WO2019038987A1 - Programme informatique, dispositif d'identification de voie de déplacement et système d'identification de voie de déplacement - Google Patents

Programme informatique, dispositif d'identification de voie de déplacement et système d'identification de voie de déplacement Download PDF

Info

Publication number
WO2019038987A1
WO2019038987A1 PCT/JP2018/016006 JP2018016006W WO2019038987A1 WO 2019038987 A1 WO2019038987 A1 WO 2019038987A1 JP 2018016006 W JP2018016006 W JP 2018016006W WO 2019038987 A1 WO2019038987 A1 WO 2019038987A1
Authority
WO
WIPO (PCT)
Prior art keywords
vehicle
teacher
target vehicle
lane
unit
Prior art date
Application number
PCT/JP2018/016006
Other languages
English (en)
Japanese (ja)
Inventor
建太朗 高木
西村　茂樹
昌一 棚田
Original Assignee
住友電気工業株式会社
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 住友電気工業株式会社 filed Critical 住友電気工業株式会社
Priority to JP2019537912A priority Critical patent/JP7120239B2/ja
Publication of WO2019038987A1 publication Critical patent/WO2019038987A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
    • G08G1/127—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station
    • G08G1/13—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams to a central station ; Indicators in a central station the indicator being in the form of a map

