Browse Topic: Sensors and actuators

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An Analysis and Comparison of Free-Space Detection Approaches and Their Evaluation for Heavy-Duty Vehicles2026-01-0763To be published on 07/01/2026
The detection of free space plays a fundamental role in ensuring the safe and efficient operation of heavy-duty vehicles, particularly in environments where the available area to maneuver is severely constrained, such as construction zones, rest areas, or loading docks. An accurate estimation of free space is essential to prevent collisions, maintaining operational continuity and minimizing vehicle downtime. As observed from the reviewed literature, despite the large number of proposed free-space detection methods, there is no concise and established definition about how free space should be determined, represented, and inferred, nor agreement on the semantic classes to be considered. This heterogeneity complicates systematic comparison and benchmarking across approaches. This paper presents a structured survey and methodological analysis of recent free-space detection and semantic segmentation approaches across automotive LiDAR-, camera-, and radar-based perception systems, as well as
Martinez, Cristian, Peters, Steven
UMV Peoplemover: Towards a modular fully automated vehicle research platform2026-01-0784To be published on 07/01/2026
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated
Pohl, Eric, Schmid, Fabian, Münster, Marco, Siefkes, Tjark, Stuebler, Tillmann, Mohammed, Shawan
Who’s Riding? Machine Learning-Powered Rider Identification from E-Bike CAN Data2026-01-0778To be published on 07/01/2026
This study investigates the feasibility of identifying individual e-bike riders based on Controller Area Network (CAN) data using machine learning techniques. Datasets from 12 test riders performing various predefined cycling tasks on a dynamometer test bench are collected and used to ensure controlled and reproducible conditions. The recorded CAN data includes various sensor signals, such as power output, cadence, torque, and the used support mode. After pre-processing, two different methods of feature extraction are tested and compared, one based on snapshots of the data and one based on driving events such as braking and accelerating, measured by calculating statistics of the riding data over sliding windows. A range of machine learning models is employed to classify riders based on their distinct riding patterns using the extracted features. The evaluated models comprise k-Nearest Neighbors (KNN), Random Forest and Naive Bayes. The findings demonstrate the efficacy of machine
Simmann, Gabriel, Rauch, Yannick, Beißert, Florian, Kriesten, Reiner
Cybersecurity Risk Assessment and Experimental Validation of Automotive Ultrasonic Sensors2026-01-0782To be published on 07/01/2026
Ultrasonic sensors are widely deployed in automotive driver assistance systems for near-range environment perception and provide safety-relevant inputs for functions such as parking assistance and automated parking. With increasing vehicle automation, the integrity and availability of ultrasonic sensor data become more critical, as compromised measurements may lead to incorrect vehicle decisions and hazardous behavior. While prior research has extensively studied physical attacks on ultrasonic sensors, a structured cybersecurity risk analysis in accordance with automotive cybersecurity standards, combined with experimental validation, is largely missing. In particular, the serial communication interface between ultrasonic sensors and control units has received limited attention despite its relevance as a potential attack surface. This paper presents a systematic security analysis of an automotive ultrasonic sensing system based on a demonstrator setup. The work applies a Threat
Gahm, Sebastian, Haller, Jonathan, Kriesten, Reiner
Development of a digital twin of an electrical powertrain in a testbench environment for efficiency estimations2026-01-0744To be published on 07/01/2026
The aim of this work is to develop a modular digital twin of an electric powertrain based on novel machine learning (ML)-based model structures and a systematic, component-oriented architecture. This results in a modular, real-time-capable digital twin that precisely maps the efficiency characteristics of the entire drivetrain and can be used in development and testing processes. The further goal here is to enable virtual testing, which can be used for frontloading and thus both prevent errors and increase the speed of product development. Based on a comprehensive set of measurement data from a test bench, a multi-stage procedure is implemented that integrates data acquisition, physically informed feature selection, modeling at the component and subsystem level, and hybrid coupling strategies. The digital twin captures inverter, electric machine, and mechanical transmission stages and generates physically consistent predictions of key variables such as torque, speed, power factors, and
Kopp, Lennart, Proksch, Daniel, Ockert, Niels, Karthaus PhD, Carsten, Kley, Markus
Route-Based Sensor-Aware Speed Planning and Support for Safety Validation for Level-3 Automated Driving2026-01-0772To be published on 07/01/2026
Level-3 and higher automated driving systems require longitudinal speed strategies that remain consistent with both physical stopping feasibility and realistic sensing constraints. This paper presents a route-based, sensor-aware speed planning method that supports safety validation and explicitly couples longitudinal driving strategy with sensor field-of-view coverage. Based on a concrete route extracted from digital maps and enriched with fleet data, point-wise maximum speeds are computed considering road curvature, speed limits, and comfort constraints. From the resulting drivable speed profile, physically consistent stopping paths and their endpoints are calculated for each route position, accounting for friction limits, scenario-dependent deceleration capabilities, and system delays between perception and braking. The set of stopping paths is aggregated into a region of interest (ROI) representing the spatial area that must be reliably perceived to guarantee safe stopping. This ROI
Kohler, Paul Leonhard, Resch, Michael
Single microphone sound source localisation in partially known environments using sequential spatial sampling2026-01-0681To be published on 06/20/2026
