Browse Topic: Imaging and visualization

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A Real-Time Vehicle Detection and Counting System Using a GUI Interface.2026-01-0156To be published on 04/07/2026
The growing need for intelligent traffic monitoring and environmental control has fuelled the importance of automated vehicle detection and counting systems. Fuel consumption estimation, vehicular emissions calculations, and traffic pattern comprehension- all play a vital role in shaping sustainable environmental policies. Therefore, accurate identification and counting of vehicles is essential. In this paper, we have shown the development of a real-time vehicle detection and counting application that uses computer vision and deep learning techniques to effectively detect, categorise, and count several types of vehicles on Indian roads. The computer vision vehicle counting application uses the YOLO (You Only Look Once) deep learning model for real-time identification and categorisation of diverse vehicle types, including cars, buses, trucks, and motorbikes [1]. A tracking module based on the SORT (Simple Online and Real-time Tracking) algorithm ensures unique identification of vehicles
Gusain, Ashna, Maurya, Akshay, PATHAK, SUNIL, Khan, Tuhin
Ensemble of Experts for Generating Digital Parking Maps from Remote Sensing Images2026-01-0107To be published on 04/07/2026
Being a vital component of transportation, parking and parking-related research are gaining increasing attention from the intelligent transportation and automotive industry research communities. A digital parking map with precise parking spot geospatial information is crucial for research such as automatic valet parking, parking spot recommendations, and parking route optimization. Currently, these maps are generated through costly and resource-intensive human annotation. To address the challenges of insufficient parking maps and the high costs of manual map creation, researchers have proposed the following two strategies for generating parking maps. The SLAM-based map generation for indoor parking lots and the remote sensing image-based map generation for outdoor parking lot. Our work mainly focuses on the outdoor high-definition parking map generation from remote sensing images. These images often suffer from occlusion, inconsistent resolution, and varying luminosity conditions. To
Shukla, Ajitesh, Cao, Xiaofei, Liu, Yongkang, Takeuchi, Yusuke, Sisbot, Akin
Defect Depth Characterization via Thermally and Pneumatically Induced Shearography for Nondestructive Component Inspection in the Automotive Sector2026-01-0205To be published on 04/07/2026
The following approach introduces a novel method for defect depth characterization using digital Shearography, which is a non-contact, full-field, and material-independent optical interferometric method that enables fast and nondestructive testing (NDT) of components, especially in industrial environments such as the automotive sector. While traditional techniques like computed-tomography, ultrasonic-testing, or thermography can offer depth approximations but they often involve high costs, longer testing times, or limited accessibility. In contrast, the method introduced utilizes various excitation methods in combination with shearographic evaluation to derive procedures for depth estimation of subsurface defects. Recent developments in Shearography have enhanced the method’s robustness and industrial applicability. By detecting the surface deformation behavior in the nanometer range under defined loading, depth-related characteristics of hidden defects can be extracted. Loading can be
Bastgen, Valentin, Plaßmann, Jessica, Petry, Christopher, von Freymann, Georg, Schuth, Michael
A Helical Compression Spring Fracture under Impact Velocity in Pump Operation by Theoretical Study and FEA2026-01-0271To be published on 04/07/2026
Helical compression springs have been used widely in various industries from automotive, aerospace and heavy-duty industries to electronics and medical devices. In the automotive industry, they appear in many places such as suspension, valvetrain, etc., especially in the discharge check valve of Gasoline Direct Injection (GDI) pump, which is the subject of study due to a recent fracture in lab testing. A theoretical study is conducted first to establish equation governing spring dynamic motion under impact velocity, which can be in high magnitude with surging shock wave along spring axis. A new spring shock wave equation is developed for spring axial motion coupled with coil torsional effect. This newly derived shock wave equation has a broad term than the classic spring formula found in engineering book. In the paper, it proves that the classic spring shock wave equation is only a special case for the new general wave equation discovered. Also, a formula on spring shock wave
Pang, Michael L., GUNTURU, SRINU, Norkin, Eugene
Radar-Based Dynamic Pixel Activation in Camera Sensors for Optimized Image Processing, Power Efficiency, and Thermal Management2026-01-0047To be published on 04/07/2026
This paper presents a radar-based dynamic imager pixel activation method designed to optimize image processing efficiency, power consumption, and thermal management in automotive In-cabin monitoring systems (DMS and OMS). By integrating radar sensing with high-resolution cameras, the proposed approach selectively activates image sensor pixels only within radar-identified regions of interest, such as moving occupants or key cabin areas. This selective pixel activation substantially reduces the computational workload and data bandwidth required, as the camera transmits high-resolution data solely for relevant zones while maintaining lower resolution or deactivating pixels elsewhere. The fusion of radar and camera data enables adaptive sensor control that caters to distinct scenarios, including driver gaze tracking and passenger monitoring, thereby enhancing DMS and OMS functionality. The architecture supports mounting configurations such as headliner or rearview mirror installations
Kasarla, Nagender Reddy, Patel, Krunal, Balaraman, Nirmal Kumar
ProGuard: A Real-time Preemptive Artificial Intelligence-Based Anti-Pinch System for Bus Entryways on Low-Power Edge Devices2026-01-0100To be published on 04/07/2026
This paper proposes ProGuard, a novel approach to preemptive pinch detection systems for buses. ProGuard utilizes state-of-the-art AI object detection algorithms to identify potential pinching events in bus entryways before pinching occurs. Modern conventional anti-pinch systems, such as pressure sensors or hall effect sensors, often rely on mechanical contact before triggering. While these systems are established safety mechanisms, they are reactive and therefore require some level of pinching before triggering. This reactive approach presents numerous safety concerns for passengers, especially when considering children on school buses. Existing preemptive detection methods, such as infrared or ultrasonic sensors, solve the problems presented by these reactive detection systems. However, these systems either lack the range or environmental resilience needed for reliable operation in buses. The critical nature of anti-pinch systems requires a robust and reliable solution that can adapt
