Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294450
Georgios Makridis, D. Kyriazis, Stathis Plitsos
One of the key challenges in the maritime industry refers to minimizing the time a vessel cannot be utilized, which has multiple effects. The latter is addressed through maintenance approaches that however in many cases are not efficient in terms of cost and downtime. Predictive maintenance provides optimized maintenance scheduling offering extended vessel lifespan, coupled with reduced maintenance costs. As in several industries, including the maritime domain, an increasing amount of data is made available through the deployment and exploitation of data sources, such as on board sensors that provide real-time information. These data provide the required ground for analysis and thus support for various types of data-driven decision making. In the maritime domain, sensors are deployed on vessels to monitor their engines and data analysis tools are needed to assist engineers towards reduced operational risk through predictive maintenance solutions that are put in place. In this paper, we present an approach for anomaly detection on time-series data, utilizing machine learning on the vessels sensor data, in order to predict the condition of specific parts of the vessel’s main engine and thus facilitate predictive maintenance. The novel characteristic of the proposed approach refers both to the inclusion of new innovative models to address the case of predictive maintenance in maritime and the combination of those different models, highlighting an improved result in terms of evaluation metrics.
{"title":"Predictive maintenance leveraging machine learning for time-series forecasting in the maritime industry","authors":"Georgios Makridis, D. Kyriazis, Stathis Plitsos","doi":"10.1109/ITSC45102.2020.9294450","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294450","url":null,"abstract":"One of the key challenges in the maritime industry refers to minimizing the time a vessel cannot be utilized, which has multiple effects. The latter is addressed through maintenance approaches that however in many cases are not efficient in terms of cost and downtime. Predictive maintenance provides optimized maintenance scheduling offering extended vessel lifespan, coupled with reduced maintenance costs. As in several industries, including the maritime domain, an increasing amount of data is made available through the deployment and exploitation of data sources, such as on board sensors that provide real-time information. These data provide the required ground for analysis and thus support for various types of data-driven decision making. In the maritime domain, sensors are deployed on vessels to monitor their engines and data analysis tools are needed to assist engineers towards reduced operational risk through predictive maintenance solutions that are put in place. In this paper, we present an approach for anomaly detection on time-series data, utilizing machine learning on the vessels sensor data, in order to predict the condition of specific parts of the vessel’s main engine and thus facilitate predictive maintenance. The novel characteristic of the proposed approach refers both to the inclusion of new innovative models to address the case of predictive maintenance in maritime and the combination of those different models, highlighting an improved result in terms of evaluation metrics.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132638593","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294736
Xiangkun He, Cong Fei, Yulong Liu, Kaiming Yang, Xuewu Ji
The challenging task of “autonomous electric vehicle” opens up a new frontier to improving traffic, saving energy and reducing emission. However, many driving decision-making problems are characterized by multiple competing objectives whose relative importance is dynamic, and that makes developing high-performance decision-making system difficult. Therefore, this paper proposes a novel entropy-constrained reinforcement learning (RL) scheme for multi-objective longitudinal decision-making of autonomous electric vehicle. Firstly, in order to prevent the policy from prematurely converging to a local optimum, the policy’s entropy is embedded in proximal policy optimization (PPO) algorithm based on actor-critic architecture. Secondly, a self-adjusting mechanism to the weight of entropy is developed to accelerate model training and improve algorithm stability through entropy constraint. Thirdly, multimodal reward signals are designed to guide the RL agent learning complex multi-modal driving policies by considering safety, comfort, economy and transport efficiency. Finally, simulation results show that, the proposed longitudinal decision-making approach for autonomous electric vehicle is feasible and effective.