Definitions

  • the present invention relates to a computer program for identifying a travel lane of a vehicle, a travel lane identification device, and a travel lane identification system.
  • a traffic information creation device that creates traffic information based on probe information collected from a plurality of vehicles is known (see, for example, Patent Document 1).
  • the traffic information creation device described in Patent Document 1 creates traffic information such as travel time of each link for each vehicle speed range by classifying probe information for each vehicle speed range and performing statistical processing.
  • a computer program acquires speed transition information in a predetermined section of a teacher vehicle which is a vehicle whose traveling lane in the predetermined section is known.
  • An acquisition unit, a second acquisition unit for acquiring speed transition information in the predetermined section of a target vehicle whose traveling lane is unknown in the predetermined section, speed transition information of each of the teacher vehicle and the target vehicle From the feature quantity extraction unit for extracting the feature quantity of each vehicle, and by classifying at least the feature quantity of the target vehicle among the feature quantities of the teacher vehicle and the feature quantity of the target vehicle into one or more groups
  • a travel lane identification device includes: a first acquisition unit that acquires speed transition information in the predetermined section of a teacher vehicle whose traveling lane is a known vehicle in the predetermined section; The second acquisition unit for acquiring speed transition information in the predetermined section of the target vehicle whose traveling lane is unknown in the predetermined section, and the speed transition information of each of the teacher vehicle and the target vehicle
  • the target vehicle is classified by classifying at least the feature amount of the target vehicle among the feature amount of the teacher vehicle and the feature amount of the target vehicle into a group of one or more of a feature amount extraction unit that extracts a feature amount;
  • a traveling lane identification unit that identifies a traveling lane of the target vehicle included in each group based on the feature amount of the teacher vehicle.
  • a travel lane identification system includes a travel lane identification device for identifying a travel lane of a target vehicle whose vehicle is an unknown vehicle in a predetermined section, and the travel lane identification device.
  • a vehicle mounted on the target vehicle for transmitting probe information in the predetermined section of the target vehicle, and the travel lane identification device is a teacher vehicle whose traveling lane in the predetermined section is a known vehicle
  • a first acquisition unit for acquiring speed transition information in the predetermined section, and the probe information acquired from the in-vehicle device, and based on the acquired probe information, speed transition information in the predetermined section of the target vehicle
  • a second acquisition unit for acquiring the feature information, and a feature quantity extraction unit for extracting feature quantities of each vehicle from the speed transition information of each vehicle of the teacher vehicle and the target vehicle;
  • a classification unit that classifies the target vehicle by classifying at least the characteristic quantity of the target vehicle among the characteristic quantities of the supervised vehicle and the characteristic quantities of the target vehicle into one or more groups, and the characteristic of the teacher vehicle
  • the vehicle speed may drop in a specific lane and congestion may occur, but in other lanes no congestion may occur.
  • the traffic situation may be different every time.
  • the driver including an automatic driving function
  • traffic congestion can be avoided.
  • GPS Global Positioning System
  • GPS complementation technology By using the GPS augmentation technology or the GPS complementation technology, it is possible to identify the traveling position of the vehicle with high accuracy, and it is considered that it becomes easy to identify the traveling lane. However, it will take some time before all the vehicles will be equipped with a receiver that can receive Quasi-Zenith satellite signals and pinpoint the travel position with high accuracy.
  • a vehicle capable of transmitting probe information including highly accurate position information to be able to specify a traveling lane and a vehicle transmitting probe information including position information of conventional accuracy where it is difficult to specify a traveling lane Will be mixed for a while.
  • the present disclosure has been made in view of such circumstances, and is a computer program capable of specifying a traveling lane in which the vehicle travels with respect to a vehicle that can transmit only probe information including position information with conventional accuracy. It is an object of the present invention to provide a lane identification device and a lane identification system.
  • a computer program includes a computer, a first acquisition unit for acquiring speed transition information in a predetermined section of a teacher vehicle which is a vehicle whose traveling lane in a predetermined section is known, and The second acquisition unit for acquiring speed transition information in the predetermined section of the target vehicle whose traveling lane is unknown in the predetermined section, and the speed transition information of each of the teacher vehicle and the target vehicle
  • the target vehicle is classified by classifying at least the feature amount of the target vehicle among the feature amount of the teacher vehicle and the feature amount of the target vehicle into a group of one or more of a feature amount extraction unit that extracts a feature amount It functions as a classification unit to be classified and a traveling lane identification unit that identifies the traveling lane of the target vehicle included in each group based on the feature amount of the teacher vehicle.
  • At least the feature quantities of the target vehicle are classified into groups, and the traveling lane of the target vehicle is specified based on the classification result of the feature quantities of the teacher vehicle into the group. Therefore, a target vehicle having a speed transition similar to that of the teacher vehicle is classified into the same group as that of the teacher vehicle, so that the traveling lane of the target vehicle can be specified.
  • the feature quantity of each vehicle extracted by the feature quantity extraction unit includes at least one of an average speed, a maximum speed, a minimum speed, a deceleration point and an acceleration point of the vehicle.
  • the first acquisition unit further acquires type information of the teacher vehicle
  • the second acquisition unit further acquires type information of the target vehicle
  • the classification unit Classifies at least the feature amount of the target vehicle into one or more groups for each type of the target vehicle
  • the traveling lane identification unit is based on the feature amount of the teacher vehicle for each type of the teacher vehicle To identify the travel lane of the target vehicle included in each group.
  • the classification unit may classify the representative feature quantity of each traveling lane of the teacher vehicle and the feature quantity of the target vehicle into one or more groups.
  • the feature quantities of the target vehicle can be classified into groups including the representative feature quantities of the teacher vehicle. Therefore, for each group, the target vehicle included in the group can be identified as having traveled in the same lane as the teacher vehicle included in the group.
  • the classification unit is characterized in that the feature quantities of the teacher vehicle and the features of the target vehicle are subject to the constraint that the feature quantities of the teacher vehicle traveling on the same lane are classified into the same group.
  • the amounts may be classified into one or more groups.
  • the feature quantities of the target vehicle can be classified into groups including the feature quantities of the teacher vehicle without the feature quantities of the teacher vehicle traveling on the same lane being classified into a plurality of groups.
  • the target vehicle included in the group can be identified as having traveled in the same lane as the teacher vehicle included in the group.
  • the traveling lane identifying unit is configured to compare the representative feature of each traveling lane of the teacher vehicle with the center of gravity of the feature of the target vehicle belonging to each group.
  • the traffic lane of the target vehicle included in each group may be specified by applying the amount to any group.
  • the target vehicle included in the group can be identified as having traveled in the same lane as the teacher vehicle.
  • the classification unit may further classify at least the feature quantities of the target vehicle into one or more groups under the constraint that the distance between the groups is equal to or more than a predetermined threshold.
  • groups including similar feature quantities can be integrated.
  • the difference in traveling speed between traveling lanes is small, there is no significant difference in travel time etc. regardless of which lane the vehicle travels. For this reason, it is not necessary to forcibly specify the traveling lane of the target vehicle, but this can be realized by performing group integration.
  • the traveling lane identification unit selects the traveling lane of the target vehicle included in the first group.
  • the travel lanes of the target vehicles included in two groups may be added.
  • candidate traveling lanes can be identified without uniquely identifying the traveling lanes of the target vehicle.
  • the traveling lane identification unit classifies the traveling lanes of the target vehicles included in the plurality of groups into a plurality of groups for a plurality of groups in which the distance between the groups is equal to or less than a predetermined distance. It may be identified as any of the driving lanes of the said teacher vehicle.
  • candidate traveling lanes can be identified without uniquely identifying the traveling lanes of the target vehicle.
  • a travel lane identification device includes: a first acquisition unit that acquires speed transition information in the predetermined section of a teacher vehicle whose traveling lane in the predetermined section is a known vehicle; The second acquisition unit for acquiring speed transition information in the predetermined section of the target vehicle whose traveling lane is unknown in the predetermined section, and the speed transition information of each of the teacher vehicle and the target vehicle