Bradley, Hudson, Zadeh, Mehrdad, TAN, Teik-Khoon
Self-Supervised Gaussian-Based Framework for Robust Static-Dynamic Decomposition and Real-Time Urban Scene Reconstruction2026-01-0010To be published on 04/07/2026
We propose a self-supervised Gaussian-based framework that enhances the robustness and efficiency of static-dynamic decomposition and high-fidelity reconstruction in autonomous driving scenarios. Existing approaches demonstrate promising performance but remain prone to noisy motion segmentation, overfitting on sparse LiDAR observations, and degraded accuracy in low-texture or distant regions, resulting in unstable geometry. To address these challenges, our method introduces a feature fusion module that jointly exploits image semantics and geometric cues, enabling more reliable motion prior extraction under noise and illumination variations. A cross-sequence geometric consistency constraint is further designed to regularize reconstruction across temporal and viewpoint changes, ensuring physically coherent scene representations. In addition, we formulate an efficient Gaussian parameter optimization strategy that constrains scale and normal updates, preventing unrealistic ellipsoid growth
Feng, Runlei, Wang, Ning, Zhang, Zhihao
An Enhanced Canny-Based Vision System for Vanishing Point Detection in Camera Calibration and Sun Glare Mitigation for ADAS2026-01-0016To be published on 04/07/2026
Abstract— Edge detection is fundamental for intelligent vehicle applications, directly supporting ADAS functions such as lane detection, obstacle recognition, and scene understanding. The conventional Canny edge detection method exhibits notable shortcomings, especially in color-image processing, adaptive threshold selection, and preserving edge integrity under noisy conditions. In this study, we present an enhanced Canny edge detection framework tailored for ADAS-oriented intelligent vehicle systems, incorporating a quaternion-based weighted averaging scheme for color preservation, adaptive thresholds derived from gradient-amplitude histograms, multiscale edge localization via scale multiplication, and a novel gravitational-field-intensity operator for improved gradient robustness. Moreover, we extend the method to vanishing-point estimation an essential ADAS capability by performing precise intersection calculations combined with clustering techniques such as DBSCAN and RANSAC
Uppala, Rohit Raj, Kaye, Murali, Zadeh, Mehrdad, TAN, Teik-Khoon
Numerical Framework for Predicting Power Loss and Oil Flow Dynamics in Dip-Lubricated Gear Systems2026-01-0411To be published on 04/07/2026
Oil churning and windage power losses in dip-lubricated gearboxes can significantly improve overall transmission efficiency, particularly at high rotational speeds. As modern gearbox systems are pushed toward higher efficiency and reliability, understanding and predicting these losses becomes increasingly important. In addition to energy dissipation, the associated multiphase flow phenomena—such as oil splashing, thin film formation along gear surfaces, and aeration of the sump—strongly influence lubrication effectiveness, heat transfer, and component durability. Capturing these effects requires a robust numerical strategy that can resolve both power loss mechanisms and multiphase flow dynamics with sufficient accuracy. In this study, a single spur gear is numerically analyzed under varying oil depths and rotational speeds to quantify total power loss and investigate oil flow patterns. The computational approach employs a volume-of-fluid multiphase framework, and the predictions are
Mahyawansi, Pratik J., Haria, Hiral, Pandey, Ashutosh, KHAJEH HOSSEINI D, NAVVAB
Investigation on Microstructure and Mechanical Properties in the Dissimilar Joining of WE43 to AA7075 Alloy using FSW2026-01-0229To be published on 04/07/2026
This research investigates the alterations in microstructure, microhardness, and joint strength resulting from the dissimilar friction stir welding (FSW) of WE43 magnesium alloy to AA7075 aluminium alloy. The study specifically analyses the role of FSW process parameters in the formation of intermetallic compounds (IMCs), the evolution of grain structure, the resultant microhardness distribution across the weld zone, and the joint tensile strength. A comprehensive microstructural characterization was performed utilizing optical microscopy (OM), field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy (FESEM-EDS), electron backscatter diffraction (EBSD), and X-ray diffraction (XRD). These analyses confirmed significant refinement of grain in the stir zone and the identification of various IMCs at the weld interface. Microhardness mapping indicated a gradient profile, with the weld nugget exhibiting superior hardness attributed to its dynamically
Ahmad, Tariq, Khan, Noor Zaman, Ahmad, Babar, Siddiquee, Arshad Noor
Analysis of In-Cylinder Flow for Hybrid Gasoline Engine using High-Speed Particle Image Velocimetry2026-01-0298To be published on 04/07/2026
To curb global warming, reduction of CO2 emissions in automotive powertrains is required. Hybridization and further improvement of thermal efficiency has been promoted in internal combustion engines. Therefore, the 2.0-liter inline 4-cylinder direct-injection engine for 2 motor-hybrid powertrain have been developed to realize rapid combustion by in-cylinder flow enhancement. In this paper, in-cylinder flow analysis using high-speed PIV on the optical single-cylinder engine was performed, and evaluated the combustion promotion concept by analyzing the bulk flow and the turbulence. To achieve rapid combustion, the intake port shape for tumble flow enhancement and bowl-in type piston which was smoothly converted the tumble flow to upward flow were examined. As a method for turbulent component decomposition, the time filter method proposed by the authors were applied. The characteristics of the time filter method is that turbulent component does not include the cycle-by-cycle fluctuations
Okura, Yasuhiro, Urata, Yasuhiro
The Effects of Collision-Related Power Loss on Toyota Safety Sense 3.0 Event Data2026-01-0548To be published on 04/07/2026