{"title":"Multi-objective Longitudinal Decision-making for Autonomous Electric Vehicle: A Entropy-constrained Reinforcement Learning Approach","authors":"Xiangkun He, Cong Fei, Yulong Liu, Kaiming Yang, Xuewu Ji","doi":"10.1109/ITSC45102.2020.9294736","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294736","url":null,"abstract":"The challenging task of “autonomous electric vehicle” opens up a new frontier to improving traffic, saving energy and reducing emission. However, many driving decision-making problems are characterized by multiple competing objectives whose relative importance is dynamic, and that makes developing high-performance decision-making system difficult. Therefore, this paper proposes a novel entropy-constrained reinforcement learning (RL) scheme for multi-objective longitudinal decision-making of autonomous electric vehicle. Firstly, in order to prevent the policy from prematurely converging to a local optimum, the policy’s entropy is embedded in proximal policy optimization (PPO) algorithm based on actor-critic architecture. Secondly, a self-adjusting mechanism to the weight of entropy is developed to accelerate model training and improve algorithm stability through entropy constraint. Thirdly, multimodal reward signals are designed to guide the RL agent learning complex multi-modal driving policies by considering safety, comfort, economy and transport efficiency. Finally, simulation results show that, the proposed longitudinal decision-making approach for autonomous electric vehicle is feasible and effective.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132800873","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294369
A. Esser, S. Rinderknecht
Synthetic Driving Cycles have been used in numerous studies to describe a certain driving profile of relevance. An important purpose of synthetic cycles is to limit the necessary time on a test-rig or to reduce the computational effort within simulations, which is achieved by compressing a larger amount of gathered operating data from a certain vehicle or a vehicle fleet to a necessary minimum. Interestingly, despite the intensive use of the synthetic driving cycles, there is only limited literature on the validation of using synthetic driving cycles. Therefore, the scope of this work is to further investigate under which conditions synthetic driving cycles can be used to replace the entirety of the relevant operating data in the evaluation of a vehicle’s consumption. We apply a longitudinal vehicle simulation model to calculate the fuel and electric consumption of vehicles with different powertrain concepts on many generated synthetic driving cycles for different compression rates. We then compare that to the consumption if considering the original driving data. A legislative driving cycle (WLTC) as well as naturalistic driving data sets are used for the evaluation. The results show, that synthetic driving cycles allow for a compact representation of the original data sets but possible compression rates depend on the specific driving data. The presented two-step process can be extended to a generalized validation process for the use of synthetic driving cycles.
{"title":"Process for the Validation of Using Synthetic Driving Cycles Based on Naturalistic Driving Data Sets","authors":"A. Esser, S. Rinderknecht","doi":"10.1109/ITSC45102.2020.9294369","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294369","url":null,"abstract":"Synthetic Driving Cycles have been used in numerous studies to describe a certain driving profile of relevance. An important purpose of synthetic cycles is to limit the necessary time on a test-rig or to reduce the computational effort within simulations, which is achieved by compressing a larger amount of gathered operating data from a certain vehicle or a vehicle fleet to a necessary minimum. Interestingly, despite the intensive use of the synthetic driving cycles, there is only limited literature on the validation of using synthetic driving cycles. Therefore, the scope of this work is to further investigate under which conditions synthetic driving cycles can be used to replace the entirety of the relevant operating data in the evaluation of a vehicle’s consumption. We apply a longitudinal vehicle simulation model to calculate the fuel and electric consumption of vehicles with different powertrain concepts on many generated synthetic driving cycles for different compression rates. We then compare that to the consumption if considering the original driving data. A legislative driving cycle (WLTC) as well as naturalistic driving data sets are used for the evaluation. The results show, that synthetic driving cycles allow for a compact representation of the original data sets but possible compression rates depend on the specific driving data. The presented two-step process can be extended to a generalized validation process for the use of synthetic driving cycles.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130801921","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294607
Zhexian Li, M. Levin, Raphael Stem, Xu Qu
In this study, we develop a traffic model to simulate network traffic evolution under the impact of controlled autonomous vehicles acting as moving bottlenecks. We first extend the Newell-Daganzo method to track the trajectories of moving bottlenecks and calculate the cumulative number of vehicles passing moving bottlenecks. By integrating the solutions to the cumulative number of vehicles passing moving bottlenecks and link nodes as boundary conditions in the link-transmission models, we can incorporate the impact of moving bottlenecks into the flow of traffic at a network scale. The numerical simulation results demonstrate the effectiveness of the developed model to track trajectories of the moving bottlenecks and simulate their impact on freeway traffic.
