JP2013104815A - Navigation device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accurately detect a location of a traffic light while preventing complication of device structure and/or cost increase required for the device structure.SOLUTION: A navigation device 10 comprises: a frequency analysis unit 26 for calculating a power spectrum corresponding to a frequency from frequency analysis of acceleration of a vehicle; a congestion prediction unit 29 for calculating a congestion indication degree on the basis of the power spectrum; and a location determination unit 24 for determining whether a traffic light exists within a predetermined range from a current location of the vehicle on the basis of location information of traffic lights stored in a map data storage unit 11 and the current location acquired by a current location detection unit 23. The congestion prediction unit 29 previously stores data indicating a correlation between magnitude of the congestion indication degree and length of a distance from a current location to a traffic light, and when the traffic light exists within the predetermined range from the current location, estimates a distance from the current location to the traffic light according to the congestion indication degree.

Description

  The present invention relates to a navigation device.

Conventionally, based on a positioning signal such as a GPS (Global Positioning System) signal, a current position of a vehicle detected by combining arithmetic processing of autonomous navigation, and map information having position information of a traffic light in advance, the traffic light When the actual position is grasped, there is a problem that it is difficult to improve the accuracy of position detection.
Conventionally, for example, map information having position information of a traffic light is combined with detection of a traffic light by image recognition processing for image data output from a camera that captures the outside of the vehicle. A navigation device that grasps an actual position is known (see, for example, Patent Document 1).
Conventionally, there is known a communication system that notifies a vehicle of position information of a traffic signal by a road-to-vehicle communication device (on-road device) that is arranged on the roadside and transmits various types of information to surrounding vehicles. .

JP 2008-82761 A

By the way, according to the navigation device according to the above prior art, as a special device for detecting a traffic light by image recognition, it is output from a camera and a camera for obtaining image data having a desired image quality with respect to the outside of the vehicle. There is a possibility that an image processing apparatus that processes image data quickly and highly needs to be additionally mounted on the vehicle, which causes a problem that the cost required for the apparatus configuration increases.
In addition, according to the communication system according to the related art, it is necessary to additionally include a road unit and a vehicle-mounted communication device capable of communicating with the road unit as dedicated devices for performing road-to-vehicle communication. There arises a problem that the cost required increases.

  The present invention has been made in view of the above circumstances, and provides a navigation device capable of accurately detecting the position of a traffic light while preventing the device configuration from becoming complicated and the cost required for the device configuration from increasing. For the purpose.

  In order to solve the above problems and achieve the object, the navigation apparatus according to claim 1 of the present invention is a storage means for storing road information having traffic signal position information (for example, map data storage in the embodiment). Unit 11), current position acquisition means for acquiring the current position of the vehicle (for example, current position detection section 23 in the embodiment), and acceleration detection means for detecting the acceleration of the vehicle (for example, in the embodiment) Various sensors 12 and an acceleration calculation unit 25) and acceleration spectrum calculation means for calculating a power spectrum corresponding to the frequency from the frequency analysis of the acceleration detected by the acceleration detection means (for example, the frequency analysis unit 26 in the embodiment) And a traffic jam sign degree calculating means for calculating a traffic jam sign degree based on the power spectrum calculated by the acceleration spectrum calculating means (example: A traffic jam prediction unit 29) according to an embodiment, wherein the traffic signal is based on the position information stored in the storage means and the current position acquired by the current position acquisition means. Is determined within a predetermined range from the current position by the position determination means (for example, the position determination unit 24 in the embodiment) and the position determination means. When it is determined that the traffic signal is present, the estimation unit that estimates the distance from the current position to the traffic signal according to the traffic jam sign degree calculated by the traffic jam sign degree calculation unit (for example, step S10 in the embodiment) , S35, and the traffic jam prediction unit 29).

  Furthermore, the navigation device according to claim 2 of the present invention calculates a single regression line of the power spectrum calculated by the acceleration spectrum calculation means, and maximizes the amount of change in the slope of the single regression line in a predetermined frequency range. The maximum value calculation means (for example, the single regression line calculation unit 27 and the inclination maximum value calculation unit 28 in the embodiment) that calculates the value as the inclination maximum value, and the inclination maximum value calculated by the maximum value calculation means Periodicity determining means for determining whether or not it has periodicity (for example, the steps S09 and S34 in the embodiment also serve as a traffic jam prediction unit 29), and the estimating means includes the periodicity determining means When it is determined that the slope maximum value has periodicity, the traffic jam sign degree and the distance from the current position to the traffic signal have a predetermined correlation, and the traffic jam It estimates the distance in accordance with a trillion times.

  Furthermore, in the navigation device according to claim 3 of the present invention, a single regression line of the power spectrum calculated by the acceleration spectrum calculation means is calculated, and a maximum amount of change in the slope of the single regression line in a predetermined frequency range is calculated. A maximum value calculating unit (for example, a single regression line calculating unit 27 and a gradient maximum value calculating unit 28 in the embodiment) that calculates a value as a gradient maximum value, and the estimating unit is calculated by the maximum value calculating unit. Further, the lighting state is estimated according to the traffic jam sign degree, assuming that the traffic jam sign degree and the lighting state of the traffic light have a predetermined correlation according to the slope maximum value.

  Furthermore, the navigation device according to claim 4 of the present invention creates travel support information for supporting travel so as to suppress acceleration and deceleration of the vehicle according to the estimation result of the lighting state of the traffic light by the estimation means. In addition, the vehicle is provided with travel support means for providing the travel support information to the vehicle (for example, steps S12 and S37 in the embodiment also serve as the traffic jam prediction unit 29).

According to the navigation device of the first aspect of the present invention, even when the accuracy of the current position acquired by the current position acquisition unit is low, in a state where it is determined that there is a traffic light within a predetermined range from the current position. The distance from the current position to the traffic signal can be accurately estimated in accordance with the traffic sign.
For example, if there is a large traffic sign indicating the possibility of traffic jam in the forward direction of the vehicle, consider that the traffic jam sign is increasing due to the presence of a traffic signal nearby. Can do.

