CN108839657B - A method of online recognition road roughness information is responded based on automobile vibration - Google Patents
A method of online recognition road roughness information is responded based on automobile vibration Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
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Abstract
The invention discloses a kind of methods based on automobile vibration response online recognition road roughness information, comprising the following steps: Step 1: calculating two different speed u on calibration road surface1And u2Corresponding first time domain broad sense international roughness indexE1With the second time domain broad sense international roughness indexE2, calibration vehicle automobile vibration response quautity in speed u is measured, establishes IRI respectivelyE1With the first regression model of automobile vibration response quautity, IRIE2With the second regression model of automobile vibration response quautity；Step 2: on road surface to be measured, measurement calibration vehicle automobile vibration response quautity in speed u；Step 3: automobile vibration response quautity is substituted into the first regression model and the second regression model respectively, the first frequency domain broad sense international roughness index on road surface to be measured is calculatedE1' and the second frequency domain broad sense international roughness indexE2′；Step 4: according to the IRIE1' and IRIE2' calculate road surface frequency index and road roughness coefficient.Method energy on-line identification simultaneously provided by the invention goes out frequency index and road roughness coefficient.
Description
Technical field
The present invention relates to the road surface characteristic identification technology fields in Vehicle Engineering, more particularly to a kind of automobile vibration that is based on to ring
The method for answering online recognition road roughness information.
Background technique
With the rapid development of automobile industry, people have better pursuit to the comfort and operational stability of automobile.
Main driving source of the road roughness as automobile vibration has larger impact to car comfort and control stability.Closely
Nian Lai, active, the control of semi-active suspension are the most direct effective means of coordination car comfort and operational stability, and road surface
Unevenness information provides indispensable foundation for this purpose.Therefore, obtaining road roughness information online has
Significance.
Currently, the identification of road roughness information is divided into two classes in Vehicle Engineering: (1) knowledge of road roughness vertical section
Other and (2) spectrum of road surface roughness identification.Certainly, the latter can be realized on the former basis by mathematic(al) manipulation.
The recognition methods of road roughness vertical section is had measurement method based on laser emitter, is handled based on photographed images
Method, neural network method, numerical value best practice, control-constrained procedure, Youla-Kucera parametric method, state subgroup
Space recognition method and the method for support vector machines etc..But these methods are by auto model precision, running conditions of vehicle, data
It manages operation efficiency and measures the restriction of operation complexity.
The recognition methods of spectrum of road surface roughness has the dummy excitation method of frequency-domain analysis method and time-frequency domain.But these methods need to will be
System is assumed to linear system, obtains the transmission function of reflection vehicle attribute and be affected by speed.In addition, compared to road surface
The recognition methods of unevenness vertical section is also considerable though the recognition methods data processing amount of spectrum of road surface roughness is less.2014
Year, Jiangsu University proposed patent application " a kind of road roughness on-line identification method " (application number: 201410581629.X), related to
And a kind of road roughness information detecting method based on unsprung mass acceleration root-mean-square value and speed, but the invention is in frequency
Rate index is fixed as 2.0 lower realizations, and the frequency index on practical road surface generally changes between 1.5 to 3.5, and frequency index
There is important influence to automobile vibration response.
Summary of the invention
The present invention is to solve current technology shortcoming, is provided a kind of based on automobile vibration response online recognition road surface
The method of unevenness information, energy on-line identification simultaneously go out frequency index and road roughness coefficient.
