Browse Topic: Sensors and actuators

Items (7,892)
Vehicular accident reconstruction is intended to explain the stages of a collision. This also includes the description of the driving trajectories of vehicles. Stored driving data is now often available for accident reconstruction, increasingly including gyroscopic sensor readings. Driving dynamics parameters such as lateral acceleration in various driving situations are already well studied, but angular rates such as those around the yaw axis are little described in the literature. This study attempts to reduce this gap somewhat by evaluating high-frequency measurement data from real, daily driving operations in the field. 813 driving maneuvers, captured by accident data recorders, were analyzed in detail and statistically evaluated. These devices also make it possible to record events without an accident. The key findings show the average yaw rates as a function of driving speed as well as the ratio between mean and associated peak yaw rate. Beyond that, considerably lower yaw rates
Fuerbeth, Uwe
This study introduces an innovative intelligent tire system capable of estimating the risk of total hydroplaning based on water pressure measurements within the tread grooves. Dynamic hydroplaning represents an important safety concern influenced by water depth, tread design, and vehicle longitudinal speed. Existing intelligent tire systems primarily assess hydroplaning risk using the water wedge effect, which occurs predominantly in deep water conditions. However, in shallow water, which is far more prevalent in real-world scenarios, the water wedge effect is absent at higher longitudinal speeds, which could make existing systems unable to reliably assess the total hydroplaning risk. Groove flow represents a key factor in hydroplaning dynamics, and it is governed by two mechanisms: water interception rate and water wedge pressure. In both the shallow water and deep water cases, the groove water flow will increase as a result of increasing the longitudinal speed of the vehicle for a
Vilsan, AlexandruSandu, CorinaAnghelache, GabrielWarfford, Jeffrey
Regarding the development of automated driving, manufacturers, technology startups, and systems developers have taken some different approaches. Some are on the path toward stand-alone vehicles, mostly relying on onboard sensors and intelligence. On the other hand, the connected, cooperative, and automated mobility (CCAM) approach relies on additional communication and information exchange to ensure safe and secure operation. CCAM holds great potential to improve traffic management, road safety, equity, and convenience. In both approaches, there are increasingly large amounts of data generated and used functions in perception, situational awareness, path prediction, and decision-making. The use of artificial intelligence is instrumental in processing such data; and in that context, “edge AI” is a more recent type of implementation. Edge Artificial Intelligence in Cooperative, Connected, and Automated Mobility explores perspectives on edge AI for CCAM, explores primary applications, and
Van Schijndel-de Nooij, MargrietBeiker, Sven
Public buses can be high-risk environments for the transmission of airborne viruses due to the confined space and high passenger density. However, advanced cabin air control systems and other measures can mitigate this risk. This research was conducted to explore various strategies aimed at reducing airborne particle transmission in bus cabins by using retrofit accessories and a redesigned parallel ventilation system. Public transit buses were used for stationary and on-road testing. Air exchange rates (ACH) were calculated using CO2 gas decay rates measured by low-cost sensors throughout each cabin. An aerosol generator (AG) was placed at various locations inside the bus and particle concentrations were measured for various experiments and ventilation configurations. The use of two standalone HEPA air filters lowered overall concentrations of particles inside the bus cabin by a factor of three. The effect of using plastic “barriers” independently showed faster particle arrival times
Lopez, BrendaSwanson, JacobDover, KevinRenck, EvanChang, M.-C. OliverJung, Heejung
The results published in this paper emphasize on the study of three-way catalytic convertor for a 1.2 L turbocharged multi-point fuel injection gasoline engine. This paper takes us through the findings on methodology used for finalizing the brick configuration for catalytic convertor along with downstream oxygen sensor placement for emission control and methods applied for catalytic convertor selection with actual testing. The advantages of dual brick configuration over single brick with downstream sensor placed in between the bricks to enable faster dew point of sensor is explained using water splash test and design confirmation of better exhaust gas flow vortices concentration at the sensor tip for better sensing. Selection of catalytic convertor loading by testing its emission conversion capability and light-off behavior. NOx conversion capability across stoichiometric ratio (14.7:1 for petrol) on selected most operational zone was tested (±5% lambda) for the design-finalized
