EP2196973A1 - Verkehrsinformationseinheit, Verkehrsinformationssystem, Fahrzeugverwaltungssystem, Fahrzeug und Verfahren zum Steuern eines Fahrzeugs - Google Patents

Verkehrsinformationseinheit, Verkehrsinformationssystem, Fahrzeugverwaltungssystem, Fahrzeug und Verfahren zum Steuern eines Fahrzeugs Download PDF

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Publication number
EP2196973A1
EP2196973A1 EP08171579A EP08171579A EP2196973A1 EP 2196973 A1 EP2196973 A1 EP 2196973A1 EP 08171579 A EP08171579 A EP 08171579A EP 08171579 A EP08171579 A EP 08171579A EP 2196973 A1 EP2196973 A1 EP 2196973A1
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EP
European Patent Office
Prior art keywords
vehicle
traffic
information
traffic 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.)
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EP08171579A
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English (en)
French (fr)
Inventor
Zoltan Papp
Gerardus Johannes Nicolaas Doodeman
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Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek TNO
TNO Institute of Industrial Technology
Original Assignee
Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek TNO
TNO Institute of Industrial Technology
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Application filed by Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek TNO, TNO Institute of Industrial Technology filed Critical Nederlandse Organisatie voor Toegepast Natuurwetenschappelijk Onderzoek TNO
Priority to EP08171579A priority Critical patent/EP2196973A1/de
Priority to EP09771424.0A priority patent/EP2370965B1/de
Priority to DK09771424.0T priority patent/DK2370965T3/da
Priority to PCT/NL2009/050760 priority patent/WO2010068107A1/en
Publication of EP2196973A1 publication Critical patent/EP2196973A1/de
Withdrawn legal-status Critical Current

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    • 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

  • 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.
  • traffic of vehicles only provided with adaptive cruise control a sudden breaking of one of the vehicles tends to cause a shock-wave, as each vehicle only changes its state in response to the change of state of its immediate predecessor.
  • 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.
  • a traffic information unit associated with a traffic infrastructure comprising
  • 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 system and the motion state of the target vehicle.
  • a vehicle with such a vehicle management system is provided.
  • 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.
  • 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. It is not necessary that a large fraction of vehicle at the traffic infrastructure is provided with a vehicle control system according to the invention.
  • 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 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. This would not be the case if the same number of vehicles were provided with a cooperative cruise control system, as the functioning of the cooperative cruise control system relies on the presence of other vehicles having the same cooperative cruise control system.
  • the necessary traffic data is provided by the traffic information system coupled to the traffic infrastructure, it becomes more attractive for owners of vehicles not provided with a vehicle management system according to the invention to achieve such a vehicle management system.
  • 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:
  • 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 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 neighboring 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 neighboring 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 of controlling traffic comprising the steps of
  • 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 80A, 80B, 80C, 80D of the infrastructure 80.
  • Figure 1 and 2 only four facilities MI are shown for clarity.
  • Figure 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 neighborhood.
  • 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 MD1, MD2, MD3.
  • Each traffic information unit MD1, 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 message interpreter MI.
  • the traffic information system further has a communication facility 60 for enabling traffic information units MD1, 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.
  • Figure 4 schematically shows a part of the traffic infrastructure that is provided with a plurality of sensor nodes j having position c j .
  • the sensor nodes have a detection area with radius R.
  • a vehicle i is present at the infrastructure having a position (v i x , v i y ). 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 MD1, MD2, MD3.
  • the transmitters T selectively transmit vehicle state information related to vehicles within their transmission range and optionally in a neighborhood thereof.
  • 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 MD1.
  • the traffic information unit MD1 transmits the motion state of the surrounding vehicles to the target vehicle (e.g. 70B) provided with a vehicle management system C, using the wireless link between the transmitter T and the communication system R of the vehicle management system C. This information is stored in a local vehicle status data base C0.
  • the vehicle management system C further has inputs C1 for receiving state information from the target vehicle 70B.
  • 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 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 C0 and the state information received at inputs C1 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 an 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 neighboring 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 neighboring vehicles in local vehicle status data base C0.
  • 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.
  • the traffic information system only provides the state information of neighboring 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 neighborhood.
  • the vehicle control system C can better anticipate for events occurring at the road in front of the target vehicle 70B. This allows for a smoother and safer control.