Sound source localization is a fundamental capability for environmental awareness in a wide range of applications, including automotive or automated vehicles. Microphone-array-based signal processing techniques are widely used for this task. However, achieving sufficient localization accuracy often requires a large number of microphones and wide array apertures, which can be incompatible with limited installation space and cost constraints. Moreover, standard array-processing methods often rely on free-field transfer functions. In environments with reflections, diffraction, and scattering, particularly under non-line-of-sight conditions, this mismatch can degrade both accuracy and interpretability. This paper presents a methodology for sound source localization in partially known environments that addresses these challenges by combining two ideas. First, the method reduces sensor requirements by exploiting sequential pressure measurements acquired at different spatial locations along a
Pirro, Giovanni Battista, Nijman, Eugene, Deckers, Elke, Denayer, Hervé
Experimental Validation of Frame-Dependent Gyroscopic Frequency Splitting in a Freely Rotating Tire Using Wireless MEMS Accelerometers and Scanning Laser Doppler Vibrometer2026-01-0700To be published on 06/20/2026
Gyroscopic effects split circumferential traveling-wave resonances of rotating structures into forward and backward branches. This work first analyzes the splitting in the co-rotating (Lagrangian) frame to provide physical intuition for the evolution of the two branches with spin speed. A transformation to the inertial (Eulerian) frame is then derived, showing that the observed frequencies are shifted by a kinematic Doppler-like term that acts with opposite sign on the forward and backward waves, leading to different Campbell-diagram slopes depending on the observation frame. The resulting framework is validated experimentally on a freely rotating, unloaded tire using two complementary sensing modalities: wireless on-tire accelerometers (co-rotating view) and a scanning laser Doppler vibrometer (inertial view). A frequency-domain SVD-based identification (FDD/ODS-SVD) is used to extract poles and deformation patterns over a range of spin speeds, enabling Campbell diagrams in both
del Fresno Zarza, Javier, Naets, Frank
System and Method of Strain Emulation using a Beam Load Cell Test Setup2026-26-0743To be published on 06/01/2026
This novel method deals with emulation of Strain of a Structural Measurement System which includes software validation, acceptance tests and training. Current methods for simulating strain and force data for developing and verifying data acquisition (DAQ) software typically rely on costly electronic simulators or specialized hardware, making it challenging and expensive for developers, researchers, and small organizations to test their solutions under realistic conditions. To verify DAQ software, multiple specialized hardware solutions are deployed, that include Electronic Simulators, Commercial DAQ Modules and Hydraulic/Pneumatic test rigs. These technologies pose a challenge with limited flexibility and scalability options for small-scale prototyping, especially in budget-constrained scenarios. The sensors on these equipment may or may not be company approved inducing acceptance challenges. Our invention is an inexpensive, scalable, and mechanically simple alternative. Using a 3D
Murthy, Harsha, Bhat Venkatesh, Aditi, K Padmanabhan, Rahul, Madhu, Sheetal, Garag, Naveen
Adaptive HVAC Strategies for Enhanced Cabin Thermal Comfort, Air Quality and Energy Efficiency2026-26-0791To be published on 06/01/2026
Passenger comfort within vehicle and aerospace cabins relies on finely tuned management of temperature, air quality, and energy use. This paper proposes an integrated HVAC framework that combines zonal climate control, intelligent airflow distribution, and real-time sensor data to maintain thermal balance across different cabin zones. Leveraging predictive thermal load modeling and machine learning, the system anticipates environmental changes—such as sudden shifts in external temperature or passenger load—and proactively adjusts heating and cooling outputs. Simultaneously, air quality is enhanced through a multistage filtration system, active air purification technologies, and dynamic CO₂ concentration monitoring. Comfort assessment integrates PMV (Predicted Mean Vote) and PPD (Predicted Percentage Dissatisfied) indices to adapt environmental conditions based on passenger feedback and physiological needs. Simulations and early-stage prototypes improve energy savings and improve
Mudavath, Lehitha Sai, Patil, Ashish, Saha, Sudipta
Neural Network-Enabled Synthetic Air Data System: Development and Validation2026-26-0720To be published on 06/01/2026
Synthetic Air Data System (SADS) provides a smart solution that can be used to predict critical air data parameters in the absence of conventional air data sensors. Traditional air data sensors, such as pitot-static tubes and vanes, are generally expensive, require regular maintenance, and can fail in harsh weather conditions. In addition, these sensors, along with their processor and computers, add weight to the aircraft. To address these issues, a synthetic air data system is proposed using a Recurrent Neural Network (RNN). Several flight variables were checked for Pearson correlation coefficient with respect to the angle-of-attack and angle-of-sideslip, and thereafter, input features were selected based on the thresholding technique. The proposed neural network has two hidden layers and regularization technique was implemented by adding two dropout layers to each hidden layer to prevent overfitting of the model. The neural network was trained using actual flight test data
Sahu, Sanju, C, Poornima, Kaliyari, Dushyant, TK, Khadeeja Nusrath, Hebbar, Archana
Electric Health-Monitoring Wiring (E-Wiring) for Aircraft2026-26-0785To be published on 06/01/2026
Modern aircraft depend on extensive electrical wiring networks for power distribution, avionics, and control systems; however, these wiring systems are vulnerable to wear, insulation degradation, and arcing over time, leading to safety risks and costly unscheduled maintenance. The proposed Electric Health-Monitoring Wiring (E-Wiring) system integrates temperature, current, insulation, vibration, and environmental sensors directly into aircraft wiring harnesses to enable continuous monitoring and intelligent fault detection. Data from these embedded sensors are processed through a distributed edge AI network, forming an Electrical Health Monitoring System (EHMS) capable of real-time diagnostics, predictive maintenance, and fault localization. The architecture comprises smart cable segments with sensor nodes, local harness gateways for edge processing, aircraft-level EHMS integration via AFDX/Ethernet, and cockpit or maintenance displays linked to ground-based cloud analytics for fleet
Tammana, Bala Sai Sri Rohit, Murthy, Harsha, Mendu, HarikaSivaniSunandha
