CN114444872A - Method and system for analyzing driving behavior of driver by adopting artificial intelligence algorithm - Google Patents
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Abstract
The method and the system for analyzing the driving behavior of the driver by adopting the artificial intelligence algorithm are characterized by comprising the following steps: step 1, collecting behavior data of a vehicle and a driver; step 2, inputting the behavior data into a trained neural network model to obtain a driving behavior score of a driver; and 3, obtaining the safety level of the driving behavior of the driver based on the driving behavior score of the driver. According to the method, the collected behavior data of the vehicle is used for obtaining the safety level of the driving behavior of the driver through the neural network model, so that safety management and danger prediction are performed, on the other hand, the probability of vehicle danger can be accurately predicted through driving behavior analysis, the prediction accuracy of the vehicle danger is greatly improved, the vehicle safety is improved, and the commercial operation income is improved.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to a method and a system for analyzing driving behaviors of a driver by adopting an artificial intelligence algorithm.
Background
DMS Driver monitoring System (Driver Monitor System) and ADAS Advanced Driving Assistance System (Advanced Driving Assistance System) are used on the car more and more, play an important role in the safe Driving of the System, and with the development of artificial intelligence, we can use a complex algorithm to analyze a large amount of data, recognize the mode in the data, and make corresponding prediction.
Disclosure of Invention
In view of the technical defects and technical drawbacks in the prior art, embodiments of the present invention provide a method and a system for analyzing driving behavior of a driver by using an artificial intelligence algorithm, which overcome the above problems or at least partially solve the above problems, and the specific scheme is as follows:
as a first aspect of the present invention, there is provided a method for performing driver driving behavior analysis using an artificial intelligence algorithm, the method comprising:
step 1, collecting behavior data of a vehicle and a driver;
step 2, inputting the behavior data into a trained neural network model to obtain a driving behavior score of a driver;
and 3, obtaining the safety level of the driving behavior of the driver based on the driving behavior score of the driver.
Further, in step 1, the behavior data includes ABS starting times, driving time data per day, DMS alarm data, FCW alarm data, following distance data, and acceleration data.
Further, the neural network model is a convolutional neural network CNN, a recurrent neural network RNN, or a long-term memory network LSTM.
Further, the neural network model comprises 6 sub-neural network models, the AABS starting times, the daily driving time data, the DMS alarm data, the FCW alarm data, the following distance data and the acceleration force data are respectively used as input data of the 6 sub-neural network models, each sub-neural network model outputs a corresponding driving behavior score based on the corresponding input data, and a final driving behavior score of a driver is obtained based on an output result of each sub-neural network model.
Further, the final driver driving behavior score is obtained based on the output result of each sub-neural network model, specifically: and for each sub-neural network model, acquiring a corresponding score weight based on the input behavior data type, and performing algorithm weighting on the output result of each sub-neural network model based on the score weight of each sub-neural network model so as to obtain a final output result.
As a second aspect of the present invention, there is provided a system for analyzing driving behavior of a driver using an artificial intelligence algorithm, the system comprising a data acquisition module, a data analysis module, and a statistics module;
the data acquisition module is used for acquiring behavior data of a vehicle and a driver;
the data analysis module is used for inputting the behavior data into a trained neural network model to obtain a driving behavior score of a driver;
the statistical module is used for obtaining the safety level of the driving behavior of the driver based on the driving behavior score of the driver.
Further, the behavior data comprises ABS starting times, daily driving time data, DMS alarm data, FCW alarm data, following distance data and acceleration data.
Further, the neural network model is a convolutional neural network CNN, a recurrent neural network RNN, or a long-term memory network LSTM.
Further, the neural network model comprises 6 sub-neural network models, the AABS starting times, the daily driving time data, the DMS alarm data, the FCW alarm data, the following distance data and the acceleration force data are respectively used as input data of the 6 sub-neural network models, each sub-neural network model outputs a corresponding driving behavior score based on the corresponding input data, and a final driving behavior score of a driver is obtained based on an output result of each sub-neural network model.
Further, the final driver driving behavior score is obtained based on the output result of each sub-neural network model, specifically: and for each sub-neural network model, acquiring a corresponding score weight based on the input behavior data type, and performing algorithm weighting on the output result of each sub-neural network model based on the score weight of each sub-neural network model so as to obtain a final output result.
The invention has the following beneficial effects:
according to the method, the collected behavior data of the vehicle is used for obtaining the safety level of the driving behavior of the driver through the neural network model, so that safety management and danger prediction are performed, on the other hand, the probability of vehicle danger can be accurately predicted through driving behavior analysis, the prediction accuracy of the vehicle danger is greatly improved, the vehicle safety is improved, and the commercial operation income is improved.
Drawings
Fig. 1 is a flowchart of a method for analyzing driving behavior of a driver by using an artificial intelligence algorithm according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As a first embodiment of the present invention, as shown in fig. 1, there is provided a method for analyzing a driving behavior of a driver using an artificial intelligence algorithm, the method including:
step 1, collecting behavior data of a vehicle and a driver;
step 2, inputting the behavior data into a trained neural network model to obtain a driving behavior score of a driver;
and 3, obtaining the safety level of the driving behavior of the driver based on the driving behavior score of the driver.
According to the neural network modeling process schematic diagram provided by the embodiment of the invention, the AI algorithm is utilized to analyze the behavior data of the vehicle and the driver, and the deep learning neural network is utilized to model so as to obtain the safety level of the driving behavior of the driver, thereby carrying out safety management and risk prediction.
The existing mainstream neural network architecture can meet the requirements of the neural network model of the invention, and the neural network model comprises a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), a long-term and short-term memory network (LSTM) and the like.
