In this paper we propose a method for estimating arbitrary lane boundaries using noisy sensor data. Lane boundaries are modelled three dimensionally as consecutive linear segments in real world coordinates fixed in the local environment of the vehicle. A detailed error model is defined in order to estimate and represent uncertainties arising during the perception process. Uncertainties are estimated in lateral direction along lane boundaries. A detailed error model of the vehicle state incorporates the uncertainties arising during the perspective mapping. A two dimensional Interval Map is employed in order to structure the environment and manage the linear segments efficiently. The proposed method enables a compensation of erroneous ego state estimates or a flat world assumption. Results demonstrating the successful three dimensional estimation of lane boundary segments using real world sensor data are presented and discussed. The estimated positions are compared to reference data for evaluation.
{"title":"3D Lane Boundary Tracking Using Local Linear Segments","authors":"M. Schmidt, U. Hofmann, Stephan Neumaier","doi":"10.1109/ITSC.2015.399","DOIUrl":"https://doi.org/10.1109/ITSC.2015.399","url":null,"abstract":"In this paper we propose a method for estimating arbitrary lane boundaries using noisy sensor data. Lane boundaries are modelled three dimensionally as consecutive linear segments in real world coordinates fixed in the local environment of the vehicle. A detailed error model is defined in order to estimate and represent uncertainties arising during the perception process. Uncertainties are estimated in lateral direction along lane boundaries. A detailed error model of the vehicle state incorporates the uncertainties arising during the perspective mapping. A two dimensional Interval Map is employed in order to structure the environment and manage the linear segments efficiently. The proposed method enables a compensation of erroneous ego state estimates or a flat world assumption. Results demonstrating the successful three dimensional estimation of lane boundary segments using real world sensor data are presented and discussed. The estimated positions are compared to reference data for evaluation.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125453165","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}
Autonomous vehicles require a reliable perception of their environment to operate in real-world conditions. Awareness of moving objects is one of the key components for the perception of the environment. This paper proposes a method for detection and tracking of moving objects (DATMO) in dynamic environments surrounding a moving road vehicle equipped with a Velodyne laser scanner and GPS/IMU localization system. First, at every time step, a local 2.5D grid is built using the last sets of sensor measurements. Along time, the generated grids combined with localization data are integrated into an environment model called local 2.5D map. In every frame, a 2.5D grid is compared with an updated 2.5D map to compute a 2.5D motion grid. A mechanism based on spatial properties is presented to suppress false detections that are due to small localization errors. Next, the 2.5D motion grid is post-processed to provide an object level representation of the scene. The detected moving objects are tracked over time by applying data association and Kalman filtering. The experiments conducted on different sequences from KITTI dataset showed promising results, demonstrating the applicability of the proposed method.
{"title":"Detection and Tracking of Moving Objects Using 2.5D Motion Grids","authors":"A. Asvadi, P. Peixoto, U. Nunes","doi":"10.1109/ITSC.2015.133","DOIUrl":"https://doi.org/10.1109/ITSC.2015.133","url":null,"abstract":"Autonomous vehicles require a reliable perception of their environment to operate in real-world conditions. Awareness of moving objects is one of the key components for the perception of the environment. This paper proposes a method for detection and tracking of moving objects (DATMO) in dynamic environments surrounding a moving road vehicle equipped with a Velodyne laser scanner and GPS/IMU localization system. First, at every time step, a local 2.5D grid is built using the last sets of sensor measurements. Along time, the generated grids combined with localization data are integrated into an environment model called local 2.5D map. In every frame, a 2.5D grid is compared with an updated 2.5D map to compute a 2.5D motion grid. A mechanism based on spatial properties is presented to suppress false detections that are due to small localization errors. Next, the 2.5D motion grid is post-processed to provide an object level representation of the scene. The detected moving objects are tracked over time by applying data association and Kalman filtering. The experiments conducted on different sequences from KITTI dataset showed promising results, demonstrating the applicability of the proposed method.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125668823","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}
Russel Aziz, Manav Kedia, Soham Dan, S. Sarkar, Sudeshna Mitra, Pabitra Mitra
GPS Data from vehicles making trips on the highway are a valuable source of information for highway data analytics. In this article we propose an algorithm for segmenting the highway network into homogenous stretches in terms of vehicle speed profiles. We have GPS data of trucks plying across India, transmitted at an interval of 10 minutes, for thousands of trips. We identify break-points for individual trips and then cluster those break-points to obtain highway segment ends. We calculate the average velocity of vehicles traversing the regions between these segment ends, i.e. the highway segments. Then we merge the segments using an iterative minimum difference merging algorithm. The segments obtained thus are meaningful and may be utilized in optimal trip planning, infrastructure management and other decision making tasks.
