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Viewing 1 to 30 of 1031
2017-09-04
Technical Paper
2017-24-0046
Richard Stone, Ben Williams, Paul Ewart
The increased efficiency and specific output with Gasoline Direct Injection (GDI) engines are well known, but so too are the higher levels of Particulate Matter emissions compared with Port Fuel Injection (PFI) engines. To minimise Particulate Matter emissions, then it is necessary to understand and control the mixture preparation process, and important insights into GDI engine combustion can be obtained from optical access engines. Such data is crucial for validating models that predict flows, sprays and air fuel ratio distributions. Mie scattering can be used for semi-quantitative measurements of the fuel spray and this can be followed with Planar Laser Induced Fluorescence (PLIF) for determining the air fuel ratio and temperature distributions. With PLIF, very careful in-situ calibration is needed, and for temperature this can be provided by Laser Induced Thermal Grating Spectroscopy (LITGS).
2017-09-04
Technical Paper
2017-24-0126
Christian Zöllner, Dieter Brueggemann
The removal of particulate matter (PM) from diesel exhaust is necessary to protect the environment and human health. To meet the strict emission standards for diesel engines an additional exhaust aftertreatment system is essential. Diesel particulate filters (DPF) are established devices to remove emitted PM from diesel exhaust. But the deposition and the accumulation of soot in the DPF influences the filter back pressure and therefore the engine performance and the fuel consumption which is why a periodical regeneration through PM oxidation is necessary. The oxidation behavior should result in an effective regeneration mode that minimizes the fuel penalty and limits the temperature rise while maintaining a high regeneration efficiency. Excessive and fast regenerations have to be avoided as well as uncontrolled oxidations leading to damages of the filter and fuel penalty.
2017-09-04
Technical Paper
2017-24-0109
Nic Van Vuuren, Lucio Postrioti, Gabriele Brizi, Federico Picchiotti
ABSTRACT: Selective Catalytic Reduction (SCR) diesel exhaust aftertreatment systems are virtually indispensable to meet NOx emissions limits worldwide. These systems generate the NH3 reductant by injecting aqueous urea solution (AUS-32/AdBlue®/DEF) into the exhaust for the SCR NOx reduction reactions. Understanding the AUS-32 injector spray performance is critical to proper optimization of the SCR system. Specifically, better knowledge is required of urea sprays under operating conditions including those where fluid temperatures exceed the atmospheric fluid boiling point. Results were previously presented from imaging of an AUS-32 injector spray which showed substantial structural differences in the spray between room temperature fluid conditions, and conditions where the fluid temperature approached and exceeded 104º C and “flash boiling” of the fluid was initiated.
2017-06-05
Technical Paper
2017-01-1874
Tongyang Shi, Yangfan Liu, J Stuart Bolton, Frank Eberhardt, Warner Frazer
Abstract Wideband Acoustical Holography (WBH), which is a monopole-based, equivalent source procedure (J. Hald, “Wideband Acoustical Holography,” INTER-NOISE 2014), has proven to offer accurate noise source visualization results in experiments with a simple noise source: e.g., a loudspeaker (T. Shi, Y. Liu, J.S. Bolton, “The Use of Wideband Holography for Noise Source Visualization”, NOISE-CON 2016). From a previous study, it was found that the advantage of this procedure is the ability to optimize the solution in the case of an under-determined system: i.e., when the number of measurements is much smaller than the number of parameters that must be estimated in the model. In the present work, a diesel engine noise source was measured by using one set of measurements from a thirty-five channel combo-array placed in front of the engine.
2017-06-05
Technical Paper
2017-01-1872
Masao Nagamatsu
Abstract The almost current sound localization methods do not have enough resolution in low frequency sound localization. To overcome this disadvantage, I am now developing the new sound localization method, Double Nearfield Acoustic Holography (DNAH) method. This method is a converted method of conventional Nearfield Acoustic Holography (NAH) method. In this proposing method, the resolution of low frequency sound localization is improved by using sound propagation information on doubled measurement planes. To prove the performance of proposing method, the basic experiments with variable conditions are conducted. In these experiments, the small speakers are used as sound sources. In this paper, to discuss the ability to apply to actual industry, the effect of measurement distance from the sound source is explained. Some experimental results with changing measurement distance are shown in this paper.
