JP2001165677A - Road surface freeze forecasting device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To forecast freeze appropriately in accordance with the situation in respective places by a simple structure. SOLUTION: Turning points or freeze accelerating factors existing in a definite area relative to an own vehicle position are extracted from map data (S10, 20), and open-air temperature is detected as a basic factor of freezing (S40). The degree of freezing is forecast by using the contributing level g(t) to freezing of the open-air temperature (t) and the contributing levels C1, C2, etc., to freezing of the respective turning points (S50). Forecast of freeze is performed not by being based on the amount of solar radiation or by previously recording the occurrence of freezing together with its date in respective areas as in the customary official reports but by being based on the open-air temperature as the basic factor and on the specific freeze accelerating factors. Because the freeze accelerating factors can be extracted from map data, typical map data used in navigation processing can be used as it is.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus for predicting freezing of a road surface.

[0002]

2. Description of the Related Art Driver assistance systems such as a navigation system and an alarm system have been developed to improve safety and comfort during driving of a vehicle. Generally, the main driver support system often uses high-performance and expensive equipment to realize the support function. Therefore, for example, in the case of a navigation system, it is preferable to realize not only the originally intended support function such as route guidance, but also a new support function using a part of the system. In other words, it is disadvantageous in terms of mounting space to mount devices completely separately in order to realize another support function, which naturally causes a reduction in cost performance.
In particular, map data having advanced information is used for route guidance in a navigation system, and it is expected that this map data is used for driver assistance other than route guidance.

For example, Japanese Unexamined Patent Publication No. Hei 9-114371 (hereinafter referred to as Conventional Publication A) and Japanese Unexamined Patent Publication No. Hei 11-1437 have been proposed based on such intentions.
Japanese Patent Publication No. 7 (hereinafter referred to as Conventional Publication B) discloses an apparatus for predicting freezing of a road surface. These conventional publications A and B are both stand-alone types that do not require external information and do not employ a method of optically monitoring the road surface condition, and thus have the advantage that the disadvantages that occur in each case are eliminated.

[0004] First, disadvantages when a method of acquiring external information is adopted will be described. If the configuration is based on obtaining external information from the infrastructure, there is no particular problem in urban areas where the infrastructure is well-equipped, but in areas such as suburbs and mountainous areas where the infrastructure is not sufficient or the infrastructure itself is not available , It is difficult or impossible to obtain adequate external information sufficiently. Further, it is conceivable to obtain external information using a mobile phone, a car phone, or the like. At present, their communication speeds are low.

Next, the disadvantages when the optical monitoring method is employed will be described. For example, in order for a driver who has been notified of an alarm to safely perform vehicle control (deceleration, etc.),
It is necessary to notify the driver that he / she has enough time to control before reaching the predicted point. Therefore, it is necessary to monitor the road surface condition considerably ahead, but in order to monitor farther away, the optical axis of the optical device and the road surface to be monitored are infinitely parallel, making it difficult to perform appropriate monitoring. Further, for example, in a situation where a sharp curve exists in a mountain area or the like, it is not possible to physically monitor a distant place.

Due to such disadvantages, the road surface freezing prediction (estimation) devices disclosed in the conventional publications A and B are:
By not employing the “method based on external information” and the “optical monitoring method”, the advantage that the above-described disadvantages do not occur can be obtained.

[0007]

However, the following inconvenience arises. First, in the case of the road surface freezing prediction device disclosed in the conventional publication A, the freezing prediction is performed based on the amount of solar radiation, so that the usable state is limited. For example, Paragraph No. 0014 of Publication A states, "... proportional to the integrated value of the amount of solar radiation from the sunrise of this day to the present time ...". Therefore, if a vehicle is present at the same position outdoors or within a limited range, the integrated value of the amount of solar radiation can be calculated, and a prediction based on the calculated amount of solar radiation can also be employed. Since the situation changes from time to time, it is not practical to respond to them.