For this reason, for example, by grasping in advance the correlation between the magnitude of the traffic jam sign degree and the length of the distance from the current position to the traffic signal, the traffic signal from the current position according to the traffic jam sign degree calculated by the traffic jam sign degree calculating means. Can be accurately estimated.
Moreover, the road information having the traffic signal position information is provided in the map data normally used by the navigation device, and the traffic jam sign is calculated based on the acceleration detected by the acceleration sensor or the like normally mounted on the vehicle. Therefore, it is not necessary to additionally provide a dedicated device for estimating the distance from the current position to the traffic light, and it is possible to prevent the device configuration from becoming complicated and the cost required for the device configuration from increasing.

  Furthermore, according to the navigation device of the second aspect of the present invention, the traffic sign is a parameter corresponding to the slope maximum value calculated by the maximum value calculation means, and the traffic signal is within a predetermined range from the current position. Can be considered to have a periodicity due to a periodic change in the lighting state of the traffic light, and when the slope maximum has a periodicity, Accordingly, the reliability of the estimation result can be improved by estimating the distance from the current position to the traffic light.

Further, according to the navigation device of the third aspect of the present invention, the traffic jam sign is a parameter corresponding to the slope maximum value calculated by the maximum value calculation means, and the traffic light is within a predetermined range from the current position. Can be considered to change according to the lighting state of the traffic light (for example, blue, yellow, red lighting or flashing), and the traffic light can be turned on according to the traffic jam sign level. The state can be estimated with high accuracy.
For example, when the traffic sign is large, it can be estimated that the traffic light is in a red and yellow lighting state. For example, when the traffic sign is small, it can be estimated that the traffic light is in a blue lighting state. it can.

  Furthermore, according to the navigation device of the fourth aspect of the present invention, the travel support information for assisting the travel so as to prevent unnecessary acceleration / deceleration of the vehicle according to the lighting state of the traffic light existing ahead in the traveling direction of the vehicle. The travel support information can be used to facilitate smooth travel of the vehicle and improvement of fuel consumption of the vehicle.

It is a lineblock diagram of a navigation device concerning an embodiment of the invention. It is a figure which shows the example of the acceleration spectrum which concerns on embodiment of this invention. It is a flowchart which shows operation | movement of the navigation apparatus which concerns on embodiment of this invention. It is a block diagram of the navigation apparatus which concerns on the modification of embodiment of this invention. It is a figure which shows the example of probability density distribution which concerns on embodiment of this invention. It is a figure which shows the example of distribution of the covariance value which concerns on embodiment of this invention. It is a figure which shows the example of the correlation map of covariance minimum value and inclination maximum value which concerns on embodiment of this invention. It is a figure which shows the example of the relationship between the traffic density and traffic volume which concerns on embodiment of this invention. It is a figure which shows the example of the correlation map with the logarithm of the covariance minimum value about the inter-vehicle distance distribution which concerns on embodiment of this invention, and the logarithm of inclination maximum value about an acceleration spectrum. It is a flowchart which shows operation | movement of the navigation apparatus which concerns on the modification of embodiment of this invention.

Hereinafter, an embodiment of a navigation device of the present invention will be described with reference to the accompanying drawings.
A navigation device 10 according to the present embodiment is mounted on, for example, a vehicle. As shown in FIG. 1, a map data storage unit 11, various sensors 12, a switch 13, various actuators 14, and a display 15 are provided. The speaker 16 and the processing device 17 are provided.

The map data storage unit 11 stores map data.
The map data includes, for example, map display data for displaying a map on the display screen of the display 15 and position coordinates on the road (for example, traffic lights) required for map matching processing based on the current position of the vehicle. Road coordinate data indicating the position coordinates of the road and road data required for processing such as route search and route guidance (for example, coordinate points consisting of latitude and longitude of predetermined positions on the road such as intersections and branch points) A certain node and a link that is a line connecting each node, a road shape, a road type, and the like).

  The various sensors 12 are, for example, a vehicle speed sensor that detects the speed of the vehicle, a yaw rate sensor that detects the yaw rate of the vehicle, and the like, and outputs a detection result signal related to the running state of the vehicle to the processing device 17.

The switch 13 outputs various signals related to, for example, vehicle travel control to the processing device 17.
The various signals output from the switch 13 are, for example, signals that relate to the operation state (for example, operation position) of the brake pedal or accelerator pedal by the driver and the vehicle's driving automatically according to the driver's input operation. Various signals related to automatic travel control for controlling the state (for example, a signal for instructing control start and control stop, and a signal for instructing increase or decrease of the target inter-vehicle distance with respect to the target vehicle speed or the preceding vehicle).

  The various actuators 14 are, for example, a throttle actuator that controls the driving force of the vehicle, a brake actuator that controls the braking of the vehicle 2, a steering actuator that controls the turning of the vehicle, and the like, which are output from the processing device 17. Drive control is performed by a control signal.

The display device 15 is, for example, various displays including a display screen such as a liquid crystal display screen, a head-up display that performs display by projection on the front window as a display screen, various lamps, and the like. Display or turn on or off according to the control signal output from the.
The speaker 16 outputs an alarm sound or a sound according to a control signal output from the processing device 17.

  The processing device 17 includes, for example, a positioning signal reception unit 21, an autonomous navigation processing unit 22, a current position detection unit 23, a position determination unit 24, an acceleration calculation unit 25, a frequency analysis unit 26, and a single regression line calculation. A unit 27, a slope maximum value calculation unit 28, a traffic jam prediction unit 29, a travel control unit 30, and a notification control unit 31 are configured.

The positioning signal receiver 21 receives a positioning signal such as a GPS (Global Positioning System) signal for measuring the position of the vehicle using an artificial satellite, for example, by the antenna 21a.
The autonomous navigation processing unit 22 performs an autonomous navigation calculation process based on the vehicle speed and yaw rate output from the various sensors 12.
The current position detecting unit 23 detects the current position of the vehicle based on the positioning signal acquired by the positioning signal receiving unit 21 and the processing result of the autonomous navigation calculation process executed by the autonomous navigation processing unit 22.

