Pub Date : 2020-09-20DOI: 10.1109/ITSC45102.2020.9294206
Keyang Zhou, Kaiwei Wang, Kailun Yang
Depth estimation is a basic problem in computer vision, which provides three-dimensional information by assigning depth values to pixels. With the development of deep learning, researchers have focused on estimating depth based on a single image, which is known as the “monocular depth estimation” problem. Moreover, panoramic images have been introduced to obtain a greater view angle recently, but the corresponding model for monocular depth estimation is scarce in the state of the art. In this paper, we propose PADENet for panoramic monocular depth estimation and re-design the loss function adapted for panoramic images. We also perform model transferring to panoramic scenes after training. A series of experiments show that our PADENet and loss function can effectively improve the accuracy of panoramic depth prediction while maintaining a high level of robustness and reaching the state of the art on the CARLA Dataset.
{"title":"PADENet: An Efficient and Robust Panoramic Monocular Depth Estimation Network for Outdoor Scenes","authors":"Keyang Zhou, Kaiwei Wang, Kailun Yang","doi":"10.1109/ITSC45102.2020.9294206","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294206","url":null,"abstract":"Depth estimation is a basic problem in computer vision, which provides three-dimensional information by assigning depth values to pixels. With the development of deep learning, researchers have focused on estimating depth based on a single image, which is known as the “monocular depth estimation” problem. Moreover, panoramic images have been introduced to obtain a greater view angle recently, but the corresponding model for monocular depth estimation is scarce in the state of the art. In this paper, we propose PADENet for panoramic monocular depth estimation and re-design the loss function adapted for panoramic images. We also perform model transferring to panoramic scenes after training. A series of experiments show that our PADENet and loss function can effectively improve the accuracy of panoramic depth prediction while maintaining a high level of robustness and reaching the state of the art on the CARLA Dataset.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"6 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":"114982295","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/ITSC45102.2020.9294246
S. Mousavi, Anastasios Kouvelas
In order to control and reduce the congestion in motorway traffic networks, it is required to measure important traffic variables, e.g. densities, that can be observed by sensors. However, to reduce the operational costs, one should look for the most efficient methods to place the minimum number of sensors in a given network. In this paper, we discuss the structural observability of a traffic system, namely, the density dynamics defined on a motorway ring road. For this purpose, LWR theory in a spatial discretization form is employed, and the nonlinear dynamics of the traffic density associated with different cells of the network have been derived. Then, by considering a linearization of the ordinary differential equations (ODEs), we derive the minimum number of sensors that are needed to render the network weakly or strongly structurally observable. In this framework, the parameters of the system can have any nonzero value, and the exact value of nonzero elements (weights) is not of interest. In this work, we also discuss optimal locations in the traffic network to place the minimum set of sensors.
{"title":"Structural Observability of Traffic Density Dynamics on a Motorway Ring Road","authors":"S. Mousavi, Anastasios Kouvelas","doi":"10.1109/ITSC45102.2020.9294246","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294246","url":null,"abstract":"In order to control and reduce the congestion in motorway traffic networks, it is required to measure important traffic variables, e.g. densities, that can be observed by sensors. However, to reduce the operational costs, one should look for the most efficient methods to place the minimum number of sensors in a given network. In this paper, we discuss the structural observability of a traffic system, namely, the density dynamics defined on a motorway ring road. For this purpose, LWR theory in a spatial discretization form is employed, and the nonlinear dynamics of the traffic density associated with different cells of the network have been derived. Then, by considering a linearization of the ordinary differential equations (ODEs), we derive the minimum number of sensors that are needed to render the network weakly or strongly structurally observable. In this framework, the parameters of the system can have any nonzero value, and the exact value of nonzero elements (weights) is not of interest. In this work, we also discuss optimal locations in the traffic network to place the minimum set of sensors.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"57 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":"115229914","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/ITSC45102.2020.9294243
Meng Lu, Jaime Ferragut, M. Kutila, Tao Chen
EU-China 5G collaboration trials will be conducted addressing two specific scenarios: (1) enhanced Mobile Broadband (eMBB) on the 3.5GHz band; and (2) Internet of Vehicles (IoV) based on LTE-V2X using the 5.9 GHz band for Vehicle-to-Vehicle (V2V) and the 3.5 GHz band for Vehicle-to-Network (V2N). This paper discussed scenario 2, and presents the use cases based on next generation communication technologies in the domain of Cooperative Intelligent Transport Systems (C-ITS) for cooperative and automated road transport. In addition, it describes for each use case the developed physical architecture. Finally, it provides an overview of the joint V2X trials to be conducted in the EU and China, in the context of the 5G-DRIVE project.