2017-05-18
Journal Article
2017-01-9678
G Agawane, Varun Jadon, Venkatesham Balide, R Banerjee
Abstract Liquid sloshing noise from an automotive fuel tank is becoming increasingly important during frequent accelerating/decelerating driving conditions. It is becoming more apparent due to significant decrease in other noise sources in a vehicle, particularly in hybrid vehicles. As a step toward understanding the dynamics of liquid sloshing and noise generation mechanism, an experimental study was performed in a partially filled rectangular tank. A systematic study was performed to understand the effects of critical parameters like fill level and acceleration/deceleration magnitude. Response parameters like dynamic pressure, dynamic force, dynamic acceleration and sound pressure levels along with high speed video images were recorded. The proposed experimental setup was able to demonstrate major events leading to sloshing noise generation. These events in the sloshing mechanism have been analysed from the dynamic sensor data and correlated with high speed video images.
2017-03-28
Journal Article
2017-01-0691
Louis-Marie Malbec, Julian Kashdan
Abstract Previous experimental data obtained in constant volume combustion vessels have shown that soot-free diffusive flames can be achieved in a Diesel spray if the equivalence ratio at the flame lift-off location is below 2. The so-called Leaner Lifted-Flame Combustion (LLFC) strategy is a promising approach to limit the levels of in-cylinder soot produced in Diesel engines. However, implementing such strategies in light-duty engines is not straightforward due to the effects of charge confinement , non-steady boundary conditions and spray-spray interactions compared to the simplified configuration of a free-jet in a constant-volume combustion vessel. The present study aims at trying to gain a better understanding of the requirements in terms of injector and engine settings in order to reach the LLFC regime in a light-duty engine. Experiments were performed on a 0.5L single-cylinder optical engine.
2017-03-28
Journal Article
2017-01-0674
Benjamin Matthew Wolk, Isaac Ekoto
Abstract Pulsed nanosecond discharges (PND) can achieve ignition in internal combustion engines through enhanced reaction kinetics as a result of elevated electron energies without the associated increases in translational gas temperature that cause electrode erosion. Atomic oxygen (O), including its electronically excited states, is thought to be a key species in promoting low-temperature ignition. In this paper, high-voltage (17-24 kV peak) PND are examined in oxygen/nitrogen/carbon dioxide/water mixtures at engine-relevant densities (up to 9.1 kg/m3) through pressure-rise calorimetry and direct imaging of excited-state O-atom and molecular nitrogen (N2) in an optically accessible spark calorimeter, with the anode/cathode gap distance set to 5 mm or with an anode-only configuration (DC corona). The conversion efficiency of pulse electrical energy into thermal energy was measured for PND with secondary streamer breakdown (SSB) and similar low-temperature plasmas (LTP) without.
2017-03-28
Technical Paper
2017-01-0673
Alessandro Cimarello, Carlo N. Grimaldi, Francesco Mariani, Michele Battistoni, Massimo Dal Re
Abstract Radio Frequency Corona ignition systems represent an interesting solution among innovative ignition strategies for their ability to stabilize the combustion and to extend the engine operating range. The corona discharge, generated by a strong electric field at a frequency of about 1 MHz, produces the ignition of the air-fuel mixture in multiple spots, characterized by a large volume when compared to a conventional spark, increasing the early flame growth speed. The transient plasma generated by the discharge, by means of thermal, kinetic and transport effects, allows a robust initialization of the combustion even in critical conditions, such as using diluted or lean mixtures. In this work the effects of Corona ignition have been analyzed on a single cylinder optical engine fueled with gasoline, comparing the results with those of a traditional single spark ignition.
2017-03-28
Technical Paper
2017-01-0753
Marcus Olof Lundgren, Zhenkan Wang, Alexios Matamis, Oivind Andersson, Mattias Richter, Martin Tuner, Marcus Alden, Andersson Arne
Abstract Gasoline partially premixed combustion (PPC) has shown potential in terms of high efficiency with low emissions of oxides of nitrogen (NOx) and soot. Despite these benefits, emissions of unburned hydrocarbons (UHC) and carbon monoxide (CO) are the main shortcomings of the concept. These are caused, among other things, by overlean zones near the injector tip and injector dribble. Previous diesel low temperature combustion (LTC) research has demonstrated post injections to be an effective strategy to mitigate these emissions. The main objective of this work is to investigate the impact of post injections on CO and UHC emissions in a quiescent (non-swirling) combustion system. A blend of primary reference fuels, PRF87, having properties similar to US pump gasoline was used at PPC conditions in a heavy duty optical engine. The start of the main injection was maintained constant. Dwell and mass repartition between the main and post injections were varied to evaluate their effect.