Toyota vehicles equipped with Toyota Safety Sense (TSS) can record detailed information surrounding various driving events. Often, this data is employed in accident reconstruction to better understand the dynamics of a collision. TSS data is comprised of three main categories: Vehicle Control History (VCH), Freeze Frame Data (FFD), and image records. During an event, it is possible that a vehicle undergoes a catastrophic power loss from the damages sustained during the event. In this paper, the effects of a sudden power loss on the VCH, FFD, and images are studied. Events are triggered on a TSS 3.0 equipped vehicle by driving toward a stationary target. After system activation, a total power loss is induced, triggered on the instrument cluster “BRAKE” alert message, at various delays after activation. This testing studies various signals recorded across VCH, FFD and image data including vehicle speed, measured accelerations and time and date. This testing also compares the acceleration
Getz, Charles, Yeakley, Adam, DiSogra, Matthew
Monocular 3D Perception and Lane-Aware Bird’s-Eye-View Mapping for Autonomous Driving2026-01-0012To be published on 04/07/2026
Accurate perception of the surrounding environment is fundamental and essential to safe and reliable autonomous driving. This work presents an integrated vision-based framework that combines object detection, 3D spatial localization, and lane segmentation to construct a unified bird’s-eye-view (BEV) representation of the driving scene. The pipeline provides geometric information on object position and orientation by employing Omni3D to infer 3D bounding boxes of objects from monocular camera frames. Detections are subsequently projected onto a 2D BEV canvas, where object instances are represented with respect to the ground plane for enhanced interpretability. To complement the object-level perception, we utilized YOLOPv2 to perform lane segmentation, producing both lane masks and lane line masks in the image domain for future coordinate transformation. By adopting a pinhole camera model, the coordinate transformation of these masks from the perspective image plane into the BEV canvas
Tan, Lin, Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, Lee, Victoria, Xu, Bin, Arjmanzdadeh, Ziba
Reconstruction of Pedestrian-Vehicle Collisions Using 3D Skeletal Estimation from Vehicle-Mounted Camera Video2026-01-0575To be published on 04/07/2026
Since 2009, pedestrian fatalities caused by traffic accidents in the United States have increased by 78%, exceeding 7,000 deaths annually. To reduce injuries, a deeper understanding of injury mechanisms based on real-world accident data is required. Recently, accident reconstruction techniques utilizing recorded video from vehicle-mounted cameras have gained increasing attention in accident analysis. However, injuries resulting from contact with the road surface are often occluded due to camera blind spots, hindering accurate capture. Additionally, the low frame rate of many vehicle-mounted cameras limits the ability to accurately analyze the detailed kinematics of pedestrian impacts. To address these limitations, this study proposes a reconstruction method that estimates pedestrian motion in occluded regions by matching observed behavior from vehicle-mounted camera recorded video with simulation results. First, to reconstruct three-dimensional pedestrian motion from vehicle-mounted
Onishi, Koji, Wang, Kewang, Uno, Eriko, Ichikawa, Koji, Tanase, Noboru, Ando, Takahiro
Comparing the Dynamic Response of a Rolling Tire using Data Collected using a 3D Scanning Laser Doppler Vibrometer2026-01-0592To be published on 04/07/2026
Detailed tire response data is necessary to advance vehicle safety, and monitor real time tire health. This study presents a non-contact experimental method to measure the vibratory characteristics of a tire subject to shaker excitation and road rolling forces. A Global Electric Motorcars (GEM) E4 electric vehicle was mounted on a chassis dynamometer to simulate loading conditions in a controlled environment. Scanning laser vibrometry (SLDV) was employed to capture out of plane velocity responses across the tire surface using a three independent laser configuration. This technique enables visualization of mode shapes under static and dynamic excitation. The results from SLDV identified the tire’s dominant resonant frequencies and corresponding operating deflection shapes under both static and dynamic conditions at different velocities. The dynamic measurements revealed that the harmonics of rotational frequency dominated higher order structural responses. Incorporating a reference
Gower, Adam, Baqersad, Javad, Gupta, Arjun
AI-Driven Data Consistency and Relationship Inference System for Agile Component Library Management2026-01-0109To be published on 04/07/2026
Reliable component libraries are the foundation of the engineering process and the starting point for all intelligence within CAD tools. In practice, however, libraries created and maintained by librarians often contain incomplete, inconsistent, or outdated data. This paper introduces the component data consistency and relationship inference AI system, developed within Amoeba software, which addresses these challenges by improving component library quality. The system uses AI to infer component attributes such as component type, gender, color, material, etc. Moreover, it can identify relationships such as the family a connector is associated with based on its attributes and geometry. The system improves data consistency in areas such as resolving mismatched wire size constraints imposed by the connector and cavity components. It also utilizes computer vision to identify common connector footprints, cavity sizes, and 2D symbol geometries. Deployed within Amoeba software, the system has
Phan, Dung, Horvat, Bryan
Development of technology to distinguish contaminants using LiDAR near-field point cloud based on real-field data2026-01-0020To be published on 04/07/2026
Sensing technologies are essential to autonomous driving, enabling environmental perception and supporting navigation and its decision-making. However, most publicly available datasets are collected under perfect weather conditions and fail to capture the challenges of real-world environments. To address this gap, we built a large-scale dataset from extensive driving campaigns across diverse regions of the United States, covering conditions such as rain, snow, dust, clear weather and anti-ice material affects. Data were acquired through a multimodal sensing devices - including perception cameras, LiDAR, infrared sensors, and external monitoring systems - providing a comprehensive representation of the vehicle's operational environment. Building on this dataset, our study focuses on LiDAR data, emphasizing contaminant classification as a critical requirement for sensor-cleaning strategies essential to reliable autonomous driving. Using raw LiDAR signals collected over 22,000km of