On the other hand, in the case of the conventional publication B, it is an essential requirement that the presence or absence of the occurrence of freezing for each date in each region is indispensable. Can not. In addition, it is necessary to prepare frozen data according to date and time instead of one type at each position, and the amount of stored frozen data increases. Above all, it is impractical to assume that such frozen data exists in any region, and in fact, it depends only on frozen data in units of a wide area. In that case, the presence or absence of freezing greatly differs depending on the circumstances in each place in the area, and it is highly likely that appropriate prediction cannot be made.

Further, the occurrence of freezing for each date in each area is not uniform every year, and varies variously depending on weather conditions. Therefore, the present invention solves such a problem,
An object of the present invention is to make it possible to perform appropriate freezing prediction according to the circumstances of each place while having a simple configuration.

[0010]

In order to achieve the above object, the road surface freezing prediction device according to the first aspect of the present invention is based on the amount of insolation as disclosed in the conventional publication A or based on the conventional publication B. Instead of storing in advance the presence or absence of occurrence of freezing for each date in each region, freezing prediction is performed based on the outside air temperature as a basic element and a predetermined freezing promoting factor. . Since the freezing promoting factor can be extracted from the map data, for example, as described in claim 2, the map data used in the navigation device can be directly used. Therefore, for example, assuming a vehicle equipped with a navigation device, an outside air temperature detecting unit (for example, a temperature sensor)
All that is required is to add a program that executes freezing prediction. As described above, it is possible to make an appropriate prediction according to the situation of each place, while being able to be easily realized.

In order to realize an appropriate prediction, a freezing promoting factor becomes important.
For example, as shown in claim 3, terrain where humidity easily rises, terrain where wind easily occurs, terrain where shade easily occurs, and the like can be considered. These terrains were extracted from the following viewpoints. That is, although the freezing basically depends on the outside air temperature, the factor which promotes the freezing is firstly the humidity. This is because moisture is needed for freezing. Wind and shade can be cited as causes for lowering the temperature. Of course, without being limited to these exemplified terrains, any terrain that can be extracted from the above-mentioned viewpoints can be a freezing promoting factor.

In order to predict freezing, it is conceivable to employ, for example, the method described in claim 4. In consideration of the fact that the outside air temperature changes and the extracted terrain does not basically change, the following calculation of the freezing contribution level can be considered. That is, when calculating the freezing contribution level corresponding to the freezing basic factor, for example, as described in claim 5, the level calculation is performed using a predetermined function using the outside air temperature as a variable. On the other hand, when calculating the freezing contribution level corresponding to the freezing promoting factor, for example, as described in claim 6, the correspondence between the freezing promoting factor and the freezing contribution level is stored, and based on the corresponding relationship. Perform level calculation. Of course, as for the freezing basic factor, the correspondence between the outside air temperature and the freezing contribution level may be stored, but in general, the relationship between the outside air temperature and the road surface freezing can be functionalized. It is considered that it is preferable to set the value as 5.

By the way, since the present device employs a technique of "predicting" rather than directly detecting the freezing of the road surface, it cannot be guaranteed that the prediction result always coincides with the actual frozen state of the road surface. However, if the prediction result can be evaluated in some way, learning can be performed based on the evaluation result. Therefore, in the road surface freezing prediction device according to claim 7, information related to the slip state of the wheels can be obtained from the traction control device, and based on the slip state related information obtained from the traction control device, it is determined whether the freezing prediction was appropriate. evaluate. Then, learning is performed using the evaluation result.

Here, the slip state-related information may be the slip rate itself or a traction control amount reflecting the slip rate. Further, when the prediction result is evaluated and learned, it is conceivable to correct the freezing contribution level corresponding to the outside temperature using the evaluation result of whether the prediction was appropriate, for example, as described in claim 8. Can be

When the freezing is predicted as described above, the user can be notified based on the prediction result (claim 10).

[0016]

Embodiments of the present invention will be described below with reference to the drawings. It is needless to say that the embodiments of the present invention are not limited to the following examples, and can take various forms as long as they belong to the technical scope of the present invention.

FIG. 1 is a block diagram showing a schematic configuration of a navigation system 2 including the function of the freezing prediction device of the present embodiment. The present navigation system 2 is configured as a so-called car navigation system mounted on a vehicle, and includes a position detector 4, a map data input device 6, an operation switch group 8, an outside air temperature sensor 9, and a control circuit 10 connected thereto. External memory 1 connected to control circuit 10
2, speaker 13, display device 14, and remote control sensor 1
5 is provided.