{"title":"Next-generation wireless networks for V2X","authors":"Meng Lu, Jaime Ferragut, M. Kutila, Tao Chen","doi":"10.1109/ITSC45102.2020.9294243","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294243","url":null,"abstract":"EU-China 5G collaboration trials will be conducted addressing two specific scenarios: (1) enhanced Mobile Broadband (eMBB) on the 3.5GHz band; and (2) Internet of Vehicles (IoV) based on LTE-V2X using the 5.9 GHz band for Vehicle-to-Vehicle (V2V) and the 3.5 GHz band for Vehicle-to-Network (V2N). This paper discussed scenario 2, and presents the use cases based on next generation communication technologies in the domain of Cooperative Intelligent Transport Systems (C-ITS) for cooperative and automated road transport. In addition, it describes for each use case the developed physical architecture. Finally, it provides an overview of the joint V2X trials to be conducted in the EU and China, in the context of the 5G-DRIVE project.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"31 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":"115695631","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/ITSC45102.2020.9294280
L. Tumash, C. Canudas-de-Wit, M. D. Monache
This paper addresses the problem of a boundary control design for traffic evolving in a large urban network. The traffic state is described on a macroscopic scale and corresponds to the vehicle density, whose dynamics are governed by a two dimensional conservation law. We aim at designing a boundary control law such that the throughput of vehicles in a congested area is maximized. Thereby, the only knowledge we use is the network’s topology, capacities of its roads and speed limits. In order to achieve this goal, we treat a 2D equation as a set of 1D equations by introducing curvilinear coordinates satisfying special properties. The theoretical results are verified on a numerical example, where an initially fully congested area is driven to a state with maximum possible throughput.
{"title":"Topology-based control design for congested areas in urban networks","authors":"L. Tumash, C. Canudas-de-Wit, M. D. Monache","doi":"10.1109/ITSC45102.2020.9294280","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294280","url":null,"abstract":"This paper addresses the problem of a boundary control design for traffic evolving in a large urban network. The traffic state is described on a macroscopic scale and corresponds to the vehicle density, whose dynamics are governed by a two dimensional conservation law. We aim at designing a boundary control law such that the throughput of vehicles in a congested area is maximized. Thereby, the only knowledge we use is the network’s topology, capacities of its roads and speed limits. In order to achieve this goal, we treat a 2D equation as a set of 1D equations by introducing curvilinear coordinates satisfying special properties. The theoretical results are verified on a numerical example, where an initially fully congested area is driven to a state with maximum possible throughput.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"2016 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":"127275816","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/ITSC45102.2020.9294328
Moritz Klischat, M. Althoff
We propose an approach for the fast computation of reachable sets of road vehicles while considering dynamic obstacles. The obtained reachable sets contain all possible behaviors of vehicles and can be used for motion planning, verification, and criticality assessment. The proposed approach precomputes computationally expensive parts of the reachability analysis. Further, we partition the reachable set into cells and construct a directed graph storing which cells are reachable from which cells at preceding time steps. Using this approach, considering obstacles reduces to deleting nodes from the directed graph. Although this simple idea ensures an efficient computation, the discretization can introduce considerable over-approximations. Thus, the main novelty of this paper is to reduce the over-approximations by intersecting reachable sets propagated from multiple points in time. We demonstrate our approach on a large range of scenarios for automated vehicles showing a faster computation time compared to previous approaches while providing the same level of accuracy.
{"title":"A Multi-Step Approach to Accelerate the Computation of Reachable Sets for Road Vehicles","authors":"Moritz Klischat, M. Althoff","doi":"10.1109/ITSC45102.2020.9294328","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294328","url":null,"abstract":"We propose an approach for the fast computation of reachable sets of road vehicles while considering dynamic obstacles. The obtained reachable sets contain all possible behaviors of vehicles and can be used for motion planning, verification, and criticality assessment. The proposed approach precomputes computationally expensive parts of the reachability analysis. Further, we partition the reachable set into cells and construct a directed graph storing which cells are reachable from which cells at preceding time steps. Using this approach, considering obstacles reduces to deleting nodes from the directed graph. Although this simple idea ensures an efficient computation, the discretization can introduce considerable over-approximations. Thus, the main novelty of this paper is to reduce the over-approximations by intersecting reachable sets propagated from multiple points in time. We demonstrate our approach on a large range of scenarios for automated vehicles showing a faster computation time compared to previous approaches while providing the same level of accuracy.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"41 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":"126088996","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/ITSC45102.2020.9294741
Ramin Niroumand, Mehrdad Tajalli, L. Hajibabai, Ali Hajbabaie
This study investigates the effects of the “white phase” on the performance of isolated signalized intersections. During the white phase, connected automated vehicles (CAV) control traffic flow through an intersection, and connected human-driven vehicles (CHV) follow their front vehicle (either CAV or CHV). Traffic controller ensures collision-free movement of vehicles through the intersection by determining 1) the sequence and duration of phases (green and white) and 2) trajectory of CAVs during white phases. White phases can be assigned to conflicting movements simultaneously. We have formulated this problem as a mixed-integer non-linear program (MINLP) and solved it using a receding horizon algorithm. Two demand patterns with five different CAV market penetration rates are used to evaluate the effects of the white phase on mobility and safety in an isolated intersection. Each case study is tested with three different control scenarios: 1) No-white-phase, 2) white-phase-only, and 3) optimal-white-phase activation (combination of white, green, and red phases). The results indicate that the white phase yields significant improvement in intersection performance while maintaining the same safety level.
