CN111027633B - Collision analysis method of automatic driving vehicle collision analysis system based on big data - Google Patents
Collision analysis method of automatic driving vehicle collision analysis system based on big data Download PDFInfo
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Abstract
The invention discloses a big data-based automatic driving vehicle collision analysis system and a big data-based automatic driving vehicle collision analysis method, wherein the collision analysis system comprises a transponder and a transmitter which are installed on a vehicle, an analysis module, an information generation module and a feedback receiving control module, the analysis module comprises a collision analysis module and a radio frequency signal analysis module, the transponder is used for receiving radio frequency signals which are sent by other vehicles and carry first mixed information, the transmitter is used for sending radio frequency signals of second mixed information to other vehicles, the collision analysis module is used for analyzing the risk level of vehicle collision, the radio frequency signal analysis module is used for analyzing the radio frequency signals which are received by the vehicles and carry the first mixed information, and the information generation module generates the second mixed information which is sent to other vehicles according to the collision analysis module and the radio frequency signal analysis module.
Description
Technical Field
The invention relates to the field of big data, in particular to a big data-based automatic driving vehicle collision analysis system and method.
Background
The automatic driving automobile depends on the cooperation of artificial intelligence, visual calculation, radar, monitoring device and global positioning system, so that the computer can operate the motor vehicle automatically and safely without any active operation of human. The automatic driving is an intelligent automobile which realizes unmanned driving through a computer system, and a driver can send a passenger to a destination without the need of the driver. The automatic driving is an important way for improving the road traffic intelligentization level and promoting the transformation and upgrading of the traffic transportation industry, and is also a favorable opportunity for driving the convergence and development of the industries such as traffic, automobiles, communication and the like. However, the existing autonomous vehicle has a high risk of vehicle collision during driving, and therefore, a system and a method for analyzing the collision of the autonomous vehicle based on big data are proposed.
Disclosure of Invention
The invention aims to provide a system and a method for analyzing the collision of an automatic driving vehicle based on big data, which aim to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
the collision analysis system comprises a transponder and a transmitter which are installed on a vehicle, an analysis module, an information generation module and a feedback receiving control module, wherein the analysis module comprises a collision analysis module and a radio frequency signal analysis module, the transponder is used for receiving radio frequency signals which are sent by other vehicles and carry first mixed information, the transmitter is used for sending radio frequency signals of second mixed information to other vehicles, the collision analysis module is used for analyzing the risk level of collision of the vehicle, the radio frequency signal analysis module is used for analyzing the radio frequency signals which are received by the vehicle and carry the first mixed information, the information generation module is used for generating the second mixed information which is sent to other vehicles according to the collision analysis module and the radio frequency signal analysis module, and the feedback receiving control module is used for generating the second mixed information which is sent to other vehicles according to the output result of the collision analysis module and the analysis result of the radio frequency signal analysis And adjusting the data or adjusting the quality of the radio frequency signals sent by the vehicle to other vehicles.
As a preferred scheme, the collision analysis module comprises a sample data acquisition module, a sample data processing module, a vehicle data acquisition module and a vehicle collision risk output module, wherein the sample data acquisition module is used for acquiring the driving data characteristics of a vehicle in a collision and adjacent collision in the driving process as a sample, the sample data processing module utilizes a K-means model to cluster the driving data characteristics in the sample and divides the driving data characteristics into different collision risk grades according to the clustering result and the collision risk, the vehicle data acquisition module is used for acquiring the driving data characteristics of the vehicle in the driving process, and the vehicle collision risk output module is used for inputting the acquired driving data characteristics into the K-means model and outputting the vehicle collision risk grade; the radio frequency signal analysis module comprises a first response information analysis module and a first received information analysis module, the first response information analysis module is used for analyzing the bit error rate of radio frequency signals received by the vehicle and making feedback suggestions, and the first received information analysis module is used for analyzing and outputting the collision risk level of surrounding vehicles.
