CN116729371B - Vehicle potential danger detection system based on radar and video linkage - Google Patents
Vehicle potential danger detection system based on radar and video linkage Download PDFInfo
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- CN116729371B CN116729371B CN202310707023.5A CN202310707023A CN116729371B CN 116729371 B CN116729371 B CN 116729371B CN 202310707023 A CN202310707023 A CN 202310707023A CN 116729371 B CN116729371 B CN 116729371B
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- 238000000034 method Methods 0.000 claims abstract description 46
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- 238000012502 risk assessment Methods 0.000 claims abstract description 14
- 230000002159 abnormal effect Effects 0.000 claims description 60
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- 239000000446 fuel Substances 0.000 claims description 25
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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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/095—Predicting travel path or likelihood of collision
- B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
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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
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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
- B60W40/02—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 ambient conditions
- B60W40/04—Traffic conditions
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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
- B60W40/02—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 ambient conditions
- B60W40/06—Road conditions
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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
- B60W40/10—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 vehicle motion
- B60W40/105—Speed
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
- G01S13/867—Combination of radar systems with cameras
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0002—Automatic control, details of type of controller or control system architecture
- B60W2050/0004—In digital systems, e.g. discrete-time systems involving sampling
- B60W2050/0005—Processor details or data handling, e.g. memory registers or chip architecture
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/143—Alarm means
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Remote Sensing (AREA)
- Mechanical Engineering (AREA)
- Physics & Mathematics (AREA)
- Radar, Positioning & Navigation (AREA)
- Transportation (AREA)
- Mathematical Physics (AREA)
- Human Computer Interaction (AREA)
- Computer Networks & Wireless Communication (AREA)
- General Physics & Mathematics (AREA)
- Electromagnetism (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention belongs to the technical field of vehicle operation safety, in particular to a vehicle potential hazard detection system based on radar and video linkage, which comprises a processor, a radar video acquisition and monitoring module, a road section accident risk analysis module, a road section difficulty analysis module, a driving safety detection module and a vehicle early warning module, wherein the radar video acquisition and monitoring module is used for acquiring radar video; according to the invention, the radar video acquisition and monitoring module is used as a carrier and is used for acquiring, monitoring and analyzing so as to effectively prevent rear-end collision of the vehicle, the road section accident risk analysis module is used for carrying out historical accident analysis on the next driving road section of the target vehicle so as to generate an accident high risk signal or an accident low risk signal, the road section difficulty analysis module is used for carrying out road section passing difficulty analysis when the accident low risk signal is generated, and the driving safety detection module is used for carrying out driving safety analysis on the target vehicle, so that reasonable analysis on potential risks in the driving process of the vehicle is realized, and the safety of the driving process is further ensured.
Description
Technical Field
The invention relates to the technical field of vehicle operation safety, in particular to a vehicle potential hazard detection system based on radar and video linkage.
Background
The concept of the vehicle refers to a vehicle without power, the concept of the vehicle is changed silently at present, and the vehicle is a collective name of all vehicles, but mainly refers to an automobile, the types of the vehicles are various, the structures are different from each other, the appearance of the vehicle is changed along with the development of society, the progress of science and technology and the change of requirements, the basic structure is not changed greatly, only the specific parts have more scientific and advanced structural design, and the vehicle mainly comprises five parts of a vehicle body, a vehicle chassis, a running part, a coupler buffer device and a brake device;
at present, in the running process of a vehicle, a driver mainly judges the potential risk of the running of the vehicle by himself, front vehicle analysis and early warning cannot be carried out in a radar and video linkage mode, rear-end collision is easy to occur, accident analysis and road difficulty analysis of a subsequent road section cannot be realized in the driving process, and vehicle driving safety analysis in the driving process cannot be combined, so that the potential risk early warning of the running process of the vehicle is not facilitated to be accurately carried out in time, corresponding adjustment is not facilitated to be carried out in time by the driver, and the driving safety of the vehicle is difficult to be ensured;
in view of the above technical drawbacks, a solution is now proposed.
Disclosure of Invention
The invention aims to provide a radar and video linkage-based vehicle potential hazard detection system, which solves the problems that the prior art cannot analyze and early warn a front vehicle in a radar and video linkage mode, rear-end collision is easy to occur, accident analysis and road difficulty analysis of a subsequent road section cannot be realized in a driving process, and vehicle driving safety analysis in the driving process cannot be combined, so that the driving safety of the vehicle is difficult to ensure.
In order to achieve the above purpose, the present invention provides the following technical solutions:
the vehicle potential hazard detection system based on radar and video linkage comprises a processor, a radar video acquisition and monitoring module, a road section accident risk analysis module, a road section difficulty analysis module, a driving safety detection module and a vehicle early warning module; the radar video acquisition monitoring module is used for generating a rear-end collision low-risk signal or a rear-end collision high-risk signal based on a camera and a radar serving as carriers through acquisition monitoring analysis, the rear-end collision low-risk signal or the rear-end collision high-risk signal is transmitted to the vehicle early warning module through the processor, and the vehicle early warning module sends corresponding early warning when receiving the rear-end collision high-risk signal;
the road section accident risk analysis module is used for marking the next driving road section of the target vehicle as an analysis road section, carrying out historical traffic accident analysis on the analysis road section, marking corresponding traffic accidents as heavy accidents, medium accidents and light accidents through analysis, generating accident low risk signals or accident high risk signals through analysis, sending the accident low risk signals or the accident high risk signals to the processor, sending the accident high risk signals to the vehicle early warning module by the processor, and sending corresponding early warning when the vehicle early warning module receives the accident high risk signals;
the road section difficulty analysis module is used for analyzing the road section passing difficulty of the analysis road section of the target vehicle, generating a road section high difficulty signal or a road section low difficulty signal through the road section passing difficulty analysis, sending the road section high difficulty signal or the road section low difficulty signal to the processor, sending the road section high difficulty signal to the vehicle early warning module by the processor, and sending a corresponding early warning when the vehicle early warning module receives the road section high difficulty signal; the driving safety detection module is used for carrying out driving safety analysis on the target vehicle, generating a driving safety qualified signal or a driving safety unqualified signal through analysis, sending the driving safety qualified signal or the driving safety unqualified signal to the processor, sending the driving safety unqualified signal to the vehicle early warning module through the processor, and sending corresponding early warning when the vehicle early warning module receives the driving safety unqualified signal.