  The position determination unit 24 is based on the information on the position coordinates of the traffic signal stored in the map data storage unit 11 and the current position of the vehicle detected by the current position detection unit 23, and the traffic signal exists within a predetermined range from the current position. It is determined whether or not to perform, and this determination result is output.

  In addition, the acceleration calculation unit 25 may change the speed over time or the current position over time based on, for example, the vehicle speed information output from the various sensors 12 or the current position information detected by the current position detection unit 23. The acceleration of the vehicle is calculated from the change.

The frequency analysis unit 26 performs frequency analysis on the vehicle acceleration calculated by the acceleration calculation unit 25 and calculates a power spectrum corresponding to the frequency.
For example, frequency analysis is performed on the acceleration of the representative vehicle detected by the acceleration calculation unit 25 in two different appropriate traveling states, so that the frequency as a power spectrum as shown in FIGS. Acceleration spectra 51 and 53 corresponding to are calculated.

The single regression line calculation unit 27 calculates a single regression line in the power spectrum calculated by the frequency analysis unit 26.
For example, simple regression lines 52 and 54 are calculated for the acceleration spectra 51 and 53 shown in FIGS.

  The slope maximum value calculation unit 28 calculates the maximum value of the change amount of the slope of the single regression line in a predetermined frequency range as the slope maximum value with respect to the single regression line calculated by the single regression line calculation unit 27.

  For example, the slope maximum value calculation unit 28 performs a predetermined frequency range Y (for example, a frequency range corresponding to a time range from several seconds to several minutes) with respect to the single regression lines 52 and 54 shown in FIGS. And slopes α1 and α2 (= Y / X) are calculated based on the change X of the spectral value at 0 to 0.5 Hz or the like.

The traffic jam prediction unit 29 calculates a traffic jam sign indicating the possibility of traffic jam or the possibility of traffic jam already occurring according to the slope maximum value calculated by the slope maximum value calculation unit 28.
The traffic jam sign degree is a parameter corresponding to, for example, the maximum value of the slope, and increases when there is a high possibility of traffic jam ahead in the traveling direction of the vehicle, and decreases when the possibility is low.
In addition, an arbitrary value can be set as the predetermined threshold value for determining the magnitude of the traffic jam sign degree, but “−45 degrees”, which is generally known as (1 / f) fluctuation characteristics, is set as the predetermined threshold value. It can be.

For example, when the slope α is small with respect to the single regression line calculated by the single regression line calculation unit 27, this corresponds to a case where the shock wave (vibration, fluctuation) received from the preceding vehicle is small, and the reaction delay with respect to the preceding vehicle is small. This corresponds to a case where the distance between the vehicles becomes long and it is difficult to form a vehicle group, that is, there is little possibility of traffic jams. In this case, the traffic jam sign is a small value.
On the contrary, when the inclination α is large, it corresponds to the case where the shock wave (vibration, fluctuation) received from the preceding vehicle is large, the reaction delay with respect to the preceding vehicle is large, and the vehicle group tends to become dense, that is, there is a possibility that the traffic congestion will occur. Corresponds to large case. In this case, the sign of congestion is a large value.
The shock wave (vibration, fluctuation) referred to here means that this operation (back and forth movement) is propagated to the rear vehicle as a kind of vibration by repeating the acceleration and deceleration operations.

Therefore, the traffic jam prediction unit 29 responds to the magnitude of the slope α of the single regression line calculated by the single regression line calculation unit 27, more specifically, the slope maximum value calculated by the slope maximum value calculation unit 28. Calculate the traffic sign.
For example, the traffic jam prediction unit 29 obtains a function (for example, y = ax + b) indicating the relationship between the slope maximum value (x) and the traffic jam sign degree (y) in advance, and is calculated by the slope maximum value calculation unit 28. The traffic jam sign degree (y) with respect to the slope maximum value (x) is calculated.
The traffic jam prediction unit 29 creates a relationship between the maximum value of the slope and the corresponding traffic sign value in advance and stores it in a memory as a table, and the traffic jam sign level for the calculated slope maximum value is stored in the table. It can also be obtained by reference.

Further, the traffic jam prediction unit 29 is based on the calculated traffic jam sign degree and the map data stored in the map data storage unit 34, and driving support information indicating driving support necessary for avoiding traffic jams and eliminating traffic jams in the vehicle. Create
The driving support information is, for example, information that is used for driving control of the vehicle in order to prevent the occurrence of traffic jams or is notified to the driver from the vehicle display 15 or the speaker 16. More specifically, for example, information on the target vehicle speed and target inter-vehicle distance in automatic travel control required for avoiding traffic jams and eliminating traffic jams in vehicles, predetermined increases such as increase of inter-vehicle distances for preceding vehicles and suppression of acceleration operations. Driving operation information, route search and route guidance information for the vehicle, and the like.

  In addition, the traffic jam prediction unit 29 determines that the traffic jam sign and the distance from the current position to the traffic signal when the traffic signal is determined to be within a predetermined range from the current position in the determination result output from the position determination unit 24. And have a predetermined correlation, the distance from the current position to the traffic light is estimated according to the traffic sign.

The traffic jam prediction unit 29 is further calculated by the slope maximum value calculation unit 28 when it is determined in the determination result output from the position determination unit 24 that the traffic light is within a predetermined range from the current position. If the slope maximum value has periodicity, and it is determined that the slope maximum value has periodicity, the traffic jam sign and the distance from the current position to the traffic light have a predetermined correlation. It is also possible to estimate the distance from the current position to the traffic signal according to the traffic sign.
In this case, the periodicity when the lighting state of the traffic light changes is stored in advance, and whether or not the periodicity of the calculated slope maximum value is related to the periodicity of the lighting state of the traffic light. You may judge.