Implementation of Wireless IoT Technology in EVAC Slide Emergency Systems2026-26-0792To be published on 06/01/2026
Emergency evacuation slides (EVAC slides) are critical safety devices used on aircraft to enable rapid egress during emergencies. While these slides provide a quick and reliable escape route, communication between separated slides during evacuation remains a challenge. Often, during raft deployment over water, slides may drift apart, impeding communication among evacuees and rescue personnel, potentially compromising safety. Existing aircraft EVAC systems on the market lack integrated wireless communication, relying on visual or shouted voice signals, which are unreliable in noisy or chaotic conditions. With the rise of the Internet of Things (IoT), wireless sensor networks have become effective tools for monitoring and communication in various safety and industrial applications. This paper explores the integration of wireless IoT technology into EVAC slide systems to facilitate inter-slide communication and monitor critical parameters, such as slide air pressure and the floating
Sengodan, Rajkumar, Talore, Suresh
Diffusion Framework with Cross-Modality Fusion Perception for Autonomous Driving in Urban Traffic2026-99-07495/15/2026
End-to-end autonomous driving in urban environments faces three core challenges. First, camera and LiDAR sensor heterogeneity causes cross-modal perception inconsistencies and sensor fusion instability. Second, diffusion models suffer from training instability due to scale variance and distribution changes, which limits generalization. Third, traditional trajectory decoders lack structured interaction with semantic elements, thereby undermining planning rationality. To address these issues, CMFPNet introduces an integrated framework with three key modules. The HGCF-Backbone integrates LiDAR and camera features using channel focus, deformable cross-focus, and state space modeling to enhance semantic alignment. The NST module maps physical trajectories to normalized space, employing truncated diffusion sampling for stable generation in just 2–4 steps. The NDA models trajectory generation as a semantic narrative, utilizing a six-stage semantic attention flow incorporating BEV context
Qu, Yanwei, Mo, Hangjie
A High-Sensitivity and High-Throughput Inductive Sensor for Abrasive Particle Detection Using Rectangular Coils2026-99-07575/15/2026
The analysis of wear particles within machinery lubricants constitutes a critical methodology for assessing equipment health and enabling the early identification of potential failures. However, conventional inductive abrasive particle sensors typically exhibit lower detection sensitivity compared to other sensing technologies, limiting their practical application in precision condition monitoring. To address this limitation, this paper introduces an inductive abrasive particle sensor with enhanced sensitivity and throughput, employing rectangular coils, together with a custom-designed signal conditioning circuit. The sensor features two symmetrically arranged rectangular excitation coils and two symmetrically arranged rectangular sensing coils, with their respective axes mutually perpendicular. This unique spatial configuration not only ensures strong magnetic field intensity within the detection region but also significantly enhances magnetic field utilization efficiency. The sensing
Jiang, Ziyang, Qian, Min, Huang, Honglian, Lu, Yanlu, Zhang, Junjian, Pan, Chengliang
A Reinforcement Learning Trajectory Planning Model with Kalman Filter-Based Dynamic Tracking for Six-Axis Robotic Manipulators2026-99-07385/15/2026
Robot Arm Tracking Control refers to the control of robot end effectors following a prescribed trajectory as their movement in robotic systems. The work presents a combination of Kalman Filter Based Dynamic System Tracking with Reinforcement Learning Based Trajectory Planning. These two aspects of tracking and planning help the robotic manipulator dynamically track a target that is located on an arbitrary moving path. In particular, by using Kalman filtering to estimate the position of a moving target and to compensate for sensor noise and sparse sampling, we take high-precision estimation values of each point’s coordinates along the target trajectory as a reliable basis to build a policy network using reinforcement learning. Based on it, the robot manipulator could produce effective motion planning under its own dynamic capabilities and physical constraint limit. Comprehensive simulation results illustrate advantages of the new algorithm against the classical control method, confirm
Yu, Jingze, Wang, Yujia, Li, Junshen, Chen, Cong, Xu, Peng
Autonomous Navigation Strategy and Docking Mechanism for Intelligent Chassis Module of Split Flying Vehicle2026-99-07435/15/2026
In response to the problems of urban traffic congestion and the limited expansion of infrastructure, this paper conducts two core research focusing on the intelligent chassis system of split-type flying vehicle. Firstly, an autonomous navigation strategy for the intelligent chassis module is proposed based on chassis module Navigation 2 architecture, which fuses LIDAR and IMU positioning to plan paths using the A* global planning algorithm on a global cost map, and update the local cost map in real time with sensor data. It is orchestrated by the BT Navigator using a behavior tree, with failures handled by the Recovery Server, to achieve autonomous driving across multiple waypoints. In simulation and closed-field experiments, the system can stably reach the preset target points. The positioning accuracy and trajectory tracking performance can meet the design requirements. Secondly, a mechanical slide rail-type docking structure adapted to the split flying vehicle architecture is
Zhao, Wenyu, Shi, Qin, Jiang, Cong, He, Zejia
Optimizing Coil Structural Parameters to Enhance the Sensitivity of Wear Particle Sensor2026-01-50335/14/2026
If wear particles generated during the operation of automobile engines are not monitored in time, they will contaminate the lubricating oil, leading to system failures or even accidents. Therefore, real-time wear particle monitoring is crucial for the stable operation of engines. Among mainstream wear particle monitoring sensors, the three-coil inductive sensor demonstrates significant application potential due to its ability to distinguish wear particle materials and strong resistance to environmental interference. However, its insufficient sensitivity to small-diameter wear particles limits further performance improvement. This paper takes the three-coil inductive wear particle monitoring sensor as the research object. First, a mathematical model of the sensor’s operation is established based on the law of electromagnetic induction, clarifying the relationship between structural parameters (such as channel radius, turns, coil spacing, and length) and the peak induced voltage
