EP2370965A1 - Unité d'informations routières, système d'informations routières, système de gestion de véhicule, véhicule, et procédé de commande d'un véhicule - Google Patents

Unité d'informations routières, système d'informations routières, système de gestion de véhicule, véhicule, et procédé de commande d'un véhicule

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Publication number
EP2370965A1
EP2370965A1 EP09771424A EP09771424A EP2370965A1 EP 2370965 A1 EP2370965 A1 EP 2370965A1 EP 09771424 A EP09771424 A EP 09771424A EP 09771424 A EP09771424 A EP 09771424A EP 2370965 A1 EP2370965 A1 EP 2370965A1
Authority
EP
European Patent Office
Prior art keywords
vehicle
traffic
information
state information
vehicles
Prior art date
Legal status (The legal status 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 status listed.)
Granted
Application number
EP09771424A
Other languages
German (de)
English (en)
Other versions
EP2370965B1 (fr
Inventor
Zoltan Papp
Gerardus Johannes Nicolaas Doodeman
Martin Willem Nelisse
Joris Sijs
Johannes Adrianus Cornelis Theeuwes
Bartholomeus Joannes Franciscus Driessen
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek TNO
Original Assignee
Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek TNO
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.)
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Application filed by Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek TNO filed Critical Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek TNO
Priority to EP09771424.0A priority Critical patent/EP2370965B1/fr
Publication of EP2370965A1 publication Critical patent/EP2370965A1/fr
Application granted granted Critical
Publication of EP2370965B1 publication Critical patent/EP2370965B1/fr
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/164—Centralised systems, e.g. external to vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/22—Platooning, i.e. convoy of communicating vehicles

Definitions

  • Traffic information unit Traffic information unit, traffic information system, vehicle management system, vehicle, and method of controlling a vehicle
  • the present invention relates to a traffic information unit.
  • the present invention further relates to a traffic information system.
  • the present invention further relates to a vehicle management system.
  • the present invention further relates to a vehicle provided with a vehicle management system.
  • the present invention further relates to a method of controlling a vehicle.
  • Cruise control systems that maintain the speed of a target vehicle at a predetermined velocity are well-known. More recently adaptive cruise control systems were developed that also adapt the speed of the target vehicle to the state (e.g. relative position and speed) of a lead vehicle, directly in front of the target vehicle. The (partial) relative motion state of the lead vehicle is for example determined by radar measurements. Still more recently cooperative cruise control systems were developed that not only take into account the state of the lead vehicle but also from the state of one or more vehicles in front of the lead vehicle. Cooperative cruise control has the potential to improve traffic safety as well as traffic flow, as the control system can better anticipate the traffic situation than an adaptive cruise control system.
  • a target vehicle provided with a cooperative cruise control system can also react to a change in state of another vehicle not directly leading the target vehicle provided with a cooperative cruise control system. This allows the target vehicle to more gradually adapt its state, e.g. its velocity. This is favorable for traffic flow and traffic safety. It is however a drawback of this system that it is dependent from input data from the motion state estimator mounted at other vehicles in the neighbourhood (the motion state estimator is typically part of the CACC installed, thus this input is available via the cooperation between CACCs (hence the name)).
  • DE 19956455 discloses a method for transmitting signals into a vehicle when passing a certain locality with local detection and transmission equipment to automatically influence the speed and/or distance regulated operation of the vehicle.
  • the local detection and transmission equipment can be controlled from a central point. The method makes it possible to timely reduce the speed of vehicles that approach a traffic jam.
  • a traffic information unit associated with a traffic infrastructure comprising - a facility for tracking vehicle state information of traffic present at the traffic infrastructure, a facility for broadcasting said vehicles state information to other vehicles at the traffic infrastructure, characterized in that the vehicle state information comprises vehicle state information of individual vehicles, including at least the instantaneous position of said individual vehicles.
  • a traffic information unit is considered associated with a traffic infrastructure if it has a sensor system using sensors that are mounted at an at least substantially fixed position related to the traffic infrastructure.
  • the sensor system may comprise sensor nodes that are embedded in the traffic infrastructure.
  • the sensor nodes may arranged movably at a holder that has a fixed position with respect to the traffic infrastructure.
  • the traffic information unit further comprises a sensor system comprising a plurality of sensor nodes for sensing vehicles arranged in the vicinity of a traffic infrastructure for carrying vehicles, communication means coupled to the sensor system, wherein the facility for tracking uses information communicated by the sensor system.
  • a vehicle management system for target vehicles comprising a communication system arranged for receiving vehicle state information relating to surrounding vehicles from a traffic information unit, inputs for receiving state information from the target vehicle and a control system for providing control signals for controlling a state of the target vehicle using the other vehicles' state information retrieved from the traffic information unit or from the traffic information system and the motion state of the target vehicle, characterized in that the vehicle state information used by the control system comprises vehicle state information of individual vehicles, including at least the instantaneous position of said individual vehicles.
  • a vehicle with such a vehicle management system is provided.
  • the present traffic information unit and system according to the invention provide vehicle state information of individual vehicles present at the vehicle infrastructure. This makes it possible the vehicle management system of the target vehicle not only to react to global traffic disruptions, but also to events that could potentially result in a traffic disruption such as a sudden braking of vehicles in front of the target vehicle, including such vehicles that are out of sight of the driver of the target vehicle. This improves the safety of the target vehicle and also vehicles following the target vehicle. Additionally an efficient managing of other traffic phenomena, is made possible, such as a shockwave damping on highways.
  • the instantaneous position of the surrounding vehicle provides the vehicle management system of the target vehicle sufficient information to derive also other motion state information of those surrounding vehicles such as velocity, orientation, and acceleration. Alternatively this other motion state information may also be provided by the traffic information unit and system in addition to the instantaneous position.
  • the traffic control system comprises a traffic information system that builds and maintains a real-time database of all vehicles currently using a traffic infrastructure. This enables a vehicle provided with a vehicle control system to receive status information of vehicles in its environment. In an embodiment said status information is only provided upon request. This allows for a power reduction as the transmitters do not have to be active when no such requests are received. Alternatively the transmitters may be active permanently and transmit this information unconditionally on a unidirectional basis. This is favorable if a large number of vehicles instrumented with a vehicle management system is present.
