CN116653980A - Driver driving habit analysis system and driving habit analysis method - Google Patents
Driver driving habit analysis system and driving habit analysis method Download PDFInfo
- Publication number
- CN116653980A CN116653980A CN202310783209.9A CN202310783209A CN116653980A CN 116653980 A CN116653980 A CN 116653980A CN 202310783209 A CN202310783209 A CN 202310783209A CN 116653980 A CN116653980 A CN 116653980A
- Authority
- CN
- China
- Prior art keywords
- driving
- driver
- habit
- habits
- level
- 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.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 claims abstract description 42
- 238000011156 evaluation Methods 0.000 claims abstract description 28
- 230000008569 process Effects 0.000 claims abstract description 17
- 238000000605 extraction Methods 0.000 claims abstract description 15
- 230000006399 behavior Effects 0.000 claims description 98
- 230000000007 visual effect Effects 0.000 claims description 16
- 238000004590 computer program Methods 0.000 claims description 15
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000012216 screening Methods 0.000 claims description 4
- 230000005540 biological transmission Effects 0.000 description 7
- 238000004422 calculation algorithm Methods 0.000 description 6
- 238000012545 processing Methods 0.000 description 6
- 238000010801 machine learning Methods 0.000 description 5
- 230000001133 acceleration Effects 0.000 description 4
- 230000006872 improvement Effects 0.000 description 4
- 238000003909 pattern recognition Methods 0.000 description 4
- 238000012549 training Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 238000012800 visualization Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 238000007635 classification algorithm Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
Classifications
-
- 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
-
- 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
Abstract
The application discloses a system and a method for analyzing driving habits of a driver, wherein the system comprises the following steps: the data acquisition module is used for acquiring driving data generated in the driving process of a driver; the feature extraction module is used for extracting driving behavior features in the driving data; the driving recognition module is used for analyzing the driving behavior characteristics and recognizing driving habits; and the driving evaluation module is used for evaluating the driving habit of the driver and generating a driving habit report of the driver. By utilizing the embodiment of the application, the accuracy and precision of driving habit analysis can be improved, and better driving evaluation and service can be provided for a driver.
Description
Technical Field
The application belongs to the technical field of intelligent driving, and particularly relates to a driving habit analysis system and a driving habit analysis method for a driver.
Background
With the popularization of automobiles and increasingly heavy road traffic, the importance of driving safety and driving habits is increasingly highlighted. The driving behavior and habit of the driver have an important influence on traffic safety and driving efficiency. However, the conventional driving habit evaluation method generally relies on manual observation and subjective judgment, and often has problems of subjectivity, inaccuracy, large workload and the like.
In recent years, with the development of technologies such as artificial intelligence and machine learning, a driving habit analysis method based on data analysis and pattern recognition has attracted attention. The method uses data processing and feature extraction technology to build a model to identify and evaluate driving habits by collecting driving behavior data of a driver, and provides personalized driving advice and service for the driver.
However, existing driving habit analysis systems still have some problems. First, the methods of the partial system for processing the driving data and extracting the characteristics are not accurate and effective enough, resulting in inaccurate driving habit evaluation results. Secondly, part of the systems have limited recognition and prediction capabilities for driving behaviors, and the driving habits of the driver cannot be accurately judged. In addition, existing systems often lack personalized driving assessment reports and services that do not meet the personalized needs of the driver.
Disclosure of Invention
The application aims to provide a system and a method for analyzing driving habits of a driver, which are used for solving the defects in the prior art, improving the accuracy and precision of the driving habit analysis and providing better driving evaluation and service for the driver.
One embodiment of the present application provides a driver driving habit analysis system including:
the data acquisition module is used for acquiring driving data generated in the driving process of a driver;
the feature extraction module is used for extracting driving behavior features in the driving data;
the driving recognition module is used for analyzing the driving behavior characteristics and recognizing driving habits;
and the driving evaluation module is used for evaluating the driving habit of the driver and generating a driving habit report of the driver.
