Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294239
Wu Xihui, A. Eskandarian
This paper addresses the challenge of safe path planning for autonomous vehicles at intersections. Rapidly exploring Random Tree (RRT) as an effective local motion planning methodology has the ability to determine a feasible path. As the number of sampled positions increases, the probability of finding an optimal path increases. However, RRT is usually applied to the static environment due to its delay or lack of efficiency in planning a path to the goal area. In dynamic environments, redundant sampling positions near dynamic obstacles are not effective. Therefore, we proposed a methodology, pRRT, that combines Gaussian Processes Regression (GPR) and RRT to generate a local path to guide the vehicle through the intersection. The procedure includes two phases: prediction and planning. Under prediction, GPR predicts the vehicle’s future trajectory points. The prediction results are combined with destination position (intersection exit) to generate a probability map for sampling such that position sample quality is increased by avoiding redundant samples. The optimal strategy is deployed to guarantee the trajectory is collision-free in both current and future time instances. A combination of both proposed improvements can thus result in a path that is collision-free under the dynamic intersection area. The proposed method also increased the speed of path generation compared to the RRT algorithm.
{"title":"Predictive Motion Planning of Vehicles at Intersection Using a New GPR and RRT","authors":"Wu Xihui, A. Eskandarian","doi":"10.1109/ITSC45102.2020.9294239","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294239","url":null,"abstract":"This paper addresses the challenge of safe path planning for autonomous vehicles at intersections. Rapidly exploring Random Tree (RRT) as an effective local motion planning methodology has the ability to determine a feasible path. As the number of sampled positions increases, the probability of finding an optimal path increases. However, RRT is usually applied to the static environment due to its delay or lack of efficiency in planning a path to the goal area. In dynamic environments, redundant sampling positions near dynamic obstacles are not effective. Therefore, we proposed a methodology, pRRT, that combines Gaussian Processes Regression (GPR) and RRT to generate a local path to guide the vehicle through the intersection. The procedure includes two phases: prediction and planning. Under prediction, GPR predicts the vehicle’s future trajectory points. The prediction results are combined with destination position (intersection exit) to generate a probability map for sampling such that position sample quality is increased by avoiding redundant samples. The optimal strategy is deployed to guarantee the trajectory is collision-free in both current and future time instances. A combination of both proposed improvements can thus result in a path that is collision-free under the dynamic intersection area. The proposed method also increased the speed of path generation compared to the RRT algorithm.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"28 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":"115180299","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/ITSC45102.2020.9294180
P. Ghorai, A. Eskandarian
The cooperative perception among connected autonomous vehicles extends the field-of-view of the individual cars and adds significantly to their sensing and collision avoidance capabilities. This feature is particularly useful and essential in avoiding collisions with pedestrians, vulnerable road users, and other objects or cars which are obscured in the typical field-of-view of an ego vehicle. This paper proposes a simple to implement but effective longitudinal control algorithm to avoid collisions in a dynamic environment for cooperative autonomous vehicles. The algorithm is applied to ego and lead vehicles to control longitudinal dynamics with appropriate braking based on safety distance modeling. Simulations using dynamic models for both vehicles and pedestrians on a hazardous traffic scenario are presented to illustrate the effectiveness of the proposed control algorithm. The proposed method is also capable of warning and avoiding collisions for several other critical situations that may appear in autonomous driving. The results demonstrate a promising solution for cooperative collision avoidance, which can be further expanded to more complex scenarios.
{"title":"Longitudinal Control Algorithm for Cooperative Autonomous Vehicles to Avoid Accident with Vulnerable Road Users","authors":"P. Ghorai, A. Eskandarian","doi":"10.1109/ITSC45102.2020.9294180","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294180","url":null,"abstract":"The cooperative perception among connected autonomous vehicles extends the field-of-view of the individual cars and adds significantly to their sensing and collision avoidance capabilities. This feature is particularly useful and essential in avoiding collisions with pedestrians, vulnerable road users, and other objects or cars which are obscured in the typical field-of-view of an ego vehicle. This paper proposes a simple to implement but effective longitudinal control algorithm to avoid collisions in a dynamic environment for cooperative autonomous vehicles. The algorithm is applied to ego and lead vehicles to control longitudinal dynamics with appropriate braking based on safety distance modeling. Simulations using dynamic models for both vehicles and pedestrians on a hazardous traffic scenario are presented to illustrate the effectiveness of the proposed control algorithm. The proposed method is also capable of warning and avoiding collisions for several other critical situations that may appear in autonomous driving. The results demonstrate a promising solution for cooperative collision avoidance, which can be further expanded to more complex scenarios.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"59 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":"114528564","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/ITSC45102.2020.9294261
Josep Maria Salanova Grau, Neofytos Boufidis, G. Aifadopoulou, Panagiotis Tzenos, Thanasis Tolikas
Safety al Level crossing (LC) is a minor issue for the road sector since it represents less than 1% of the accident mortality, but it is highly important for the railway sector, which accounts for thousands of accidents and collisions every year. In total, more than 500 causalities occur every year in the surroundings of LCs in the United States and the European Union Member States combined. This paper presents a multimodal cooperative safety system to alert about the vicinity of a LC and, if any, an approaching train. The system processes spatial data of trains and private nearby LCs and generate alerts about the presence of a nearby LC and the estimated time of arrival for approaching trains. The system was tested in the LCs of Thessaloniki by professional taxi drivers during a 12month period. Qualitative analyses indicate positive acceptance by the drivers as well strong perception of reliability and safety impact of the system.