  • the traffic information system will also transmit the status information of vehicle 70D, indicating that this vehicle intends to change from the rightmost lane to the middle lane of the traffic infrastructure 80.
  • the traffic control system C of vehicle 70B may respond more gradually to the maneuver of vehicle 70D, than would be the case if vehicle 70B 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 70A, ..., 70E are driving in the same lane, and that none of the vehicles 70A, ..., 70E 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 70E 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 neighborhood of a target vehicle, e.g. 70B may include vehicles 70C,...,70E, may additionally or alternatively include vehicles 70A behind the target vehicle 70B.
  • This vehicle status information may be used by the control system C2 to of vehicle 70B to moderate a breaking power of said vehicle 70B to prevent that a collision occurs with a vehicle 70A succeeding it.
  • FIG. 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 R1 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 VS1 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 VS1 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.
  • FIG 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 S1 Starting (Step S1) 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 T L . 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 T L 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 T L 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 unoccupied in step S7, and a message signaling this is transmitted in step S8.
  • 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 T H .
  • the second predetermined value T H may be higher than the first predetermined value T L . If the signal value does not exceed the second predetermined value T H program flow continues with step S2. If however the value is higher than said second predetermined value T H then program flow continues with step S10.
  • step S10 it is verified whether the signal value v remains above the second predetermined value T H for a second predetermined time period, which may be equal to the first predetermined time period. During step S10 the retrieval of input from the A/D convertor is continued.
  • step S2 If the signal value v returns to a value lower then said predetermined value T H 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 S11, and a message signaling this is transmitted in step S12.
  • a message interpreter shown in Figure 10 and 11 , 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.
  • 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. Once the messages D are associated with a particular vehicle 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 neighboring message interpreters via network interface 60 (wired or wireless), for example to exchange the motion state of crossing objects.
  • the microcontroller 24 of Figure 11 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.
  • separate memories may be present for storing each of these maps and for the software.
  • 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 MD1, MD2, MD3 comprises hardware as described with reference to Figure 10 and 11 .
  • Figure 12 schematically shows a part of a traffic infrastructure 80 having sections R j-1 , R j , R j+1 .
  • a vehicle moves in a direction indicated by arrow X from R j-1 , via R j , to R j+1 .
  • Figure 13 shows an overview of a method for detecting the vehicle performed by the message interpreter for section R j , using the messages obtained from the sensor nodes.
  • step S20 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 neighbors one of the neighboring sections R j-1 or R j+1 . If that is the case, then in step S23 the event is communicated via the communication network interface to the message interpreter for that neighboring section. In 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 neighboring 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 t 0 associated with the current state S 0 .
  • the state S determined by the estimation method is the state update of S0 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 S0 to S0 in step S64. What does it mean?
  • vehicles could be provided with a transponder that signals their momentaneous position to the traffic information system.
  • A1 Estimation and association for multiple target tracking based on spatially, distributed detections
  • 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.
  • the set defines the integer values and 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 x ( t ) ⁇ is defined as a vector depending on time t and is sampled using some sampling method.
  • the time t at sampling instant k ⁇ is defined as t k ⁇ .
  • the matrix A ( t 2 - t 1 ) ⁇ depends on the difference between two time instants t 2 > t 1 and is shortly denotes as A t 2 -r 1
  • each object also has a certain shape or geometry which covers a certain set of positions in , i.e. the grey area of Figure 16 .
  • To define the vectors ⁇ i we equidistant sample the rectangular box defined by using a grid with a distance r .
  • Each ⁇ i is a grid point within the set S as graphically depicted in Figure 17 .
  • T i represents the i th object's rotation-matrix dependent on ⁇ i .
  • s i t x i t ⁇ y i t ⁇ ⁇ i t ⁇ d ⁇ x i t dt ⁇ d ⁇ y i t dt T .
  • the objects are observed in by a camera or a network of sensors. For that M 'detection' points are marked within and collected in the set D ⁇ .
  • the position of a detection point is denoted as d ⁇ D .
  • Figure 18 shows an example of object i which is detected by multiple detection points. The covariance ⁇ of each detection point is also indicated.
