Pub Date : 2023-06-26DOI: 10.1109/MED59994.2023.10185834
Arash Jalilian, Norman Schwarz, Andreas Völz, Robert Ritschel
This paper investigates the task of lateral disturbance compensation based on model predictive control (MPC) for autonomous vehicles. By considering external disturbances and parameter perturbations in the model term of the MPC, the steady-state offset can be compensated. However, in the presence of more dynamic disturbances, like side wind, the lateral path tracking performance deteriorates. To overcome this limitation, a cascaded approach is presented, which is a combination of an MPC-based and an underlying direct compensation. The performance of this approach is validated in simulations as well as in practice with real vehicle tests.
{"title":"Cascaded Disturbance Compensation for MPC-based Autonomous Vehicle Guidance","authors":"Arash Jalilian, Norman Schwarz, Andreas Völz, Robert Ritschel","doi":"10.1109/MED59994.2023.10185834","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185834","url":null,"abstract":"This paper investigates the task of lateral disturbance compensation based on model predictive control (MPC) for autonomous vehicles. By considering external disturbances and parameter perturbations in the model term of the MPC, the steady-state offset can be compensated. However, in the presence of more dynamic disturbances, like side wind, the lateral path tracking performance deteriorates. To overcome this limitation, a cascaded approach is presented, which is a combination of an MPC-based and an underlying direct compensation. The performance of this approach is validated in simulations as well as in practice with real vehicle tests.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121663458","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185841
Spyridon Syntakas, K. Vlachos
This paper presents the design of a robust tube-based nonlinear Model Predictive Control (MPC) law for a triangular marine platform, that is over-actuated with three rotating jets. The goal is safe navigation and dynamic positioning of the platform under realistic wind and wave environmental disturbances, as well as real-time obstacle avoidance employing Control Barrier Functions (CBF) as constraints in the robust MPC strategy. Extensive Monte Carlo simulations have been conducted under a control allocation scheme, taking into account the actuator thrust and rotation dynamics, sensor noise, as well as additional state and input constraints. The simulation results show that the nonlinear controller ensures robust and safe navigation with obstacle avoidance and accomplishes accurate positioning of the floating platform at a given goal pose, while satisfying the actuator limits.
{"title":"Tube-based Nonlinear MPC of an Over-actuated Marine Platform for Navigation and Obstacle Avoidance using Control Barrier Functions","authors":"Spyridon Syntakas, K. Vlachos","doi":"10.1109/MED59994.2023.10185841","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185841","url":null,"abstract":"This paper presents the design of a robust tube-based nonlinear Model Predictive Control (MPC) law for a triangular marine platform, that is over-actuated with three rotating jets. The goal is safe navigation and dynamic positioning of the platform under realistic wind and wave environmental disturbances, as well as real-time obstacle avoidance employing Control Barrier Functions (CBF) as constraints in the robust MPC strategy. Extensive Monte Carlo simulations have been conducted under a control allocation scheme, taking into account the actuator thrust and rotation dynamics, sensor noise, as well as additional state and input constraints. The simulation results show that the nonlinear controller ensures robust and safe navigation with obstacle avoidance and accomplishes accurate positioning of the floating platform at a given goal pose, while satisfying the actuator limits.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121803293","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185787
A. Merola, Francesca Nesci, Donatella Dragone, F. Amato, C. Cosentino
In this paper, the mixed Finite-Time Stability (FTS)/$mathscr{H}_{infty}$ control problem is investigated for the class of nonlinear quadratic systems (NLQSs), which have several relevant applications, e.g., in robotics, systems biology and other domains of applied sciences. Sufficient conditions are provided here to solve synthesis problems, in the presence of both norm-bounded disturbances, constraints on initial and terminal conditions, and finite-time bounds on the output transient. More specifically, taking into account such constraints within the design phase, allows to achieve a desired $mathscr{H}_{infty}$ performance with nonzero initial conditions, while simultaneously guaranteeing that a given NLQS is finite-time stable for all admissible uncertainties and disturbances. Such conditions can be formulated as Linear Matrix Inequalities (LMIs) optimization problem. The applicability of the proposed results is illustrated by means of a numerical example.