Preferably, the feedback receiving control module includes a vehicle driving data adjusting module and a radio frequency signal adjusting module, the vehicle driving data adjusting module includes an internal stimulation adjusting module and an external stimulation adjusting module, the internal stimulation adjusting module adjusts driving data of the vehicle according to a collision risk level of the vehicle, the external stimulation adjusting module adjusts driving data of the vehicle according to a collision risk level of surrounding vehicles, and the radio frequency signal adjusting module is configured to receive feedback suggestions of other vehicles on radio frequency signal quality of the vehicle sending the signal, and adjust the radio frequency signal quality accordingly.
A big-data based autonomous vehicle collision analysis method, the collision analysis method comprising: the transponder mounted on the center vehicle receives the radio frequency signals carrying the first mixed information and sent by the vehicles around the center vehicle, the transmitter mounted on the center vehicle sends the radio frequency signals carrying the second mixed information to the vehicles around the center vehicle according to the received radio frequency signals carrying the first mixed information and the collision risk level of the center vehicle, and the collision risk level is transmitted by signals between the running vehicles, so that other vehicles can take countermeasures for avoiding collision.
Preferably, the collision analysis method further includes the following steps:
the method comprises the steps that a transponder installed on a center vehicle receives radio frequency signals which are sent by vehicles around the center vehicle and carry first mixed information, wherein the first mixed information comprises first response information and first receiving information, the first response information is used for the center vehicle to analyze the quality of the received radio frequency signals, and the first receiving information is the collision risk level of the vehicle sending the radio frequency signals;
the central vehicle analyzes the first received information, judges the collision risk level of the central vehicle after adjusting the running data characteristics of the central vehicle according to the first received information, and sends radio frequency signals carrying second mixed information to vehicles around the central vehicle by a transmitter arranged on the central vehicle, wherein the second mixed information comprises second feedback information and second analysis information, the second feedback information comprises feedback of the quality of the received radio frequency signals, and the second analysis information is the collision risk level of the central vehicle.
Preferably, the quality of the radio frequency signal received by the center vehicle is analyzed by analyzing the bit error rate of the radio frequency signal of the center vehicle;
the feeding back the quality of the received radio frequency signal comprises: and when the measured value of the bit error rate of the radio frequency signal is greater than the bit error rate threshold value, controlling the transmitter of the corresponding vehicle for transmitting the radio frequency signal to increase the transmitting power.
In the technical scheme, the received radio frequency signals are fed back according to the bit error rate of the received radio frequency signals, so that the transmitting power of the transmitter is ensured to be in a reasonable state, the transmitting power of the transmitter is prevented from being too high to influence the transmission of other signals, the transmitting power of the transmitter is also prevented from being too low to clearly analyze the first received information, and the vehicle is prevented from being collided due to unclear information reception.
Preferably, the driving data characteristics include a vehicle speed, a vehicle acceleration, and a vehicle-to-surrounding vehicle distance.
Preferably, the determining the level of risk of collision of the center vehicle includes:
step S1: taking vehicle data with collision and adjacent collision as samples, collecting driving data characteristics of the vehicle samples in the driving process, clustering the driving data characteristics by using a K-means model, sequentially dividing collision risks from high to low into 4 collision risk grades according to the clustering result and the collision risk, wherein the collision risk grades are respectively high collision risk, medium and high collision risk, low collision risk and no collision risk, and setting the high collision risk and the medium and high collision risk as alarm grades;
step S2: collecting the driving data characteristics of the central vehicle, inputting the driving data characteristics into a K mean value model, outputting the collision risk level, judging whether the vehicle gives an alarm or not, and if the vehicle gives the alarm, adjusting the driving data characteristics by the vehicle.
In the technical scheme, the vehicle can adjust the driving data characteristics of the vehicle when the collision risk level of the vehicle is high, and can also adjust the driving data characteristics of the vehicle when the collision risk level of other vehicles is high, so that the vehicle can not respond to the collision of other vehicles in time to avoid the occurrence of a chain of collision accidents.
