Gerald Richter, Lukas Grohmann, P. Nitsche, G. Lenz
Expectations are that automated and connected mobility will increase road safety and traffic efficiency. However, due to possible shortcomings of new technologies , road users may be confronted with disturbances and potential safety risks. The mitigation of such risks will bring necessary changes to road infrastructure, vehicles and road-users’ behavior. In a traffic environment that was built to fit the human perception, preemptive simulation of parametrized scenarios can provide guidelines for what changes and difficulties are to be expected. Utilizing SUMO in varied scenarios, this paper outlines the creation of virtual models that correspond to interaction hot spots on the Austrian road network from digitizing the infrastructure, to calibrating a simulation scenario with congruent traffic measurements while it concludes with the evaluation of scenario simulation results. The approach is demonstrated for a selected motorway ramp scenario, varying rates of automated vehicles and different infrastructure layouts. Performance indicators like vehicle speed distributions and traffic disruptions are defined and analyzed to investigate how adaptations can mitigate risks, influence traffic flow and hence support progressing vehicle automation.
{"title":"Anticipating Automated Vehicle Presence and the Effects on Interactions with Conventional Traffic and Infrastructure","authors":"Gerald Richter, Lukas Grohmann, P. Nitsche, G. Lenz","doi":"10.29007/S6M7","DOIUrl":"https://doi.org/10.29007/S6M7","url":null,"abstract":"Expectations are that automated and connected mobility will increase road safety and traffic efficiency. However, due to possible shortcomings of new technologies , road users may be confronted with disturbances and potential safety risks. The mitigation of such risks will bring necessary changes to road infrastructure, vehicles and road-users’ behavior. In a traffic environment that was built to fit the human perception, preemptive simulation of parametrized scenarios can provide guidelines for what changes and difficulties are to be expected. Utilizing SUMO in varied scenarios, this paper outlines the creation of virtual models that correspond to interaction hot spots on the Austrian road network from digitizing the infrastructure, to calibrating a simulation scenario with congruent traffic measurements while it concludes with the evaluation of scenario simulation results. The approach is demonstrated for a selected motorway ramp scenario, varying rates of automated vehicles and different infrastructure layouts. Performance indicators like vehicle speed distributions and traffic disruptions are defined and analyzed to investigate how adaptations can mitigate risks, influence traffic flow and hence support progressing vehicle automation.","PeriodicalId":201953,"journal":{"name":"International Conference on Simulation of Urban Mobility","volume":"141 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126757121","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}
L. Lücken, Evangelos Mintsis, Kallirroi N. Porfyri, Robert Alms, Yun-Pang Flötteröd, D. Koutras
Transitions of Control (ToC) play an important role in the simulative impact assessment of automated driving because they may represent major perturbations of smooth and safe traffic operation. The drivers' efforts to take back control from the automation are accompanied by a change of driving behavior and may lead to increased error rates, altered headways, safety critical situations, and, in the case of a failing takeover, even to minimum risk maneuvers. In this work we present modeling approaches for these processes, which have been introduced into SUMO recently in the framework of the TransAID project. Further, we discuss the results of an evaluation of some hierarchical traffic management (TM) procedures devised to ameliorate related disturbances in transition areas, i.e., zones of increased probability for the automation to request a ToC.