  • the target vehicle is classified by classifying at least the feature amount of the target vehicle among the feature amount of the teacher vehicle and the feature amount of the target vehicle into a group of one or more of a feature amount extraction unit that extracts a feature amount;
  • a traveling lane identification unit that identifies a traveling lane of the target vehicle included in each group based on the feature amount of the teacher vehicle.
  • the traffic lane specifying device includes, as a configuration, a processing unit in which a computer functions according to the above-described computer program. For this reason, the same operation and effect as the above-described computer program can be exhibited.
  • a travel lane identification system includes a travel lane identification device for identifying a travel lane of a target vehicle whose vehicle is an unknown vehicle in a predetermined section, and the travel lane identification device.
  • a vehicle mounted on the target vehicle for transmitting probe information in the predetermined section of the target vehicle, and the travel lane identification device is a teacher vehicle whose traveling lane in the predetermined section is a known vehicle
  • a first acquisition unit for acquiring speed transition information in the predetermined section, and the probe information acquired from the in-vehicle device, and based on the acquired probe information, speed transition information in the predetermined section of the target vehicle
  • a second acquisition unit for acquiring the feature information, and a feature quantity extraction unit for extracting feature quantities of each vehicle from the speed transition information of each vehicle of the teacher vehicle and the target vehicle;
  • a classification unit that classifies the target vehicle by classifying at least the characteristic quantity of the target vehicle among the characteristic quantities of the supervised vehicle and the characteristic quantities of the target vehicle into one or more groups, and the characteristic of the teacher vehicle
  • This travel lane specification system includes, as a configuration, a processing unit in which a computer functions by the above-described computer program. For this reason, the same operation and effect as the above-described computer program can be exhibited.
  • FIG. 1 is a diagram showing a configuration of a traffic information provision system according to Embodiment 1 of the present invention.
  • a traffic information providing system capable of specifying a traveling lane of a road on which the vehicle travels from the probe information collected from the vehicle and providing traffic information for each traveling lane to the vehicle will be described.
  • traffic information provision system 1 functions as a travel lane identification system, and includes server 2, on-vehicle device 6 installed on target vehicle 5 traveling on road 9, and a teacher vehicle traveling on road 9. And an on-vehicle device 8 installed in the vehicle.
  • the target vehicle 5 indicates a vehicle whose traveling lane on the road 9 is unknown. That is, a vehicle whose position accuracy of the vehicle detected by the on-vehicle device 6 is not high enough to identify the traveling lane is regarded as the target vehicle 5. Specifically, a vehicle whose traveling position is determined by a conventional GPS receiver is taken as the target vehicle 5.
  • the teacher vehicle 7 indicates a vehicle whose traveling lane on the road 9 is known. That is, a vehicle whose position accuracy of the vehicle detected by the on-vehicle device 8 is high enough to be able to specify the traveling lane is the teacher vehicle 7. Specifically, a vehicle capable of correcting the traveling position measured by the conventional GPS receiver with the reinforcement signal received from the quasi-zenith satellite is the teacher vehicle 7.
  • the in-vehicle device 6 generates probe information including at least a traveling position of the target vehicle 5 on which the in-vehicle device 6 is installed and information of a passing time when the traveling position passes, at a predetermined time interval or a predetermined distance interval.
  • the in-vehicle device 6 transmits the generated probe information to the server 2 via the base station 4 and the network 3.
  • the on-vehicle device 8 has probe information including at least a traveling position of the teacher vehicle 7 on which the on-vehicle device 8 is installed and passing time when passing the traveling position. Generate at distance intervals.
  • the in-vehicle device 8 transmits the generated probe information to the server 2 via the base station 4 and the network 3.
  • the in-vehicle devices 6 and 8 may be dedicated devices such as in-vehicle communication devices installed in the target vehicles 5 and 7, respectively, or general-purpose devices such as smartphones carried by passengers of the target vehicles 5 and 7 It may be.
  • the server 2 is installed, for example, in a traffic control center or the like.
  • the server 2 functions as a travel lane identification device, receives probe information from the in-vehicle devices 6 and 8, and identifies the lane of the road 9 on which the target vehicle 5 travels based on the received probe information.
  • FIG. 2 is a block diagram showing the configuration of the in-vehicle device 6 installed in the target vehicle 5 according to the first embodiment of the present invention.
  • the on-vehicle apparatus 6 includes a GPS receiver 61, a speed sensor 62, an azimuth sensor 63, an acceleration sensor 64, a position detection unit 65, a probe information generation unit 66, and a probe information provision unit. 67, a communication I / F (Interface) unit 68, and a traffic information acquisition unit 69.
  • the GPS receiver 61 measures the position of the target vehicle 5 based on radio waves received from GPS satellites, and outputs position information.
  • the position information of the target vehicle 5 includes latitude information and longitude information of the target vehicle 5.
  • the speed sensor 62 measures, for example, the traveling speed of the target vehicle 5 by measuring the number of rotations of the wheels of the target vehicle 5, and outputs the measurement result.
  • the method of measuring the traveling speed is not limited to this.
  • the direction sensor 63 includes, for example, a magnetic sensor or a gyro sensor, measures the direction of the target vehicle 5, and outputs the measurement result.
  • the acceleration sensor 64 measures the traveling acceleration of the target vehicle 5 according to a capacitance detection method or a piezoresistive method, and outputs the measurement result.
  • the position detection unit 65 detects the position of the target vehicle 5 based on the outputs of the GPS receiver 61, the speed sensor 62, the direction sensor 63, and the acceleration sensor 64. For example, when the GPS receiver 61 outputs position information, the position detection unit 65 detects the position indicated by the position information as the position of the target vehicle 5. However, there may be a case where the position of the target vehicle 5 can not be determined by the GPS receiver 61 in a place where a radio wave from a GPS satellite is disturbed, such as in a tunnel. In such a case, the position of the target vehicle 5 is interpolated and detected based on the outputs of the speed sensor 62, the direction sensor 63, and the acceleration sensor 64.
  • the probe information generation unit 66 generates probe information including information on the position of the target vehicle 5 detected by the position detection unit 65.
  • FIG. 3 is a diagram showing an example of a data structure of probe information generated by the probe information generation unit 66.
  • the probe information 150 includes in-vehicle apparatus identification information, position information, speed information, time information, and position accuracy information.
  • the in-vehicle apparatus identification information is information for identifying the in-vehicle apparatus 6 and is information uniquely assigned to the in-vehicle apparatus 6. Vehicle identification information for identifying the target vehicle 5 may be used instead of the in-vehicle apparatus identification information.
  • the position information is information on the position of the target vehicle 5 detected by the position detection unit 65.
  • the speed information is information on the traveling speed of the target vehicle 5 measured by the speed sensor 62.
  • the time information is information on the time when the target vehicle 5 has passed the position of the target vehicle 5 detected by the position detection unit 65.
  • the position accuracy information is information indicating the accuracy of the position of the target vehicle 5 detected by the position detection unit 65. That is, it is information indicating whether the position information is high enough to identify the traveling lane. Information that is high enough to identify the driving lane is hereinafter referred to as “high accuracy information”, and information that is not high enough to identify the traveling lane is hereinafter referred to as “low accuracy information”.
  • the in-vehicle device 6 measures the position only by the GPS receiver 61. Therefore, the position accuracy information indicates low accuracy information.
  • the traveling speed of the target vehicle 5 can be calculated from the position and time of the target vehicle 5. Therefore, the velocity information may not be included in the probe information 150.
  • the in-vehicle apparatus identification information may not be included.
  • the position accuracy information can be specified from the in-vehicle apparatus identification information or the like, the position accuracy information may not necessarily be included in the probe information 150.
  • position accuracy information when position accuracy information is not included in the probe information 150, it may be determined that the position accuracy indicated by the probe information 150 is low accuracy that is not high enough to identify the traveling lane.
  • probe information provision unit 67 transmits the probe information generated by probe information generation unit 66 to server 2 via communication I / F unit 68.
  • the communication I / F unit 68 is a communication interface for wirelessly transmitting data.
  • the communication I / F unit 68 conforms to a communication standard such as 3G or LTE (Long Term Evolution), and the in-vehicle device 6 and the base Establish a connection for communication with station 4
  • the communication I / F unit 68 transmits probe information to the server 2 via the base station 4 and the network 3.
  • the traffic information acquisition unit 69 acquires traffic information for each lane from the server 2 via the communication I / F unit 68. For example, the traffic information acquisition unit 69 acquires travel time for each lane. The traffic information acquisition unit 69 displays the acquired traffic information on a display device or provides the navigation system with the travel time for each lane to the destination.
