U.S. patent application number 09/816207 was filed with the patent office on 2002-05-30 for navigation system for vehicles.
Invention is credited to Ohshima, Yuichiro.
Application Number | 20020065602 09/816207 |
Document ID | / |
Family ID | 18835169 |
Filed Date | 2002-05-30 |
United States Patent
Application |
20020065602 |
Kind Code |
A1 |
Ohshima, Yuichiro |
May 30, 2002 |
Navigation system for vehicles
Abstract
A navigation system for vehicles which extracts a target
facility by conducting a search using fuzzy search words inputted
by user. A facility search section 13 includes a fuzziness
interpretation section 131 for converting any fuzzy search word
included in the inputted character string into a defined condition
(quantified criterion) and retrieves target facility using facility
information of the facility to be searched on the basis of the
defined condition.
Inventors: |
Ohshima, Yuichiro; (Tokyo,
JP) |
Correspondence
Address: |
BIRCH STEWART KOLASCH & BIRCH
PO BOX 747
FALLS CHURCH
VA
22040-0747
US
|
Family ID: |
18835169 |
Appl. No.: |
09/816207 |
Filed: |
March 26, 2001 |
Current U.S.
Class: |
701/425 ;
340/995.19 |
Current CPC
Class: |
G01C 21/3679
20130101 |
Class at
Publication: |
701/207 ;
340/995 |
International
Class: |
G01C 021/32 |
Foreign Application Data
Date |
Code |
Application Number |
Nov 30, 2000 |
JP |
2000-364182 |
Claims
What is claimed is:
1. A navigation system for vehicles comprising: a location detector
section for detecting a location of a vehicle; a mapping data
memory section for memorizing mapping data including various kinds
of facility information; an input section; an facility search
section for determining a facility to be searched according to a
character string inputted from said input section and for
retrieving facility information of the facility to be searched from
said mapping data memory section; and a display section for
displaying a location of the vehicle and the facility information
outputted from said facility search section; wherein said facility
search section includes a fuzziness interpretation section for
converting any fuzzy search word included in the inputted character
string into a defined condition, and extracts and outputs the
target facility using the facility information of said facility to
be searched on the basis of the defined condition.
2. The navigation system for vehicles according to claim 1, wherein
the defined condition can be selectively changed.
3. The navigation system for vehicles according to claim 1, wherein
the defined condition is an approximated condition and a target
facility can be extracted and outputted using the facility
information of said facility to be searched on the basis of the
approximately defined condition.
4. The navigation system for vehicles according to claim 1, wherein
the fuzziness interpretation section converts a plurality of
inputted fuzzy search words into a plurality of defined conditions
and, at the same time, judges a conjunctive relation between said
plurality of fuzzy search words.
5. The navigation system for vehicles according to claim 4, wherein
the fuzziness interpretation section converts a plurality of
inputted fuzzy search words into a plurality of approximately
defined conditions and, at the same time, judges a conjunctive
relation between said plurality of fuzzy search words.
6. The navigation system for vehicles according to claim 3, wherein
fuzzy search words are converted into approximately defined
conditions and reliability in the mentioned fuzzy search words is
acknowledged for the facility extracted on the basis of the
approximately defined conditions.
7. The navigation system for vehicles according to claim 6, wherein
fuzzy search words are converted into approximately defined
conditions and reliability in the mentioned fuzzy search words is
acknowledged for the facility extracted on the basis of the
approximately defined conditions, using a membership function
established for the mentioned fuzzy search words.
8. The navigation system for vehicles according to claim 3, wherein
fuzzy search words of negative meaning are converted into
approximately defined conditions and reliability in the mentioned
fuzzy search words of negative meaning is acknowledged for the
facility extracted on the basis of the approximately defined
conditions, using an established membership function.
Description
BACKGROUND OF THE INVENTION
[0001] 1. Technical Field
[0002] The present invention relates to a navigation system for
vehicles that retrieves and extracts a target facility.
[0003] 2. Background Art
[0004] In one of conventional navigation systems, for example,
disclosed by the Japanese Patent Publication (unexamined) No.
