Cuthbert Ruseruka, Judith Mwakalonge, G. Comert, Saidi Siuhi, Judy Perkins
Road authorities worldwide can leverage the advances in vehicle technology by continuously monitoring their roads’ conditions to minimize road maintenance costs. The existing methods for carrying out road condition surveys involve manual observations using standard survey forms, performed by qualified personnel. These methods are expensive, time-consuming, infrequent, and can hardly provide real-time information. Some automated approaches also exist but are very expensive since they require special vehicles equipped with computing devices and sensors for data collection and processing. This research aims to leverage the advances in vehicle technology in providing a cheap and real-time approach to carry out road condition monitoring (RCM). This study developed a deep learning model using the You Only Look Once, Version 5 (YOLOv5) algorithm that was trained to capture and categorize flexible pavement distresses (FPD) and reached 95% precision, 93.4% recall, and 97.2% mean Average Precision. Using vehicle built-in cameras and GPS sensors, these distresses were detected, images were captured, and locations were recorded. This was validated on campus roads and parking lots using a car featured with a built-in camera and GPS. The vehicles’ built-in technologies provided a more cost-effective and efficient road condition monitoring approach that could also provide real-time road conditions.
世界各地的道路管理部门可以利用车辆技术的进步，持续监测道路状况，最大限度地降低道路维护成本。进行道路状况调查的现有方法包括由合格人员使用标准调查表格进行人工观察。这些方法昂贵、耗时、不频繁，而且很难提供实时信息。一些自动化方法也存在，但非常昂贵，因为它们需要配备计算设备和传感器的特殊车辆来收集和处理数据。这项研究旨在利用先进的车辆技术，提供一种廉价和实时的方法来进行道路状况监测(RCM)。本研究使用You Only Look Once, Version 5 (YOLOv5)算法开发了一个深度学习模型，该模型经过训练，可以捕捉和分类柔性路面破损(FPD)，准确率达到95%，召回率为93.4%，平均平均精度为97.2%。使用车载内置摄像头和GPS传感器，检测到这些遇险，捕获图像并记录位置。在校园道路和停车场上，使用了一辆内置摄像头和GPS的汽车进行了验证。这些车辆的内置技术提供了一种更具成本效益和效率的路况监测方法，还可以提供实时路况。
{"title":"Road Condition Monitoring Using Vehicle Built-in Cameras and GPS Sensors: A Deep Learning Approach","authors":"Cuthbert Ruseruka, Judith Mwakalonge, G. Comert, Saidi Siuhi, Judy Perkins","doi":"10.3390/vehicles5030051","DOIUrl":"https://doi.org/10.3390/vehicles5030051","url":null,"abstract":"Road authorities worldwide can leverage the advances in vehicle technology by continuously monitoring their roads’ conditions to minimize road maintenance costs. The existing methods for carrying out road condition surveys involve manual observations using standard survey forms, performed by qualified personnel. These methods are expensive, time-consuming, infrequent, and can hardly provide real-time information. Some automated approaches also exist but are very expensive since they require special vehicles equipped with computing devices and sensors for data collection and processing. This research aims to leverage the advances in vehicle technology in providing a cheap and real-time approach to carry out road condition monitoring (RCM). This study developed a deep learning model using the You Only Look Once, Version 5 (YOLOv5) algorithm that was trained to capture and categorize flexible pavement distresses (FPD) and reached 95% precision, 93.4% recall, and 97.2% mean Average Precision. Using vehicle built-in cameras and GPS sensors, these distresses were detected, images were captured, and locations were recorded. This was validated on campus roads and parking lots using a car featured with a built-in camera and GPS. The vehicles’ built-in technologies provided a more cost-effective and efficient road condition monitoring approach that could also provide real-time road conditions.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88227235","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}
Lucas Koch, Dennis Roeser, Kevin Badalian, Alexander Lieb, Jakob Andert
Automotive control functions are becoming increasingly complex and their development is becoming more and more elaborate, leading to a strong need for automated solutions within the development process. Here, reinforcement learning offers a significant potential for function development to generate optimized control functions in an automated manner. Despite its successful deployment in a variety of control tasks, there is still a lack of standard tooling solutions for function development based on reinforcement learning in the automotive industry. To address this gap, we present a flexible framework that couples the conventional development process with an open-source reinforcement learning library. It features modular, physical models for relevant vehicle components, a co-simulation with a microscopic traffic simulation to generate realistic scenarios, and enables distributed and parallelized training. We demonstrate the effectiveness of our proposed method in a feasibility study to learn a control function for automated longitudinal control of an electric vehicle in an urban traffic scenario. The evolved control strategy produces a smooth trajectory with energy savings of up to 14%. The results highlight the great potential of reinforcement learning for automated control function development and prove the effectiveness of the proposed framework.