Optionally, the analyzing the driving behavior feature and identifying the driving habit includes:
screening driving behavior characteristics belonging to driving habits from the driving behavior characteristics;
and classifying the screened driving behavior characteristics according to the preset driving habit level to obtain the driving habits of each level.
Optionally, the driving habit report includes: visual display of driving habit scores and driving habit histories;
the method for evaluating the driving habit of the driver and generating the driving habit report of the driver comprises the following steps:
for each level of driving habits, determining the behavior feature times in the level of driving habits;
calculating a driving habit score of a driver, wherein the calculation mode is as follows: the sum of the products of the behavior characteristic times of each level of driving habit and the corresponding level of driving habit weights;
and carrying out real-time visual display on the driving habits of each level, and searching the driving habit history record of the driver.
Optionally, the system further comprises: and the driving early warning module is used for carrying out early warning on specific-level driving habits of which the behavior characteristic times exceed a preset threshold value.
Yet another embodiment of the present application provides a driving habit analysis method including:
collecting driving data generated in the driving process of a driver;
extracting driving behavior characteristics in the driving data;
analyzing the driving behavior characteristics and identifying driving habits;
and evaluating the driving habit of the driver and generating a driving habit report of the driver.
Optionally, the analyzing the driving behavior feature and identifying the driving habit includes:
screening driving behavior characteristics belonging to driving habits from the driving behavior characteristics;
and classifying the screened driving behavior characteristics according to the preset driving habit level to obtain the driving habits of each level.
Optionally, the driving habit report includes: visual display of driving habit scores and driving habit histories;
the method for evaluating the driving habit of the driver and generating the driving habit report of the driver comprises the following steps:
for each level of driving habits, determining the behavior feature times in the level of driving habits;
calculating a driving habit score of a driver, wherein the calculation mode is as follows: the sum of the products of the behavior characteristic times of each level of driving habit and the corresponding level of driving habit weights;
and carrying out real-time visual display on the driving habits of each level, and searching the driving habit history record of the driver.
Optionally, the method further comprises: and carrying out early warning on the specific level driving habit of which the behavior characteristic times exceed the preset threshold value.
A further embodiment of the application provides a storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of the preceding claims when run.
Yet another embodiment of the application provides an electronic device comprising a memory having a computer program stored therein and a processor configured to run the computer program to perform the method recited in any of the preceding claims.
Compared with the prior art, the system for analyzing the driving habit of the driver provided by the application comprises the following components: the data acquisition module is used for acquiring driving data generated in the driving process of a driver; the feature extraction module is used for extracting driving behavior features in the driving data; the driving recognition module is used for analyzing the driving behavior characteristics and recognizing driving habits; the driving evaluation module is used for evaluating the driving habit of the driver and generating a driving habit report of the driver, so that the accuracy and precision of driving habit analysis can be improved, and better driving evaluation and service are provided for the driver.
Drawings
Fig. 1 is a schematic diagram of a driving habit analysis system of a driver according to an embodiment of the present application;
fig. 2 is a flow chart of a driving habit analysis method according to an embodiment of the present application;
fig. 3 is a hardware block diagram of a computer terminal of a driving habit analysis method according to an embodiment of the present application.
Detailed Description
The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application.
An embodiment of the present application provides a driving habit analysis system of a driver, as shown in fig. 1, which may include:
the data acquisition module 101 is used for acquiring driving data generated in the driving process of a driver;
specifically, the module is used for collecting driving data generated in the driving process of a driver. The data can be acquired by means of vehicle-mounted sensors, cameras, mobile phone application programs and the like. The data acquisition module is responsible for collecting the data and transmitting the data to the subsequent feature extraction module for processing.
In practical applications, various sensors, such as an acceleration sensor, a brake sensor, a steering angular velocity sensor, etc., are installed on a vehicle for collecting data generated during driving. Meanwhile, the vehicle-mounted camera can be used for recording the behavior of a driver or connected with Bluetooth of a vehicle through a mobile phone application program to collect data of the vehicle. The acquired data can be sent to the feature extraction module for processing in a wireless transmission mode.
