Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235246
Máté Fazekas, P. Gáspár, B. Németh
The article proposes a parameter identification algorithm for a kinematic vehicle model from real measurements of on-board sensors. The motivation of the paper is to improve the localization in poor sensor performance cases. For example, when the GNSS signals are unavailable, or when the vision-based methods are incorrect due to the poor feature number, furthermore, when the IMU-based method fails due to the lack of frequent accelerations. In these situations the wheel encoder-based odometry can be an appropriate choice for pose estimation, however, this method suffers from parameter uncertainty. The proposed method combines the Gauss-Newton non-linear estimation techniques with Kalman-filtering in an iterative loop and identifies the wheel circumferences and track width parameters in three steps. The estimation architecture eliminates the convergence to a local optimum and the divergence resulted in the highly uncertain initial parameter values. The identification performance is verified by a real test of a compact car. The results are compared with the nominal setting, which should be applied in the lack of identification.
{"title":"Identification of kinematic vehicle model parameters for localization purposes","authors":"Máté Fazekas, P. Gáspár, B. Németh","doi":"10.1109/MFI49285.2020.9235246","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235246","url":null,"abstract":"The article proposes a parameter identification algorithm for a kinematic vehicle model from real measurements of on-board sensors. The motivation of the paper is to improve the localization in poor sensor performance cases. For example, when the GNSS signals are unavailable, or when the vision-based methods are incorrect due to the poor feature number, furthermore, when the IMU-based method fails due to the lack of frequent accelerations. In these situations the wheel encoder-based odometry can be an appropriate choice for pose estimation, however, this method suffers from parameter uncertainty. The proposed method combines the Gauss-Newton non-linear estimation techniques with Kalman-filtering in an iterative loop and identifies the wheel circumferences and track width parameters in three steps. The estimation architecture eliminates the convergence to a local optimum and the divergence resulted in the highly uncertain initial parameter values. The identification performance is verified by a real test of a compact car. The results are compared with the nominal setting, which should be applied in the lack of identification.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125644267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235215
P. Kulmon
This paper deals with bistatic track association in classical Frequency Modulation (FM) based Multi Static Primary Surveillance Radar (MSPSR). We formulate deghosting procedure as Bayesian inference of association matrix between bistatic tracks and targets as well as target positions. To do that, we formulate prior probability distribution for the association matrix and develop custom Monte Carlo Markov Chain (MCMC) sampler, which is necessary to solve such a hybrid inference problem. Using simulated data, we compare the performance of the proposed algorithm with two others and show its superior performance in such a setup. At the end of the paper, we also outline further research of the algorithm.
{"title":"Bayesian Deghosting Algorithm for Multiple Target Tracking","authors":"P. Kulmon","doi":"10.1109/MFI49285.2020.9235215","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235215","url":null,"abstract":"This paper deals with bistatic track association in classical Frequency Modulation (FM) based Multi Static Primary Surveillance Radar (MSPSR). We formulate deghosting procedure as Bayesian inference of association matrix between bistatic tracks and targets as well as target positions. To do that, we formulate prior probability distribution for the association matrix and develop custom Monte Carlo Markov Chain (MCMC) sampler, which is necessary to solve such a hybrid inference problem. Using simulated data, we compare the performance of the proposed algorithm with two others and show its superior performance in such a setup. At the end of the paper, we also outline further research of the algorithm.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128413326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235253
Hauke Kaulbersch, J. Honer, M. Baum
Extended target models often approximate complex structures of real-world objects. Yet, these structures can have a significant impact on the interpretation of the measurements. A prime example for such a scenario is a dimensional reduction, i.e. a target that generates three-dimensional measurements is estimated by a two-dimensional model. We present an approach that introduces asymmetric surface noise to the Random Hypersurface Model (RHM). This allows for a different generation interpretation of measurements depending on their location relative to the target surface, and in turn provides a way to model extended targets that generate measurements primarily but not exclusively at the surface. The benefits of this model are demonstrated on automotive LIDAR data and a large-scale comparison to the literature approach is provided on the Nuscenes data set.