{"title":"Multimodal Cooperative ITS Safety System at Level-Crossings*","authors":"Josep Maria Salanova Grau, Neofytos Boufidis, G. Aifadopoulou, Panagiotis Tzenos, Thanasis Tolikas","doi":"10.1109/ITSC45102.2020.9294261","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294261","url":null,"abstract":"Safety al Level crossing (LC) is a minor issue for the road sector since it represents less than 1% of the accident mortality, but it is highly important for the railway sector, which accounts for thousands of accidents and collisions every year. In total, more than 500 causalities occur every year in the surroundings of LCs in the United States and the European Union Member States combined. This paper presents a multimodal cooperative safety system to alert about the vicinity of a LC and, if any, an approaching train. The system processes spatial data of trains and private nearby LCs and generate alerts about the presence of a nearby LC and the estimated time of arrival for approaching trains. The system was tested in the LCs of Thessaloniki by professional taxi drivers during a 12month period. Qualitative analyses indicate positive acceptance by the drivers as well strong perception of reliability and safety impact of the system.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"82 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":"114824322","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/ITSC45102.2020.9294238
Kay Massow, F. Thiele, Karl Schrab, Sebastian Bunk, I. Tschinibaew, I. Radusch
Today’s Advanced Driver Assistance Systems (ADAS) adopt an autonomous approach with all instrumentation and intelligence on board of one vehicle. In order to further enhance their benefit, ADAS need to cooperate in the future. This enables, for instance, to solve hazardous situations by coordinated maneuvers for safety intervention on multiple vehicles at the same point in time. Our prototyping environment presented in previous work addresses developing such cooperative ADAS. Its underlying approach is to either bring ideas for cooperative ADAS through the prototyping stage towards plausible candidates for further development, or to discard them as quickly as possible. This is enabled by an iterative process of refining and assessment. In this paper, we focus on handling the application specific parameter space, and more precisely on the scenario related aspects. As a part of our iterative prototyping process, defining and tuning scenarios and application parameters are highly repetitive tasks which needs to be designed very efficiently. We, therefore, strive to create a scenario definition methodology, which provides best flexibility and a minimal expenditure of time on the developer side.
{"title":"Scenario Definition for Prototyping Cooperative Advanced Driver Assistance Systems","authors":"Kay Massow, F. Thiele, Karl Schrab, Sebastian Bunk, I. Tschinibaew, I. Radusch","doi":"10.1109/ITSC45102.2020.9294238","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294238","url":null,"abstract":"Today’s Advanced Driver Assistance Systems (ADAS) adopt an autonomous approach with all instrumentation and intelligence on board of one vehicle. In order to further enhance their benefit, ADAS need to cooperate in the future. This enables, for instance, to solve hazardous situations by coordinated maneuvers for safety intervention on multiple vehicles at the same point in time. Our prototyping environment presented in previous work addresses developing such cooperative ADAS. Its underlying approach is to either bring ideas for cooperative ADAS through the prototyping stage towards plausible candidates for further development, or to discard them as quickly as possible. This is enabled by an iterative process of refining and assessment. In this paper, we focus on handling the application specific parameter space, and more precisely on the scenario related aspects. As a part of our iterative prototyping process, defining and tuning scenarios and application parameters are highly repetitive tasks which needs to be designed very efficiently. We, therefore, strive to create a scenario definition methodology, which provides best flexibility and a minimal expenditure of time on the developer side.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"643 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":"116089640","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/ITSC45102.2020.9294304
Kunming Li, Mao Shan, K. Narula, Stewart Worrall, E. Nebot
Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions of the pedestrians’ future state as human motion prediction has a large variance. To overcome this problem, we propose a new method, SARL-SGAN-KCE, that combines a deep socially aware attentive value network with a human multimodal trajectory prediction model to help identify the optimal driving policy. We also introduce a novel technique to extend the discrete action space with minimal additional computational requirements. The kinematic constraints of the vehicle are also considered to ensure smooth and safe trajectories. We evaluate our method against the state of art methods for crowd navigation and provide an ablation study to show that our method is safer and closer to human behaviour.