  • FIG. 4 is a block diagram showing the configuration of the in-vehicle apparatus 8 installed in the teacher vehicle 7 according to the first embodiment of the present invention.
  • the on-vehicle device 8 includes a GPS receiver 61, a speed sensor 62, an azimuth sensor 63, an acceleration sensor 64, a reinforcement signal receiver 81, a camera 82, and a position detection unit 83.
  • a probe information generation unit 84, a probe information provision unit 67, a communication I / F unit 68, and a traffic information acquisition unit 69 are provided.
  • the processing units 61 to 64 and 67 to 69 having the same reference numerals as those of the in-vehicle apparatus 6 shown in FIG. 2 have the same configuration as those of the in-vehicle apparatus 6. However, the difference is that the vehicle to be processed is not the target vehicle 5 but the teacher vehicle 7.
  • the reinforcement signal receiver 81 receives the reinforcement signal transmitted by the quasi-zenith satellite.
  • a reinforcement signal is a signal for improving the positioning accuracy by GPS, for example, a LEX signal corresponds to it.
  • the L1-SAIF signal may be used instead of the LEX signal.
  • the nonpatent literature 1 for example.
  • the camera 82 is installed in the teacher vehicle 7 and captures an image of the surroundings of the teacher vehicle 7. For example, the camera 82 shoots the front of the teacher vehicle 7.
  • the image captured by the camera 82 may be a still image or a moving image.
  • At least one of the reinforcement signal receiver 81 and the camera 82 may be included in the in-vehicle device 8.
  • the position detection unit 83 detects a position with a positioning accuracy of about several cm to 1 m by correcting the position of the teacher vehicle 7 measured by the GPS receiver 61 using the reinforcement signal received by the reinforcement signal receiver 81. Do. As a result, the position detection unit 83 can detect a position having an accuracy with which the traveling lane of the teacher vehicle 7 can be identified. However, the position detection unit 83 detects the position of the teacher vehicle 7 based on the outputs of the speed sensor 62, the direction sensor 63, and the acceleration sensor 64 in a tunnel or the like where radio waves from GPS satellites or quasi-zenith satellites are interrupted. Interpolate and detect.
  • the probe information generation unit 84 generates probe information including information on the position of the teacher vehicle 7 detected by the position detection unit 83 and an image around the teacher vehicle 7 photographed by the camera 82.
  • the data structure of the probe information of the teacher vehicle 7 generated by the probe information generation unit 84 is the data structure of the probe information of the target vehicle 5 generated by the probe information generation unit 66, an example of which is shown in FIG. It is.
  • the position accuracy information indicates that the information is high accuracy information.
  • the position accuracy information indicates that the information is low accuracy information.
  • the image captured by the camera 82 may be transmitted to the server 2 without being included in the probe information.
  • FIG. 5 is a block diagram showing the configuration of the server 2 according to Embodiment 1 of the present invention.
  • server 2 includes communication I / F unit 21, probe information acquisition unit 22, storage device 23, link matching unit 24, traveling lane estimation unit 25, and feature quantity extraction unit 26. , A clustering unit 27, a travel lane identification unit 28, and a traffic information calculation unit 29.
  • the communication I / F unit 21 is a communication interface for connecting the server 2 to the network 3.
  • the communication I / F unit 21 receives probe information transmitted by the in-vehicle devices 6 and 8 via the network 3.
  • the probe information acquisition unit 22 functions as a first acquisition unit and a second acquisition unit, and acquires probe information from the in-vehicle devices 6 and 8 via the communication I / F unit 21.
  • the probe information acquisition unit 22 stores the acquired probe information in the storage device 23.
  • the probe information acquisition unit 22 causes the storage device 23 to store probe information whose position accuracy information is low accuracy information and does not contain an image as probe information of the target vehicle 5.
  • the probe information acquisition unit 22 stores the probe information of which the position accuracy information is high accuracy information as the probe information of the teacher vehicle 7 in the storage device 23. Further, the probe information acquisition unit 22 stores the probe information including the image whose position accuracy information is low accuracy information and including an image in the storage device 23 as probe information of the teacher vehicle 7.
  • the storage unit 23 stores various information.
  • the storage device 23 is configured of, for example, a magnetic storage device such as a hard disk drive (HDD) or a semiconductor storage device such as a flash memory.
  • HDD hard disk drive
  • flash memory a semiconductor storage device
  • the storage device 23 stores, for example, the probe information 23A and the map database 23B acquired by the probe information acquisition unit 22.
  • the probe information 23A is probe information acquired by the probe information acquisition unit 22 from the in-vehicle devices 6 and 8 as described above.
  • the map database 23B is map information including information on road links on which the target vehicle 5 and the teacher vehicle 7 can travel, and information on lane links forming each road link.
  • the map database 23B also includes information of a target section (see FIG. 1) to be determined for the lane in which the target vehicle 5 has traveled.
  • FIG. 6 is a diagram showing an example of road link, lane link, and target segment information included in the map database 23B.
  • the map database 23B includes information for specifying the road links 101-105. As shown in the figure, the road link 101, the road link 102, and the road link 103 are connected in this order from the upstream, and the road link 103 branches to the road link 104 and the road link 105 at the lower end.
  • the target section S is, for example, a section from the middle of the road link 101 to the end of the road link 102, and is a three-lane road including three lane links 106A to 106C.
  • the link matching unit 24 estimates the road link on which the target vehicle 5 has traveled, based on the probe information 23A and the map database 23B. That is, the link matching unit 24 estimates the road link on which the target vehicle 5 has traveled by estimating the road link having the position information closest to the position information indicated by the probe information of the target vehicle 5.
  • the traveling lane estimation unit 25 estimates a road link and a traveling lane (lane link) on which the teacher vehicle 7 has traveled, based on the probe information 23A and the map database 23B.
  • Each road link is composed of a plurality of lane links each corresponding to a traveling lane. Therefore, when the position accuracy information included in the probe information of the teacher vehicle 7 indicates the high accuracy information, the traveling lane estimation unit 25 has a road having the position information closest to the position information indicated by the probe information of the teacher vehicle 7.
  • links and lane links road links and lane links on which the teacher vehicle 7 travels are estimated.
  • the traveling lane estimation unit 25 when the position accuracy information included in the probe information of the teacher vehicle 7 indicates low accuracy information, the traveling lane estimation unit 25 has a road link having position information closest to the position information indicated by the probe information of the teacher vehicle 7. Estimate In addition, the traveling lane estimation unit 25 estimates the lane link on which the teacher vehicle 7 has traveled by inputting the image included in the probe information to the classifier learned in advance. For example, in the case where the classifier is a neural network, a neural network is previously learned by using, as teacher data, a set of a lane link and an image captured by a vehicle traveling the lane link. The traffic lane estimation unit 25 estimates a lane link on which the teacher vehicle 7 has traveled by inputting an image to a learned neural network.
  • the feature amount extraction unit 26 specifies the probe information of the target vehicle 5 that has traveled the predetermined target section based on the road link estimated by the link matching unit 24, and extracts the feature amount from the specified probe information. Further, the feature quantity extraction unit 26 specifies the probe information of the teacher vehicle 7 traveling on the target section based on the road link estimated by the traveling lane estimation unit 25 and extracts the feature quantity from the specified probe information.
  • the feature amount includes, for example, an average velocity, a maximum velocity, a minimum velocity, and a deceleration point in the target section. The deceleration point indicates the first position at which the velocity changes from the predetermined velocity Vth or more to less than the predetermined velocity Vth by searching from the upper end of the target section.
  • the feature amount may include an acceleration point instead of or in addition to the deceleration point.
  • the acceleration point indicates the first position at which the velocity changes from less than the predetermined velocity Vth to at least the predetermined velocity Vth by searching from the upper end of the target section.
  • FIG. 7 is a diagram showing feature quantities extracted from probe information of the target vehicle 5.
  • the vehicles A to F are the target vehicles 5, (a) to (f) in FIG. 7 indicate speed transition information in the target sections of the respective target vehicles 5 (vehicles A to F).
  • the horizontal axis indicates the distance from the upper end of the target section, and the vertical axis indicates the traveling speed at each point.
  • the feature quantity extracted by the feature quantity extraction unit 26 from the speed transition information of the target vehicle 5 is shown in a rectangular frame.
  • the clustering unit 27 functions as a classification unit, and the feature quantity (hereinafter referred to as “feature quantity of the target vehicle 5”) extracted from the probe information of the target vehicle 5 and of the teacher vehicle 7.
  • a clustering process is performed to classify representative feature quantities obtained from feature quantities (hereinafter referred to as “feature quantities of the teacher vehicle 7") indicated by the probe information into a plurality of clusters (groups).