{"title":"Assymetric Noise Tailoring for Vehicle Lidar data in Extended Object Tracking","authors":"Hauke Kaulbersch, J. Honer, M. Baum","doi":"10.1109/MFI49285.2020.9235253","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235253","url":null,"abstract":"Extended target models often approximate complex structures of real-world objects. Yet, these structures can have a significant impact on the interpretation of the measurements. A prime example for such a scenario is a dimensional reduction, i.e. a target that generates three-dimensional measurements is estimated by a two-dimensional model. We present an approach that introduces asymmetric surface noise to the Random Hypersurface Model (RHM). This allows for a different generation interpretation of measurements depending on their location relative to the target surface, and in turn provides a way to model extended targets that generate measurements primarily but not exclusively at the surface. The benefits of this model are demonstrated on automotive LIDAR data and a large-scale comparison to the literature approach is provided on the Nuscenes data set.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131277691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235256
Sebastian Woischneck, D. Fränken
This paper discusses algorithms that can be used to estimate the position and possibly in addition the velocity of an object by means of bistatic measurements. Concerning position-only estimation based on bistatic range measurements, improved versions of an approximate maximum-likelihood estimator will be introduced and compared with methods known from literature. The new estimators will then be extended to also estimate velocity based on additional range-rate measurements. Simulation results confirm that the proposed estimators yield errors close to the Cramer-Rao lower bound.
{"title":"Localization and velocity estimation based on multiple bistatic measurements","authors":"Sebastian Woischneck, D. Fränken","doi":"10.1109/MFI49285.2020.9235256","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235256","url":null,"abstract":"This paper discusses algorithms that can be used to estimate the position and possibly in addition the velocity of an object by means of bistatic measurements. Concerning position-only estimation based on bistatic range measurements, improved versions of an approximate maximum-likelihood estimator will be introduced and compared with methods known from literature. The new estimators will then be extended to also estimate velocity based on additional range-rate measurements. Simulation results confirm that the proposed estimators yield errors close to the Cramer-Rao lower bound.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"359 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114712534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235216
Daniel Pollithy, Marcel Reith-Braun, F. Pfaff, U. Hanebeck
In multitarget tracking, finding an association between the new measurements and the known targets is a crucial challenge. By considering both the uncertainties of all the predictions and measurements, the most likely association can be determined. While Kalman filters inherently provide the predicted uncertainties, they require a predefined model. In contrast, neural networks offer data-driven possibilities, but provide only deterministic predictions. We therefore compare two common approaches for uncertainty estimation in neural networks applied to LSTMs using our multitarget tracking benchmark for optical belt sorting. As a result, we show that the estimation of measurement uncertainties improves the tracking results of LSTMs, posing them as a viable alternative to manual motion modeling.
{"title":"Estimating Uncertainties of Recurrent Neural Networks in Application to Multitarget Tracking","authors":"Daniel Pollithy, Marcel Reith-Braun, F. Pfaff, U. Hanebeck","doi":"10.1109/MFI49285.2020.9235216","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235216","url":null,"abstract":"In multitarget tracking, finding an association between the new measurements and the known targets is a crucial challenge. By considering both the uncertainties of all the predictions and measurements, the most likely association can be determined. While Kalman filters inherently provide the predicted uncertainties, they require a predefined model. In contrast, neural networks offer data-driven possibilities, but provide only deterministic predictions. We therefore compare two common approaches for uncertainty estimation in neural networks applied to LSTMs using our multitarget tracking benchmark for optical belt sorting. As a result, we show that the estimation of measurement uncertainties improves the tracking results of LSTMs, posing them as a viable alternative to manual motion modeling.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121841867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235230
Seyed Mahdi Shamsi, Gian Pietro Farina, Marco Gaboardi, N. Napp
We present a robotic development framework called ROSPPL, which can accomplish many of the essential probabilistic tasks that comprise modern autonomous systems and is based on a general purpose probabilistic programming language (PPL). Benefiting from ROS integration, a short PPL program in our framework is capable of controlling a robotic system, estimating its current state online, as well as automatically calibrating parameters and detecting errors, simply through probabilistic model and policy specification. The advantage of our approach lies in its generality which makes it useful for quickly designing and prototyping of new robots. By directly modeling the interconnection of random variables, decoupled from the inference engine, our design benefits from robustness, re-usability, upgradability, and ease of specification. In this paper, we use a SDV as an example of a complex autonomous system, to show how different sub-components of such system could be implemented using a probabilistic programming language, in a way that the system is capable of reasoning about itself. Our set of use-cases include localization, mapping, fault detection, calibration, and planning.