A feature extraction module 102, configured to extract driving behavior features in the driving data;
specifically, the module is used for extracting driving behavior characteristics from driving data. The driving behavior characteristics may include acceleration, braking distance, steering angular velocity, etc. The feature extraction module processes and analyzes the driving data according to a predefined feature extraction algorithm, and extracts features related to driving behaviors.
In practical applications, various feature extraction algorithms may be used to extract driving behavior features from driving data. Common feature extraction methods include statistical feature extraction, frequency domain analysis, time domain analysis, machine learning, and the like. Specifically, the characteristics such as the mean value and variance of acceleration, the rate of change of braking distance, the frequency distribution of steering angular velocity, and the like can be calculated. According to the definition of actual demand and driving habit, a suitable feature extraction algorithm can be selected.
A driving recognition module 103, configured to analyze the driving behavior feature and recognize driving habits;
specifically, the module is used for analyzing the extracted driving behavior characteristics and identifying driving habits. The driving recognition module may use a machine learning algorithm or a pattern recognition technique to determine the driving habit of the driver by training and classifying the driving behavior features. For example, the driver is classified into a habit such as aggressive driving, steady driving, or conservative driving, or a driving habit such as excellent, good, normal, bad, or the like, according to a specific driving behavior feature pattern.
In practical applications, machine learning algorithms or pattern recognition techniques may be used to analyze the extracted driving behavior characteristics and to recognize driving habits. For example, training and classification may be performed using classification algorithms such as support vector machines, random forests, or pattern recognition techniques such as statistical models, neural networks, and the like. In the training phase, driving data with labels need to be prepared for training and constructing a driving habit model.
In one implementation, driving behavior features belonging to driving habits among the driving behavior features may be screened; and classifying the screened driving behavior characteristics according to the preset driving habit level to obtain the driving habits of each level.
First, the driving recognition module screens the extracted driving behavior features, and selects features belonging to driving habits from the extracted driving behavior features. This step may be accomplished by setting a set of predefined driving habit feature rules or using a machine learning algorithm. For example, the number of times a certain characteristic exceeds a certain threshold is set as a flag for aggressive driving.
Next, aiming at the screened driving habit characteristics, the driving recognition module classifies the driving habit characteristics according to preset driving habit levels. This process may divide the different levels of driving habits according to the intensity, frequency, or other indicators of driving behavior characteristics. For example, the rapid acceleration and rapid braking behavior features are classified as low-level aggressive driving, while the speeding and hazard-changing lane behavior features are classified as high-level aggressive driving.
Through the steps, the driving recognition module can classify the driving behavior characteristics according to the driving habit and the level to which the driving recognition module belongs. Such classification results are important for the driver and subsequent evaluation and report generation of the system. The driver can know the driving behavior of himself by looking at different levels and characteristic times of his driving habit and can improve the driving behavior in a targeted way. The system can calculate the driving habit score of the driver according to the classification result and generate a corresponding driving habit report.
Through analysis of driving behavior characteristics and recognition of driving habits, the driving habit analysis system of the driver can evaluate driving behaviors of the driver more accurately and provide personalized driving habit reports and suggestions. This helps the driver to understand his driving habit and take corresponding measures to improve driving behavior.
The driving evaluation module 104 is configured to evaluate driving habits of the driver and generate a driving habit report of the driver.
Specifically, the module is used for evaluating the driving habit of the driver and generating a driving habit report. And the driving evaluation module calculates the driving habit score of the driver according to the identified driving habits and by combining preset evaluation standards and weights. The evaluation module can also generate a driving habit report of the driver according to the score, wherein the driving habit report comprises information such as a scoring result, visual display, a history record and the like. The driver can learn about his driving habit through reports and make improvements and optimizations accordingly.
In practical application, the driving habit score of the driver can be calculated according to the identified driving habits and by combining preset evaluation standards and weights. The evaluation module may set different weights according to different evaluation criteria, such as aggressive driving, steady driving, conservative driving, etc. The score may be calculated by means of weighted summation or based on a certain evaluation model.