{"title":"Mixed FTS/H∞ Control for Nonlinear Quadratic Systems Subject to Norm-Bounded Disturbances","authors":"A. Merola, Francesca Nesci, Donatella Dragone, F. Amato, C. Cosentino","doi":"10.1109/MED59994.2023.10185787","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185787","url":null,"abstract":"In this paper, the mixed Finite-Time Stability (FTS)/$mathscr{H}_{infty}$ control problem is investigated for the class of nonlinear quadratic systems (NLQSs), which have several relevant applications, e.g., in robotics, systems biology and other domains of applied sciences. Sufficient conditions are provided here to solve synthesis problems, in the presence of both norm-bounded disturbances, constraints on initial and terminal conditions, and finite-time bounds on the output transient. More specifically, taking into account such constraints within the design phase, allows to achieve a desired $mathscr{H}_{infty}$ performance with nonzero initial conditions, while simultaneously guaranteeing that a given NLQS is finite-time stable for all admissible uncertainties and disturbances. Such conditions can be formulated as Linear Matrix Inequalities (LMIs) optimization problem. The applicability of the proposed results is illustrated by means of a numerical example.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126368424","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185721
Z. Abdin, A. Rachid
Photovoltaic-thermal (PV-T) systems are expected to fulfil an increasingly vital role in future energy production. The current research endeavors to showcase machine learning modeling and control of a water-based PV-T collector. In this work, the PV-T collector is modeled using a decision tree algorithm and artificial neural network (ANN). The predicted outputs are compared with the actual outputs to validate the models. The ANN-based model performed better and proved its efficacy in training and testing. Further, various control strategies are implemented and their performance is compared. All the techniques presented are illustrated through simulation results.
{"title":"Modeling and control of a hybrid PV-T collector using machine learning","authors":"Z. Abdin, A. Rachid","doi":"10.1109/MED59994.2023.10185721","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185721","url":null,"abstract":"Photovoltaic-thermal (PV-T) systems are expected to fulfil an increasingly vital role in future energy production. The current research endeavors to showcase machine learning modeling and control of a water-based PV-T collector. In this work, the PV-T collector is modeled using a decision tree algorithm and artificial neural network (ANN). The predicted outputs are compared with the actual outputs to validate the models. The ANN-based model performed better and proved its efficacy in training and testing. Further, various control strategies are implemented and their performance is compared. All the techniques presented are illustrated through simulation results.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133896188","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185863
Oussama Djadane, Salama Makni, A. Hajjaji
This paper is concerned with the VGT (Variable Geometry Turbocharger) and EGR (Exhaust Gas Recirculation) control design for the air path of a Diesel engine. The purpose is to validate a Diesel engine model by choosing the right operating conditions based on a series of open-loop tests. For the control design, an observer-based control with integrator is developed for the estimation of the compressor power and a good tracking of reference pressure signals which correspond to emission standards. The efficiency of this method is illustrated through simulation results on the Amesim software.
{"title":"Observer-based state feedback air path control for a turbocharged diesel engine with EGR and VGT","authors":"Oussama Djadane, Salama Makni, A. Hajjaji","doi":"10.1109/MED59994.2023.10185863","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185863","url":null,"abstract":"This paper is concerned with the VGT (Variable Geometry Turbocharger) and EGR (Exhaust Gas Recirculation) control design for the air path of a Diesel engine. The purpose is to validate a Diesel engine model by choosing the right operating conditions based on a series of open-loop tests. For the control design, an observer-based control with integrator is developed for the estimation of the compressor power and a good tracking of reference pressure signals which correspond to emission standards. The efficiency of this method is illustrated through simulation results on the Amesim software.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131187636","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185840
Elisa Mostacciuolo, L. Iannelli, Silvio Baccari, F. Vasca
The problem of simultaneous online co-estimation of the battery state of charge (SOC) and the parameters of the open circuit voltage (OCV) vs. SOC characteristic is investigated. It is shown that any co-estimation technique requires at least one known point in the function that approximates the OCV vs. SOC map. A third-order bilinear co-estimator based on the equivalent circuit model of the battery is then proposed and its well-posedness is analyzed. The technique is validated on real data coming from an automotive application.