{"title":"The Effects of the “White Phase” on Intersection Performance with Mixed-Autonomy Traffic Stream","authors":"Ramin Niroumand, Mehrdad Tajalli, L. Hajibabai, Ali Hajbabaie","doi":"10.1109/ITSC45102.2020.9294741","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294741","url":null,"abstract":"This study investigates the effects of the “white phase” on the performance of isolated signalized intersections. During the white phase, connected automated vehicles (CAV) control traffic flow through an intersection, and connected human-driven vehicles (CHV) follow their front vehicle (either CAV or CHV). Traffic controller ensures collision-free movement of vehicles through the intersection by determining 1) the sequence and duration of phases (green and white) and 2) trajectory of CAVs during white phases. White phases can be assigned to conflicting movements simultaneously. We have formulated this problem as a mixed-integer non-linear program (MINLP) and solved it using a receding horizon algorithm. Two demand patterns with five different CAV market penetration rates are used to evaluate the effects of the white phase on mobility and safety in an isolated intersection. Each case study is tested with three different control scenarios: 1) No-white-phase, 2) white-phase-only, and 3) optimal-white-phase activation (combination of white, green, and red phases). The results indicate that the white phase yields significant improvement in intersection performance while maintaining the same safety level.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"96 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116105936","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/ITSC45102.2020.9294188
Yongqiu Zhu, Hongrui Wang, R. Goverde
Real-time railway traffic management is important for the daily operations of railway systems. It predicts and resolves operational conflicts caused by events like excessive passenger boardings/alightings. Traditional optimization methods for this problem are restricted by the size of the problem instances. Therefore, this paper proposes a reinforcement learning-based timetable rescheduling method. Our method learns how to reschedule a timetable off-line and then can be applied online to make an optimal dispatching decision immediately by sensing the current state of the railway environment. Experiments show that the rescheduling solution obtained by the proposed reinforcement learning method is affected by the state representation of the railway environment. The proposed method was tested to a part of the Dutch railways considering scenarios with single initial train delays and multiple initial train delays. In both cases, our method found high-quality rescheduling solutions within limited training episodes.
{"title":"Reinforcement Learning in Railway Timetable Rescheduling","authors":"Yongqiu Zhu, Hongrui Wang, R. Goverde","doi":"10.1109/ITSC45102.2020.9294188","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294188","url":null,"abstract":"Real-time railway traffic management is important for the daily operations of railway systems. It predicts and resolves operational conflicts caused by events like excessive passenger boardings/alightings. Traditional optimization methods for this problem are restricted by the size of the problem instances. Therefore, this paper proposes a reinforcement learning-based timetable rescheduling method. Our method learns how to reschedule a timetable off-line and then can be applied online to make an optimal dispatching decision immediately by sensing the current state of the railway environment. Experiments show that the rescheduling solution obtained by the proposed reinforcement learning method is affected by the state representation of the railway environment. The proposed method was tested to a part of the Dutch railways considering scenarios with single initial train delays and multiple initial train delays. In both cases, our method found high-quality rescheduling solutions within limited training episodes.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"11 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":"114180121","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/ITSC45102.2020.9294344
H. Davis, Paul Kornyoh, O. Osman, Peter R. Bakhit, Divya Kolasani
This study presents a driving simulator experiment conducted on 47 drivers to investigate how different populations of users respond to automated system failure. On this account, a major takeover scenario of a level 3 automated vehicle malfunctioning at three high-speed critical curves along a freeway was designed. The drivers are notified with an auditory warning that is triggered instantaneously with the malfunctions, thus indicating a demand to takeover. The reaction time, time to regain control, frequency of time to regain control, frequency of unsafe curves, and type of control were used as measures of users’ behavior. The results show that conservative users may be able to learn how to take control of the car safely compared to aggressive users as they experience more malfunctions. However, there is enough evidence that such group of users are more likely to drop their level of trust in automation if they experience unsafe maneuvers or lose control. These findings are promising as they can help auto-makers better design autonomous vehicles and officials better establish educational programs, which can accommodate different groups of users.