Further, the acquisition monitoring analysis process of the radar video acquisition monitoring module is specifically as follows:
in the running process of the target vehicle, acquiring the distance between the target vehicle and the front vehicle and the relative speed of the target vehicle and the front vehicle, calculating the relative speed of the target vehicle and the front vehicle to obtain a relative speed difference value, marking the distance between the target vehicle and the front vehicle as vehicle distance data, generating a rear-end collision low risk signal if the vehicle distance data exceeds a preset vehicle distance data threshold value and the relative speed difference value does not exceed a preset relative speed difference threshold value, otherwise, calculating the ratio of the vehicle distance data to the relative speed difference value to obtain a rear-end collision risk value, generating a rear-end collision low risk signal if the rear-end collision risk value exceeds a preset rear-end collision risk threshold value, and generating a rear-end collision high risk signal if the rear-end collision risk value does not exceed the preset rear-end collision risk threshold value.
Further, the specific operation process of the radar video acquisition monitoring module further comprises:
when the target vehicle is in a state of waiting to drive in a queue, the moment of converting the front vehicle from a stationary state to a starting state is marked as an initial moment, if the target vehicle is not started at the moment, timing analysis is carried out, time difference calculation is carried out between the timing detection moment and the initial moment to obtain a starting dullness coefficient, if the starting dullness coefficient exceeds a preset starting dullness coefficient threshold value, a starting reminding signal is generated, the starting reminding signal is sent to a vehicle early warning module through a processor, and the vehicle early warning module sends corresponding early warning after receiving the starting reminding module.
Further, the specific operation process of the road section accident risk analysis module comprises the following steps:
acquiring the current position of a target vehicle in a detection period, setting an analysis end point by taking the current position of the target vehicle as an analysis starting point and based on a running navigation path of the target vehicle, wherein the path distance between the analysis end point and the analysis starting point is L1, and marking the next running road section between the analysis end point and the analysis starting point as an analysis road section; acquiring historical traffic accident frequency of a corresponding analysis road section in unit time, and carrying out numerical calculation on the frequency of the severe accident, the frequency of the moderate accident, the frequency of the mild accident and the historical traffic accident frequency to obtain an accident risk coefficient based on the casualty condition of the corresponding traffic accident and by analyzing and marking the corresponding traffic accident as a severe accident, a moderate accident or a mild accident; if the accident risk coefficient exceeds a preset accident risk coefficient threshold, generating an accident high risk signal, otherwise, generating an accident low risk signal, and sending the accident high risk signal or the accident low risk signal to the processor.
Further, the specific analysis process for marking the corresponding traffic accident as a heavy accident, a moderate accident or a light accident is as follows:
if the corresponding traffic accident has the death of the personnel, the corresponding traffic accident is marked as a serious accident, if the corresponding traffic accident does not have the death of the personnel, the number of the serious injury and the light injury caused by the corresponding traffic accident is obtained, the number of the serious injury and the light injury caused by the corresponding traffic accident is marked as the number of the serious injury and the light injury, the number of the serious injury and the light injury caused by the corresponding traffic accident are calculated to obtain an accident feedback value, the accident feedback value is compared with a preset accident feedback range in a numerical mode, if the accident feedback value exceeds the maximum value of the preset accident feedback range, the corresponding traffic accident is marked as a serious accident, if the accident feedback value is positioned in the preset accident feedback range, the corresponding traffic accident is marked as a moderate accident, and if the accident feedback value does not exceed the minimum value of the preset accident feedback range, the corresponding traffic accident is marked as a mild accident.
Further, the specific operation process of the road section difficulty analysis module comprises the following steps:
acquiring the current moment, acquiring average vehicle flow and average vehicle speed in the period corresponding to the current moment in the previous e adjacent days of the corresponding analysis road section, carrying out numerical calculation on the average vehicle flow and the average vehicle speed to acquire a vehicle passing value of the corresponding date, marking the vehicle passing value exceeding a preset vehicle passing threshold as an abnormal passing value, carrying out ratio calculation on the number of the abnormal passing values and the numerical value e to acquire an abnormal passing ratio, carrying out summation calculation on the vehicle passing values in the previous e adjacent days, taking an average value to acquire a passing average value, and carrying out numerical calculation on the abnormal passing ratio and the passing average value to acquire a road section abnormal coefficient;
and obtaining the road section complexity coefficient of the corresponding analysis road section through analysis, respectively carrying out numerical comparison on the road section abnormal coefficient and the road section complexity coefficient of the corresponding analysis road section and a preset road section abnormal coefficient threshold value and a preset road section complexity coefficient threshold value, and generating a road section high-difficulty signal if the road section abnormal coefficient exceeds the preset road section abnormal coefficient threshold value or the road section complexity coefficient exceeds the preset road section complexity coefficient threshold value.