  • the traffic information unit may have a first mode wherein vehicle status information is only transmitted upon request, e.g. when a low traffic density is detected and a second mode wherein the vehicle status information is permanently transmitted, e.g. during rush hours.
  • the traffic information system may broadcasts vehicle state information for the part of the infrastructure observed by the traffic information system. If desired the information may be restricted to information related to vehicles within a predetermined radius of a transmitter. Information to be transmitted may include not only vehicle state information relating to the lead vehicle (i.e. the vehicle directly in front of the target vehicle), but also vehicle state information relating to other vehicles in front of the lead vehicle that could not be observed by an on-board radar system. Also vehicle state information relating to vehicles behind the target vehicle may be included in the query set. As the traffic control system can provide status information, not only of the lead vehicle, but also of other vehicles in front of the target vehicle, the vehicle control system can better anticipate for events occurring at the road in front of the target vehicle, allowing for a smoother and safer control.
  • each instrumented vehicle will operate reliably using the information transmitted by the traffic information system.
  • Each of these instrumented vehicles can use the full vehicle map provided by the traffic information system according to the present invention and therewith reliable adapt its own motion to the motion of preceding and possibly succeeding vehicles. If only a relatively modest fraction of the vehicles present at the road is provided with the inventive vehicle control system, these vehicles will already act as a buffer for smoothing traffic flow. The smoother traffic flow allows for a reduction in fuel consumption and air pollution.
  • the traffic information system provides the vehicles instrumented with a vehicle control system with state information in its environment, and therewith allows the vehicle control system to anticipate for events ahead of the target vehicle, the vehicle control system can maintain short distances to its predecessor.
  • Incident management is a further example.
  • the traffic management system can provide information to a target vehicle about incidents ahead of the target vehicle and enforce safety measures.
  • the safety measures may include a gradual braking of the target vehicle, a deviation of the target vehicle to an alternative route, a warning to the driver of the vehicle and/or a warning to other drivers by light signals.
  • the sensor system comprises a plurality of sensor nodes that each provides a message indicative for an occupancy status of a detection area of a traffic infrastructure monitored by said sensor node.
  • the traffic information system further comprises at least one message interpreter that includes: - a vehicle database facility comprising motion state information of vehicles present at the traffic infrastructure, the state information of the vehicles including at least the vehicle position, an association facility for associating the messages provided by the sensor nodes with the state information present in the vehicle data base facility, a state updating facility for updating the state information on the basis of the messages associated therewith.
  • vehicles can be tracked with relatively simple and cheap means. It is sufficient that the sensor nodes merely provide a message that indicates whether a detection area associated with the sensor node is occupied by a vehicle or not. This makes it economically feasible to apply the traffic information unit to large traffic infrastructures.
  • Suitable sensor elements for use in a sensor node are for example magnetic loop sensors, magneto restrictive sensors. These sensor elements determine whether their associated detection area is occupied by detection of a perturbation of the earth magnetic field.
  • each sensor node is provided with a wireless transmission facility that transmits the sensed data to a receiver facility coupled to the association facility.
  • a sensor node may have a set of sensor elements that are clustered in a sensor module.
  • a sensor module is for example a camera that monitors a part of the traffic infrastructure, wherein each photosensitive element of the camera serves as a sensor element of the vehicle tracking system.
  • a camera may be used for example if a perturbation of the earth magnetic field can not be measured. This is the case for example if (parts of) the infra structure comprises metal components e.g. a bridge.
  • the detection areas of the sensor elements are complementary.
  • the detection areas may overlap, or spaces may exist between the detection areas. It is sufficient that the detection areas have a scale that is smaller than the vehicle to be tracked, e.g. a size of at most 1 m 2 and a maximum diameter of not more than 1 m.
  • the sensor elements are randomly distributed over the traffic infrastructure. As compared to an arrangement wherein the sensor elements are regularly distributed with the same average number of sensor elements per unit of area, a more accurate estimation of the state of the vehicles can be obtained.
  • Independent traffic information units are particularly suitable for providing vehicle state information for relatively small traffic infrastructures.
  • a traffic information system is provided that comprises at least a first and a second traffic information unit according to the present invention.
  • the first and the second traffic information unit are associated with mutually neighbouring sections of the traffic infrastructure and are arranged to mutually exchange state information.
  • a traffic information system is provided that can be easily expanded with one or more additional traffic information units if required.
  • a new traffic information unit needs only to communicate with the traffic information units arranged for neighbouring sections. For example if a certain road is already provided with a traffic information system, it is sufficient to provide for a communication facility between the information unit for the last section of said traffic information system and the new traffic information unit for the appended section.
  • the traffic information units merely exchange state information and not the unprocessed messages from the sensor nodes the amount of communication between the traffic information units is modest.
  • An embodiment of a vehicle management system further comprises communication means for exchanging vehicle state information with surrounding vehicles and a selection facility for selecting one or more of vehicle state information obtained from the surrounding vehicles and information received from the traffic information system as the vehicle state information to be used by the control system.
  • the selection made by the selection facility may for example depend on the availability of reliable information. For example in an area where the traffic infrastructure is provided with a traffic information system, the selection facility may automatically select the traffic information system as the source of state information. In an area where no traffic information system is present, it may select the information provided by surrounding vehicles. Alternatively the selection may be more fine grained. It may select for example to receive velocity information from the surrounding vehicles themselves if such information is available and to receive the remaining information from the traffic information system.
  • a method is provided of controlling a vehicle on a traffic intrastructure for carrying vehicles and that is instrumented with a vehicle management system as presented above, comprising the steps of observing vehicles from a fixed position by a sensor system comprising a plurality of sensor nodes for sensing vehicles arranged in the vicinity of the traffic infrastructure, communicating the observations to a message interpreter, - with said message interpreter tracking vehicle state information of traffic present at the traffic infrastructure using the communicated observations, transmitting said vehicle state information to the instrumented vehicle, the instrumented vehicle controlling its own state using the vehicle state information, characterized in that the transmitted vehicle state information used by the instrumented vehicle comprises vehicle state information of individual vehicles.