2017-03-28
Technical Paper
2017-01-0755
Karthik Nithyanandan, Yongli Gao, Han Wu, Chia-Fon Lee, Fushui Liu, Junhao Yan
Abstract Dual-fuel combustion combining a premixed charge of compressed natural gas (CNG) and a pilot injection of diesel fuel offer the potential to reduce diesel fuel consumption and drastically reduce soot emissions. In this study, dual-fuel combustion using methane ignited with a pilot injection of No. 2 diesel fuel, was studied in a single cylinder diesel engine with optical access. Experiments were performed at a CNG substitution rate of 70% CNG (based on energy) over a wide range of equivalence ratios of the premixed charge, as well as different diesel injection strategies (single and double injection). A color high-speed camera was used in order to identify and distinguish between lean-premixed methane combustion and diffusion combustion in dual-fuel combustion. The effect of multiple diesel injections is also investigated optically as a means to enhance flame propagation towards the center of the combustion chamber.
2017-03-28
Journal Article
2017-01-0716
Randy Hessel, Zongyu Yue, Rolf Reitz, Mark Musculus, Jacqueline O'Connor
Abstract One way to develop an understanding of soot formation and oxidation processes that occur during direct injection and combustion in an internal combustion engine is to image the natural luminosity from soot over time. Imaging is possible when there is optical access to the combustion chamber. After the images are acquired, the next challenge is to properly interpret the luminous distributions that have been captured on the images. A major focus of this paper is to provide guidance on interpretation of experimental images of soot luminosity by explaining how radiation from soot is predicted to change as it is transmitted through the combustion chamber and to the imaging. The interpretations are only limited by the scope of the models that have been developed for this purpose. The end-goal of imaging radiation from soot is to estimate the amount of soot that is present.
2017-03-28
Journal Article
2017-01-0555
Salvatore Iaccarino, Sebastiano Breda, Alessandro D'Adamo, Stefano Fontanesi, Adrian Irimescu, Simona Merola
Abstract The increasing limitations in engine emissions and fuel consumption have led researchers to the need to accurately predict combustion and related events in gasoline engines. In particular, knock is one of the most limiting factors for modern SI units, severely hindering thermal efficiency improvements. Modern CFD simulations are becoming an affordable instrument to support experimental practice from the early design to the detailed calibration stage. To this aim, combustion and knock models in RANS formalism provide good time-to-solution trade-off allowing to simulate mean flame front propagation and flame brush geometry, as well as “ensemble average” knock tendency in end-gases. Still, the level of confidence in the use of CFD tools strongly relies on the possibility to validate models and methodologies against experimental measurements.
2017-03-28
Journal Article
2017-01-0551
Alessandro D'Adamo, Sebastiano Breda, Salvatore Iaccarino, Fabio Berni, Stefano Fontanesi, Barbara Zardin, Massimo Borghi, Adrian Irimescu, Simona Merola
Abstract Engine knock is one of the most limiting factors for modern Spark-Ignition (SI) engines to achieve high efficiency targets. The stochastic nature of knock in SI units hinders the predictive capability of RANS knock models, which are based on ensemble averaged quantities. To this aim, a knock model grounded in statistics was recently developed in the RANS formalism. The model is able to infer a presumed log-normal distribution of knocking cycles from a single RANS simulation by means of transport equations for variances and turbulence-derived probability density functions (PDFs) for physical quantities. As a main advantage, the model is able to estimate the earliest knock severity experienced when moving the operating condition into the knocking regime.
2017-03-28
Technical Paper
2017-01-0573
Mohammed Jaasim Mubarak ali, Francisco Hernandez Perez, R Vallinayagam, S Vedharaj, Bengt Johansson, Hong Im
Abstract Full cycle simulations of KAUST optical diesel engine were conducted in order to provide insights into the details of fuel spray, mixing, and combustion characteristics at different start of injection (SOI) conditions. Although optical diagnostics provide valuable information, the high fidelity simulations with matched parametric conditions improve fundamental understanding of relevant physical and chemical processes by accessing additional observables such as the local mixture distribution, intermediate species concentrations, and detailed chemical reaction rates. Commercial software, CONVERGE™, was used as the main simulation tool, with the Reynolds averaged Navier-Stokes (RANS) turbulence model and the multi-zone (SAGE) combustion model to compute the chemical reaction terms. SOI is varied from late compression ignition (CI) to early partially premixed combustion (PPC) conditions.
