JP2009162740A - Navigation device, road slope arithmetic method, and altitude arithmetic method - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To continue road slope calculation even during brake operation and to improve the accuracy of the altitude of one's own vehicle position to be calculated. <P>SOLUTION: A flat road acceleration arithmetic section 1 calculates flat road acceleration based on engine power torque and vehicle weight, an estimation acceleration arithmetic section 2 calculates estimation acceleration based on the vehicle speed, a road slope arithmetic section 3 calculates road slope based on the flat road acceleration and estimation acceleration, and an altitude arithmetic section 6 calculates altitude using the road slope. At this time, when a brake SW turns on, the road slope arithmetic section 3 predicts the slope of the road where the own vehicle travels based on the road slope calculated just before the brake SW turns on or the rate of change of the road slope until the brake SW turns off. A vehicle weight learning section 7 calculates the present vehicle weight based on the differential value of the estimation acceleration and drive force differential value of the own vehicle, and corrects the vehicle weight suitably using the past vehicle weight. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

  The present invention relates to a vehicle navigation device and a road gradient calculation method and altitude calculation method used in the navigation device.

  In a vehicle navigation device, the vehicle position is detected by GPS (Global Positioning System) or autonomous navigation. The detected vehicle position is clearly indicated on a map around the vehicle position. The position is displayed on the display device together with the map. In that case, the driver can grasp the road information more appropriately as the displayed vehicle position is closer to the actual vehicle position, that is, as the position detection accuracy of the vehicle is higher. That is, the comfort of using the navigation device is improved for the driver.

  Conventionally, when the navigation device intends to obtain the altitude of the vehicle position, it uses the altitude of a predetermined reference point included in the map information, the travel distance from the reference point, and the road gradient of the road, It is necessary to calculate the altitude of the vehicle position. At that time, in order to accurately calculate the altitude of the vehicle position, the navigation device needs to acquire a road gradient.

For example, in Patent Document 1, a reference acceleration based on a flat road calculated based on an engine output torque is compared with an actual acceleration calculated based on a vehicle speed, and a road gradient is estimated. An example of a vehicle shift control device for changing a shift pattern is disclosed.
JP 2007-183004 A

  However, according to Patent Document 1, the estimation of the road gradient is stopped during the driver's braking operation. This is because the reference acceleration is calculated based on the engine output torque, and when the braking torque is applied by the brake operation, it is difficult to estimate the road gradient. If the reference acceleration is obtained using the braking torque, the road gradient can be estimated, but it is difficult to detect the braking torque at present.

  Further, when the road gradient is estimated using this method, an error occurs in the road gradient estimation result if the vehicle weight varies. In a general sedan type passenger car, when it is considered that the passenger car is used from a baggage for one passenger to a full baggage for four passengers, the vehicle weight may vary from 300 to 400 kg. Furthermore, since the fluctuation of the vehicle weight occurs only by getting on and off a person, it cannot be ignored.

  An object of the present invention is to provide a navigation device capable of continuing calculation of a road gradient even during a brake operation and improving the accuracy of the altitude of the vehicle position calculated using the road gradient. Another object is to provide a road gradient calculation method and an altitude calculation method.

  In order to achieve the above object, a navigation device of the present invention includes a flat road acceleration calculation unit that calculates flat road acceleration in a flat road running state based on an engine output torque of the own vehicle and a vehicle weight of the own vehicle. And an estimated acceleration calculating unit that calculates an estimated acceleration based on the vehicle speed of the host vehicle, and a road gradient calculating unit that calculates a road gradient based on the flat road acceleration and the estimated acceleration. Then, when the brake operation signal of the host vehicle is input, the road gradient calculation unit calculates the road gradient of the road along the road on which the host vehicle travels until the brake operation signal is not input thereafter. Predictive calculation is performed based on at least one of the road gradient calculated before the brake operation signal is input and the change rate of the road gradient.

  Therefore, the navigation device of the present invention integrates the predicted and calculated gradient even when the brake operation signal is input, that is, when the brake operation is performed, to Can be obtained.

  Furthermore, the navigation device of the present invention provides a driving force obtained by time-differentiating an estimated acceleration differential value obtained by time-differentiating the estimated acceleration calculated by the estimated acceleration calculating unit and a driving force of the own vehicle calculated using the engine output torque of the own vehicle. A vehicle weight learning unit that calculates the current vehicle weight based on the differential value and corrects the current vehicle weight based on the vehicle weight calculated in the past is provided.

  Therefore, the navigation device of the present invention can calculate the current vehicle weight with high accuracy. Since the vehicle weight is a parameter used for calculating the flat road acceleration, the accuracy of the road gradient and the altitude of the vehicle position calculated using the flat road acceleration is further improved.

  ADVANTAGE OF THE INVENTION According to this invention, the navigation apparatus which can continue calculation of a road gradient even during brake operation, and can improve the precision of the altitude of the own vehicle position calculated using the road gradient, road A gradient calculation method and an advanced calculation method can be provided.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

<First Embodiment>
FIG. 1 is a diagram showing an example of the configuration of a navigation device according to the first embodiment of the present invention. As shown in FIG. 1, the navigation apparatus 100 includes a flat road acceleration calculation unit 1, an estimated acceleration calculation unit 2, a road gradient calculation unit 3, a road gradient storage unit 4, a gradient change rate calculation unit 5, an altitude calculation unit 6, and a vehicle. It includes functional blocks such as a weight learning unit 7, a vehicle position detection unit 8, a map information storage unit 9, a vehicle surrounding map information acquisition unit 10, and a vehicle position correction unit 11. These functional blocks are functional blocks related to the correction function of the own vehicle position such as road gradient calculation and altitude calculation. In FIG. 1, description of functional blocks for known functions such as route search and route guidance is omitted. ing.

  The navigation device 100 is configured by a computer including a CPU (Central Processing Unit) and a storage device (not shown). The operation of each functional block shown in FIG. 1 is realized by the CPU executing a predetermined program stored in the storage device. Note that the storage device referred to here is configured by a high-speed semiconductor memory or the like, and may include a large-capacity hard disk device or the like.

  Next, with reference to FIG. 1, a schematic operation of each functional block of the navigation device 100 will be described.

The flat road acceleration calculation unit 1 uses information such as engine output torque, engine speed, and vehicle speed input from various sensors via communication means such as an in-vehicle LAN (Local Area Network) to estimate driving force F and flatness. The road acceleration αf is calculated. The estimated driving force F and the flat road acceleration αf can be obtained from the well-known expressions (1) and (2).
F = engine torque × transmission gear ratio / tire diameter (1)
M x αf = F-Rolling resistance-Speed resistance (2)
Here, M is a vehicle weight, and the value learned by the vehicle weight learning unit 7 is used as the vehicle weight.