  • Figure 1 schematically shows a spatial arrangement for various components of a traffic information system according to the invention
  • Figure 2 shows a functional relationship between various units of the system of Figure 1
  • Figure 3 shows another schematic view of the traffic information system
  • Figure 4 schematically shows a part of the traffic infrastructure provided with a plurality of sensor elements
  • Figure 5 schematically shows an overview of interactions between a traffic information system for a traffic infrastructure and a vehicle management system of vehicles using the traffic infrastructure
  • Figure 6 shows an embodiment of a vehicle management system according to the invention
  • Figure 7 shows an example of an embodiment of a sensor node in a traffic information system according to the invention
  • Figure 8 shows a possible hardware implementation of the sensor node of
  • Figure 9 schematically shows a method performed by the sensor node of Figures 7 and 8,
  • Figure 10 shows a message interpreter in a traffic information system according to the invention
  • FIG 11 shows a possible hardware implementation of the message interpreter of Figure 10
  • Figure 12 schematically shows a part of a traffic infrastructure
  • Figure 13 shows an overview of a method performed by the message interpreter
  • Figure 14 shows a first aspect of the method in more detail
  • Figure 15 shows a second aspect of the method in more detail.
  • Figure 16 shows an example of an object to be detected at a reference position and orientation and at a different position and orientation
  • Figure 17 shows a definition of a set S and the equidistant sampled set
  • Figure 18 shows detection of an object at multiple detection points
  • Figure 19 shows a definition of the set O n of possible positions o ⁇ for a single detection point
  • Figure 20 shows a definition of the set ON of possible positions o ⁇ for multiple detection points
  • Figure 21 shows a derivation of ON(q ) given 2 detections and 2 different samples of q
  • Figure 22 shows (Left) determination of - ⁇ , (right) the object's possible position set On given d n and q
  • Figure 23 shows (left) the mean of all Gaussians from f(o
  • Figure 24 shows an association result with event-based data-association
  • Figure 25 shows an association result with Nearest Neighbor data- association
  • Figure 26 shows time sampling of a signal y(t)
  • Figure 27 shows event sampling of a signal y(t)
  • Figure 28 shows event sampling: Send-on-Delta
  • Figure 29 shows the Gaussian function
  • Figure 30 shows a top view of the Gaussian function
  • Figure 31 shows an approximation of A H (y n ) as a sum of Gaussian functions
  • Figure 32 shows position, speed and acceleration of a simulated object
  • Figure 33 shows a position estimation error for various methods
  • Figure 34 shows a speed estimation speed for various methods
  • Figure 35 shows a factor of increase in estimation error after z k , or y ka .
  • Figure 1 and 2 schematically show a traffic information system comprising a plurality of traffic information units.
  • the traffic information units comprise a sensor system with a plurality of sensors (indicated as black dots) for sensing vehicles (indicated by open hexagons) arranged in the vicinity of a traffic infrastructure 80 for carrying vehicles.
  • the sensors are provided with communication means to transmit sensed information to a facility MI for identifying and tracking states of individual vehicles using information communicated by the sensors.
  • the sensors are only capable of transmitting information towards the facilities MI, in another embodiment, they may also be capable of bidirectional communication.
  • sensors can form a network, that can guide the information in an indirect way to the facilities MI.
  • each of the facilities MI is responsible for monitoring a particular section 8OA, 8OB, 8OC, 8OD of the infrastructure 80.
  • FIG 1 and 2 only four facilities MI are shown for clarity.
  • FIG 3 is another schematic view of the traffic information system.
  • Figure 3 shows how sensor nodes 10 transmit (detection) messages D to a message interpreter MI in their neighbourhood.
  • the message interpreters MI may also communicate to each other via a communication channel 60 to indicate that a vehicle crosses a boundary between their respective sections and to exchange a status of such a vehicle.
  • the traffic information system comprises a plurality of traffic information units MDl, MD2, MD3.
  • Each traffic information unit MDl, MD2, MD3 comprises a respective subset of the plurality of sensor nodes 10 for monitoring a respective section of the traffic infrastructure and a respective respective message interpreter MI.
  • the traffic information system further has a communication facility 60 for enabling traffic information units MDl, MD2, MD3 of mutually neighboring sections to exchange state information.
  • the traffic information system further comprises client information modules CIM for providing status information related to the infrastructure 80.
  • the status comprises for example statistical information, such as an occupation density and an average speed as a function of a position at the traffic infrastructure 80.
  • the facilities MI and the client information modules CIM are coupled to each other via a communication backbone. This allows the client information modules CIM to request said information for arbitrary regions (indicated by dashed boxes) of the infrastructure 80 that may extend beyond the boundaries for individual facilities MI.
  • FIG 4 schematically shows a part of the traffic infrastructure that is provided with a plurality of sensor nodes j having position Cj.
  • the sensor nodes have a detection area with radius R.
  • a vehicle i is present at the infrastructure having a position (V 1 X, vV). In this case if the vehicle substantially covers the detection area, e.g. more than 50%, the sensor node sends a message D that the detection area is occupied (indicated in gray). Otherwise the sensor node sends a message that the detection area is not occupied (white).
  • the traffic information system is further provided with a facility T for transmitting state information derived by the traffic information system to a particular vehicle upon request. Each transmitter T has a transmission range TR.
  • the transmission ranges of the transmitters together define a continuous area having a substantial length and over a full width of the infrastructure where state information is available.
  • a plurality of transmitters may be coupled to each traffic information unit MDl, MD2, MD3.
  • the transmitters T selectively transmit vehicle state information related to vehicles within their transmission range and optionally in a neighbourhood thereof.
  • some 7OB, 7OE of the vehicles 7OA,... ,7OE present at the traffic infrastructure 80 are provided with a vehicle management system C.
  • the vehicle management system C comprises a communication system R arranged for receiving vehicle state information relating to surrounding vehicles from the traffic information system, e.g. here from the traffic information unit MDl.
  • the traffic information unit MDl transmits the motion state of the surrounding vehicles to the target vehicle (e.g.