The position detector 4 is a well-known geomagnetic sensor 1.
6. GPS (Gl) for detecting the position of the vehicle based on radio waves from the gyroscope 18, the distance sensor 20, and satellites
GPS receiver 22 for obal Positioning System)
have. These sensors and the like 16, 18, 20, 2
2 is configured to be used while interpolating each with a plurality of sensors because each has an error having a different property. It should be noted that depending on the accuracy, a part of the above-described components may be used, and a rotation sensor for the steering wheel, a wheel sensor for each rolling wheel, or the like may be used.

The map data input device 6 is a device for inputting various data including so-called map matching data, map data and landmark data for improving the accuracy of position detection. As a medium, CD-R
Although OM is generally used, another medium such as a memory card may be used.

The outside temperature sensor 9 is a sensor for detecting the temperature outside the vehicle. The speaker 13 is connected to the control circuit 1
The user is notified of various information processed in step 0 or voice information for calling attention to freeze described later. The display device 14 is a color display device. The screen of the display device 14 displays the current vehicle position mark input from the position detector 4, the map data input from the map data input device 6, and further displays on the map. And additional data such as a guide route to be set and a mark of a set point can be displayed in a superimposed manner. The display device 14 also notifies the user of image information for calling attention to freezing described later.

The navigation system 2 is a remote control terminal (hereinafter, referred to as a remote controller).
When the position of the destination is input from the remote control sensor 15 via the operation switch group 8 via the remote control sensor 15a, an optimum route from the current position to the destination is automatically selected, and a guidance route is formed and displayed. It also has a route guidance function. As a technique for automatically setting the optimum route, a technique such as the Dijkstra method is known. As the operation switch group 8, for example, a touch switch or a mechanical switch integrated with the display device 14 is used, and is used for various inputs.

The control circuit 10 is configured as a normal computer, and has a well-known CPU, ROM, R
A bus line for connecting AM, I / O, and these components is provided. The control circuit 10 controls the entire device, but can also acquire information from the traction control device 30. The navigation system 2 and the traction control device 30 may be connected by, for example, a LAN.

The traction control device 30 executes a known traction control. This traction control will be briefly described. From the difference between the rotation speed of the driving wheel and the driven wheel, the difference between the turning radius expected from the steering angle obtained from a steering sensor (not shown), and the turning radius predicted from the difference between the rotation speeds of the inner wheel and the outer wheel, etc. , A slip state such as wheel slip or side slip is detected. Then, in order to suppress the slip state, an engine control device (not shown) is instructed to reduce the engine output.

The control circuit 10 of the navigation system 2
Is configured to acquire slip information from the traction control device 30, evaluate the result of freezing prediction, and further learn. Next, the operation of the navigation system 2 of the present embodiment will be described. In addition, since a portion related to freezing prediction and notification is a characteristic, a typical operation as the navigation system 2 will be briefly described, and then an operation of a portion related to freezing prediction and notification, and a result of freezing prediction will be described. The operation of the part related to evaluation / learning will be described in detail.

After the power of the navigation system 2 is turned on, the driver displays a guide route from a menu displayed on the display device 14 by using the remote controller 15a (the same operation can be performed with the operation switches 8; the same applies to the following description). When the route information display process is selected to be displayed on the device 14, the following process is performed. That is, when the driver inputs a destination by operating a remote controller or the like based on a map on the display device 14, the GPS
The current position of the vehicle is obtained based on the satellite data obtained from the receiver 22, the cost is calculated by the Dijkstra method between the destination and the current position, and the shortest route from the current position to the destination is set as the guidance route. The required processing is performed. Then, the guidance route is displayed on the road map on the display device 14 so as to guide the driver to an appropriate route.
The calculation process and the guidance process for obtaining such a guide route are generally well-known processes, and a description thereof will be omitted.

Next, the freeze prediction / notification will be described.
The freezing prediction / notification process is executed in parallel with the processes such as the route calculation and the guidance that are typical operations of the navigation system 2 described above. That is, in the case of the present embodiment, the freeze prediction / notification process is executed regardless of the result of the route calculation and the content of the guidance process.