  The estimated acceleration calculation unit 2 calculates the estimated acceleration αs by differentiating the vehicle speed input from a vehicle speed sensor or the like via communication means such as an in-vehicle LAN. However, simply differentiating the vehicle speed increases noise, so it is preferable to differentiate after appropriately filtering the vehicle speed.

The road gradient calculation unit 3 uses the flat road acceleration αf calculated by the flat road acceleration calculation unit 1 and the estimated acceleration αs calculated by the estimated acceleration calculation unit 2 to calculate and calculate according to the well-known formula (3). The road gradient is stored in the road gradient storage unit 4.
Road gradient = tan θ≈sin θ = (αf−αs) / g (3)
Here, θ is the inclination angle of the road, and g is the acceleration of gravity.

  Further, the road gradient calculation unit 3 inputs ON / OFF information of a brake switch (hereinafter referred to as brake SW) via communication means such as an in-vehicle LAN, and the gradient change rate calculated by the gradient change rate calculation unit 5. Is used to predict the road gradient when the brake SW is ON.

  The vehicle weight learning unit 7 uses the estimated driving force F calculated by the flat road acceleration calculation unit 1 and the acceleration obtained by time-differentiating the vehicle speed input using a communication means such as an in-vehicle LAN, to obtain the current vehicle weight. , And the vehicle weight used in the flat road acceleration calculation unit 1 is corrected.

  The own vehicle position detection unit 8 detects the own vehicle position based on the signal from the GPS, and also combines the vehicle speed input via the communication means such as the in-vehicle LAN, information from the gyro sensor, etc. The vehicle position may be calculated by generating and integrating. Furthermore, the vehicle position may be calculated by combining the vehicle position detected based on the signal from the GPS and the integrated value of the vehicle movement vector.

  The map information storage unit 9 stores road map information including node information, link information, and the like, and the link information includes at least altitude information. The map information storage unit 9 is configured to be included in a storage device that constitutes the navigation device 100. In this case, a hard disk device is used as the storage device, and a CD-ROM, DVD-ROM, or the like is acceptable. A drive device of a portable storage medium may be used. In addition, the navigation device 100 takes in the map information provided from the map information center into the map information storage unit 9 via a portable storage medium such as a CD-ROM or DVD-ROM. You may acquire map information by communication.

  The own vehicle surrounding map information acquisition unit 10 accesses the map information storage unit 9 based on the own vehicle position detected by the own vehicle position detection unit 8 and acquires map information around the own vehicle position.

  The own vehicle position correcting unit 11 compares the altitude calculated by the altitude calculating unit 6 with the altitude acquired by the own vehicle surrounding map information acquiring unit 10 and corrects the own vehicle position.

  Next, with reference to FIG. 2, the content of the whole process in the navigation apparatus 100 of this embodiment, ie, the process from the detection of the own vehicle position to the correction of the own vehicle position including the altitude correction will be described. Here, FIG. 2 is an example of a flowchart showing the entire processing contents of the navigation device 100 according to the present embodiment. This process is repeatedly executed at a predetermined time period.

  First, in a process 300, the CPU of the navigation device 100 (hereinafter simply referred to as a CPU) detects the vehicle position and acquires map information around the vehicle position (the vehicle position detection unit 8 and the vehicle periphery). Process of map information acquisition unit 10). Details of the process 300 will be separately described with reference to FIG.

  Next, in the process 201, the CPU calculates a flat road acceleration αf in a flat road running state (process of the flat road acceleration calculation unit 1). Specifically, the current gear ratio is estimated from the engine speed and the vehicle speed, and the driving force F acting on the vehicle is estimated based on the estimated gear ratio, the engine output torque, and the tire radius. Then, the flat road acceleration αf is calculated from the estimated driving force F, the running resistance on the flat road, and the vehicle weight.

  Next, in the process 202, the CPU calculates the estimated acceleration αs by differentiating the vehicle speed with respect to time (process of the estimated acceleration calculation unit 2). However, since the noise component is large when the vehicle speed is simply differentiated, the calculation value is filtered using a low-pass filter or a change amount limit.

  Next, in the process 203, the CPU calculates the slope of the currently traveling road using the flat road acceleration αf calculated in the process 201 and the estimated acceleration αs calculated in the process 202. Specifically, the difference between the flat road acceleration and the estimated acceleration is calculated, and a road gradient value (unit:%) is obtained by multiplying the acceleration difference by a coefficient.

  However, the gradient obtained by the processing 203 is effective when the brake is not operated (when the brake SW OFF signal is input), but when the brake is operated (the brake SW ON signal). Is invalid).

  Therefore, in the processing 204, processing 205 and processing 206, the CPU classifies cases according to whether or not the brake is operated, and predicts the gradient when the brake is operated (when the brake SW is ON).

  That is, when the brake SW is switched from OFF to ON (YES in process 204), the CPU proceeds to process 400, and when the brake SW continues to be ON (YES in process 205), the process 500 is performed. Proceed to Although details will be described later, in the process 400 and the process 500, the CPU predicts the gradient at that time based on the gradient immediately before the brake SW switches to ON, and further predicts the altitude.

  Further, when the brake SW is switched from ON to OFF (YES in process 206), the process proceeds to process 600. In other cases, that is, when the brake SW continues to be OFF (NO in process 206). The process proceeds to process 207. Although details will be described later, in the process 600, the CPU matches the predicted gradient with the gradient obtained by the process 203 and corrects the altitude. In step 207, the CPU obtains an altitude by integrating the road gradient calculated in step 203.

  The processing 203 to processing 207, processing 400, processing 500, and processing 600 described above correspond to the processing of the road gradient calculation unit 3 and the altitude calculation unit 6.

  Next, in step 208, the CPU compares the altitude calculated in steps 400, 500, 600, and 207 with the altitude obtained from the map information in step 300 to correct the vehicle position ( Process of own vehicle position correction unit 11). Details thereof will be separately described with reference to FIG.

  Next, with reference to FIG. 3, the specific processing content of the process 300 (own vehicle surrounding map information acquisition process) in FIG. 2 is demonstrated. FIG. 3 is an example of a flowchart showing the processing contents of the vehicle surrounding map information acquisition processing.

  First, in the process 301, the CPU detects the own vehicle position using the information on the own vehicle position (latitude, longitude, etc.) received from the GPS, the vehicle speed, the information from the gyro sensor, etc. The map information around the vehicle position is read from the unit 9. Here, reading the information by the CPU means, for example, storing information read from a low-speed storage medium such as a hard disk device in a main memory including a high-speed semiconductor RAM (Random Access Memory). To do.