  • the vehicle management system C further has inputs Cl for receiving state information from the target vehicle 7OB.
  • the state information may include information related to a momentaneous position, e.g. obtained by GPS, speed obtained by GPS or using odometry, an acceleration derived by odometry or by an inertial sensor and a direction e.g by using a compass or a by a gyro.
  • a control system C2 uses this information in the local vehicle status database CO and the state information received at inputs Cl to provide control signals at output C3 for controlling a state of the target vehicle, e.g. a speed or an orientation of the target vehicle (70B).
  • the vehicle management system C also has a bidirectional link C4 for additional communication purposes.
  • This link can be used to negotiate and coordinate actions among vehicles (e.g. requesting/granting free space, joining/leaving platoon, etc.).
  • the system C further has an input C5 for receiving user control commands.
  • This allows the user to set an authorization level, i.e. control the extent to which the system C controls the vehicle, e.g. the user may allow the system only to provide warnings, may allow the system to regulate a speed, to break the vehicle up to a predetermined maximum deceleration, and to control a travelling direction. In the latter case a user may for example instruct the system to carry out certain maneuvers, e.g. a merging between a sequence of vehicles in a neighbouring lane.
  • a further input C6 is present to receive navigation information.
  • This information may be used for global control.
  • the control system C2 may control the vehicle to another lane, taking into account the state of neighbouring vehicles in local vehicle status data base CO.
  • Output C7 may provide the user information about the current authorization level, about a current activity of the system C, to show warnings, and to request for input.
  • the C7 output represents a man-machine interface and may be implemented in any form; it may use auditory, visual or sensory channels.
  • An embodiment, wherein the traffic information system only provides the state information of neighbouring vehicles upon request has the advantage that power is saved during intervals that no information is requested.
  • the transmitters T may permanently transmit the information relating to the vehicles present in its neighbourhood.
  • the traffic information system can provide status information to an instrumented vehicle, e.g.
  • the vehicle control system C can better anticipate for events occurring at the road in front of the target vehicle 7OB. This allows for a smoother and safer control.
  • the traffic information system will also transmit the status information of vehicle 7OD, indicating that this vehicle intends to change from the rightmost lane to the middle lane of the traffic infrastructure 80. Using this information, the traffic control system C of vehicle 7OB may respond more gradually to the maneuver of vehicle 7OD, than would be the case if vehicle 7OB had only a simple cruise control system that merely responds to the behavior of a vehicle immediately in front.
  • vehicle control system of each vehicle will operate reliably using the information transmitted by the traffic information system. If only a relatively modest fraction of the vehicles present at the road is provided with the inventive vehicle control system, these vehicles will already act as a buffer for smoothing traffic flow. This can be illustrated by way of the following example. Presume that the vehicles 7OA, ..., 7OE are driving in the same lane, and that none of the vehicles 7OA, ..., 7OE is instrumented with a vehicle control system or is only instrumented with an adaptive cruise control system. In that case a sudden breaking of vehicle 7OE would result in a shock effect that ripples through the chain of vehicles.
  • the set of vehicles for which vehicle status information is transmitted by a transmitter T in the neighbourhood of a target vehicle may include vehicles 7OC,..., 7OE, may additionally or alternatively include vehicles 7OA behind the target vehicle 7OB.
  • This vehicle status information may be used by the control system C2 to of vehicle 7OB to moderate a breaking power of said vehicle 7OB to prevent that a collision occurs with a vehicle 7OA succeeding it.
  • Figure 6 shows a further embodiment of a vehicle management system C according to the invention. Parts therein corresponding to those in Figure 5 have the same reference.
  • the vehicle management system of Figure 6 further comprises communication means Rl for exchanging vehicle state information VS2 with surrounding vehicles.
  • the vehicle management system C shown therein further comprises a selection facility SL for selecting one or more of vehicle state information VS2 obtained from the surrounding vehicles and vehicle state information VSl received from the traffic information system as the vehicle state information VS to be used by the control system C2.
  • the control system C2 further receives state information from the target vehicle (ST).
  • the selection made by the selection facility SL may for example depend on the availability of reliable information. For example in an area where the traffic infrastructure is provided with a traffic information system, the selection facility may automatically select the state information VSl provided by said traffic information system as the source of state information VS. In an area where no traffic information system is present, it may select the information VS2 provided by surrounding vehicles. Alternatively the selection may be more fine grained. For example it may select for example to receive velocity information from the surrounding vehicles themselves if such information is available and to receive the remaining information from the traffic information system.
  • FIG. 7 shows an example of a sensor node 10.
  • the sensor node 10, shown in Figure 7, is an assembly of a sensor element 12, a processing unit 14 (with memory), a clock-module 18 and a radio link 16.
  • the sensor element 12 is capable of sensing the proximity of the vehicles to be tracked.
  • the processing unit 14 determines if an object (vehicle) is present or absent on the basis of the signals from the sensor element 12. If an occupancy status of the detection area of the sensor changes, the processing unit 14 initiates a transmission of a message D indicating the new occupancy status and including a time stamp indicative of the time t at which the new occupancy status occurred.
  • the message D sent should reach at least one message interpreter MI.
  • a concrete implementation of the sensor node 10 is used for road vehicle tracking: in this case the sensor element 12 is a magnetoresistive component, which measures the disturbance on the earth magnetic field induced by the vehicles. Alternatively, a magnetic rod or loop antenna may be used for this purpose.
  • Figure 8 shows a possible implementation of the hardware involved for the sensor node 10 of Figure 7.
  • the sensor element 12 is coupled via an A/D converter 13 to a microcontroller 14 that has access to a memory 15, and that further controls a radio transmitter 16 coupled to an antenna 17.
  • Figure 9 schematically shows a method performed by a sensor node to generate a message indicative for occupancy status of a detection area of the sensor node.
  • Step Sl Starting (Step Sl) from an off-state of the sensor node, input from the A/D converter is received (Step S2). In a next step S3, offset is removed from the sensed value.
  • step S4 it is determined whether the occupancy state of the detection area as reported by the last message transmitted by the sensor node was ON (selection YES) (vehicle present in the detection range) or OFF (selection NO) (no vehicle present in the detection range. This occupancy state is internally stored in the sensor node.