Yin, Hao, Zhao, Lijun, Shen, Yitao
Visual–Language Model–Driven Annotation and Analysis of Maneuver Data for Enhanced Vehicle Safety09-14-01-00325/4/2026
Vehicle maneuver data are essential for perception and planning in advanced driver-assistance systems (ADAS) and automated driving systems (ADS). While high-quality annotations improve machine-learning performance, existing maneuver datasets remain fragmented, labor-intensive to annotate, and inconsistent in semantic richness. Challenges persist in scalability, interpretability, and contextual labeling. This article establishes a structured framework for maneuver data analysis by combining a systematic review of existing resources with the development of a new multimodal dataset. First, we conduct a systematic review of publicly available datasets such as HDD, KITTI, BDD-X, D2CAV, Brain4Cars, DrivingDojo, and the Driving Behavior Database. We further evaluate the data modality and sensor configurations including event data recorders, onboard logging systems, and smartphone sensing. We then propose the Matt3r Data Collection System with modern metadata management, which integrates video
Bai, Ling, Yuan, Chongyu, Osman, Islam, Lin, Zirui, Mirab, Ghazal, Saheb, Amir, Parnian, Neda, Shapiro, Evgeny, Shehata, Mohamed S., Liu, Zheng
ProGuard: A Real-Time Preemptive Artificial Intelligence-Based Anti-Pinch System for Bus Entryways on Low-Power Edge Devices2026-01-01004/7/2026
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
A Comparison of Road Grade Preview Signals from Lidar and Maps2026-01-00264/7/2026
Road grade can impact the energy efficiency, safety, and comfort associated with automated vehicle control systems. Currently, control systems that attempt to compensate for road grade are designed with one of two assumptions. Either the grade is only known once the vehicle is driving over the road segment through proprioception, or complete knowledge of the oncoming road grade is known from a pre-made map. Both assumptions limit the performance of a control system, as not having a preview signal prevents proactive grade compensation, whereas relying only on map data potentially subjects the control system to missing or outdated information. These limits can be avoided by measuring the oncoming grade in real-time using on-board lidar sensors. In this work, we use point returns accumulated during travel to estimate the grade at each waypoint along a path. The estimated grade is defined as the difference in height between the front and rear wheelbase at a given waypoint. Kalman filtering
Schexnaydre, Logan, Poovalappil, Aman, Robinette, Darrell, Bos, Jeremy
Backward Fuzzy Driving Control for 6×4 Off-Road Vehicles2026-01-01484/7/2026
Off-road autonomous vehicle systems must be able to operate across unstructured and variable terrain while avoiding obstacles. This presents significant challenges in vehicle and control system design, especially for less conventional platforms such as 6×4 vehicles. While forward driving autonomy has developed and matured in recent years, effective reverse navigation remains an under-explored area of vehicle co-design. Reversing 6×4 vehicles have limited rear steering authority, an extended wheelbase, and asymmetric traction, which introduce complex dynamics into any control system that is used. To address this need, a robust and experimentally validated fuzzy logic control architecture for 6×4 reverse navigation was developed during the course of this project. This architecture incorporates both near-field and long-range path data with adaptive outputs controlling steering and velocity based on a rule base that covers the whole vehicle state space. This method has low computational
Dekhterman, Samuel R., Sreenivas, Ramavarapu S., Norris, William R., Patterson, Albert E., Soylemezoglu, Ahmet, Nottage, Dustin
The In-Vehicle Connectivity Foundation Powering Autonomous Driving2026-01-00094/7/2026
Achieving full vehicle autonomy is not just about adding sensors or compute - it requires a fundamental shift in how vehicles are architected. Autonomous systems rely on higher-resolution sensors, massive processing power, and the ability to fuse data from multiple sources in real time. Centralized in-vehicle architectures, which consolidate computing and enable sensor fusion, place unprecedented demands on connectivity. Precise time synchronization across systems becomes critical, as does advanced control to ensure safe and reliable operation. Any delay or data loss can impact decision-making, making robust, resilient communication links essential. High-performance connectivity is the backbone of this evolution. It must deliver the highest bandwidth to handle massive streams of sensor data, support long-reach connections across the vehicle, and maintain error-free performance even in the most challenging electromagnetic environments. This combination of speed, reach, and reliability
Shwartzberg, Daniel
Development of a Tire Blowout Experimental Platform for Dynamics and Control Validation2026-01-02024/7/2026
Tires are critical to vehicle dynamics, transmitting traction, braking, and cornering forces to the road. A tire blowout, the sudden and rapid loss of inflation pressure due to puncture or structural failure, can cause severe instability, rollover, or collisions. Understanding vehicle response during blowout events is essential for developing robust safety systems and control strategies. Earlier developed simulation models are used to study and understand vehicle behavior during blowouts, but there is a lack of on-road testing platforms to validate these models experimentally. In this paper, an experimental platform integrating a tire blowout device and an instrumentation system has been developed to address this gap. The blowout device consists of multiple solenoid valves mounted on the wheel surface and powered by a 12V power supply. All valves can be triggered at the same time using an RF remote, producing rapid and synchronized deflation. As an extension of this implementation, an
Kanthala, Maha Vishnu Vardhan Reddy, Krishnakumar, Ashwin, Lin, Wen-Chiao, Chen, Yan
Ultra-Low Latency Video Pipeline Optimization for Perception-Centric Autonomous Systems2026-01-00224/7/2026
Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages—sensor acquisition, video encoding, data transmission, decoding, and display—undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software
Indrakanti, Rama Kiran Kumar
Developing Safety Case for Automated Driving following UL 4600 & ISO 50832026-01-05274/7/2026