{"title":"An interlaced co-estimation technique for batteries","authors":"Elisa Mostacciuolo, L. Iannelli, Silvio Baccari, F. Vasca","doi":"10.1109/MED59994.2023.10185840","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185840","url":null,"abstract":"The problem of simultaneous online co-estimation of the battery state of charge (SOC) and the parameters of the open circuit voltage (OCV) vs. SOC characteristic is investigated. It is shown that any co-estimation technique requires at least one known point in the function that approximates the OCV vs. SOC map. A third-order bilinear co-estimator based on the equivalent circuit model of the battery is then proposed and its well-posedness is analyzed. The technique is validated on real data coming from an automotive application.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127323805","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185791
Kisan Sarda, A. Yerudkar, C. D. Vecchio
With the increasing interconnectivity of cyber-physical systems (CPSs) in various fields, such as manufacturing plants, power plants, and smart networked systems, large amounts of multivariate data are generated through sensors and actuators, also other data sources such as measurements and images. This paper focuses on the anomaly detection (AD) problem, also known as fault detection or outlier detection, depending on the type of dataset, which involves identifying anomalous values in the dataset using analytical methods. However, datasets often contain missing values, which can lead to incorrect outcomes and affect the availability of anomalous samples that are fewer in amount, making incomplete datasets. Therefore, a generalized AD method is proposed for incomplete datasets, which involves two steps: data imputation (DI) to obtain complete datasets using GAN and later AD for the complete datasets. While statistical-based imputation methods are commonly used, they do not consider data distribution for datasets with anomalous samples. The capabilities of GANbased DI are tested under different hyperparameter settings and percentages of missing values. The AD problem is then addressed using seven unsupervised anomaly detection methods on six different datasets, including a real dataset from a steel manufacturing plant in Italy. Each dataset is analyzed to determine which DI and AD method combination performs the best. The results show that GAN-imputed data provides the best DI performance, while the reweighted minimum covariance determinant (RMCD) method offers the overall best AD results combined with GAN.
{"title":"Unsupervised Anomaly Detection for Multivariate Incomplete Data using GAN-based Data Imputation: A Comparative Study","authors":"Kisan Sarda, A. Yerudkar, C. D. Vecchio","doi":"10.1109/MED59994.2023.10185791","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185791","url":null,"abstract":"With the increasing interconnectivity of cyber-physical systems (CPSs) in various fields, such as manufacturing plants, power plants, and smart networked systems, large amounts of multivariate data are generated through sensors and actuators, also other data sources such as measurements and images. This paper focuses on the anomaly detection (AD) problem, also known as fault detection or outlier detection, depending on the type of dataset, which involves identifying anomalous values in the dataset using analytical methods. However, datasets often contain missing values, which can lead to incorrect outcomes and affect the availability of anomalous samples that are fewer in amount, making incomplete datasets. Therefore, a generalized AD method is proposed for incomplete datasets, which involves two steps: data imputation (DI) to obtain complete datasets using GAN and later AD for the complete datasets. While statistical-based imputation methods are commonly used, they do not consider data distribution for datasets with anomalous samples. The capabilities of GANbased DI are tested under different hyperparameter settings and percentages of missing values. The AD problem is then addressed using seven unsupervised anomaly detection methods on six different datasets, including a real dataset from a steel manufacturing plant in Italy. Each dataset is analyzed to determine which DI and AD method combination performs the best. The results show that GAN-imputed data provides the best DI performance, while the reweighted minimum covariance determinant (RMCD) method offers the overall best AD results combined with GAN.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114499416","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185776
Kyriacos Theocharides, C. Menelaou, Y. Englezou, S. Timotheou