  • step S5 it is determined whether a signal value v obtained from the A/D converter, and indicative for an occupied status of the detection area is below a first predetermined value TL. If this is not the case program flow continues with step S2. If however the value is lower than said first predetermined value then program flow continues with step S6. In step S6 it is verified whether the signal value v remains below the first predetermined value TL for a first predetermined time period. During step S6 the retrieval of input from the A/D convertor is continued. If the signal value v returns to a value higher then said predetermined value TL before the end of said predetermined time-period then processing flow continues with step S2.
  • step S9 it is determined whether the signal value v obtained from the A/D converter, and indicative for an occupied status of the detection area exceeds a second predetermined value TH.
  • the second predetermined value TH may be higher than the first predetermined value TL. If the signal value does not exceed the second predetermined value TH program flow continues with step S2. If however the value is higher than said second predetermined value TH then program flow continues with step SlO. In step SlO it is verified whether the signal value v remains above the second predetermined value TH for a second predetermined time period, which may be equal to the first predetermined time period.
  • step SlO the retrieval of input from the A/D convertor is continued. If the signal value v returns to a value lower then said predetermined value TH before the end of said predetermined time- period then processing flow continues with step S2. Otherwise the value for the occupancy state is internally saved as occupied in step SlI, and a message signaling this is transmitted in step S12.
  • a message interpreter shown in Figure 10, consists of a radio receiver 20, coupled to antenna 22, a processing unit 24 (with memory 28) and a network interface 65, as well as a real-time clock 26.
  • the network interface 65 couples the message interpreter MI via the communication channel 60 to other message interpreters.
  • the radio receiver 20 receives the binary "object present" signals D (with timestamp) from the sensor nodes 10 via the radio link and runs a model based state estimator algorithm to calculate the motion states of the objects individually (i.e. each real world object is represented in the message interpreter).
  • the accuracy and the uncertainty of the estimation depends on the sensor density. For accurate object tracking it is preferred to have coverage of multiple sensors per object.
  • the message interpreter MI has a vehicle database facility 32, 34 that comprises state information of vehicles present at the traffic infrastructure.
  • the message interpreter MI further has a sensor map 45 indicative for the spatial location of the sensor nodes 10. Alternatively, the sensor nodes may transmit their location or their position could even be derived by a triangulation method.
  • the message interpreter MI further has an association facility 40 for associating the messages D provided by the sensor nodes 10 with the state information present in the vehicle data base facility 32, 34.
  • the association facility 40 may associate the messages received with state information for example with one of the methods Gating, Nearest Neighbor (NN), (Joint) Probabilistic Data Association ((J)DPA), Multiple Hypothesis Tracker (MHT) and the MCMCDA.
  • the message interpreter further has a state updating facility 50 for updating the state information on the basis of the messages D associated therewith by the association facility 40.
  • a state updating facility 50 for updating the state information on the basis of the messages D associated therewith by the association facility 40.
  • the state of that vehicle in a local vehicle data base is updated by the update facility 50.
  • a global map builder 65 may exchange this updated information with global map builders of neighbouring message interpreters via network interface 60 (wired or wireless), for example to exchange the motion state of crossing objects.
  • the microcontroller 24 of Figure 10 processes the received messages D.
  • the memory 28 stores the local and global vehicle map and the sensor map as well as the software for carrying out the data estimation and state estimation tasks. In an alternative embodiment separate memories may be present for storing each of these maps and for the software. Likewise dedicated hardware may be present to perform one or more of these tasks.
  • the result of the processing i.e. the estimation of the motion states of all sensed objects
  • the result of the processing is present in the memory of the message interpreters in a distributed way.
  • Message interpreters may run additional (cooperative) algorithms to deduct higher level motion characteristics and/or estimate additional object characteristics (e.g. geometry).
  • the vehicle tracking system may comprise only a single traffic information unit.
  • the global map builder is superfluous, and local vehicle map is identical to the global vehicle map.
  • each message interpreter MI for a respective traffic information unit MDl, MD2, MD3 comprises hardware as described with reference to Figure 10 and 11. Operation of the message interpreter is further illustrated with respect to
  • Figure 12 schematically shows a part of a traffic infrastructure 80 having sections Rj i, Rj, Rj+i.
  • a vehicle moves in a direction indicated by arrow X from Rj i, via Rj, to Rj+i.
  • Figure 13 shows an overview of a method for detecting the vehicle performed by the message interpreter for section Rj, using the messages obtained from the sensor nodes.
  • the method waits for a message D from a sensor node.
  • program flow continues with step S21, where the time t associated with the message is registered.
  • the registered time t associated with the message may be a time- stamp embedded in the message or a time read from an internal clock of the message interpreter.
  • messages are indirectly transmitted to a message interpreter, e.g. by a network formed by sensor nodes it is advantageous if the embeds the time stamp in the message, so that it is guaranteed that the registered time corresponds to the observed occupancy status regardless any delays in the transmission of the message.
  • step S22 it is verified whether the detection is made by a sensor node in a location of section Rj that neighbours one of the neighbouring sections Rj i or Rj+i. If that is the case, then in step S23 the event is communicated via the communication network interface to the message interpreter for that neighbouring section.
  • step S24 it is determined which vehicle O in the vehicle data base facility is responsible for the detected event. An embodiment of a method used to carry out step S24 is described in more detail in Figure 14. After the responsible object O is identified in step S25, i.e. an association is made with existing object state information, it is determined in Step 26 whether it is present in the section Rj. If that is the case, control flow continues with Step S27.
  • step S28 it is determined whether the state information implies that the vehicle O has a position in a neighboring region R j-1 or R j+1 . In that case the updated state information is transmitted in step S29 to the message interpreter for the neighbouring region and control flow returns to step S20. Otherwise the control flow returns immediately to Step S20.
  • the current state known for the vehicle with that index i is retrieved from the vehicle database facility.
  • a probability is determined that the vehicle O caused the detection reported by the message D at time t.
  • the vehicle index i is incremented in step S43 and if it is determined in step S44 that i is less than the number of vehicles, the steps S41 to S43 are repeated. Otherwise in step S45 it is determined which vehicle caused the detection reported by the message D at time t with the highest probability.