{"title":"Cloud-Based Reinforcement Learning in Automotive Control Function Development","authors":"Lucas Koch, Dennis Roeser, Kevin Badalian, Alexander Lieb, Jakob Andert","doi":"10.3390/vehicles5030050","DOIUrl":"https://doi.org/10.3390/vehicles5030050","url":null,"abstract":"Automotive control functions are becoming increasingly complex and their development is becoming more and more elaborate, leading to a strong need for automated solutions within the development process. Here, reinforcement learning offers a significant potential for function development to generate optimized control functions in an automated manner. Despite its successful deployment in a variety of control tasks, there is still a lack of standard tooling solutions for function development based on reinforcement learning in the automotive industry. To address this gap, we present a flexible framework that couples the conventional development process with an open-source reinforcement learning library. It features modular, physical models for relevant vehicle components, a co-simulation with a microscopic traffic simulation to generate realistic scenarios, and enables distributed and parallelized training. We demonstrate the effectiveness of our proposed method in a feasibility study to learn a control function for automated longitudinal control of an electric vehicle in an urban traffic scenario. The evolved control strategy produces a smooth trajectory with energy savings of up to 14%. The results highlight the great potential of reinforcement learning for automated control function development and prove the effectiveness of the proposed framework.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135015163","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}
Ingrid J. Moreno, Dina Ouardani, D. Chaparro-Arce, A. Cárdenas
Reducing costs and time spent in experiments in the early development stages of vehicular technology such as off-road and agricultural semi-autonomous robots could help progress in this research area. In particular, evaluating path tracking strategies in the semi-autonomous operation of robots becomes challenging because of hardware costs, the time required for preparation and tests, and constraints associated with external aspects such as meteorological or weather conditions or limited space in research laboratories. This paper proposes a methodology for the real-time hardware-in-the-loop emulation of path tracking strategies in low-cost agricultural robots. This methodology enables the real-time validation of path tracking strategies before their implementation on the robot. To validate this, we propose implementing a path tracking strategy using only the information of motor’s angular speed and robot yaw velocity obtained from encoders and a low-cost inertial measurement unit (IMU), respectively. This paper provides a simulation with MATLAB/Simulink, hardware-in-the-loop with Qube-servo (Quanser), and experimental results with an Agribot platform to confirm its validity.
{"title":"Real-Time Hardware-in-the-Loop Emulation of Path Tracking in Low-Cost Agricultural Robots","authors":"Ingrid J. Moreno, Dina Ouardani, D. Chaparro-Arce, A. Cárdenas","doi":"10.3390/vehicles5030049","DOIUrl":"https://doi.org/10.3390/vehicles5030049","url":null,"abstract":"Reducing costs and time spent in experiments in the early development stages of vehicular technology such as off-road and agricultural semi-autonomous robots could help progress in this research area. In particular, evaluating path tracking strategies in the semi-autonomous operation of robots becomes challenging because of hardware costs, the time required for preparation and tests, and constraints associated with external aspects such as meteorological or weather conditions or limited space in research laboratories. This paper proposes a methodology for the real-time hardware-in-the-loop emulation of path tracking strategies in low-cost agricultural robots. This methodology enables the real-time validation of path tracking strategies before their implementation on the robot. To validate this, we propose implementing a path tracking strategy using only the information of motor’s angular speed and robot yaw velocity obtained from encoders and a low-cost inertial measurement unit (IMU), respectively. This paper provides a simulation with MATLAB/Simulink, hardware-in-the-loop with Qube-servo (Quanser), and experimental results with an Agribot platform to confirm its validity.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83720504","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}
Since large articulated vehicles have uncertainties in trailer articulation angle as well as dynamic complexity, it is not easy to accurately establish a reliable motion plan. In this paper, two geometric path plans constructed based on the empirical rules of driving experts are presented so that articulated vehicles can automatically perform perpendicular parking on a reverse path. By analyzing the empirical parking methods of professional drivers, these path plans were constructed by appropriately combining several standardized simple basic motions to facilitate implementation in real vehicles. In addition, the path plans included appropriate complementary motions to effectively respond to uncertainties arising from articulation angles, etc. The complementary motions developed in this study are based on the results of qualitative analysis on the behavior of articulated vehicles. The usefulness of the proposed articulated vehicle parking method has been proven through hundreds of experimental tests using a scaled model automated vehicle.