{"title":"A network traffic model with controlled autonomous vehicles acting as moving bottlenecks","authors":"Zhexian Li, M. Levin, Raphael Stem, Xu Qu","doi":"10.1109/ITSC45102.2020.9294607","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294607","url":null,"abstract":"In this study, we develop a traffic model to simulate network traffic evolution under the impact of controlled autonomous vehicles acting as moving bottlenecks. We first extend the Newell-Daganzo method to track the trajectories of moving bottlenecks and calculate the cumulative number of vehicles passing moving bottlenecks. By integrating the solutions to the cumulative number of vehicles passing moving bottlenecks and link nodes as boundary conditions in the link-transmission models, we can incorporate the impact of moving bottlenecks into the flow of traffic at a network scale. The numerical simulation results demonstrate the effectiveness of the developed model to track trajectories of the moving bottlenecks and simulate their impact on freeway traffic.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130978047","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294256
Evangelos Mintsis, L. Lücken, V. Karagounis, Kallirroi N. Porfyri, Michele Rondinone, A. Correa, Julian Schindler, E. Mitsakis
Highway work zones can induce significant delays and undermine traffic safety. The recent advent of connected and automated vehicles (CAVs) can pose an additional threat to traffic flow performance and safety around highway work zones. CAVs equipped with low – medium level automation systems that cannot reliably address work zone scenarios under all circumstances could induce control transitions and imminent Minimum Risk Manoeuvers (MRMs) that would result in significant traffic disruption and multiple safety critical events. The latter negative effects could be mitigated via the introduction of highly automated vehicles that could utilize sophisticated infrastructure assistance to traverse highway work zones without disengaging automation systems. This study develops novel and utilizes existing vehicle-driver models to simulate manual driving, mixed traffic and infrastructure-assisted highly automated traffic around highway work zones. Traffic operations are evaluated for the latter fleet mixes and three different traffic demand levels. Simulation results indicate that joint deployment of infrastructure-assisted traffic management and cooperative driving can ensure increased traffic efficiency and safety levels for high traffic intensity in a fully connected and automated road environment.
{"title":"Joint Deployment of Infrastructure-Assisted Traffic Management and Cooperative Driving around Work Zones","authors":"Evangelos Mintsis, L. Lücken, V. Karagounis, Kallirroi N. Porfyri, Michele Rondinone, A. Correa, Julian Schindler, E. Mitsakis","doi":"10.1109/ITSC45102.2020.9294256","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294256","url":null,"abstract":"Highway work zones can induce significant delays and undermine traffic safety. The recent advent of connected and automated vehicles (CAVs) can pose an additional threat to traffic flow performance and safety around highway work zones. CAVs equipped with low – medium level automation systems that cannot reliably address work zone scenarios under all circumstances could induce control transitions and imminent Minimum Risk Manoeuvers (MRMs) that would result in significant traffic disruption and multiple safety critical events. The latter negative effects could be mitigated via the introduction of highly automated vehicles that could utilize sophisticated infrastructure assistance to traverse highway work zones without disengaging automation systems. This study develops novel and utilizes existing vehicle-driver models to simulate manual driving, mixed traffic and infrastructure-assisted highly automated traffic around highway work zones. Traffic operations are evaluated for the latter fleet mixes and three different traffic demand levels. Simulation results indicate that joint deployment of infrastructure-assisted traffic management and cooperative driving can ensure increased traffic efficiency and safety levels for high traffic intensity in a fully connected and automated road environment.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132924746","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294487
E. Sofronova, A. Diveev
A traffic flows optimal control problem in an urban road network is considered. It is assumed that all information on the traffic flows and maneuvers is known. The optimal control problem is to find duration of signal phases at controlled intersections that provide optimal traffic estimation in the considered road network. optimization criteria include penalties for violation of constraints. To find the optimal control program a variation genetic algorithm is used. The traffic flow model under study is a mathematical model based on the controlled networks theory. Some properties of the model are discussed. The problem of determining the critical states is formulated. An optimal control problem for a network of four controlled intersections is solved.