Further, if the abnormal road section coefficient does not exceed the preset abnormal road section coefficient threshold and the complex road section coefficient does not exceed the preset complex road section coefficient threshold, subtracting the abnormal road section coefficient from the abnormal road section coefficient threshold to obtain an abnormal road section difference value, subtracting the complex road section coefficient from the complex road section coefficient to obtain a complex road section difference value, carrying out numerical calculation on the abnormal road section difference value and the complex road section difference value to obtain a traffic difficulty difference value, carrying out numerical comparison on the traffic difficulty difference value and the preset traffic difficulty difference value, generating a low road section difficulty signal if the traffic difficulty difference value exceeds the preset traffic difficulty difference value, and generating a high road section difficulty signal if the traffic difficulty difference value does not exceed the preset traffic difficulty difference value.
Further, the analysis and acquisition method of the road section complexity coefficient specifically comprises the following steps:
the method comprises the steps of obtaining the number of the pothole uneven areas in an analysis road section and the areas of the corresponding pothole uneven areas, marking the pothole uneven areas exceeding a preset area threshold as abnormal areas, calculating the ratio of the number of the abnormal areas to the number of the pothole uneven areas in the corresponding sub road sections to obtain an abnormal area occupation ratio, calculating the numerical value of the abnormal area occupation ratio to the number of the pothole uneven areas to obtain an area flatness value, obtaining the straight road section length, the number of traffic lights and the turning-around number of the corresponding analysis road section, calculating the ratio of the straight road section length to the path distance L1 to obtain a straight road section occupation ratio, and calculating the numerical value of the straight road section occupation ratio, the number of the traffic lights, the turning-around number and the area flatness value to obtain a road section complexity coefficient.
Further, the specific analysis process of the driving safety analysis includes:
the method comprises the steps of respectively comparing a vehicle detection value and an environment detection value with a preset vehicle detection threshold value and a preset environment detection threshold value through analysis to obtain the vehicle detection value and the environment detection value of a target vehicle in a detection period, generating a driving safety unqualified signal if the vehicle detection value exceeds the preset vehicle detection threshold value or the environment detection value exceeds the preset environment detection threshold value, and generating a driving safety qualified signal if the vehicle detection value does not exceed the preset vehicle detection threshold value and the environment detection value does not exceed the preset environment detection threshold value.
Further, the analysis and acquisition method of the vehicle detection value and the environment detection value is as follows:
obtaining vibration data and generated noise data of a target vehicle in a detection period, obtaining a driving distance and fuel consumption of the target vehicle in the detection period, calculating the ratio of the fuel consumption to the driving distance to obtain a fuel consumption analysis value, calculating the difference between the fuel consumption analysis value and the median of a preset fuel consumption analysis value range, taking an absolute value to obtain a fuel consumption analysis difference value, and calculating the fuel consumption analysis difference value, the vibration data and the noise data to obtain a vehicle detection value; and acquiring brightness data, rainfall data and visibility data of the external environment corresponding to the target vehicle in the detection period, and performing numerical calculation on the brightness data, the rainfall data and the visibility data to acquire an environment detection value.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, a radar video acquisition monitoring module is used for acquiring, monitoring and analyzing based on a camera and a radar as carriers to generate a rear-end collision low risk signal or a rear-end collision high risk signal, and whether a start reminding signal is generated or not is judged through analysis under the condition that a target vehicle is in queuing for waiting to run, and a vehicle early warning module sends out corresponding early warning when receiving the rear-end collision high risk signal or the start reminding signal so as to prevent the rear-end collision of the vehicle, thereby ensuring the running safety; analyzing the next driving road section of the target vehicle through a road section accident risk analysis module to generate an accident low risk signal or an accident high risk signal, analyzing road section passing difficulty through a road section difficulty analysis module when the accident low risk signal is generated, and sending out corresponding early warning when the accident high risk signal or the road section high difficulty signal is received by a vehicle early warning module, so that reasonable analysis of potential risks in the driving process of the vehicle is realized, and the safety of the driving process is further ensured;
2. according to the invention, the driving safety detection module is used for carrying out driving safety analysis on the target vehicle to generate the driving safety qualified signal or the driving safety unqualified signal, the driving safety unqualified signal is sent to the vehicle early warning module through the processor, and the vehicle early warning module sends out corresponding early warning when receiving the driving safety unqualified signal, so that the driving safety detection module is beneficial to drivers to know the driving safety condition in time, carry out driving adjustment in time and stop driving according to the requirement, and has high intelligent degree and powerfully ensures the safety of the drivers and the vehicle.