The traffic jam prediction unit 29 increases the traffic jam predicting factor due to the presence of a traffic signal nearby, for example, when the traffic jam predicting factor indicating the possibility of traffic jam occurring in the forward direction of the vehicle is large. Based on the determination that the periodicity of the maximum value of the slope is due to a periodic change in the lighting condition of the traffic light. Estimate the distance to.
For this reason, the traffic jam prediction unit 29 stores in advance data such as a map indicating the correlation between the magnitude of the traffic jam sign degree and the length of the distance from the current position to the traffic light. Get the distance to the traffic light.

Furthermore, the traffic jam prediction unit 29 determines that the slope maximum value calculated by the slope maximum value calculation unit 28 changes according to the lighting state of the traffic light (for example, blue, yellow, red lighting or blinking). Based on the above, the lighting state of the traffic light is estimated according to the traffic sign.
For this reason, the traffic jam prediction unit 29 stores data such as a map indicating a correlation between the magnitude of the traffic jam sign degree and the lighting state of the traffic signal in advance, and acquires the lighting status of the traffic signal with reference to this data.
In this data, for example, when the traffic sign is large, the traffic lights are associated with red and yellow lighting states. For example, when the traffic sign is small, the traffic light is associated with a blue lighting state. .

Then, the traffic jam prediction unit 29 suppresses unnecessary acceleration / deceleration in the vehicle based on the acquired information on the distance from the current position to the traffic signal and the lighting state of the traffic signal and the map data stored in the map data storage unit 34. The travel support information indicating the travel support required for the vehicle is created.
This driving support information is, for example, information that is used for driving control of the vehicle so that the traffic light can be smoothly passed or detoured, or is notified to the driver from the vehicle display 15 or speaker 16. . More specifically, for example, information on the target vehicle speed and target inter-vehicle distance in the automatic traveling control required for the smooth passage of the traffic light in the vehicle, the increase of the inter-vehicle distance with respect to the preceding vehicle, suppression of acceleration operation, etc. Information on driving operations, route search and route guidance for vehicles, and the like.

  The travel control unit 30 includes a traffic jam sign degree and travel support information calculated by the traffic jam prediction unit 29, various signals output from the switch 13, and detection results relating to the travel state of the vehicle output from the various sensors 12. Based on the signal, for example, the drive of the throttle actuator, the brake actuator, and the steering actuator are controlled to drive the vehicle.

  For example, the traveling control unit 30 starts or stops the execution of the automatic traveling control according to a signal output from the switch 13, and sets or changes the target vehicle speed and the target inter-vehicle distance in the automatic traveling control.

In addition, for example, the travel control unit 30 has a high possibility that a traffic jam will occur in the forward direction of the vehicle at the traffic jam sign degree calculated by the traffic jam prediction unit 29 (or there is a possibility that a traffic jam has already occurred). If the vehicle is high), make sure that the vehicle avoids traffic jams, and that subsequent vehicles are less likely to cause traffic jams, or that the traffic around the vehicle is resolved. Thus, the required target vehicle speed and target inter-vehicle distance are set, or the running state of the vehicle is changed.
Then, automatic travel control that maintains these target vehicle speed and target inter-vehicle distance (for example, constant speed travel control that matches the actual vehicle speed to the target vehicle speed, and actual inter-vehicle distance with respect to other vehicles (for example, preceding vehicles)) The inter-vehicle distance control (for example, follow-up traveling control) is performed so as to match the target inter-vehicle distance.

  In addition, for example, the travel control unit 30 avoids traffic jams according to the travel support information created by the traffic jam prediction unit 29, and further prevents subsequent vehicles from causing traffic jams. Alternatively, the necessary target vehicle speed and target inter-vehicle distance are set, or the running state of the vehicle is changed so as to eliminate traffic congestion around the vehicle.

  In addition, for example, the travel control unit 30 is configured when the travel support information created by the traffic jam prediction unit 29 is based on the information on the distance from the current position to the traffic signal and the lighting state of the traffic signal according to the traffic jam sign degree. In accordance with this travel support information, unnecessary acceleration / deceleration is suppressed, and the required target vehicle speed and target inter-vehicle distance are set, or the travel state of the vehicle is changed.

  The notification control unit 31 controls various notification operations by controlling the display unit 15 and the speaker 16 based on the traffic jam sign degree and the travel support information calculated by the traffic jam prediction unit 29.

  For example, the notification control unit 31 has a high possibility that a traffic jam will occur ahead of the direction of travel of the vehicle in the traffic jam sign degree calculated by the traffic jam prediction unit 29 (or that there is a high possibility that a traffic jam has already occurred). Etc.) is displayed, for example, display on the display screen of the display 15 and lighting / extinguishing of the lamp are controlled, and output of alarm sound and sound from the speaker 16 is controlled. Then, the vehicle occupant is notified of information related to these traffic jams.

  In addition, for example, the notification control unit 31 avoids traffic jams according to the traffic jam sign degree or the driving support information calculated by the traffic jam prediction unit 29, and further, subsequent vehicles of the vehicles are less likely to cause traffic jams. In this manner, or in order to eliminate traffic congestion around the vehicle, a necessary driving operation instruction (for example, increase in the inter-vehicle distance with respect to the preceding vehicle or suppression of acceleration operation) is notified.

For example, the display 15 switches the display of a two-color signal (blue and red, etc.) indicating whether or not a traffic jam has occurred, turns on or blinks a single color lamp indicating the occurrence of a traffic jam, or indicates an alarm indicating the occurrence of a traffic jam. A message is output.
For example, the speaker 16 outputs an alarm sound or an alarm sound indicating that a traffic jam has occurred.
Further, for example, the content of the driving support information is notified to the driver from the display 15 or the speaker 16.

  In addition, for example, when the traffic prediction unit 29 acquires information on the distance from the current position to the traffic signal and the lighting state of the traffic signal according to the traffic congestion sign degree, the notification control unit 31 displays the display on the display unit 15, for example. Information on these traffic lights is notified to the vehicle occupant by controlling the display on the screen, lighting or extinguishing of the lamp, and controlling the output of alarm sound or voice from the speaker 16.