Arun Selvan, S. A.Paul, Arun AugustineSelvaraj, Manimaran
Industries that require high-accuracy automation in the creation of high-mix/low-volume parts, such as aerospace, often face cost constraints with traditional robotics and machine tools due to the need for many pre-programmed tool paths, dedicated part fixtures, and rigid production flow. This paper presents a new machine learning (ML) based vision mapping and planning technique, created to enhance flexibility and efficiency in robotic operations, while reducing overall costs. The system is capable of mapping discrete process targets in the robot work envelope that the ML algorithms have been trained to identify, without requiring knowledge of the overall assembly. Using a 2D camera, images are taken from multiple robot positions across the work area and are used in the ML algorithm to detect, identify, and predict the 6D pose of each target. The algorithm uses the poses and target identifications to automatically develop a part program with efficient tool paths, including
Langan, DanielHall, MichaelGoldberg, EmilySchrandt, Sasha
A Northwestern University-led team of researchers has developed a new fuel cell that harvests energy from microbes living in dirt. About the size of a standard paperback book, the completely soil-powered technology could fuel underground sensors used in precision agriculture and green infrastructure. This potentially could offer a sustainable, renewable alternative to batteries, which hold toxic, flammable chemicals that leach into the ground, are fraught with conflict-filled supply chains and contribute to the ever-growing problem of electronic waste.
An invention that uses microchip technology in implantable devices and other wearable products such as smart watches can be used to improve biomedical devices including those used to monitor people with glaucoma and heart disease.
In late July to October 2022, residents of the Manu’a Islands in American Samoa felt the earth shake several times a day, raising concerns of an imminent volcanic eruption or tsunami.
A major challenge in self-powered wearable sensors for health care monitoring is distinguishing different signals when they occur at the same time. Researchers from Penn State and China’s Hebei University of Technology addressed this issue by uncovering a new property of a sensor material, enabling the team to develop a new type of flexible sensor that can accurately measure both temperature and physical strain simultaneously but separately to more precisely pinpoint various signals.
MEMS is a more complex technology than traditional semiconductors. They are 3D structures with moving parts, making them much more difficult to fabricate. If you’re designing a semiconductor, you may be able to take advantage of an existing process development kit (PDK), which your foundry can provide to you. There is no equivalent approach in MEMS. It’s a “one process, one product” paradigm that requires a high level of customization. That takes time, money, and resources.
This article conducts a thorough review of contemporary air suspension systems on the market for passenger cars. The evolution of suspension structures and control methodologies are briefly discussed. The layout of air suspension systems is introduced in detail, with each component receiving a comprehensive description and analysis. The open-loop and closed-loop arrangements are explained. Various types of air springs are discussed and compared. The sensory system, special working conditions, and failure analysis are also elaborated. In the case studies, some example models are listed to show a complete guide of how air suspension is implemented on passenger cars, which includes functionalities, air spring configurations, control methods, signal flow, service modes, and diagnostic messages. The major sources are OEMs’ official websites and previously released documents, such as user manuals and maintenance manuals, which are valid up to April 2023. Finally, the article concludes with a
Ma, ChangyeLu, YukunZhen, RanLiu, YegangPan, BingweiKhajepour, Amir
In the automobile industry, ensuring the safety of automated vehicles equipped with the automated driving system (ADS) is becoming a significant focus due to the increasing development and deployment of automated driving. Automated driving depends on sensing both the external and internal environments of a vehicle, utilizing perception sensors and algorithms, and electrical/electronic (E/E) systems for situational awareness and response. ISO 21448 is the standard for Safety of the Intended Functionality (SOTIF) that aims to ensure that the ADS operate safely within their intended functionality. SOTIF focuses on preventing or mitigating potential hazards that may arise from the limitations or failures of the ADS, including hazards due to insufficiencies of specification, or performance insufficiencies, as well as foreseeable misuse of the intended functionality. However, the challenge lies in ensuring the safety of vehicles despite the limited availability of extensive and systematic
Patel, MilinJung, RolfKhatun, Marzana