Drawings
For the convenience of those skilled in the art, the present invention will be further described with reference to the accompanying drawings;
FIG. 1 is a system block diagram of a first embodiment of the present invention;
fig. 2 is a system block diagram of a second embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
as shown in fig. 1, the radar and video linkage-based vehicle potential hazard detection system provided by the invention comprises a processor, a radar video acquisition and monitoring module, a road section accident risk analysis module, a road section difficulty analysis module and a vehicle early warning module, wherein the processor is in communication connection with the radar video acquisition and monitoring module, the road section accident risk analysis module, the road section difficulty analysis module and the vehicle early warning module; the radar video acquisition monitoring module is used for generating a rear-end collision low-risk signal or a rear-end collision high-risk signal based on a camera and a radar as carriers through acquisition monitoring analysis, the rear-end collision low-risk signal or the rear-end collision high-risk signal is transmitted to the vehicle early warning module by the processor, the vehicle early warning module sends corresponding early warning when receiving the rear-end collision high-risk signal, and a corresponding driver timely adjusts the speed when receiving the early warning so as to ensure the running safety; the acquisition monitoring analysis process of the radar video acquisition monitoring module specifically comprises the following steps:
in the running process of a target vehicle, acquiring the distance between the target vehicle and a front vehicle and the relative speed of the target vehicle and the front vehicle, calculating the relative speed of the target vehicle and the front vehicle to obtain a relative speed difference value XC, marking the distance between the target vehicle and the front vehicle as distance data CS, respectively carrying out numerical comparison on the distance data CS and the relative speed difference data XC and a preset distance data threshold value and a preset relative speed difference threshold value which are recorded and stored in advance, and generating a rear-end collision low risk signal if the distance data CS exceeds the preset distance data threshold value and the relative speed difference value XC does not exceed the preset relative speed difference threshold value; otherwise, calculating the ratio of the vehicle distance data CS to the relative speed difference value XC to obtain a rear-end collision risk value ZF, comparing the rear-end collision risk value ZF with a preset rear-end collision risk threshold value in a numerical mode, generating a rear-end collision low risk signal if the rear-end collision risk value exceeds the preset rear-end collision risk threshold value, and generating a rear-end collision high risk signal if the rear-end collision risk value ZF does not exceed the preset rear-end collision risk threshold value.
When the target vehicle is in a state of waiting in line for running, marking the moment when the front vehicle starts to be converted from a static state to a starting forward state as an initial moment, if the target vehicle is not started at the moment, performing timing analysis, performing time difference calculation on the timing detection moment and the initial moment to obtain a starting dullness coefficient QC, and performing numerical comparison on the starting dullness coefficient QC and a preset starting dullness coefficient threshold value which is recorded and stored in advance, wherein the preset starting dullness coefficient threshold value is preferably 2 seconds; if the starting dullness coefficient QC exceeds a preset starting dullness coefficient threshold value, a starting reminding signal is generated and sent to a vehicle early warning module through a processor, the vehicle early warning module sends corresponding early warning after receiving the starting reminding module, and a corresponding driver timely starts the vehicle when receiving the corresponding early warning so as to prevent rear-end collision of the vehicle after being in a rear-end collision of the vehicle, and safety is further improved.
The road section accident risk analysis module marks the next driving road section of the target vehicle as an analysis road section, carries out historical traffic accident analysis on the analysis road section, marks corresponding traffic accidents as heavy accidents, medium accidents and light accidents through analysis, generates accident low risk signals or accident high risk signals through analysis, sends the accident low risk signals or the accident high risk signals to the processor, sends the accident high risk signals to the vehicle early warning module, sends corresponding early warning when the vehicle early warning module receives the accident high risk signals, and the corresponding drivers should pay more attention to and concentrate on the following driving process when receiving the corresponding early warning, and properly decelerates slowly as required; the specific operation process of the road section accident risk analysis module is as follows:
the method comprises the steps of obtaining the current position of a target vehicle in a detection period, setting an analysis end point by taking the current position of the target vehicle as an analysis starting point and based on a driving navigation path of the target vehicle, wherein the path distance between the analysis end point and the analysis starting point is L1, and marking a road section between the analysis end point and the analysis starting point as an analysis road section, namely a next driving road section; acquiring historical traffic accident frequency of a corresponding analysis road section in unit time, marking the historical traffic accident frequency as SP, and preferably, the unit time is six months before adjacent; grading the corresponding traffic accidents, specifically: if the corresponding traffic accident has the death of the personnel, marking the corresponding traffic accident as a serious accident; if no person dies in the corresponding traffic accident, acquiring the number of heavy injury and light injury caused by the corresponding traffic accident, and marking the number of heavy injury and light injury caused by the corresponding traffic accident as a heavy injury number ZS and a light injury number QS;
the method comprises the steps of calculating the number ZS of heavy injuries and the number QS of light injuries of corresponding traffic accidents according to a formula SF=rt1+rt2 to obtain an accident feedback value SF; wherein, rt1 and rt2 are preset weight coefficients with values larger than zero, and rt1 is more than rt2; and, the larger the value of the accident feedback value SF is, the more serious the corresponding traffic accident is; comparing the accident feedback value SF with a preset accident feedback range which is recorded and stored in advance, marking the corresponding traffic accident as a serious accident if the accident feedback value SF exceeds the maximum value of the preset accident feedback range, marking the corresponding traffic accident as a moderate accident if the accident feedback value SF is positioned in the preset accident feedback range, and marking the corresponding traffic accident as a mild accident if the accident feedback value SF does not exceed the minimum value of the preset accident feedback range;
obtaining the frequency of severe accidents, the frequency of moderate accidents and the frequency of mild accidents of the corresponding analysis road sections in unit time, marking the three as ZP, FP and QP respectively, and passing through the formulaCarrying out numerical calculation on the frequency ZP of severe accidents, the frequency FP of moderate accidents, the frequency QP of mild accidents and the frequency SP of historical traffic accidents to obtain an accident risk coefficient FX; wherein tg1, tg2, tg3, tg4 are preset proportionality coefficients, tg2 > tg3 > tg4 > tg1, alpha is a preset correction factor and alpha has a value of 3.658;
it should be noted that, the larger the value of the accident risk coefficient FX of the corresponding analysis road section is, the larger the risk of the accident of the corresponding analysis road section is, and the more careful is needed in the driving process; and comparing the accident risk coefficient FX with a preset accident risk coefficient threshold value which is recorded and stored in advance, generating an accident high risk signal if the accident risk coefficient FX exceeds the preset accident risk coefficient threshold value, generating an accident low risk signal if the accident risk coefficient FX does not exceed the preset accident risk coefficient threshold value, and transmitting the accident high risk signal or the accident low risk signal to a processor.