  • feature quantities of the teacher vehicle 7 indicated by the probe information into a plurality of clusters (groups).
  • the clustering unit 27 classifies the feature amount of the target vehicle 5 and the representative feature amount of the teacher vehicle 7 into three clusters, for example, using the k-means method.
  • the clustering unit 27 classifies the target vehicle 5 into clusters by classifying the feature quantities of the target vehicle 5 into clusters.
  • FIG. 8 is a diagram for explaining clustering processing by the clustering unit 27.
  • the feature quantity of the target vehicle 5 can be indicated as a point in the four-dimensional vector space. That is, the feature quantity of the target vehicle 5 is a four-dimensional feature quantity vector consisting of an average speed, a maximum speed, a minimum speed, and a deceleration point.
  • the representative feature quantity of the teacher vehicle 7 is generated from the feature quantity vector of the teacher vehicle 7 for each traveling lane.
  • the clustering unit 27 generates a representative vector 121 as a representative feature amount from the feature amount (feature amount vector) of the teacher vehicle 7 traveling in the first lane. An average vector (centroid vector) of feature amounts may be used as the representative vector 121.
  • the clustering unit 27 generates a representative vector 122 of the teacher vehicle 7 traveling in the second lane and a representative vector 123 of the teacher vehicle 7 traveling in the third lane.
  • the clustering unit 27 clusters these feature quantity vectors into three clusters. Thus, cluster 1 including feature quantity vector 111 and representative vector 121, cluster 2 including feature quantity vector 112 and representative vector 122, and cluster 3 including feature quantity vector 113 and representative vector 123 are generated. Note that, as preprocessing for clustering, the clustering unit 27 is assumed to perform normalization of feature quantities of each dimension.
  • FIG. 9 is a diagram showing an example of the result of the clustering process by the clustering unit 27. As shown in FIG.
  • cluster 1 includes feature quantities of vehicles A, B, and F
  • cluster 2 includes feature quantities of vehicles C and E
  • cluster 3 includes vehicle D.
  • the travel lane identification unit 28 identifies the travel lane of the target vehicle 5 included in the cluster from the representative vector included in each cluster.
  • the cluster 1 includes a representative vector 121 of the teacher vehicle 7 traveling in the first lane.
  • the traveling lane identification unit 28 identifies the traveling lane of the target vehicle 5 corresponding to the feature quantity vector 111 included in the cluster 1 as the first lane.
  • the traveling lanes of the vehicles A, B and F are identified as the first lane.
  • the representative vector 122 of the teacher vehicle 7 traveling in the second lane belongs to the cluster 2
  • the traveling lanes of the vehicles C and E are identified as the second lane.
  • the representative vector 123 of the teacher vehicle 7 traveling in the third lane belongs to the cluster 3
  • the traveling lane of the vehicle D is identified as the third lane.
  • the traffic information calculation unit 29 detects the probe information of the teacher vehicle 7 whose travel lane is estimated by the travel lane estimation unit 25 and the target vehicle 5 whose travel lane is identified by the travel lane identification unit 28.
  • the traffic information for each lane is calculated based on the probe information of For example, the traffic information calculation unit 29 calculates, for each lane, the travel time of the predetermined section and the traffic jam end position.
  • the traffic information calculation unit 29 transmits the calculated traffic information to the in-vehicle devices 6 and 8 via the communication I / F unit 21.
  • FIG. 10 is a sequence diagram showing a flow of processing performed by the traffic information providing system 1.
  • the on-vehicle apparatus 6 and the on-vehicle apparatus 8 detect the positions of the target vehicle 5 and the teacher vehicle 7, respectively (S1).
  • the in-vehicle devices 6 and 8 generate probe information based on the detected position (S2).
  • the in-vehicle devices 6 and 8 transmit the generated probe information to the server 2, and the server 2 receives the probe information from the in-vehicle devices 6 and 8 (S3).
  • the server 2 stores the probe information received from the in-vehicle devices 6 and 8 in the storage device 23.
  • the server 2 specifies the traveling lane of the target vehicle 5 in the target section based on the probe information received from the in-vehicle devices 6 and 8 (S4).
  • the server 2 generates traffic information for each lane in the target section, and transmits the generated traffic information to the in-vehicle devices 6 and 8 (S5).
  • FIG. 11 is a flowchart showing details of the travel lane identification process (S4 in FIG. 10).
  • the server 2 stands by until the predetermined traffic information generation timing T is reached (S11). For example, the traffic information generation timing T periodically arrives at an interval of 30 minutes.
  • the traveling lane estimation unit 25 determines the most downstream of the target section S from time (TC) to timing T from the probe information of the teacher vehicle 7
  • the traveling lane of the passing teacher vehicle 7 is estimated (S13).
  • the link matching unit 24 identifies the road link of the target vehicle 5 that has passed the most downstream of the target section S from time (TC) to timing T (S14).
  • the feature amount extraction unit 26 extracts a feature amount from the probe information of the target vehicle 5 and the teacher vehicle 7 that have passed the most downstream of the target section S from time (TC) to timing T (S15).
  • the clustering unit 27 clusters the feature quantity vector of the target vehicle 5 and the representative vector of the teacher vehicle 7 (S16).
  • the traveling lane identification unit 28 identifies the traveling lane of the target vehicle 5 included in the cluster as the traveling lane of the teacher vehicle 7 corresponding to the representative vector included in the cluster for each cluster (S17).
  • the travel lane of the target vehicle 5 is specified by clustering the feature quantity vector of the target vehicle 5 and the representative vector of the teacher vehicle 7. Therefore, the target vehicle 5 having a speed transition similar to the speed transition of the teacher vehicle 7 is classified into the same cluster as the teacher vehicle 7. Therefore, the target vehicle 5 included in a certain cluster can be identified as having traveled in the same lane as the teacher vehicle 7 included in the cluster. Thus, the travel lane of the target vehicle 5 can be identified.
  • the traveling lane identification unit 28 of the server 2 determines a cluster to which the teacher vehicle 7 belongs using the representative vector generated from the feature amount of the teacher vehicle 7, and the traveling lane of the target vehicle 5 for each cluster. It was determined. In this modification, not the representative vector of the teacher vehicle 7 but the feature amount of the teacher vehicle 7 is used to determine the cluster to which the teacher vehicle 7 belongs.
  • the feature quantities of the teacher vehicle 7 traveling in the same lane are the feature quantities of the target vehicle 5 and the features of the teacher vehicle 7 under the constraint that they belong to the same cluster. Cluster without distinction from quantity.
  • FIG. 12 is a diagram for describing clustering processing by the clustering unit 27.
  • the clustering process is performed such that the feature amount vector 131, the feature amount vector 132 and the feature amount vector 133 of the teacher vehicle 7 each traveling in the same lane are classified into the same cluster.
  • the feature vector 131 is a feature vector of the teacher vehicle 7 traveling in the first lane
  • the feature vector 132 is a feature vector of the teacher vehicle 7 traveling in the second lane
  • the feature vector 133 Is a feature quantity vector of the teacher vehicle 7 traveling in the third lane.
  • the feature amount vector 111 of the target vehicle 5 and the feature amount vector 131 of the teacher vehicle 7 are classified into the cluster 1. Further, the feature quantity vector 112 of the target vehicle 5 and the feature quantity vector 132 of the teacher vehicle 7 are classified into clusters 2. Furthermore, the feature amount vector 113 of the target vehicle 5 and the feature amount vector 133 of the teacher vehicle 7 are classified into clusters 3.
  • the feature amount vector 131 is a feature amount vector of the teacher vehicle 7 traveling in the first lane. Therefore, the travel lane identification unit 28 identifies the travel lane of the target vehicle 5 corresponding to the feature quantity vector 111 belonging to the same cluster 1 as the feature quantity vector 131 as the first lane. Similarly, the traveling lane identification unit 28 identifies the traveling lane of the target vehicle 5 belonging to the cluster 2 as the second lane, and identifies the traveling lane of the target vehicle 5 belonging to the cluster 3 as the third lane.
  • the feature quantities of the target vehicle 5 are classified into clusters including the feature quantities of the teacher vehicle 7 without the feature quantities of the teacher vehicle 7 traveling on the same lane being classified into a plurality of clusters. Can.
  • the target vehicle 5 included in the cluster can be identified as having traveled in the same lane as the teacher vehicle 7 included in the cluster.
  • a target vehicle 5 whose speed transition is similar between the lanes like the above-mentioned vehicle traveling in the second and third lanes is a candidate without being uniquely identified the traveling lane. Identify the driving lane.
  • the traveling lane identification unit 28 of the server 2 calculates the distance between the clusters.
  • the traveling lane identification unit 28 calculates the distance between the centers of gravity of the feature quantity vectors of the target vehicle 5 included in the cluster as the distance between the clusters.
  • the method of calculating the distance between clusters is not limited to this.
  • the traffic lane specifying unit 28 specifies a plurality of clusters in which the distance between clusters is equal to or less than a predetermined threshold. For example, in clusters 1 to 3 shown in FIG. 8, the distance between cluster 2 and cluster 3 is equal to or less than a predetermined threshold, and the distance between cluster 1 and cluster 2 and the distance between cluster 1 and cluster 3 are predetermined threshold Suppose that it is larger than.
  • the travel lane identification unit 28 does not uniquely identify the travel lane for the target vehicles 5 included in the clusters 2 and 3.