  Next, in step 303, the CPU performs processing for matching the vehicle position with the map information read in step 302 using the information on the vehicle position detected in step 301. As an example of the matching process, create a mesh on the map, compare the vehicle position (latitude, longitude) with the position of the mesh grid point on the road included in the map, Map matching that matches the position of the vehicle to a certain mesh grid point is common.

  Next, in step 304, the CPU updates the vehicle position according to the result of the matching process executed in step 303. In step 305, the CPU outputs map information based on the updated vehicle position. Here, the output map information includes at least altitude information of a road on which the vehicle is traveling, such as a road on which the vehicle is traveling and a nearby branch road.

  As described above, the CPU obtains map information in the vicinity of the host vehicle by the processing by the host vehicle position detection unit 8 and the host vehicle surrounding map information acquisition unit 10.

  Next, with reference to FIG. 4, the specific processing content of the processing 400 in FIG. 2 (brake corresponding processing 1: road gradient and altitude prediction calculation processing when the brake SW is switched from OFF to ON) will be described. FIG. 4 is an example of a flowchart showing the processing contents of the brake response processing 1.

  First, in the process 401, the CPU calculates the change rate of the road gradient using the road gradient information stored in the road gradient storage unit 4 (the process of the gradient change rate calculation unit 5). As a method of calculating the rate of change of the road gradient, there is a method of differentiating the road gradient with time, but this method is likely to contain a lot of noise in the calculation result. It is desirable to adopt a method in which a slope is obtained by a least square method using a sample and this is used as a change rate.

  Next, in the process 402, the CPU determines whether or not the gradient change rate immediately before the brake SW is turned on is smaller than a predetermined threshold value. If the gradient change rate is small (that is, a slope having a substantially constant gradient: process) If YES in step 402), the process proceeds to step 409. If it is large (that is, a road portion where the slope changes, for example, a road where a flat road without a slope and a slope are connected: NO in step 402), the processing is performed. Go to 403.

  Next, in step 403, the CPU sets the gradient prediction calculation mode when the brake is ON to “prediction mode”, and in step 404 calculates the gradient lower limit value. This slope lower limit value is a value necessary for the limiter processing described later, and is the vehicle surrounding road acquired by the vehicle surrounding map information acquisition unit 10 (on the road on which the vehicle is traveling and the traveling path of the vehicle). If there is a branch road, the road gradient is calculated using the altitude of the branch road where the altitude change is noticeable, and the road gradient with the smallest value in the vicinity of the host vehicle is used as the lower slope limit value. .

  Next, in a process 405, the CPU predicts the road gradient at that time. Specifically, the gradient is continuously changed with the gradient change rate immediately before the brake is turned on, and this is set as the predicted gradient. Further, in the process 406, the CPU determines whether or not the predicted gradient obtained in the process 405 exceeds the gradient lower limit value obtained in the process 404. If the gradient lower limit value is exceeded, a limiter process is performed in the process 407. (In other words, the gradient lower limit value is substituted for the predicted gradient) and the process proceeds to process 408. If the gradient lower limit value is not exceeded, the process proceeds to process 408 as it is.

  In step 409, the CPU sets the gradient prediction calculation mode when the brake is ON to “hold mode”. In step 410, the CPU performs gradient hold processing, and proceeds to step 408. This gradient hold process is a process of continuously using the gradient immediately before the brake is turned on.

  Finally, in step 408, the CPU integrates the gradient calculated in step 405, step 407 or step 410 to calculate the altitude, and returns to the original step.

  Next, with reference to FIG. 5, the specific processing contents of the process 500 in FIG. 2 (brake handling process 2: road gradient and altitude prediction calculation process when the brake SW is ON) will be described. . FIG. 5 is an example of a flowchart showing the processing contents of the brake handling process 2.

  First, in the process 501, the CPU determines whether or not the calculation mode when the brake is ON is the “prediction mode”. If the calculation mode is the “prediction mode”, the CPU proceeds to the process 405, and in the case of the “hold mode” ( In other words, if it is not “prediction mode”, the process proceeds to process 410. In addition, since the process after process 405 and the process 410 are the same as the process demonstrated in FIG. 4, the description is omitted.

  Next, with reference to FIG. 6, the specific processing content of the process 600 in FIG. 2 (Brake corresponding process 3: Road gradient and altitude prediction calculation process when the brake SW is switched from ON to OFF) will be described. FIG. 6 is an example of a flowchart showing the processing contents of the brake response processing 3.

First, in the process 601, the CPU calculates the gradient (that is, the gradient predicted by the process when the brake SW is ON) s ₁ calculated in the previous calculation cycle, and the process 203 in the current calculation cycle (see FIG. 2). in the calculated slope (i.e., the calculated gradient when the brake SW is turned OFF from oN) from s ₂ Prefecture, calculates the gradient difference s (= s ₂ -s _1), the gradient difference s is given its If it is greater than or equal to the positive threshold s ₀ , the process proceeds to process 602, and if the gradient difference s is less than the predetermined positive threshold s ₀ , the process proceeds to process 604. It is assumed that s ₂ and s ₁ are positive when the slope is upward and negative when the slope is downward.

Next, in the process 602, the CPU calculates an altitude correction amount for returning the altitude predicted to be low to the altitude close to the actual level (adding the deficiency) because the predicted slope s ₁ is smaller than the actual _one . This specific method will be described in detail with reference to FIGS.

Further, in the process 604, the CPU uses the gradient difference s (= s ₂ −s ₁ ) calculated in the process 601, and if the gradient difference is equal to or less than a predetermined negative threshold (−s ₀ ), the process is performed. Proceeding to 605, if the difference is not less than or equal to the predetermined negative threshold (−s ₀ ), proceeding to processing 603. Note that s ₀ is positive.

Next, in the process 605, the CPU calculates an altitude correction amount for returning the altitude predicted to be high to the altitude close to the actual level (subtracting the excess amount) because the predicted gradient s ₁ is larger than the actual _one . This specific method will be described in detail with reference to FIGS.

  Finally, in the process 603, the CPU calculates the altitude using the altitude correction amount calculated in the process 602 or 605 or the gradient calculated in the process 203 in FIG. 2, and returns to the original process.

  Next, with reference to FIG. 7 to FIG. 9, the above-described gradient calculation and altitude calculation processes are applied to an actual road condition.

  Here, in FIG. 7 to FIG. 9, a road composed of a highway 701 at a high position, a general road 703 at a low position, and an attachment road 702 (branch road) connecting the two is assumed, and the vehicle It is assumed that 700 enters the attachment road 702 from the highway 701 and joins the general road 703.