The processor sends the accident low-risk signal to the road section difficulty analysis module, the road section difficulty analysis module receives the accident low-risk signal and then analyzes the road section passing difficulty of the analysis road section of the target vehicle, a road section high-difficulty signal or a road section low-difficulty signal is generated through the road section passing difficulty analysis, the road section high-difficulty signal or the road section low-difficulty signal is sent to the processor, the processor sends the road section high-difficulty signal to the vehicle early warning module, the vehicle early warning module sends corresponding early warning when receiving the road section high-difficulty signal, and the corresponding driver should pay more careful attention and concentrate on the following driving process when receiving the corresponding early warning and properly decelerate and slow down as required; the specific operation process of the road section difficulty analysis module is as follows:
acquiring the current moment, acquiring the average vehicle flow PL and the average vehicle speed PH of the period corresponding to the current moment in the first e adjacent days of the corresponding analysis road section, wherein the value of e is preferably fifteen days; calculating the average vehicle flow PL and the average vehicle speed PH by a formula CZ=u1, namely PL+u2/PH, and obtaining a vehicle passing value CZ of a corresponding date, wherein u1 and u2 are preset weight coefficients, and u2 is more than u1 and more than 0; it should be noted that, the magnitude of the vehicle passing value CZ is in a direct proportion relation with the average vehicle flow PL, and in an inverse proportion relation with the average vehicle speed PH, the larger the magnitude of the vehicle passing value CZ is, the more unsmooth the passing of the corresponding date is indicated;
invoking a preset vehicle passing threshold value which is recorded and stored in advance, marking a vehicle passing value which exceeds the preset vehicle passing threshold value as an abnormal passing value, calculating the ratio of the number of the abnormal passing values to a numerical value e to obtain an abnormal passing occupation ratio YZ, summing the vehicle passing values in the previous e days, taking an average value to obtain a passing average value TP, and calculating the abnormal passing occupation ratio YZ and the passing average value TP to obtain a road section abnormal coefficient LY through a formula LY=k1; wherein k1 and k2 are preset weight coefficients, and k1 is more than k2 and more than 1; the larger the value of the road section abnormal coefficient LY is, the worse the traffic condition of the corresponding analysis road section is indicated;
the method comprises the steps of obtaining the number of the pothole uneven areas in an analysis road section, marking the number as WS, obtaining the area of the corresponding pothole uneven areas, marking the pothole uneven areas exceeding a preset area threshold as abnormal areas, obtaining the number of the abnormal areas in a corresponding sub road section, carrying out ratio calculation on the number of the abnormal areas and the number WS of the pothole uneven areas in the corresponding sub road section to obtain an abnormal area occupation ratio ZB, and carrying out numerical calculation on the abnormal area occupation ratio ZB and the number WS of the pothole uneven areas through a formula PZ=fu1+fu2×ZB to obtain an area flatness value PZ; wherein, fu1 and fu2 are preset weight coefficients, and fu1 is more than fu2 is more than 0; and, the larger the value of the regional flatness value PZ is, the worse the surface road condition of the corresponding analysis road section is indicated;
obtaining the length of a straight road section, the number of traffic lights and the number of turning around of a corresponding analysis road section, and marking the number of the traffic lights and the number of the turning around as HL and GD respectively; calculating the ratio of the length of the straight road section to the path distance L1 to obtain a ratio ZX of the straight road section, and calculating the ratio ZX of the straight road section, the number HL of red and green lamps, the number GD of turning around and the regional flatness value PZ by using a road section complexity analysis formula FZ=tp 1/ZX+tp2, HL+tp3 and GD+tp4 to obtain a road section complexity coefficient FZ; wherein tp1, tp2, tp3 and tp4 are preset proportionality coefficients, tp1 > tp4 > tp3 > tp2; and, the larger the value of the road section complexity coefficient FZ is, the more complex the corresponding analysis road section is;
respectively carrying out numerical comparison on the road section abnormal coefficient LY and the road section complex coefficient FZ of the corresponding analysis road section and a preset road section abnormal coefficient threshold value and a preset road section complex coefficient threshold value which are recorded and stored in advance, and generating a road section high-difficulty signal if the road section abnormal coefficient LY exceeds the preset road section abnormal coefficient threshold value or the road section complex coefficient FZ exceeds the preset road section complex coefficient threshold value; if the road section abnormal coefficient LY does not exceed the preset road section abnormal coefficient threshold value and the road section complex coefficient FZ does not exceed the preset road section complex coefficient threshold value, subtracting the road section abnormal coefficient LY from the preset road section abnormal coefficient threshold value to obtain a road section abnormal difference value LC, and subtracting the road section complex coefficient FZ from the preset road section complex coefficient threshold value to obtain a road section complex difference value FC;
calculating the numerical value of the abnormal road section difference LC and the complex road section difference FC through a formula NC=st1+st2 to obtain a traffic difficulty difference NC; wherein st1 and st2 are preset weight coefficients, and st1 is more than 0 and st2 is more than 2; and the larger the numerical value of the passing difficulty difference NC is, the more favorable the driving of the road section corresponding to the analysis road section is; carrying out numerical comparison on the traffic difficulty difference NC and a preset traffic difficulty difference threshold, generating a road section low difficulty signal if the traffic difficulty difference NC exceeds the preset traffic difficulty difference threshold, and generating a road section high difficulty signal if the traffic difficulty difference NC does not exceed the preset traffic difficulty difference threshold.