  • the sampling method of the observation vectors z 0: k is a form of event sampling [4, 5,7]. 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 k i . Suppose that all associated observation-vectors z n i are collected in the set Z k i ⁇ z 0 : k . Then the second method is to estimate m k i from the observation-set Z k i . This is used in the third method, which is a state-estimator.
  • Z k i is defined as the set with all observation-vectors from z 0: k that were associated with object i .
  • Z k i is defined as the set with all observation-vectors from z 0: k that were associated with object i .
  • the set Z k i 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 - 1 i and the observation z k was associated to object i , i.e. z k i . Now if the object's edge is detected at d k for the first time, then z k i is added to the set Z k - 1 i . However, if the object's edge is detected at d k for the second time, then z k i .
  • Estimation of the measurement-vector m k i given the observation set Z k i results in calculating p m k i
  • the set Z consists of the observation vectors z n , for all n ⁇ N ⁇ [0, k ], that were associated to the same object.
  • the detection point at time-step n are defined as d n ⁇ . Meaning that the objects orientation is not directly.
  • Z ) is approximated by sampling in ⁇ , i.e.: p m
  • the main aspect of equation (13a) is to determine p ( o
  • O n ( ⁇ ) ⁇ to be equal to all possible object positions o , given that the object is detected at position d n ⁇ z n ( ⁇ Z ) and that the object's rotation is equal to ⁇ .
  • Z, ⁇ ) and ⁇ l are related to the set O N ( ⁇ ) due to the fact that it O N ( theta ) defines the set of possible object positions o for a given ⁇ .
  • Z, ⁇ ) and ⁇ l we define the functions f ( o
  • z n , ⁇ : ⁇ 0 if o ⁇ O N ⁇ , 1 if o ⁇ O N ⁇ , g o
  • Z , ⁇ : ⁇ n ⁇ N ⁇ f o
  • z n , ⁇ ⁇ 0 if o ⁇ O N ⁇ , 1 if o ⁇ O N ⁇ ,
  • 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
  • Figure 22 (right) graphically depicts the determination of ⁇ n from the set ⁇ for a given ⁇ and detection point d n .
  • z n , ⁇ ), as defined in (15), is approximated by placing a Gaussian function at each sampled position ô i ⁇ ⁇ n with a certain covariance dependent on the grid-size r : f o
  • the aim of this section is to calculate the function g ( o
  • Equation (22) If N contains m elements, then calculating equation (22) would result in K m products of m Gaussian functions and sum them afterwards. This would take too much processing power if m is large. That is why equation (22) is calculated differently.
  • each detection point d n defines a rectangular set denoted with ( ⁇ ) dependent on rotation ⁇ .
  • the intersection of all these rectangular sets is defined with the set ( ⁇ ).
  • 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 , ⁇ n ⁇ N .
  • O n ( ⁇ ) ⁇ ( ⁇ ) and O N ( ⁇ ) ⁇ ( ⁇ ). Meaning that only within the set ( ⁇ ) all the functions f ( o
  • Z, ⁇ ) of (22) is therefore approximated as: g o
  • Equation (25) is reduced to: g o
  • 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.
  • Equation (29) is approximated as a single Gaussian function: g o
  • Equation (30) is substituted into equation (16) together with f ( o
  • 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 i given the observation set Z k - 1 i ⁇ z k .
  • Z k - 1 i , 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.
  • ⁇ i and K i are equal to ⁇ and K respectively, which define the approximation of the function f ( m k i
  • the probability of (32) 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 A. Meaning that association and estimation can be done with different sizes of A.
  • 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 6.
  • the second one is a combination of Gating and Nearest Neighbor.
  • This paper presents a method for estimating the position- and rotation-vectar 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 orthoganal. 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.
  • x ⁇ p x d x ⁇ - ⁇ ⁇ ⁇ G m ⁇ ⁇ x ⁇ M ⁇ G x ⁇ u ⁇ U d x .
  • R defines the set of real numbers whereas the set defines the non-negative real numbers.
  • the set defines the integer numbers and 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 ) ⁇ is defined to depend on time t ⁇ 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 ⁇ .
  • the i th and maximum eigenvalue of a square matrix A are denoted as ⁇ i ( A ) and ⁇ max ( A ) respectively.
  • a ⁇ and B ⁇ are positive definite, denoted with A > 0 and B > 0, then A > B denotes A - B > 0 .