{"title":"Path Planning for Perpendicular Parking of Large Articulated Vehicles Based on Qualitative Kinematics and Geometric Methods","authors":"I. Han","doi":"10.3390/vehicles5030048","DOIUrl":"https://doi.org/10.3390/vehicles5030048","url":null,"abstract":"Since large articulated vehicles have uncertainties in trailer articulation angle as well as dynamic complexity, it is not easy to accurately establish a reliable motion plan. In this paper, two geometric path plans constructed based on the empirical rules of driving experts are presented so that articulated vehicles can automatically perform perpendicular parking on a reverse path. By analyzing the empirical parking methods of professional drivers, these path plans were constructed by appropriately combining several standardized simple basic motions to facilitate implementation in real vehicles. In addition, the path plans included appropriate complementary motions to effectively respond to uncertainties arising from articulation angles, etc. The complementary motions developed in this study are based on the results of qualitative analysis on the behavior of articulated vehicles. The usefulness of the proposed articulated vehicle parking method has been proven through hundreds of experimental tests using a scaled model automated vehicle.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84078423","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}
The purpose of this study is to investigate the uncertainty of the design variables of a front suspension lower control arm under fatigue-loading circumstances to estimate a reliable and robust product. This study offers a method for systematic uncertainty quantification (UQ), and the following steps were taken to achieve this: First, a finite element model was built to predict the fatigue life of the control arm under bump-loading conditions. Second, a sensitivity scheme, based on one of the global analyses, was developed to identify the model’s most and least significant design input variables. Third, physics-based and data-driven uncertainty quantification schemes were employed to quantify the model’s input parameter uncertainties via a Monte Carlo simulation. The simulations were conducted using 10,000 samples of material properties and geometrical uncertainty variables, with the coefficients of variation ranging from 1 to 3%. Finally, the confidence interval results show a deviation of about 21.74% from the mean (the baseline). As a result, by applying systematic UQ, a more reliable and robust automobile suspension control arm can be designed during the early stages of design to produce a more efficient and better approximation of fatigue life under uncertain conditions.
{"title":"Fatigue Life Uncertainty Quantification of Front Suspension Lower Control Arm Design","authors":"Misganaw Abebe, Bonyong Koo","doi":"10.3390/vehicles5030047","DOIUrl":"https://doi.org/10.3390/vehicles5030047","url":null,"abstract":"The purpose of this study is to investigate the uncertainty of the design variables of a front suspension lower control arm under fatigue-loading circumstances to estimate a reliable and robust product. This study offers a method for systematic uncertainty quantification (UQ), and the following steps were taken to achieve this: First, a finite element model was built to predict the fatigue life of the control arm under bump-loading conditions. Second, a sensitivity scheme, based on one of the global analyses, was developed to identify the model’s most and least significant design input variables. Third, physics-based and data-driven uncertainty quantification schemes were employed to quantify the model’s input parameter uncertainties via a Monte Carlo simulation. The simulations were conducted using 10,000 samples of material properties and geometrical uncertainty variables, with the coefficients of variation ranging from 1 to 3%. Finally, the confidence interval results show a deviation of about 21.74% from the mean (the baseline). As a result, by applying systematic UQ, a more reliable and robust automobile suspension control arm can be designed during the early stages of design to produce a more efficient and better approximation of fatigue life under uncertain conditions.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74619084","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}
Daigo Uchino, I. Kobayashi, J. Kuroda, K. Ogawa, K. Ikeda, T. Kato, A. Endo, H. Kato, T. Narita
As automated driving has not yet been established, on narrow roads where there is no separation between pedestrians and vehicles, it is essential to switch to manual driving. However, when the driver turns the steering wheel from one hand to another on narrow roads, it causes steering burdens and operational errors if the steering feel or burden is not proper. Thus, this study aims to construct an active steering wheel system that provides an appropriate steering feel or burden by controlling the steering reaction torque, driving position and steering gear ratio for each driver. In this paper, we focused on and examined the driving position among these. A two-dimensional steering model that considers the size of the arms for each driver was established to evaluate steering burden. In addition, a basic study was conducted on the appropriate driving position. Then, based on the joint movements and angles calculation, the appropriate driving position that considers the size of the arms was studied by evaluating the joint power. As a result, it was found that if the steering wheel position is too close to the driver, the amount of joint movement increases, and if it is too far away, the joint movement decreases. Therefore, it was found that the appropriate steering wheel position for each driver’s arm length can be considered by using the joint power.