{"title":"Probabilistic Programming Languages for Modeling Autonomous Systems","authors":"Seyed Mahdi Shamsi, Gian Pietro Farina, Marco Gaboardi, N. Napp","doi":"10.1109/MFI49285.2020.9235230","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235230","url":null,"abstract":"We present a robotic development framework called ROSPPL, which can accomplish many of the essential probabilistic tasks that comprise modern autonomous systems and is based on a general purpose probabilistic programming language (PPL). Benefiting from ROS integration, a short PPL program in our framework is capable of controlling a robotic system, estimating its current state online, as well as automatically calibrating parameters and detecting errors, simply through probabilistic model and policy specification. The advantage of our approach lies in its generality which makes it useful for quickly designing and prototyping of new robots. By directly modeling the interconnection of random variables, decoupled from the inference engine, our design benefits from robustness, re-usability, upgradability, and ease of specification. In this paper, we use a SDV as an example of a complex autonomous system, to show how different sub-components of such system could be implemented using a probabilistic programming language, in a way that the system is capable of reasoning about itself. Our set of use-cases include localization, mapping, fault detection, calibration, and planning.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115805368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235220
F. Pfaff, Kailai Li, U. Hanebeck
Estimation for multiple correlated quantities generally requires considering a domain whose dimension is equal to the sum of the dimensions of the individual quantities. For multiple correlated angular quantities, considering a hyper-toroidal manifold may be required. Based on a Cartesian product of d equidistant one-dimensional grids for the unit circle, a grid for the d-dimensional hypertorus can be constructed. This grid is used for a novel filter. For n grid points, the update step is in O(n) for arbitrary likelihoods and the prediction step is in O(n2) for arbitrary transition densities. The run time of the latter can be reduced to O(n log n) for identity models with additive noise. In an evaluation scenario, the novel filter shows faster convergence than a particle filter for hypertoroidal domains and is on par with the recently proposed Fourier filters.
{"title":"Estimating Correlated Angles Using the Hypertoroidal Grid Filter","authors":"F. Pfaff, Kailai Li, U. Hanebeck","doi":"10.1109/MFI49285.2020.9235220","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235220","url":null,"abstract":"Estimation for multiple correlated quantities generally requires considering a domain whose dimension is equal to the sum of the dimensions of the individual quantities. For multiple correlated angular quantities, considering a hyper-toroidal manifold may be required. Based on a Cartesian product of d equidistant one-dimensional grids for the unit circle, a grid for the d-dimensional hypertorus can be constructed. This grid is used for a novel filter. For n grid points, the update step is in O(n) for arbitrary likelihoods and the prediction step is in O(n2) for arbitrary transition densities. The run time of the latter can be reduced to O(n log n) for identity models with additive noise. In an evaluation scenario, the novel filter shows faster convergence than a particle filter for hypertoroidal domains and is on par with the recently proposed Fourier filters.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126885853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235244
Khoder Makkawi, Nourdine Ait Tmazirte, Maan El Badaoui El Najjar, N. Moubayed
A combination of a robust optimality criterion, the Maximum Correntropy Criterion (MCC), and a powerful Fault Detection and Exclusion (FDE) strategy for a robust and fault-tolerant multi-sensor fusion approach is presented in this paper taking advantage of the information theory. The used estimator is called the MCCNIF, which is in the Nonlinear Information Filter (NIF) under the MCC. The NIF deals well with Gaussian noises but, its performance decreases when abruptly facing heavy non-Gaussian noises causing a divergence. Conversely, the NIF deals fairly with nonlinearity problems. Hence, to deal with non-Gaussian noises, the MCC shows good performance especially with shot noises and Gaussian mixture noises. To detect and exclude the erroneous measurements, an FDE layer, based on α-Rényi Divergence (α-RD) between the a priori and a posteriori probability distributions, is created. Then an adaptive threshold is calculated as a decision support based on the α-Rényi criterion (α-Rc).In order to test in real conditions the proposed framework, an autonomous vehicle multi-sensor localization example is taken. Indeed, for this application, in stringent environments (such as urban canyon, building, forests…), it is necessary to ensure both integrity and accuracy. The proposed solution is to combine the Global Navigation Satellite System (GNSS) data with the odometer (odo) data by a tight integration. The main contributions of this paper are the design and development of unique framework integrating a robust filter the MCCNIF and an FDE method using residual based on α-RD with an adaptive threshold. Real experimental data are presented and encourages the validation of the proposed approach.