Through cooperation of the modules, the driving habit analysis system of the driver can comprehensively collect driving data, extract driving behavior characteristics, identify driving habits and evaluate the driving habits. The system can provide accurate and objective driving habit analysis, help a driver to improve driving behaviors and improve driving safety and efficiency.
Specifically, the driving habit report includes: and the driving habit score, the visual display of the driving habit and the driving habit history record.
First, in the driving evaluation module, the driving habit of the driver is evaluated, and a score of the driving habit is calculated according to a preset evaluation criterion and weight. This score may reflect how well the driver's overall driving habits are.
When the driving habit report is generated, the driving habit score is presented to the driver to reflect the difference between the driving behavior and the standard. The assessment module may represent the score in percent form or rank it as a good, a medium, a bad, etc. grade.
In addition to the driving habit score, the driving habit report also includes a visual presentation of driving habits. This may be presented graphically, or otherwise. Through the visualization, the driver can more clearly know the driving behaviors of the driver in different aspects, such as aggressive driving, fatigue driving, overspeed driving and the like. Visual displays can also help drivers find problems and room for improvement in their driving habits.
In addition, the driving habit report also includes a history of driving habits. These records may provide trends in driver driving habits over different time periods. Through the history record, the driver can compare the driving habits of the driver in different time periods and know the improvement and progress of the driver.
And the driving habit report generation module generates a driving habit report comprising scores, visual display and historical records according to the calculation result of the evaluation module and corresponding data. Such reports can be timely fed back to the driver to help him/her to understand his/her driving behavior and to take corresponding improvement measures.
In one implementation, the number of behavioral characteristics in each level of driving habits may be determined for that level of driving habits; in this step, the driving evaluation module will count the behavior features in each driving habit level, and calculate the number of times each behavior feature appears in each driving habit level. The specific operation may be traversing driving behavior features, counting the number of occurrences in different levels of driving habits for each feature, and saving the results.
Calculating a driving habit score of a driver, wherein the calculation mode is as follows: the sum of the products of the behavior characteristic times of each level of driving habit and the corresponding level of driving habit weights; in this step, the driving assessment module will calculate a driving habit score for the driver. The calculation mode is the sum of the products of the behavior characteristic times of each level driving habit and the corresponding level driving habit weight. Firstly, setting a weight for each level of driving habit, then calculating the driving habit score of each level according to the behavior characteristic statistical result, and adding the scores of different levels to obtain a final driving habit score.
And carrying out real-time visual display on the driving habits of each level, and searching the driving habit history record of the driver. In this step, the driving evaluation module performs real-time visual display on the driving habit of each level, so that the driver can intuitively understand the driving habit condition of the driver. A chart library or visualization tool may be used to demonstrate the scoring of each level of driving habits. Meanwhile, the driving evaluation module can also search the driving habit history record of the driver, so that the driver can compare the change trend and the history expression of the driving habit of the driver.
Specifically, in practical applications, the system may further include: and the driving early warning module is used for carrying out early warning on specific-level driving habits of which the behavior characteristic times exceed a preset threshold value. And judging whether the threshold value is exceeded or not by monitoring the times of driving behavior characteristics and comparing the times with a preset threshold value. When the behavior characteristic times of the driving habit at the specific level exceed a preset threshold, the driving early warning module triggers an early warning mechanism to give a warning to the driver to remind the driver to pay attention to the driving behavior so as to improve the driving habit.
In the driving early warning module, corresponding preset thresholds are set according to driving habits of different levels. These thresholds may be set according to driving safety standards, regulatory requirements, or individual driver needs. For example, for high-level aggressive driving habits, the number of overspeed behaviors may be set to a preset threshold. When the characteristic times of the driving behaviors exceed the preset thresholds, the early warning module triggers early warning.
The early warning module can transmit early warning information to a driver in a sound, visual or vibration mode and the like. For example, the driver is alerted to the driving behavior by means of a warning light, audible prompts, or vibrating a driver seat on the dashboard of the vehicle. Therefore, the driver can be helped to realize that the driving habit of the driver is bad, and timely take measures to correct the driving habit, so that the driving safety is improved.