  In addition, for example, the notification control unit 31 is based on the case where the travel support information created by the traffic jam prediction unit 29 is based on the information on the distance from the current position to the traffic signal and the lighting state of the traffic signal according to the traffic jam sign degree. Is capable of smoothly passing or detouring the traffic light according to this driving support information, suppressing unnecessary acceleration / deceleration, and instructing the required driving operation (for example, the inter-vehicle distance to the preceding vehicle) And speed increase / decrease and route guidance).

  The navigation device 10 according to the present embodiment has the above-described configuration. Next, the operation of the navigation device 10 will be described.

First, for example, in step S01 shown in FIG. 3, the vehicle speed is detected by the vehicle speed sensors of the various sensors 12, and the current position of the vehicle is detected by the current position detector 23.
Next, in step S02, based on the vehicle speed or the current position, the acceleration of the vehicle is calculated from the change in speed over time or the change in current position over time.

Next, in step S03, frequency analysis is performed on the acceleration of the vehicle, and a power spectrum corresponding to the frequency is calculated.
Next, in step S04, a single regression line is calculated in the power spectrum, and a maximum value of the amount of change in the slope of the single regression line in a predetermined frequency range is calculated as a slope maximum value.

Next, in step S05, it is determined whether or not a slope maximum value (for example, a slope maximum value greater than or equal to a predetermined value) has been calculated.
If this determination is “NO”, the flow returns to step S 01 described above.
On the other hand, if this determination is “YES”, the flow proceeds to step S 06.
In step S06, a traffic jam sign indicating the possibility of traffic jams or the possibility of traffic jams is calculated according to the slope maximum value.

Next, in step S07, the position coordinate information of the traffic light stored in the map data storage unit 11 is acquired.
Next, in step S08, it is determined whether the traffic light is within a predetermined range from the current position of the vehicle.
If this determination is “NO”, the flow proceeds to step S 12 described later.
On the other hand, if this determination is “YES”, the flow proceeds to step S 09.

Next, in step S09, it is determined whether the slope maximum value has periodicity.
If this determination is “NO”, the flow proceeds to step S 12 described later.
On the other hand, if the determination is “YES”, the flow proceeds to step S10.
Next, in step S10, referring to data such as a map showing the correlation between the magnitude of the traffic jam sign level stored in advance and the length of the distance from the current position to the traffic light, the current position is determined according to the traffic jam sign level. Get the distance from the traffic light.
Next, in step S11, with reference to data such as a map indicating the correlation between the pre-stored magnitude of the traffic jam sign level and the traffic light lighting state, the traffic light lighting state is obtained according to the traffic jam sign level. .

Then, in step S12, based on the calculated traffic sign and the map data, travel support information indicating the travel support necessary for avoiding the traffic and for eliminating the traffic is generated in the vehicle.
In addition, in step S12, in addition to the calculated traffic jam sign and map data, when information on the distance from the current position of the vehicle to the traffic signal and the lighting state of the traffic signal is acquired, for example, the traffic signal passes smoothly. Alternatively, it is possible to make a detour and create travel support information indicating the travel support required to suppress unnecessary acceleration / deceleration in the vehicle.

  Then, in step S13, according to the traffic jam sign and the driving support information, if information on the distance from the current position of the vehicle to the traffic light and the lighting state of the traffic light is acquired, the information also corresponds to the information. Then, the notification control and the traveling control are executed, and the process proceeds to the end.

As described above, according to the navigation device 10 according to the present embodiment, even if the accuracy of the current position detected by the current position detection unit 23 is low, it is determined that there is a traffic signal within a predetermined range from the current position. In this state, it is possible to accurately estimate the distance from the current position to the traffic signal according to the traffic sign.
For example, if there is a large traffic sign indicating the possibility of traffic jam in the forward direction of the vehicle, consider that the traffic jam sign is increasing due to the presence of a traffic signal nearby. Can do.

For this reason, for example, by grasping in advance the correlation between the magnitude of the traffic jam sign degree and the length of the distance from the current position to the traffic signal, the current position to the traffic signal according to the traffic jam sign degree calculated by the traffic jam prediction unit 29. Can be accurately estimated.
Moreover, the information on the coordinate position of the traffic light is provided in the map data normally used by the navigation device, and the traffic jam sign is calculated based on the acceleration detected by the acceleration sensor normally mounted on the vehicle. It is not necessary to additionally provide a dedicated device for estimating the distance from the position to the traffic light, and it is possible to prevent the device configuration from becoming complicated and the cost required for the device configuration from increasing.

  Further, in addition to the parameter according to the slope maximum value calculated by the slope maximum value calculation unit 28, the traffic congestion predicting degree is a periodicity of the traffic signal when the traffic light is within a predetermined range from the current position. It can be considered that the slope maximum value has periodicity due to the change of the lighting state, and when the slope maximum value has periodicity, the distance from the current position to the traffic light is estimated according to the traffic sign. As a result, the reliability of the estimation result can be improved.

Furthermore, in addition to being a parameter corresponding to the slope maximum value calculated by the slope maximum value calculation unit 28, the traffic jam sign degree is determined by the signal maximum when the traffic light is within a predetermined range from the current position. Can be considered to change according to the lighting state (for example, blue, yellow, red lighting or blinking), and the lighting state of the traffic light can be accurately estimated according to the traffic jam sign.
For example, when the traffic sign is large, it can be estimated that the traffic light is in a red and yellow lighting state. For example, when the traffic sign is small, it can be estimated that the traffic light is in a blue lighting state. it can.

  Further, it is possible to create travel support information that assists in traveling so as to prevent unnecessary acceleration / deceleration of the vehicle in accordance with the lighting state of a traffic light that exists in front of the traveling direction of the vehicle. Driving and improving fuel efficiency of the vehicle can be promoted.