Embodiment two:
as shown in fig. 2, the difference between this embodiment and embodiment 1 is that the processor is communicatively connected to a driving safety detection module, and the driving safety detection module performs driving safety analysis on the target vehicle, and a specific analysis process of the driving safety analysis is as follows:
obtaining vibration data and generated noise data of a target vehicle in a detection period and marking the data as ST and SE respectively, wherein the noise data SE is a data value of the magnitude of a decibel value of the generated noise of the target vehicle, the larger the generated noise decibel value is, the larger the value of the noise data is, the vibration data ST is a data value representing the magnitude of both the vibration frequency and the vibration amplitude of the target vehicle, and the larger the vibration frequency and the vibration amplitude are, the larger the value of the vibration data is; the method comprises the steps of obtaining the driving distance and the fuel consumption of a target vehicle in a detection period, calculating the ratio of the fuel consumption to the driving distance to obtain a fuel consumption analysis value, calling a preset fuel consumption analysis range from a server, calculating the difference between the fuel consumption analysis value and the median of the preset fuel consumption analysis value range, and obtaining a fuel consumption analysis difference value YC by taking the absolute value, wherein the larger the numerical value of the fuel consumption analysis value YC is, the larger the deviation degree of the fuel consumption performance of the target vehicle in the detection period compared with the proper range is, and the greater the possibility that the vehicle is abnormal is;
the fuel consumption analysis difference YC, the vibration data ST and the noise data SE are subjected to numerical calculation through a vehicle detection analysis formula SJ=a1, YC+a2, ST+a3, and then a vehicle detection value SJ of the target vehicle is obtained; wherein a1, a2 and a3 are preset weight coefficients with values larger than zero, and a1 is more than a2 and more than a3; it should be noted that, the magnitude of the vehicle detection value SJ is in a proportional relationship with the fuel consumption analysis difference YC, the vibration data ST and the noise data SE, the greater the magnitude of the vehicle detection value SJ, the worse the running performance of the target vehicle is, and the greater the possibility of abnormality of the target vehicle is;
acquiring brightness data, rainfall data and visibility data of an external environment corresponding to a target vehicle in a detection period, and marking the brightness data, the rainfall data and the visibility data as FL, FY and FN respectively, wherein the brightness data are data values representing the brightness condition of the environment, and the brighter the environment is, the larger the value of the brightness data is; the rainfall data is a data value representing the amount of rainfall in unit time, and the larger the rainfall in unit time is, the larger the value of the rainfall data is, so that the safety running of the vehicle is affected; the visibility data is a data value representing the visibility condition of the environment, and the larger the visibility of the environment is, the larger the visibility data is, so that the safety running of the vehicle is facilitated;
calculating the brightness data FL, the rainfall data FY and the visibility data FN by using an environmental impact formula HJ=b1/FL+b2 x FY+b3/FN to obtain an environmental detection value HJ; wherein b1, b2 and b3 are preset proportionality coefficients with values larger than zero, and b2 is larger than b3 and larger than b1; and, the magnitude of the environmental detection value HJ is in inverse proportion to the brightness data FL and the visibility data FN, and in direct proportion to the rainfall data FY, the larger the magnitude of the environmental detection value HJ is, the better the environment where the target vehicle is located in the detection period is, and the more favorable the safe running of the vehicle is;
the method comprises the steps of calling a preset vehicle detection threshold value and a preset environment detection threshold value which are recorded and stored in advance, respectively comparing a vehicle detection value SJ and an environment detection value HJ with the preset vehicle detection threshold value and the preset environment detection threshold value in numerical value, generating a driving safety disqualification signal if the vehicle detection value SJ exceeds the preset vehicle detection threshold value or the environment detection value HJ exceeds the preset environment detection threshold value, indicating that the driving risk of a target vehicle corresponding to a detection period is large, and generating the driving safety qualification signal if the vehicle detection value SJ does not exceed the preset vehicle detection threshold value and the environment detection value HJ does not exceed the preset environment detection threshold value, indicating that the driving risk of the target vehicle corresponding to the detection period is small.
The driving safety detection module is used for carrying out driving safety analysis on the target vehicle so as to generate a driving safety qualified signal or a driving safety unqualified signal, the driving safety qualified signal or the driving safety unqualified signal is sent to the processor, the processor is used for sending the driving safety unqualified signal to the vehicle early warning module, and the vehicle early warning module is used for sending corresponding early warning when receiving the driving safety unqualified signal, so that a driver can know the driving safety condition in time, and can drive and adjust in time and stop driving according to the requirement, and the safety of the driver and the vehicle is ensured.