{"title":"From Automated to Manual - Modeling Control Transitions with SUMO","authors":"L. Lücken, Evangelos Mintsis, Kallirroi N. Porfyri, Robert Alms, Yun-Pang Flötteröd, D. Koutras","doi":"10.29007/SFGK","DOIUrl":"https://doi.org/10.29007/SFGK","url":null,"abstract":"Transitions of Control (ToC) play an important role in the simulative impact assessment of automated driving because they may represent major perturbations of smooth and safe traffic operation. The drivers' efforts to take back control from the \u0000automation are accompanied by a change of driving behavior and may lead to increased error rates, altered headways, safety critical situations, and, in the case of a failing takeover, even to minimum risk maneuvers. In this work we present modeling \u0000approaches for these processes, which have been introduced into SUMO recently in the framework of the TransAID project. Further, we discuss the results of an evaluation of some hierarchical traffic management (TM) procedures devised to ameliorate related disturbances in transition areas, i.e., zones of increased probability for the automation to request a ToC.","PeriodicalId":201953,"journal":{"name":"International Conference on Simulation of Urban Mobility","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130801892","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}
G. Grigoropoulos, L. Lücken, J. Erdmann, Heather Kaths
Bicycle traffic is becoming an increasingly important part of urban traffic. Thus, the simulation and accurate representation of bicycle traffic in microscopic traffic simulation software is gaining importance. As bicycle traffic increases, dedicated bicycle infrastructure is designed to accommodate bicycle traffic. Especially at intersections, the design of intersection approaches follows specific rules and geometric limitations as defined by official design guidelines used in different countries across the world. However, when special environmental factors that affect the intersection layout, such as available space or gradient are not considered, specific standard forms of intersection approaches can be determined based on the number of traffic lanes, the traffic signal control and in the case of this study, the availability as well as the type of dedicated bicycle infrastructure. Categories with available bicycle infrastructure include the cases of bicycle lanes or advisory cycle lanes with advance stop lines for direct left turning bicyclists, the bicycle lanes or advisory bicycle lanes with bicycle boxes and bicycle lanes or bicycle paths with advanced stop lines and a stop area downstream for facilitating an indirect left turn or a two-stage (left) turn of bicyclists. The simulation of such bicycle infrastructure is not natively supported in microscopic traffic simulation software and is mostly only possible through intuitive adjustment of existing network design elements. In this paper, fictional intersections with special bicycle infrastructure are modelled in SUMO. Bicycle traffic data is collected at intersections in Germany with different types of bicycle infrastructure. The collected bicycle traffic data is then used to evaluate the intersection models. Specific recommendations for modelling bicycle infrastructure at intersection approaches in SUMO are provided, and limitations of the proposed methodologies and software limitations are discussed. Results show that the developed solutions can be used to model the bicycle traffic behavior with a reasonable degree of accuracy only for simulation scenarios and traffic situations unaffected by the identified software limitations.
{"title":"Modelling Bicycle Infrastructure in SUMO","authors":"G. Grigoropoulos, L. Lücken, J. Erdmann, Heather Kaths","doi":"10.29007/6CS5","DOIUrl":"https://doi.org/10.29007/6CS5","url":null,"abstract":"Bicycle traffic is becoming an increasingly important part of urban traffic. Thus, the simulation and accurate representation of bicycle traffic in microscopic traffic simulation software is gaining importance. As bicycle traffic increases, dedicated bicycle infrastructure is designed to accommodate bicycle traffic. Especially at intersections, the design of intersection approaches follows specific rules and geometric limitations as defined by official design guidelines used in different countries across the world. However, when special environmental factors that affect the intersection layout, such as available space or gradient are not considered, specific standard