Traffic state estimation (TSE) is a challenging task due to the collection of sparse and noisy measurements from fixed points in the traffic network. Unmanned Aerial Vehicles (UAVs) have been gaining popularity as traffic sensors due to their ability to monitor a number of important traffic parameters over space and time. In this work, we develop a novel UAV-based sensing architecture which provides sparse, noisy measurements of traffic densities and transfer flows of the traffic network. Assuming free-flow conditions, we construct a Kalman filter approach that utilises knowledge of regional split ratios along with the UAV-based measurements. To avoid the assumption of known split ratios, we further develop a weighted least-squares optimization approach that minimizes measurement and process errors over a moving horizon window subject to linear traffic dynamics to accurately estimate traffic densities. We compare the UAV-based sensing architecture to an all-measurement method where we assume that measurements for all traffic densities and transfer flows are available at every time-step. Results show that the UAV-based sensing architecture compares favourably to the all-measurement scenario and the proposed optimization based estimator achieves similar results to the Kalman filter, even when regional split ratios are unknown.
{"title":"Towards efficient traffic state estimation using sparse UAV-based data in urban networks","authors":"Kyriacos Theocharides, C. Menelaou, Y. Englezou, S. Timotheou","doi":"10.1109/MED59994.2023.10185776","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185776","url":null,"abstract":"Traffic state estimation (TSE) is a challenging task due to the collection of sparse and noisy measurements from fixed points in the traffic network. Unmanned Aerial Vehicles (UAVs) have been gaining popularity as traffic sensors due to their ability to monitor a number of important traffic parameters over space and time. In this work, we develop a novel UAV-based sensing architecture which provides sparse, noisy measurements of traffic densities and transfer flows of the traffic network. Assuming free-flow conditions, we construct a Kalman filter approach that utilises knowledge of regional split ratios along with the UAV-based measurements. To avoid the assumption of known split ratios, we further develop a weighted least-squares optimization approach that minimizes measurement and process errors over a moving horizon window subject to linear traffic dynamics to accurately estimate traffic densities. We compare the UAV-based sensing architecture to an all-measurement method where we assume that measurements for all traffic densities and transfer flows are available at every time-step. Results show that the UAV-based sensing architecture compares favourably to the all-measurement scenario and the proposed optimization based estimator achieves similar results to the Kalman filter, even when regional split ratios are unknown.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117161265","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185716
Steve Yuwono, Andreas Schwung
In the modern era of manufacturing automation, the integration of sensor technology into the system ensures that data acquisition and analysis from complex systems become more efficient than ever. With the support of such developments, artificial intelligence-powered control in industrial control domains gains popularity and enhances the traditional human-based PLC control, where the machines can monitor themselves, learn from the experience, and make their own decisions. However, despite advances in sensor technologies, there are some limitations of the current applications of sensors in industries, for instance, sensors for observing the current status of the system often provide Boolean output data instead of continuous output. Therefore, such limitation forms a low dimensional state representation of the system, which could be problematic to develop a self-control policy, e.g. using a model-free deep reinforcement learning. In this paper, we present an effective model predictive controller with adaptive PLC-based policy on low dimensional state representation specifically for industrial control domains. First, we learn the model of the production system using the deep learning method to get the representation of the system dynamics, in case its digital representation is not available. Second, we set up a native implementation of model predictive control. Third, we enhance the model predictive control with adaptive PLC-based policy. The proposed method is implemented into a bulk good system showing its potential to self-optimize the system by satisfying the production objective without overflow and low power consumption.