Automated Driving Systems (ADS) rely on AI algorithms, machine learning, and sensor fusion to perform autonomous driving tasks. Safety challenges arise due to the probabilistic behavior of AI/ML algorithms and the need to ensure safety within defined Operational Design Domains (ODDs). Traditional standards such as ISO 26262[3] (Functional Safety) and ISO 21448[4] (SOTIF) address hardware and software failures or functional deficiencies but are insufficient for higher-level autonomous systems (SAE Levels 3–5). To close this gap, additional standards such as UL 4600[1] and ISO 5083[2] provide complementary frameworks for ADS safety assurance. UL 4600[1] establishes a claim-based safety case encompassing the vehicle, infrastructure, and processes, emphasizing structured arguments supported by evidence and reasoning. It offers guidance on autonomy functions, V & V, tool qualification, dependability, and safety culture. ISO 5083[2] focuses on design, verification, and validation of ADS
Mudunuri, Venkateswara Raju, Almasri, Hossam, Fan, Hsing-Hua
Development of Technology to Distinguish Contaminants using LiDAR Near-Field Point Cloud based on Real-Field Data2026-01-00204/7/2026
Reliable environmental perception under adverse and contaminated conditions is a critical requirement for autonomous driving systems. Although LiDAR sensors play a central role in such perception, their performance is significantly degraded by surface contamination caused by environmental factors such as rain, snow, dust, anti-icing materials, and bug splatter impacts. However, most existing public datasets and prior studies rely on simulated or laboratory-generated contamination scenarios, which limit their applicability to real-world autonomous driving. To address this gap, we construct a large-scale real-world dataset collected from approximately 22,000 km of on-road driving across diverse regions of the United States, covering a wide range of naturally occurring environmental contamination conditions. The dataset was acquired using a multimodal sensing platform integrating LiDAR, perception RGB cameras, infrared camera sensors, and external monitoring systems, enabling
Kim, Hunjae
Virtual Sensor Development for Real-Time Estimation of Transient NOx Emissions for Internal Combustion Engine2026-01-03704/7/2026
Accurately measuring NOx emissions under transient engine conditions is becoming increasingly important with upcoming Euro 7 and EPA 2027 regulations. Traditional physical sensors often struggle with cost and response time, especially with aging of sensors in dynamic operation. This paper introduces a machine-learning–based virtual NOx sensor that can provide real-time emission estimates while reducing reliance on hardware sensors. The approach uses multiple machine-learning methods (Random Forest, Bootstrap Aggregating, Adaptive Boosting, Gradient Boosting, Extreme Gradient Boosting) and selected best one to establish correlations between engine operating parameters, measured steady-state data, and transient duty cycle NOx emissions. Validation across different duty cycles has shown strong alignment with physical sensor readings, with R2 values above 99.95% for training cycle data sets and above 95.34% for held-out cycles during training. The model needs to be trained with larger
Kumar, Chandan, Dahodwala, Mufaddel, Thawrani, Kiran
Design and Performance Evaluation of a Dust Contamination Simulation Test Apparatus for Automotive Cameras2026-01-00594/7/2026
This study presents the development and validation of a muddy water spray apparatus designed to simulate dust contamination on vehicle sensors for sensor cleaning system testing. It is important to have a constant and quantifiable test environment for the vehicle development process. For verifying the apparatus, muddy water, prepared by mixing standardized dust powder, salt, and water to maintain constant contamination test conditions, was sprayed onto glass specimens to evaluate equipment consistency. Deposited dust weight and thickness were measured across multiple spray cycles, with statistical analyses confirming consistent and reliable deposition. Paired t-tests indicated no significant difference between sample positions, demonstrating uniform spray distribution. The apparatus was further applied to individual infrared (IR) cameras to observe performance degradation under dry and wet contamination conditions showing statistically consistent increases in contamination levels
Jinhyeok, Gong
Radar-Based Dynamic Pixel Activation in Camera Sensors for Optimized Image Processing, Power Efficiency, and Thermal Management2026-01-00474/7/2026
The increased integration of radar and vision sensors in modern vehicles has significantly improved environmental perception, safety, and automation. Nevertheless, conventional camera modules capture images in fixed, continuous frames, leading to unnecessary data processing, power consumption, and heat generation in the limited space of small sensors. The paper discusses the technology of Radar Based Dynamic Pixel Activation (RDPA); whereby radar data can be used to dynamically activate specific pixels on the camera sensor, optimizing image capture and processing. Through a systematic literature review of peer-reviewed articles published between 2021 and 2025, we examined the literature on radar-camera fusion, adaptive imaging, and sensor design that is efficient in power consumption. The review indicates a research gap that there is no current paradigm that dynamically activates sensor pixels at the hardware level using radar data. We aggregated ten topical studies and proposed a
Kasarla, Nagender Reddy
Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections2026-01-00454/7/2026
Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents a novel approach to curvature prediction using deep neural networks trained on GPS-derived ground truth data, rather than model predictions, providing a more accurate training signal that reflects actual vehicle motion. We develop a multi-modal neural network architecture with temporal GRU encoders that processes vision features, driver intent signals, historical curvature, and vehicle state parameters to predict curvature. A key innovation is the use of GPS-based actual curvature measurements computed from vehicle motion data (κ = ωz/v) as training supervision, enabling the model to learn from real-world driving patterns. The model is trained on 5,322 samples from real-world driving data collected on The University of Oklahoma’s Norman Campus using a Comma 3X device and a 2025 Nissan Leaf electric vehicle. Experimental results
Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin
A System-Level Calibration Framework for Embedded Vision: Integration of Sensor, Firmware, and Software Enhancements2026-01-00134/7/2026