337361/1999, the system obtains date, weather information, etc. and
retrieves or searches for specific facilities conforming to the
obtained conditions. In this case, the search condition on date is
unique, while the search condition on weather is also uniquely
appointed by selecting from rain, cloudiness and fine.
[0005] However, a problem exists in that the conventional
navigation systems cannot make any search taking into consideration
for fuzziness. For example, it is impossible to conduct "search for
an accommodation in the neighborhood" and also, in case of "search
for an accommodation located within 5 km", the search is conducted
for within a definitely predetermined distance, but not conducted
for any accommodation slightly over the threshold value including
fuzziness.
SUMMARY OF THE INVENTION
[0006] The present invention was made to solve the above-discussed
problems and has an object of providing a navigation system for
vehicles by which user (e.g., driver) can make search using natural
fuzzy words.
[0007] A navigation system for vehicles according to the invention
comprises: a location detector section for detecting a location of
a vehicle; a mapping data memory section for memorizing mapping
data including various kinds of facility information; an input
section; an facility search section for determining a facility to
be searched according to a character string inputted from the
mentioned input section and for retrieving facility information of
the facility to be searched from the mentioned mapping data memory
section; and a display section for displaying a location of the
vehicle and the facility information outputted from the mentioned
facility search section; in which the mentioned facility search
section includes a fuzziness interpretation section for converting
any fuzzy search word included in the inputted character string
into a defined condition (quantified criterion) and retrieves the
target facility using the facility information of the mentioned
facility to be searched on the basis of the defined condition.
[0008] As a result, it becomes possible for user to extract any
target facility by the search using natural fuzzy words.
[0009] It is preferable that the defined condition can be
selectively changed.
[0010] As a result, any fussy search word is converted into one of
the defined conditions required by user.
[0011] It is also preferable that the defined condition is an
approximated condition, and a target facility can be extracted and
outputted using the facility information of the facility to be
searched on the basis of the approximately defined condition.
[0012] As a result, it becomes possible to define the fuzziness
around a certain threshold value and to retrieve and extract the
target facility from the fuzzy word.
[0013] It is also preferable that the fuzziness interpretation
section converts a plurality of inputted fuzzy search words into a
plurality of defined conditions and, at the same time, judges a
conjunctive relation between the mentioned plurality of fuzzy
search words.
[0014] As a result, it becomes possible to appropriately retrieve
and extract the target facility from the plurality of fuzzy search
words having the conjunctive relation with each other.
[0015] It is also preferable that the fuzziness interpretation
section converts a plurality of inputted fuzzy search words into a
plurality of approximately defined conditions and, at the same
time, judges a conjunctive relation between the mentioned plurality
of fuzzy search words. As a result, it becomes possible to define
the fuzziness around a certain threshold value and, it becomes
possible to appropriately retrieve and extract the target facility
from the plurality of fuzzy search words having the conjunctive
relation with each other.
[0016] It is also preferable that fuzzy search words are converted
into approximately defined conditions and reliability in the
mentioned fuzzy search words is acknowledged for the facility
extracted on the basis of the approximately defined conditions.
[0017] As a result, it becomes possible to find a target facility
with higher reliability.
[0018] It is also preferable that fuzzy search words are converted
into approximately defined conditions and reliability in the
mentioned fuzzy search words is acknowledged for the facility
extracted on the basis of the approximately defined conditions
using a membership function established for the mentioned fuzzy
search words.
[0019] As a result, it becomes possible to find a target facility
with higher reliability.
[0020] It is preferable that fuzzy search words of negative meaning
are converted into approximately defined conditions and reliability
in the mentioned fuzzy search words of negative meaning is
acknowledged for the facility extracted on the basis of the
approximately defined conditions, using an established membership
function.
[0021] As a result, it becomes possible to find a target facility
with higher reliability.
BRIEF DESCRIPTION OF THE DRAWINGS
[0022] FIG. 1 is a block diagram of a navigation system for
vehicles according to Embodiment 1 of the present invention.
[0023] FIG. 2 is a schematic block diagram showing a schematic
construction shown in FIG. 1.
[0024] FIG. 3 is a flow chart showing operation of Embodiment
1.
[0025] FIG. 4 is a diagram showing dictionary data for converting a
fuzzy word into defined condition.
