CN106570560B - Driving style quantitative evaluation method based on standardization driving behavior and phase space reconfiguration - Google Patents
Driving style quantitative evaluation method based on standardization driving behavior and phase space reconfiguration Download PDFInfo
- Publication number
- CN106570560B CN106570560B CN201610944269.4A CN201610944269A CN106570560B CN 106570560 B CN106570560 B CN 106570560B CN 201610944269 A CN201610944269 A CN 201610944269A CN 106570560 B CN106570560 B CN 106570560B
- Authority
- CN
- China
- Prior art keywords
- driving
- standardization
- driving behavior
- phase space
- space reconfiguration
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000011158 quantitative evaluation Methods 0.000 title claims abstract description 7
- 238000012360 testing method Methods 0.000 claims abstract description 10
- 238000013528 artificial neural network Methods 0.000 claims description 5
- 230000006399 behavior Effects 0.000 description 33
- 230000001133 acceleration Effects 0.000 description 8
- 238000010586 diagram Methods 0.000 description 7
- 230000006870 function Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 239000013598 vector Substances 0.000 description 2
- 206010000117 Abnormal behaviour Diseases 0.000 description 1
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 230000009471 action Effects 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 239000000295 fuel oil Substances 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- ZRHANBBTXQZFSP-UHFFFAOYSA-M potassium;4-amino-3,5,6-trichloropyridine-2-carboxylate Chemical compound [K+].NC1=C(Cl)C(Cl)=NC(C([O-])=O)=C1Cl ZRHANBBTXQZFSP-UHFFFAOYSA-M 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 238000010561 standard procedure Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
-
- 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
- B60W40/08—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 drivers or passengers
- B60W40/09—Driving style or behaviour
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- General Engineering & Computer Science (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Automation & Control Theory (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Combined Controls Of Internal Combustion Engines (AREA)
Abstract
The invention discloses a kind of driving style quantitative evaluation methods based on standardization driving behavior and phase space reconfiguration, the influence of environment is eliminated by this method, it proposes by carrying out standard condition test based on personalized driving person's model to realize the standardization of driving behavior, then the different driving style of quantitative assessment, phase space reconfiguration is carried out to standardization driving behavior, it proposes a kind of driving style index based on correlation dimension, drives radical degree for being quantitatively evaluated.
Description
Technical field
The present invention relates to intelligent transportation fields, more particularly to one kind based on standardization driving behavior and phase space reconfiguration
The driving style quantitative evaluation method of (Phase Space Reconstruction).
Background technique
An important factor for driving style is intelligent transportation, safe driving and new car testing field common concern.Driving style
It is mainly different because of driver personality, while also by environmental factor, it is mainly shown as to the manipulation of throttle/brake pedal.Drive wind
Lattice and traffic accident and fuel economy are closely related, so-called " ten times accident nine times fast ", and radical driving often consumes more
More fuel oils.Automobile industry, which generallys use the pilot model similar to PID controller, can not show any driving style.But
It is that driving style must be taken into consideration again, for example, the fuel economy of radical type driver is poor, therefore, motor corporation would generally be employed
The driver for hiring different-style tests automotive performance on public way.On the other hand, many researchs are dedicated to driving
The classification and identification of style, for example, generally can be divided into mild, moderate type, radical type, however, due to the shadow by subjective factor
It rings, classification results are usually fuzzy and rough.Therefore, objective and quantitative assessment is carried out with important research to driving style
Meaning.
Has more research to the identification of driving style.Kedar-Dongarkar is based on energy consumption and optimizes driving style
Driver is divided into conservative, moderate type and radical by acceleration, brake, speed and throttle opening by classification method
Type；Langari classifies to driving style by the ratio of average acceleration and acceleration standard deviation and acceleration；
Murphey etc. proposes the Style Analysis Methods based on acceleration inverse；Qi etc. is according to follow-up time and apart from driving style
It is divided into vigilant type, moderate type and radical type；Vaitkus is based on acceleration information and use KNN algorithm, realizes oneself of driving style
Dynamic identification；Finally, Aljafreh utilizes the norm and speed of automobile transverse direction/longitudinal acceleration, establish based on fuzzy reasoning
Driving style categorizing system.