This freezing prediction / notification process will be described with reference to FIGS. FIG. 2 is a flowchart showing the freeze prediction / notification process, and FIG. 3 is an explanatory diagram showing a specific example of a turning point described later. As shown in the flowchart of FIG. 2, first, the current position and the traveling direction (azimuth) of the own vehicle are detected based on the detection signal from the position detector 4 (S10), and the traveling as viewed from the own vehicle position is performed. Turning point (T) existing in the direction (traveling direction)
P) is extracted from the map data (S20). The turning point is a terrain that can be a factor that promotes freezing of the road surface, and is predetermined from terrain information existing in the map data.

As the turning point, terrain where the humidity is easily increased, terrain where the wind is easily generated, terrain where the shade is easily generated and the like are considered. Freezing basically depends on the outside air temperature, but the factor that promotes this is firstly humidity. Further, the outside air temperature at the position of the host vehicle is not always uniform around the vehicle, and wind and shade may be cited as a cause of the local temperature decrease. Therefore, a turning point was determined from these viewpoints. An example of these terrain that can be extracted from the map data is as follows. First, the above-mentioned `` topography that easily increases humidity '' refers to the case where water itself such as a river or a pond exists, or the case where it is assumed that there is a large amount of water content due to a large amount of forest such as a mountain area Can be considered. The “terrain where wind is likely to occur” may be, for example, a case where a bridge is present or a mountainous area. further,
The “terrain where shade tends to occur” may be, for example, a case where a road is sharply curved or a mountainous area. Of course, without being limited to these exemplified terrains, any terrain that can be extracted from the above-mentioned viewpoints can be a freezing promoting factor.

As shown in FIG. 3, it is assumed that the vehicle is located before the branch point and the vehicle is traveling in the direction of the branch point. In this case, a turning point within a predetermined range on the traveling direction side is extracted with the vehicle position as the center. In the case of the own vehicle position shown in FIG. 3, four turning points TP1 to TP4 are extracted. Specifically, turning points TP1 and TP3 are bridges, and turning points TP2 and 4 are rivers.

After the turning point is extracted in S20 as described above, if there is a branch point in front of the own vehicle, predetermined grouping is performed (S30). In the example of FIG. 3,
When the vehicle travels straight at the fork and travels on route A, there are turning points TP1 and TP2. When the vehicle turns right at the fork and travels on route B, there are turning points TP3 and TP4. Therefore, when there are different turning points depending on the route, the turning points are grouped in order to separate them.

At S40, the outside temperature (t) is detected based on the detection signal from the outside temperature sensor 9. And S
The degree of freezing is predicted using the outside temperature (t) detected at 40 and the freezing contribution level of each turning point (S50). The prediction of the degree of freezing is performed for each group described above.

Here, the freezing contribution level is a level that contributes to freezing the road surface. For the freezing contribution level corresponding to the outside temperature (t), a predetermined function using the outside temperature (t) as a variable is used. Level calculation is performed using g (t). On the other hand, as for the turning point, the correspondence between the turning point type and the frozen contribution level is stored in advance, and the level is calculated based on the correspondence. This correspondence may be stored, for example, in the external memory 12 (see FIG. 1). Of course, as for the outside temperature (t), the correspondence between the outside temperature (t) and the freezing contribution level may be stored, but in general, the relationship between the outside temperature and the road surface freezing can be made a function. Therefore, a calculation formula is used here.

Further, the degree of freezing is calculated by using the following prediction formula. Freezing degree prediction formula = f (g (t), C1, C2, C3
C4, C5 ... g (t): Freezing contribution level based on outside air temperature t C1: Freezing contribution level due to the presence of bridge C2 ... Freezing contribution level due to the presence of river C3 ... Freezing contribution level due to the presence of pond C4 ... Mountainous area Contribution level due to the presence of C5 Freezing contribution level due to the presence of a sharp curve Therefore, before reaching the branch point, the degree of freezing when traveling on route A is determined by the freezing contribution level g (t) based on the outside air temperature t. And the freezing contribution level C1 corresponding to the turning point TP1, and the turning point T
Using the frozen contribution level C2 corresponding to P2, the prediction formula f
(G (t), C1, C2). Similarly, the degree of freezing when traveling on route B is calculated.