In the above-described embodiment, for example, as in the navigation device 10 according to the modified example shown in FIG. 4, in addition to the acceleration of the vehicle, the congestion prediction is performed based on the information on the inter-vehicle distance between the vehicle and the preceding vehicle. An operation may be performed.
The configuration of the navigation device 10 according to this modification differs from the configuration of the navigation device 10 of the above-described embodiment in that, for example, the radar device 41, the preceding vehicle detection unit 42, the inter-vehicle distance calculation unit 43, and the inter-vehicle distance A distance distribution estimation unit 44, a covariance minimum value calculation unit 45, and a correlation calculation unit 46 are added.

That is, the navigation device 10 of the modified example includes a map data storage unit 11, various sensors 12, switches 13, various actuators 14, a display unit 15, a speaker 16, a processing device 17, and a radar device 41. It is prepared for.
The processing device 17 includes, for example, a positioning signal receiving unit 21, an autonomous navigation processing unit 22, a current position detecting unit 23, a position determining unit 24, an acceleration calculating unit 25, a frequency analyzing unit 26, and a single regression. Straight line calculation unit 27, slope maximum value calculation unit 28, traffic jam prediction unit 29, travel control unit 30, notification control unit 31, preceding vehicle detection unit 42, inter-vehicle distance calculation unit 43, and inter-vehicle distance distribution estimation A unit 44, a minimum covariance calculation unit 45, and a correlation calculation unit 46.

  For example, the radar device 41 divides a detection target region set in front of the traveling direction of the vehicle into a plurality of angular regions, and scans each angular region so that an electromagnetic wave such as an infrared laser or a millimeter wave is generated. Send a call signal. And each reflected signal receives the reflected signal of the reflected wave generated by being reflected by an object outside the vehicle (for example, a preceding vehicle) or a pedestrian. Then, a signal corresponding to the transmission signal and the reflection signal is output to the processing device 17.

The preceding vehicle detection unit 42 detects a preceding vehicle that exists ahead of the traveling direction of the vehicle based on the signal output from the radar device 41.
The inter-vehicle distance calculation unit 43 detects the inter-vehicle distance for each preceding vehicle of the vehicle detected by the preceding vehicle detection unit 42.

  The inter-vehicle distance distribution estimation unit 44 estimates the inter-vehicle distance distribution based on the inter-vehicle distances of the vehicles detected by the inter-vehicle distance calculation unit 43 with respect to each preceding vehicle and the number of detected preceding vehicles.

For example, the inter-vehicle distance distribution estimation unit 44 may use variational Bayes when a vehicle group in front of the vehicle (that is, a set of preceding vehicles having a relatively precise inter-vehicle distance) is detected from the information on the inter-vehicle distance and the number of vehicles. The Gaussian distribution (probability density distribution) is applied to each vehicle group using the distribution estimation method.
For example, when two vehicle groups are detected, the two vehicle groups can be regarded as a distribution obtained by linearly combining two Gaussian distributions. For example, as shown in FIG. 5, a probability function P1 ( A probability function P (X) representing the entire distribution is obtained as the sum (superposition) of X) and P2 (X).

  Here, when the Gaussian distribution (probability function) is represented by N (x | μ, Σ), the superposition of a plurality of Gaussian distributions as illustrated in FIG. 5 is described as shown in the following formula (1). The

In the above formula (1), for example, for an arbitrary natural number k, an expected value (average value) μ _k represents a position having the highest density. The covariance value (matrix) Σ _k represents the distortion of the distribution, that is, how the density decreases when moving away from the expected value μ _k . A mixing coefficient (mixing ratio) π _k (0 ≦ π _k ≦ 1) of the Gaussian distribution represents a ratio of how much each Gaussian distribution contributes, and is a so-called probability.

The covariance minimum value calculation unit 45 performs a calculation process using variational Bayes or the like in order to obtain a parameter (covariance) that maximizes the likelihood function obtained from the above-described probability function P (X), for example.
For example, for the probability function P (X) obtained as a superposition of a plurality of Gaussian distributions as illustrated in FIG. 5, the covariance minimum value calculating unit 45 performs the covariance value Σ for each Gaussian distribution. _k is calculated. Then, the minimum value of the plurality of covariance values Σ _k obtained for each Gaussian distribution is calculated.

For example Figure 6 the graph of the distribution of the covariance value sigma _k as shown in (A) 56, to the variable of the covariance values sigma _k [delta] (e.g., the covariance value sigma _k itself, etc.), the variable [delta] = 0 A sharp graph indicates that there is no fluctuation in the vehicle group, that is, the vehicle distance is in a substantially constant traveling state.
On the other hand, the distribution of the covariance value Σ _k as shown in FIG. 6B is represented by a graph 57 having a peak in the negative region value δ1 of the variable δ related to the covariance value Σ _k and a positive region value δ2. The graph 58 is composed of two graphs 58 having peaks. Each of the graphs 57 and 58 has a predetermined fluctuation range with respect to the variable δ related to the covariance value Σ _k , and there are fluctuations in the vehicle group, in other words, there are a plurality of sets of vehicles 2 having different inter-vehicle distances. It suggests.
For example, in FIG. 6A, the minimum value (covariance minimum value) of the covariance value Σ _k is substantially zero. For example, in FIG. 6B, the minimum value of the covariance value Σ _k is two values δ1. , Δ2 is the smaller value δ1.

The correlation calculation unit 46 creates a correlation map between the gradient maximum value calculated by the gradient maximum value calculation unit 28 and the covariance minimum value calculated by the covariance minimum value calculation unit 45.
For example, in the image (concept) diagram of the correlation map between the maximum slope value and the minimum covariance value shown in FIG. 7, the horizontal (X) axis is the minimum covariance value X, and the vertical (Y) axis is the maximum slope value Y. The correlation of variables (X, Y) is mapped.