  FIG. 7 is a diagram illustrating an example of changes in actual altitude and gradient when the vehicle 700 travels in the order of the highway 701 → the attachment road 702 → the general road 703. As shown in FIG. 7, when the vehicle 700 branches from the highway 701 to the attachment road 702 and reaches the point A, the attachment road 702 is a downhill, so that the gradient (solid line 720) is large in the negative direction. At the same time, the altitude (solid line 710) begins to drop. Thereafter, the gradient (solid line 720) stabilizes at a certain value, and then gradually decreases and approaches 0, and the altitude (solid line 710) starts to converge to the same altitude as the general road 703. When reaching point B, the gradient (solid line 720) is 0 and the altitude (solid line 710) is the same as that of the general road 703, and the vehicle 700 merges from the attachment road 702 to the general road 703.

  FIG. 8 and FIG. 9 show examples of changes in altitude and gradient calculated when the vehicle 700 travels in the order of highway 701 → attached road 702 → general road 703 when the brake operation timing is different. It is the figure shown together with the state of SW. 8 and 9, the solid line 800 and the solid line 900 indicate the state of the brake SW, the solid line 810 and the solid line 910 indicate the calculation altitude, and the solid line 820 and the solid line 920 indicate the road gradient calculated in the process 203 of FIG. With respect to (calculation gradient), a broken line 821 and a broken line 921 indicate predicted gradients calculated by the processing 405 to the processing 407 in FIG. 4 or FIG.

  First, FIG. 8 will be described. After the vehicle 700 branches from the highway 701 to the attachment road 702 and passes through the point A, when the brake operation of the vehicle 700 is performed at the point C, the brake SW is turned on, but the vehicle 700 has reached this point C. Sometimes the slope (solid line 820) has already begun to change. Since the braking force works when the brake SW is turned on, it is impossible to calculate a normal gradient in the process 203 shown in FIG. Therefore, the CPU uses the gradient change rate calculated immediately before the brake SW is turned on, and obtains a predicted gradient (broken line 821) according to the gradient change rate.

  Next, the CPU calculates the gradient lower limit value Sd using the altitude information of the attachment road 702 obtained from the map information by the process 404 in FIG. 4, and uses the lower limit value Sd for the predicted gradient (broken line 821). Apply limiter processing. After that, when the brake SW is turned off at the point B, the calculation result of the road gradient by the process 203 in FIG. 2 is normally output. In this example, the flat road gradient (gradient 0) is output. Is done.

  At this time, at the point B, a large step is generated between the predicted gradient (broken line 821) and the calculation gradient by the process 203. Therefore, when the CPU calculates the altitude using the predicted gradient (broken line 821), the calculation result of the altitude becomes lower than the actual altitude (the altitude is lowered too much) as indicated by the solid line 810. Therefore, in order to correct the difference in altitude, the CPU gradually changes the gradient as shown by a dotted line 822 to obtain the altitude corresponding to the area of the area indicated by the hatched area 823 obtained when the gradient is connected to the solid line 820 ( If the gradient is integrated by distance, it becomes altitude) and the calculated altitude (solid line 810) is added.

  However, when the altitude corresponding to the area of the hatched area 823 is added to the calculated altitude (solid line 810), the amount of change or the low-pass filter calculation is applied to prevent the altitude of calculation (solid line 810) from changing suddenly. . In that case, the calculated altitude (solid line 810) changes smoothly in the vicinity of the point B like a region 811 surrounded by a dotted line.

  Next, FIG. 9 will be described. The brake operation is performed at a point C where the vehicle 700 starts to branch from the highway 701 to the attachment road 702, and the brake SW is turned on. Since there is no change in gradient at this point C, the predicted gradient is held at the previous value and is represented by a broken line 921. Thereafter, when the brake SW is turned off at the point D when the vehicle 700 starts passing through the point A and descends, the calculation result of the road gradient by the process 203 in FIG. 2 is normally output.

  At this time, a step is generated between the predicted gradient (broken line 921) and the calculated gradient (solid line 920) by the process 203. Therefore, when the CPU calculates the altitude using the predicted gradient (broken line 921), the calculation result of the altitude becomes higher than the actual altitude as indicated by the solid line 910. Therefore, in order to correct the height difference, the CPU gradually changes the gradient as shown by the dotted line 922 to obtain the height corresponding to the area of the area indicated by the hatched area 923 obtained when the slope is connected to the solid line 920. , Subtract from the calculation altitude (solid line 910).

  However, when the altitude corresponding to the area of the hatched area 923 is subtracted from the calculated altitude (solid line 910), a change amount limit or a low-pass filter calculation is performed so that the calculated altitude (solid line 910) does not change suddenly. In that case, the calculated altitude (solid line 910) changes smoothly in the vicinity of the point D as in a region 911 surrounded by a dotted line.

  Also, in FIG. 9, when the vehicle 700 is traveling on the attachment road 702 and a brake operation is performed at the point E and the brake SW is turned on, the predicted gradient (broken line 921) is calculated in the same manner as the broken line 821 described in FIG. . Further, when the brake SW is turned off at the point F, the calculation result of the road gradient by the processing 203 in FIG. 2 is normally output again.

  At this time, a difference occurs between the predicted gradient (broken line 921) and the calculation gradient (solid line 920) by the process 203 at the point F, and a further difference in altitude caused by the difference is corrected. In the same manner as described above, the height corresponding to the area of the hatched region 927 is added to the calculated height (solid line 910).

  However, when the altitude corresponding to the area of the hatched area 927 is added to the calculated altitude (solid line 910), a change amount restriction or a low-pass filter calculation is performed so that the calculated altitude (solid line 910) does not change suddenly. . In this case, the calculated altitude (solid line 910) changes smoothly like a region 912 surrounded by dotted lines near points B and F.

  As described above, even in a state where the road gradient cannot be normally calculated by the processing 203 in FIG. 2 while the brake is being operated, the road during the brake operation can be obtained by using the road gradient immediately before the brake operation and the rate of change thereof. The gradient can be predicted. In addition, by calculating the altitude using the predicted gradient information, it is possible to output sufficient information for performing map matching.

  Next, with reference to FIG. 10, the vehicle position correction process by the vehicle position correction unit 11 will be described. Here, FIG. 10 is a diagram showing an example of the movement of the car mark 1000 displayed on the display screen of the navigation device 100 when the vehicle travels in the order of the highway 701 ⇒ the attachment road 702 ⇒ the general road 703. is there.