Technical solution provided by the invention are as follows:
A method of online recognition road roughness information is responded based on automobile vibration, comprising the following steps:
Step 1: calculating two different speed u on calibration road surface1And u2Corresponding first time domain broad sense world flatness
Index IRIE1With the second time domain broad sense international roughness indexE2, the automobile vibration response in speed u of measurement calibration vehicle
Amount, establishes the first time domain broad sense international roughness index respectivelyE1With the first regression model of automobile vibration response quautity,
The second time domain broad sense international roughness indexE2With the second regression model of automobile vibration response quautity；
Step 2: on road surface to be measured, measurement calibration vehicle automobile vibration response quautity in speed u；
Step 3: the automobile vibration response quautity measured in the step 2 is substituted into first regression model and institute respectively
The second regression model is stated, the first frequency domain broad sense international roughness index on road surface to be measured is calculated separatelyE1' wide with the second frequency domain
Adopted international roughness indexE2′；
Step 4: according to the first frequency domain broad sense international roughness indexE1' smooth with the second frequency domain broad sense world
Spend index IRIE2' calculating road surface frequency index and road roughness coefficient, wherein the frequency index W meets:
The road roughness coefficient Gq(n0) meet:
Wherein, F (Gq(n0), W) meet:
Or
F (W) meets:
Wherein, n0For reference frequency, n0=0.1m-1,For the spring carried mass and non-spring charge material of gold vehicle
The frequency response function of Relative vertical speed is measured, f is temporal frequency, flFor temporal frequency lower limit, fuFor the temporal frequency upper limit.
Preferably, the gold vehicle meets:
Wherein, msFor spring carried mass, muFor nonspring carried mass, ksFor suspension rate, ktFor tire stiffness, csFor suspension resistance
Buddhist nun.
Preferably, in said step 1,
The automobile vibration response quautity is that front suspension moves in stroke accumulated value or mileage in unit mileage travelled
Rear suspension moves stroke accumulated value or front axle nonspring carried mass mass center normal acceleration root-mean-square value or rear axle nonspring carried mass mass center
Normal acceleration root-mean-square value.
Preferably, establish regression model in the step 1 the following steps are included:
Step a, the unevenness on the calibration road surface is measured；
Step b, it is based on the unevenness, calculates the first time domain broad sense international roughness indexE1With described second
Time domain broad sense international roughness indexE2；
Step c, on the calibration road surface, measure speed u when mileage in front suspension move stroke accumulated value and
Front axle nonspring carried mass mass center normal acceleration root-mean-square value；
Step d, the first time domain broad sense international roughness index is establishedE1It is vertical with front axle nonspring carried mass mass center
First regression model of acceleration root-mean-square value；Establish the second time domain broad sense international roughness indexE2It is travelled with unit
Front suspension moves the second regression model of stroke accumulated value in mileage.
Preferably, u1=40km/h, u2=80km/h, u=80km/h.
Preferably, first regression model meets:
Wherein,For front axle nonspring carried mass mass center normal acceleration root-mean-square value, unit is m/s2。
Preferably, second regression model meets:
IRIE2=-2444.924cfd 2+1031.190cfd-0.038
Wherein, cfdStroke accumulated value is moved for front suspension in unit mileage travelled, unit is m/m.
Preferably, it is non-to choose the front axle for demarcating vehicle for automobile vibration response quautity described in first regression model
Spring carried mass mass center normal acceleration root-mean-square value, automobile vibration response quautity described in second regression model choose the mark
Determine front suspension in the mileage of vehicle and moves stroke accumulated value.
Preferably, in said step 1, the time domain broad sense international roughness indexEiMeet:
Wherein, L is the total distance of running car；WithThe spring carried mass and non-spring charge material of the respectively described gold vehicle
The vertical speed of amount is the running car timetFunction.
Preferably, in the step 4, the spring carried mass and nonspring carried mass Relative vertical speed of the gold vehicle
Frequency response functionSatisfaction:
Wherein, j is imaginary unit；μ is the ratio of spring carried mass and nonspring carried mass；C is suspension rate and spring carried mass
Ratio；k1For the ratio of tire stiffness and spring carried mass；k2For the ratio of suspension rate and spring carried mass.
It is of the present invention the utility model has the advantages that 1) utilize broad sense international roughness indexEWith returning for automobile vibration response quautity
Return model, realize the online recognition of road roughness information, significantly reduce data processing amount, real-time operation amount very little improves
Online recognition efficiency；2) it online recognition frequency index and road roughness coefficient, road pavement unevenness information can mention simultaneously
It takes more fully；3) sensor carried by automobile suspension system measures automobile vibration response quautity, reduces measurement cost,
Strong operability；4) without obtaining auto model parameter and measurement vehicle transfer function, there is preferable be applicable in car category
Property, it is portable good.