This document defines a set of standard application layer interfaces called JAUS Manipulator Services. JAUS Services provide the means for software entities in an unmanned system or system of unmanned systems to communicate and coordinate their activities. The Manipulator Services represent platform-independent capabilities commonly found across domains and types of unmanned systems. At present, twenty-five (25) services are defined in this document. These services are categorized as: Low Level Manipulator Control Services – The one service in this category allows for low-level command of the manipulator joint actuation efforts. This is an open-loop command that could be used in a simple tele-operation scenario. The service in this category is listed as follows: Primitive Manipulator Service Manipulator Sensor Services – These services, when queried, return instantaneous sensor data. Three services are defined that return respectively joint positions, joint velocities, and joint
AS-4JAUS Joint Architecture for Unmanned Systems Committee
Small size engines feature several peculiarities that render them a challenge with respect to implementing measurements required for characterizing specific phenomena such as combustion evolution. Measuring in-cylinder pressure is well established as standard procedure for determining combustion characteristics, but in the case of small size units actually applying it can require alternative approaches. Fitting a crank angle encoder may be extremely difficult, as a consequence of the actual size of the power unit. Cost is another essential driver for small engine development that also influences how measurements are implemented. Within this context, the present work describes the development and implementation of a method that employs an algorithm that practically generates a ‘virtual’ encoder. Only a basic phasing signal is required, such as an inductive crankshaft position sensor output or that of an ignition pulser. The software was developed on an experimental engine with a crank
Irimescu, AdrianCecere, GiovanniMerola, Simona SilviaVaglieco, Bianca Maria
The objective of this experimental study was to investigate the change of shifting rate of metal V-belt type CVT during speed up/down under quasi-idle loading condition. Changes in the rotational speeds of the driving and driven pulleys were simultaneously measured by the rotational speed sensors installed on the driving and driven shafts during speed up/down shifting, respectively. In addition, the interaxial force applied to the driving and driven pulleys was measured by a load cell. The shifting rate was defined as the ratio of the calculated radial displacement to the tangential displacement of the belt in the pulley groove. This study found that the shifting rate was determined not only by the slippage between the pulley and the belt element, but also by the elastic deformation of the belt element in the pulley groove. The power transmission performance was improved when the elastic deformation was small even though radial slippage between the pulley and the belt element was
Mori, YuichirouOkubo, KazuyaObunai, Kiyotaka
The previously developed capacitance sensor for detecting a liquid fuel film was modified to apply to the in-cylinder measurement. On the developed sensor surface, comb-shaped electrodes were circularly aligned. The capacitance between the electrodes varies with the liquid fuel film adhering. The capacitance variation between the electrodes on the sensor surface was converted to the frequency variation of the oscillation circuit. In the previous study, it was revealed that the frequency of the oscillation circuit varies with the variation of the liquid fuel coverage area on the sensor surface. The developed sensor was installed in the combustion chamber of the rapid compression and expansion machine, and the performance of the developed sensor was examined. Iso-octane was used as a test fuel to explore the sensor that had been developed. As a result, the adherence of the liquid fuel directly injected into the cylinder was successfully detected under the quiescent and motoring
Kuboyama, TatsuyaMoriyoshi, YasuoTakayama, SatoshiNakabeppu, Osamu
Hurricane evacuations generate high traffic demand with increased crash risk. To mitigate such risk, transportation agencies can adopt high-resolution vehicle data to predict real-time crash risks. Previous crash risk prediction models mainly used limited infrastructure sensor data without covering many road segments. In this article, we present methods to determine potential crash risks during hurricane evacuation from an emerging alternative data source known as connected vehicle data that contain vehicle speed and acceleration information collected at a high frequency (mean = 14.32, standard deviation = 6.82 s). The dataset was extracted from a database of connected vehicle data for the evacuation period of Hurricane Ida on Interstate-10 in Louisiana. Five machine learning models were trained considering weather features and different traffic characteristics extracted from the connected vehicle data. The results indicate that the Gaussian process boosting and extreme gradient