2017-03-28
Technical Paper
2017-01-0262
Taewon Kim, Xi Luo, Mustafa Al-Sadoon, Ming-Chia Lai, Marcis Jansons, Doohyun Kim, Jason Martz, Angela Violi, Eric Gingrich
Abstract Three jet fuel surrogates were compared against their target fuels in a compression ignited optical engine under a range of start-of-injection temperatures and densities. The jet fuel surrogates are representative of petroleum-based Jet-A POSF-4658, natural gas-derived S-8 POSF-4734 and coal-derived Sasol IPK POSF-5642, and were prepared from a palette of n-dodecane, n-decane, decalin, toluene, iso-octane and iso-cetane. Optical chemiluminescence and liquid penetration length measurements as well as cylinder pressure-based combustion analyses were applied to examine fuel behavior during the injection and combustion process. HCHO* emissions obtained from broadband UV imaging were used as a marker for low temperature reactivity, while 309 nm narrow band filtered imaging was applied to identify the occurrence of OH*, autoignition and high temperature reactivity.
2017-03-28
Technical Paper
2017-01-0107
Arvind Jayaraman, Ashley Micks, Ethan Gross
Abstract Recreating traffic scenarios for testing autonomous driving in the real world requires significant time, resources and expense, and can present a safety risk if hazardous scenarios are tested. Using a 3D virtual environment to enable testing of many of these traffic scenarios on the desktop or cluster significantly reduces the amount of required road tests. In order to facilitate the development of perception and control algorithms for level 4 autonomy, a shared memory interface between MATLAB, Simulink, and Unreal Engine 4 can send information (such as vehicle control signals) back to the virtual environment. The shared memory interface conveys arbitrary numerical data, RGB image data, and point cloud data for the simulation of LiDAR sensors.
2017-03-28
Technical Paper
2017-01-0104
Maryam Moosaei, Yi Zhang, Ashley Micks, Simon Smith, Madeline J. Goh, Vidya Nariyambut Murali
Abstract In this work, we outline a process for traffic light detection in the context of autonomous vehicles and driver assistance technology features. For our approach, we leverage the automatic annotations from virtually generated data of road scenes. Using the automatically generated bounding boxes around the illuminated traffic lights themselves, we trained an 8-layer deep neural network, without pre-training, for classification of traffic light signals (green, amber, red). After training on virtual data, we tested the network on real world data collected from a forward facing camera on a vehicle. Our new region proposal technique uses color space conversion and contour extraction to identify candidate regions to feed to the deep neural network classifier. Depending on time of day, we convert our RGB images in order to more accurately extract the appropriate regions of interest and filter them based on color, shape and size.
2017-03-28
Technical Paper
2017-01-0102
Mahdi Heydari, Feng Dang, Ankit Goila, Yang Wang, Hanlong Yang
In this paper, a sensor fusion approach is introduced to estimate lane departure. The proposed algorithm combines the camera, inertial navigation sensor, and GPS data with the vehicle dynamics to estimate the vehicle path and the lane departure time. The lane path and vehicle path are estimated by using Kalman filters. This algorithm can be used to provide early warning for lane departure in order to increase driving safety. By integrating inertial navigation sensor and GPS data, the inertial sensor biases can be estimated and the vehicle path can be estimated where the GPS data is not available or is poor. Additionally, the algorithm can be used to reduce the latency of information embedded in the controls, so that the vehicle lateral control performance can be significantly improved during lane keeping in Advanced Driver Assistance Systems (ADAS) or autonomous vehicles. Furthermore, it improves lane detection reliability in situations when camera fails to detect lanes.
2017-03-28
Technical Paper
2017-01-0109
Yi Zhang, Madeline J. Goh, Vidya Nariyambut Murali
Abstract This work describes a single camera based object distance estimation system. As technology on vehicles is constantly advancing on the road to autonomy, it is critical to know the locations of objects in 3D space for safe behavior of the vehicle. Though significant progress has been made on object detection in 2D sensor space from a single camera, this work additionally estimates the distance to said object without requiring stereo vision or absolute knowledge of vehicle motion. Specifically, our proposed system is comprised of three modules: vision based ego-motion estimation, object-detection, and distance estimation. In particular, we compensate for the vehicle ego-motion by using pin-hole camera model to increase the accuracy of the object distance estimation.