In S60 of FIG. 2, it is determined whether or not grouping has been performed in S30. If grouping has been performed (S60: YES), it is determined whether or not the vehicle has passed the branch point that caused the grouping. (S70). In the case of the own vehicle position shown in FIG. 3, it is determined whether or not the vehicle has passed the branch point. Then, wait until it passes the branch point (S
70: NO), when the vehicle passes the branch point (S70: YE)
S), it is determined whether or not notification is necessary (S80). S50
If the degree of freezing predicted in the step is a level that requires notification, it is determined that notification is necessary (S80: YES), and notification processing is performed (S90).

Here, a supplementary description of the necessity of notification in S80 will be given. In the example of FIG. 3, when the vehicle travels straight on the branch point and travels on route A, the turning point TP
1, the necessity of notification is determined based on the degree of freezing using TP2, and when traveling on route B by turning right at a junction, the necessity of notification is required based on the degree of freezing using turning points TP3 and TP4. Judge the gender. Therefore, since it is necessary to determine which of these degrees of freezing is to be based on, the passage of the branch point is waited in S70. Conversely, if they are not grouped (S6
0: NO), since there is no need to wait for passage through the branch point,
The process directly proceeds to S80 without performing the process of 0.

The notification process in S90 is performed by using the speaker 13 to generate a predetermined warning sound or to generate a warning sentence. Of course, the display device 14 may also display a warning message or the like. If a warning sentence is to be uttered or displayed, a more specific warning, for example, "Please note that there is a possibility of freezing at a bridge a few meters ahead" is possible. Note that the degree of warning may be set stepwise. For example,
It is preferable that the warnings at the "possible freezing" level and the warnings at the "mostly frozen level and need sufficient attention" level be changed. There will be.

Although the processing relating to the freezing prediction / notification has been described, the case where the own vehicle position further moves in FIG. 3 will be briefly described. It is conceivable that the vehicle turns right at a fork and passes a bridge which is a turning point TP3. Turning points considered at this point include a peak (TP5) and a sharp curve (TP6).
Therefore, the frozen contribution level g (t) based on the outside temperature t
And the freezing contribution level C4 corresponding to the turning point TP5 and the freezing contribution level C5 corresponding to the turning point TP6, the prediction formula f (g (t), C4, C4
It is calculated by 5). Similarly, the degree of freezing when traveling on route B is calculated.

After passing through the mountain, there is a bridge as a turning point TP7 before turning right at the branch point. Therefore, the freezing contribution level g (t) based on the outside temperature t and the freezing contribution level corresponding to the turning point TP7 Using C1,
It is calculated by the prediction formula f (g (t), C1). Note that, as in the case described above, the notification is made only when there is a right turn at the branch point (if necessary). In particular, in the case of a sharp curve such as the turning point TP6, the possibility of a slip accident increases, so that a special warning may be issued to give sufficient attention.

Next, the operation of the part related to the evaluation and learning of the result of the freeze prediction based on the slip information from the traction control device 30 will be described. The above-described freezing prediction employs a method of “predicting” the road surface freezing, instead of directly detecting it, and therefore cannot guarantee that the prediction result always matches the actual road surface freezing state. However, if the prediction result can be evaluated in some way, learning can be performed based on the evaluation result.
Therefore, information related to the slip state of the wheels is obtained from the traction control device 30. The “slip state related information” to be obtained may be the slip rate itself or a traction control amount reflecting the slip rate. This is because the degree of slip can be indirectly grasped from the traction control amount.

Then, based on the slip state-related information, it is evaluated whether the freezing prediction was appropriate. For example, if the freezing is predicted, but the slip state, which is assumed to occur during the freezing, is not reached, the evaluation content indicates that the freezing is excessively predicted. Further, learning is performed using the evaluation result. At the time of this learning, for example, the freezing contribution level corresponding to the outside temperature is corrected using the evaluation result of whether the prediction was appropriate.