Syed, Zaheen E MuktadiHasan, Samiul
Automotive signal processing is dealt with in several contributions that propose various techniques to make the most out of the available data, typically for enhancing safety, comfort, or performance. Specifically, the accurate estimation of tire–road interaction forces is of high interest in the automotive world. A few years ago the T.R.I.C.K. tool was developed, featuring a vehicle model processing experimental data, collected through various vehicle sensors, to compute several relevant virtual telemetry channels, including interaction forces and slip indices. Following years of further development in collaboration with motorsport companies, this article presents T.R.I.C.K. 2.0, a thoroughly renewed version of the tool. Besides a number of important improvements of the original tool, including, e.g., the effect of the limited slip differential, T.R.I.C.K. 2.0 features the ability to exploit advanced sensors typically used in motorsport, including laser sensors, potentiometers, and
Napolitano Dell’Annunziata, GuidoFarroni, FlavioTimpone, FrancescoLenzo, Basilio
Shear-polarized ultrasonic sensors have been instrumented onto the outer liner surface of an RTX-6 large marine diesel engine. The sensors were aligned with the first piston ring at top dead center and shear ultrasonic reflectometry (comparing the variation in the reflected ultrasonic waves) was used to infer metal–metal contact between the piston ring and cylinder liner. This is possible as shear waves are not supported by fluids and will only transmit across solid-to-solid interfaces. Therefore, a sharp change in the reflected wave is an indicator of oil film breakdown. Two lubricant injection systems have been evaluated—pulse jet and needle lift-type injectors. The needle lift type is a prototype injector design with a reduced rate of lubricant atomization relative to pulse jet injectors. This is manifested as a smaller reduction in the reflected ultrasonic wave, showing less metal–metal contact had occurred. During steady-state testing, the oil feed rate was varied; the high flow
Rooke, JackLi, XiangweiDwyer-Joyce, Robert S.
Brake-by-wire systems have received more and more attention in the recent years, but a close look on the available systems shows, that they have not reached full by-wire level yet. Most systems are still using hydraulic connections between main cylinder and the brake calipers on at least one axle to ensure functional safety. Mostly, this is the front axle, since the front brakes have to convert more kinetic energy during braking manoeuvers. Electromechanical actuators are currently used for rear brakes in hybrid brake-by-wire applications solely, since a loss of the front brake calipers can lead to severe conditions and control loss of the vehicle during braking. Further, the higher mass of battery electric vehicles (BEVs) leads to much higher braking forces on both axles and to increased sizes of the electromechanical calipers. This article presents a concept for a brake-by-wire system for battery electric vehicles, which features electromechanical brake actuators on all corners and a
Heydrich, MariusLenz, MatthiasIvanov, ValentinStoev, JulianLecoutere, Johan
Driving automation systems rely heavily on sophisticated electronics to function effectively, and economic pressure poses new challenges in manufacturing. Tightly integrated sensors, processors, and communication modules monitor and control the vehicle's operation at any time. Size, weight, power, and cost constraints put pressure on manufactures to reduce stack electronics, miniaturize boards, and innovate over the traditional sequential assemble/test cycle. Consequently, designers and manufacturers reduce access to boards, remove test points, co-locate RF with other components, and break the sequential SMT line. Radio-frequency (RF) reflectometry is a mature and reliable technology essential for characterizing materials, components, and analog circuits. It provides precise insights into electromagnetic properties like impedance and permittivity, crucial for optimizing RF and microwave designs. Widely used in fields from material science to quantum computing, RF reflectometry is key
Moreno, CarlosSharma, RakshitPabbi, SrijanFischmeister, Sebastian
Optical sensors serve as the backbone of numerous scientific and technological endeavors, from detecting gravitational waves to imaging biological tissues for medical diagnostics. These sensors use light to detect changes in properties of the environment they’re monitoring, including chemical biomarkers and physical properties like temperature. A persistent challenge in optical sensing has been enhancing sensitivity to detect faint signals amid noise.
Researchers have combined miniaturized hardware and intelligent algorithms to create a cost-effective, compact powerful tool capable of solving real-world problems in areas like healthcare.
Just one year after signing a ground-breaking trilateral agreement, the Deep Space Advanced Radar Capability partnership is completing facilities construction at the first of three sites that will host a global network of advanced ground-based sensors.