Embedded vision systems are essential for contemporary applications, including robotics, advanced driver assistance systems (ADAS), and intelligent surveillance; yet they frequently experience diminished image quality due to resource constraints, environmental variability, and inconsistent illumination conditions. Such degradations impact multiple visual attributes—sharpness, contrast, color accuracy, noise levels, and structural similarity—that are critical for reliable perception in safety- and performance-driven domains. This study introduces a comprehensive system-level calibration architecture that integrates three coordinated layers: sensor-level adjustment, firmware optimization, and adaptive software enhancements. At the sensor level, exposure control, gain tuning, and white balance adjustments mitigate luminance imbalance and color shifts under changing light conditions. Firmware optimization leverages image signal processor (ISP) parameters to reduce temporal and spatial
Indrakanti, Rama Kiran Kumar, Vishnoi, Nitin, Kamadi, Venkata
Mitigation of Sensor-Level Cyber-Physical Attacks on Electric Vehicle Powertrains: A Simulation-Based Study2026-01-00914/7/2026
Electric vehicles (EVs) rely extensively on sensor feedback for safe and efficient powertrain operation. However, this dependency introduces cyber-physical vulnerabilities, especially when sensor signals are maliciously manipulated. This paper presents a simulation-based investigation into sensor-level cyberattacks on a mid-sized EV powertrain model developed in MATLAB/Simulink. The study quantifies mechanical consequences and evaluates mitigation strategies to enhance system resilience. Four representative attack scenarios were simulated. Speed sensor spoofing led the controller to misinterpret vehicle velocity, causing a 41% overshoot beyond the 50 km/h setpoint. False data injection into torque/current sensors triggered an unintended torque surge of approximately 20%, resulting in inverter current saturation within 2 seconds. Battery temperature spoofing delayed thermal protection, allowing a deviation of 1.5 °C/min beyond safe operating limits. A hybrid attack combining frozen
Tariq, Usama, Sahandabadi, Sahereh, Dianat, Ali
Sensor Fusion Method for In-Cabin Humidity Prediction2026-01-01314/7/2026
Maintaining optimal in-cabin humidity levels is part of occupant comfort, air quality, and the effective operation of climate control systems, particularly for functions like windshield defogging. This paper introduces a novel sensor fusion methodology for predicting in-cabin humidity distribution without dedicated humidity sensor. The proposed approach leverages readily available vehicle data, integrating information from ambient temperature sensors, in-cabin temperature sensors, occupant detection systems, window status, and climate control settings. By intelligently fusing these diverse data streams, a predictive model is developed to infer the dynamic humidity conditions within the vehicle cabin. We discuss the complex interactions between these parameters, such as the moisture contribution from occupants, the influence of external air ingress through open windows, and the dehumidifying or humidifying effects of the Heating, Ventilation, and Air Conditioning system. The paper
Ghannam, Mahmoud, Schroeter, Robert, Shaik, Faizan
EV Propulsion Drive Converter SiC Power Device Over-Temperature Protection under Low Motor Speed Operation2026-01-04364/7/2026
Monitoring power device temperature in an electric vehicle propulsion drive converter is extremely important to achieve full power delivery within the maximum power capability envelope. Usually, on-die temperature sensors are installed on Si-IGBT power devices in electric vehicle propulsion drive converters to enable monitoring device temperature and achieve over-temperature protection. Currently, SiC MOSFET is a promising power device in power converters of electric drives because of its lower loss, higher switching speed, higher voltage capability, and higher junction temperature limit in comparison with the widely used Si-IGBT. However, SiC MOSFET is a more expensive device, installation of an on-die temperature sensor on SiC MOSFET will significantly increase its cost and complexity. So presently, there is no junction temperature sensor installed in SiC MOSFET due to which there is great difficulty protecting SiC MOSFET from over temperature. When a junction temperature estimation
Thongam, Jogendra Singh, Ge, Baoming, Bradford, Steven, Kulkarni, Milind
Advanced Integrated Chassis Control for Improved Energy Efficiency and Driving Experience in Electric Vehicle Applications2026-01-06434/7/2026
Complexity of modern ground vehicles grows constantly, since car manufacturers want to provide functionality, while customers are expecting innovation and recent technologies to be integrated into the latest models released to the market. Recent advances in hard- and software opened the gates for new means of vehicle control and operation. Especially the transition to electric propulsion systems and decoupled chassis actuators offer completely new opportunities of dynamics control and manipulation. This paper presents an approach for integrated chassis and vehicle motion control in (battery) electric vehicle applications by using new and innovative controllers as well as mechatronic chassis systems. In several experiments on public roads with a fully instrumented vehicle demonstrator, that features in-wheel based rear-wheel drive and a hybrid brake-by-wire-system, the proposed control is tested under real environmental and traffic conditions with respect to aspects like energy
Heydrich, Marius, Mitsching, Thomas, Ivanov, Valentin
BEV Drivetrain Parameters Sensitivity for Efficient and Effective Active Damping Controls Modeling2026-01-00044/7/2026
Effective active damping control is critical for battery electric vehicle (BEV) drive quality. Central to this control is the open loop frequency response function between actuator (motor torque) and response (motor speed). While many driveline parameters influence the system dynamics, sensitivity analysis reveals that only a few of them significantly shape the resonance frequency and amplitude of crucial low frequency driveline modes. In this study, an orthogonal array-based sensitivity analysis is conducted and validated using 1D simulation tool Amesim. The results show that identifying the most influential parameters enables meaningful model simplification without loss of accuracy, guiding quick and efficient modelling to help design control systems. This approach provides engineers with a practical framework to focus on critical variables of driveline torsional system, reduce model complexity and implement effective control strategies.