Therefore, the system for analyzing the driving habit of the driver provided by the application comprises the following components: the data acquisition module is used for acquiring driving data generated in the driving process of a driver; the feature extraction module is used for extracting driving behavior features in the driving data; the driving recognition module is used for analyzing the driving behavior characteristics and recognizing driving habits; the driving evaluation module is used for evaluating the driving habit of the driver and generating a driving habit report of the driver, so that the accuracy and precision of driving habit analysis can be improved, and better driving evaluation and service are provided for the driver.
A further embodiment of the present application provides a driving habit analysis method, corresponding to the system shown in fig. 1, referring to fig. 2, the method may include:
s201, collecting driving data generated in the driving process of a driver;
s202, extracting driving behavior characteristics in the driving data;
s203, analyzing the driving behavior characteristics and identifying driving habits;
s104, evaluating the driving habit of the driver and generating a driving habit report of the driver.
It can be seen that the driving data generated by the driver driving process is collected; extracting driving behavior characteristics in the driving data; analyzing the driving behavior characteristics and identifying driving habits; and evaluating the driving habit of the driver and generating a driving habit report of the driver, so that the accuracy and precision of driving habit analysis can be improved, and better driving evaluation and service are provided for the driver.
The following describes the operation of the computer terminal in detail by taking it as an example. Fig. 3 is a hardware block diagram of a computer terminal of a driving habit analysis method according to an embodiment of the present application. As shown in fig. 3, the computer terminal may comprise one or more (only one is shown in fig. 3) processors 302 (the processor 302 may comprise, but is not limited to, a microprocessor MCU or a processing means such as a programmable logic device FPGA) and a memory 304 for storing data, and optionally the computer terminal may further comprise a transmission means 306 for communication functions and an input output device 308. It will be appreciated by those skilled in the art that the configuration shown in fig. 3 is merely illustrative and is not intended to limit the configuration of the computer terminal described above. For example, the computer terminal may also include more or fewer components than shown in FIG. 3, or have a different configuration than shown in FIG. 3.
The memory 304 may be used to store software programs and modules of application software, such as program instructions/modules corresponding to the driving habit analysis method in the embodiment of the present application, and the processor 302 executes the software programs and modules stored in the memory 304 to perform various functional applications and data processing, that is, implement the method described above. Memory 304 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, memory 304 may further include memory located remotely from processor 302, which may be connected to the computer terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means 306 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission means 306 comprises a network adapter (Network Interface Controller, NIC) that can be connected to other network devices via a base station to communicate with the internet. In one example, the transmission device 306 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
The embodiment of the application also provides a storage medium, in which a computer program is stored, wherein the computer program is configured to perform the steps of any of the method embodiments described above when run.
Specifically, in the present embodiment, the above-described storage medium may be configured to store a computer program for executing the steps of:
s201, collecting driving data generated in the driving process of a driver;
s202, extracting driving behavior characteristics in the driving data;
s203, analyzing the driving behavior characteristics and identifying driving habits;
s104, evaluating the driving habit of the driver and generating a driving habit report of the driver.
Specifically, in the present embodiment, the storage medium may include, but is not limited to: a usb disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing a computer program.
It can be seen that the driving data generated by the driver driving process is collected; extracting driving behavior characteristics in the driving data; analyzing the driving behavior characteristics and identifying driving habits; and evaluating the driving habit of the driver and generating a driving habit report of the driver, so that the accuracy and precision of driving habit analysis can be improved, and better driving evaluation and service are provided for the driver.
The present application also provides an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the method embodiments described above.
Specifically, the electronic apparatus may further include a transmission device and an input/output device, where the transmission device is connected to the processor, and the input/output device is connected to the processor.
Specifically, in the present embodiment, the above-described processor may be configured to execute the following steps by a computer program:
s201, collecting driving data generated in the driving process of a driver;
s202, extracting driving behavior characteristics in the driving data;
s203, analyzing the driving behavior characteristics and identifying driving habits;
s104, evaluating the driving habit of the driver and generating a driving habit report of the driver.