  • the cluster 2 includes the representative vector 122 of the teacher vehicle 7 traveling in the second lane
  • the cluster 3 includes the representative vector 123 of the teacher vehicle 7 traveling in the third lane.
  • the travel lane specifying unit 28 specifies that the target vehicle 5 included in the cluster 2 and the cluster 3 has traveled in one of the second lane and the third lane. That is, the travel lane identification unit 28 identifies the second lane and the third lane as travel lanes of the target vehicle 5 included in the cluster 2 and the cluster 3.
  • the traveling lane identification unit 28 is included in the traveling lane of the target vehicle 5 included in the first group in the second group when there is a second group whose distance to the first group is equal to or less than a predetermined threshold.
  • Second Embodiment In the first embodiment and its modification, clustering of feature quantities is performed using the k-means method. However, the clustering method is not limited to this. In the second embodiment, an example in which the x-means method is used instead of the k-means method will be described. The configuration of the traffic information providing system 1 is the same as that of the first embodiment.
  • the clustering unit 27 illustrated in FIG. 5 classifies the feature quantities of the target vehicle 5 into clusters using the x-means method. Unlike the k-means method, the x-means method changes the number of classified clusters.
  • the clustering unit 27 features the features of the target vehicle 5 Categorize into quantities. Thereby, clusters including similar feature quantities can be integrated.
  • the difference in traveling speed between traveling lanes is small, there is no significant difference in travel time etc. regardless of which lane the vehicle travels. For this reason, it is not necessary to forcibly specify the traveling lane of the target vehicle 5, but this can be realized by performing the above-described cluster integration.
  • FIG. 13 is a diagram for describing clustering processing by the clustering unit 27 and traveling lane identification processing by the traveling lane identification unit 28.
  • the clustering unit 27 clusters the feature quantities of the target vehicle 5 using the x-means method, whereby the feature quantity vectors 111 are classified into clusters 1 and the feature quantity vectors 112 into clusters 2 as shown in FIG. 13. It is assumed that the feature amount vector 113 is classified into cluster 3 after classification.
  • the traveling lane identification unit 28 obtains the center of gravity vector 141 by calculating the center of gravity of the feature amount vector 111 belonging to the cluster 1. Similarly, the traveling lane identification unit 28 obtains the gravity center vector 142 by calculating the gravity center of the feature amount vector 112 belonging to the cluster 2 and calculates the gravity center vector 143 by calculating the gravity center of the feature amount vector 113 belonging to the cluster 3 Ask.
  • the traveling lane identification unit 28 calculates the representative vector 121 from the feature amount vector of the teacher vehicle 7 traveling the first lane. Further, the traveling lane identification unit 28 calculates a representative vector 122 from the feature amount vector of the teacher vehicle 7 traveling in the second lane, and calculates the representative vector 123 from the feature amount vector of the teacher vehicle 7 traveling in the third lane .
  • the traveling lane identification unit 28 determines, for each of the representative vectors 121 to 123, a gravity center vector having the closest Euclidean distance from the representative vector from among the gravity center vectors 141 to 143.
  • the cluster to which the representative vectors 121 to 123 belong is determined.
  • the centroid vector closest to the representative vector 121 is the centroid vector 141. Therefore, the traveling lane identification unit 28 determines that the cluster to which the representative vector 121 belongs is the cluster 1 to which the feature amount vector 111 which is the source of the calculation of the gravity center vector 141 belongs.
  • the travel lane identification unit 28 identifies that the target vehicle 5 included in the cluster 1 has traveled the same first lane as the teacher vehicle 7 corresponding to the representative vector 121.
  • the travel lane identification unit 28 identifies the travel lane of the target vehicle 5 included in the cluster 2 as the second lane, and identifies the travel lane of the target vehicle 5 included in the cluster 3 as the third lane.
  • the target vehicle 5 included in the cluster can be identified as having traveled in the same lane as the teacher vehicle 7.
  • the travel lane of the target vehicle 5 is specified without distinguishing the type of vehicle.
  • the traveling speed is different between a large vehicle such as a truck or a bus and a small vehicle such as a passenger car.
  • motorcycles such as motorcycles run differently from four-wheeled vehicles by traveling on the side roads. For this reason, target vehicles 5 which originally travel in the same lane are classified into different clusters, and it is determined that they travel in different lanes, or target vehicles 5 which travel in different lanes originally It may be determined that the vehicles are classified into the same cluster and travel in the same lane.
  • the travel lane of the target vehicle 5 is specified by performing clustering processing for each type of vehicle.
  • the type information of the vehicle is included in the probe information that the target vehicle 5 and the teacher vehicle 7 transmit to the server 2.
  • the type information includes, for example, information on large vehicles, small vehicles and motorcycles. The type information may be further subdivided.
  • the clustering unit 27 performs clustering processing of probe information for each type of vehicle.
  • the clustering process is the same as that shown in the first and second embodiments except that it is performed for each type of vehicle.
  • the traveling lane identification unit 28 identifies the traveling lane of the target vehicle 5 from the result of the clustering for each type of vehicle.
  • the lane identification processing is similar to that shown in the first and second embodiments except that it is performed for each type of vehicle.
  • the travel lane of the target vehicle 5 can be specified for each type of vehicle. Therefore, the travel lane of the target vehicle 5 can be identified accurately.
  • the server 2 estimates the road link on which the target vehicle 5 travels, and estimates the road link and lane link on which the teacher vehicle 7 travels.
  • the on-vehicle apparatus 6 of 5 or the on-vehicle apparatus 8 of the teacher vehicle 7 may perform.
  • the link matching unit 24 of the in-vehicle device 6 links the probe information and the map database 23B.
  • a matching process is performed to estimate a road link on which the target vehicle 5 has traveled.
  • the in-vehicle device 6 transmits the information of the estimated road link to the server 2.
  • the traveling lane estimation unit 25 of the on-vehicle apparatus 8 is based on the probe information and the map database 23B.
  • the road link and the lane link on which the teacher vehicle 7 travels are estimated.
  • the in-vehicle device 8 transmits the information of the estimated road link and lane link to the server 2.
  • the traveling lane estimation unit 25 of the teacher vehicle 7 may estimate the lane link using a classifier such as the above-described neural network.
  • the above-described server and in-vehicle apparatus are configured as a computer system including a microprocessor, a read only memory (ROM), a random access memory (RAM), a hard disk drive (HDD), a display unit, and the like. It is also good.
  • a computer program is stored in the RAM or the HDD. Each device achieves its function by the microprocessor operating according to the computer program.
  • the computer program is configured by combining a plurality of instruction codes indicating instructions to the computer in order to achieve a predetermined function.
  • the system LSI is a super-multifunctional LSI manufactured by integrating a plurality of components on one chip, and more specifically, a computer system including a microprocessor, a ROM, a RAM, and the like. .
  • a computer program is stored in the RAM.
  • the system LSI achieves its functions by the microprocessor operating according to the computer program.
  • the microprocessor of the on-vehicle apparatus 6 is RAM or HDD. It is realized by executing a stored computer program. That is, the microprocessor of the in-vehicle apparatus 6 functionally includes a probe information providing unit 67, a traffic information acquisition unit 69, a probe information generation unit 66, and a position detection unit 65.
  • the microprocessor of the on-vehicle apparatus 8 stores the computer program stored in the RAM or HDD. It is realized by executing. That is, the microprocessor of the on-vehicle apparatus 8 functionally includes a probe information providing unit 67, a traffic information acquisition unit 69, a probe information generation unit 84, and a position detection unit 83. Furthermore, the probe information acquisition unit 22, the link matching unit 24, the travel lane estimation unit 25, the feature extraction unit 26, the clustering unit 27, the travel lane identification unit 28, and the traffic information calculation unit 29 of the server 2 are included in the server 2.
  • the microprocessor is realized by executing a computer program stored in the RAM or the HDD. That is, the microprocessor included in the server 2 includes the probe information acquisition unit 22, the link matching unit 24, the travel lane estimation unit 25, the feature extraction unit 26, the clustering unit 27, the travel lane identification unit 28, and the traffic information calculation unit 29. It has functionally.
  • the present invention may be the method described above. Furthermore, the present invention may be a computer program that implements these methods by a computer.
  • the computer program may be recorded in a computer readable non-transitory recording medium, such as an HDD, a CD-ROM, a semiconductor memory, or the like.
  • the computer program may be transmitted via a telecommunication line, a wireless or wired communication line, a network represented by the Internet, data broadcasting or the like. Further, each of the above-described devices may be realized by a plurality of computers.
  • some or all of the functions of the above-described devices may be provided by cloud computing. That is, some or all of the functions of each device may be realized by the cloud server. Furthermore, the above embodiment and the above modification may be combined respectively.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Traffic Control Systems (AREA)