{"title":"A Basic Study for Active Steering Wheel System for Steering Burden Evaluation by Driving Position Focus on Driver’s Arm Size","authors":"Daigo Uchino, I. Kobayashi, J. Kuroda, K. Ogawa, K. Ikeda, T. Kato, A. Endo, H. Kato, T. Narita","doi":"10.3390/vehicles5030046","DOIUrl":"https://doi.org/10.3390/vehicles5030046","url":null,"abstract":"As automated driving has not yet been established, on narrow roads where there is no separation between pedestrians and vehicles, it is essential to switch to manual driving. However, when the driver turns the steering wheel from one hand to another on narrow roads, it causes steering burdens and operational errors if the steering feel or burden is not proper. Thus, this study aims to construct an active steering wheel system that provides an appropriate steering feel or burden by controlling the steering reaction torque, driving position and steering gear ratio for each driver. In this paper, we focused on and examined the driving position among these. A two-dimensional steering model that considers the size of the arms for each driver was established to evaluate steering burden. In addition, a basic study was conducted on the appropriate driving position. Then, based on the joint movements and angles calculation, the appropriate driving position that considers the size of the arms was studied by evaluating the joint power. As a result, it was found that if the steering wheel position is too close to the driver, the amount of joint movement increases, and if it is too far away, the joint movement decreases. Therefore, it was found that the appropriate steering wheel position for each driver’s arm length can be considered by using the joint power.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76993812","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}
Electric vehicles (EVs) are experiencing explosive growth in public adoption, causing a major shift in research and development priorities by OEMs toward electrified powertrains. To verify EV drivetrain platforms and software models in the design phase, testbeds with specific capabilities are essential. Full-scale vehicle testbeds are expensive, bulky, dissipative, and not easily reconfigurable or movable, making scaled testbeds more attractive, especially for education and research institutes. To support this cause, this paper reports on the development of a small-scale, modular, hardware-in-the-loop (HIL) testbed platform for the drivetrain of EVs that is cost-effective, efficient, and easily movable and reconfigurable and allows integration of a battery pack. The testbed is comprised of two directly coupled electric machines. The first machine emulates the traction motor and is used to control vehicle speed according to a specified drive cycle. The second machine is used to impose a torque profile on the first machine’s shaft—based on the vehicle’s parameters and driving environment—and emulates a gearbox (if necessary). A systematic two-way scaling approach is adopted to downscale the parameters and driving environment of full-size EVs to a level that can be handled by the testbed and to upscale the test results obtained from the testbed to the full-size vehicle level. The power consumption of the testbed is limited to system losses. A case study involving a full-size EV was performed and the HIL simulation results were compared to the computer simulation results to verify the performance of the testbed.
