Connected and Autonomous Vehicles(CAVs)hold great potential to improve traffic efficiency,emissions and safety in freeway on-ramp bottlenecks through coordination between mainstream and on-ramp vehicles.This study pro...Connected and Autonomous Vehicles(CAVs)hold great potential to improve traffic efficiency,emissions and safety in freeway on-ramp bottlenecks through coordination between mainstream and on-ramp vehicles.This study proposes a bi-level coordination strategy for freeway on-ramp merging of mixed traffic consisting of CAVs and human-driven vehicles(HDVs)to optimize the overall traffic efficiency and safety in congested traffic scenarios at the traffic flow level instead of platoon levels.The macro level employs an optimization model based on fundamental diagrams and shock wave theories to make optimal coordination decisions,including optimal minimum merging platoon size to trigger merging coordination and optimal coordination speed,based on macroscopic traffic state in mainline and ramp(i.e.,traffic volume and penetration rates of CAVs).Furthermore,the micro level determines the real platoon size in each merging cycle as per random arrival patterns and designs the coordinated trajectories of the mainline facilitating vehicle and ramp platoon.A receding horizon scheme is implemented to accommodate human drivers’stochastics as well.The developed bi-level strategy is tested in terms of improving efficiency and safety in a simulation-based case study under various traffic volumes and CAV penetration rates.The results show the proposed coordination addresses the uncertainties in mixed traffic as expected and substantially improves ramp merging operation in terms of merging efficiency and traffic robustness,and reducing collision risk and emissions,especially under high traffic volume conditions.展开更多
For the integrity monitoring of a multi-source PNT(Positioning,Navigation,and Timing)resilient fusion navigation system,a theoretical framework of multi-level autonomous integrity monitoring is proposed.According to t...For the integrity monitoring of a multi-source PNT(Positioning,Navigation,and Timing)resilient fusion navigation system,a theoretical framework of multi-level autonomous integrity monitoring is proposed.According to the mode of multi-source fusion navigation,the framework adopts the top-down logic structure and establishes the navigation source fault detection model based on the multi-combination separation residual method to detect and isolate the fault source at the system level and subsystem level.For isolated non-redundant navigation sources,the system level recovery verification model is used.For the isolated multi-redundant navigation sources,the sensor fault detection model optimized with the dimension-expanding matrix is used to detect and isolate the fault sensors,and the isolated fault sensors are verified in real-time.Finally,according to the fault detection and verification results at each level,the observed information in the fusion navigation solution is dynamically adjusted.On this basis,the integrity risk dynamic monitoring tree is established to calculate the Protection Level(PL)and evaluate the integrity of the multi-source integrated navigation system.The autonomous integrity monitoring method proposed in this paper is tested using a multi-source navigation system integrated with Inertial Navigation System(INS),Global Navigation Satellite System(GNSS),Long Baseline Location(LBL),and Ultra Short Baseline Location(USBL).The test results show that the proposed method can effectively isolate the fault source within 5 s,and can quickly detect multiple faulty sensors,ensuring that the positioning accuracy of the fusion navigation system is within 5 m,effectively improving the resilience and reliability of the multi-source fusion navigation system.展开更多
BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct ...BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct a comprehensive study of the range of measurement results for a single BGP monitor.In this paper,we take the first step to describe the observed topology of each BGP monitor.To that end,we first investigate the construction and theoretical up-limit of the measured topology of a BGP monitor based on the valley-free model,then we evaluate the individual parts of the measured topology by comparing such theoretical results with the actually observed data.We find that:1)for more than 90%of the monitors,the actually observed peer-peer links merely takes a small part of all theoretical visible links;2)increasing the BGP monitors in the same AS may improve the measurement result,but with limited improvement;and 3)deploying multiple BGP monitors in different ASs can significantly improve the measurement results,but non-local BGP monitors can hardly replace the local AS BGP monitors.We also propose a metric for monitor selection optimization,and prove its effectiveness with experiment evaluation.展开更多
The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings(RMs).Obviously,we can build the lane-level map by running a mobile mapping system(MMS)which is ...The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings(RMs).Obviously,we can build the lane-level map by running a mobile mapping system(MMS)which is equipped with a high-end 3D LiDAR and a number of high-cost sensors.This approach,however,is highly expensive and ineffective since a single high-end MMS must visit every place for mapping.In this paper,a lane-level RM mapping system using a monocular camera is developed.The developed system can be considered as an alternative to expensive high-end MMS.The developed RM map includes the information of road lanes(RLs)and symbolic road markings(SRMs).First,to build a lane-level RM map,the RMs are segmented at pixel level through the deep learning network.The network is named RMNet.The segmented RMs are then gathered to build a lane-level RM map.Second,the lane-level map is improved through loop-closure detection and graph optimization.To train the RMNet and build a lane-level RM map,a new dataset named SeRM set is developed.The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images.Finally,the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.展开更多
基金VINNOVA(ICV-safety),National Key R&D Program of China(2019YFE0108300)the Area of Advance Transport and AI Center(CHAIR)at Chalmers University of Technology for funding this research.