[0026] FIG. 5 is a flow char t showing operation of Embodiment
2.
[0027] FIG. 6 is a flow chart showing operation of Embodiment
4.
[0028] FIG. 7 is a diagram showing dictionary data for converting
fuzzy words into approximately defined conditions.
[0029] FIG. 8 is a graph showing a membership function of a fuzzy
word.
[0030] FIG. 9 is a graph showing a membership function of another
fuzzy word.
[0031] FIG. 10 is a diagram showing reliability of a facility
extracted in Embodiment 4.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0032] Embodiment 1
[0033] Referring to FIG. 1 showing a block diagram of a navigation
system for vehicles according to Embodiment 1 of the present
invention, reference numeral 11 is a mapping data memory section
for memorizing mapping data including information of various
facilities, and numeral 12 is a location detector section for
detecting a location of a vehicle. Numeral 13 is a facility search
section for judging a concerned facility to be searched from a
character string inputted through an input section 15. .Yen.The
facility search section 13 searches for facility information of the
concerned facility to be searched from the mapping data memory 11
and includes a fuzziness interpretation section 131 for converting
a fuzzy word included in the inputted character string into a
defined condition. Numeral 14 is a display section, such as a
display monitor, for displaying a location of the vehicle, facility
information outputted from the facility search engine 13, etc. to
user. Numeral 15 is an input section, through which user inputs a
character string, comprising a key board, remote control keys, a
touch panel, keys disposed in a front panel, a voice input unit
with a voice recognition function, etc.
[0034] Referring now to FIG. 2 showing a schematic block diagram of
the schematic construction shown in FIG. 1, reference numeral 21 is
an azimuth sensor such as gyroscope, and numeral 22 is a car speed
sensor to determine car speed from car speed pulses. Numeral 23 is
a GPS (Global Positioning System) receiver, which outputs the
present location of the vehicle in the form of, e.g., information
of longitude and latitude degrees. Numerals 21, 22 and 23 are
included in the location detector section 12 shown in FIG. 1.
Numeral 24 is an operating switch corresponding to the input
section 15 shown in FIG. 1. Numeral 25 is mapping data including
facility information corresponding to the mapping data memory
section 11 shown in FIG. 1. Memorized in the facility information
are varieties of information such as name, location, business hour,
price, or parking space relating to the facility concerned.
[0035] A navigation ECU (electronic control unit) 27 carries out
predetermined calculations on the basis of each output value using
an external memory 26. The facility search section 13 and the
fuzziness interpretation section 131 are implemented as respective
functions of this navigation ECU 27. The location of the vehicle
and the retrieved facility information are displayed on the display
monitor 28.
[0036] Now, operation of this Embodiment 1 is hereinafter described
with reference to FIG. 3 showing a flow chart of operation of
Embodiment 1.
[0037] Referring to FIG. 3, in the first step, location of the
vehicle is detected (S31). Then, from the inputted character string
(e.g., neighboring restaurant), a concerned facility to be searched
(a restaurant) is determined and, from the mapping data memory,
facility information of the concerned facility to be searched (the
restaurant) is read in (S32). Subsequently, fuzziness
interpretation is conducted. A fuzzy search word, e.g.,
"neighboring" is converted into a defined condition "less than 10
km" using the dictionary data shown in FIG. 4 and, a facility
corresponding to the fuzziness decision, i.e., a facility
corresponding to the defined condition "less than 10 km" is
extracted from the facility information of the target facility to
be searched (restaurant) and is outputted as the target facility
(S33). In this case, as the search object is a facility in the
neighborhood of the present location of the vehicle, the facility
located within "less than 10 km" from the vehicle location is
searched. On the other hand, in case that the facility in the
neighborhood of a destination is searched, the facility located
within "less than 10 km" from the destination is searched.
[0038] Embodiment 2
[0039] In the foregoing fuzziness interpretation process, it is
also possible that the fuzzy search word, e.g., "neighboring" is
converted into an approximated criterion "less than 10 km.+-.10%"
(either less than 9 km or less than 11 km) using the dictionary
data shown in FIG. 4, and the facility corresponding to the
fuzziness interpretation, i.e., the facility corresponding to the
defined condition "less than 10 km.+-.10%" is extracted from the
facility information of the concerned facility to be searched
(restaurant) and is outputted as the target facility (S33). In this
case, the facility to be searched is preferably retrieved in
combination with search conditions such as number of search
facilities, etc. Then the facility most suited for the search
conditions such as number of search facilities, etc. within the
range of mentioned .+-.10% is extracted.