  • a ⁇ 0 denotes A is positive semi-definite.
  • PDF probability density function
  • the exact description of the set H k e ( z k e -1 , t ) depends on the actual sampling method.
  • H k e ( z k e -1 , t ) is derived for the method "Send-on-Delta", with y ( t ) ⁇ .
  • the event instant k e occurs whenever
  • exceeds a predefined level ⁇ , see Figure 28 , which results in H k e ( z k e -1 , t ) ⁇ y ⁇
  • H k e (z k e -1 , t ) should contain the set of all possible values that y ( t ) can take in between the event instants k e - 1 and k e . Meaning that if t k e -1 ⁇ t ⁇ t k e , then y ( t ) ⁇ H k e ( z k e -1 , t ).
  • the state vector x ( t ) of this system is to be estimated from the observation vectors z 0 e : k e .
  • 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 .
  • the problem of interest in this paper is to construct a state-estimator suitable for the general event sampling method introduced in Section 3 and which is computationally tractable. Furthermore, it is desirable to guarantee that P 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 e is received, then t n - t k e ⁇ ⁇ and ⁇ i ( P n
  • 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.
  • Equation (25) is explicitly solved by applying Proposition 1: p ⁇ x n
  • n - 1 + C T ⁇ R n i - 1 ⁇ y n i , P n i : P n
  • n - 1 - 1 + C T ⁇ R n i - 1 ⁇ C - 1 and ⁇ n i : G ⁇ y n i , Cx n
  • the third step is to approximate (27) as a single Gaussian to retrieve a computationally tractable algorithm. For if both p ( x n -1
  • y 0: n ⁇ Y 0: n ) of (27) is approximated as a single Gaussian with an equal expectation and covariance matrix, i.e.: p x n
  • the first two estimators are the EBSE and the asynchronous Kalman filter (AKF) of [13].
  • ⁇ 0.1 [ nt ].
  • the AKF estimates the states only at the event instants t k e .
  • the states at t k 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 af y k a .
  • QKF quantized Kalman filter
  • 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 k a is the quantized version of y k a with quantization level 0.1, which corresponds to the "Send-on-Delta" method. Hence, a comparison can be made.
  • i - 1 , which is a measure of the change in the estimation-error after the measurement update with either z k e or y k a was done. Notice that if ⁇ ⁇ 1 the estimation error decreased after an update, if ⁇ > 1 the error increased and if ⁇ 1 the error remained the same.
  • 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 k e and x ka were estimated and it took 0.094 seconds.
  • ⁇ 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 EBSE calculates P n
  • n 2 as (29) in which V is replaced with R : V + V . Notice that for these estimators we have that ⁇ n ⁇ t s and R n ⁇ R , for all n .
  • the first step of induction is to prove that P 1
  • 1 1 A ⁇ 1 ⁇ P 0 ⁇ A ⁇ 1 T + B ⁇ 1 ⁇ Q ⁇ B ⁇ 1 T - 1 + C T ⁇ R 1 - 1 ⁇ C - 1
  • 1 2 A t s ⁇ P 0 ⁇ A t s T + B t s ⁇ Q ⁇ B t s T - 1 + C T ⁇ R - 1 ⁇ C - 1 .