Specifically, the specific examples in this embodiment may refer to the examples described in the foregoing embodiments and the optional implementation manners, and this embodiment is not repeated herein.
It can be seen that the driving data generated by the driver driving process is collected; extracting driving behavior characteristics in the driving data; analyzing the driving behavior characteristics and identifying driving habits; and evaluating the driving habit of the driver and generating a driving habit report of the driver, so that the accuracy and precision of driving habit analysis can be improved, and better driving evaluation and service are provided for the driver.
While the foregoing is directed to embodiments of the present application, other and further embodiments of the application may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (10)
1. A system for analyzing driving habits of a driver, the system comprising:
the data acquisition module is used for acquiring driving data generated in the driving process of a driver;
the feature extraction module is used for extracting driving behavior features in the driving data;
the driving recognition module is used for analyzing the driving behavior characteristics and recognizing driving habits;
and the driving evaluation module is used for evaluating the driving habit of the driver and generating a driving habit report of the driver.
2. The system of claim 1, wherein the analyzing the driving behavior feature and identifying driving habits comprises:
screening driving behavior characteristics belonging to driving habits from the driving behavior characteristics;
and classifying the screened driving behavior characteristics according to the preset driving habit level to obtain the driving habits of each level.
3. The system of claim 2, wherein the driving habit report comprises: visual display of driving habit scores and driving habit histories;
the method for evaluating the driving habit of the driver and generating the driving habit report of the driver comprises the following steps:
for each level of driving habits, determining the behavior feature times in the level of driving habits;
calculating a driving habit score of a driver, wherein the calculation mode is as follows: the sum of the products of the behavior characteristic times of each level of driving habit and the corresponding level of driving habit weights;
and carrying out real-time visual display on the driving habits of each level, and searching the driving habit history record of the driver.
4. A system according to claim 3, wherein the system further comprises: and the driving early warning module is used for carrying out early warning on specific-level driving habits of which the behavior characteristic times exceed a preset threshold value.
5. A driving habit analysis method according to any one of claims 1-4, characterized in that the method comprises:
collecting driving data generated in the driving process of a driver;
extracting driving behavior characteristics in the driving data;
analyzing the driving behavior characteristics and identifying driving habits;
and evaluating the driving habit of the driver and generating a driving habit report of the driver.
6. The method of claim 5, wherein analyzing the driving behavior feature and identifying driving habits comprises:
screening driving behavior characteristics belonging to driving habits from the driving behavior characteristics;
and classifying the screened driving behavior characteristics according to the preset driving habit level to obtain the driving habits of each level.
7. The method of claim 6, wherein the driving habit report comprises: visual display of driving habit scores and driving habit histories;
the method for evaluating the driving habit of the driver and generating the driving habit report of the driver comprises the following steps:
for each level of driving habits, determining the behavior feature times in the level of driving habits;
calculating a driving habit score of a driver, wherein the calculation mode is as follows: the sum of the products of the behavior characteristic times of each level of driving habit and the corresponding level of driving habit weights;
and carrying out real-time visual display on the driving habits of each level, and searching the driving habit history record of the driver.
8. The method of claim 7, wherein the method further comprises: and carrying out early warning on the specific level driving habit of which the behavior characteristic times exceed the preset threshold value.