{"title":"Automation Malfunction: Behavioral Characteristics of Different User Groups under Forced Transfer of Control*","authors":"H. Davis, Paul Kornyoh, O. Osman, Peter R. Bakhit, Divya Kolasani","doi":"10.1109/ITSC45102.2020.9294344","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294344","url":null,"abstract":"This study presents a driving simulator experiment conducted on 47 drivers to investigate how different populations of users respond to automated system failure. On this account, a major takeover scenario of a level 3 automated vehicle malfunctioning at three high-speed critical curves along a freeway was designed. The drivers are notified with an auditory warning that is triggered instantaneously with the malfunctions, thus indicating a demand to takeover. The reaction time, time to regain control, frequency of time to regain control, frequency of unsafe curves, and type of control were used as measures of users’ behavior. The results show that conservative users may be able to learn how to take control of the car safely compared to aggressive users as they experience more malfunctions. However, there is enough evidence that such group of users are more likely to drop their level of trust in automation if they experience unsafe maneuvers or lose control. These findings are promising as they can help auto-makers better design autonomous vehicles and officials better establish educational programs, which can accommodate different groups of users.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"23 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":"114377238","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/ITSC45102.2020.9294566
D. Mahajan, Yashaswi Karnati, A. Rangarajan, S. Ranka
The modern road network infrastructure (signal controllers and detectors) continuously generates data that can be transformed and used to evaluate the performance of signalized intersections. In order to automatically make meaningful observations about signal performance, we propose the application of data summarization and compression techniques in order to intelligently group together intersections and/or time intervals during the day and certain days of the week. This work details the use of linear and nonlinear dimensionality reduction techniques to achieve the aforementioned goals. The approach is also extended to perform change detection so that significant changes at intersections and corridors can be highlighted.
{"title":"Unsupervised Summarization and Change Detection in High-Resolution Signalized Intersection Datasets","authors":"D. Mahajan, Yashaswi Karnati, A. Rangarajan, S. Ranka","doi":"10.1109/ITSC45102.2020.9294566","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294566","url":null,"abstract":"The modern road network infrastructure (signal controllers and detectors) continuously generates data that can be transformed and used to evaluate the performance of signalized intersections. In order to automatically make meaningful observations about signal performance, we propose the application of data summarization and compression techniques in order to intelligently group together intersections and/or time intervals during the day and certain days of the week. This work details the use of linear and nonlinear dimensionality reduction techniques to achieve the aforementioned goals. The approach is also extended to perform change detection so that significant changes at intersections and corridors can be highlighted.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"6 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":"116835482","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/ITSC45102.2020.9294748
Weizhi Qiu, W. Shangguan, B. Cai, Linguo Chai, Junjie Chen
Digital twins and parallel system have already been regarded as one of the most effective approaches that give a great impetus to the development of transportation system, especially for the testing of vehicle intelligence. State synchronization, as the main influencer of real-time interaction in a parallel system, determines the testing accuracy and computational efficiency. Despite the fact that the synchronization control is already a well-explored field, the traffic state synchronization in the application of the vehicle testing via virtual-real interaction is still a topic for further research. In this paper, to achieve better synchronization, a path generation method based on the Frenet frame is firstly designed to decompose the object’s motion into the longitudinal and lateral directions, and achieves the trajectory tracking based on the near real-time data sent from the physical space. Then to eliminate the stochastic latency, the advance estimate-based path modification method is proposed to generate a stretch of path in advance. Finally, the integrated approach is implemented in a testing platform and the experimental results prove that the proposed method improves the synchronous rate by an average of 78.4% and 63.7% in the scenario of straight driving and lane changing.
{"title":"Advance Estimate-based Traffic State Synchronization for Parallel Testing","authors":"Weizhi Qiu, W. Shangguan, B. Cai, Linguo Chai, Junjie Chen","doi":"10.1109/ITSC45102.2020.9294748","DOIUrl":"https://doi.org/10.1109/ITSC45102.2020.9294748","url":null,"abstract":"Digital twins and parallel system have already been regarded as one of the most effective approaches that give a great impetus to the development of transportation system, especially for the testing of vehicle intelligence. State synchronization, as the main influencer of real-time interaction in a parallel system, determines the testing accuracy and computational efficiency. Despite the fact that the synchronization control is already a well-explored field, the traffic state synchronization in the application of the vehicle testing via virtual-real interaction is still a topic for further research. In this paper, to achieve better synchronization, a path generation method based on the Frenet frame is firstly designed to decompose the object’s motion into the longitudinal and lateral directions, and achieves the trajectory tracking based on the near real-time data sent from the physical space. Then to eliminate the stochastic latency, the advance estimate-based path modification method is proposed to generate a stretch of path in advance. Finally, the integrated approach is implemented in a testing platform and the experimental results prove that the proposed method improves the synchronous rate by an average of 78.4% and 63.7% in the scenario of straight driving and lane changing.","PeriodicalId":394538,"journal":{"name":"2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)","volume":"20 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":"129718415","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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