文摘Connected and Autonomous Vehicles(CAVs)hold great potential to improve traffic efficiency,emissions and safety in freeway on-ramp bottlenecks through coordination between mainstream and on-ramp vehicles.This study proposes a bi-level coordination strategy for freeway on-ramp merging of mixed traffic consisting of CAVs and human-driven vehicles(HDVs)to optimize the overall traffic efficiency and safety in congested traffic scenarios at the traffic flow level instead of platoon levels.The macro level employs an optimization model based on fundamental diagrams and shock wave theories to make optimal coordination decisions,including optimal minimum merging platoon size to trigger merging coordination and optimal coordination speed,based on macroscopic traffic state in mainline and ramp(i.e.,traffic volume and penetration rates of CAVs).Furthermore,the micro level determines the real platoon size in each merging cycle as per random arrival patterns and designs the coordinated trajectories of the mainline facilitating vehicle and ramp platoon.A receding horizon scheme is implemented to accommodate human drivers’stochastics as well.The developed bi-level strategy is tested in terms of improving efficiency and safety in a simulation-based case study under various traffic volumes and CAV penetration rates.The results show the proposed coordination addresses the uncertainties in mixed traffic as expected and substantially improves ramp merging operation in terms of merging efficiency and traffic robustness,and reducing collision risk and emissions,especially under high traffic volume conditions.
基金The project is supported by the National key research and development program of China(Grant No.2020YFB0505804)the National Natural Science Foundation of China(Grant No.42274037,41874034)the Beijing Natural Science Foundation(Grant No.4202041).
文摘For the integrity monitoring of a multi-source PNT(Positioning,Navigation,and Timing)resilient fusion navigation system,a theoretical framework of multi-level autonomous integrity monitoring is proposed.According to the mode of multi-source fusion navigation,the framework adopts the top-down logic structure and establishes the navigation source fault detection model based on the multi-combination separation residual method to detect and isolate the fault source at the system level and subsystem level.For isolated non-redundant navigation sources,the system level recovery verification model is used.For the isolated multi-redundant navigation sources,the sensor fault detection model optimized with the dimension-expanding matrix is used to detect and isolate the fault sensors,and the isolated fault sensors are verified in real-time.Finally,according to the fault detection and verification results at each level,the observed information in the fusion navigation solution is dynamically adjusted.On this basis,the integrity risk dynamic monitoring tree is established to calculate the Protection Level(PL)and evaluate the integrity of the multi-source integrated navigation system.The autonomous integrity monitoring method proposed in this paper is tested using a multi-source navigation system integrated with Inertial Navigation System(INS),Global Navigation Satellite System(GNSS),Long Baseline Location(LBL),and Ultra Short Baseline Location(USBL).The test results show that the proposed method can effectively isolate the fault source within 5 s,and can quickly detect multiple faulty sensors,ensuring that the positioning accuracy of the fusion navigation system is within 5 m,effectively improving the resilience and reliability of the multi-source fusion navigation system.
基金This work was supported in part by the Guangdong Province Key Research and Development Plan(Grant No.2019B010137004)the National Key research and Development Plan(Grant No.2018YFB0803504).
文摘BGP monitors are currently the main data resource of AS-level topology measurement,and the integrity of measurement result is limited to the location of such BGP monitors.However,there is currently no work to conduct a comprehensive study of the range of measurement results for a single BGP monitor.In this paper,we take the first step to describe the observed topology of each BGP monitor.To that end,we first investigate the construction and theoretical up-limit of the measured topology of a BGP monitor based on the valley-free model,then we evaluate the individual parts of the measured topology by comparing such theoretical results with the actually observed data.We find that:1)for more than 90%of the monitors,the actually observed peer-peer links merely takes a small part of all theoretical visible links;2)increasing the BGP monitors in the same AS may improve the measurement result,but with limited improvement;and 3)deploying multiple BGP monitors in different ASs can significantly improve the measurement results,but non-local BGP monitors can hardly replace the local AS BGP monitors.We also propose a metric for monitor selection optimization,and prove its effectiveness with experiment evaluation.
基金This work was supported by the Industry Core Technology Development Project,20005062Development of Artificial Intelligence Robot Autonomous Navigation Technology for Agile Movement in Crowded Space,funded by the Ministry of Trade,industry&Energy(MOTIE,Republic of Korea).
文摘The essential requirement for precise localization of a self-driving car is a lane-level map which includes road markings(RMs).Obviously,we can build the lane-level map by running a mobile mapping system(MMS)which is equipped with a high-end 3D LiDAR and a number of high-cost sensors.This approach,however,is highly expensive and ineffective since a single high-end MMS must visit every place for mapping.In this paper,a lane-level RM mapping system using a monocular camera is developed.The developed system can be considered as an alternative to expensive high-end MMS.The developed RM map includes the information of road lanes(RLs)and symbolic road markings(SRMs).First,to build a lane-level RM map,the RMs are segmented at pixel level through the deep learning network.The network is named RMNet.The segmented RMs are then gathered to build a lane-level RM map.Second,the lane-level map is improved through loop-closure detection and graph optimization.To train the RMNet and build a lane-level RM map,a new dataset named SeRM set is developed.The set is a large dataset for lane-level RM mapping and it includes a total of 25157 pixel-wise annotated images and 21000 position labeled images.Finally,the proposed lane-level map building method is applied to SeRM set and its validity is demonstrated through experimentation.