Path tracking control, which is one of the most important foundations of autonomous driving, could help the vehicle to precisely and smoothly follow the preset path by actively adjusting the front wheel steering angle. Although there are a number of advanced control methods with simple structure and reliable robustness that could assist vehicles achieving path tracking, these controllers have many parameters to be calibrated, and there is a lack of guidance documents to help non-professional test site engineers quickly master calibration methods. Therefore, this paper proposes a parameter virtual calibration method based on the deep reinforcement learning, which provides an effective solution for parameter calibration of vehicle path tracking controller. Firstly, the vehicle trajectory tracking model is established through the kinematic relationship between the vehicle and the target path, combined with the Taylor series expansion linearization method. Next, a vehicle path tracking
Zhao, JianGuo, ChenghaoZhao, HuiChaoZhao, YongqiangYu, ZhenZhu, BingChen, Zhicheng
Door sunshade in a vehicle has proven to be very comfortable and luxurious feature to the customers. Luxury vehicles provide power sunshade which is electrically operated with the activation of a switch, whereas cost conscious vehicles provide manual sunshade which requires manual coiling and uncoiling. This study is to develop a door panel structure that can accommodate both the manual sunshade and power sunshade, thereby serving both cost conscious as well as luxury seeking customers. Manual sunshade consists only of cassette, pull bar, spindle mechanism and hooks whereas the power sunshade consists of cassette, pull bar, spindle mechanism, flap mechanism, bowden cable mechanism, actuator and motor. Due to this difference in package, it becomes difficult to accommodate both variants of sunshade into the same body system. However, this study helps in developing a common body structure by ways of effective packaging, modifying the cable and actuator mechanism and critical packaging of
S M, Rahuld, AnanthaKakani, Phani Kumar
The performance of a second-generation Toyota Mirai fuel cell was characterized as part of the SwRI internal research program. This data was used to develop a supervisory controller scheme designed to balance the plant for the fuel cell system during steady-state and transient vehicle conditions. This was accomplished using a Supervisory Integrated Controller (SIC) implemented on a Real-time Power Electronics Control System (RPECS) with a Simulink-based control algorithm. The actuators of interest are the three hydrogen injectors at anode inlet, air compressor and three air side valves on at the cathode inlet. The FC power measurement and pressure sensor readings at the anode and cathode were utilized as real-time feedback for the controller operation. The aim of the controller was to achieve and maintain the power target set by the hybrid powertrain ECU present on the vehicle, which is responsible for balancing power on the fuel cell and battery over the high-voltage bus. These
Chundru, Venkata RajeshKubesh, MatthewLegala, Adithya
Clamping force control in Electromechanical Brake (EMB) systems must overcome various nonlinear characteristics, such as motor distorted voltage, Back Electromotive Force (EMF), and actuator friction disturbances. Therefore, modeling and parameter identification of these nonlinearities are necessary. This paper first proposes a motor parameter identification method based on the mathematical model of a Permanent Magnet Synchronous Motor (PMSM). A combination of the Least Square Method and Particle Swarm Optimization (PSO) is used to stepwise identify both the electrical and mechanical parameters of the motor. The accuracy of the identified parameters is validated by comparing simulation results with test bench responses. The identified parameters are applied to design the motor Back EMF compensation module, the distorted voltage compensation module, and to tune the current loop parameters. Next, a lumped parameter friction model suitable for closed-loop clamping force control in EMB
Qiao, LeXiong, LuZhuo, GuirongShu, Qiang
Camera matching photogrammetry is widely used in the field of accident reconstruction for mapping accident scenes, modeling vehicle damage from post collision photographs, analyzing sight lines, and video tracking. A critical aspect of camera matching photogrammetry is determining the focal length and Field of View (FOV) of the photograph being analyzed. The intent of this research is to analyze the accuracy of the metadata reported focal length and FOV. The FOV from photographs captured by over 20 different cameras of various makes, models, sensor sizes, and focal lengths will be measured using a controlled and repeatable testing methodology. The difference in measured FOV versus reported FOV will be presented and analyzed. This research will provide analysts with a dataset showing the possible error in metadata reported FOV. Analysts should consider the metadata reported FOV as a starting point for photogrammetric analysis and understand that the FOV calculated from the image