Compared with the prior art, the invention has the beneficial effects that: according to the invention, the received radio frequency signal is fed back according to the bit error rate of the received radio frequency signal, so that the transmitting power of the transmitter is ensured to be in a reasonable state, the transmitting power of the transmitter is prevented from being too high to influence the transmission of other signals, the transmitting power of the transmitter is also prevented from being too low to clearly analyze the first received information, and the vehicle is prevented from being collided due to unclear information reception.
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FIG. 1 is a block diagram of a big data based crash analysis system for an autonomous vehicle according to 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 embodiments of the present invention, and not all of the 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.
Referring to fig. 1, in an embodiment of the present invention, an automatic driving vehicle collision analysis system based on big data includes a transponder and a transmitter mounted on a vehicle, an analysis module, an information generation module, and a feedback receiving control module, where the analysis module includes a collision analysis module and a radio frequency signal analysis module, the transponder is configured to receive a radio frequency signal carrying first mixed information sent by another vehicle, the transmitter is configured to send a radio frequency signal carrying second mixed information to another vehicle, the collision analysis module is configured to analyze a risk level of a collision of the vehicle, the radio frequency signal analysis module is configured to analyze the radio frequency signal carrying the first mixed information received by the vehicle, the information generation module generates the second mixed information sent to the other vehicle according to the collision analysis module and the radio frequency signal analysis module, and the feedback receiving control module adjusts the running data of the vehicle according to the output result of the collision analysis module and the analysis result of the radio frequency signal analysis, or adjusts the quality of the radio frequency signal sent by the vehicle to other vehicles.
The collision analysis module comprises a sample data acquisition module, a sample data processing module, a vehicle data acquisition module and a vehicle collision risk output module, wherein the sample data acquisition module is used for acquiring the driving data characteristics of a vehicle which is collided and collided nearby in the driving process as a sample, the sample data processing module is used for clustering the driving data characteristics in the sample by using a K-means model and dividing the driving data characteristics into different collision risk grades according to the clustering result and the collision risk, the vehicle data acquisition module is used for acquiring the driving data characteristics of the vehicle in the driving process, and the vehicle collision risk output module is used for inputting the acquired driving data characteristics into the K-means model and outputting the vehicle collision risk grade; the radio frequency signal analysis module comprises a first response information analysis module and a first received information analysis module, the first response information analysis module is used for analyzing the bit error rate of radio frequency signals received by the vehicle and making feedback suggestions, and the first received information analysis module is used for analyzing and outputting the collision risk level of surrounding vehicles.
The feedback receiving control module comprises a vehicle driving data adjusting module and a radio frequency signal adjusting module, the vehicle driving data adjusting module comprises an internal stimulation adjusting module and an external stimulation adjusting module, the internal stimulation adjusting module adjusts the driving data of the vehicle according to the collision risk level of the vehicle, the external stimulation adjusting module adjusts the driving data of the vehicle according to the collision risk level of surrounding vehicles, and the radio frequency signal adjusting module is used for receiving feedback suggestions of other vehicles on the quality of radio frequency signals of the vehicle sending the signals and adjusting the quality of the radio frequency signals according to the feedback suggestions.
A big-data based autonomous vehicle collision analysis method, the collision analysis method comprising: the transponder installed on the center vehicle receives the radio frequency signals carrying the first mixed information sent by the vehicles around the center vehicle, and the transmitter installed on the center vehicle sends the radio frequency signals carrying the second mixed information to the vehicles around the center vehicle according to the received radio frequency signals carrying the first mixed information and the collision risk level of the center vehicle.
The collision analysis method further comprises the following steps:
the method comprises the steps that a transponder installed on a center vehicle receives radio frequency signals which are sent by vehicles around the center vehicle and carry first mixed information, wherein the first mixed information comprises first response information and first receiving information, the first response information is used for the center vehicle to analyze the quality of the received radio frequency signals, and the first receiving information is the collision risk level of the vehicle sending the radio frequency signals;
the central vehicle analyzes the first received information, judges the collision risk level of the central vehicle after adjusting the running data characteristics of the central vehicle according to the first received information, and sends radio frequency signals carrying second mixed information to vehicles around the central vehicle by a transmitter arranged on the central vehicle, wherein the second mixed information comprises second feedback information and second analysis information, the second feedback information comprises feedback of the quality of the received radio frequency signals, and the second analysis information is the collision risk level of the central vehicle.