  • the index of that vehicle is returned as the result if the method.
  • step S60 the messages D 1 ,..., D n associated with vehicle O are selected?
  • step S61 a probability density function is constructed on the basis of the associated messages.
  • step S62 the current state So and time to for object O are determined.
  • step S63 it is determined whether the time for which the state S of the vehicle O has to be determined is less than the time to associated with the current state So. If that is the case, the state S determined by the estimation method is the state update of SO to t, performed in step S65. If that is not the case, the state S determined by the estimation method is the state update of SO to SO in step S64. What does it mean?
  • vehicles could be provided with a transponder that signals their momentaneous position to the traffic information system.
  • condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present). More details relevant for the present invention are described in the following Annexes:
  • multiple target tracking [1-3] one aims to track all the objects/targets, which are moving in a certain area.
  • Section 2 defines background knowledge such as the notation of (object) variables and functions that are used throughout this paper. After that the problem is formulated in section 3 together with existing methods. Section 4 describes the approach which is taken in the design. A more detailed description of the estimation and associated is presented in Section 5 and 6 respectively. Finally both methods are tested in a small application example presented in Section 6 and conclusions are drawn in section 7. But let's start with the background information.
  • R defines the set of real numbers whereas the set R + defines the non-negative real numbers.
  • the set Z defines the integer values and Z + defines the set of non-negative integer numbers.
  • the variable 0 is used either as null, the null-vector or the null-matrix. Its size will become clear from the context.
  • Vector jc(t)e R" is defined as a vector depending on time t and is sampled using some sampling method.
  • the time t at sampling instant ⁇ e Z + is defined as ⁇ e R .
  • the variables ⁇ k e R , ⁇ e R" and x o k e R" xi+1 are defined as:
  • the matrix 2 1 depends on the difference between two time instants t 2 > t v and is shortly denotes as
  • transpose, inverse and determinant of a matrix Ae R" x " are denoted as A ⁇ , A “1 and I A I respectively.
  • E[x] , E[x I u] and cov(x) can be found in [6] sections B4 and B7.
  • the Gaussian function shortly noted as Gaussian, depending on vectors x G R" and w e R" and on matrix Pe R" x " is defined as:
  • each object also has a certain shape or geometry which covers a certain set of positions in R xy , i.e. the grey area of Figure 16.
  • This closed set is denoted with ⁇ c R 1 and is defined as the union of the open set of the object's body S B a R xy and the closed set of the object's edge S E a R xy , i.e. .
  • the set S is approximated by a set of sampled position-vectors , with I 1 G R 1 .
  • To define the vectors A 1 we equidistant sample the rectangular box defined by C 0 using a grid with a distance r .
  • Each A 1 is a grid point within the set S as graphically depicted in Figure 17.
  • the aim is to estimate position, speed and rotation of the object in the case that its acceleration and rotational speed are unknown. Therefore the object's state- vector s(t) e. R 5 and process-noise w(t)e R 2 are defined as:
  • T' represents the i th object's rotation-matrix dependent on ⁇ ' .
  • the dynamical process of object i with state-vector s' , process-noise w' and measurement-vector m is defined with the following state-space model:
  • the objects are observed in R by a camera or a network of sensors. For that
  • M 'detection' points are marked within R ⁇ and collected in the set D c R xy .
  • the position of a detection point is denoted as J e D .
  • the k th detection of the system generates the observation vector z ⁇ e ⁇ R X ⁇ ,, R ⁇ if the edge of the i th object covers one of the detection points d k G D at time t k :
  • the system does not know which object was detected for it can be any object. As a result the system will not generate z k ' but a general observation vector z k e (R ⁇ 5 R) , which is yet to be associated with an object. Therefore, due to the k th detection, the observation vector z k is generated whenever one of the E object covers a detection point d k G D at time t k :
  • the sampling method of the observation vectors Z 0 k is a form of event sampling [2, 10, 12]. For a new observation vector is sampled whenever an event, i.e. object detection, takes place. With these event samples all N objects are to be tracked. To accomplish that three methods are needed. The first one is the association of the new observation-vector z k to an object i and therefore denote it with z ⁇ . Suppose that all associated observation-vectors z n ' are collected in the set Z k ' e ⁇ Z o t l - Then the second method is to estimate m k ' from the observation- set Z k ' .
  • Z k ' is defined as the set with all observation-vectors from z o k that were associated with object i .
  • the set Z k ' G [z o k ] is defined as the set of all observation-vectors Z n which were associated with object i , from which their detection point is still covered by the object. We will first show how this is done.
  • At time step k we have the observation- set Z k ⁇ _ ⁇ and the observation z k was associated to object i , i.e.
  • the first step is to position the object on each detection point d n and mirror its set S into the set O n , as shown in Figure 19 for a single detection. This way we transform the points that are covered by the object, into possible vectors of the object's position d k e O n given that it is detected at the detection point d n .
  • the second step is to turn all sets O n simultaneously around their detection point d n .
  • each possible orientation ⁇ k ' of the object results in a corresponding possible object's position o ⁇ .
  • O N VM e N , which is denoted as O N . Therefore if we apply these two steps for a number of orientations ⁇ k ' , then at each orientation we have a set O N which has to contain the object's position o[ . From all these orientations we can calculate p ⁇ m k l ⁇ Z k ' j as shown in the next section.
  • the set Z consists of the observation vectors Z n , for all tiG N c [0, k] , that were associated to the same object.
  • the detection point at time-step n are defined as d n e H x . Meaning that the objects orientation is not directly. However, because every observation vector z n G Z detects the object for one and the same ⁇ , the PDF p(m ⁇ Z) is approximated by sampling in ⁇ , i.e.: ⁇
  • the main aspect of equation (17) is to determine p(o ⁇ Z, ⁇ ) .
  • O n ( ⁇ ) G R xy to be equal to all possible object positions o , given that the object is detected at position d n e z n (e Z) and that the object's rotation is equal to ⁇ .