forms of intersection approaches can be determined based on the number of traffic lanes, the traffic signal control and in the case of this study, the availability as well as the type of dedicated bicycle infrastructure. Categories with available bicycle infrastructure include the cases of bicycle lanes or advisory cycle lanes with advance stop lines for direct left turning bicyclists, the bicycle lanes or advisory bicycle lanes with bicycle boxes and bicycle lanes or bicycle paths with advanced stop lines and a stop area downstream for facilitating an indirect left turn or a two-stage (left) turn of bicyclists. The simulation of such bicycle infrastructure is not natively supported in microscopic traffic simulation software and is mostly only possible through intuitive adjustment of existing network design elements. In this paper, fictional intersections with special bicycle infrastructure are modelled in SUMO. Bicycle traffic data is collected at intersections in Germany with different types of bicycle infrastructure. The collected bicycle traffic data is then used to evaluate the intersection models. Specific recommendations for modelling bicycle infrastructure at intersection approaches in SUMO are provided, and limitations of the proposed methodologies and software limitations are discussed. Results show that the developed solutions can be used to model the bicycle traffic behavior with a reasonable degree of accuracy only for simulation scenarios and traffic situations unaffected by the identified software limitations.","PeriodicalId":201953,"journal":{"name":"International Conference on Simulation of Urban Mobility","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130888228","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}
Bangkok is notorious for its chronic traffic congestion due to the rapid urbanization and the haphazard city plan. The Sathorn Road network area stands to be one of the most critical areas where gridlocks are a normal occurrence during rush hours. This stems from the high volume of demand imposed by the dense geographical placement of 3 big educational institutions and the insufficient link capacity with strict routes. Current solutions place heavy reliance on human traffic control expertises to prevent and disentangle gridlocks by consecutively releasing each queue length spillback through inter-junction coordination. A calibrated dataset of the Sathorn Road network area in a microscopic road traffic simulation package SUMO (Simulation of Urban MObility) is provided in the work of Chula-Sathorn SUMO Simulator (Chula-SSS). In this paper, we aim to utilize the Chula-SSS dataset with extended vehicle flows and gridlocks in order to further optimize the present traffic signal control policies with reinforcement learning approaches by an artificial agent. Reinforcement learning has been successful in a variety of domains over the past few years. While a number of researches exist on using reinforcement learning with adaptive traffic light control, existing studies often lack pragmatic considerations concerning application to the physical world especially for the traffic system infrastructure in developing countries, which suffer from constraints imposed from economic factors. The resultant limitation of the agent’s partial observability of the whole network state at any specific time is imperative and cannot be overlooked. With such partial observability constraints, this paper has reported an investigation on applying the Ape-X Deep Q-Network agent at the critical junction in the morning rush hours from 6 AM to 9 AM with practically occasional presence of gridlocks. The obtainable results have shown a potential value of the agent’s ability to learn despite physical limitations in the traffic light control at the considered intersection within the Sathorn gridlock area. This suggests a possibility of further investigations on agent applicability in trying to mitigate complex interconnected gridlocks in the future.
由于快速的城市化和杂乱无章的城市规划，曼谷因长期的交通拥堵而臭名昭著。萨索恩道路网区域是最关键的区域之一，交通堵塞是高峰时段的正常现象。这源于三大教育机构密集的地理位置和严格路线的连接能力不足所带来的高需求。目前的解决方案严重依赖于人工交通控制专家，通过交叉路口的协调，连续释放每个队列长度的溢出来预防和解决交通堵塞。在Chula-Sathorn SUMO Simulator (Chula-SSS)的工作中，提供了微观道路交通模拟软件包SUMO (simulation of Urban MObility)中Sathorn路网区域的校准数据集。在本文中，我们的目标是利用Chula-SSS数据集扩展车辆流和交通阻塞，通过人工智能体的强化学习方法进一步优化当前的交通信号控制策略。在过去的几年里，强化学习在许多领域都取得了成功。虽然已有大量研究将强化学习应用于自适应交通灯控制，但现有研究往往缺乏对现实世界应用的实用考虑，特别是发展中国家的交通系统基础设施，受到经济因素的制约。由此产生的智能体在任何特定时间对整个网络状态的部分可观察性的限制是必要的，不可忽视的。在这种部分可观察性约束下，本文报道了在早高峰时间从上午6点到上午9点的关键路口应用Ape-X深度Q-Network代理的研究，实际上偶尔会出现交通堵塞。可获得的结果显示了智能体的学习能力的潜在价值，尽管在萨索恩交通堵塞区域内的十字路口的交通信号灯控制存在物理限制。这表明，未来有可能进一步研究智能体的适用性，以缓解复杂的互联交通堵塞。