The center vehicle analyzing the quality of the received radio frequency signal comprises analyzing a bit error rate of the center vehicle radio frequency signal;
the feeding back the quality of the received radio frequency signal comprises: when the measured value of the bit error rate of the radio frequency signal is smaller than the bit error rate threshold value, controlling the transmitter of the corresponding vehicle for transmitting the radio frequency signal to reduce the transmitting power, and when the measured value of the bit error rate of the radio frequency signal is larger than the bit error rate threshold value, controlling the transmitter of the corresponding vehicle for transmitting the radio frequency signal to increase the transmitting power; the received radio frequency signals are fed back according to the bit error rate of the received radio frequency signals, the transmitting power of the transmitter is guaranteed to be in a reasonable state, the transmitting power of the transmitter is prevented from being too high to influence the transmission of other signals, the transmitting power of the transmitter is also prevented from being too low to clearly analyze the first received information, and the vehicle is prevented from being collided due to unclear information reception.
The driving data characteristics include vehicle speed, vehicle acceleration, and vehicle-to-surrounding vehicle distance.
The judging of the risk level of the central vehicle collision includes:
step S1: taking vehicle data with collision and adjacent collision as samples, collecting driving data characteristics of the vehicle samples in the driving process, clustering the driving data characteristics by using a K-means model, sequentially dividing collision risks from high to low into 4 collision risk grades according to the clustering result and the collision risk, wherein the collision risk grades are respectively high collision risk, medium and high collision risk, low collision risk and no collision risk, and setting the high collision risk and the medium and high collision risk as alarm grades;
step S2: the method comprises the steps of collecting driving data characteristics of a central vehicle, inputting the driving data characteristics into a K-means model, outputting the collision risk level, judging whether the vehicle gives an alarm or not, if the vehicle gives the alarm, adjusting the driving data characteristics by the vehicle, adjusting the vehicle speed, the vehicle acceleration and the distance between the vehicle and surrounding vehicles when the driving data characteristics are adjusted by the vehicle, adjusting the data simultaneously, and selecting one of the data to adjust.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (3)
1. A collision analysis method of an automatic driving vehicle collision analysis system based on big data is characterized in that: the collision analysis system comprises a transponder and a transmitter which are installed on a vehicle, an analysis module, an information generation module and a feedback receiving control module, wherein the analysis module comprises a collision analysis module and a radio frequency signal analysis module, the transponder is used for receiving radio frequency signals which are sent by other vehicles and carry first mixed information, the transmitter is used for sending radio frequency signals of second mixed information to other vehicles, the collision analysis module is used for analyzing the risk level of collision of the vehicle, the radio frequency signal analysis module is used for analyzing the radio frequency signals which are received by the vehicle and carry the first mixed information, the information generation module generates the second mixed information which is sent to other vehicles according to the collision analysis module and the radio frequency signal analysis module, and the feedback receiving control module adjusts the running data of the vehicle according to the output result of the collision analysis module and the analysis result of the radio frequency signal analysis, or adjusting the quality of radio frequency signals sent by the vehicle to other vehicles;
the collision analysis module comprises a sample data acquisition module, a sample data processing module, a vehicle data acquisition module and a vehicle collision risk output module, wherein the sample data acquisition module is used for acquiring the driving data characteristics of a vehicle which is collided and collided nearby in the driving process as a sample, the sample data processing module is used for clustering the driving data characteristics in the sample by using a K-means model and dividing the driving data characteristics into different collision risk grades according to the clustering result and the collision risk, the vehicle data acquisition module is used for acquiring the driving data characteristics of the vehicle in the driving process, and the vehicle collision risk output module is used for inputting the acquired driving data characteristics into the K-means model and outputting the vehicle collision risk grade; the radio frequency signal analysis module comprises a first response information analysis module and a first received information analysis module, the first response information analysis module is used for analyzing the bit error rate of radio frequency signals received by the vehicle and making feedback suggestions, and the first received information analysis module is used for analyzing and outputting the collision risk level of surrounding vehicles;