  • the determination of O n ( ⁇ ) e R is presented in the n the next section. Therefore, if one object is detected at multiple detection points d n , Vne JV , then the set of all possible object positions o given a certain ⁇ equals O n ( ⁇ ) :
  • O N (theta) defines the set of possible object positions o for a given ⁇ .
  • p(o I Z, ⁇ ) and CC 1 we define the functions f(o I Z, ⁇ ) and g(o I Z, ⁇ ) :
  • both p(m I Z) is calculated according to (13).
  • the rest of this section is divided into two parts. The first part derives the probability function based on a single detection, i.e. f(o ⁇ z n , ⁇ ) . While the second part derives the probability function based on a multiple detections, i.e. g(o I Z, ⁇ ) .
  • Figure 22 (right) graphically depicts the determination of O n from the set ⁇ for a given ⁇ and detection point d n .
  • Proposition 1 Let there exist two Gaussian functions of the random vectors X G R" and rriG H q and the matrix T G R ?X " ; G(X, U, U) and G(m,Tx,M) . Then they have the following property: Proof. The proof can be found in Section 9.
  • Equation (28) is calculated differently.
  • each detection point d n defines a rectangular set denoted with
  • the first set, O n ( ⁇ ) shown in Figure 19 defines all possible object positions o based on a single detection at d n .
  • the second set, i.e. O N ( ⁇ ) shown in Figure 20, defines all possible object positions o based on all detections at d n , VnG N .
  • O n (6>) c C n (6>)
  • O N ( ⁇ ) C C N ( ⁇ ) Meaning that only within the set C N ( ⁇ ) all the functions f(o ⁇ z n , ⁇ ) have an overlapping area in which they are 1.
  • the calculation of (26) is done by applying the following two propositions.
  • the first one i.e. Proposition 2
  • the second one i.e. Proposition 3, proofs that a product of Gaussians results in a single Gaussian.
  • Proposition 2 The product of a summation of Gaussians can be written into a summation of a product of Gaussian:
  • Equation (29) is approximated as a single Gaussian function:
  • Equation (30) is substituted into equation (16) together with f(o ⁇ Z n , ⁇ ) of (27) to calculate p(o ⁇ Z, ⁇ ) and CC 1 . Substituted these results into (13) gives:
  • the PDF p(m ⁇ Z) also gives us the probability that a new observation vector is generated by an certain object i . This is discussed in the next section.
  • the total probability that a new observation vector z k is generated by object i is equal to the total probability of the measurement-vector m k ' given the observation set [Z j-1 , Z k ] .
  • p(m k ⁇ Z k l _ l , z k ) which is equal to equation (31).
  • the definition of a PDF is that its total probability, i.e. its integral from - ⁇ to ⁇ , is equal to 1.
  • the variables y' and K' are equal to J and K respectively, which define the approximation of the function f(m k ' I Z n , ⁇ [ as shown in (6.1).
  • the probability of (42) one can design a method which associates an observation-vector due to a new detection, to its most probable object i .
  • the estimation method requires a certain amount of processing power, one can reduce this by reducing the number of samples in the set ⁇ . Meaning that association and estimation can be done with different sizes of ⁇ .
  • the objects have a rectangular shape, then with some tricks one can reduce the amount of processing power to a level at which both association as well as estimation can run real-time.
  • the simulation case is made such that it contains two interesting situation.
  • the objects are tracked using two different association methods.
  • the first one is a combination of Gating and detection association of 7.
  • the second one is a combination of Gating and Nearest Neighbor.
  • This paper presents a method for estimating the position- and rotation-vector of objects from spatially, distributed detections of that object. Each detection is generated at the event that the edge of an object crosses a detection point. From the estimation method a detection associator is also designed. This association method calculates the probability that a new detection was generated by an object i .
  • An example of a parking lot shows that the detection association method has no incorrect associated detections in the case that two vehicles cross each other both in parallel as well as orthogonal. If the association method of Nearest Neighbor was used, a large amount of incorrect associated detections were noticed, resulting in a higher state-estimation error.
  • the data-assimilation can be further improved with two adjustments.
  • the first one is replacing the set S with S E only at the time-instants that the observation vector is received.
  • the second improvement is to take the detection points that have not detected anything also in account.
  • Equation (38) is equal to (28) for:
  • R defines the set of real numbers whereas the set R + defines the non-negative real numbers.
  • the set Z defines the integer numbers and Z + defines the set of non- negative integer numbers.
  • the notation 0 is used to denote either the null-vector or the null-matrix. Its size will become clear from the context.
  • a vector x(t) G R" is defined to depend on time tG R and is sampled using some sampling method. Two different sampling methods are discussed. The first one is time sampling in which samples are generated whenever time t equals some predefined value. This is either synchronous in time or asynchronous. In the synchronous case the time between two samples is constant and defined as t s e R + .
  • a transition-matrix A is defined to relate the vector u ⁇ t v )& H b to a vector x(t 2 ) e R a as follows
  • the transpose, inverse and determinant of a matrix Ae R" x " are denoted as A ⁇ , A “1 and I A I respectively.
  • the i th and maximum eigenvalue of a square matrix A are denoted as A 1 (A) and ⁇ max (A) respectively.
  • Ae R" x " and BG R" X " are positive definite, denoted with A >- 0 and ByO, then Ay B denotes A-ByO.
  • a ⁇ O denotes A is positive semi-definite.
  • PDF probability density function
  • the Gaussian function (shortly noted as Gaussian) of vectors XG R" and we R" and matrix Pe R" x " is defined as G(x,u,P) : R" xR"xR" x " ⁇ R , i.e.:
  • the set PDF is defined as A ⁇ (x) :R" — > ⁇ 0,v ⁇ with ve R defined as the Lebesque measure [8] of the set Y , i.e.:
  • time sampling defines that the next sampling instant, i.e. k a , takes place whenever present time t exceeds the set H k (z k _. ) . Therefore z k is defined a a a as:
  • y(t) in between the two samples can have any value within R q .
  • asynchronous sampling methods have emerged, such as, for example "Send-on- Delta” [9, 10] and “Integral sampling” [U]. Opposed to time sampling, these sampling methods are not controlled by time t , but by y(t) itself.