{"title":"Combination of Maximum Correntropy Criterion & α-Rényi Divergence for a Robust and Fail-Safe Multi-Sensor Data Fusion","authors":"Khoder Makkawi, Nourdine Ait Tmazirte, Maan El Badaoui El Najjar, N. Moubayed","doi":"10.1109/MFI49285.2020.9235244","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235244","url":null,"abstract":"A combination of a robust optimality criterion, the Maximum Correntropy Criterion (MCC), and a powerful Fault Detection and Exclusion (FDE) strategy for a robust and fault-tolerant multi-sensor fusion approach is presented in this paper taking advantage of the information theory. The used estimator is called the MCCNIF, which is in the Nonlinear Information Filter (NIF) under the MCC. The NIF deals well with Gaussian noises but, its performance decreases when abruptly facing heavy non-Gaussian noises causing a divergence. Conversely, the NIF deals fairly with nonlinearity problems. Hence, to deal with non-Gaussian noises, the MCC shows good performance especially with shot noises and Gaussian mixture noises. To detect and exclude the erroneous measurements, an FDE layer, based on α-Rényi Divergence (α-RD) between the a priori and a posteriori probability distributions, is created. Then an adaptive threshold is calculated as a decision support based on the α-Rényi criterion (α-Rc).In order to test in real conditions the proposed framework, an autonomous vehicle multi-sensor localization example is taken. Indeed, for this application, in stringent environments (such as urban canyon, building, forests…), it is necessary to ensure both integrity and accuracy. The proposed solution is to combine the Global Navigation Satellite System (GNSS) data with the odometer (odo) data by a tight integration. The main contributions of this paper are the design and development of unique framework integrating a robust filter the MCCNIF and an FDE method using residual based on α-RD with an adaptive threshold. Real experimental data are presented and encourages the validation of the proposed approach.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129354853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235264
Gorkem Anil Al, P. Estrela, Uriel Martinez-Hernandez
In this paper, we present a multimodal sensor interface that is capable of recognizing hand gestures for human-robot interaction. The proposed system is composed of an array of proximity and gesture sensors, which have been mounted on a 3D printed bracelet. The gesture sensors are employed for data collection from four hand gesture movements (up, down, left and right) performed by the human at a predefined distance from the sensorised bracelet. The hand gesture movements are classified using Artificial Neural Networks. The proposed approach is validated with experiments in offline and real-time modes performed systematically. First, in offline mode, the accuracy for recognition of the four hand gesture movements achieved a mean of 97.86%. Second, the trained model was used for classification in real-time and achieved a mean recognition accuracy of 97.7%. The output from the recognised hand gesture in real-time mode was used to control the movement of a Universal Robot (UR3) arm in the CoppeliaSim simulation environment. Overall, the results from the experiments show that using multimodal sensors, together with computational intelligence methods, have the potential for the development of intuitive and safe human-robot interaction.