KIM, HUNJAE
Evaluating Cloud Platforms for Real-Time Cooperative Perception Systems Using RSU Cameras: A Comparative Study of Google Colab, Azure, and AWS2026-01-0018To be published on 04/07/2026
This paper presents a comparative study of three widely used cloud platforms, Google Colab, Microsoft Azure, and Amazon Web Services (AWS), for running a real-time cooperative perception system based on roadside unit (RSU) cameras. The goal is to evaluate the performance, scalability, and cost-efficiency of each platform when handling high-volume video data for object detection, a key task in autonomous driving. A unified perception pipeline using the YOLOv8 Small model was deployed on all platforms, with the same dataset and settings to ensure fair comparison. The evaluation focused on key metrics such as latency, frame processing rate, scalability, and cost per frame. The results show that Google Colab is a cost-effective starting point but has limitations in uptime and scalability. Azure offers stable performance and balanced cost, making it suitable for medium-scale applications. AWS delivers the best scalability and speed but at a higher cost. This study provides practical
Alkharabsheh, Ekhlass, Alawneh, Shadi, Rawashdeh, Osamah
Accelerated Computed Tomography for Spray Characterization2026-01-0341To be published on 04/07/2026
Computed tomography (CT) is a valuable diagnostic technique for visualizing plume direction and assessing mixture quality within combustion chambers under engine-relevant conditions. High-speed extinction imaging followed by tomographic reconstruction enables temporally and spatially resolved measurements of liquid volume fraction and plume evolution in multi-jet sprays. Traditionally, tomographic reconstruction requires capturing multiple angular views by rotating the injector and averaging over numerous injections to ensure statistical convergence. This process is time-intensive, particularly due to the large volume of data acquisition and the corresponding delays in data saving, often requiring dozens of injections per view angle. In this study, we investigate the minimum number of injections required to achieve sufficient CT image quality, thereby significantly reducing experimental time. Two injectors are evaluated: a symmetric 8-hole Spray M injector from the Engine Combustion
Yi, Junghwa, Wan, Kevin, Pickett, Lyle
Displacement measurement in impact tests based on machine vision2026-01-0477To be published on 04/07/2026
Contact measurement is difficult to arrange sensors under various impact tests. And the method of measuring displacement by machine vision has significant advantages. This paper proposes a displacement measurement method based on machine vision. Firstly, YOLO (You Only Look Once) v5 is used to automatically extract the recognition targets in various test scenarios. Secondly, the extracted recognition target graphics are processed by image enhancement and threshold segmentation technology. Then, the geometric shape of the processed graphics is identified by edge detection technology, and the scale factor is calculated. Finally, the displacement of the target structure is calculated through coordinate transformation in multiple coordinate systems. The results show that for a single recognition target, the relative error between machine vision and sensor measurement is within 5%, and for multiple recognition targets, the relative error between machine vision and industrial software
Sun, Jiawei, Du, Dan, Zhang, Bin
Investigation of CDI and TCI Ignition Charactertistics for Lean Hydrogen–Air Mixtures in a Constant Volume Combustion Chamber2026-01-0327To be published on 04/07/2026
The discharge characteristics of ignition systems strongly influence the formation of flame kernels under lean-burn conditions. In this study, two ignition strategies are compared. A capacitor discharge ignition (CDI) system characterized by discharge energy of 1–5 mJ with discharge current around 300 mA and short discharge duration (~50 μs). A transistor coil ignition (TCI) system delivering discharge energy of 30–50 mJ with lower discharge current (~100 mA) and millisecond-scale discharge duration. These two systems represent distinct discharge current profiles, namely “high-current/short-duration” and “low-current/long-duration.” The influence of discharge current profile on flame kernel formation and early flame development in lean hydrogen–air mixtures is investigated. Experiments are conducted in a constant-volume combustion chamber with hydrogen–air mixtures at various global equivalence ratios. Both quiescent and flow conditions are considered. High speed shadowgraph imaging is
Cong, Binghao, Jin, Long, Yu, Xiao, Zheng, Ming
SAE Truck & Off-Highway Engineering: February 202626TOFHP022/12/2026
AI influence on ICE design Experts offer their outlook on the future of heavy-duty internal combustion engines, and of course artificial intelligence will play a role. Accelerating down the road to autonomy Commercial success of the autonomous truck may be closer than we think. Digital tech a Las Vegas headliner With the rise of AI and other new digital technologies on the horizon, ACT Expo 2026 will be a crucial intersection for industry leaders to map out the route ahead. Editorial CONEXPO debuts - from micro to massive Rolls-Royce pursues pure methanol for marine engines Integrated architectures reshape the mobile-hydraulic ecosystem A proactive approach to hydraulic health in Class 8 trucks Precast concrete bases pave the way for rapid EV charger deployment CONEXPO 2026: Volvo CE to reveal new articulated hauler and short-swing, compact and wheeled excavators Product Briefs SPOTLIGHTS: Imaging/Cameras; Simulation/Analysis Software Q&A Bosch Rexroth: Hydraulics remain relevant in
Multi-Storey AI Based Parking System2026-28-01292/12/2026
This paper presents a novel AI-based parking management system designed to enhance efficiency, reduce manual intervention, and optimize operational costs in modern parking facilities. By integrating computer vision with infrared (IR) sensors, the system continuously monitors parking areas in real time, accurately detecting vehicle occupancy and dynamically updating the space availability. The hybrid approach minimizes reliance on conventional sensors, improving accuracy and environmental robustness. Additional features include intelligent navigation assistance guiding drivers to available spots and integrated video surveillance for enhanced security through AI-driven suspicious activity detection. The user interface provides real-time updates ensuring a seamless and convenient parking experience. Overall, this system offers a comprehensive solution that advances parking technology through automation, real-time monitoring, and secure, user-friendly operation.