  • V 1 : A ⁇ 1 ⁇ P 0 ⁇ A ⁇ 1 T + B ⁇ 1 ⁇ Q ⁇ B ⁇ 1 T
  • V 2 : A t s ⁇ P 0 ⁇ A t s T + B t s ⁇ Q ⁇ B t s T
  • 1 1 and U 2 : P 1
  • the second and last step of induction is to show that if P n - 1
  • V 1 : A ⁇ n ⁇ P n - 1
  • V 2 : A t s ⁇ P n - 1

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* Cited by examiner, † Cited by third party
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GB2584964A (en) * 2020-06-29 2020-12-23 I R Kinetics Ltd Systems and methods for interactive vehicle transport networks
GB2585165A (en) * 2020-09-25 2020-12-30 I R Kinetics Ltd Systems and methods for interactive vehicle transport networks

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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US9272711B1 (en) 2014-12-31 2016-03-01 Volkswagen Ag Congestion-friendly adaptive cruise control
JP7069944B2 (ja) * 2018-03-28 2022-05-18 住友電気工業株式会社 環境検出装置、環境検出システム、環境検出方法、及びコンピュータプログラム
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19956455A1 (de) * 1999-11-24 2001-05-31 Volkswagen Ag Verfahren und Einrichtung zum geschwindigkeits- und/oder abstandsgeregelten Fahrbetrieb
US20040145495A1 (en) * 2003-01-27 2004-07-29 Makio Komada Security method for vehicle safe driving support system
EP1569183A2 (de) * 2004-02-24 2005-08-31 Robert Bosch GmbH System zum Steuern und/oder zum Regeln von Fahrerassistenzsystemen sowie hierauf bezogenes Verfahren
US20070216528A1 (en) * 2006-03-09 2007-09-20 Denso Corporation Operation support system, sending device, and receiving device

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US2002A (en) 1841-03-12 Tor and planter for plowing
JP4984974B2 (ja) * 2007-03-02 2012-07-25 富士通株式会社 運転支援システム及び車載装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE19956455A1 (de) * 1999-11-24 2001-05-31 Volkswagen Ag Verfahren und Einrichtung zum geschwindigkeits- und/oder abstandsgeregelten Fahrbetrieb
US20040145495A1 (en) * 2003-01-27 2004-07-29 Makio Komada Security method for vehicle safe driving support system
EP1569183A2 (de) * 2004-02-24 2005-08-31 Robert Bosch GmbH System zum Steuern und/oder zum Regeln von Fahrerassistenzsystemen sowie hierauf bezogenes Verfahren
US20070216528A1 (en) * 2006-03-09 2007-09-20 Denso Corporation Operation support system, sending device, and receiving device

Non-Patent Citations (35)

* Cited by examiner, † Cited by third party
Title
"Asymptotic Effectiveness of the Event-Based Sampling according to the Integral Criterion", SENSORS, vol. 7, 2007, pages 16 - 37
A. POORE; S. GADALETA: "Some assignment problems arising from multiple target tracking", MATHEMATICAL AND COMPUTER MODELLING, vol. 43, 2006, pages 1074 - 1091, XP024904058, DOI: doi:10.1016/j.mcm.2005.05.026
A. V. BALAKRISHNAN, KALMAN FILTERING THEORY. OPTIMIZATION SOFTWARE, 1987
B. RISTIC; S. ARULAMPALAM; N. GORDON, BEYOND THE KALMAN FILTER: PARTICLE FILTER FOR TRACKING APPLICATIONS, 2002
B. RISTIC; S. ARULAMPALAM; N. GORDON: "Beyond the Kalman filter: Particle filter for tracking applications", 2004, ARTECH HOUSE
D. C. MONTGOMERY; G. C. RUNGER: "Applied Statistics and Probability for Engineers", 2007, JOHN WILEY AND SONS
D. KELLY: "Introduction to probability", 1994, MACMILLAN PUBLISHING COMPANY
D. S. BERNSTEIN: "Matrix Mathematics", 2005, PRINCETON UNIVERSITY PRESS
E. JOHANNESSON; T. HENNINGSSON; A. CERVIN: "Hybrid Systems: Computation and Control, ser. Lecture Notes in Computer Science", vol. 4416, 2007, SPRINGER VERIAG, article "Sporadic control of first-order linear stochastic systems", pages: 301 - 314
G. F. FRANKLIN; J. D. POWEL; A. EMAMI-NAEINI: "Feedback control of dynamic systems", 1995, ADDISON-WESLEY
H. DURANT-WHYTE; B. RAO; H. HU: "Towards a fully decentralized architecture for multisensor data fusion", 1990 IEEE INT. CONF. ON ROBOTICS AND AUTOMATION, 1990, pages 1331 - 1336
H. L. LEBESQUE: "Integrule, longueur, aire", PH.D. DISSERTATION, 1902
H. W. SORENSON; D. L. ALSPACH: "Recursive Bayesian estimation using Gaussian sums", AUTOMATICA, vol. 7, 1971, pages 465 - 479
J. H. KOTECHA; P. M. DJURIÉ: "Gaussian sum particle filtering", IEEE TRANSACTION SIGNAL PROCESSING, vol. 51, no. 10, 2003, pages 2602 - 2612
K. ÅSTRÖM; B. BEMHARDSSON: "Comparison of Riemann and Lebesque sampling for first order stochastic systems", 41ST IEEE CONFERENCE ON DECISION AND CONTROL, 2002
K. J. ÅSTRÖM; B. M. BERNHARDSSON: "Comparison of Riemann and Lebesque sampling for first order stochastic systems", 41ST IEEE CONF. ON DEC. AND CONTR., 2002