{"title":"Design and Experimental Evaluation of a Scaled Modular Testbed Platform for the Drivetrain of Electric Vehicles","authors":"Martin Kardasz, Mehrdad Kazerani","doi":"10.3390/vehicles5030045","DOIUrl":"https://doi.org/10.3390/vehicles5030045","url":null,"abstract":"Electric vehicles (EVs) are experiencing explosive growth in public adoption, causing a major shift in research and development priorities by OEMs toward electrified powertrains. To verify EV drivetrain platforms and software models in the design phase, testbeds with specific capabilities are essential. Full-scale vehicle testbeds are expensive, bulky, dissipative, and not easily reconfigurable or movable, making scaled testbeds more attractive, especially for education and research institutes. To support this cause, this paper reports on the development of a small-scale, modular, hardware-in-the-loop (HIL) testbed platform for the drivetrain of EVs that is cost-effective, efficient, and easily movable and reconfigurable and allows integration of a battery pack. The testbed is comprised of two directly coupled electric machines. The first machine emulates the traction motor and is used to control vehicle speed according to a specified drive cycle. The second machine is used to impose a torque profile on the first machine’s shaft—based on the vehicle’s parameters and driving environment—and emulates a gearbox (if necessary). A systematic two-way scaling approach is adopted to downscale the parameters and driving environment of full-size EVs to a level that can be handled by the testbed and to upscale the test results obtained from the testbed to the full-size vehicle level. The power consumption of the testbed is limited to system losses. A case study involving a full-size EV was performed and the HIL simulation results were compared to the computer simulation results to verify the performance of the testbed.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88284632","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}
Integration, testing, and release of complex Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) is one of the main challenges in the field of automated driving. In order for the systems to be accepted by customers and to compete in the market, they have to feature functional, comfortable, safe, efficient, and natural driving behavior. The calibration process acquires increasing importance in the achievement of this objective. Complex ADAS/ADS require the optimization of interacting calibration parameters in a large number of different scenarios—a task that can hardly be performed with feasible effort and cost using conventional calibration methods. Virtual calibration in simulation enables reproducible and automated testing of different data sets of calibration parameters in various scenarios. These capabilities facilitate different use cases to extend the conventional calibration process of ADAS/ADS through virtual testing. This paper discusses the different use cases of virtual calibration and methods to achieve the desired objectives. A special focus is on a multi-scenario-level method that can be used to iteratively calibrate ADAS/ADS for optimal behavior in a variety of scenarios, resulting in a more comfortable, safe, and natural behavior of the system and still a feasible number of test cases. The presented methods are implemented for the virtual calibration of an Adaptive Cruise Control model for evaluation.
{"title":"Use Cases and Methods of Virtual ADAS/ADS Calibration in Simulation","authors":"Moritz Markofsky, Max Schäfer, D. Schramm","doi":"10.3390/vehicles5030044","DOIUrl":"https://doi.org/10.3390/vehicles5030044","url":null,"abstract":"Integration, testing, and release of complex Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) is one of the main challenges in the field of automated driving. In order for the systems to be accepted by customers and to compete in the market, they have to feature functional, comfortable, safe, efficient, and natural driving behavior. The calibration process acquires increasing importance in the achievement of this objective. Complex ADAS/ADS require the optimization of interacting calibration parameters in a large number of different scenarios—a task that can hardly be performed with feasible effort and cost using conventional calibration methods. Virtual calibration in simulation enables reproducible and automated testing of different data sets of calibration parameters in various scenarios. These capabilities facilitate different use cases to extend the conventional calibration process of ADAS/ADS through virtual testing. This paper discusses the different use cases of virtual calibration and methods to achieve the desired objectives. A special focus is on a multi-scenario-level method that can be used to iteratively calibrate ADAS/ADS for optimal behavior in a variety of scenarios, resulting in a more comfortable, safe, and natural behavior of the system and still a feasible number of test cases. The presented methods are implemented for the virtual calibration of an Adaptive Cruise Control model for evaluation.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82790429","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}
Automated guided vehicles undertake complex transportation tasks, for instance, in production and storage systems. In recent years, an increased focus on sustainability has occurred as the effects of ongoing climate change have become more apparent. Engineers are searching intensively for ways to design technical systems that are not only environmentally sustainable, but are also resilient to the challenges of the changing climate and other environmental conditions. The production of automated guided vehicles requires considerable resources; therefore, a long operation time is desirable for overall sustainability. The performance of transportation tasks requires certain processes, such as control, path planning, coordination/synchronization, and maintenance and update processes—the latter are also very important for a long operation time. This article proposes understanding these processes as services and to explore product service systems with automated guided vehicles. Due to their complexity, the efficient and safe operation of such systems can be at risk because of several factors, such as component faults, external attacks and disturbances. For several years both resilient control and resilience engineering have been researched as possible remedies. An extension of these two concepts to the early stages of system development processes and including the system’s hardware is proposed in this article. This extension is referred to as resilient design. A primary purpose of resilient design is sustainability through extended usability and planned updates. The main intention of this article is to provide a comprehensive understanding of resilient design through application to product service systems with automated guided vehicles. The basis for this contribution is an extensive literature review and detailed system analyses on different levels. The main research results include novel application modes for product development methods. The explanation of the results is supported by means of an illustrative example based on a product service system with automated guided vehicles.