Sharma, Pranjal, Kolluri, Murali Mohan, Lin, Chihang
Predictive Maintenance of EV Drive Unit Oil Pumps through Machine Learning on Fleet Telemetry Data2026-01-01614/7/2026
The reliability of Drive Unit (DU) oil pumps is critical to the performance and safety of electric vehicles, as these pumps provide essential lubrication and thermal management. In modern EV architectures, real-time health monitoring of these pumps typically relies on indirect signals than dedicated sensing hardware, a design choice optimized for cost, weight, and system complexity. This makes early fault detection a non-trivial challenge. To address this limitation, we present a novel, data-driven anomaly detection framework that leverages large-scale customer fleet telemetry and advanced machine learning to identify incipient pump degradation that traditional diagnostic methods often fail to capture. Specifically, we develop an XGBoost regression model trained on time-series features—including commanded pump speed, oil temperature, and historical pump current—to predict expected current behavior under nominal conditions. Deviations are quantified using the Mean Absolute Percentage
Li, Jingman, Yao, Mengqi, Rahimi, Sahil, Lin, Joanne
Accelerometer Location Study for Non-Intrusive Start of Combustion (SOC) Detection in Compression-Ignition Engines2026-01-02884/7/2026
This study investigates the impact of sensor location on accelerometer-based sensing of combustion phasing for compression-ignition engines. Ten accelerometer locations were studied on a light-duty compression-ignition engine for a set of conditions with variations in engine load, speed, injection timing, and injection strategy. Start of combustion (SOC) was identified from the filtered acceleration signal using a previously developed approach. Each location was assessed using both signal-based metrics, including magnitude squared coherence (MSC) between block surface acceleration and in-cylinder pressure, as well as SOC outcome-based metrics, such as detection success rate. Results demonstrate that the mounting location has a significant impact on the ability to extract combustion phasing information from the accelerometer signal. Sensors mounted on the front face of the engine produced the strongest signals for an individual cylinder. For multi-cylinder sensing, side-mounted
Hegge, Graydon, Hanson, Reed, Kim, Kenneth, Rothamer, David
In-Situ Raman Measurements of Fuel Dilution of Lubricant Films in a Direct-Injection Spark-Ignition Engine2026-01-03554/7/2026
To measure the fuel proportion within the lubricant film, an in-situ Raman spectroscopy technique was employed in a specially modified single-cylinder direct-injection spark-ignition engine. The engine block was engineered for optical access with a fused silica window, enabling a focused laser beam to probe the lubricant film on the engine liner under motoring conditions. The lubricant used was GTL8 base oil with ZDDP additive, and iso-octane was injected as a model fuel to study fuel-lubricant mixing. A calibration curve was established by recording Raman spectra of known mixtures of GTL8 oil and iso-octane. The Raman intensity ratio of the iso-octane peak to the oil peak was used as a quantitative indicator of fuel concentration. During engine operation, Raman spectra were acquired in real time, on a cycle-by-cycle basis, through the optical window. Upon iso-octane injection, its characteristic Raman peak appeared in the spectrum, and the intensity ratio was referenced against the
Bolle, Bastien, Augoye, Kobi, Wong, Janet, Aleiferis, Pavlos, Hall, Jonathan, Bassett, Mike, Cracknell, Roger
PRODUCT BRIEFS26AUTP03_163/1/2026
Aumovio's remote temp sensor far more accurate for e-mobility26AUTP03_123/1/2026
At the U.S. headquarters for Aumovio SE (formerly Continental Auto Group), the company showed its new remote temperature sensor for EV motors as part of its post-CES tech day presentations. The tech, which provides a more accurate reading of the rotor temperature of an EV motor, could lead to more sustainable motor designs by reducing the amount of rare earth materials used to increase the heat resistance of magnets. It can also improve potential motor performance. The e-motor rotor temperature sensor (e-RTS) is placed directly near the rotor, improving its tolerance range from 15 degrees C (59 F) to 3 degrees C (37 F). It communicates wirelessly to a wired transceiver elsewhere on the motor module (it can be moved around for better packaging).