{"title":"Reinforcement Learning Agent under Partial Observability for Traffic Light Control in Presence of Gridlocks","authors":"Thanapapas Horsuwan, C. Aswakul","doi":"10.29007/BDGN","DOIUrl":"https://doi.org/10.29007/BDGN","url":null,"abstract":"Bangkok is notorious for its chronic traffic congestion due to the rapid urbanization and the haphazard city plan. The Sathorn Road network area stands to be one of the most critical areas where gridlocks are a normal occurrence during rush hours. This stems from the high volume of demand imposed by the dense geographical placement of 3 big educational institutions and the insufficient link capacity with strict routes. Current solutions place heavy reliance on human traffic control expertises to prevent and disentangle gridlocks by consecutively releasing each queue length spillback through inter-junction coordination. A calibrated dataset of the Sathorn Road network area in a microscopic road traffic simulation package SUMO (Simulation of Urban MObility) is provided in the work of Chula-Sathorn SUMO Simulator (Chula-SSS). In this paper, we aim to utilize the Chula-SSS dataset with extended vehicle flows and gridlocks in order to further optimize the present traffic signal control policies with reinforcement learning approaches by an artificial agent. Reinforcement learning has been successful in a variety of domains over the past few years. While a number of researches exist on using reinforcement learning with adaptive traffic light control, existing studies often lack pragmatic considerations concerning application to the physical world especially for the traffic system infrastructure in developing countries, which suffer from constraints imposed from economic factors. The resultant limitation of the agent’s partial observability of the whole network state at any specific time is imperative and cannot be overlooked. With such partial observability constraints, this paper has reported an investigation on applying the Ape-X Deep Q-Network agent at the critical junction in the morning rush hours from 6 AM to 9 AM with practically occasional presence of gridlocks. The obtainable results have shown a potential value of the agent’s ability to learn despite physical limitations in the traffic light control at the considered intersection within the Sathorn gridlock area. This suggests a possibility of further investigations on agent applicability in trying to mitigate complex interconnected gridlocks in the future.","PeriodicalId":201953,"journal":{"name":"International Conference on Simulation of Urban Mobility","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121018947","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 electrification of transport is one of the key parts of the present aim to reduce undesirable vehicular emissions in the atmosphere. While the full electrification of personal vehicles is mostly associated with employing a big battery pack on the board and charging on (static) charging stations, another interesting possibility appears in the case of public transport – dynamic drawing of the power from overhead wires. Regarding vehicles moving on the road, this concept is used by trolleybuses or hybrid trolleybuses, i.e. vehicles combining power from the overhead wires and batteries. A replacement of classic buses (with a combustion engine) with (hybrid) trolleybuses is hardly possible without an appropriate adjustment of public transport lines and the necessary infrastructure. For this purpose, a simulation of the adjusted public transport service may be used to identify weaknesses of the proposed solution. This paper presents a new vehicle device and a new additional part of road infrastructure in SUMO. It introduces device.elecHybrid based on existing device.battery, extending its functionality and tailoring it for the needs of hybrid trolleybuses. In addition, overhead wires and traction substations are implemented. As the voltage and electric currents in the overhead wires depend on traffic, the overhead wire parameters are optionally evaluated by a built-in electric circuit solver using Kirchhoff’s laws. The proposed changes allow us to simulate hybrid trolleybus in-motion charging under the overhead wire. The extensions can be immediately used in micro-simulations or even (in a simplified version) in the meso-simulation mode.