{"title":"Segmenting Highway Network Based on Speed Profiles","authors":"Russel Aziz, Manav Kedia, Soham Dan, S. Sarkar, Sudeshna Mitra, Pabitra Mitra","doi":"10.1109/ITSC.2015.469","DOIUrl":"https://doi.org/10.1109/ITSC.2015.469","url":null,"abstract":"GPS Data from vehicles making trips on the highway are a valuable source of information for highway data analytics. In this article we propose an algorithm for segmenting the highway network into homogenous stretches in terms of vehicle speed profiles. We have GPS data of trucks plying across India, transmitted at an interval of 10 minutes, for thousands of trips. We identify break-points for individual trips and then cluster those break-points to obtain highway segment ends. We calculate the average velocity of vehicles traversing the regions between these segment ends, i.e. the highway segments. Then we merge the segments using an iterative minimum difference merging algorithm. The segments obtained thus are meaningful and may be utilized in optimal trip planning, infrastructure management and other decision making tasks.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121719366","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}
Intersections remain among the most accident-prone subsystems in modern traffic. With the introduction of vehicle-to-infrastructure communication, it is possible for the intersection to become aware of the incoming stream of vehicles and issue warnings when needed. We consider an approach where vehicles can act automatically on those warnings, leaving drivers maximal freedom of manoeuvre while guaranteeing safety with minimal intervention. We also quantify the impact of imperfect communication in the uplink (from vehicle to infrastructure) and the downlink (from infrastructure to vehicle).
{"title":"Cooperative Intersection Collision Avoidance in a Constrained Communication Environment","authors":"A. Colombo, H. Wymeersch","doi":"10.1109/ITSC.2015.70","DOIUrl":"https://doi.org/10.1109/ITSC.2015.70","url":null,"abstract":"Intersections remain among the most accident-prone subsystems in modern traffic. With the introduction of vehicle-to-infrastructure communication, it is possible for the intersection to become aware of the incoming stream of vehicles and issue warnings when needed. We consider an approach where vehicles can act automatically on those warnings, leaving drivers maximal freedom of manoeuvre while guaranteeing safety with minimal intervention. We also quantify the impact of imperfect communication in the uplink (from vehicle to infrastructure) and the downlink (from infrastructure to vehicle).","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115866812","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 evaluation and testing of future driving assistance systems based on Vehicular Ad-hoc Networks (VANETs) in real testbeds is difficult due to the need for repeatable scenarios and large-scale experiments. Therefore, a novel framework based on multi-resolution modeling is presented to test automotive software both accurately and efficiently in large-scale scenarios using virtual test drives in a simulated environment. The approach enables the precise and large-scale evaluation of real-world implementations. This is done through the synchronized execution of simulation models of multiple resolutions representing the vehicle and network domain, as well as the applications encapsulated in virtual Electronic Control Units. This paper provides a detailed and formal description of the applied multi-resolution methodology and explains the developed generic testing platform and the components it is comprised of.
{"title":"Multi-resolution-Modeling for Testing and Evaluation of VANET Applications","authors":"M. Schiller, Thomas Behrens, A. Knoll","doi":"10.1109/ITSC.2015.64","DOIUrl":"https://doi.org/10.1109/ITSC.2015.64","url":null,"abstract":"The evaluation and testing of future driving assistance systems based on Vehicular Ad-hoc Networks (VANETs) in real testbeds is difficult due to the need for repeatable scenarios and large-scale experiments. Therefore, a novel framework based on multi-resolution modeling is presented to test automotive software both accurately and efficiently in large-scale scenarios using virtual test drives in a simulated environment. The approach enables the precise and large-scale evaluation of real-world implementations. This is done through the synchronized execution of simulation models of multiple resolutions representing the vehicle and network domain, as well as the applications encapsulated in virtual Electronic Control Units. This paper provides a detailed and formal description of the applied multi-resolution methodology and explains the developed generic testing platform and the components it is comprised of.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130049603","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}
Manuel Kehl, M. Enzweiler, B. Fröhlich, Uwe Franke, W. Heiden
In this paper, we present a stereo-vision based approach for road sign detection. As opposed to traffic signs, which are typically made up of well-defined pictographs, road signs can contain arbitrary information. Here, color and shape are the main two cues that represent different classes of road signs, e.g. signs on the highway vs. signs on country roads. To that extent, the proposed model couples efficient low-level color-based segmentation in HSL space with higher-level constraints that integrate prior knowledge on sign geometry in 3D through stereo-vision. Additional robustness is obtained by temporal integration as well as by matching detected signs against the results of object detectors for other traffic participants. The effectiveness of our approach is demonstrated on a real-world stereo-vision dataset (3700 images) that has been captured from a moving vehicle on German highways and country roads. Our results indicate competitive performance at real-time speeds.