For example, in the correlation map shown in FIG. 7, two regions 59 and 60 are shown, and there is a boundary region 61 where the two regions 59 and 60 overlap. The region 59 corresponds to a state where the covariance minimum value is relatively small and the variation of the vehicle group is small, in other words, a state where the inter-vehicle distance is relatively constant. Conversely, the region 60 corresponds to a state where the covariance minimum value is relatively large and the variation of the vehicle group is large, in other words, a state where there are a plurality of sets of vehicles having different inter-vehicle distances.
The boundary region 61 is a region where a change in the vehicle group changes from a small state to a large state, and traffic congestion can be predicted by quantitatively finding the state of the vehicle group corresponding to the boundary region 61.

For example, in the diagram showing the relationship between the traffic density and the traffic volume as shown in FIG. 8, the horizontal (X) axis of the graph is the traffic density that means the number of other vehicles existing within a predetermined distance from an appropriate vehicle. The reciprocal of this traffic density corresponds to the inter-vehicle distance. The vertical (Y) axis is a traffic volume that means the number of vehicles passing through a predetermined position.
For example, the diagram showing the relationship between the traffic density and the traffic volume as shown in FIG. 8 can be regarded as representing a traffic flow that means the flow of vehicles.

The traffic flow illustrated in FIG. 8 can be roughly divided into four states (regions).
The first state is a free flow state in which the possibility of traffic congestion is low, and here, a certain amount of acceleration and inter-vehicle distance can be secured.
The second state is a mixed flow state in which the braking state and the acceleration state of the vehicle are mixed. The state of this mixed flow is the state before the transition to the congestion flow, and the driver's freedom of driving decreases, and the probability of transition to the congestion flow due to the increase in traffic density (reduction of the inter-vehicle distance). It is in a high state.
The third state is a traffic flow state indicating a traffic jam.
The fourth state is a critical region that is a transition state that exists during the transition from the free flow state to the mixed flow state. This critical region is a state where the traffic volume and the traffic density are higher than those of the free flow, and the state transitions to a mixed flow due to a decrease in traffic volume and an increase in traffic density (a reduction in the inter-vehicle distance). The critical region is sometimes called metastable flow or metastable flow.

Then, for example, the region 59 shown in FIG. 7 includes the free flow and critical region shown in FIG. 6, for example, and the region 60 shown in FIG. 7, for example, includes the mixed flow and the jam flow shown in FIG. Will be included.
Therefore, for example, the boundary region shown in FIG. 7 is a boundary state including both the critical region shown in FIG. 8 and the mixed flow state, for example, and is the boundary of the critical region shown in FIG.
By quantitatively grasping the critical region including the boundary of this critical region, it is possible to prevent the occurrence of traffic congestion by suppressing the transition to the mixed flow state.

The critical region quantification will be described below with reference to FIGS. 9A and 9B showing a correlation map between the logarithm of the covariance minimum value for the inter-vehicle distance distribution and the logarithm of the slope maximum value for the acceleration spectrum, for example. explain.
FIG. 9A is a simplified drawing of the traffic flow map shown in FIG. 8, and FIG. 9B shows a correlation map between the logarithm of the covariance minimum value and the logarithm of the slope maximum value.
The logarithm of the covariance minimum value and the slope maximum value shown in FIG. 9B are the slope maximum value calculated by the slope maximum value calculation unit 28 and the covariance minimum calculated by the covariance minimum value calculation unit 45. It is calculated as a logarithmic value with respect to the value and depicts the parameterization of the phase transition state in the critical region.

For example, in FIG. 9B, the region 62 includes the critical region shown in FIG. 9A, and the region 63 includes the mixed flow state shown in FIG. 9A. The critical line 64 means a critical point where there is a high possibility that traffic congestion will occur if the critical line 64 is shifted to the mixed flow state beyond this. The boundary region 65 between the regions 62 and 63 corresponds to the boundary of the critical region immediately before the critical line 64.
Note that the correlation map illustrated in FIG. 9B is stored in a memory in the processing device 17.

  The traffic jam prediction unit 29 of this modified example determines whether or not the boundary state of the critical region exists in the correlation map created by the correlation calculation unit 46, and calculates the traffic jam sign degree according to the determination result. . Furthermore, when there is a critical area boundary state in the correlation map, the travel support information is created by referring to the map data stored in the map data storage unit 11 in order to prevent the transition to the traffic jam. To do.

  Note that the traffic jam sign degree in this modification is higher than a predetermined threshold value, for example, in the case where the boundary state of the critical region exists in the correlation map, and the boundary state of the critical region in the correlation map is Corresponding to the case where it does not exist, it becomes lower than a predetermined threshold value.

  The navigation apparatus 10 according to this modification has the above-described configuration. Next, the operation of the navigation apparatus 10 will be described.

First, for example, in step S21 shown in FIG. 10, the vehicle speed is detected by the vehicle speed sensors of the various sensors 12, and the current position of the vehicle is detected by the current position detector 23.
Next, in step S22, based on the vehicle speed or the current position, the acceleration of the vehicle is calculated from the change in speed over time or the change in current position over time.

Next, in step S23, frequency analysis is performed on the acceleration of the vehicle, and a power spectrum corresponding to the frequency is calculated.
Next, in step S24, a single regression line is calculated in the power spectrum, and the maximum value of the amount of change in the slope of the single regression line in the predetermined frequency range is calculated as the slope maximum value.

Next, in step S25, it is determined whether or not a slope maximum value (for example, a slope maximum value greater than or equal to a predetermined value) has been calculated.
If this determination is “NO”, the flow returns to step S 21 described above.
On the other hand, if the determination is “YES”, the flow proceeds to step S26.

Next, in step S26, a preceding vehicle existing ahead in the traveling direction of the vehicle is detected, and an inter-vehicle distance of the vehicle with respect to each preceding vehicle is calculated.
Next, in step S27, the inter-vehicle distance distribution is estimated based on the inter-vehicle distance of the vehicle with respect to each preceding vehicle and the number of detected preceding vehicles.
Next, in step S28, the minimum value of covariance is calculated from the inter-vehicle distance distribution.
Next, in step S29, the vehicle group distribution ahead of the traveling direction of the vehicle is estimated from the correlation between the minimum covariance value and the maximum slope value.