{"title":"A Vehicle Device Tailored for Hybrid Trolleybuses and Overhead Wires Implementation in SUMO","authors":"J. Ševčík, J. Přikryl","doi":"10.29007/6PQR","DOIUrl":"https://doi.org/10.29007/6PQR","url":null,"abstract":"The electrification of transport is one of the key parts of the present aim to reduce undesirable vehicular emissions in the atmosphere. While the full electrification of personal vehicles is mostly associated with employing a big battery pack on the board and charging on (static) charging stations, another interesting possibility appears in the case of public transport – dynamic drawing of the power from overhead wires. Regarding vehicles moving on the road, this concept is used by trolleybuses or hybrid trolleybuses, i.e. vehicles combining power from the overhead wires and batteries. A replacement of classic buses (with a combustion engine) with (hybrid) trolleybuses is hardly possible without an appropriate adjustment of public transport lines and the necessary infrastructure. For this purpose, a simulation of the adjusted public transport service may be used to identify weaknesses of the proposed solution. This paper presents a new vehicle device and a new additional part of road infrastructure in SUMO. It introduces device.elecHybrid based on existing device.battery, extending its functionality and tailoring it for the needs of hybrid trolleybuses. In addition, overhead wires and traction substations are implemented. As the voltage and electric currents in the overhead wires depend on traffic, the overhead wire parameters are optionally evaluated by a built-in electric circuit solver using Kirchhoff’s laws. The proposed changes allow us to simulate hybrid trolleybus in-motion charging under the overhead wire. The extensions can be immediately used in micro-simulations or even (in a simplified version) in the meso-simulation mode.","PeriodicalId":201953,"journal":{"name":"International Conference on Simulation of Urban Mobility","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128871120","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}
Large majority of control methodologies used in traffic applications require short-time prediction of the environment. For instance, in widely-used Model Predictive Control [1] employed to reduce fuel and energy consumption of vehicles in a platoon, information about future velocity profiles of leading vehicles is necessary. In such case, the dynamic model should provide information more detailed than prediction of averaged and global quantities. Additionally, if the control input is to be applied at high-frequencies, traffic model must be solved in a short period of time. We propose a novel framework which addresses aforementioned problems by estimating the vehicle velocity at any location in the domain based on the real-time information from induction loops downstream. Additionally, our formulation is linear and low-dimensional (i.e. consists of few degrees of freedom) meaning that the estimation can be executed at high frequencies. First a mapping is constructed from velocities at discrete locations to the smooth continuous field, which is subsequently projected onto its most significant principal components. Next, current state of such system is estimated using Kalman filter by combining the linear, wave-like dynamics of the traffic with the instantaneous information provided by induction loops. Short-term traffic prediction is then achieved by integration of the model forward in time. The proxy methodology is validated using SUMO simulation on the test case of the vehicles approaching a traffic junction. The performance is evaluated based on sampling reconstructed continuous waveform at the locations and timestamps of the vehicles in the reference data and calculating velocity errors. Separate cases are considered where drivers follow Intelligent Driver Model perfectly and with varying levels of uncertainty.
{"title":"Low-dimensional estimation and prediction framework for description of the oscillatory traffic dynamics","authors":"Jakub Król, Bani Anvari, R. Lot","doi":"10.29007/4GLX","DOIUrl":"https://doi.org/10.29007/4GLX","url":null,"abstract":"Large majority of control methodologies used in traffic applications require short-time prediction of the environment. For instance, in widely-used Model Predictive Control [1] employed to reduce fuel and energy consumption of vehicles in a platoon, information about future velocity profiles of leading vehicles is necessary. In such case, the dynamic model should provide information more detailed than prediction of averaged and global quantities. Additionally, if the control input is to be applied at high-frequencies, traffic model must be solved in a short period of time. We propose a novel framework which addresses aforementioned problems by estimating the vehicle velocity