Smith, Connor A.Erickson, MichaelHashemian, Alireza
Advancements in sensor technologies have led to increased interest in detecting and diagnosing “driver states”—collections of internal driver factors generally associated with negative driving performance, such as alcohol intoxication, cognitive load, stress, and fatigue. This is accomplished using imperfect behavioral and physiological indicators that are associated with those states. An example is the use of elevated heart rate variability, detected by a steering wheel sensor, as an indicator of frustration. Advances in sensor technologies, coupled with improvements in machine learning, have led to an increase in this research. However, a limitation is that it often excludes naturalistic driving environments, which may have conditions that affect detection. For example, reductions in visual scanning are often associated with cognitive load [1]; however, these reductions can also be related to novice driver inexperience [2] and alcohol intoxication [3]. Through our analysis of the
Seaman, SeanZhong, PeihanAngell, LindaDomeyer, JoshuaLenneman, John
Given the promising prospects of retired lithium-ion batteries in second-life utilization, enhancing their consistency through a rational sorting process has become a pressing priority. Traditional capacity-based sorting methods have significant limitations as it takes high time costs and fails to provide internal dynamic information about the batteries. To address this, the present study introduces a novel approach by incorporating electrochemical impedance spectroscopy (EIS) into the sorting process. Firstly, principal component analysis (PCA) analysis is applied to extract the first principal component from the EIS data, which has a strong correlation with battery capacity. It serves as a key feature for assessing the residual value of retired batteries. Accurate estimation of battery capacity is then achieved using a simple linear equation: For retired nickel-cobalt-manganese (NCM) batteries, the mean absolute percentage error (MAPE) and root mean squared percentage error (RMSPE
Fan, WenjunWang, XueyuanJin, YiqunJiang, BoZhu, JiangongWei, XuezheDai, Haifeng
Vehicle-to-Infrastructure (V2I) cooperation has emerged as a fundamental technology to overcome the limitations of the individual ego-vehicle perception. Onboard perception is limited by the lack of information for understanding the environment, the lack of anticipation, the drop of performance due to occlusions and the physical limitations of embedded sensors. The perception of V2I in a cooperative manner improves the perception range of the ego vehicle by receiving information from the infrastructure that has another point of view, mounted with sensors, such as camera and LiDAR. This technical paper presents a perception pipeline developed for the infrastructure based on images with multiple viewpoints. It is designed to be scalable and has five main components: the image acquisition for the modification of camera settings and to get the pixel data, the object detection for fast and accurate detection of four wheels, two wheels and pedestrians, the data fusion module for robust
Picard, QuentinMorice, MaloFadili, MaryemPechberti, Steve
Automotive seating systems have become increasingly sophisticated, providing consumers with more flexible configurations and comfort functionalities. Traditional power seating, which relied on a few motors to adjust the seat position, has evolved into more technically advanced reconfigurable systems equipped with additional feedback sensors and actuators. These advancements include features such as Easy Entry, Zero Gravity, Stadium Swivel, IP Nesting, Auto Lumbar/Bolster Adjustment and Power Long Rails. All the features indicate that the overall control of seating systems now resembles robotic arm control or multi-body control, involving numerous coordinated movements. In this paper, we propose a novel control strategy for the coordinated speed control of multiple motors. Unlike traditional seating controls, which typically use direct switches or open-loop systems, we introduce a feedback approach that incorporates Kalman-filter-based speed estimation using raw signals directly from
Yang, HanlongLi, Miranda
This paper presents a Digital Twin approach based on Machine Learning (ML), aimed at creating software-based sensors to reduce the auxiliary devices of the vehicle and enabling predictive maintenance, thus reducing carbon footprint. The solution is applied to the electric Lubrication Oil Pump (eLOP), a crucial component within a vehicle's powertrain system. The proposed eLOP Digital Twin integrates ML-based sensors to estimate critical parameters such as temperature, pressure and flow rate, reducing the reliance on physical sensors and associated hardware. This approach minimizes manufacturing complexity and cost, enhancing energy efficiency during both production and operation. Furthermore, the Digital Twin facilitates predictive maintenance by continuously monitoring the component's performance, enabling early detection of potential failures and optimizing maintenance schedules. This leads to lower energy consumption and reduced emissions throughout the component's lifecycle. The