  • Z t -i : (yl -i ,h _ ⁇ ) r e R ?+1 .
  • H k (z k 4 ,0 c R' +1 , which depends on both z k _ : and t .
  • z k is defined as: The exact description of the set depends on the actual sampling method.
  • e e should contain the set of all possible values that y(t) can take in between the event instants k e - ⁇ and k e . Meaning that if then y ( ) ( ) .
  • a sufficient condition is that , which for "Send-on-Delta" results in y(t) e [y k _ : - ⁇ , y _ : + ⁇ ] for all
  • the state vector x(t) of this system is to be estimated from the observation vectors Z n e t e ⁇
  • our goal is to construct an event-based state-estimator (EBSE) that provides an estimate of x(t) not only at the event instants t k but also at the sampling instants t k . Therefore, we define a new set of sampling instants t n as the combination of sampling instants due to event sampling, i.e. k e , and time sampling, i.e. k a : ) and
  • the estimator calculates the PDF of the state-vector x n given all the observations until t n . This results in a hybrid state-estimator, for at time t n an event can either occur or not, which further implies that measurement data is received or not, respectively. In both cases the estimated state must be updated (not predicted) with all information until t n . Therefore, depending on t n a different PDF must be calculated, i.e.:
  • the important parameters for the performance of any state-estimator are the expectation and error-covariance matrix of its calculated PDF. Therefore, from (9) we define:
  • the PDFs of (5) can be described as the Gaussian G(x n ,x nin ,P nin ) .
  • the square root of the eigenvalues of define the shape of this Gaussian function.
  • the problem of interest in this paper is to construct a state-estimator suitable for the general event sampling method introduced in Section 4 and which is computationally tractable. Furthermore, it is desirable to guarantee that P nXn has bounded eigenvalues for all n .
  • Existing state estimators can be divided into two categories.
  • the first one contains estimators based on time sampling: the (a) synchronous Kalman filter [12, 13] (linear process, Gaussian PDF), the Particle filter [14] and the Gaussian sum filter [4, 5] (nonlinear process, non-Gaussian PDF).
  • These estimators cannot be directly employed in event based sampling as if no new observation vector z k is received, then t -t ⁇ e — > ⁇ and X (P, k e_, ) —> ⁇ .
  • the second category contains estimators based on event sampling. In fact, to the best of our knowledge, only the method proposed in [15] fits this category.
  • a H [y n ) can be approximated as a summation of N Gaussians, i.e. ke
  • Proposition 1 [12, 14] Let there exist two Gaussians of random vectors X G R" and r ⁇ i_ ⁇ Te R qxn : G(m,Tx,M) and G(x,u,U) . Then they satisfy:
  • Equation (33) is explicitly solved by applying Proposition 1: ( ) The expression of p(x n I J 0 n e F 0 n ) as a sum of N Gaussians is the result of the following substitutions: (26) into (13), (26) into (14c) to obtain p(y n e F n I J 0 n-1 e F 0 n-1 ) and the latter into (13) again. This yields
  • the third step is to approximate (27) as a single Gaussian to retrieve a computationally tractable algorithm. For if both /?(x n-1 I J 0 n-1 e F 0 n-1 ) and are approximated using N Gaussians, the estimate of X n in (36) is described with M n Gaussians. The value of M n equals M n _ ⁇ N , meaning that M n increases after each sample instant and with it also the processing demand of the EBSE increases.
  • the first two estimators are the EBSE and the asynchronous Kalman filter (AKF) of [13].
  • the AKF estimates the states only at ke the event instants t k e .
  • the states at t a are calculated by applying the prediction- step of (14b).
  • the third estimator is based on the quantized Kalman filter (QKF) introduced in [21] that uses synchronous time sampling of y k a .
  • the QKF can deal with quantized data, which also results in less data transfer, and therefore can be considered as an alternative to EBSE.
  • y. a is the quantized version of y k with quantization level 0.1 , which corresponds to the ⁇ Send-on-Delta" method.
  • the error sometimes decreases but it can also increase considerably after an update.
  • ⁇ of the QKF converges to 1. Meaning that for t > 5.5 the estimation error does not change after an update and new samples are mostly used to bound .
  • the last aspect on which the three estimators are compared is the total amount of processing time which was needed to estimate all state-vectors.
  • both x, and x were estimated and it took 0.094 seconds.
  • the AKF e a estimated x. e and predicted x. a in a total time of 0.016 seconds and the QKF estimated x k and its total processing time equaled 0.022 seconds.
  • Ae R" x " and B G R" xm are defined as the state-space matrices for the time-continuous counterpart of (7). Then it is known [22] that for any sampling period ⁇ > 0 , A ⁇ and B ⁇ of (7) are obtained from their corresponding continuous- time matrices A and B as follows:
  • ⁇ max (P ⁇ ) The upper bound on ⁇ max (P ⁇ ) is proven by induction, considering the asymptotic behavior of a KF that runs in parallel with the EBSE, as follows.
  • the first step of induction is to prove that P 111 ⁇ 0 P 111 ⁇ .

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Abstract

L'invention porte sur une unité d'informations routières (MD1, MD2, MD3), qui comprend une installation (MI) pour suivre des informations d'état de véhicule relatives à l'état de véhicules individuels présents dans une infrastructure routière, et une installation (T) pour transmettre lesdites informations d'état de véhicule à un véhicule (70B, 70E). Un système d'informations routière peut comprendre une pluralité de ces unités d'informations routières. L'invention porte en outre sur un système de gestion de véhicule (C) pour un véhicule cible (70B, 70E), qui est apte à recevoir et à utiliser les informations d'état de véhicule, et sur un véhicule comportant ce système. L'invention porte en outre sur un procédé de régulation du trafic.
EP09771424.0A 2008-12-12 2009-12-11 Unité d'informations de trafic, système d'informations de trafic, système de gestion de véhicule, véhicule, et procédé de commande d'un véhicule Active EP2370965B1 (fr)

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PCT/NL2009/050760 WO2010068107A1 (fr) 2008-12-12 2009-12-11 Unité d'informations routières, système d'informations routières, système de gestion de véhicule, véhicule, et procédé de commande d'un véhicule
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