In the present embodiment, the freezing contribution level is calculated using the predetermined function g (t) using the outside air temperature (t) as a variable. The freezing contribution level is corrected by adding the correction value calculated based on the result. That is, g (t) → g (t) +
This is a correction value.

In this embodiment, the outside temperature sensor corresponds to "outside temperature detecting means", and the position detector 4 and the control circuit 10 correspond to "current position detecting means". The control circuit 10 corresponds to “freezing promoting factor extracting means”, “freezing prediction means”, “prediction result evaluation / learning means”, and “notification control means”, and the speaker 13 and the display device 14 function as “notification means”. Equivalent to.

As described above, when the navigation system 2 of the present embodiment exhibits the road surface freezing prediction function, it is based on the outside air temperature as the freezing basic factor and the freezing promoting factor extracted from the map data. Predict the freezing of the road surface around the current position. In other words, instead of storing the occurrence of freezing for each date in each area in advance as in the conventional publication B based on the amount of insolation as in the conventional publication A, the prediction of the freezing is basically performed. It was performed based on the outside air temperature as a factor and a predetermined freezing promoting factor. And
Since this freezing promoting factor can be extracted from map data, it is possible to directly use map data used in typical navigation processing. Therefore, assuming the configuration of a general navigation system 2, it is only necessary to add the outside temperature sensor 9 and a program for executing freezing prediction. As described above, it is possible to make an appropriate prediction according to the situation of each place, while being able to be easily realized.

As a result of such freezing prediction, if notification to the user is necessary, the notification can be made via the speaker 13 or the display device 14. Therefore, the user who has received the notification can drive the vehicle with more care. In this case as well, since the speaker 13 and the display device 14 are also used in a typical navigation process, they can be used as they are for notification based on the freeze prediction result.

Further, since the prediction result can be evaluated and learned, the prediction error can be minimized and more appropriate prediction can be realized. In this example, among the factors for promoting freezing, a sharp curve or a mountainous part is exemplified as a cause of shading. Actually, there are many shades if high-rise buildings are in the forest, but we do not consider the existence of such buildings here, just pure terrain data generally used as map data (rivers, ponds, mountainous areas ), Bridges as public facilities, and sharp curves from road data. Therefore, prediction can be easily performed without preparing dedicated data for freezing prediction again.
That is, whether or not the building is a high-rise building is basically unnecessary information in route guidance or the like in a navigation device. Therefore, adding information about whether or not the building is high enough to allow shading to the map data itself is equivalent to preparing dedicated data for freezing prediction.

[Others] (1) In the above embodiment, the description has been made such that the turning point type and the freezing contribution level correspond one to one. For example, if the type of terrain is "river", the freezing contribution level of the river is uniquely determined. However, turning points of the same kind may be further subdivided. For example, in the case of a river, the degree of promotion of freezing may vary depending on the size of the river. In other words, it is considered that a wider river may supply a larger amount of water, and the degree of promoting freezing is higher. So, for example, the first-class river is C21.
It is also possible to say 22. In this case, assuming that the higher the level, the higher the contribution to freezing, C21>
It becomes C22. Also, with respect to a sharp curve, the freezing contribution level may be increased as the curve is steeper (as the curvature is larger). In addition, from this viewpoint, the freezing promoting factor is
This includes not only a factor that promotes actual freezing but also a factor that causes a vehicle running state that impairs safety during freezing.

(2) In the above embodiment, it has been described that the freezing prediction is performed separately from the route calculation / guidance which is a typical process in the navigation system. But,
If a route has been set, the vehicle normally travels in that direction. Therefore, the route information can be added to the determination of the range in the map data from which the turning point is extracted in S20 of FIG. 2 described above. . In other words, the turning point is extracted not only from the distance from the own vehicle position but also from a portion farther than a portion outside the set route for a portion related to the set route.

[Brief description of the drawings]

FIG. 1 is a block diagram showing a schematic configuration of a navigation system as an embodiment.

FIG. 2 is a flowchart illustrating freeze prediction / notification processing executed by a control circuit according to the embodiment.

FIG. 3 is an explanatory diagram showing a specific example of a turning point.