Detailed description of the invention
Fig. 1 is the flow chart of the present invention based on vehicle vibration response online recognition road roughness information.
Fig. 2 is gold auto model of the present invention and model parameter.
Fig. 3 is 1/2 automobile 6DOF vibrational system mechanical model of the invention.
Fig. 4 is IRI of the inventionE1With the correlativity figure of front axle nonspring carried mass mass center normal acceleration root-mean-square value.
Fig. 5 is IRI of the inventionE2The correlativity figure of stroke accumulated value is moved with front suspension in mileage.
Specific embodiment
Present invention will be described in further detail below with reference to the accompanying drawings, to enable those skilled in the art referring to specification text
Word can be implemented accordingly.
As shown in Figure 1, the present invention provides a kind of method based on automobile vibration response online recognition road roughness information,
Include the following steps:
Step 1: on calibration road surface, two different speed u are calculated1And u2Corresponding first time domain broad sense world flatness
Index IRIE1With the second time domain broad sense international roughness indexE2, the automobile vibration response in speed u of measurement calibration vehicle
Amount, establishes IRI respectivelyE1With the first regression model of automobile vibration response quautity, IRIE2It is returned with the second of automobile vibration response quautity
Model:
Gold mechanics of vehicles model and parameter, as shown in Fig. 2, the meaning that parameters represent in model are as follows: msAnd muRespectively
For spring carried mass and nonspring carried mass, ksAnd ktRespectively suspension rate and tire stiffness, csFor suspension damping, zsAnd zuRespectively
The vertical displacement of spring carried mass and nonspring carried mass, μ are the ratio of spring carried mass and nonspring carried mass, and c is suspension rate and spring
The ratio of mounted mass, k1For the ratio of tire stiffness and spring carried mass, k2For the ratio of suspension rate and spring carried mass.And meet:
Based on the definition of international roughness index (international roughness index), it is expanded, is defined
Any uniformly speed uiGold vehicle suspension moves stroke cumulant in lower mileage, is broad sense international roughness index
IRIEi, time domain mathematic(al) representation are as follows:
Wherein, L is the total distance of running car；WithThe respectively spring carried mass of gold vehicle and nonspring carried mass
Vertical speed is the function of running car time t.
On calibration road surface, the regression model of time domain broad sense international roughness index Yu automobile vibration response quautity is established, is wrapped
Include following steps:
(1) on the road surface for calibration, using laser cross section instrument, the unevenness on measurement calibration road surface；
(2) two different speed u are sought1=40km/h and u2The corresponding first time domain broad sense world flatness of=80km/h
Index IRIE1With the second time domain broad sense international roughness indexE2Value；
(3) it with fixed vehicle speed u=80km/h, demarcates vehicle and is travelled on calibration road surface, the automobile vibration of the present embodiment measurement
Dynamic response amount is that front suspension moves stroke accumulated value in unit mileage travelled and front axle nonspring carried mass mass center normal acceleration is square
Root；
(4) the first time domain broad sense international roughness index is established respectivelyE1(front axle is non-with automobile vibration response quautity
Spring carried mass mass center normal acceleration root-mean-square value) the first regression model, the second time domain broad sense international roughness index
IRIE2With the second regression model of automobile vibration response quautity (front suspension moves stroke accumulated value in mileage).
Fig. 3 is 1/2 automobile 6DOF vibrational system mechanical model of certain calibration vehicle, known to model parameter.In figure, m1
And m2Respectively axle nonspring carried mass, m3And m4yRespectively spring carried mass and spring carried mass longitudinally rotating around its mass center
Inertia, m5And m6Body mass on respectively forward and backward seat, csfAnd csrRespectively forward and backward chair vertical damped coefficient, cfAnd cr
The vertical damping coefficient of respectively forward and backward suspension, ksfAnd ksrRespectively forward and backward chair vertical rigidity, kfAnd krIt is respectively forward and backward
The vertical stiffness of suspension, ktfAnd ktrThe vertical stiffness of respectively forward and backward tire, a and b arrive forward and backward respectively for spring carried mass mass center
The fore-and-aft distance of axis, l1And l2Arrive the fore-and-aft distance of forward and backward row's seat, z respectively for spring carried mass mass center1And z2It is respectively forward and backward
The vertical displacement of axis nonspring carried mass mass center, z3And θ4Respectively the vertical displacement of spring carried mass mass center and spring carried mass mass center around
Longitudinal angular displacement of its mass center, z5And z6The vertical displacement of body mass mass center, q on respectively forward and backward seat1And q2Respectively
The road excitation at forward and backward place.