N, Kalaiarasi, Gupta, Shivansh, Hajarnis, Mihir, Anand, Vikas
Accidental Dooring Warning and Assist System2026-28-00532/12/2026
Dooring accidents occur when a vehicle door is opened into the path of an approaching cyclist, motorcyclist, or other road user, often causing serious collisions and injuries. These incidents are a major road safety concern, particularly in densely populated urban areas where heavy traffic, narrow roads, and inattentive behavior increase the likelihood of such events. To address this challenge, this project presents an intelligent computer vision based warning system designed to detect approaching vehicles and alert occupants before they open a door. The system can operate using either the existing rear parking camera in a vehicle or a USB webcam in vehicles without such a feature. The captured live video stream is processed by a Raspberry Pi 4 microprocessor, chosen for its compact size, low power consumption, and ability to support machine learning frameworks. The video feed is analyzed in real time using MobileNetSSD, a lightweight deep learning object detection model optimized
C, Jegadheesan, T, Karthi, Gurusamy, Varun Sankar, Balraj, Tharun, Murugaiya, Tamilselvan
SBD-YOLO: An Efficient Speed Bump Detector for Active Suspension Preview Control12-09-03-00181/28/2026
Speed bump detection through computer vision and deep learning is essential for advancing active suspension preview control and intelligent driving. Although substantial progress has been made in this field, there remains a need to enhance detection accuracy while reducing computational demands. This article introduces a novel single-stage speed bump detector, the Speed Bump Detector Based on You Only Look Once (SBD-YOLO), which utilizes the YOLOv9 architecture for speed bump identification. To better capture the deep global features of speed bumps, we propose an innovative convolutional module—specifically, a lightweight building block designed for efficient feature extraction—named the Aggregated-MBConv. Furthermore, we design a new YOLO backbone by stacking Mobile Inverted Bottleneck Convolution (MBConv) and Aggregated-MBConv modules, which reduces computational cost while enhancing detection accuracy. Additionally, we introduce a Squeeze-aggregated Excitation (SaE) attention
Mao, Ruichi, Wu, Jian, Wu, Yukai, Wang, Huiliang, Li, Jun, Wu, Guangqiang
Niobium Alloyed Brake Disc for reduced NVH and Emission Control2026-26-02851/16/2026
Recent regulations limiting brake dust emissions have presented many challenges to the brake engineering community. The objective of this paper is to provide a low cost, mass production solution utilizing well known existing technologies to meet brake emissions requirements. The proposed process is to alloy the Gray Cast Iron with Niobium and subsequently Ferritic Nitrocarburize (FNC) the disc. The Niobium addition will improve the wear resistance of the FNC case, reducing wear debris. The test methodology included: 1. Manufacture of disc samples alloyed with Niobium, 2. Finish machining and ferritic nitrocarburizing and 3. Evaluation of airborne wear debris utilizing a pin-on-disc tribometer equipped with emission collection capability. The airborne emission and wear surfaces were further analyzed by Scanning Electron Microscopy, Energy Dispersive techniques (SEM-EDS), X-Ray Diffraction and Optical Microscopy. The cast iron test matrix included four groups; Unalloyed eutectic 4.3
Barile, Bernardo, Holly, Mike
Computer Vision Based Driver Health and Wellness Monitoring System: A) Fatigue Detection, B) Emotional Wellbeing2026-26-06391/16/2026
Computer vision has evolved from a supportive driver-assistance tool into a core technology for intelligent, non-intrusive occupant health monitoring in modern vehicles. Leveraging deep learning, edge optimization, and adaptive image processing, this work presents a dual-module Driver Health and Wellness Monitoring System that simultaneously performs fatigue detection and emotional wellbeing assessment using existing in-cabin RGB cameras without requiring additional sensors or intrusive wearables. The fatigue module employs MediaPipe-based facial and skeletal landmark analysis to track Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), head posture, and gaze dynamics, detecting early drowsiness and postural deviations. Adaptive, driver-specific thresholds combined with CAN-bus data fusion minimize false positives, achieving over 92% detection accuracy even under variable lighting and demographics. The emotional wellbeing module analyzes micro-expressions and facial action units to
Iqbal, Shoaib, Imteyaz, Shahma
Failure Prevention of Permanent Deformation in HNBR Elastomeric Diaphragms Used in CNG Valve Applications2026-26-02941/16/2026
Hydrogenated nitrile butadiene rubbers (HNBR) and their derivatives have gained significant importance in automotive compressed natural gas (CNG) valve applications. In one of the four-wheelers, CNG valve application, HNBR elastomeric diaphragms are being used for their excellent sealing and pressure regulation properties. The HNBR elastomeric diaphragm was developed to sustain CNG higher pressure However, it was found permanently deformed under lower pressures. In this research work, number of experiments was carried out to find out the primary root cause of diaphragm permanent deformation and to prevent the failure for safe usage of the CNG gas. HNBR diaphragm deformation investigation was carried out using advanced qualitative and quantitative analysis methods such as Soxhlet Extraction Column, Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), Optical Microscopy (OM), Scanning Electron Microscopy (SEM), and Thermogravimetric Analysis (TGA). For