{"title":"Traffic Flows Optimal Control Problem with Full Information*","authors":"E. Sofronova, A. Diveev","doi":"10.1109/ITSC45102.2020.9294487","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294487","url":null,"abstract":"A traffic flows optimal control problem in an urban road network is considered. It is assumed that all information on the traffic flows and maneuvers is known. The optimal control problem is to find duration of signal phases at controlled intersections that provide optimal traffic estimation in the considered road network. optimization criteria include penalties for violation of constraints. To find the optimal control program a variation genetic algorithm is used. The traffic flow model under study is a mathematical model based on the controlled networks theory. Some properties of the model are discussed. The problem of determining the critical states is formulated. An optimal control problem for a network of four controlled intersections is solved.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133042152","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294722
S. Belyaev, I. Popov, V. Shubnikov, P. Popov, E. Boltenkova, Daniil A. Savchuk
Recent advances in machine learning research could significantly alter the railroad industry by deploying fully autonomous trains. To achieve effective interaction between self-driving trains and the environment, an accurate long-range railway detection should be provided. In this paper, we propose a framework for the rail tracks segmentation on high-resolution images ($2168times 4096$). The announced approach accelerates inference speed 6 times, by using two neural networks. The proposed architecture and its training approach provide a long-range railway segmentation within 150 meters, achieving 20 fps. Also, we propose an auxiliary algorithm detecting possible paths among all the found ones. To determine which data labeling approach has a higher impact, additional experiments were performed. The proposed framework provides a balanced tradeoff between computing efficiency and performance in the railroad segmentation problem.
{"title":"Railroad semantic segmentation on high-resolution images","authors":"S. Belyaev, I. Popov, V. Shubnikov, P. Popov, E. Boltenkova, Daniil A. Savchuk","doi":"10.1109/ITSC45102.2020.9294722","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294722","url":null,"abstract":"Recent advances in machine learning research could significantly alter the railroad industry by deploying fully autonomous trains. To achieve effective interaction between self-driving trains and the environment, an accurate long-range railway detection should be provided. In this paper, we propose a framework for the rail tracks segmentation on high-resolution images ($2168times 4096$). The announced approach accelerates inference speed 6 times, by using two neural networks. The proposed architecture and its training approach provide a long-range railway segmentation within 150 meters, achieving 20 fps. Also, we propose an auxiliary algorithm detecting possible paths among all the found ones. To determine which data labeling approach has a higher impact, additional experiments were performed. The proposed framework provides a balanced tradeoff between computing efficiency and performance in the railroad segmentation problem.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133378818","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294602
Fei Yang, Yuan Shen
Vehicle scheduling at the intersection is a challenging topic for the intelligent transportation system (ITS), and various efficient methods have been developed based on different methods. Essentially, compared with the traditional traffic-light-based approach, the improvement of the scheduling performance comes from the increasing information on the states of vehicles collected by the controller. In this paper, we focus on specifying the relative significance of the information for the scheduling performance and propose an efficient vehicle scheduling algorithm when the total information is constrained. Specifically, the scheduling problem is modelled as a sequential decision process, and the required information is the arrival times of vehicles. We first propose a method to determine the decision time sequence by analyzing the state of the intersection. Then an adaptive scheduling algorithm is developed at the decision times, where the information demand is different for each decision according to its relative importance defined by the regret of a wrong choice. The proposed algorithm is verified by simulations that competitive performance can be obtained with much less information required.