Khan, JalalD'Alessandro, StefanoTramaglia, FedericoFauda, Alessandro
Automotive technologies have been rapidly evolving with the introduction of electric powertrains, Advanced Driver-Assistance Systems (ADAS) and Over-The-Air (OTA) upgradability. Existing decentralized architectures are not an optimal choice for these applications, due to significant increases in cost and complexity. The transition to centralized architectures enables heavy computation to be delegated to a limited number of powerful Electronic Control Units (ECUs) called domain or zone controllers. The remaining ECUs, known as smart actuators, will perform well defined and specific tasks, receiving new parameters from the dedicated domain/zone controller over a network. Network bandwidth and time synchronization are the two major challenges in this transition. New automotive standards have been developed to address these challenges. Automotive Ethernet and Time Sensitive Networking (TSN) are two standards that are well-suited for centralized architectures. This paper presents a
Ayesh, MostafaBandur, VictorPantelic, VeraWassyng, AlanWasacz, BryonLawford, Mark
This paper investigates the problem of nonlinear model predictive control (NMPC) strategy for a class of nonlinear systems with multiple actuators’ response time-delays. Conventional approaches that incorporate these time-delays into the NMPC formulation typically result in a significant increase in the optimization problem's scale. To address these problems, we propose a novel NMPC strategy. In the first stage, the NMPC strategy is designed for the nonlinear system without considering actuator’s response time-delay, thereby maintaining the original scale of the optimization problem. The optimal control sequence derived from this NMPC is then fitted to a time-continuous polynomial function, serving as a reference signal for the actuators' response time-delay models. In the second stage, combining inverse model and inverse Laplace transform techniques, a novel inverse model compensation control (IMCC) strategy is designed for actuators’ response time-delays. This IMCC strategy enables
Wang, Bin
The application of multi-sensor fusion for enhanced distance estimation accuracy in dynamic environments is crucial for advanced driver assistance systems (ADAS) and autonomous vehicles. The Limitations of single sensors such as cameras or radar in adverse conditions motivate the use of combined camera and radar data to improve reliability, adaptability, and object recognition. A multi-sensor fusion approach using an Extended Kalman Filter (EKF) is proposed to combine sensor measurements with a dynamic system model, achieving robust and accurate distance estimation. The research utilizes the Mississippi State University Autonomous Vehicular Simulator (MAVS), a physics-based simulation platform, to generate realistic synthetic datasets incorporating sensor imperfections such as noise and missed detections to create a controlled environment for data collection. Data analysis is performed using MATLAB. Qualitative metrics such as visualization of fused data vs ground truth and
Ebu, Iffat AraIslam, FahmidaRafi, Mohammad AbdusShahidRahman, MahfuzurIqbal, UmarBall, John
Deliberate modifications to infrastructure can significantly enhance machine vision recognition of road sections designed for Vulnerable Road Users, such as green bike lanes. This study evaluates how green bike lanes, compared to unpainted lanes, enhance machine vision recognition and vulnerable road users safety by keeping vehicles at a safe distance and preventing encroachment into designated bike lanes. Conducted at the American Center for Mobility, this study utilizes a vehicle equipped with a front-facing camera to assess green bike lane recognition capabilities across various environmental conditions including dry daytime, dry nighttime, rain, fog, and snow. Data collection involved gathering a comprehensive dataset under diverse conditions and generating masks for lane markings to perform comparative analysis for training Advanced Driver Assistance Systems. Quality measurement and statistical analysis are used to evaluate the effectiveness of machine vision recognition using
Ponnuru, Venkata Naga RithikaDas, SushantaGrant, JosephNaber, JeffreyBahramgiri, Mojtaba
The Automated Mobility Partnership (AMP) is a consortium of industry and academic stakeholders dedicated to advancing Automated Driving Systems (ADS) through a comprehensive suite of tools, datasets, and methodologies. The AMP portal integrates events from over 35 million miles of naturalistic driving data including thousands of annotated crashes and near-crashes and a decade of U.S. police-reported crash data curated by the Virginia Tech Transportation Institute. The portal enables data discovery, visualization, processing, and analysis through secured web access. This paper briefly describes the AMP portal and examines its utility in developing and evaluating the safety of ADS using standardized processes. For the examination, we provide examples based on generic automated driving functions, guided by the Safety of the Intended Functionality (SOTIF) framework. The results show that AMP is instrumental in identifying recorded real-world cases in which the hazardous behavior of a