Patil, Bhushan Gulab, NAIKWADI, AMOL, Mali, Manoj, Tata, Srikanth
Assessing the Impact of Hole Piercing Edge Quality on Fatigue Samples for S550MC Steel Sheet2026-26-03061/16/2026
The exceptional strength, formability, and weldability of S550MC steel sheets make them a cornerstone material in the automotive industry. These properties translate into the creation of high-performance automotive components like chassis parts, structural reinforcements, ultimately contributing to enhanced vehicle safety and overall performance. Furthermore, S550MC steel boasts excellent fatigue resistance, a critical factor for ensuring long-term reliability in demanding automotive applications that experience repeated stress cycles. However, optimizing the performance of S550MC components depends on a fine understanding of the critical relationship between hole edge quality and fatigue failure. This study highlights the impact of hole-piercing clearance on the edge quality of the hole in modified fatigue samples manufactured from S550MC steel, and its effect on fatigue life. The surface morphology was characterized by using stereoscope for edge quality of hole piercing operation
Nahalde, Sujay, Hingalje, Abhijeet, Ughade, Vikas, Singh, Udita, More, Hemant
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
Artificial Intelligence in Passenger Cars: Unlocking Intelligent Mobility and Human-Centric Vehicle Innovation2026-26-06721/16/2026
Artificial Intelligence (AI) is radically transforming the automotive industry, particularly in the domain of passenger vehicles where personalization, safety, diagnostics, and efficiency. This paper presents an exploration of AI/ML applications through quadrant of the key pillars: Customer Experience (CX), Vehicle Diagnostics, Lifecycle Management, and Connected Technologies. Through detailed use cases, including AI-powered active suspension systems, intelligent fault code prioritization, and eco-routing strategies, we demonstrate how AI models such as machine learning, deep learning, and computer vision are reshaping both the user experience and engineering workflow of modern electric vehicles (EVs). This paper combines simulations, pseudo-algorithms and data-centric examples of the combined depth of functionality and deployment readiness of these technologies. In addition to technical effectiveness, the paper also discusses the challenges at field level in adopting AI at scale i.e
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
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
Mane, Suvidya, Makam, Sri Lalith Madhav, Varghese, Rixson, Desu, Harsha
Advanced Defect Detection Techniques Using AI, CAD Data, and Computer Vision2026-26-06441/16/2026
In area of modern manufacturing, ensuring product quality and minimizing defects are utmost important for maintaining competitive advantage and customer satisfaction. This paper presents an innovative approach to detect defect by leveraging Artificial Intelligence (AI) models trained using Computer-Aided Design (CAD) data. Traditional defect detection methods often rely on physical inspection, which can be time-consuming and prone to human error. The conventional method of developing an AI model requires a physical part data, By utilizing CAD data, the time to develop an AI model and implementing it to production line station can be saved drastically. This approach involves the use of AI algorithms trained on CAD models to detect and classify defects in real-time. The field trial results demonstrate the effectiveness of this approach in various industrial applications, highlighting its potential to revolutionize defect detection in manufacturing.
Kulkarni, Prasad Ramesh, Sahu, Dilip, Joshi, Chandrashekhar, Khatavkar, Akshay, Poddar, Shivani, Deep, Amar
Comparative Analysis of Pothole Depth Estimation Using LIDAR and Stereo Imaging with Polarized Lenses: A DOE Approach2026-26-02691/16/2026
Potholes are a common road hazard that significantly compromise road safety. Water filled potholes can be particularly dangerous. These hidden hazards may cause vehicles to hydroplane [1], leading to a loss of control and potential collisions. At night or in low visibility conditions, such potholes can appear deceptively shallow, increasing the risk of severe suspension damage or tire blowouts. Additionally, deep water intrusion can affect critical components such as the exhaust system, air intake, or electrical wiring, potentially leading to engine stalling or short circuits. This research proposes a novel approach for identifying and determining the depth of potholes, especially those that are filled with water. By integrating YOLO, cutting edge computer vision methods like stereo imaging and Lidar. We hope to create a system that can precisely detect and evaluate potholes' severity, reducing the risks connected to these road hazards. A structured 2k factorial Design of Experiment
Ashok, Deeksha, Kumar, Pradeep, Singh, Amandeep
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
Khan, Aamir Naved, Saraswat, Vivek, Jha, Kartik, Singh, Hemendra, Seenivasan, Gokulram, Khan, Nafees
Map-Less Yet Accurate: Trajectory Prediction for Traffic Agents Using Online HD Map Reconstruction for Autonomous Driving2026-26-00391/16/2026