{"title":"Resilient Design of Product Service Systems with Automated Guided Vehicles","authors":"R. Stetter","doi":"10.3390/vehicles5030043","DOIUrl":"https://doi.org/10.3390/vehicles5030043","url":null,"abstract":"Automated guided vehicles undertake complex transportation tasks, for instance, in production and storage systems. In recent years, an increased focus on sustainability has occurred as the effects of ongoing climate change have become more apparent. Engineers are searching intensively for ways to design technical systems that are not only environmentally sustainable, but are also resilient to the challenges of the changing climate and other environmental conditions. The production of automated guided vehicles requires considerable resources; therefore, a long operation time is desirable for overall sustainability. The performance of transportation tasks requires certain processes, such as control, path planning, coordination/synchronization, and maintenance and update processes—the latter are also very important for a long operation time. This article proposes understanding these processes as services and to explore product service systems with automated guided vehicles. Due to their complexity, the efficient and safe operation of such systems can be at risk because of several factors, such as component faults, external attacks and disturbances. For several years both resilient control and resilience engineering have been researched as possible remedies. An extension of these two concepts to the early stages of system development processes and including the system’s hardware is proposed in this article. This extension is referred to as resilient design. A primary purpose of resilient design is sustainability through extended usability and planned updates. The main intention of this article is to provide a comprehensive understanding of resilient design through application to product service systems with automated guided vehicles. The basis for this contribution is an extensive literature review and detailed system analyses on different levels. The main research results include novel application modes for product development methods. The explanation of the results is supported by means of an illustrative example based on a product service system with automated guided vehicles.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85536023","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}
Alessandro Saldarini, S. Miraftabzadeh, M. Brenna, M. Longo
The Electric Vehicle (EV) market has been growing exponentially in recent years, which is why the distribution network of public charging stations will be subject to expansion and upgrading. In order to improve the public charging infrastructure, this paper aims to develop a model capable of analyzing the current situation of a stretch of highway, identifying the congestion points, created by the formation of queues at the charging points. A specific section of a highway in Spain was selected as a case study to evaluate the performance of the model, allowing for rigorous testing and thorough analysis of its performance in a real-world scenario. The first step is to define and evaluate the effects of factors affecting EV consumption, such as the slope of the road, weather conditions, and driving style. Subsequently, a simulation model is developed using the agent-based simulation software AnyLogic, which simulates the journey of a fleet of electric vehicles, taking into account the battery charging and discharging process. Based on the obtained results, the charging infrastructure is improved to minimize the total travel time of an electric vehicle on a long-distance trip.
{"title":"Strategic Approach for Electric Vehicle Charging Infrastructure for Efficient Mobility along Highways: A Real Case Study in Spain","authors":"Alessandro Saldarini, S. Miraftabzadeh, M. Brenna, M. Longo","doi":"10.3390/vehicles5030042","DOIUrl":"https://doi.org/10.3390/vehicles5030042","url":null,"abstract":"The Electric Vehicle (EV) market has been growing exponentially in recent years, which is why the distribution network of public charging stations will be subject to expansion and upgrading. In order to improve the public charging infrastructure, this paper aims to develop a model capable of analyzing the current situation of a stretch of highway, identifying the congestion points, created by the formation of queues at the charging points. A specific section of a highway in Spain was selected as a case study to evaluate the performance of the model, allowing for rigorous testing and thorough analysis of its performance in a real-world scenario. The first step is to define and evaluate the effects of factors affecting EV consumption, such as the slope of the road, weather conditions, and driving style. Subsequently, a simulation model is developed using the agent-based simulation software AnyLogic, which simulates the journey of a fleet of electric vehicles, taking into account the battery charging and discharging process. Based on the obtained results, the charging infrastructure is improved to minimize the total travel time of an electric vehicle on a long-distance trip.","PeriodicalId":73282,"journal":{"name":"IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79337265","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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