Fig. 4 is IRIE1With the correlativity of front axle nonspring carried mass mass center normal acceleration root-mean-square value, IRI is establishedE1With
First regression model of front axle nonspring carried mass mass center normal acceleration root-mean-square value are as follows:
Wherein,For front axle nonspring carried mass mass center normal acceleration root-mean-square value, unit is m/s2；
Fig. 5 is IRIE2The correlativity that stroke accumulated value is moved with front suspension in mileage, establishes IRIE2And unit
Front suspension moves the second regression model of stroke accumulated value in mileage travelled are as follows:
IRIE2=-2444.924cfd 2+1031.190cfd- 0.038, R=0.9997 (3)
Wherein, cfdStroke accumulated value is moved for front suspension in unit mileage travelled, unit is m/m.
Step 2: on road surface to be measured, the selected automobile vibration response quautity of the step 1 is measured:
On road surface to be measured, with fixed vehicle speed u=80km/h in the step 1, setting sample frequency is 100Hz, respectively
Front suspension moves row in the front axle nonspring carried mass mass center normal acceleration root-mean-square value and mileage of measurement calibration vehicle
Journey accumulated value.
Step 3: the frequency domain broad sense international roughness index on road surface to be measured is soughtE' value:
Step S310: the front axle nonspring carried mass mass center normal acceleration root-mean-square value based on step 2 measurement, knot
Box-like (2) seek the first frequency domain broad sense international roughness index on road surface to be measuredE1' value；
Step S320: front suspension moves stroke accumulated value, convolution in the mileage based on step 2 measurement
(3), the second frequency domain broad sense international roughness index on road surface to be measured is soughtE2' value；
Step 4: online recognition spectrum of road surface roughness parameter, i.e. frequency index and road roughness coefficient:
The present invention describes spectrum of road surface roughness using formula (4):
Wherein, GqIt (n) is road roughness spatial frequency power spectrum density, W is frequency index, Gq(n0) it is Uneven road
Spend coefficient, n0For reference frequency, n0=0.1m-1。
(1) online recognition of frequency index
Online recognition frequency index W is
Wherein, u1=40km/h and u2=80km/h is two different speeds, IRIE1' and IRIE2' it is u respectively1=
40km/h and u2The corresponding first frequency domain broad sense international roughness index of=80km/h and the second frequency domain broad sense world flatness refer to
Number.
(2) online recognition of road roughness coefficient
Road roughness coefficient Gq(n0) be
Wherein, F (Gq(n0), W) and F (W) is respectively
Wherein, n0For reference frequency, n0=0.1m-1,For the spring carried mass and non-spring charge material of gold vehicle
The frequency response function of Relative vertical speed is measured, f is temporal frequency, temporal frequency lower limit fl=0Hz, temporal frequency upper limit fu=
30Hz。
The spring carried mass of gold vehicle and the frequency response function of nonspring carried mass Relative vertical speedIt is specific
Expression formula is
Wherein, j is imaginary unit；μ is the ratio of spring carried mass and nonspring carried mass；C is suspension rate and spring carried mass
Ratio；k1For the ratio of tire stiffness and spring carried mass；k2For the ratio of suspension rate and spring carried mass.
For the ease of the understanding and implementation of the invention patent, it is given below and seeks road roughness coefficient and frequency index
Derivation process:
Frequency domain broad sense international roughness indexEi' mathematic(al) representation are as follows:
Wherein, GqIt (f) is road roughness temporal frequency power spectral density.