Next, in step S30, it is determined whether or not the boundary state of the critical region exists in the correlation map between the minimum covariance value and the gradient maximum value of the acceleration spectrum.
If this determination is “NO”, the flow returns to step S 21 described above.
On the other hand, if the determination is “YES”, the flow proceeds to step S31.
Next, in step S31, a traffic jam sign indicating the possibility of traffic jams or the possibility of traffic jams is calculated according to the slope maximum value.

Next, in step S32, information on the position coordinates of the traffic light stored in the map data storage unit 11 is acquired.
Next, in step S33, it is determined whether the traffic light is within a predetermined range from the current position of the vehicle.
If this determination is “NO”, the flow proceeds to step S 37 described later.
On the other hand, if this determination is “YES”, the flow proceeds to step S34.

Next, in step S34, it is determined whether the slope maximum value has periodicity.
If this determination is “NO”, the flow proceeds to step S 37 described later.
On the other hand, if the determination is “YES”, the flow proceeds to step S35.
Next, in step S35, referring to data such as a map showing the correlation between the magnitude of the traffic jam sign level stored in advance and the distance from the current position to the traffic light, the current position is determined according to the traffic jam sign level. Get the distance from the traffic light.
Next, in step S36, with reference to data such as a map indicating the correlation between the pre-stored magnitude of the traffic jam sign degree and the traffic light lighting state, the traffic light lighting state is acquired according to the traffic jam sign degree. .

Then, in step S37, based on the calculated traffic sign and the map data, travel support information indicating travel support necessary for avoiding traffic congestion and eliminating traffic congestion is created in the vehicle.
In addition, in step S37, in addition to the calculated traffic sign and map data, when information on the distance from the current position of the vehicle to the traffic light and information on the lighting state of the traffic light is acquired, for example, the traffic light passes smoothly. Alternatively, it is possible to make a detour and create travel support information indicating the travel support required to suppress unnecessary acceleration / deceleration in the vehicle.

  In step S38, according to the traffic congestion predictor and the driving support information, if information on the distance from the current position of the vehicle to the traffic light and the lighting state of the traffic light is acquired, the information is also received. Then, the notification control and the traveling control are executed, and the process proceeds to the end.

  According to the navigation device 10 according to this modified example, in addition to the acceleration of the vehicle, the traffic congestion sign degree is calculated by combining the easily obtainable information such as the inter-vehicle distance between the vehicle and the preceding vehicle. The calculation accuracy and reliability can be improved, and the estimation accuracy of the distance from the current position to the traffic light and the lighting state of the traffic light can be improved.

  In the modification of the above-described embodiment, for example, instead of the radar device 41, an in-vehicle communication device capable of communicating with another vehicle is provided, and information on the current position of the other vehicle is acquired by the in-vehicle communication device, The inter-vehicle distance between the vehicle and the preceding vehicle may be calculated.

10 navigation device 11 map data storage unit (storage means)
12 Various sensors (acceleration detection means)
23 Current position detector (current position acquisition means)
24 position determination unit (position determination means)
25 Acceleration calculation unit (acceleration detection means)
26 Frequency analyzer (acceleration spectrum calculation means)
27 Simple regression line calculation unit (maximum value calculation means)
28 Inclination maximum value calculation unit (maximum value calculation means)
29 Congestion prediction unit (congestion sign calculation means, estimation means, periodicity determination means, travel support means)
42 preceding vehicle detection unit 43 inter-vehicle distance calculation unit 44 inter-vehicle distance distribution estimation unit 45 covariance minimum value calculation unit 46 inter-phase calculation unit steps S09, S34 periodicity determination unit steps S10, S35 estimation unit steps S12, S37 travel support unit

Claims (4)

Storage means for storing road information having position information of traffic lights;
Current position acquisition means for acquiring the current position of the vehicle;
Acceleration detecting means for detecting acceleration of the vehicle;
An acceleration spectrum calculating means for calculating a power spectrum corresponding to the frequency detected from the acceleration frequency analysis detected by the acceleration detecting means;
A traffic jam sign degree calculating means for calculating a traffic jam sign degree based on the power spectrum calculated by the acceleration spectrum calculating means,
Position determining means for determining whether or not the traffic light is within a predetermined range from the current position based on the position information stored in the storage means and the current position acquired by the current position acquiring means;
The distance from the current position to the traffic signal according to the traffic congestion sign degree calculated by the traffic congestion sign degree calculation means when the position determination means determines that the traffic signal is within a predetermined range from the current position Estimating means for estimating
A navigation device comprising: A maximum value calculating means for calculating a single regression line of the power spectrum calculated by the acceleration spectrum calculating means, and calculating a maximum value of a change amount of the inclination of the single regression line in a predetermined frequency range as an inclination maximum value;
Periodicity determining means for determining whether or not the slope maximum value calculated by the maximum value calculating means has periodicity,
When the estimation means determines that the slope maximum value has periodicity by the periodicity determination means, the traffic sign and the distance from the current position to the traffic light have a predetermined correlation. The navigation apparatus according to claim 1, wherein the distance is estimated according to the traffic jam sign degree. A maximum value calculating means for calculating a single regression line of the power spectrum calculated by the acceleration spectrum calculating means and calculating a maximum value of a change amount of the slope of the single regression line in a predetermined frequency range as an inclination maximum value; ,
The estimation means determines that the traffic jam sign degree and the lighting state of the traffic light have a predetermined correlation according to the slope maximum value calculated by the local maximum value calculation means, and the lighting is performed according to the traffic jam sign degree. The navigation device according to claim 1, wherein the state is estimated. According to the estimation result of the lighting state of the traffic light by the estimation means, travel support information for supporting travel so as to suppress acceleration / deceleration of the vehicle is created, and the travel support information for providing the travel support information to the vehicle The navigation apparatus according to claim 3, further comprising:

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