Abstract

Dans la présente invention, un ordinateur est amené à fonctionner comme : une première unité d'acquisition pour acquérir, pour un intervalle prescrit, des informations de transition de vitesse concernant un véhicule d'apprentissage ayant une voie de déplacement établie dans l'intervalle prescrit; une seconde unité d'acquisition pour acquérir, pour l'intervalle prescrit, des informations de transition de vitesse concernant d'autres véhicules ayant des voies de déplacement inconnues dans l'intervalle prescrit; une unité d'extraction de valeur de caractéristiques pour extraire, à partir des informations de transition de vitesse à la fois du véhicule d'apprentissage et des autres véhicules, une valeur de caractéristique de chacun des véhicules; une unité de classification pour classer les autres véhicules en classant, en un ou plusieurs groupes, au moins les valeurs de caractéristiques des autres véhicules parmi la valeur de caractéristique du véhicule d'apprentissage et les valeurs de caractéristiques des autres véhicules; et une unité d'identification de voie de déplacement pour identifier les voies de déplacement des autres véhicules inclus dans chacun des groupes sur la base de la valeur de caractéristique du véhicule d'apprentissage.
PCT/JP2018/016006 2017-08-25 2018-04-18 Programme informatique, dispositif d'identification de voie de déplacement et système d'identification de voie de déplacement WO2019038987A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
JP2019537912A JP7120239B2 (ja) 2017-08-25 2018-04-18 コンピュータプログラム、走行車線特定装置および走行車線特定システム