2017-03-28
Technical Paper
2017-01-0099
Jose E. Solomon, Francois Charette
Abstract The proposed technique is a tailored deep neural network (DNN) training approach which uses an iterative process to support the learning of DNNs by targeting their specific misclassification and missed detections. The process begins with a DNN that is trained on freely available annotated image data, which we will refer to as the Base model, where a subset of the categories for the classifier are related to the automotive theater. A small set of video capture files taken from drives with test vehicles are selected, (based on the diversity of scenes, frequency of vehicles, incidental lighting, etc.), and the Base model is used to detect/classify images within the video files. A software application developed specifically for this work then allows for the capture of frames from the video set where the DNN has made misclassifications. The corresponding annotation files for these images are subsequently corrected to eliminate mislabels.
2017-03-28
Journal Article
2017-01-0083
Yi Tian, Hangxin Liu, Tomonari Furukawa
Abstract This paper presents a novel infrastructural traffic monitoring approach that estimates traffic information by combining two sensing techniques. The traffic information can be obtained from the presented approach includes passing vehicle counts, corresponding speed estimation and vehicle classification based on size. This approach uses measurement from an array of Lidars and video frames from a camera and derives traffic information using two techniques. The first technique detects passing vehicles by using Lidars to constantly measure the distance from laser transmitter to the target road surface. When a vehicle or other objects pass by, the measurement of the distance to road surface reduces in each targeting spot, and triggers detection event. The second technique utilizes video frames from camera and performs background subtraction algorithm in each selected Region of Interest (ROI), which also triggers detection when vehicle travels through each ROI.
2017-03-28
Technical Paper
2017-01-0395
Xin Xie, Danielle Zeng, Boyang Zhang, Junrui Li, Liping Yan, Lianxiang Yang
Abstract Vehicle front panel is an interior part which has a major impact on the consumers’ experience of the vehicles. To keep a good appearance during long time aging period, most of the front panel is designed as a rough surface. Some types of surface defects on the rough surface can only be observed under the exposure of certain angled sun light. This brings great difficulties in finding surface defects on the production line. This paper introduces a novel polarized laser light based surface quality inspection method for the rough surfaces on the vehicle front panel. By using the novel surface quality inspection system, the surface defects can be detected real-timely even without the exposure under certain angled sun light. The optical fundamentals, theory derivation, experiment setup and testing result are shown in detail in this paper.
2017-03-28
Technical Paper
2017-01-0394
Junrui Li, Ruiyan Yang, Zhen Li, Changqing Du, Dajun Zhou, Lianxiang Yang
Abstract Advanced high-strength steel (AHSS) is gaining popularity in the automotive industry due to its higher final part strength with the better formability compares to the conventional steel. However, the edge fracture occurs during the forming procedure for the pre-strained part. To avoid the edge fracture that happens during the manufacturing, the effect of pre-strain on edge cracking limit needs to be studied. In this paper, digital image correlation (DIC), as an accurate optical method, is adopted for the strain measurement to determining the edge cracking limit. Sets of the wide coupons are pre-strained to obtain the samples at different pre-strain level. The pre-strain of each sample is precisely measured during this procedure using DIC. After pre-straining, the half dog bone samples are cut from these wide coupons. The edge of the notch in the half dog bone samples is created by the punch with 10% clearance for the distinct edge condition.
2017-03-28
Technical Paper
2017-01-1675
Genís Mensa, Núria Parera, Alba Fornells
Abstract Nowadays, the use of high-speed digital cameras to acquire relevant information is a standard for all laboratories and facilities working in passive safety crash testing. The recorded information from the cameras is used to develop and improve the design of vehicles in order to make them safer. Measurements such as velocities, accelerations and distances are computed from high-speed images captured during the tests and represent remarkable data for the post-crash analysis. Therefore, having the exact same position of the cameras is a key factor to be able to compare all the values that are extracted from the images of the tests carried out within a long-term passive safety project. However, since working with several customers involves a large amount of different cars and tests, crash facilities have to readapt for every test mode making it difficult for them to reproduce the correct and precise position of the high-speed cameras throughout the same project.