K. MARDIA; J. KENT; J. BIBBY: "Mutlivariate analysis", 1979, ACADEMIC PRESS, INC.
K. V. MARDIA; J. T. KENT; J. M. BIBBY: "Mutlivariate analysis.", 1979, ACADEMIC PRESS
L F. AKYILDIZ ET AL.: "Computer Networks", vol. 38, 2002, ELSEVIER, article "Wireless Sensor Networks: a survey", pages: 393 - 422
L. AGGOUN; R. ELLIOT: "Measure Theory and Filtering", 2004, CAMBRIDGE UNIVERSITY PRESS
M. L. J. HAUTUS: "Controllability and observability conditions of linear autonomous systems", INDAGATIONES MATHEMATHICAE, vol. 32, 1972, pages 448 - 455
M. MALLICK; S. CORALUPPI; C. CARTHEL: "Advances in Asynchronous and Decentralized Estimation", PMCEEDING OF THE 2001 AEROSPACE CONFERENCE, 2001
M. MISKOWICZ: "Send-on-delta concept an event-based data-reporting strategy", SENSORS, vol. 6, 2006, pages 49 - 63, XP055179444, DOI: doi:10.3390/s6010049
M. MISKOWICZ: "Send-on-delta concept: an event-based data-reporting strategy", SENSORS, vol. 6, 2006, pages 49 - 63, XP055179444, DOI: doi:10.3390/s6010049
N. JOHNSON; S. KOTZ; A. KEMP: "Univariate discrete distributions", 1992, JOHN WILEY AND SONS, INC
N. L. JOHNSON; S. KOTZ; A. W. KEMP: "Univariate discrete distributions", 1992, JOHN WILEY AND SONS
O. SONGHWAI; S. SASTRY; L. SCHENATO: "A Hierarchical Multiple-Target Tracking Algorithm for Sensor Networks", PROC. OF THE 2005 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, 2005
R. E. CURRY: "Estimation and control with quantized measurements", 1970, MIT PRESS
R. E. KALMAN: "A new approach to linear filtering and prediction problems", TRANSACTION OF THE ASME JOURNAL OF BASIC ENGINEERING, vol. 82, no. D, 1960, pages 35 - 42
R. KARLSSON; F. GUSTAFSSON: "Monte Carlo data association for multiple target tracking", IEE INTERNATIONAL SEMINAR ON TARGET TRACKING: ALGORITHMS AND APPLICATIONS, 2001
S. BLACKMAN; R. POPOLI: "Design and Analysis of Modern Tracking Systerns", 1999, ARTECH HOUSE
V. H. NGUYEN; Y. S. SUH: "Improving estimation performance in Networked Control Systems applying the Send-on-delta transmission method", SENSORS, vol. 7, 2007, pages 2128 - 2138
V. NGUYEN; Y SUH: "Improving estimation performance in Networked Control Systems applying the Send-on-delta transmission method", SENSORS, vol. 7, 2007, pages 2128 - 2138
W. P. M. H. HEEMELS; J. H. SANDEE; P. P. J. VAN DEN BOSCH: "Analysis of event-driven controllers for linear systems", INTERNATIONAL JOURNAL OF CONTROL, vol. 81, no. 4, 2008
Y BAR-SHALOM; R. LI: "Multitarget-Multisensor Tracking: Principles and Techniques", YBS, 1995

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CN105144663A (zh) * 2013-03-15 2015-12-09 德纳有限公司 使用多级网络的用于数据收集与分析的系统和方法
US9571562B2 (en) 2013-03-15 2017-02-14 Dana Limited System and method for data collection and analysis using a multi-level network
CN106327919A (zh) * 2015-06-20 2017-01-11 联芯科技有限公司 实现行驶警告的方法及实现自动行驶警告的系统
CN106327919B (zh) * 2015-06-20 2020-04-10 联芯科技有限公司 实现行驶警告的方法及实现自动行驶警告的系统
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