR and camera. In such cases, the performance of cooperative perception is constrained by individual sensor limitations. To exploit the multi-modality of sensors to further improve distant object detection accuracy, in this paper, we propose a unified multi-modal multi-agent cooperative perception framework that integrates camera and LiDAR data to enhance perception performance in intelligent transportation systems. By leveraging the complementary strengths of LiDAR and camera sensors, our framework utilizes the geometry information from LiDAR and the semantic information from cameras to achieve an accurate cooperative perception
Meng, Zonglin, Xia, Xin, Zheng, Zhaoliang, Gao, Letian, Liu, Wei, Zhu, Jiaqi, Ma, Jiaqi
Low-Cost Fusion Odometry Algorithm Based on 4D Radar and Pseudo-LiDAR: Bridging the Gap between 4D Radar and Images in 3D Space2024-01-703112/13/2024
Recently, four-dimensional (4D) radar has shown unique advantages in the field of odometry estimation due to its low cost, all-weather use, and dynamic and static recognition. These features complement the performance of monocular cameras, which provide rich information but are easily affected by lighting. However, the construction of deep radar visual odometry faces the following challenges: (1) the 4D radar point cloud is very sparse; (2) due to the penetration ability of 4D radar, it will produce mismatches with pixels when projected onto the image plane. In order to enrich the point cloud information and improve the accuracy of modal correspondence, this paper proposes a low-cost fusion odometry method based on 4D radar and pseudo-LiDAR, 4DRPLO-Net. This method proposes a new framework that uses 4D radar points and pseudo-LiDAR points generated by images to construct odometry, bridging the gap between 4D radar and images in three-dimensional (3D) space. Specifically, the pseudo
Huang, Minqing, Lu, Shouyi, Zhuo, Guirong
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