2017-03-28
Technical Paper
2017-01-1422
Toby Terpstra, Seth Miller, Alireza Hashemian
Abstract Photogrammetry and the accuracy of a photogrammetric solution is reliant on the quality of photographs and the accuracy of pixel location within the photographs. A photograph with lens distortion can create inaccuracies within a photogrammetric solution. Due to the curved nature of a camera’s lens(s), the light coming through the lens and onto the image sensor can have varying degrees of distortion. There are commercially available software titles that rely on a library of known cameras, lenses, and configurations for removing lens distortion. However, to use these software titles the camera manufacturer, model, lens and focal length must be known. This paper presents two methodologies for removing lens distortion when camera and lens specific information is not available. The first methodology uses linear objects within the photograph to determine the amount of lens distortion present. This method will be referred to as the straight-line method.
2017-03-28
Technical Paper
2017-01-1409
Markus Schratter, Susie Cantu, Thomas Schaller, Peter Wimmer, Daniel Watzenig
Abstract Highly Automated Driving (HAD) opens up new middle-term perspectives in mobility and is currently one of the main goals in the development of future vehicles. The focus is the implementation of automated driving functions for structured environments, such as on the motorway. To achieve this goal, vehicles are equipped with additional technology. This technology should not only be used for a limited number of use cases. It should also be used to improve Active Safety Systems during normal non-automated driving. In the first approach we investigate the usage of machine learning for an autonomous emergency braking system (AEB) for the active pedestrian protection safety. The idea is to use knowledge of accidents directly for the function design. Future vehicles could be able to record detailed information about an accident. If enough data from critical situations recorded by vehicles is available, it is conceivable to use it to learn the function design.
2017-03-28
Technical Paper
2017-01-1401
Trong-Duy Nguyen, Joseph Lull, Satish Vaishnav
Abstract In this paper, a method of improving the automated vehicle’s perception using a multi-pose camera system (MPCS) is presented. The proposed MPCS is composed of two identical colored and high frame-rate cameras: one installed in the driver side and the other in the passenger side. Perspective of MPCS varies depending on the width of vehicle type in which MPCS is installed. To increase perspective, we use the maximum width of the host vehicle as camera to camera distance for the MPCS. In addition, angular positions of the two cameras in MPCS are controlled by two separate electric motor-based actuators. Steering wheel angle, which is available from the vehicle Controller Area Network (CAN) messages, is used to supply information to the actuators to synchronize MPCS camera positions with the host vehicle steering wheel.
2017-03-28
Technical Paper
2017-01-1405
Tzu-Sung Wu
Abstract Autonomous Emergency Braking Systems (AEBS) usually contain radar, (stereo) camera and/or LiDAR-based technology to identify potential collision partners ahead of the car, such that to warn the driver or automatically brake to avoid or mitigate a crash. The advantage of camera is less cost: however, is inevitable to face the defects of cameras in AEBS, that is, the image recognition cannot perform good accuracy in the poor or over-exposure light condition. Therefore, the compensation of other sensors is of importance. Motivated by the improvement of false detection, we propose a Pedestrian-and-Vehicle Recognition (PVR) algorithm based on radar to apply to AEBS. The PVR employs the radar cross section (RCS) and standard deviation of width of obstacle to determine whether a threshold value of RCS and standard deviation of width of the pedestrian and vehicle is crossed, and to identity that the objective is a pedestrian or vehicle, respectively.
2017-03-28
Journal Article
2017-01-1403
Alexander Koenig, Michael Gutbrod, Sören Hohmann, Julian Ludwig
Abstract Highly automated driving (HAD) is under rapid development and will be available for customers within the next years. However the evidence that HAD is at least as safe as human driving has still not been produced. The challenge is to drive hundreds of millions of test kilometers without incidents to show that statistically HAD is significantly safer. One approach is to let a HAD function run in parallel with human drivers in customer cars to utilize a fraction of the billions of kilometers driven every year. To guarantee safety, the function under test (FUT) has access to sensors but its output is not executed, which results in an open loop problem. To overcome this shortcoming, the proposed method consists of four steps to close the loop for the FUT. First, sensor data from real driving scenarios is fused in a world model and enhanced by incorporating future time steps into original measurements.
Viewing 1 to 30 of 1031

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