As can be seen that above-mentioned driver style identification is normally based on different-style driver and is acquired in various environment
Driving data including brake pressure, accelerator open degree, throttle opening, acceleration, speed etc., the qualitative classification carried out
And identification.In addition, the difference of road conditions can bring difficulty to the identification of driving style, because driving behavior can change correspondingly, for example,
Mild driver can frequently touch on the brake in the case where congestion and throttle, shows certain radical style, and radical type is in height
It can keep relatively stable travel speed on fast road, therefore that judgement is carried out under identical operating condition is just more reasonable for style identification.
Summary of the invention
The purpose of the invention is to overcome shortcoming and defect of the existing technology, and one kind is provided and is driven based on standardization
The driving style quantitative evaluation method for sailing behavior and phase space reconfiguration eliminates the influence of environment by this method, proposes logical
It crosses and standard condition test is carried out to realize the standardization of driving behavior based on personalized driving person's model, then quantitative assessment is different
Driving style, to standardization driving behavior carry out phase space reconfiguration, propose a kind of driving style index based on correlation dimension,
Radical degree is driven for being quantitatively evaluated.
To achieve the above object, the technical scheme is that the following steps are included:
(1) travelling data that true environment acquires is standardized and obtains standardized driving behavior, the driving
Data are characterized by throttle opening；
(2) phase space reconfiguration is carried out to the driving behavior after standardization, and calculates correlation dimension, when obtaining throttle opening
Between sequence correlation dimension TDR；
(3) style index StyIn is calculated using formula (1), for the radical degree driven to be quantitatively evaluated
In formulaIndicate that numerical value std_e rounds up, TDR indicates the correlation dimension of throttle opening time series
Number；The standard deviation of std_e expression velocity error.
It includes following sub-step that further setting, which is the step (1):
Firstly, based on the Typical Representative RBF Function Network of locality neural network, using the direct inverse of study control
Modelling establishes pilot model；
Secondly, being tested using the simulative automobile that the pilot model carries out federal test circulation FTP-72 standard condition, survey
The rate curve that standard condition FTP-72 is followed when examination realizes the standardization of driving behavior.
Further setting is that the step (2) specifically includes following sub-step:
Firstly, carrying out phase space reconfiguration to the driving behavior after standardization using time-delay technique；
Secondly, calculating correlation dimension according to G-P algorithm.According to G-P algorithm, attractor correlation dimension is by following correlation intergal
Export:
In formula, H (u) is Heaviside function, works as u>0, H (u)=1, when u<0, H (u)=0；||Xi-Xj| | it is two vectors
Between distance；R is the radius of hypersphere in phase space, is lesser positive number；N is sequence length.And have
When r → 0 (3)
Wherein, DRAs correlation dimension, DRIt can be found out by following formula
Asymptote of the curve lnr~lnC (r) in r → 0 is straight line, and slope is exactly DR。
The beneficial effects of the present invention are:
1, the driving behavior standardized system proposed by the invention based on personalized driving person's model and standard condition can
To effectively eliminate influence of the road conditions to driving style, keep driving style evaluation more reasonable.
2, driving behavior phase space reconfiguration proposed by the present invention then can effectively characterize the feature of driving style, therefore, this
Invention in fact can carry out refinement assessment to driving style, and be not limited to mild/moderate/radical such rude classification.
3, the present invention also provides one for driving behavior living things feature recognition, abnormal behaviour early warning and automotive test etc.
New effective way.
The present invention is described further with specific embodiment with reference to the accompanying drawings of the specification.
Detailed description of the invention
Fig. 1 is the driver style quantitative assessment process of the invention based on standardization driving behavior and phase space reconfiguration
Figure；
Fig. 2 is driving behavior standardized system block diagram of the invention；
Phase space reconfiguration track example of original throttle opening (TP) signal of Fig. 3 present invention when Embedded dimensions are equal to 2
Display diagram；
Fig. 4 is phase space reconfiguration rail of original throttle opening (TP) signal of the present invention when Embedded dimensions are equal to 3 respectively
Mark example display diagram；
Fig. 5 is phase space reconfiguration rail of throttle opening (TP) signal when Embedded dimensions are equal to 2 after present invention standardization
Mark example display diagram；
Fig. 6 is phase space reconfiguration track example of the throttle opening (TP) when Embedded dimensions are equal to 3 after present invention standardization
Sub- display diagram.
Specific embodiment
The present invention is specifically described below by embodiment, is served only for that invention is further explained, no
It can be interpreted as limiting the scope of the present invention, the technician in the field can be according to the content of foregoing invention to the present invention
Make some nonessential modifications and adaptations.