{"title":"Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles","authors":"Kunming Li, Mao Shan, K. Narula, Stewart Worrall, E. Nebot","doi":"10.1109/ITSC45102.2020.9294304","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294304","url":null,"abstract":"Seamlessly operating an autonomous vehicles in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. However, these methods can have very poor performance due to inaccurate predictions of the pedestrians’ future state as human motion prediction has a large variance. To overcome this problem, we propose a new method, SARL-SGAN-KCE, that combines a deep socially aware attentive value network with a human multimodal trajectory prediction model to help identify the optimal driving policy. We also introduce a novel technique to extend the discrete action space with minimal additional computational requirements. The kinematic constraints of the vehicle are also considered to ensure smooth and safe trajectories. We evaluate our method against the state of art methods for crowd navigation and provide an ablation study to show that our method is safer and closer to human behaviour.","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":"124464423","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/ITSC45102.2020.9294608
Yeltsin Valero, A. Antonelli, Z. Christoforou, N. Farhi, Bachar Kabalan, Christos Gioldasis, Nicolas Foissaud
The Personal Mobility Vehicles (PMV) and in particular the electric scooters enjoy increasing popularity and their use has become widespread in the urban environment. The use of existing infrastructure, such as the sidewalks, by escooter drivers, poses a new challenge to policy makers trying to regulate the use of this new mode of transport so that it will be smoothly integrated in the urban networks. So far, there is limited research on the movement of electric scooters and their interaction with pedestrians, depriving the authorities of tools to draw and enforce effective policies. In this paper, we explore the applicability of the social force model for pedestrian dynamics to simulate the movement of e-scooters and the interaction between e-scooters and pedestrians. To conduct this study, we extract electric scooter and pedestrian trajectories through image analysis of videos containing pedestrian and e-scooter movement. Based on the extracted trajectories, scenarios and the respective initial conditions are generated. The social force model is used for the scenarios, and simulated trajectories of escooter and pedestrian movement are produced. The simulated trajectories are compared to the experimental trajectories with the Root Mean Squared Error (RMSE). Finally, the parameters of the social force model and the free speed of the vehicle are estimated with the Cross Entropy Method (CEM).
{"title":"Adaptation and calibration of a social force based model to study interactions between electric scooters and pedestrians","authors":"Yeltsin Valero, A. Antonelli, Z. Christoforou, N. Farhi, Bachar Kabalan, Christos Gioldasis, Nicolas Foissaud","doi":"10.1109/ITSC45102.2020.9294608","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294608","url":null,"abstract":"The Personal Mobility Vehicles (PMV) and in particular the electric scooters enjoy increasing popularity and their use has become widespread in the urban environment. The use of existing infrastructure, such as the sidewalks, by escooter drivers, poses a new challenge to policy makers trying to regulate the use of this new mode of transport so that it will be smoothly integrated in the urban networks. So far, there is limited research on the movement of electric scooters and their interaction with pedestrians, depriving the authorities of tools to draw and enforce effective policies. In this paper, we explore the applicability of the social force model for pedestrian dynamics to simulate the movement of e-scooters and the interaction between e-scooters and pedestrians. To conduct this study, we extract electric scooter and pedestrian trajectories through image analysis of videos containing pedestrian and e-scooter movement. Based on the extracted trajectories, scenarios and the respective initial conditions are generated. The social force model is used for the scenarios, and simulated trajectories of escooter and pedestrian movement are produced. The simulated trajectories are compared to the experimental trajectories with the Root Mean Squared Error (RMSE). Finally, the parameters of the social force model and the free speed of the vehicle are estimated with the Cross Entropy Method (CEM).","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"22 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":"124686491","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/ITSC45102.2020.9294229
T. Seo
Traffic state estimation (TSE), which reconstructs complete traffic states from partial observation data, is an essential component in intelligent transportation systems. In this study, a novel traffic state estimation method using connected vehicles and a single detector based on Kalman filtering and Rauch–Tung–Striebel (RTS) smoothing is proposed. To the author’s knowledge, while filtering is common approach for TSE, smoothing has not been employed to TSE in the literature. The important features of the proposed method are twofold. First, thanks to RTS smoothing, it can estimate accurate traffic state using a single detector, and it does not require detectors in every entries and exits of a road section. In addition, the estimation accuracy is not significantly sensitive to detector location. Second, it does not require parameter calibration thanks to the method’s data-driven nature. These features will make the method flexibly applicable for practical conditions. Estimation accuracy of the proposed method was empirically evaluated by using actual vehicle trajectories data, and the effectiveness of the above two features was confirmed.