  In the example of FIG. 10, the vehicle branches from the highway 701 to the attachment road 702 and enters the general road 703. However, even if the vehicle branches to the attachment road 702 on the display screen of the navigation device 100, the current position of the vehicle The car mark 1000 indicating that does not immediately branch and remains on the highway 701 for a while (point B). This is a phenomenon that occurs because the highway 701 is a priority road and it is difficult to detect minute movements of the vehicle in the lateral direction.

  However, when the vehicle further travels on the attachment road 702, the altitude difference from the highway 701 gradually increases. In the navigation device 100 according to the present embodiment, the CPU matches the vehicle position to the attachment road 702 when the vehicle height correction unit 11 processes the vehicle height difference to a predetermined height difference or more (point C). Let Therefore, since the display position of the car mark 1000 moves to the attachment road 702 side at the point C, the vehicle coincides with the road on which the vehicle is actually traveling. Thereafter, the car mark 1000 sequentially moves from the point D on the attachment road 702 to the point F on the general road 703 as the vehicle travels.

  That is, when the CPU detects a branch road such as the attachment road 702 in the own vehicle position correction process, the CPU compares the calculated altitude information of the own vehicle with the altitude information of the expressway 701 that is running. When the difference in altitude exceeds a predetermined altitude difference, it is recognized that the vehicle has deviated from the expressway 701, and the display position of the car mark 1000 is moved from the expressway 701 to a branch road such as the attachment road 702. .

  As described above, according to the present embodiment, even when the brake is operated and the brake SW is turned on, the altitude of the host vehicle can be calculated smoothly. As a result, it is possible to correct the position of the vehicle on the side of the running road even on a branch road such as a highway attachment road.

Next, processing contents of the vehicle weight learning unit 7 will be described. The equation of motion of the running vehicle is expressed by the following equation (4) by using the vehicle weight M, acceleration α, driving force F acting on the vehicle, rolling resistance Rr, speed resistance Rv, and gradient resistance Ri.
M × α = F−Rr−Rv−Ri (4)
Here, the driving force F is an estimated driving force obtained by calculating the engine output torque and the like, and the acceleration α is an estimated acceleration obtained by differentiating the vehicle speed.

  Here, it is considered to eliminate the term of resistance by differentiating both sides of the equation (4) with respect to time. First, since the rolling resistance Rr does not change with time, the time derivative of this term is zero. Further, the speed resistance Rv can be ignored as it does not cause a large speed difference for a short time. Similarly, regarding the gradient resistance Ri, it can be ignored that a large difference does not occur for a short time. Then, a relationship of M × (dα / dt) = dF / dt is obtained.

  That is, the current vehicle weight M can be calculated from the ratio between the differential value of the driving force F and the differential value of the acceleration α. However, since noise increases when both are differentiated with respect to time, it is desirable to perform calculation by appropriately filtering.

  Therefore, as one method of calculating the vehicle weight M in consideration of filtering, when the vehicle accelerates / decelerates under a predetermined condition, the temporal change of the driving force and acceleration is stored in the storage device, and the driving force and the acceleration are calculated by the least square method. There is a method of obtaining the slope of each acceleration and taking the ratio.

  FIG. 11 is a diagram showing an example of the relationship between the estimated driving force and the estimated acceleration obtained when the vehicle has accelerated and decelerated in the past. In FIG. 11, the broken line 1101 is a straight line that approximates the estimated acceleration to the least square, and the alternate long and short dash line 1102 is a straight line that approximates the estimated driving force to the least square. The current vehicle weight can be estimated from the ratio of the slopes of the two. As described above, the estimated driving force is an amount calculated based on the gear ratio, the engine output torque, and the tire radius, and the estimated acceleration is an amount calculated from the vehicle speed.

  Therefore, when a predetermined vehicle speed or a predetermined estimated acceleration is obtained as the processing of the vehicle weight learning unit 7, the CPU stores the estimated acceleration and the time variation of the estimated driving force at that time in the storage device, and thereafter The slope of each of the estimated acceleration and the estimated driving force is calculated by least square approximation of the time change, and the vehicle weight of the host vehicle is estimated by calculating the ratio of the two. The CPU repeatedly performs the estimation of the vehicle weight as described above as needed to learn the vehicle weight of the own vehicle.

  In consideration of the terms neglected when differentiating both sides of the equation (4), vehicle weight estimation learning is not performed when acceleration / deceleration is abrupt or when the gradient change is significant. Even when the brake SW is OFF, normal estimated driving force cannot be obtained, so learning is not performed. That is, in order for the CPU to learn the vehicle weight, the slope of acceleration is not less than the first predetermined value and not more than the second predetermined value, the vehicle speed is not less than the first predetermined speed and not more than the second predetermined speed, and the brake SW is There are conditions such as OFF. Therefore, the CPU learns the vehicle weight after confirming that the traveling state of the vehicle satisfies these conditions.

  As described above, according to the present embodiment, the calculation of the road gradient is continued even during the driver's braking operation, and the vehicle weight is appropriately calculated and the learning is performed. The error can be suppressed, and the vehicle position can be output with high accuracy. Furthermore, in the conventional system, there is a method of estimating the gradient using an acceleration sensor, but in this embodiment, since the acceleration sensor is unnecessary, an effect of reducing the device manufacturing cost can be expected.

<Second Embodiment>
FIG. 12 is a diagram showing an example of the configuration of the navigation device according to the second embodiment of the present invention. As shown in FIG. 12, in the altitude calculation unit 6a of the navigation device 100a according to this embodiment, the altitude calculation unit 6 (see FIG. 1) of the navigation device 100 according to the first embodiment includes the own vehicle altitude storage unit. 12 and the vehicle altitude correction unit 13 are added. In the following description, the same components as those in the first embodiment are denoted by the same reference numerals, and detailed description thereof is omitted.

  The own vehicle altitude storage unit 12 stores the altitude of the own vehicle obtained by integrating the road gradient calculated by the road gradient calculating unit 3 in time series. The own vehicle altitude correction unit 13 corrects the altitude of the own vehicle stored in the own vehicle altitude storage unit 12 to a value close to the actual road shape.

  Next, with reference to FIG. 13, a calculation method of the altitude calculation unit 6 in the second embodiment will be described. FIG. 13 illustrates an example of changes in altitude and gradient that are calculated when the vehicle 700 travels in the order of the highway 701 → the attachment road 702 → the general road 703 in the same situation as the road configuration described in FIG. It is the figure shown with the state of.

  In FIG. 13, the solid line 800 indicates the state of the brake SW, the solid line 1300 and the dotted line 1301 indicate the altitude calculation results, the solid line 820 indicates the road gradient calculated by the process 203 in FIG. 2, and the broken line 821 indicates the FIG. 5 shows the predicted gradient calculated by the process 405 to the process 407.