The behavior data comprise ABS starting times, daily driving time length data, DMS alarm data, FCW alarm data, following distance data and acceleration force data.
The method comprises 6 sub-neural network models, wherein the 6 sub-neural network models respectively correspond to behavior data of 6 types including ABS starting times, daily driving time data, DMS (digital distribution system) alarm data, FCW (fuzzy control) alarm data, following distance data and acceleration force data, each sub-neural network model outputs a corresponding driving behavior score based on corresponding input data, and a final driving behavior score of a driver is obtained based on an output result of each sub-neural network model; for example, the following distance data is used as an input of one of the sub-neural network models, the following distance data passes through the corresponding sub-neural network model, a driving behavior score of 0 to 1 is output, and the safety level of the driving behavior is obtained based on the driving behavior score.
And performing algorithm weighting on the output result of each sub-neural network model based on the score weight of each sub-neural network model, thereby obtaining the final output result.
According to the invention, the score of the whole driving behavior is determined according to the monitoring data, including the weighting of the data sub-neural network model with multiple dimensions such as ABS starting times, driving time per day, DMS alarm, FCW alarm, following distance, acceleration force and the like, the driving behavior analysis result is determined, and according to the driving behavior analysis, the probability of vehicle insurance can be accurately predicted through the deep learning model, so that the prediction accuracy of vehicle insurance is greatly improved, the vehicle safety is improved, and the commercial operation income is improved.
As a second embodiment of the present invention, there is also provided a system for analyzing a driving behavior of a driver using an artificial intelligence algorithm, the system including a data acquisition module, a data analysis module, and a statistical module;
the data acquisition module is used for acquiring behavior data of a vehicle and a driver;
the data analysis module is used for inputting the behavior data into a trained neural network model to obtain a driving behavior score of a driver;
the statistical module is used for obtaining the safety level of the driving behavior of the driver based on the driving behavior score of the driver.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. A method for analyzing driving behavior of a driver using an artificial intelligence algorithm, the method comprising:
step 1, collecting behavior data of a vehicle and a driver;
step 2, inputting the behavior data into a trained neural network model to obtain a driving behavior score of a driver;
and 3, obtaining the safety level of the driving behavior of the driver based on the driving behavior score of the driver.
2. The method for analyzing the driving behavior of a driver by using an artificial intelligence algorithm as claimed in claim 1, wherein the behavior data includes ABS activation times, driving time data per day, DMS warning data, FCW warning data, following distance data, and acceleration data in step 1.
3. The method for analyzing the driving behavior of the driver by adopting the artificial intelligence algorithm as claimed in claim 1, wherein the neural network model is a Convolutional Neural Network (CNN), a cyclic neural network (RNN) or a long-term memory network (LSTM).
4. The method of claim 2, wherein the neural network model comprises 6 sub-neural network models, the AABS activation times, daily driving duration data, DMS alarm data, FCW alarm data, following distance data, and acceleration data are respectively used as input data of the 6 sub-neural network models, each sub-neural network model outputs a corresponding driving behavior score based on the corresponding input data, and a final driving behavior score is obtained based on an output result of each sub-neural network model.
5. The method for analyzing the driving behavior of the driver by adopting the artificial intelligence algorithm as claimed in claim 4, wherein the final driving behavior score is obtained based on the output result of each sub-neural network model, and specifically comprises: and for each sub-neural network model, acquiring a corresponding score weight based on the input behavior data type, and performing algorithm weighting on the output result of each sub-neural network model based on the score weight of each sub-neural network model so as to obtain a final output result.
6. A system for analyzing the driving behavior of a driver by adopting an artificial intelligence algorithm is characterized by comprising a data acquisition module, a data analysis module and a statistic module;
the data acquisition module is used for acquiring behavior data of a vehicle and a driver;
the data analysis module is used for inputting the behavior data into a trained neural network model to obtain a driving behavior score of a driver;
the statistical module is used for obtaining the safety level of the driving behavior of the driver based on the driving behavior score of the driver.
7. The system of claim 6, wherein the behavior data comprises ABS activation times, daily driving duration data, DMS alarm data, FCW alarm data, following distance data, and acceleration data.
8. The system for analyzing the driving behavior of the driver by adopting the artificial intelligence algorithm as claimed in claim 6, wherein the neural network model is a Convolutional Neural Network (CNN), a cyclic neural network (RNN) or a long-term memory network (LSTM).
9. The system of claim 7, wherein the neural network model comprises 6 sub-neural network models, the AABS activation times, the daily driving duration data, the DMS alarm data, the FCW alarm data, the following distance data and the acceleration data are respectively used as input data of the 6 sub-neural network models, each sub-neural network model outputs a corresponding driving behavior score based on the corresponding input data, and a final driving behavior score is obtained based on an output result of each sub-neural network model.
10. The system for analyzing driving behavior of a driver using artificial intelligence algorithm as claimed in claim 9, wherein the final driving behavior score is obtained based on the output result of each sub-neural network model, specifically: and for each sub-neural network model, acquiring a corresponding score weight based on the input behavior data type, and performing algorithm weighting on the output result of each sub-neural network model based on the score weight of each sub-neural network model so as to obtain a final output result.
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| CN116739437A (en) * | 2023-07-14 | 2023-09-12 | 鱼快创领智能科技(南京)有限公司 | Comprehensive transport capacity grading method based on Internet of vehicles data |
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| CN116739437A (en) * | 2023-07-14 | 2023-09-12 | 鱼快创领智能科技(南京)有限公司 | Comprehensive transport capacity grading method based on Internet of vehicles data |
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