{"title":"An Adaptive Approach for Intersection Vehicle Scheduling with Limited State Information","authors":"Fei Yang, Yuan Shen","doi":"10.1109/ITSC45102.2020.9294602","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294602","url":null,"abstract":"Vehicle scheduling at the intersection is a challenging topic for the intelligent transportation system (ITS), and various efficient methods have been developed based on different methods. Essentially, compared with the traditional traffic-light-based approach, the improvement of the scheduling performance comes from the increasing information on the states of vehicles collected by the controller. In this paper, we focus on specifying the relative significance of the information for the scheduling performance and propose an efficient vehicle scheduling algorithm when the total information is constrained. Specifically, the scheduling problem is modelled as a sequential decision process, and the required information is the arrival times of vehicles. We first propose a method to determine the decision time sequence by analyzing the state of the intersection. Then an adaptive scheduling algorithm is developed at the decision times, where the information demand is different for each decision according to its relative importance defined by the regret of a wrong choice. The proposed algorithm is verified by simulations that competitive performance can be obtained with much less information required.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133620043","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294353
Kaiping Wang, Rong Yang, Xi Lin, Fang He, M. Li
With the recent development of Unmanned Aerial Vehicles (UAV) applications, traffic police might utilize UAV to conduct Service Patrol (SP) tasks. However, a major limitation of existing UAV systems is their limited flight endurance. To address this issue, by implementing the auto-rechargeable mechanism, we explicitly optimize hardware setting and system strategy required for regional SP with predefined initial tasks and stochastic incidents by solving a heuristic facility location problem and multi-objective path planning problem based on cooperative auto-recharging facilities, and fleet management center. The proposed fleet size and system performance are leveraged in a grid network with respect to different infrastructure settings and service coverage. The field experiments were conducted in Xi’an for SP tasks in complete vehicle coverage trajectory reconstruction, and results show that the proposed system is capable of unmanned SP tasks and large-scale application in urban scenarios.
{"title":"Automated and Connected Unmanned Aerial Vehicles (AC-UAV) for Service Patrol: System Design and Field Experiments","authors":"Kaiping Wang, Rong Yang, Xi Lin, Fang He, M. Li","doi":"10.1109/ITSC45102.2020.9294353","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294353","url":null,"abstract":"With the recent development of Unmanned Aerial Vehicles (UAV) applications, traffic police might utilize UAV to conduct Service Patrol (SP) tasks. However, a major limitation of existing UAV systems is their limited flight endurance. To address this issue, by implementing the auto-rechargeable mechanism, we explicitly optimize hardware setting and system strategy required for regional SP with predefined initial tasks and stochastic incidents by solving a heuristic facility location problem and multi-objective path planning problem based on cooperative auto-recharging facilities, and fleet management center. The proposed fleet size and system performance are leveraged in a grid network with respect to different infrastructure settings and service coverage. The field experiments were conducted in Xi’an for SP tasks in complete vehicle coverage trajectory reconstruction, and results show that the proposed system is capable of unmanned SP tasks and large-scale application in urban scenarios.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133693049","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 : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294519
Alyssa Byrnes, C. Sturton
In situations where humans and computers cooperate, mode confusion on the part of the human can be dangerous. We present a methodology to evaluate a semi-autonomous system for its support of mode awareness. The methodology uses discrete-state model checking of the specification and real-valued model checking of the system in operation. Using the ISO standard for adaptive cruise control as a case study, we exhaustively enumerate the instances of four design flaws known to contribute to mode confusion. We then build a real-valued model of eight driving scenarios to find which of those instances of possible mode confusion may lead to a dangerous situation. We find 116 property violations, and determine 62 of them to be potentially dangerous.
{"title":"Evaluating a Specification for its Support of Mode Awareness using Discrete and Continuous Model Checking","authors":"Alyssa Byrnes, C. Sturton","doi":"10.1109/ITSC45102.2020.9294519","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294519","url":null,"abstract":"In situations where humans and computers cooperate, mode confusion on the part of the human can be dangerous. We present a methodology to evaluate a semi-autonomous system for its support of mode awareness. The methodology uses discrete-state model checking of the specification and real-valued model checking of the system in operation. Using the ISO standard for adaptive cruise control as a case study, we exhaustively enumerate the instances of four design flaws known to contribute to mode confusion. We then build a real-valued model of eight driving scenarios to find which of those instances of possible mode confusion may lead to a dangerous situation. We find 116 property violations, and determine 62 of them to be potentially dangerous.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133646517","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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