As shown in figures 1 to 6, the present invention is that a kind of driver style based on standardization driving behavior and phase space reconfiguration is fixed
Evaluation method is measured, the hardware and programming language of method carrying out practically of the invention are not intended to limit, are write and be ok with any language
It completes, other operating modes repeat no more thus.
The embodiment of the present invention is using a computer with Intel Core-i5 central processing unit and 4G byte of memory
And the work based on standardization driving behavior and the driver style quantitative assessment of phase space reconfiguration has been worked out with Matlab language
Program realizes method of the invention.
Before introducing specific steps, we first introduce the meaning of the symbol to be used below.
VS: car speed (Vehicle Speed)；
TP: throttle opening (Throttle Position)；
BP: brake pedal pressure size (Brake Pressure)；
Driver style quantitative evaluation method based on standardization driving behavior and phase space reconfiguration of the invention is mainly wrapped
It includes the following three steps: driving behavior is standardized, standardizes driving behavior phase space reconfiguration, referred to based on correlation dimension formulation style
Number, it is described that specific step is as follows:
(1) in order to eliminate the influence of environment, a kind of driving based on personalized driving person's model and standard condition is proposed
Sail behavioral standard method.It mainly includes:
A) personalized driving person's model is established.As Fig. 2 (a) show the original of the pilot model based on Direct Inverse Model method
Manage block diagram.In view of the highly discrete and local mutability, the present invention of real driving data use the allusion quotation of Local neural network
Type represents foundation of the RBF Function Neural Network for pilot model.Assuming that the speed of t moment is VS (t), at this point, driver
Throttle opening TP (t) or brake pedal BP (t) are operated, due to time delay, this operation influences vs (t+k), and (wherein k representative is prolonged
The slow time).Using in practical travelling data vs (t+k) and Δ vs (t+k)=vs (t+k)-vs (t) as the defeated of neural network
Enter, TP (t) and BP (t) signal are desired output, carry out repetition training to RBF network using practical travelling data, obtain final
Pilot model.
B) driving behavior standardizes.Based on pilot model and standard condition, the driving behavior mark as shown in Fig. 2 (b) is established
Quasi-ization system.Firstly, extracting (t+k) from the rate curve that the simulative automobile of federal test circulation FTP-72 standard condition is tested
Desired speed vs of the moment speed vs (t+k) as t moment*(t), and its difference DELTA vs with actual vehicle speed vs ' (t) is calculated*
[t]=vs*[t]-vs'[t]；Secondly, with vs*(t) and Δ vs*The input of [t] as pilot model and pilot controller, point
It is not sought and exports TPm (t), BPm (t) and TPp (t), BPp (t), is then output to the TP (t) and BP (t) of car model respectively
It is
TP (t)=TPm (t)+TPp (t)
BP (t)=BPm (t)+BPp (t)
Finally, using current vehicle speed vs ' (t) and current throttle opening TP (t) and BP (t) as the defeated of car model
Enter, due to time-lag action (z in Fig. 2 (b)-kIt is shown), TP (t) and BP (t) influences whether the speed vs ' (t+k) at (t+k) moment；
Above step repeats, and completes the standardization of driving behavior.
(2) driving behavior phase space reconfiguration is standardized.With mild driver #1~#2, moderate type driver #1~#2,
For radical type driver #1~#2.
First pass around step (1) driving behavior standardization.
Secondly, the time-delay technique again based on propositions such as Packard carries out phase space to standardization driving behavior (TP)
Reconstruct.Assuming that TP time series is { tp (k), k=1,2 ..., N }, then some state vectors reconstructed in phase space can be with
It indicates are as follows:
TPi=[tp (i), tp (i+ τ) ..., tp (i+ (m-1) τ)] i=1,2 ..., M (5)
Wherein M is the number thought in phase space reconstruction a little, and M=N- (m-1) τ, m and τ is Embedded dimensions and the time of system respectively
Delay.
Fig. 5 and Fig. 6 is three kinds of styles respectively after the driving behavior standardization of totally 6 drivers in Embedded dimensions m=2 and m
Phase space reconfiguration trajectory diagram when=3.(such as Fig. 3 and Fig. 4), standard compared with the phase space reconfiguration track of original driving behavior
The phase space reconfiguration track for changing driving behavior becomes apparent from the discrimination of different-style, while the driving characteristics of identical style are more
Add it is similar, illustrate driving behavior standardization can effectively promote the representational of driving characteristics.