[0040] Embodiment 3
[0041] FIG. 5 is a flowchart showing operation of the navigation
system according to Embodiment 2. Steps S31 and S32 are the same as
those in the foregoing Embodiment 1. Then, fuzziness interpretation
is conducted. User selects one of the defined conditions "less than
10 km", "less than 8 km" and "less than 5 km" (using the dictionary
data shown in FIG. 4), which corresponds to the fuzzy word
"neighboring" (S34). The facility corresponding to the fuzziness
interpretation on the basis of the selected criterion, e.g., "less
than 5 km" is extracted from the facility information of the
concerned facility to be searched (restaurant) and is outputted as
the target facility (S35)
[0042] Embodiment 4
[0043] FIG. 6 is a flow chart explaining a method for the fuzziness
interpretation according to Embodiment 4. Referring to FIG. 6, a
key word of the facility such as restaurant, recreation ground,
shop, public office etc. is searched from among the inputted
character string to determine whether or not any character string
to be searched is found in the inputted character string. If it is
found, judgment of compound sentence about whether or not there
exists any punctuation mark "," is conducted at the same time (S41:
Yes) Then, the facility information of the concerned facility to be
searched is read from the mapping data on the basis of the
character string (e.g., restaurant) to be searched (S42). On the
other hand, if any mentioned character string to be searched is not
found (S41: No), the search becomes fault and goes on return step
(S47).
[0044] In Step S43, the fuzzy search words in the inputted
character string are converted into approximately defined
conditions using dictionary data shown in FIG. 7. FIG. 7 is a
diagram showing an example of dictionary data for converting each
of fuzzy words into the approximately defined condition.
[0045] For example, a fuzzy word, "neighboring" is converted into
an approximately defined condition "less than 10 km approx.".
Likewise, in case of "not cheap", considering that this word is a
combination of "cheap" (fuzzy word) + (plus) "not" (negation), that
is, "less than about 3,000 Yen" plus "not" (negation), it is
interpreted as "approximately 3,000 Yen or over". In this manner,
all fuzzy search words are converted into approximately defined
conditions (S43: Yes). On the other hand, in case that conversion
of any fuzzy word is impossible because of not entered in the
mentioned dictionary data or so, the search becomes fault and goes
on return step S47 (S43: No).
[0046] In Step 44, judgment of the conjunctive relation between the
approximately defined condition is conducted using the dictionary
data of logical sum (.orgate.) comprising the disjunctive words
such as "or", "otherwise", or "either" and, the dictionary data of
logical product (.andgate.) comprising the conjunctive words such
as "and", "as well as" "also" or "with" in the character strings of
the approximately defined conditions converted from every fuzzy
search words. Then, referring to the judgment of compound sentence
in Step S41, every conjunctive word in the character strings is
converted (S44: Yes). On the other hand, if any mentioned character
string to be searched is not found (S41: No), the search becomes
fault and goes on return step (S47).
[0047] Through the mentioned Steps, for example, the character
string of the compound sentence "a restaurant in the neighborhood
located within approximately 5 km but not cheap, or a restaurant
distant and cheap" is converted to "restaurant (located within
approximately 5 km .andgate. less than approximately 10 km
.andgate. approximately 3,000 Yen or over) .orgate. (longer than
approximately 10 km .andgate. less than approximately 3,000
Yen)".
[0048] Then, in Step 45, the approximately defined condition is
further converted to an approximately defined condition being more
specific. For example, "approximately" in the approximately defined
condition is more specifically converted into ".+-.10%" so that the
character string of the mentioned compound sentence is expressed as
"restaurant located within (5 km.+-.10% .andgate. less than 10
km.+-.10% .andgate. 3,000 Yen or over) .orgate. (10 km.+-.10% or
over .andgate. less than 3,000 Yen.+-.10%)". Thus, a facility,
which meets the mentioned character string, is extracted from the
facility information previously read in (S45).