{"title":"Vision-Based Road Sign Detection","authors":"Manuel Kehl, M. Enzweiler, B. Fröhlich, Uwe Franke, W. Heiden","doi":"10.1109/ITSC.2015.89","DOIUrl":"https://doi.org/10.1109/ITSC.2015.89","url":null,"abstract":"In this paper, we present a stereo-vision based approach for road sign detection. As opposed to traffic signs, which are typically made up of well-defined pictographs, road signs can contain arbitrary information. Here, color and shape are the main two cues that represent different classes of road signs, e.g. signs on the highway vs. signs on country roads. To that extent, the proposed model couples efficient low-level color-based segmentation in HSL space with higher-level constraints that integrate prior knowledge on sign geometry in 3D through stereo-vision. Additional robustness is obtained by temporal integration as well as by matching detected signs against the results of object detectors for other traffic participants. The effectiveness of our approach is demonstrated on a real-world stereo-vision dataset (3700 images) that has been captured from a moving vehicle on German highways and country roads. Our results indicate competitive performance at real-time speeds.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134017632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Automated vehicle detection is a research field in constant evolution due to the new technological advances and security requirements demanded by the current intelligent transportation systems. For these reasons, in this paper we present a vision-based vehicle detection and tracking pipeline, which is able to run on an iPhone in real time. An approach based on smartphone cameras supposes a versatile solution and an alternative to other expensive and complex sensors on the vehicle, such as LiDAR or other range-based methods. A multi-scale proposal and simple geometry consideration of the roads based on the vanishing point are combined to overcome the computational constraints. Our algorithm is tested on a publicly available road dataset, thus demonstrating its real applicability to ADAS or autonomous driving.
{"title":"A Real-Time Multi-scale Vehicle Detection and Tracking Approach for Smartphones","authors":"Eduardo Romera, L. Bergasa, R. Arroyo","doi":"10.1109/ITSC.2015.213","DOIUrl":"https://doi.org/10.1109/ITSC.2015.213","url":null,"abstract":"Automated vehicle detection is a research field in constant evolution due to the new technological advances and security requirements demanded by the current intelligent transportation systems. For these reasons, in this paper we present a vision-based vehicle detection and tracking pipeline, which is able to run on an iPhone in real time. An approach based on smartphone cameras supposes a versatile solution and an alternative to other expensive and complex sensors on the vehicle, such as LiDAR or other range-based methods. A multi-scale proposal and simple geometry consideration of the roads based on the vanishing point are combined to overcome the computational constraints. Our algorithm is tested on a publicly available road dataset, thus demonstrating its real applicability to ADAS or autonomous driving.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134043393","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}
Research on advanced driver-assistance systems (ADASs) aims at increasing traffic safety. In such systems, assistance of maneuver decision making is a hot research topic. This paper proposes a merging assistance decision system, to perceive the dynamic and real-time environment of vehicles and provide decisions of merging maneuvers during urban driving. In particular, the algorithmic background for this system is described. According to detect and track lane marking by image processing, a compact representation of the region of interest (ROI) in driving environment based on an occupancy grid is constructed. Then sensor measurements of vehicles are mapped into the grid. Finally, we formulate the merging assistance decision system to recommend the required acceleration to safely merging. Real world traffic data is used to simulate and verify the proposed system and algorithm.
{"title":"Merging Driver Assistance Decision System Using Occupancy Grid-Based Traffic Situation Representation","authors":"Kenan Mu, F. Hui, Xiangmo Zhao","doi":"10.1109/ITSC.2015.47","DOIUrl":"https://doi.org/10.1109/ITSC.2015.47","url":null,"abstract":"Research on advanced driver-assistance systems (ADASs) aims at increasing traffic safety. In such systems, assistance of maneuver decision making is a hot research topic. This paper proposes a merging assistance decision system, to perceive the dynamic and real-time environment of vehicles and provide decisions of merging maneuvers during urban driving. In particular, the algorithmic background for this system is described. According to detect and track lane marking by image processing, a compact representation of the region of interest (ROI) in driving environment based on an occupancy grid is constructed. Then sensor measurements of vehicles are mapped into the grid. Finally, we formulate the merging assistance decision system to recommend the required acceleration to safely merging. Real world traffic data is used to simulate and verify the proposed system and algorithm.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134531573","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}
Jan Micha Borrmann, Frederik Haxel, A. Viehl, O. Bringmann, W. Rosenstiel
In this paper, we present a novel runtime resource management approach that obeys automotive safety constraints. We specifically target emerging heterogeneous embedded plat-forms which promise potential to ease the ever-growing gap between demanded processing power and feasible efficient em-bedded realization of modern assistance systems by allowing both, hardware and software implementations of automotive driver assistance tasks. Our approach proposes runtime concepts that are mandatory for efficiently utilizing those heterogeneous architectures, specifically taking into account hard automotive safety requirements. Our dynamic management is complemented by a fail-operational scheme that ensures permanent safe vehicle operation. For evaluation, we implement a modern heterogeneous embedded platform as both, an in-vehicle prototype platform using a near-series CMOS sensor and as hardware-in-the-loop prototype, concurrently executing two complex assistance applications, a traffic light recognition and a traffic sign recognition, demonstrating the feasibility of our approach.