at any location in the domain based on the real-time information from induction loops downstream. Additionally, our formulation is linear and low-dimensional (i.e. consists of few degrees of freedom) meaning that the estimation can be executed at high frequencies. First a mapping is constructed from velocities at discrete locations to the smooth continuous field, which is subsequently projected onto its most significant principal components. Next, current state of such system is estimated using Kalman filter by combining the linear, wave-like dynamics of the traffic with the instantaneous information provided by induction loops. Short-term traffic prediction is then achieved by integration of the model forward in time. The proxy methodology is validated using SUMO simulation on the test case of the vehicles approaching a traffic junction. The performance is evaluated based on sampling reconstructed continuous waveform at the locations and timestamps of the vehicles in the reference data and calculating velocity errors. Separate cases are considered where drivers follow Intelligent Driver Model perfectly and with varying levels of uncertainty.","PeriodicalId":201953,"journal":{"name":"International Conference on Simulation of Urban Mobility","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115571963","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}
Recent developments such as increasing automation and connectivity of vehicles as well as new regulations for real driving emissions lead to a stronger consideration of traffic and traffic control in automotive development. The increasing complexity of vehicular systems requires a highly virtualized development process. Therefore, a cosimulation solution of DYNA4’s virtual vehicle with SUMO’s microscopic traffic is presented here. Despite increasing automation, virtual test drives often still require a virtual test driver. Thus, the co-simulation solution is extended by combining the driver models of both tools. The operational decision making level of DYNA4 is extended by SUMO’s tactical driver decisions, aiming at virtual test drives in complex surrounding traffic with realistic reaction on traffic and traffic control and reduced parametrization effort. By comparing two variants it is shown that a higher reference speed and more aggressive lane change parameters lead to an increase of usage of the left lane and an increase in achieved speeds.
{"title":"Co-simulation of the virtual vehicle in virtual traffic considering tactical driver decisions","authors":"J. Kaths, B. Schott, F. Chucholowski","doi":"10.29007/QZG2","DOIUrl":"https://doi.org/10.29007/QZG2","url":null,"abstract":"Recent developments such as increasing automation and connectivity of vehicles as well as new regulations for real driving emissions lead to a stronger consideration of traffic and traffic control in automotive development. The increasing complexity of vehicular systems requires a highly virtualized development process. Therefore, a cosimulation solution of DYNA4’s virtual vehicle with SUMO’s microscopic traffic is presented here. Despite increasing automation, virtual test drives often still require a virtual test driver. Thus, the co-simulation solution is extended by combining the driver models of both tools. The operational decision making level of DYNA4 is extended by SUMO’s tactical driver decisions, aiming at virtual test drives in complex surrounding traffic with realistic reaction on traffic and traffic control and reduced parametrization effort. By comparing two variants it is shown that a higher reference speed and more aggressive lane change parameters lead to an increase of usage of the left lane and an increase in achieved speeds.","PeriodicalId":201953,"journal":{"name":"International Conference on Simulation of Urban Mobility","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124768205","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}
In many cases, driving simulator studies target how test persons interact with surrounding traffic and with traffic signals. Traffic simulations like SUMO specialize in modeling traffic flow, which includes signal control. Consequently, driving and traffic simulation are coupled to benefit from the advantages of both. This means that all except the driven (ego) vehicle are controlled by the traffic simulation. Essential vehicle dynamics data are exchanged and applied frequently to make the test person interact with SUMO-generated traffic. Additionally, traffic lights are controlled by SUMO and transferred to the driving simulation. The system is used to evaluate an Adaptive Cruise Control (ACC) system, which considers current and future traffic light states. Measures include objective terms like traffic flow as well as the subjective judgement of the signal program, the ACC and the simulation environment.