{"title":"Calibration-free traffic state estimation method using single detector and connected vehicles with Kalman filtering and RTS smoothing","authors":"T. Seo","doi":"10.1109/ITSC45102.2020.9294229","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294229","url":null,"abstract":"Traffic state estimation (TSE), which reconstructs complete traffic states from partial observation data, is an essential component in intelligent transportation systems. In this study, a novel traffic state estimation method using connected vehicles and a single detector based on Kalman filtering and Rauch–Tung–Striebel (RTS) smoothing is proposed. To the author’s knowledge, while filtering is common approach for TSE, smoothing has not been employed to TSE in the literature. The important features of the proposed method are twofold. First, thanks to RTS smoothing, it can estimate accurate traffic state using a single detector, and it does not require detectors in every entries and exits of a road section. In addition, the estimation accuracy is not significantly sensitive to detector location. Second, it does not require parameter calibration thanks to the method’s data-driven nature. These features will make the method flexibly applicable for practical conditions. Estimation accuracy of the proposed method was empirically evaluated by using actual vehicle trajectories data, and the effectiveness of the above two features was confirmed.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"1 4 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":"124997052","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/ITSC45102.2020.9294674
Goodarz Mehr, A. Eskandarian
This paper presents a model to estimate the probability of reaching a target position on the road using multiple lane changes based on parameters corresponding to traffic flow and driving behavior. Knowing this information can help design advance warning systems that increase driver safety and traffic efficiency. The model is first developed for a two-lane road segment where traffic conditions are simplified to reach an abstract formulation. It is then extended to cases with a higher number of lanes using the law of total probability. Finally, the model is used in two sample cases to illustrate its predictions and the effect of different parameters on the results.
{"title":"Estimating the Likelihood of Reaching a Road Target Using Multiple Lane Changes for Driver Assistance","authors":"Goodarz Mehr, A. Eskandarian","doi":"10.1109/ITSC45102.2020.9294674","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294674","url":null,"abstract":"This paper presents a model to estimate the probability of reaching a target position on the road using multiple lane changes based on parameters corresponding to traffic flow and driving behavior. Knowing this information can help design advance warning systems that increase driver safety and traffic efficiency. The model is first developed for a two-lane road segment where traffic conditions are simplified to reach an abstract formulation. It is then extended to cases with a higher number of lanes using the law of total probability. Finally, the model is used in two sample cases to illustrate its predictions and the effect of different parameters on the results.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"62 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":"121720716","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/ITSC45102.2020.9294474
S. Henning, Kevin Malena, C. Link, Sandra Gausemeier, A. Trächtler
In previous researches, a new approach in the field of traffic flow control using consensus algorithms was studied by using microscopic traffic simulations and led to promising results. However, these studies based on some assumptions and uncertainties within the microscopic traffic model since the control variables are defined within the domain of macroscopic traffic values and therefore have to be appropriately converted into the domain of microscopic traffic values for analysis. In contrast to this, in this work an analysis of the consensus-based traffic flow control approach within the domain of macroscopic values only is presented. Consequently, a second order macroscopic traffic flow model with multiple extensions is developed to model a road network and to study the consensus-based control approach without needing to consider microscopic traffic model characteristics.
{"title":"Macroscopic Traffic Flow Control using Consensus Algorithms","authors":"S. Henning, Kevin Malena, C. Link, Sandra Gausemeier, A. Trächtler","doi":"10.1109/ITSC45102.2020.9294474","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294474","url":null,"abstract":"In previous researches, a new approach in the field of traffic flow control using consensus algorithms was studied by using microscopic traffic simulations and led to promising results. However, these studies based on some assumptions and uncertainties within the microscopic traffic model since the control variables are defined within the domain of macroscopic traffic values and therefore have to be appropriately converted into the domain of microscopic traffic values for analysis. In contrast to this, in this work an analysis of the consensus-based traffic flow control approach within the domain of macroscopic values only is presented. Consequently, a second order macroscopic traffic flow model with multiple extensions is developed to model a road network and to study the consensus-based control approach without needing to consider microscopic traffic model characteristics.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"32 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":"122057402","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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