Accurate trajectory prediction of traffic agents is critical for enabling safer and more reliable autonomous driving, particularly in urban driving scenarios where close-range interactions are most safety critical. High-definition (HD) and standard-definition (SD) maps play a vital role in this process by providing lane topology and directional cues for forecasting agent movements. However, HD maps are expensive and resource-intensive to create, often requiring specialized sensors, while SD maps lack the precision needed for reliable autonomous navigation. To address this, we propose a novel framework for trajectory prediction that leverages online reconstruction of HD maps using vehicle-mounted cameras, offering a scalable and cost-effective alternative. Our method achieves improvements in predicting accuracy, particularly in close-range scenarios, the most crucial for urban driving, while also performing robustly in settings without pre-built maps. Furthermore, we introduce a new
Upreti, Minali, Girijal, Rahul, B A, NaveenKumar, Thontepu, Phani, Ghosh, Shankhanil, Chakraborty, Bodhisattwa
Simulation-Driven Load Spectrum Prediction: Virtual RLDA for Chassis and Suspension System2026-26-00951/16/2026
In the rapidly evolving and highly competitive automotive industry, manufacturers are under immense pressure to bring products to market quickly while meeting customer expectations. As a result, optimizing the product development timeline has become essential. Structural integrity analysis for chassis and suspension systems lies in the accurate acquisition of operational load spectra, conventionally executed through Road Load Data Acquisition (RLDA) on instrumented vehicles subjected to proving ground excitation. At this point, RLDA is mainly used for final validation and fine-tuning. If any performance shortfalls, such as premature component failure or durability issues, are discovered, they often trigger design revisions, prototype rework, and additional testing. This study proposes a Virtual Road Load Data Acquisition (vRLDA) methodology employing a high-fidelity full-vehicle multibody dynamic (MBD) representation developed in Adams Car. The system is parameterized and uses high
Goli, Naga Aswani Kumar, Prasad, Tej Pratap
Adaptability Survey and Analysis of Camera Monitoring Systems (CMS) on Various Driving Population for Passenger Vehicles in Indian Driving Scenario2026-26-06011/16/2026
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
Failure Analysis of Dissimilar Metal Weld Joint of an Air Spring Bracket within an Automotive Active Seat Suspension System2026-26-04871/16/2026
In modern four-wheelers, seat suspension systems play a crucial role in enhancing occupant comfort by mitigating the effects of road unevenness and vibrations. Among these systems, active suspension mechanisms offer advanced performance through complex assemblies involving welded, riveted, and bolted joints. This study investigates the failure of an air spring bracket - a critical component of a pneumatic active suspension system - manufactured by Gas Metal Arc Welding (GMAW) of two dissimilar ferrous materials which are likely to be SAPH440 and S355J2. These different materials were used based on mechanical properties required to perform by their particular part. System level validation tests were conducted to ensure the reliability of the seat suspension system. The one of the validation tests is continuous cyclic fatigue test which is carried out on the complete seat assembly. However, during vibration / cyclic endurance testing, premature failures were observed near the weld joints
Patale Jr, Reshma, Pinjari, Jayant Namdev, Bali, Shirish
Co-Developing Illumination and Imaging Systems for Medical Fluorescence EndoscopyTBMG-544401/1/2026
Endoscopic imaging system development requires coordination between various engineering disciplines, especially for optical illumination and imaging engines, particularly when adding fluorescence imaging capabilities. The optical illumination and imaging engines set the foundation for building intuitive and effective imaging products around and become even more critical when adding fluorescence imaging (FI) capabilities to user needs.
Advanced High-Repetition-Rate Lasers Drive Next-Gen ImagingTBMG-544131/1/2026
Since the advent of laser-based imaging techniques in the early 2000s, image acquisition has faced a fundamental challenge: the imaging speed and signal averaging was directly tied to the firing rate of the laser. Because a minimum of one laser pulse generates a single data point, traditional flashlamp-based lasers operating at relatively low repetition rates were constrained in their ability to capture fine spatial or temporal detail quickly. For applications requiring real-time analysis or high-resolution mapping, these limitations often reduced the practicality of otherwise powerful imaging technologies.
Countering the New Threat from the Sky: Advanced IR Imaging Zoom Lenses for C-UAS SystemsTBMG-544341/1/2026
Current world conflicts have proven that drones are now indispensable tools in modern warfare. Whether for reconnaissance, loitering munitions, or asymmetric tactics that exploit vulnerabilities in conventional defenses, unmanned aerial systems (UAS) are redefining the rules of engagement.
A Guide to Cost-Effective Thermal Lens Integration: What to Know for Defense and Autonomous Vehicle ApplicationsTBMG-544361/1/2026
In complete darkness, through smoke, glare and fog, thermal infrared (IR) imaging is indispensable for modern defense and autonomous systems. Enabling autonomous vehicles (AVs) to detect pedestrians or threats at night or providing critical sensing capabilities for unmanned aerial vehicles and counter-UAS operations, thermal imaging has become the essential “eyes” when visible camera systems fail.