  After the vehicle 700 branches from the highway 701 to the attachment road 702 and passes through the point A, when the brake operation of the vehicle 700 is performed at the point C, the brake SW is turned on, but the vehicle 700 has reached this point C. Sometimes the slope (solid line 820) has already begun to change. Since the braking force works when the brake SW is turned on, it is impossible to calculate a normal gradient in the process 203 shown in FIG. Therefore, the CPU uses the gradient change rate calculated immediately before the brake SW is turned on, and obtains a predicted gradient (broken line 821) according to the gradient change rate.

  Next, the CPU calculates the gradient lower limit value Sd using the altitude information of the attachment road 702 obtained from the map information by the process 404 in FIG. 4, and uses the lower limit value Sd for the predicted gradient (broken line 821). Apply limiter processing. Thereafter, when the brake SW is turned off at the point B, the calculation result of the road gradient by the processing 203 in FIG. 2 is normally output, and here, the gradient of the flat road (gradient 0) is output.

  At this time, at the point B, a step is generated between the predicted gradient (broken line 821) and the calculation gradient (solid line 820) by the process 203. Therefore, when the CPU calculates the altitude using the prediction gradient (broken line 821), the calculation result of the altitude is lower than the actual altitude (the altitude is lowered too much) as shown by the solid line 1300. Therefore, in order to correct the difference in altitude, the CPU gradually changes the gradient as shown by a dotted line 822 to obtain the altitude corresponding to the area of the area indicated by the hatched area 823 obtained when the gradient is connected to the solid line 820 ( If the gradient is integrated with the distance, the altitude is added to the calculation altitude.

  In that case, as shown by the solid line 1300, the calculated altitude has a step at point B, which may cause a problem when the vehicle position correction unit 11 corrects the vehicle position. Therefore, as indicated by a dotted line 1301, the CPU corrects the calculated altitude so that it gradually converges from the point D, which is the starting point of the dotted line 822, to the altitude Hg of the general road 703.

  Specifically, the CPU stores the solid line 1300 in the host vehicle altitude storage unit 12, and as a process of the host vehicle altitude correction unit 13, for example, from a point D higher than the altitude Hg of the general road 703 by a predetermined height Assuming that the gradient changes to a point B as indicated by a dotted line 822, an altitude such as a dotted line 1301 is obtained by calculating the altitude. However, in reality, at the time of calculating the gradient and altitude, it is impossible to predict where the point B where the brake SW is turned off will be, but here, the rate of change of the gradient is as indicated by the dotted line 822. It is assumed that the point is a constant value, and the point where the gradient is 0 is determined as the point B.

  As described above, even in a state where the road gradient cannot be normally calculated by the processing 203 in FIG. 2 while the brake is being operated, the road during the brake operation can be obtained by using the road gradient immediately before the brake operation and the rate of change thereof. The gradient can be predicted. In addition, by calculating the altitude using the predicted gradient information, it is possible to output sufficient information for performing map matching.

  Next, with reference to FIG. 14, the vehicle position correction process by the vehicle position correction unit 11 will be described. Here, FIG. 14 is a diagram illustrating an example of a state of movement of the car mark 1000 on the display screen of the navigation device 100a when the vehicle travels in the order of the highway 701 → the attachment road 702 → the general road 703. Further, FIG. 14 also shows a high-level calculation result.

  In the example of FIG. 14, the vehicle branches from the highway 701 to the attachment road 702 and enters the general road 703. However, even if the vehicle branches to the attachment road 702 on the display screen of the navigation device 100a, the current position of the vehicle The car mark 1000 indicating that does not immediately branch and remains on the highway 701 for a while (point B). This is a phenomenon that occurs because the highway 701 is a priority road and it is difficult to detect minute movements of the vehicle in the lateral direction.

  However, when the vehicle further travels on the attachment road 702, the altitude difference from the highway 701 gradually increases. In the navigation device 100a of the present embodiment, the CPU attaches the vehicle position when the vehicle position correction unit 11 performs processing of the vehicle position correction unit 11 and the height difference becomes equal to or greater than a predetermined height difference (arrow 1402) (point C). Match with road 702. Therefore, since the display position of the car mark 1000 moves to the attachment road 702 side at the point C, the vehicle coincides with the road on which the vehicle is actually traveling. Thereafter, the car mark 1000 sequentially moves from the point D on the attachment road 702 to the point F on the general road 703 as the vehicle travels.

  As described above, according to the present embodiment, even when the brake is operated and the brake SW is turned on, the altitude of the host vehicle can be calculated smoothly. As a result, it is possible to correct the position of the vehicle on the side of the running road even on a branch road such as a highway attachment road.

  Also, when map matching the vehicle position, rather than using only the altitude, the shape is compared with the shape of the altitude information (solid line 1400) shown in FIG. 14 and the altitude information of the map information, A method of matching a highly correlated road is also conceivable. In the case of a road with many undulations, an effect can be expected to improve the matching accuracy.

<Third Embodiment>
FIG. 15 is a diagram illustrating an example of a configuration of a vehicle weight learning unit included in the navigation device according to the third embodiment of the present invention. The configuration of the navigation device 100b according to the third embodiment is different from the configuration of the vehicle weight learning unit 7 in the navigation device 100 (100a) according to the first (second) embodiment in the configuration of the vehicle weight learning unit 7b. Except for this, the configuration is the same as that of the navigation device 100 (100a) according to the first (second) embodiment. Therefore, in the following description, the same components as those in the first (second) embodiment are denoted by the same reference numerals, and detailed description thereof is omitted.

  As shown in FIG. 15, the configuration of the vehicle weight learning unit 7b according to this embodiment is the same as the configuration of the vehicle weight learning unit 7 in the case of the first (second) embodiment (see FIG. 1 (FIG. 12)). The seating sensor information acquisition unit 14, the door opening / closing information acquisition unit 15, and the trunk room opening / closing information acquisition unit 16 are added.

  The seating sensor information acquisition unit 14 is installed in the seat of the vehicle, acquires information from the seating sensor that detects that a person is seated via a communication means such as an in-vehicle LAN, and further gets on the vehicle. Get the number of passengers.

  The door opening / closing information acquisition unit 15 acquires information from a sensor that detects the opening / closing state of the door of the vehicle via communication means such as an in-vehicle LAN, and stores door opening / closing date / time information and the like.

  The trunk room opening / closing information acquisition unit 16 acquires information from a sensor for detecting the opening / closing state of the trunk room of the vehicle via a communication means such as an in-vehicle LAN, and stores the opening / closing date / time information of the trunk room.