The working principle of the invention is as follows: when the vehicle early warning system is used, the radar video acquisition monitoring module is used as a carrier based on a camera and a radar, generates a rear-end collision low risk signal or a rear-end collision high risk signal through acquisition monitoring analysis, judges whether to generate a starting reminding signal through analysis under the condition that a target vehicle is in queuing for waiting to run, and sends out corresponding early warning when the vehicle early warning module receives the rear-end collision high risk signal or the starting reminding signal so as to prevent the vehicle from rear-end collision, thereby ensuring the running safety; the road section accident risk analysis module marks the next driving road section of the target vehicle as an analysis road section, generates an accident low risk signal or an accident high risk signal through analysis, sends out corresponding early warning when the vehicle early warning module receives the accident high risk signal, carries out road section passing difficulty analysis through the road section difficulty analysis module when generating the accident low risk signal so as to generate a road section high difficulty signal or a road section low difficulty signal, sends out corresponding early warning when the vehicle early warning module receives the road section high difficulty signal, and the corresponding driver should pay more careful attention and concentrate attention in the following driving process when receiving the corresponding early warning, and properly decelerates and slows down as required, so that reasonable analysis of the potential risk in the driving process of the vehicle is realized, and the safety of the driving process is further ensured.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation. The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (7)
1. The vehicle potential hazard detection system based on radar and video linkage is characterized by comprising a processor, a radar video acquisition and monitoring module, a road section accident risk analysis module, a road section difficulty analysis module, a driving safety detection module and a vehicle early warning module; the radar video acquisition monitoring module is used for generating a rear-end collision low-risk signal or a rear-end collision high-risk signal based on a camera and a radar serving as carriers through acquisition monitoring analysis, the rear-end collision low-risk signal or the rear-end collision high-risk signal is transmitted to the vehicle early warning module through the processor, and the vehicle early warning module sends corresponding early warning when receiving the rear-end collision high-risk signal;
the road section accident risk analysis module is used for marking the next driving road section of the target vehicle as an analysis road section, carrying out historical traffic accident analysis on the analysis road section, marking corresponding traffic accidents as heavy accidents, medium accidents and light accidents through analysis, generating accident low risk signals or accident high risk signals through analysis, sending the accident low risk signals or the accident high risk signals to the processor, sending the accident high risk signals to the vehicle early warning module by the processor, and sending corresponding early warning when the vehicle early warning module receives the accident high risk signals;
the road section difficulty analysis module is used for analyzing the road section passing difficulty of the analysis road section of the target vehicle, generating a road section high difficulty signal or a road section low difficulty signal through the road section passing difficulty analysis, sending the road section high difficulty signal or the road section low difficulty signal to the processor, sending the road section high difficulty signal to the vehicle early warning module by the processor, and sending a corresponding early warning when the vehicle early warning module receives the road section high difficulty signal; the driving safety detection module is used for carrying out driving safety analysis on the target vehicle, generating a driving safety qualified signal or a driving safety unqualified signal through analysis, sending the driving safety qualified signal or the driving safety unqualified signal to the processor, sending the driving safety unqualified signal to the vehicle early warning module by the processor, and sending a corresponding early warning when the vehicle early warning module receives the driving safety unqualified signal;
the specific operation process of the road section difficulty analysis module comprises the following steps:
acquiring the current moment, acquiring average vehicle flow and average vehicle speed in the period corresponding to the current moment in the previous e adjacent days of the corresponding analysis road section, carrying out numerical calculation on the average vehicle flow and the average vehicle speed to acquire a vehicle passing value of the corresponding date, marking the vehicle passing value exceeding a preset vehicle passing threshold as an abnormal passing value, carrying out ratio calculation on the number of the abnormal passing values and the numerical value e to acquire an abnormal passing ratio, carrying out summation calculation on the vehicle passing values in the previous e adjacent days, taking an average value to acquire a passing average value, and carrying out numerical calculation on the abnormal passing ratio and the passing average value to acquire a road section abnormal coefficient;
the road section complexity coefficient of the corresponding analysis road section is obtained through analysis, the road section abnormal coefficient and the road section complexity coefficient of the corresponding analysis road section are respectively compared with a preset road section abnormal coefficient threshold value and a preset road section complexity coefficient threshold value in numerical value, and if the road section abnormal coefficient exceeds the preset road section abnormal coefficient threshold value or the road section complexity coefficient exceeds the preset road section complexity coefficient threshold value, a road section high difficulty signal is generated;
subtracting the road section abnormal coefficient from the preset road section abnormal coefficient threshold to obtain a road section abnormal difference value if the road section abnormal coefficient does not exceed the preset road section abnormal coefficient threshold and the road section complex coefficient does not exceed the preset road section complex coefficient threshold, subtracting the road section complex coefficient from the preset road section complex coefficient threshold to obtain a road section complex difference value, carrying out numerical calculation on the road section abnormal difference value and the road section complex difference value to obtain a traffic difficulty difference value, carrying out numerical comparison on the traffic difficulty difference value and the preset traffic difficulty difference value, generating a road section low difficulty signal if the traffic difficulty difference value exceeds the preset traffic difficulty difference value threshold, and generating a road section high difficulty signal if the traffic difficulty difference value does not exceed the preset traffic difficulty difference value;
the analysis and acquisition method of the road section complexity coefficient comprises the following steps:
the method comprises the steps of obtaining the number of the pothole uneven areas in an analysis road section and the areas corresponding to the pothole uneven areas, marking the pothole uneven areas exceeding a preset area threshold as abnormal areas, calculating the ratio of the number of the abnormal areas to the number of the pothole uneven areas in a corresponding sub road section to obtain an abnormal area occupation ratio, and calculating the numerical value of the abnormal area occupation ratio and the number of the pothole uneven areas to obtain an area flatness value; the method comprises the steps of obtaining the length of a straight road section, the number of traffic lights and the number of turning around of a corresponding analysis road section, calculating the ratio of the length of the straight road section to the path distance L1 to obtain the occupation ratio of the straight road section, and calculating the numerical values of the occupation ratio of the straight road section, the number of the traffic lights, the number of turning around and the regional flatness value to obtain the road section complexity coefficient.