[0049] In Step 46, reliability is found. FIGS. 8 and 9 are graphs
respectively showing examples of membership functions of the fuzzy
words on the basis of the more specified approximately defined
conditions.
[0050] FIG. 8 is a graph showing a membership function of the more
specified approximately defined wording "distance: less than 10
km.+-.10%" converted more specifically from the fuzzy word
"neighboring". FIG. 9 is a graph showing a membership function of
the more specified approximately defined word "price: 3,000
Yen.+-.10% or over" converted more specifically from the fuzzy word
"not cheap".
[0051] As shown in FIGS. 8 and 9, the membership functions of the
more specified approximately defined conditions are established for
every fuzzy word mentioned above, and reliability of the extracted
facilities is obtained for every fuzzy word and summed up. In this
manner, the facilities are sorted from one having the highest
reliability to the others each having lower reliability and, as a
result, the facilities found by such sorting are outputted in
return Step S47, e.g., as shown in FIG. 10.
[0052] In this Step S46, at the time of acknowledging a reliability
of the facility for every more specified approximately defined
condition, if price is a critical factor, it is preferable to apply
a multiplication by a predetermined weight such as 1.5 times.
[0053] It is also preferable to apply a classification in order to
extract the superlative like "most". For example, in case that "the
most neighboring restaurant" is input, it is not enough to show
only the extracted result of "restaurants within approx. 10
km.+-.10%", but desired to extract the most reliable facility.
[0054] It is also preferable to make a distinction between the
compound sentences. For example, in case that "the most neighboring
and the cheapest restaurant" is inputted, as it is hard to
distinguish whether it means "the most neighboring" .andgate.
"cheap" or "the most neighboring" .andgate. "the cheapest".
Accordingly, if any facility is extracted with each word
distinguished by the superlative like "the most neighboring"
.andgate. "the cheapest", then the extraction of the facility is
interrupted at that stage. If not, it is preferable to extract
facilities one after another while removing the distinction by the
superlative.
[0055] It is also preferable to make a distinction between the
imperative or requesting words such as "search", "want to see" or
"want to go" and the interrogative words such as "be there?", "be
able to come?" or "which?". For example, in case that "want to go
to a neighboring and cheap restaurant" is inputted, it is not
enough to display only an extracted result, but desirable to
display a facility extracted with the mentioned distinction,
thereby extracting the superlative to get the target facility.
[0056] It is also preferable to make a distinction by multiple
meanings. For example, in case that "high" is inputted, it is
sometimes hard to distinguish whether it means "high in price
(expensive)" or "high in altitude". If a facility for eating and
drinking like a "restaurant" is requested, it is desired to choose
"high in price". On the other hand, if a facility for resting or
parking to enjoy a panoramic view like "observatory" is requested,
it is desired to choose "high in altitude".
[0057] It is also preferable to add facility information to the
mapping data as much as possible, because the more number of
adjectives and adverbs for search are input, the more increases
recognition rate. General information stored in the navigation
system includes, for example, longitude and latitude, price,
telephone number, address, number of floors, gross floor area,
height above sea level, existence of infant facility. Specific
information includes number of stars (.star.) indicated in
magazines or the like to show restaurants, number of attractions in
association with recreation ground or the like, number of species
in association with zoo, aquarium or the like, number of sights or
hot springs in association with tourist resorts, classification of
religion and so forth. In case that an inputted character string is
"place with a fine view", it is possible to search it on the basis
of the mentioned height above sea level or number of floors.
[0058] In this Embodiment 4, it is also preferable to utilize,
e.g., a function expressing distribution probability instead of the
membership function used in fuzzy theory described above.
[0059] It is also preferable to retrieve data from, e. g., media
such as DVD-ROM, etc. or to store the data in ROM or RAM other than
DVD-ROM, instead of holding predetermined dictionary data in the
program.
[0060] It is also preferable to make it possible to externally
input the mentioned weight giving an importance to price, etc. in
order to reflect user's option.
[0061] It is also preferable that any character string is inputted
by user's keystrokes or by effect of the voice recognition
succeeding to the preliminary voice input.
* * * * *