  Then, with reference to FIG. 16, the calculation method in the vehicle weight learning part 7b which concerns on 3rd Embodiment is demonstrated. FIG. 16 is an example of a flowchart showing the processing contents of the vehicle weight learning unit 7b.

  First, in a process 1601, the CPU calculates an estimated vehicle weight (current vehicle weight). That is, the current vehicle weight can be acquired based on an equation obtained by differentiating both sides of the equation (4) as described in the first embodiment. Specifically, as described with reference to FIG. 11, when a predetermined vehicle speed or a predetermined estimated acceleration is obtained, the inclination is calculated by least square approximation of the time change for each of the estimated acceleration and the estimated driving force, Furthermore, the vehicle weight of the own vehicle is estimated by calculating the ratio between the two.

  Next, in the process 1602, the CPU determines whether or not there is a change in the number of passengers acquired by the seating sensor information acquisition unit 14, and if there is a change in the number of passengers, the CPU proceeds to a process 1603 and there is no change. In the case, the process proceeds to process 1604.

  Next, the CPU calculates a reference vehicle weight in processing 1603 and proceeds to processing 1604. Here, the reference vehicle weight is the “vehicle weight (filled with necessary liquids such as fuel full, lubricating oil / cooling water, etc.) and loaded with spare tires and tools, as described in car catalogs. ) ”Plus the weight of passengers. Normally, one passenger is calculated at 55kg. For example, when two people are on a vehicle having a vehicle weight of 1500 kg, the reference vehicle weight is 1500 + 55 × 2 = 1610 kg. However, the weight per passenger can be set to other than 55 kg, and when the weight can be estimated by the seating sensor, the estimated weight may be adopted. In that case, it is possible to increase the accuracy of the vehicle weight.

  Next, in the process 1604, the CPU calculates the estimated vehicle weight or the reference vehicle weight from the information acquired by the door opening / closing information acquisition unit 15 or the trunk room opening / closing information acquisition unit 16, and then calculates the door or trunk room within a predetermined time. It is determined whether or not the door or the trunk room is opened or closed. If the door or the trunk room is opened or closed, the process proceeds to process 1605;

  Next, in processing 1605 and processing 1607, the CPU sets parameters for learning the vehicle weight. In other words, if it is determined in the process 1604 that the door or the trunk room has been opened and closed within a predetermined time, it is highly likely that the number of passengers has been changed or luggage has been taken in and out, and the vehicle weight may have changed significantly. Because of the high nature, the CPU sets a parameter that prioritizes the learning time in order to shorten the learning convergence time in processing 1605. On the other hand, if it is determined in process 1604 that the door or the trunk room has not been opened or closed within a predetermined time, it is considered that the change in the vehicle weight is small. Therefore, in process 1607, a parameter giving priority to learning accuracy is set. . The parameters will be described in the processing 1606.

Next, in process 1606, the CPU calculates a vehicle weight learning value. The vehicle weight learning value ML is expressed by the following equation (5) using the previous value MLz of the vehicle weight learning value, the estimated vehicle weight (current vehicle weight) M, and the learning coefficient A (0 ≦ A ≦ 1). Is done.
ML = A × MLz + (1−A) × M (5)

  Here, the learning coefficient A is a parameter that affects the learning time and the learning accuracy. If the value of A is increased, it takes time to converge, but it is strong against fluctuation (variation), and conversely, the value of A is decreased. Then, there is a characteristic that it converges quickly but weakens against fluctuations. In other words, the parameter that prioritizes the learning time set in the process 1605 may reduce the value of A, and the parameter that prioritizes the learning accuracy set in the process 1607 may increase the value of A.

If it is determined that the number of passengers has changed in process 1602 and the reference vehicle weight MB is calculated in process 1603, the reference vehicle weight MB is set to the previous value MLz of the vehicle weight learning value in equation (5). The vehicle weight learning value ML is calculated using the replaced equation (6).
ML = A * MB + (1-A) * M (6)

  As described above, when it is predicted that there has been a large change in the vehicle weight using the information of the seating sensor, the door opening / closing sensor, and the trunk room opening / closing sensor, the vehicle weight learning condition (parameter: learning coefficient A ) Can be kept in balance between learning time and learning accuracy. This makes it possible to always keep the latest value while learning the vehicle weight information that is indispensable for the calculation of the road gradient.

The figure which showed the example of the structure of the navigation apparatus which concerns on the 1st Embodiment of this invention. The example of the flowchart which shows the processing content of the whole navigation apparatus which concerns on the 1st Embodiment of this invention. The example of the flowchart which shows the processing content of the own vehicle periphery map information acquisition process. The example of the flowchart which shows the processing content of the brake corresponding | compatible process 1. FIG. The example of the flowchart which shows the processing content of the brake corresponding | compatible process 2. FIG. The example of the flowchart which shows the processing content of the brake corresponding | compatible process 3. FIG. The figure which showed the example of the change of the actual height and gradient when a vehicle drive | worked in the order of the expressway-> attachment road-> general road. The figure which showed the 1st example of the change of the altitude and the gradient which are calculated when a vehicle drive | works in order of a highway-> attachment road-> general road. The figure which showed the 2nd example of the change of the altitude and the gradient which are calculated when a vehicle drive | works in order of highway-> attachment road-> general road. The figure which showed the example of the mode of the movement of the car mark displayed on the display screen of a navigation apparatus, when a vehicle drive | works in order of a highway-> attachment road-> general road. The figure which showed the example of the relationship between the estimated driving force and estimated acceleration which were obtained when the vehicle accelerated and decelerated in the past. The figure which showed the example of the structure of the navigation apparatus which concerns on the 2nd Embodiment of this invention. The figure which showed the example of the change of the altitude and the gradient calculated when the vehicle drive | worked in the order of the highway ⇒ attachment road ⇒ general road in the same situation as the road composition of Drawing 7 with the state of brake SW. The figure which showed the example of the mode of the movement of the car mark displayed on the display screen of a navigation apparatus, when the vehicle drive | worked in order of the highway-> attachment road-> general road in the 2nd Embodiment of this invention. The figure which showed the example of a structure of the vehicle weight learning part contained in the navigation apparatus which concerns on the 3rd Embodiment of this invention. The example of the flowchart which shows the processing content of the vehicle weight learning part which concerns on the 3rd Embodiment of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Flat road acceleration calculating part 2 Estimated acceleration calculating part 3 Road gradient calculating part 4 Road gradient memory | storage part 5 Gradient change rate calculating part 6, 6a Altitude calculating part 7, 7b Vehicle weight learning part 8 Own vehicle position detection part 9 Map information storage Unit 10 Own vehicle periphery map information acquisition unit 11 Own vehicle position correction unit 12 Own vehicle altitude storage unit 13 Own vehicle altitude correction unit 14 Seating sensor information acquisition unit 15 Door opening / closing information acquisition unit 16 Trunk room opening / closing information acquisition unit 100, 100a, 100b Navigation device 700 Vehicle 701 Expressway 702 Attached road 703 General road 1000 Car mark