{"title":"Safe and Efficient Runtime Resource Management in Heterogeneous Systems for Automated Driving","authors":"Jan Micha Borrmann, Frederik Haxel, A. Viehl, O. Bringmann, W. Rosenstiel","doi":"10.1109/ITSC.2015.67","DOIUrl":"https://doi.org/10.1109/ITSC.2015.67","url":null,"abstract":"In this paper, we present a novel runtime resource management approach that obeys automotive safety constraints. We specifically target emerging heterogeneous embedded plat-forms which promise potential to ease the ever-growing gap between demanded processing power and feasible efficient em-bedded realization of modern assistance systems by allowing both, hardware and software implementations of automotive driver assistance tasks. Our approach proposes runtime concepts that are mandatory for efficiently utilizing those heterogeneous architectures, specifically taking into account hard automotive safety requirements. Our dynamic management is complemented by a fail-operational scheme that ensures permanent safe vehicle operation. For evaluation, we implement a modern heterogeneous embedded platform as both, an in-vehicle prototype platform using a near-series CMOS sensor and as hardware-in-the-loop prototype, concurrently executing two complex assistance applications, a traffic light recognition and a traffic sign recognition, demonstrating the feasibility of our approach.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133889964","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}
Bus bunching is an instability problem where buses operating on high frequency public transport lines arrive at stops in bunches. In this work, we unveil that bus-following models can be used to design bus-to-bus cooperative control strategies and mitigate bunching. The use of bus-following models avoids the explicit modelling of bus-stops, which would render the resulting problem discrete, with events occurring at arbitrary time intervals. In a "follow-the-leader" two-bus system, bus-to-bus communication allows the driver of the following bus to observe (from a remote distance) the position and speed of a lead bus operating in the same transport line. The information transmitted from the lead bus is then used to control the speed of the follower to eliminate bunching. In this context, we first propose practical linear and nonlinear control laws to regulate space headways and speeds, which would lead to bunching cure. Then a combined state estimation and remote control scheme, which is based on the Linear-Quadratic Gaussian theory, is developed to capture the effect of bus stops, traffic disturbances, and randomness in passenger arrivals. To investigate the behaviour and performance of the developed approaches the 9-km 1-California line in San Francisco with about 50 arbitrary spaced bus stops is used. Simulations with real passenger data obtained from the San Francisco Municipal Transportation Agency are carried out. Results show bunching avoidance and significant improvements in terms of schedule reliability of bus services and delays. The proposed control is robust, scalable in terms of public transport network size, and thus easy to implement in real-world settings.
{"title":"Mitigating Bunching with Bus-following Models and Bus-to-Bus Cooperation","authors":"K. Ampountolas, Malcolm Kring","doi":"10.1109/ITSC.2015.18","DOIUrl":"https://doi.org/10.1109/ITSC.2015.18","url":null,"abstract":"Bus bunching is an instability problem where buses operating on high frequency public transport lines arrive at stops in bunches. In this work, we unveil that bus-following models can be used to design bus-to-bus cooperative control strategies and mitigate bunching. The use of bus-following models avoids the explicit modelling of bus-stops, which would render the resulting problem discrete, with events occurring at arbitrary time intervals. In a \"follow-the-leader\" two-bus system, bus-to-bus communication allows the driver of the following bus to observe (from a remote distance) the position and speed of a lead bus operating in the same transport line. The information transmitted from the lead bus is then used to control the speed of the follower to eliminate bunching. In this context, we first propose practical linear and nonlinear control laws to regulate space headways and speeds, which would lead to bunching cure. Then a combined state estimation and remote control scheme, which is based on the Linear-Quadratic Gaussian theory, is developed to capture the effect of bus stops, traffic disturbances, and randomness in passenger arrivals. To investigate the behaviour and performance of the developed approaches the 9-km 1-California line in San Francisco with about 50 arbitrary spaced bus stops is used. Simulations with real passenger data obtained from the San Francisco Municipal Transportation Agency are carried out. Results show bunching avoidance and significant improvements in terms of schedule reliability of bus services and delays. The proposed control is robust, scalable in terms of public transport network size, and thus easy to implement in real-world settings.","PeriodicalId":124818,"journal":{"name":"2015 IEEE 18th International Conference on Intelligent Transportation Systems","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133979609","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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