the feedback receiving control module comprises a vehicle running data adjusting module and a radio frequency signal adjusting module, the vehicle running data adjusting module comprises an internal stimulation adjusting module and an external stimulation adjusting module, the internal stimulation adjusting module adjusts the running data of the vehicle according to the collision risk level of the vehicle, the external stimulation adjusting module adjusts the running data of the vehicle according to the collision risk level of surrounding vehicles, and the radio frequency signal adjusting module is used for receiving feedback suggestions of other vehicles on the radio frequency signal quality of the vehicle sending the signals and adjusting the radio frequency signal quality according to the feedback suggestions;
the collision analysis method includes the following:
the transponder mounted on the central vehicle receives radio frequency signals carrying first mixed information and sent by vehicles around the central vehicle, and the transmitter mounted on the central vehicle sends radio frequency signals carrying second mixed information to the vehicles around the central vehicle according to the received radio frequency signals carrying the first mixed information and the collision risk level of the central vehicle;
the collision analysis method further comprises the following steps:
the method comprises the steps that a transponder installed on a center vehicle receives radio frequency signals which are sent by vehicles around the center vehicle and carry first mixed information, wherein the first mixed information comprises first response information and first receiving information, the first response information is used for the center vehicle to analyze the quality of the received radio frequency signals, and the first receiving information is the collision risk level of the vehicle sending the radio frequency signals;
the central vehicle analyzes the first received information, judges the collision risk level of the central vehicle after adjusting the running data characteristics of the central vehicle according to the first received information, and sends radio frequency signals carrying second mixed information to vehicles around the central vehicle by a transmitter arranged on the central vehicle, wherein the second mixed information comprises second feedback information and second analysis information, the second feedback information comprises feedback of the quality of the received radio frequency signals, and the second analysis information is the collision risk level of the central vehicle;
the center vehicle analyzing the quality of the received radio frequency signal comprises analyzing a bit error rate of the center vehicle radio frequency signal;
the feeding back the quality of the received radio frequency signal comprises: and when the measured value of the bit error rate of the radio frequency signal is greater than the bit error rate threshold value, controlling the transmitter of the corresponding vehicle for transmitting the radio frequency signal to increase the transmitting power.
2. The collision analysis method of big-data-based autonomous vehicle collision analysis system according to claim 1, characterized in that: the driving data characteristics include vehicle speed, vehicle acceleration, and vehicle-to-surrounding vehicle distance.
3. The collision analysis method of big-data-based autonomous vehicle collision analysis system according to claim 2, characterized in that: the judging of the risk level of the central vehicle collision includes:
step S1: taking vehicle data with collision and adjacent collision as samples, collecting driving data characteristics of the vehicle samples in the driving process, clustering the driving data characteristics by using a K-means model, sequentially dividing collision risks from high to low into 4 collision risk grades according to the clustering result and the collision risk, wherein the collision risk grades are respectively high collision risk, medium and high collision risk, low collision risk and no collision risk, and setting the high collision risk and the medium and high collision risk as alarm grades;
step S2: collecting the driving data characteristics of the central vehicle, inputting the driving data characteristics into a K mean value model, outputting the collision risk level, judging whether the vehicle gives an alarm or not, and if the vehicle gives the alarm, adjusting the driving data characteristics by the vehicle.
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| CN107958269B (en) * | 2017-11-28 | 2020-01-24 | 江苏大学 | Driving risk degree prediction method based on hidden Markov model |
| CN108082184A (en) * | 2017-12-19 | 2018-05-29 | 戴姆勒股份公司 | Collision-prevention system and the method for preventing vehicle collision |
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