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
Pan, Deng, Chen, Yuhan, Shi, Yicui, Li, Jie, Li, Guofa
GP-LGFusion: Deep Fusion of Infrared-Visible Driving Images Based on Gradient Preservation and Local Guidance2025-01-730512/31/2025
Infrared and visible driving image fusion represents a pivotal technology in multi-source perception for automated driving. The objective of this technology is to generate fused images that exhibit significant targets and comprehensive road information in complex traffic scenes. However, the existing image fusion algorithms demonstrate inconsistent capacity to complement information in diverse environments. Additionally, there are limitations in their ability to extract features, such as the detailed texture of traffic targets under complex lighting conditions, including low-light scenes and multi-exposure scenes. To overcome these limitations, we propose a novel gradient-preserving and locally guided fusion method (GP-LGFusion). Our primary contribution is a Multi-scale Gradient Residual Block (MGRRB), an encoder module specifically designed to capture and retain both strong and weak texture features across different scales, a capability lacking in conventional approaches. Second, we
Meng, Zhangjie, Shi, Yicui, Chen, Yuhan, Zhou, Xiaoji, Li, Jie, Li, Guofa
Detection of Dangerous Driving Postures Based on Depth Images Using an Anchor Regression Method2025-01-734312/31/2025
Perceiving the movement characteristics of specific body parts of a driver is crucial for determining their activity. Moreover, the driver’s body posture significantly impacts personnel safety during collision. This study investigates the creation of a dataset using Kinect depth camera for acquiring, organizing, annotating with skeleton tracking assistance, and optimizing interpolation. The pose recognition methods enhanced through an anchor regression mechanism, leading to the refinement of a lightweight anchor regression network capable of end-to-end learning ability from depth images. The improved backbone neck head structure offers advantages of reduced model parameters and enhanced accuracy. This engineering optimization makes it better suited for practical applications within vehicles with limited computational resources limitations and high real-time demands.
Xu, Hailan, Li, Wuhuan, Lu, Jun, Wang, Xin, He, Wenhao, Chen, Zhenming, Liu, Yunjie
Long-Term Temporal Hierarchical Fusion Bird’s-Eye View Perception Method Based on Multiple Position Encodings2025-01-730712/31/2025
With the rapid development of autonomous driving technology, environmental perception, as its core module, has attracted much attention. Among them, the pure visual bird's-eye-view (BEV) 3D detection scheme has become a research hotspot due to its high spatial resolution and excellent semantic recognition ability in specific scenarios. Existing methods mainly utilize the Transformer encoder structure to perform position encoding in the BEV domain to achieve 3D perspective transformation, but they often fail to fully exploit the potential value of multi-perspective image information. To address this challenge, this paper proposes an improved Transformer-based visual BEV vehicle perception method that enhances perception performance by deeply fusing BEV domain and image domain information: an innovative multi-perspective position encoding mechanism is designed, which decouples camera parameters to more efficiently learn the mapping from images to 3D space; at the same time, a cyclic
Chen, Pengyu, Wei, Xiaoxu, Chen, Zhenwei
Research and Implementation of a Neural Radiance Fields-Based 3D Reconstruction Method for Autonomous Driving Scenes2025-01-731012/31/2025
This paper addresses the scarcity of training and testing data in autonomous driving scenarios. We propose a 3D reconstruction framework for autonomous driving scenes based on Neural Radiance Fields (NeRF). Compared to traditional multi-view geometry methods, NeRF offers superior scene representation and novel view synthesis capabilities but suffers from low training efficiency and limited generalization. To overcome these limitations, we integrate existing NeRF optimization techniques and introduce a scene-specific data reuse strategy tailored for autonomous driving, enabling continuous 3D reconstruction directly from 2D images without requiring elaborate calibration. This approach significantly improves reconstruction efficiency, achieving reliable reconstruction and real-time visualization in complex traffic environments. Furthermore, we develop an interactive scene editing plugin in Unreal Engine 5, supporting the addition, removal, and adjustment of static objects. This extension
Pan, Deng, Zou, Jie, Chen, Yuhan, Meng, Zhangjie, Li, Jie, Li, Guofa
STO-VINS: A Robust Monocular Visual-Inertial SLAM Based on a Fusion Feature Extraction Algorithm Combining Shi-Tomasi and ORB2025-01-730612/31/2025
The VINS-Mono algorithm, which is based on a visual-inertial SLAM framework, faces challenges in extracting feature points in regions with weak or repetitive textures and struggles to achieve accurate localization under unstable lighting conditions. This paper proposes STO-VINS, a robust monocular visual-inertial SLAM algorithm that introduces several key innovations in feature extraction. Key innovations of STO-VINS include: (1) an adaptive multi-scale image preprocessing pipeline that combines image scaling, CLAHE enhancement, and Gaussian filtering, reducing computational complexity by 64% while maintaining feature quality; (2) bidirectional Lucas-Kanade optical flow consistency verification with geometric constraint validation, which significantly reduces false tracking rates by 30-40%; (3) a grid-based multi-feature fusion detection strategy combining Shi-Tomasi corner detection and ORB feature extraction, ensuring uniform spatial distribution of features and feature diversity; (4
Li, Jing, Wu, Jing, Liu, Bo, Gong, Zeyuan, Zhang, Guofang
Trajectory Reconstruction at Signalized Intersections in a Vehicle-to-Infrastructure Environment2025-99-021712/23/2025
Vehicle trajectories encapsulate critical spatial-temporal information essential for traffic state estimation, congestion analysis, and operational parameter optimization. In a Vehicle-to-Infrastructure (V2I) environment, connected automated vehicles (CAVs) not only continuously transmit their own real-time trajectory data but also utilize onboard sensors to perceive and estimate the motion states of surrounding regular vehicles (RVs) within a defined communication range. These multi-source data streams, when integrated with fixed infrastructure-based detectors such as speed cameras at intersections, create a robust foundation for reconstructing full-sample vehicle trajectories, thereby addressing data sparsity issues caused by incomplete CAV penetration. Building upon classical car-following (CF) theory, this study introduces a novel trajectory reconstruction framework that fuses CAV-generated trajectories and infrastructure-based speed detection data. The proposed method specifically
Bai, Wei, Fu, Chengxin, Yao, Zhihong
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