[Explanation of symbols]

2 ... Car navigation system 4 ... Position detector 6 ... Map data input device 8 ... Operation switch group 9 ... Outside air temperature sensor 10 ... Control circuit 12 ... External memory 13 ... Speaker 14 ... Display device 15 ... Remote control sensor 15a ... Remote control 16 ... Geomagnetic sensor 18 Gyroscope 20 Distance sensor 22 GPS receiver 30 Traction control device

Continued on the front page F term (reference) 2F029 AA02 AB01 AB07 AB09 AC02 AC04 AC13 AC14 AC18 3D046 BB29 HH00 HH15 HH45 HH57 JJ25 5H180 AA01 BB12 BB13 EE12 FF04 FF05 FF22 FF25 FF27 FF32 LL07 BB16H03 BB03 CCB DD 03H03 BB07 CCB JJ77 JJ78 KK15 KK16 KK19 KK31 KK37 LL08

Claims (10)

[Claims] 1. An outside air temperature detecting means for detecting an outside air temperature as a basic factor for predicting freezing, a current position detecting means for detecting a current position of a vehicle, and a vicinity of a current position detected by the current position detecting means. A freezing promoting factor extracting means for extracting a factor that promotes freezing of the road surface from the map data, an outside air temperature as a freezing basic factor detected by the outside air temperature detecting means, and the freezing promoting factor extracting means A road surface freezing prediction device, comprising: a freezing prediction unit that predicts freezing of a road surface around the current position based on the freezing promotion factor extracted in (1). 2. The road surface freezing prediction device according to claim 1, wherein the map data from which the freezing promoting factor extracting means extracts the freezing promoting factor is map data used in a navigation device. Road surface freezing prediction device. 3. The road surface freezing prediction device according to claim 1, wherein the freezing promoting factor extracting means includes, as the freezing promoting factor, a terrain where a humidity is easily increased, a terrain where a wind is easily generated, and a shade are generated. A road surface freezing prediction device characterized by extracting at least one of easy topography. 4. The road surface freezing prediction device according to claim 1, wherein the freezing prediction unit calculates a level of each of the basic freezing factor and the freezing promoting factor that contributes to freezing the road surface. A road surface freezing prediction device which calculates a freezing degree according to a freezing degree prediction formula using a predetermined function having the freezing contribution level as a variable, and predicts road surface freezing based on the calculated freezing degree. 5. The road surface freezing prediction device according to claim 4, wherein the freezing prediction means calculates the freezing contribution level corresponding to the freezing basic factor using a predetermined function having the outside temperature as a variable. Road surface freezing prediction device characterized by the above-mentioned. 6. The road surface freezing prediction device according to claim 4, wherein the freezing prediction means stores a correspondence relationship between the freezing promoting factor and the freezing contribution level, and based on the correspondence relationship, The road surface freezing prediction device, wherein the freezing contribution level corresponding to the freezing promoting factor is calculated. 7. The road surface freezing prediction device according to claim 1, wherein information relating to a wheel slip state is available from a traction control device, and the slip obtained from the traction control device is provided. A road surface freezing prediction device, comprising: a prediction result evaluation / learning unit that evaluates whether or not the prediction by the freezing prediction unit is appropriate based on state-related information, and performs learning using the evaluation result. 8. The road surface freezing prediction device according to claim 7, wherein the prediction result evaluation / learning unit uses an evaluation result of whether or not the prediction is appropriate.
A road surface freezing prediction device, wherein a freezing contribution level corresponding to the outside temperature is corrected. 9. The road surface freezing prediction device according to claim 8, wherein the freezing contribution level corresponding to the freezing basic factor is calculated using a predetermined function having the outside air temperature as a variable, The prediction result evaluation / learning means adds a correction value calculated based on the evaluation result to a freezing contribution level calculated using a predetermined function having the outside temperature as a variable,
A road surface freezing prediction device, wherein the freezing contribution level is corrected. 10. The road surface freezing prediction device according to any one of claims 1 to 9, further comprising: when a result of the prediction by the road surface freezing prediction device indicates that the user needs to be notified of the situation. And a notification control means for notifying the user via the notification means.