{"title":"Towards an intuitive human-robot interaction based on hand gesture recognition and proximity sensors","authors":"Gorkem Anil Al, P. Estrela, Uriel Martinez-Hernandez","doi":"10.1109/MFI49285.2020.9235264","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235264","url":null,"abstract":"In this paper, we present a multimodal sensor interface that is capable of recognizing hand gestures for human-robot interaction. The proposed system is composed of an array of proximity and gesture sensors, which have been mounted on a 3D printed bracelet. The gesture sensors are employed for data collection from four hand gesture movements (up, down, left and right) performed by the human at a predefined distance from the sensorised bracelet. The hand gesture movements are classified using Artificial Neural Networks. The proposed approach is validated with experiments in offline and real-time modes performed systematically. First, in offline mode, the accuracy for recognition of the four hand gesture movements achieved a mean of 97.86%. Second, the trained model was used for classification in real-time and achieved a mean recognition accuracy of 97.7%. The output from the recognised hand gesture in real-time mode was used to control the movement of a Universal Robot (UR3) arm in the CoppeliaSim simulation environment. Overall, the results from the experiments show that using multimodal sensors, together with computational intelligence methods, have the potential for the development of intuitive and safe human-robot interaction.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116364987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-09-14DOI: 10.1109/MFI49285.2020.9235239
D. Sacharny, T. Henderson, Michael Cline, Ben Russon, EJay Guo
The Federal Aviation Administration (FAA) and NASA have provided guidelines for Unmanned Aircraft Systems (UAS) to ensure adequate safety separation of aircraft, and in terms of UAS Traffic Management (UTM) have stated[1]:A UTM Operation should be free of 4-D intersection with all other known UTM Operations prior to departure and this should be known as Strategic Deconfliction within UTM … A UTM Operator must have a facility to negotiate deconfliction of operations with other UTM Operators … There needs to be a capability to allow for intersecting operations.The latter statement means that UTM Operators must be able to fly safely in the same geographic area. The current FAA-NASA approach to strategic deconfliction is to provide a set of geographic grid elements, and then have every new flight pairwise deconflict with UTM Operators with flights in the same grid elements. Note that this imposes a high computational burden in resolving these 4D flight paths, and has side effects in terms of limiting access to the airspace (e.g., if a new flight is deconflicted and added to the common grid elements during this analysis, then the new flight must start all over).We have proposed a lane-based approach to large-scale UAS traffic management [2], [3] which uses one-way lanes, and roundabouts at lane intersections to allow a much more efficient analysis and guarantee of separation safety. We present here the results of an in-depth comparison of FAA-NASA strategic deconfliction (FNSD) and Lane-based strategic deconfliction (LSD) and demonstrate that FNSD suffers from several types of complexity which are generally absent from the lane-based method. This algorithm is based on optimization methods which form the core origins of artificial intelligence.
{"title":"FAA-NASA vs. Lane-Based Strategic Deconfliction","authors":"D. Sacharny, T. Henderson, Michael Cline, Ben Russon, EJay Guo","doi":"10.1109/MFI49285.2020.9235239","DOIUrl":"https://doi.org/10.1109/MFI49285.2020.9235239","url":null,"abstract":"The Federal Aviation Administration (FAA) and NASA have provided guidelines for Unmanned Aircraft Systems (UAS) to ensure adequate safety separation of aircraft, and in terms of UAS Traffic Management (UTM) have stated[1]:A UTM Operation should be free of 4-D intersection with all other known UTM Operations prior to departure and this should be known as Strategic Deconfliction within UTM … A UTM Operator must have a facility to negotiate deconfliction of operations with other UTM Operators … There needs to be a capability to allow for intersecting operations.The latter statement means that UTM Operators must be able to fly safely in the same geographic area. The current FAA-NASA approach to strategic deconfliction is to provide a set of geographic grid elements, and then have every new flight pairwise deconflict with UTM Operators with flights in the same grid elements. Note that this imposes a high computational burden in resolving these 4D flight paths, and has side effects in terms of limiting access to the airspace (e.g., if a new flight is deconflicted and added to the common grid elements during this analysis, then the new flight must start all over).We have proposed a lane-based approach to large-scale UAS traffic management [2], [3] which uses one-way lanes, and roundabouts at lane intersections to allow a much more efficient analysis and guarantee of separation safety. We present here the results of an in-depth comparison of FAA-NASA strategic deconfliction (FNSD) and Lane-based strategic deconfliction (LSD) and demonstrate that FNSD suffers from several types of complexity which are generally absent from the lane-based method. This algorithm is based on optimization methods which form the core origins of artificial intelligence.","PeriodicalId":446154,"journal":{"name":"2020 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116495914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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