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
JP2017161706 2017-08-25
JP2017-161706 2017-08-25

Publications (1)

Publication Number Publication Date
WO2019038987A1 true WO2019038987A1 (fr) 2019-02-28

Family

ID=65438859

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2018/016006 WO2019038987A1 (fr) 2017-08-25 2018-04-18 Programme informatique, dispositif d'identification de voie de déplacement et système d'identification de voie de déplacement

Country Status (2)

Country Link
JP (1) JP7120239B2 (fr)
WO (1) WO2019038987A1 (fr)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020193954A (ja) * 2019-05-30 2020-12-03 住友電気工業株式会社 位置補正サーバ、位置管理装置、移動体の位置管理システム及び方法、位置情報の補正方法、コンピュータプログラム、車載装置並びに車両

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010043917A (ja) * 2008-08-11 2010-02-25 Sumitomo Electric Ind Ltd 経路特定装置、コンピュータプログラム及び経路特定方法
JP2010102575A (ja) * 2008-10-24 2010-05-06 Panasonic Corp 交通情報生成装置および交通情報生成方法
JP2013084126A (ja) * 2011-10-11 2013-05-09 Toyota Motor Corp 走行車線認識装置
JP2014502724A (ja) * 2010-12-31 2014-02-03 トムトム ベルギー ネムローゼ フエンノートシャップ ナビゲーションの方法及びシステム
JP2014228736A (ja) * 2013-05-23 2014-12-08 住友電気工業株式会社 回生率推定装置、経路探索システム、及びコンピュータプログラム

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012194031A (ja) 2011-03-16 2012-10-11 Aisin Aw Co Ltd 交通情報送信装置、方法およびプログラム
JP6330651B2 (ja) 2014-12-19 2018-05-30 株式会社デンソー 異常検出装置
JP6074553B1 (ja) 2015-04-21 2017-02-01 パナソニックＩｐマネジメント株式会社 情報処理システム、情報処理方法、およびプログラム

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010043917A (ja) * 2008-08-11 2010-02-25 Sumitomo Electric Ind Ltd 経路特定装置、コンピュータプログラム及び経路特定方法
JP2010102575A (ja) * 2008-10-24 2010-05-06 Panasonic Corp 交通情報生成装置および交通情報生成方法
JP2014502724A (ja) * 2010-12-31 2014-02-03 トムトム ベルギー ネムローゼ フエンノートシャップ ナビゲーションの方法及びシステム
JP2013084126A (ja) * 2011-10-11 2013-05-09 Toyota Motor Corp 走行車線認識装置
JP2014228736A (ja) * 2013-05-23 2014-12-08 住友電気工業株式会社 回生率推定装置、経路探索システム、及びコンピュータプログラム

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2020193954A (ja) * 2019-05-30 2020-12-03 住友電気工業株式会社 位置補正サーバ、位置管理装置、移動体の位置管理システム及び方法、位置情報の補正方法、コンピュータプログラム、車載装置並びに車両
JP7310313B2 (ja) 2019-05-30 2023-07-19 住友電気工業株式会社 位置補正サーバ、位置管理装置、移動体の位置管理システム及び方法、位置情報の補正方法、コンピュータプログラム、車載装置並びに車両

Also Published As

Publication number Publication date
JP7120239B2 (ja) 2022-08-17
JPWO2019038987A1 (ja) 2020-08-06

Similar Documents

Publication Publication Date Title
JP2006208223A (ja) 車両位置認識装置及び車両位置認識方法
US11227487B2 (en) Server device and congestion identification method
US20230066476A1 (en) System and method for monitoring a vehicle
US10109191B2 (en) Method of quickly detecting road distress
US10415981B2 (en) Anomaly estimation apparatus and display apparatus
CN111183464B (zh) 基于车辆轨迹数据估计信号交叉路口的饱和流量的系统和方法
US10565876B2 (en) Information processing apparatus, onboard device, information processing system, and information processing method
JP7091620B2 (ja) コンピュータプログラム、走行道路判定方法、走行道路判定装置および車載装置
US20230365154A1 (en) Determining a state of a vehicle on a road
WO2010101199A1 (fr) Dispositif de création d'informations sur la circulation routière et procédé de création d'informations sur la circulation routière
JP7120239B2 (ja) コンピュータプログラム、走行車線特定装置および走行車線特定システム
US20230090338A1 (en) Method and system for evaluation and development of automated driving system features or functions
CN115039158A (zh) 通信装置、车辆、程序和通信方法
JP7211513B2 (ja) 地図データ生成装置
CN114968189A (zh) 用于自动驾驶系统的感知系统开发的平台
CN116416780A (zh) 一种路侧感知系统的测试方法、装置、测试设备和介质
Jomrich et al. Lane Accurate Detection of Map Changes based on Low Cost Smartphone Data.
JP2021124633A (ja) 地図生成システム及び地図生成プログラム
US20240232715A9 (en) Lane-assignment for traffic objects on a road
EP4357944A1 (fr) Identification d'objets de trafic inconnus
JP7446872B2 (ja) 道路変化検出システム、道路変化検出方法、道路変化検出プログラム、および記憶媒体
EP4394664A1 (fr) Génération automatisée de données par des ensembles de réseaux neuronaux
US20240212319A1 (en) Classification of objects present on a road
US20230091986A1 (en) Method and system for evaluation and development of automated driving system features
CN118069497A (zh) 基于adas有效性条件进行评测的方法及装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 18848307

Country of ref document: EP

Kind code of ref document: A1

ENP Entry into the national phase

Ref document number: 2019537912

Country of ref document: JP

Kind code of ref document: A

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 18848307

Country of ref document: EP

Kind code of ref document: A1