{"title":"Model Predictive Control with Adaptive PLC-based Policy on Low Dimensional State Representation for Industrial Applications","authors":"Steve Yuwono, Andreas Schwung","doi":"10.1109/MED59994.2023.10185716","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185716","url":null,"abstract":"In the modern era of manufacturing automation, the integration of sensor technology into the system ensures that data acquisition and analysis from complex systems become more efficient than ever. With the support of such developments, artificial intelligence-powered control in industrial control domains gains popularity and enhances the traditional human-based PLC control, where the machines can monitor themselves, learn from the experience, and make their own decisions. However, despite advances in sensor technologies, there are some limitations of the current applications of sensors in industries, for instance, sensors for observing the current status of the system often provide Boolean output data instead of continuous output. Therefore, such limitation forms a low dimensional state representation of the system, which could be problematic to develop a self-control policy, e.g. using a model-free deep reinforcement learning. In this paper, we present an effective model predictive controller with adaptive PLC-based policy on low dimensional state representation specifically for industrial control domains. First, we learn the model of the production system using the deep learning method to get the representation of the system dynamics, in case its digital representation is not available. Second, we set up a native implementation of model predictive control. Third, we enhance the model predictive control with adaptive PLC-based policy. The proposed method is implemented into a bulk good system showing its potential to self-optimize the system by satisfying the production objective without overflow and low power consumption.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116031978","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 : 2023-06-26DOI: 10.1109/MED59994.2023.10185802
Athanasios Sersemis, Alexandros Papadopoulos, Georgios Spanos, Antonios Lalas, K. Votis, D. Tzovaras
The topic of in-vehicle and V2X communication in autonomous vehicles consists of a variety of different communication protocols, mechanisms, and devices. The implementation and cooperation between these entities and protocols in such a complex system is a rigorous and complicated process that should not only be efficient, robust, flexible, and scalable, but also secure. The security of critical systems such as autonomous vehicles requires a deep understanding of all the individual and distinct components that compose the system. This paper presents a cybersecurity architecture having as purpose to shield the communication security in the autonomous vehicles. For this reason, several well-established cybersecurity tools (e.g. Keycloak, Cloudflare) and communication mechanisms (e.g. MQTT, Kafka) have been combined in this architecture along with a novel statistical-based Intrusion Detection System. All the aforementioned cybersecurity defense mechanisms were selected to protect the entire system pipeline and meet the requirements for Confidentiality, Integrity, and Availability regarding vehicle communication. To test the performance of the proposed architecture abnormal data have been injected to the system and the results from the experiments conducted highlighted that the proposed solution can achieve its purpose of increased cybersecurity.
{"title":"Cybersecurity Oriented Architecture to Ensure the Autonomous Vehicles Communication","authors":"Athanasios Sersemis, Alexandros Papadopoulos, Georgios Spanos, Antonios Lalas, K. Votis, D. Tzovaras","doi":"10.1109/MED59994.2023.10185802","DOIUrl":"https://doi.org/10.1109/MED59994.2023.10185802","url":null,"abstract":"The topic of in-vehicle and V2X communication in autonomous vehicles consists of a variety of different communication protocols, mechanisms, and devices. The implementation and cooperation between these entities and protocols in such a complex system is a rigorous and complicated process that should not only be efficient, robust, flexible, and scalable, but also secure. The security of critical systems such as autonomous vehicles requires a deep understanding of all the individual and distinct components that compose the system. This paper presents a cybersecurity architecture having as purpose to shield the communication security in the autonomous vehicles. For this reason, several well-established cybersecurity tools (e.g. Keycloak, Cloudflare) and communication mechanisms (e.g. MQTT, Kafka) have been combined in this architecture along with a novel statistical-based Intrusion Detection System. All the aforementioned cybersecurity defense mechanisms were selected to protect the entire system pipeline and meet the requirements for Confidentiality, Integrity, and Availability regarding vehicle communication. To test the performance of the proposed architecture abnormal data have been injected to the system and the results from the experiments conducted highlighted that the proposed solution can achieve its purpose of increased cybersecurity.","PeriodicalId":270226,"journal":{"name":"2023 31st Mediterranean Conference on Control and Automation (MED)","volume":"12 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123724778","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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