Claims (13)

A navigation device that includes a position detection unit of the own vehicle, and displays the own vehicle position detected by the position detection unit and a road map including the own vehicle position on a display device,
A flat road acceleration calculation unit for calculating a flat road acceleration in a flat road running state based on the engine output torque of the own vehicle and the vehicle weight of the own vehicle;
An estimated acceleration calculator that calculates an estimated acceleration based on the vehicle speed of the host vehicle;
A road gradient calculation unit that calculates a road gradient based on the flat road acceleration and the estimated acceleration,
When the brake operation signal of the host vehicle is input, the road gradient calculation unit calculates the road gradient of the road along the road on which the host vehicle travels until the brake operation signal is not input thereafter. A navigation apparatus that performs prediction calculation based on at least one of a road gradient calculated before the brake operation signal is input and a change rate of the road gradient. The road gradient calculation unit predicts the road gradient along the road on which the vehicle travels, and when the predicted road gradient exceeds a predetermined threshold, the brake operation signal is input at the latest after that time. The navigation device according to claim 1, wherein the road gradient predicted by the road gradient calculation unit is fixed to the predetermined threshold until it disappears. The navigation apparatus according to claim 1, further comprising an altitude calculation unit that calculates an altitude of the vehicle position based on the road gradient calculated by the road gradient calculation unit. The altitude calculation unit calculates the altitude along the road on which the host vehicle travels, and the calculated altitude has a predetermined relationship with an altitude at a point where the slope of the road ahead of the host vehicle is zero. After that, the road gradient predicted by the road gradient calculation unit is used as the road gradient used for the altitude calculation until the brake operation signal is not input thereafter. The navigation device according to claim 3. When the altitude difference between the altitude of the road on which the vehicle position detected by the position detecting means is map-matched and the altitude calculated by the altitude calculating unit is larger than a predetermined altitude difference, the road With reference to the map, the other road that branches from the rear of the own vehicle position of the road where the own vehicle position is map-matched is extracted, and is calculated by the altitude and the altitude calculation unit of the extracted other road A vehicle position correction unit that remap-maps the vehicle position to the other road when the difference in height from the vehicle height is smaller than the predetermined height difference is further provided. 3. The navigation device according to 3. Based on the estimated acceleration differential value obtained by time differentiation of the estimated acceleration calculated by the estimated acceleration calculation unit, and the driving force differential value obtained by time differentiation of the driving force of the own vehicle calculated using the engine output torque of the own vehicle. The navigation device according to claim 1, further comprising a vehicle weight learning unit that calculates a current vehicle weight and corrects the current vehicle weight based on a vehicle weight calculated in the past. The vehicle weight learning unit calculates a reference vehicle weight based on the number of passengers input from a seating sensor that detects the seating of a person on the seat of the host vehicle before learning the vehicle weight. The navigation device according to claim 6. When the vehicle weight learning unit detects an opening / closing signal from at least one of a door opening / closing sensor and a trunk room opening / closing sensor of the host vehicle before starting learning of the vehicle weight, the vehicle weight learning unit sets a learning condition for learning the vehicle weight. The navigation device according to claim 6, wherein switching is performed so as to converge quickly. A road gradient prediction calculation method in a navigation device that includes a position detection unit of the own vehicle, and displays the own vehicle position detected by the position detection unit and a road map including the own vehicle position on a display device,
The navigation device includes:
A flat road acceleration calculation unit for calculating a flat road acceleration in a flat road running state based on the engine output torque of the own vehicle and the vehicle weight of the own vehicle;
An estimated acceleration calculator that calculates an estimated acceleration based on the vehicle speed of the host vehicle;
A road gradient calculation unit that calculates a road gradient based on the flat road acceleration and the estimated acceleration,
When the brake operation signal of the host vehicle is input, the road gradient calculating unit calculates the road gradient of the road along the road on which the host vehicle travels until the brake operation signal is not input thereafter. A road gradient prediction calculation method comprising: performing prediction calculation based on at least one of a road gradient calculated before the brake operation signal is input and a change rate of the road gradient. The road gradient calculation unit predicts the road gradient along the road on which the vehicle travels, and when the predicted road gradient exceeds a predetermined threshold, the brake operation signal is input at the latest after that time. The navigation method according to claim 9, wherein the road gradient predicted by the road gradient calculation unit is fixed to the predetermined threshold until it disappears. An altitude calculation method in a navigation device that includes a position detection means of the own vehicle, and displays the position of the own vehicle detected by the position detection means and a road map including the position of the own vehicle on a display device,
The navigation device includes:
A flat road acceleration calculation unit for calculating a flat road acceleration in a flat road running state based on the engine output torque of the own vehicle and the vehicle weight of the own vehicle;
An estimated acceleration calculator that calculates an estimated acceleration based on the vehicle speed of the host vehicle;
A road gradient calculation unit for calculating a road gradient based on the flat road acceleration and the estimated acceleration;
An altitude calculation unit that calculates the altitude of the vehicle position based on the road gradient calculated by the road gradient calculation unit;
With
When the brake operation signal of the host vehicle is input, the road gradient calculating unit calculates the road gradient of the road along the road on which the host vehicle travels until the brake operation signal is not input thereafter. An altitude calculation method comprising: performing a prediction calculation based on at least one of a road gradient calculated before the brake operation signal is input and a change rate of the road gradient. The altitude calculation unit calculates the altitude along the road on which the host vehicle travels, and the calculated altitude has a predetermined relationship with an altitude at a point where the slope of the road ahead of the host vehicle is zero. After that, the road gradient predicted by the road gradient calculation unit is used as the road gradient used for the altitude calculation until the brake operation signal is not input thereafter. The advanced calculation method according to claim 11. The navigation device includes:
When the altitude difference between the altitude of the road on which the vehicle position detected by the position detecting means is map-matched and the altitude calculated by the altitude calculating unit is larger than a predetermined altitude difference, the road With reference to the map, the other road that branches from the rear of the own vehicle position of the road where the own vehicle position is map-matched is extracted, and is calculated by the altitude and the altitude calculation unit of the extracted other road The altitude calculation method according to claim 12, wherein when the difference in altitude with respect to the altitude is smaller than the predetermined altitude difference, the vehicle position is re-map-matched with the other road.

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