Road roughness temporal frequency power spectral density Gq(f) with road roughness spatial frequency power spectrum density Gq(n)
Relationship is
Wherein, f=uin。
Formula (4) is updated in formula (11), is had
Formula (12) is updated in formula (10), is had
It introduces
Then formula (13) is further converted into
The speed u different corresponding to two1And u2, frequency domain broad sense international roughness indexE1' and IRIE2' be respectively
It is divided by formula (15) is corresponding with formula (16) left and right ends, obtains
Natural logrithm is taken to formula (17) both ends, is had
Because of u1≠u2, then frequency index W be
Convolution (14) and formula (15), road roughness coefficient Gq(n0) be
Wherein,Or
Equally, which is suitable for known time domain broad sense international roughness index, seeks road surface frequency index and road surface not
Pingdu coefficient.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed
With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily
Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited
In specific details and legend shown and described herein.
Claims (9)
1. a kind of method based on automobile vibration response online recognition road roughness information, which is characterized in that including following step
It is rapid:
Step 1: calculating two different speed u on calibration road surface1And u2Corresponding first time domain broad sense international roughness index
IRIE1With the second time domain broad sense international roughness indexE2；Automobile vibration response quautity of the measurement calibration vehicle in speed u,
The first time domain broad sense international roughness index is established respectivelyE1It is described with the first regression model of automobile vibration response quautity
Second time domain broad sense international roughness indexE2With the second regression model of automobile vibration response quautity；
Step 2: on road surface to be measured, measurement calibration vehicle automobile vibration response quautity in speed u；
Step 3: the automobile vibration response quautity measured in the step 2 is substituted into first regression model and described respectively
Two regression models calculate separately the first frequency domain broad sense international roughness index on road surface to be measuredE1' and the second frequency domain broad sense state
Border flatness index IRIE2′；
Step 4: according to the first frequency domain broad sense international roughness indexE1' refer to the second frequency domain broad sense world flatness
Number IRIE2' calculating road surface frequency index and road roughness coefficient, wherein the frequency index W meets:
The road roughness coefficient Gq(n0) meet:
Wherein, F (Gq(n0), W) meet:
Or
F (W) meets:
Wherein, n0For reference frequency, n0=0.1m-1,For the spring carried mass and nonspring carried mass phase of gold vehicle
To the frequency response function of vertical speed, f is temporal frequency, flFor temporal frequency lower limit, fuFor the temporal frequency upper limit；
The gold vehicle meets:
Wherein, msFor spring carried mass, muFor nonspring carried mass, ksFor suspension rate, ktFor tire stiffness, csFor suspension damping.
2. the method according to claim 1 based on automobile vibration response online recognition road roughness information, feature
It is, in said step 1,
The automobile vibration response quautity is that front suspension moves rear overhang in stroke accumulated value or mileage in unit mileage travelled
Frame moves stroke accumulated value or front axle nonspring carried mass mass center normal acceleration root-mean-square value or rear axle nonspring carried mass mass center is vertical
Acceleration root-mean-square value.
3. the method according to claim 2 based on automobile vibration response online recognition road roughness information, feature
Be, establish regression model in the step 1 the following steps are included:
Step a, the unevenness on the calibration road surface is measured；
Step b, it is based on the unevenness, calculates the first time domain broad sense international roughness indexE1With second time domain
Broad sense international roughness indexE2；
Step c, on the calibration road surface, front suspension moves stroke accumulated value and front axle in mileage when measuring speed u
Nonspring carried mass mass center normal acceleration root-mean-square value；
Step d, the first time domain broad sense international roughness index is establishedE1Vertically accelerate with front axle nonspring carried mass mass center
Spend the first regression model of root-mean-square value；Establish the second time domain broad sense international roughness indexE2And mileage
Interior front suspension moves the second regression model of stroke accumulated value.
4. the method according to claim 3 based on automobile vibration response online recognition road roughness information, feature
It is,
u1=40km/h, u2=80km/h, u=80km/h.
5. the method according to claim 4 based on automobile vibration response online recognition road roughness information, feature
It is, first regression model meets:
Wherein,For front axle nonspring carried mass mass center normal acceleration root-mean-square value, unit is m/s2。
6. the method according to claim 4 based on automobile vibration response online recognition road roughness information, feature
It is, second regression model meets:
IRIE2=-2444.924cfd 2+1031.190cfd-0.038
Wherein, cfdStroke accumulated value is moved for front suspension in unit mileage travelled, unit is m/m.