Antona-Makoshi, JacoboWilliams, VickiAli, GibranSullivan, KayeTerranova, PaoloKefauver, KevinHatchett, Alex
In this study, we introduce RGB2BEV-Net, an end-to-end pipeline that extends traditional BEV segmentation models by utilizing raw RGB images with Bird’s Eye View (BEV) generation. While previous work primarily focused on pre-segmented images to generate corresponding BEV maps, our approach expands this by collecting RGB images alongside their affiliated segmentation masks and BEV representations. This enables direct input of RGB camera sensors into the pipeline, reflecting real-world autonomous driving scenarios where RGB cameras are commonly used as sensors, rather than relying on pre-segmented images. Our model processes four RGB images through a segmentation layer before converting them into a segmented BEV, implemented in the PyTorch framework after being adapted from an original implementation that utilized a different framework. This adaptation was necessary to improve compatibility and ensure better integration of the entire system within autonomous vehicle applications. We
Hossain, SabirLin, Xianke
Path tracking is a key function of intelligent vehicles, which is the basis for the development and realization of advanced autonomous driving. However, the imprecision of the control model and external disturbances such as wind and sudden road conditions will affect the path tracking effect and even lead to accidents. This paper proposes an intelligent vehicle path tracking strategy based on Tube-MPC and data-driven stable region to enhance vehicle stability and path tracking performance in the presence of external interference. Using BP-NN combined with the state-of-the-art energy valley optimization algorithm, the five eigenvalues of the stable region of the vehicle β−β̇ phase plane are obtained, which are used as constraints for the Tube-MPC controller and converted into quadratic forms for easy calculation. In the calculation of Tube invariant sets, reachable sets are used instead of robust positive invariant sets to reduce the calculation. Simulation results demonstrates that the
Zhang, HaosenLi, YihangWu, Guangqiang
Trajectory tracking control is a key component of vehicle autonomous driving technology. Compared with traditional vehicles, Distributed Driven Electric Vehicle (DDEV) is an ideal vehicle for trajectory tracking control because of its high space utilization, redundant control freedom and fast system response. However, the chassis execution system of DDEV has a relatively large number of sensors, which significantly increases its probability of failure. In this paper, we propose a trajectory tracking fault-tolerant control method for DDEV considering steering actuator faults. Firstly, we establish the dynamic model of the steering actuator and the trajectory tracking model of DDEV. The model is linearized and discretized by using Taylor series expansion and forward Euler method. Next, considering multi-objective constraints such as motion comfort, actuator saturation and road adhesion boundary, the trajectory tracking control strategy of DDEV is designed by using model predictive
Wang, DepingLi, LunTeng, YuhanZhu, BingChen, Zhicheng
In this paper, based on the cylindrical flow theory of incompressible viscous fluids and the equivalent circuit model of resonant sensing elements, a theoretical model for the measurement of liquid viscosity with a U-Shaped tungsten wire resonance sensor was established. This model can measure the liquid viscosity independently without liquid density or coupled detection of liquid density. The experimental results show that the decoupling of liquid viscosity and its density can be achieved at Re<1. The liquid viscosity is strongly linear with the resonant conductance. The viscosity measurement error is less than 7.24% in the viscosity range of 7.235cP to 85.2cP.
Shan, BaoquanShen, YitaoYang, JianguoWu, Dehong
To address the issue of signal aliasing when multiple particles pass through a metallic particle sensor, which can lead to misidentification of particle count, we employ numerical simulation methods for an in-depth investigation. We developed a mathematical model of a three-coil inductive metal particle sensor to explore the signal variations induced by the passage of a single particle. We utilized micro-element simulation analysis to dissect the signal generated by a single particle, elucidating the underlying change process. Focusing on dual ferromagnetic particles as the subject of study, we conducted simulations and demodulation of the induced voltage under various combinations of sizes and spacings to investigate the influence patterns of dual adjacent ferromagnetic particles on the sensor's induced signal. Further research into the peak signals of different diameter particles at a constant spacing revealed that, for a given spacing, the ratio of peak signals between particles of
Chen, SenShen, YitaoQiang, GuiyanZheng, ZhengWang, ZheyuHao, YinHu, Ting
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