(3) style index is calculated based on correlation dimension.It mainly includes:
A) driving behavior is standardized according to step (1), driving behavior TP, BP and VS after being standardized, and counted
Calculate velocity error VS-VSFTPStandard deviation std_e；
B) phase space reconfiguration is carried out to the driving behavior after standardization according to step (2), and is based on G-P algorithm, calculate TP
Correlation dimension TDR.
C) according to resulting TDR and std_e is calculated, style index StyIn is calculated, i.e.,
Claims (2)
1. a kind of driving style quantitative evaluation method based on standardization driving behavior and phase space reconfiguration, it is characterised in that including
Following steps:
(1) travelling data that true environment acquires is standardized obtain standardized driving behavior data TP, BP,
VS, and calculating speed error VS-VSFTPStandard deviation std_e, wherein VS indicate car speed, TP indicate throttle opening, BP
Indicate brake pedal pressure, VSFTPIndicate the car speed based on federal test circulation FTP-72 standard condition；
(2) phase space reconfiguration is carried out to the driving behavior after standardization, and calculates correlation dimension, obtain throttle opening time sequence
The correlation dimension TDR of column；The step (2) specifically includes following sub-step:
Firstly, carrying out phase space reconfiguration to the driving behavior after standardization using time-delay technique；
Secondly, calculating correlation dimension according to G-P algorithm；
(3) style index StyIn is calculated using formula (1), for the radical degree driven to be quantitatively evaluated
In formulaIndicate that numerical value std_e rounds up, TDR indicates the correlation dimension of throttle opening time series.
2. the driving style quantitative assessment side according to claim 1 based on standardization driving behavior and phase space reconfiguration
Method, it is characterised in that:
The step (1) includes following sub-step:
Firstly, based on the Typical Representative RBF Function Network of locality neural network, using the Direct Inverse Model of study control
Method establishes pilot model；
Secondly, being tested using the simulative automobile that the pilot model carries out federal test circulation FTP-72 standard condition, when test
The rate curve for following standard condition FTP-72 realizes the standardization of driving behavior.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610944269.4A CN106570560B (en) | 2016-11-02 | 2016-11-02 | Driving style quantitative evaluation method based on standardization driving behavior and phase space reconfiguration |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610944269.4A CN106570560B (en) | 2016-11-02 | 2016-11-02 | Driving style quantitative evaluation method based on standardization driving behavior and phase space reconfiguration |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106570560A CN106570560A (en) | 2017-04-19 |
| CN106570560B true CN106570560B (en) | 2019-01-04 |
Family
ID=58536582
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610944269.4A Active CN106570560B (en) | 2016-11-02 | 2016-11-02 | Driving style quantitative evaluation method based on standardization driving behavior and phase space reconfiguration |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106570560B (en) |
Families Citing this family (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108926352B (en) * | 2017-05-22 | 2020-10-09 | 北京大学 | Driving fatigue detection method and system |
| CN107521501B (en) * | 2017-07-11 | 2020-06-30 | 上海蔚来汽车有限公司 | Game theory-based driver assistance system decision method, system and others |
| CN108622103B (en) | 2018-05-08 | 2019-02-19 | 清华大学 | The scaling method and system of driving Risk Identification model |
| CN111126556B (en) * | 2018-10-31 | 2023-07-25 | 百度在线网络技术（北京）有限公司 | Training method and device for artificial neural network model |
| CN112035942A (en) * | 2019-06-03 | 2020-12-04 | 上海汽车集团股份有限公司 | Energy consumption simulation method and device based on driving behaviors |
| CN111547064B (en) * | 2020-05-26 | 2022-07-12 | 吉林大学 | Driving style recognition and classification method for automobile adaptive cruise system |
| CN113188814A (en) * | 2021-05-17 | 2021-07-30 | 联合汽车电子有限公司 | Automatic driving reproduction method, system and storage medium |
| CN117698762A (en) * | 2023-12-12 | 2024-03-15 | 海识(烟台)信息科技有限公司 | Intelligent driving assistance system and method based on environment perception and behavior prediction |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104832299A (en) * | 2015-03-05 | 2015-08-12 | 东软集团股份有限公司 | High-fuel consumption driving state judgment method, apparatus and system |