7. the method according to claim 2 based on automobile vibration response online recognition road roughness information, feature
It is,
Automobile vibration response quautity described in first regression model chooses the front axle nonspring carried mass mass center of the calibration vehicle
Normal acceleration root-mean-square value, automobile vibration response quautity described in second regression model choose the unit of the calibration vehicle
Front suspension moves stroke accumulated value in mileage travelled.
8. the method according to claim 1 based on automobile vibration response online recognition road roughness information, feature
It is, in said step 1, the time domain broad sense international roughness indexEiMeet:
Wherein, L is the total distance of running car；WithThe spring carried mass of the respectively described gold vehicle and nonspring carried mass
Vertical speed is the function of running car time t.
9. the method according to claim 8 based on automobile vibration response online recognition road roughness information, feature
It is, in the step 4, the spring carried mass of the gold vehicle and the frequency response letter of nonspring carried mass Relative vertical speed
NumberSatisfaction:
Wherein, j is imaginary unit；μ is the ratio of spring carried mass and nonspring carried mass；C is the ratio of suspension rate and spring carried mass
Value；k1For the ratio of tire stiffness and spring carried mass；k2For the ratio of suspension rate and spring carried mass.
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| CN109476310B (en) * | 2016-12-30 | 2021-11-12 | 同济大学 | Automatic driving vehicle speed control method based on comfort level |
| CN111504436B (en) * | 2020-04-17 | 2021-09-17 | 清华大学 | Vehicle load and road condition monitoring method and device based on vehicle vibration data |
| CN111976731B (en) * | 2020-08-04 | 2023-06-13 | 大连民族大学 | Road surface unevenness recognition method based on vehicle frequency domain response |
| CN112498361B (en) * | 2020-11-04 | 2022-01-11 | 江苏大学 | Vehicle suspension self-checking system and self-checking method |
| CN113932758B (en) * | 2021-09-15 | 2022-12-20 | 同济大学 | Road surface flatness prediction method and device |
| CN114261408B (en) * | 2022-01-10 | 2024-05-03 | 武汉路特斯汽车有限公司 | Automatic driving method and system capable of identifying road conditions and vehicle |
| CN114541222B (en) * | 2022-02-17 | 2024-01-26 | 同济大学 | Road network grade pavement flatness detection method based on multi-vehicle crowd funding vibration data |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102837702A (en) * | 2011-06-24 | 2012-12-26 | 株式会社普利司通 | Method and apparatus for determining road surface condition |
| KR20150000016A (en) * | 2013-06-19 | 2015-01-02 | 에스케이플래닛 주식회사 | Method for analyzing road environment using vehicle condition, system and apparatus thereof |
| CN106548651A (en) * | 2017-01-17 | 2017-03-29 | 吉林大学 | A kind of On-line testing method of vehicle traveling road ahead fine information |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20070208484A1 (en) * | 2006-03-06 | 2007-09-06 | Denso Corporation | Vehicle cruise control system and road condition estimating system |
| US8897959B1 (en) * | 2013-02-26 | 2014-11-25 | Faster Faster, Inc. | Feedback system for electric motorcycles |
| US20150203120A1 (en) * | 2014-01-21 | 2015-07-23 | Yung-Chi Liang | Vehicle with an electric energy consumption prediction module |
| JP6330398B2 (en) * | 2014-03-18 | 2018-05-30 | 株式会社Ｓｏｋｅｎ | Vehicle misstart suppression device |
-
2018
- 2018-06-05 CN CN201810566297.6A patent/CN108839657B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102837702A (en) * | 2011-06-24 | 2012-12-26 | 株式会社普利司通 | Method and apparatus for determining road surface condition |
| KR20150000016A (en) * | 2013-06-19 | 2015-01-02 | 에스케이플래닛 주식회사 | Method for analyzing road environment using vehicle condition, system and apparatus thereof |
| CN106548651A (en) * | 2017-01-17 | 2017-03-29 | 吉林大学 | A kind of On-line testing method of vehicle traveling road ahead fine information |
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