SG11201809423VA - Driving model training method, driver identification method, apparatuses, device and medium - Google Patents
Driving model training method, driver identification method, apparatuses, device and mediumInfo
- Publication number
- SG11201809423VA SG11201809423VA SG11201809423VA SG11201809423VA SG11201809423VA SG 11201809423V A SG11201809423V A SG 11201809423VA SG 11201809423V A SG11201809423V A SG 11201809423VA SG 11201809423V A SG11201809423V A SG 11201809423VA SG 11201809423V A SG11201809423V A SG 11201809423VA
- Authority
- SG
- Singapore
- Prior art keywords
- training
- driving model
- driving
- acquiring
- apparatuses
- Prior art date
Links
- 238000000034 method Methods 0.000 title abstract 8
- 238000012360 testing method Methods 0.000 abstract 3
Classifications
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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
- B60W40/08—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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
- G06F18/24133—Distances to prototypes
- G06F18/24137—Distances to cluster centroïds
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/285—Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
-
- 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
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- 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
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- 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
- B60W2040/0809—Driver authorisation; Driver identity check
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
DRIVING MODEL TRAINING METHOD, DRIVER IDENTIFICATION METHOD, APPARATUSES, DEVICE AND MEDIUM A driving model training method, a driver identification method, apparatuses, a device and a medium are provided. The driving model training method comprises: acquiring training behavior data of a user wherein the training behavior data are associated with a user iden tifier; acquiring training drivin g data associated with the user identifier based on the training behavior data; acquiring positive and negative samples from the training driving data based on the user identifier, and dividing the positive and negative samples into a training set and a test set; training the training set using a bagging algorithm, and acquiring an original driving model; and testing the original driving model using the test set, and acquiring a ta rget driving model. The driving model training method effectively enhances generalization of the driving model, solves the problem of a poor iden tification result of the current driving identification model, and improves an accuracy rate of identifying driving of a driver. FIG. 1
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201710846499.1A CN107679557B (en) | 2017-09-19 | 2017-09-19 | Driving model training method, driver identification method, device, equipment and medium |
PCT/CN2017/108517 WO2019056497A1 (en) | 2017-09-19 | 2017-10-31 | Driving model training method, driver recognition method, device, apparatus and medium |
Publications (1)
Publication Number | Publication Date |
---|---|
SG11201809423VA true SG11201809423VA (en) | 2019-04-29 |
Family
ID=61137308
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
SG11201809423VA SG11201809423VA (en) | 2017-09-19 | 2017-10-31 | Driving model training method, driver identification method, apparatuses, device and medium |
Country Status (4)
Country | Link |
---|---|
US (2) | US20210188290A1 (en) |
CN (1) | CN107679557B (en) |
SG (1) | SG11201809423VA (en) |
WO (1) | WO2019056497A1 (en) |
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CN108921200B (en) * | 2018-06-11 | 2021-07-30 | 百度在线网络技术(北京)有限公司 | Method, apparatus, device and medium for classifying driving scene data |
CN108791302B (en) * | 2018-06-25 | 2020-05-19 | 大连大学 | Driver behavior modeling system |
CN108791308B (en) * | 2018-06-25 | 2020-05-19 | 大连大学 | System for constructing driving strategy based on driving environment |
CN108891421B (en) * | 2018-06-25 | 2020-05-19 | 大连大学 | Method for constructing driving strategy |
CN108819948B (en) * | 2018-06-25 | 2020-05-19 | 大连大学 | Driver behavior modeling method based on reverse reinforcement learning |
CN108944940B (en) * | 2018-06-25 | 2020-05-19 | 大连大学 | Driver behavior modeling method based on neural network |
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-
2017
- 2017-09-19 CN CN201710846499.1A patent/CN107679557B/en active Active
- 2017-10-31 US US16/093,633 patent/US20210188290A1/en active Pending
- 2017-10-31 SG SG11201809423VA patent/SG11201809423VA/en unknown
- 2017-10-31 WO PCT/CN2017/108517 patent/WO2019056497A1/en active Application Filing
-
2023
- 2023-12-25 US US18/395,671 patent/US20240132078A1/en active Pending
Also Published As
Publication number | Publication date |
---|---|
CN107679557A (en) | 2018-02-09 |
WO2019056497A1 (en) | 2019-03-28 |
US20210188290A1 (en) | 2021-06-24 |
CN107679557B (en) | 2020-11-27 |
US20240132078A1 (en) | 2024-04-25 |
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