{"title":"Testing an Adaptive Cruise Controller with coupled traffic and driving simulations","authors":"Mirko Barthauer, A. Hafner","doi":"10.29007/84rc","DOIUrl":"https://doi.org/10.29007/84rc","url":null,"abstract":"In many cases, driving simulator studies target how test persons interact with surrounding traffic and with traffic signals. Traffic simulations like SUMO specialize in modeling traffic flow, which includes signal control. Consequently, driving and traffic simulation are coupled to benefit from the advantages of both. This means that all except the driven (ego) vehicle are controlled by the traffic simulation. Essential vehicle dynamics data are exchanged and applied frequently to make the test person interact with SUMO-generated traffic. Additionally, traffic lights are controlled by SUMO and transferred to the driving simulation. The system is used to evaluate an Adaptive Cruise Control (ACC) system, which considers current and future traffic light states. Measures include objective terms like traffic flow as well as the subjective judgement of the signal program, the ACC and the simulation environment.","PeriodicalId":201953,"journal":{"name":"International Conference on Simulation of Urban Mobility","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124774749","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}
Philipp Heisig, S. Jeroschewski, Johannes Kristan, Robert Höttger, A. Banijamali, S. Sachweh
The emerging usage of connected vehicles promises new business models and a high level of innovation, but also poses new challenges for the automotive domain and in particular for the connectivity dimension, i. e. the connection between vehicles and cloud environments including the architecture of such systems. Among other challenges, IoT Cloud platforms and their services have to scale with the number of vehicles on the road to provide functionality in a reliable way, especially when dealing with safety-related functions. Testing the scalability, functionality, and availability of IoT Cloud platform architectures for connected vehicles requires data from real world scenarios instead of hypothetical data sets to ensure both the proper functionality of distinct connected vehicle services and that the architecture scales with a varying number of vehicles. However, the closed and proprietary nature of current connected vehicle solutions aggravate the availability of both vehicle data and test environments to evaluate different architectures and cloud solutions. Thus, this paper introduces an approach for connecting the Eclipse SUMO traffic simulation with the open source connected vehicle ecosystem Eclipse Kuksa. More precisely, Eclipse SUMO is used to simulate traffic scenarios including microscopic properties like the position or emission. The generated data of each vehicle is then be sent to the message gateway of the Kuksa IoT Cloud platform and delegated to an according example service that consumes the data. In this way, not only the scalability of connected vehicle IoT architectures can be tested based on real world scenarios, but also the functionality of cloud services can be ensured by providing context-specific automotive data that goes beyond rudimentary or fake data-sets. M. Weber, L. Bieker-Walz, R. Hilbrich and M. Behrisch (eds.), SUMO2019 (EPiC Series in Computing, vol. 62), pp. 213–229 Bridging SUMO & Kuksa Heisig, Jeroschewski, Kristan, Höttger, Banijamali and Sachweh
联网汽车的新兴应用承诺了新的商业模式和高水平的创新，但也给汽车领域带来了新的挑战，特别是在连接方面，即车辆与云环境之间的连接，包括这些系统的架构。在其他挑战中，物联网云平台及其服务必须随着道路上车辆的数量进行扩展，以可靠的方式提供功能，特别是在处理与安全相关的功能时。测试联网车辆的物联网云平台架构的可扩展性、功能和可用性需要来自真实世界场景的数据，而不是假设的数据集，以确保不同的联网车辆服务的正确功能，以及架构随不同数量的车辆进行扩展。然而，当前联网汽车解决方案的封闭性和专有性加剧了车辆数据和测试环境的可用性，从而无法评估不同的架构和云解决方案。因此，本文介绍了一种将Eclipse SUMO交通模拟与开源互联汽车生态系统Eclipse Kuksa连接起来的方法。更准确地说，Eclipse SUMO用于模拟交通场景，包括位置或发射等微观属性。然后，每辆车生成的数据被发送到Kuksa物联网云平台的消息网关，并委托给使用这些数据的相应示例服务。通过这种方式，不仅可以基于真实世界的场景测试联网汽车物联网架构的可扩展性，还可以通过提供超越基本或虚假数据集的特定环境的汽车数据来确保云服务的功能。M. Weber, L. Bieker-Walz, R. Hilbrich和M. Behrisch(编)，SUMO2019 (EPiC Series in Computing, vol. 62)， pp. 213-229 Bridging SUMO & Kuksa Heisig, Jeroschewski, Kristan, Höttger, Banijamali和Sachweh
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