| CN105185112A (en) * | 2015-08-21 | 2015-12-23 | 深圳市北斗软核信息技术有限公司 | Driving behavior analysis and recognition method and system |
| CN105844913A (en) * | 2016-04-15 | 2016-08-10 | 苏州爱诺信信息科技有限公司 | Correlation analyzing method based on vehicle, road conditions and safe travel big data in network of vehicles |
-
2016
- 2016-11-02 CN CN201610944269.4A patent/CN106570560B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104832299A (en) * | 2015-03-05 | 2015-08-12 | 东软集团股份有限公司 | High-fuel consumption driving state judgment method, apparatus and system |
| CN105185112A (en) * | 2015-08-21 | 2015-12-23 | 深圳市北斗软核信息技术有限公司 | Driving behavior analysis and recognition method and system |
| CN105844913A (en) * | 2016-04-15 | 2016-08-10 | 苏州爱诺信信息科技有限公司 | Correlation analyzing method based on vehicle, road conditions and safe travel big data in network of vehicles |
Non-Patent Citations (1)
| Title |
|---|
| 驾驶员行为模式的因子分析和模糊聚类;张磊;《交通运输工程学报》;20091031;第9卷(第5期);第121-126页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106570560A (en) | 2017-04-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106570560B (en) | Driving style quantitative evaluation method based on standardization driving behavior and phase space reconfiguration | |
| Xie et al. | A data-driven lane-changing model based on deep learning | |
| Zhu et al. | Typical-driving-style-oriented personalized adaptive cruise control design based on human driving data | |
| CN107742193B (en) | A kind of driving Risk Forecast Method based on time-varying state transition probability Markov chain | |
| CN110949398B (en) | Method for detecting abnormal driving behavior of first-vehicle drivers in vehicle formation driving | |
| Gu et al. | A novel lane-changing decision model for autonomous vehicles based on deep autoencoder network and XGBoost | |
| US7860813B2 (en) | Monitoring driving safety using semi-supervised sequential learning | |
| Deng et al. | Driving style recognition method using braking characteristics based on hidden Markov model | |
| Tang et al. | Driver lane change intention recognition of intelligent vehicle based on long short-term memory network | |
| Lyu et al. | Using naturalistic driving data to identify driving style based on longitudinal driving operation conditions | |
| Li et al. | Driver-identified supervisory control system of hybrid electric vehicles based on spectrum-guided fuzzy feature extraction | |
| EP3725633B1 (en) | Method for determining vehicle paths | |
| EP3726436A1 (en) | Method for determining vehicle paths | |
| CN114655227A (en) | Driving style recognition method, driving assistance method and device | |
| EP3726434B1 (en) | Method for reducing exhaust emissions of a vehicle drive system with combustion engine | |
| Yang et al. | A time-efficient approach for decision-making style recognition in lane-changing behavior | |
| Chu et al. | A review of driving style recognition methods from short-term and long-term perspectives | |
| Sun et al. | Machine-learning-based hybrid recognition approach for longitudinal driving behavior in noisy environment | |
| Liu et al. | Estimation of driver lane change intention based on the LSTM and Dempster–Shafer evidence theory | |
| Jensen et al. | Mathematical models of human drivers using artificial risk fields | |
| Liu et al. | Real-time comprehensive driving ability evaluation algorithm for intelligent assisted driving | |
| Li et al. | Modeling and verification of heavy-duty truck drivers’ car-following characteristics | |
| Mechernene et al. | Lane change decision algorithm based on risk prediction and fuzzy logic method | |
| Mumcuoğlu | Driving behavior classification for Heavy-Duty vehicles using LSTM Networks | |
| Shi et al. | A normalized approach for evaluating driving styles based on personalized driver modeling |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20170419 Assignee: Big data and Information Technology Research Institute of Wenzhou University Assignor: Wenzhou University Contract record no.: X2020330000098 Denomination of invention: Driving style quantitative evaluation method based on standardized driving behavior and phase space reconstruction Granted publication date: 20190104 License type: Common License Record date: 20201115 |
|
| EE01 | Entry into force of recordation of patent licensing contract | ||
| EE01 | Entry into force of recordation of patent licensing contract |
Application publication date: 20170419 Assignee: Wenzhou Jicheng Network Co.,Ltd. Assignor: Wenzhou University Contract record no.: X2023330000909 Denomination of invention: A quantitative evaluation method for driving style based on standardized driving behavior and phase space reconstruction Granted publication date: 20190104 License type: Common License Record date: 20231208 |