2. The radar and video linkage based vehicle potential hazard detection system of claim 1, wherein the radar video acquisition monitoring module acquires, monitors and analyzes the process specifically as follows:
in the running process of the target vehicle, acquiring the distance between the target vehicle and the front vehicle and the relative speed of the target vehicle and the front vehicle, calculating the relative speed of the target vehicle and the front vehicle to obtain a relative speed difference value, marking the distance between the target vehicle and the front vehicle as vehicle distance data, generating a rear-end collision low risk signal if the vehicle distance data exceeds a preset vehicle distance data threshold value and the relative speed difference value does not exceed a preset relative speed difference threshold value, otherwise, calculating the ratio of the vehicle distance data to the relative speed difference value to obtain a rear-end collision risk value, generating a rear-end collision low risk signal if the rear-end collision risk value exceeds a preset rear-end collision risk threshold value, and generating a rear-end collision high risk signal if the rear-end collision risk value does not exceed the preset rear-end collision risk threshold value.
3. The radar and video linkage based vehicle hazard potential detection system of claim 2, wherein the specific operation of the radar video acquisition monitoring module further comprises:
when the target vehicle is in a state of waiting to drive in a queue, the moment of converting the front vehicle from a stationary state to a starting state is marked as an initial moment, if the target vehicle is not started at the moment, timing analysis is carried out, time difference calculation is carried out between the timing detection moment and the initial moment to obtain a starting dullness coefficient, if the starting dullness coefficient exceeds a preset starting dullness coefficient threshold value, a starting reminding signal is generated, the starting reminding signal is sent to a vehicle early warning module through a processor, and the vehicle early warning module sends corresponding early warning after receiving the starting reminding module.
4. The radar and video linkage based vehicle potential hazard detection system of claim 1, wherein the specific operation of the road segment accident risk analysis module comprises:
acquiring the current position of a target vehicle in a detection period, setting an analysis end point by taking the current position of the target vehicle as an analysis starting point and based on a running navigation path of the target vehicle, wherein the path distance between the analysis end point and the analysis starting point is L1, and marking the next running road section between the analysis end point and the analysis starting point as an analysis road section; acquiring historical traffic accident frequency of a corresponding analysis road section in unit time, and carrying out numerical calculation on the frequency of the severe accident, the frequency of the moderate accident, the frequency of the mild accident and the historical traffic accident frequency to obtain an accident risk coefficient based on the casualty condition of the corresponding traffic accident and by analyzing and marking the corresponding traffic accident as a severe accident, a moderate accident or a mild accident; if the accident risk coefficient exceeds a preset accident risk coefficient threshold, generating an accident high risk signal, otherwise, generating an accident low risk signal, and sending the accident high risk signal or the accident low risk signal to the processor.
5. The radar and video linkage based vehicle hazard potential detection system of claim 4, wherein the specific analysis process for marking the corresponding traffic accident as a heavy accident, a medium accident or a light accident is as follows:
if the corresponding traffic accident has the death of the personnel, the corresponding traffic accident is marked as a serious accident, if the corresponding traffic accident does not have the death of the personnel, the number of the serious injury and the light injury caused by the corresponding traffic accident is obtained, the number of the serious injury and the light injury caused by the corresponding traffic accident is marked as the number of the serious injury and the light injury, the number of the serious injury and the light injury caused by the corresponding traffic accident are calculated to obtain an accident feedback value, the accident feedback value is compared with a preset accident feedback range in a numerical mode, if the accident feedback value exceeds the maximum value of the preset accident feedback range, the corresponding traffic accident is marked as a serious accident, if the accident feedback value is positioned in the preset accident feedback range, the corresponding traffic accident is marked as a moderate accident, and if the accident feedback value does not exceed the minimum value of the preset accident feedback range, the corresponding traffic accident is marked as a mild accident.
6. The radar and video linkage based vehicle potential hazard detection system of claim 1, wherein the specific analysis process of the driving safety analysis comprises:
the method comprises the steps of respectively comparing a vehicle detection value and an environment detection value with a preset vehicle detection threshold value and a preset environment detection threshold value through analysis to obtain the vehicle detection value and the environment detection value of a target vehicle in a detection period, generating a driving safety unqualified signal if the vehicle detection value exceeds the preset vehicle detection threshold value or the environment detection value exceeds the preset environment detection threshold value, and generating a driving safety qualified signal if the vehicle detection value does not exceed the preset vehicle detection threshold value and the environment detection value does not exceed the preset environment detection threshold value.
7. The radar and video linkage based vehicle hazard detection system of claim 6, wherein the vehicle detection value and the environmental detection value are obtained by the following analysis:
obtaining vibration data and generated noise data of a target vehicle in a detection period, obtaining a driving distance and fuel consumption of the target vehicle in the detection period, calculating the ratio of the fuel consumption to the driving distance to obtain a fuel consumption analysis value, calculating the difference between the fuel consumption analysis value and the median of a preset fuel consumption analysis value range, taking an absolute value to obtain a fuel consumption analysis difference value, and calculating the fuel consumption analysis difference value, the vibration data and the noise data to obtain a vehicle detection value; and acquiring brightness data, rainfall data and visibility data of the external environment corresponding to the target vehicle in the detection period, and performing numerical calculation on the brightness data, the rainfall data and the visibility data to acquire an environment detection value.
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| CN116729371A (en) | 2023-09-12 |
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