9. A storage medium having a computer program stored therein, wherein the computer program is arranged to perform the method of any of claims 5-8 when run.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, the processor being arranged to run the computer program to perform the method of any of claims 5-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310783209.9A CN116653980A (en) | 2023-06-28 | 2023-06-28 | Driver driving habit analysis system and driving habit analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310783209.9A CN116653980A (en) | 2023-06-28 | 2023-06-28 | Driver driving habit analysis system and driving habit analysis method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116653980A true CN116653980A (en) | 2023-08-29 |
Family
ID=87722464
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310783209.9A Pending CN116653980A (en) | 2023-06-28 | 2023-06-28 | Driver driving habit analysis system and driving habit analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116653980A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015053423A1 (en) * | 2013-10-11 | 2015-04-16 | (주)나노포인트 | System and method for analyzing and diagnosing driving habit |
| CN105679020A (en) * | 2016-01-29 | 2016-06-15 | 深圳市美好幸福生活安全系统有限公司 | Driving behavior analysis device and method |
| CN109033332A (en) * | 2018-07-20 | 2018-12-18 | 汉纳森（厦门）数据股份有限公司 | Driving behavior analysis method, medium and system |
| CN109325705A (en) * | 2018-10-11 | 2019-02-12 | 北京三驰惯性科技股份有限公司 | A kind of driving habit methods of marking and system based on inertia integration technology |
| CN113762755A (en) * | 2021-08-30 | 2021-12-07 | 一汽解放汽车有限公司 | Method and device for pushing driver analysis report, computer equipment and storage medium |
-
2023
- 2023-06-28 CN CN202310783209.9A patent/CN116653980A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015053423A1 (en) * | 2013-10-11 | 2015-04-16 | (주)나노포인트 | System and method for analyzing and diagnosing driving habit |
| CN105679020A (en) * | 2016-01-29 | 2016-06-15 | 深圳市美好幸福生活安全系统有限公司 | Driving behavior analysis device and method |
| CN109033332A (en) * | 2018-07-20 | 2018-12-18 | 汉纳森（厦门）数据股份有限公司 | Driving behavior analysis method, medium and system |
| CN109325705A (en) * | 2018-10-11 | 2019-02-12 | 北京三驰惯性科技股份有限公司 | A kind of driving habit methods of marking and system based on inertia integration technology |
| CN113762755A (en) * | 2021-08-30 | 2021-12-07 | 一汽解放汽车有限公司 | Method and device for pushing driver analysis report, computer equipment and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP2020524632A (en) | System and method for obtaining occupant feedback in response to an autonomous vehicle driving event | |
| US11900738B2 (en) | Systems and methods to obtain feedback in response to autonomous vehicle failure events | |
| EP3075621B1 (en) | Driving diagnosis method and driving diagnosis apparatus | |
| JP2020530578A (en) | Driving behavior scoring method and equipment | |
| CN103150900B (en) | Traffic jam event automatic detecting method based on videos | |
| CN108091132B (en) | Traffic flow prediction method and device | |
| US10877999B2 (en) | Programmatically identifying a personality of an autonomous vehicle | |
| JP6918137B2 (en) | Driving behavior evaluation method, device and computer-readable storage medium | |
| CN113155173B (en) | Perception performance evaluation method and device, electronic device and storage medium | |
| CN108091131B (en) | Traffic incident identification method and device | |
| CN112466118A (en) | Vehicle driving behavior recognition method, system, electronic device and storage medium | |
| CN115423035A (en) | User portrait generation method based on feature variable scoring, equipment, automobile and storage medium | |
| CN109784586B (en) | Prediction method and system for danger emergence condition of vehicle danger | |
| CN113283548A (en) | Vehicle safety scoring method, device, equipment and storage medium | |
| CN116653980A (en) | Driver driving habit analysis system and driving habit analysis method | |
| CN114822044B (en) | Driving safety early warning method and device based on tunnel | |
| CN113192340B (en) | Method, device, equipment and storage medium for identifying highway construction vehicles | |
| CN114662691A (en) | Characteristic knowledge base system and method for automatic driving vehicle | |
| CN111382631A (en) | Identification method, identification device, terminal, server and storage medium | |
| CN116485020B (en) | Supply chain risk identification early warning method, system and medium based on big data | |
| CN117421692B (en) | Garbage illegal delivery identification method, device and equipment for garbage delivery station | |
| CN117922595A (en) | Obstacle monitoring and early warning method and system based on automatic driving | |
| CN114897295A (en) | Model training and driving behavior evaluation method, device, equipment and storage medium | |
| WO2024008880A1 (en) | Driving style based personalizable driver assistance system and method | |
| CN115082905A (en) | Method and device for detecting operation behavior, storage medium and electronic device |
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 |