Clonts, Chris
The Enduring Value of Highly Optimized, Non-Standardized Systems26AERD03_033/1/2026
The roles of aerospace and defense engineers have profoundly changed. Systems integrators and acquisition programs require “standardized openness” while also wishing for boxes that take up less space, weight, and power. Despite the push towards Modular Open Systems Approach (MOSA), Sensor Open Systems Architecture (SOSA), and similar open standards, there are still opportunities to use non-standardized, small form factor (SFF) designs. As outlined in the project examples below, a purpose-built SFF module can address technical system requirements better than any current approach focused on open standards.
New MEMS Modulator Offers Significant Advancements in Optical EfficiencyTBMG-547123/1/2026
A new Microelectromechanical system (MEMS) grating modulator has been developed, offering significant advancements in optical efficiency and scalability for communication systems. By integrating a tunable sinusoidal grating with broadside-constrained continuous ribbons, a large-scale aperture of 30 × 30 mm is achieved and supports high-speed modulation up to 250 kHz.
A Thin-Film Flexible Temperature-Strain Multimodal Sensor for Lithium-Ion Battery Monitoring Based of Laser Direct Writing2026-01-70262/27/2026
Multimodal sensors, capable of simultaneously acquiring multiple physical or chemical signals, have shown broad application potential in fields such as health monitoring, soft robotics, and energy systems. However, current multimodal sensors often suffer from complex fabrication processes and signal decoupling challenges, which limit their practical deployment. To address these issues, this work presents a thin-film temperature–strain multimodal sensor (FTSMS) fabricated via laser processing. The temperature-sensing unit, based on the Seebeck effect, achieves a sensitivity of 9.08 μV/°C, while the strain-sensing unit, utilizing BaTiO₃/AlN@PDMS as the sensitive layer, exhibits a gauge factor (GF) of 43.2. By integrating distinct sensing mechanisms (thermovoltage for temperature and capacitance change for strain), the FTSMS enables self-decoupled measurements over 20–90 °C. Applied in LIB monitoring, it successfully captures real-time temperature and strain variations during charge
Wang, Ziwei, Li, Zhenglin, Gao, Yang, Xuan, Fuzhen
Failure Modes for High-Lift Actuation Load PathsAIR7061 (Current)2/17/2026
This SAE Aerospace Information Report provides examples of single failure modes for components used in fixed-wing, high-lift actuation systems’ load paths, as well as the typical hazards posed by those failures at the aircraft level.
A-6B3 Electro-Mechanical Actuation Committee
IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility2026-28-01132/12/2026
This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless
Deepan Kumar, Sadhasivam, S, Balakrishnan, Dhayaneethi, Sivaji, Boobalan, Saravanan, Abdul Rahim, Mohamed Arshad, S, Manikandan, R, Jamuna, L, Rishi Kannan
Miner’s Safety Bot: An ESP32-Based Autonomous Mobile Mine Safety Monitoring System2026-28-01162/12/2026
Mining operations are important to industrial growth, but they expose the mining workers to risk including hazardous gases, elevated ambient temperatures, and dynamic structural instabilities within underground environments. Safety systems in the past, typically based on fixed sensor networks or manual patrols, fall short in accurate hazard detection amidst shifting mine conditions. The proposed project Miner's Safety Bot advanced this paradigm by leveraging an ESP 32 microcontroller as a mobile platform that integrates gas sensing, thermal monitoring, visual inspection and autonomous obstacle avoidance. The system incorporates MQ7 semiconductor gas sensor to monitor real time carbon monoxide (CO), offering detection range from 5 to 2000 ppm with accuracy of 5 ppm. Temperature and humidity are monitored through DHT11 digital sensor, calibrated to ensure reliability across the harsh microclimates in mines. Navigation and autonomous movement are enabled by Ultrasonic Sensor (HC-SR04
D, Suchitra, D, Anitha, Muthukumaran, Balasubramaniam, Mohanraj, Siddharth, Subash Chandra Bose, Rohan
Development of Advanced Rider Assistance System (ARAS) for Electric Motorcycles: A Level-1 Collision Avoidance and Emergency Braking Prototype Using Ultrasonic Sensors2026-28-01182/12/2026
This paper presents the design, development, and validation of an Advanced Rider Assistance System (ARAS) tailored for electric motorcycles, with a specific focus on a Level-1 collision-avoidance and emergency-braking prototype employing ultrasonic sensing. The study is motivated by the disproportionately high accident exposure of two-wheeler riders and the slow adoption of ARAS technologies relative to the well-established Advanced Driver Assistance Systems (ADAS) in passenger vehicles. The proposed system utilizes front and rear ultrasonic sensors operating at 40 kHz, offering a measurement range of 2 cm to 4 m with ±1% accuracy, and maintaining reliable performance at motorcycle lean angles of up to 30°. Sensor data are processed using an STM32-series microcontroller running a real-time collision-risk estimation algorithm based on obstacle distance and relative velocity. A configurable safety threshold (typically 3 m) initiates a hierarchical warning strategy comprising visual
Deepan Kumar, Sadhasivam, Karu, Ragupathy, Karthick, K N, R, Vishnu Ramesh Kumar, Kumar, V, Manojkumar, R, M, Karthick, M, Rishab
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
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