Traffic global safety early warning system based on real-time safety information fusion technology

Abstract

The invention discloses a traffic global safety early warning system based on a real-time safety information fusion technology. The traffic global safety early warning system comprises a traffic global big data cloud center, a vehicle-mounted intelligent terminal OBU, an intelligent terminal provided with a client APP and a road side unit RSU. The OBU is used for acquiring vehicle self-acquisition information serving as a first-layer information source; the road side unit RSU is used for acquiring road side RSU information serving as a second-layer information source; the intelligent terminal provided with the client APP is used for acquiring global traffic information serving as a third-layer information source; and the traffic global big data cloud center corrects the precision of dynamic traffic data including vehicle self-acquisition information, a roadside RSU information source and global traffic information, and fuses the dynamic traffic data to a unified traffic electronic map. According to the invention, various individuals entering the public transportation space can be found in advance, and the information transparency, the operation efficiency and the travel safety of the transportation system are improved.

Description

Technical Field

[0001] The present invention relates to the field of traffic safety technology, and in particular to a traffic global safety warning system based on real-time safety information fusion technology. Background Art

[0002] High-level intelligent driving and vehicle-road collaboration require rich multi-source dynamic data. In addition to roadside traffic information sources (coils, microwaves, videos, radars), it is still necessary to consider incorporating all other dynamic information of people or vehicles moving in the public transportation environment into the intelligent transportation decision-making environment. Traditional traffic information sources are mainly obtained through direct collection, such as relying on the active collection function of smart driving vehicles or smart roadside equipment (RSU). This collection method is the basic data foundation for smart transportation and intelligent driving now and in the future, but if we want to promote the deepening of intelligent transportation or support the further development of vehicle-road collaboration, we still need to improve the abundance and accuracy of traffic information. Only when "all traffic information is under control" can we achieve ultimate safety and support the implementation of smart transportation and smart driving.

[0003] The biggest problem with the traditional active traffic information collection model is that the types and scope of information are limited, and recognition errors are inevitable (blind spots, short warning time, and severe weather impact, etc.), which greatly limits the scope and extent of application of smart transportation and smart driving. By adopting a reasonable approach to allow all types of traffic participants in the public transportation environment to actively contribute real-time dynamic information and access the global traffic information space, the abundance, scope, and quality of traditional traffic information sources can be improved, and better serve the application scenarios of future smart transportation.

[0004] At present, portable smart devices (mobile phones, bracelets, etc.) and on-board smart devices (OBU) are becoming more and more popular, and have initially met the conditions to cover all mobile transportation individuals. In the future, the breadth and depth of the popularization of smart portable devices will be further enhanced.

[0005] Therefore, how to improve traffic efficiency and travel safety based on smart devices has become a technical problem that technical personnel in this field urgently need to solve. Summary of the invention

[0006] In view of the above-mentioned defects of the prior art, the present invention provides a traffic full-area safety warning system based on real-time safety information fusion technology, the purpose of which is to synchronize (access) the system in real time based on mobile information (after desensitization processing) actively activated by smart devices, to ensure that all types of individuals (individuals, vehicles) entering the public transportation space can be discovered in advance and accurately, thereby improving the information transparency and operation efficiency of the transportation system, and also providing corresponding feedback information (real-time information and conflict warnings of surrounding mobile transportation bodies) for the actively activated individuals, thereby improving traffic efficiency and travel safety.

[0007] To achieve the above-mentioned purpose, the present invention discloses a traffic global safety warning system based on real-time safety information fusion technology, including a traffic global big data cloud center, an on-board intelligent terminal OBU, an intelligent terminal with a client APP installed, and a roadside unit RSU;

[0008] The vehicle-mounted intelligent terminal OBU is used to obtain the vehicle's own collected information as the first layer of information source, including traffic information directly obtained through various sensors of the vehicle itself and the driver's vision;

[0009] The roadside unit RSU is used to obtain roadside RSU information as a second-layer information source, including surrounding traffic environment information provided by intelligent information devices in the road environment, specifically including road congestion, signal and road network geographical environment information collected by roadside monitoring equipment;

[0010] The smart terminal with the client APP installed is used to obtain global traffic information as the third-layer information source, including non-aggregate traffic information resources provided by various traffic individuals for the global traffic environment through the application program in the smart terminal;

[0011] The global traffic big data cloud center corrects the accuracy of dynamic traffic data including vehicle self-collected information, roadside RSU information sources and global traffic information, and integrates them into a unified traffic electronic map.

[0012] Preferably, the various sensors of the automobile include radars and / or cameras; the roadside monitoring equipment includes cameras, coils and / or radars; and the smart terminal installed with the client APP includes a smart phone, a tablet computer, a smart watch and / or a smart bracelet.

[0013] More preferably, the global traffic information is the desensitized Beidou / GPS information, movement direction information, and speed information uploaded by various types of traffic individuals, which are collected on the global traffic information platform.

[0014] Preferably, the traffic global big data cloud center includes a processing module, a deduction and prediction module, and a warning information release module;

[0015] The processing module performs desensitization processing on the information uploaded by various types of traffic individuals by region, and processes the traffic movement data including location, speed and direction of various types of traffic individuals;

[0016] The deduction and prediction module deduces and predicts short-term conflicts of various types of traffic individuals in different regions;

[0017] The warning information release module pushes various types of traffic conflict warning information by region.

[0018] Preferably, the workflow is as follows:

[0019] Step 1: All the smart terminals installed with the client APP and all the on-board smart terminals OBU upload the smart terminal's own dynamic traffic information to the traffic global big data cloud center;

[0020] The intelligent terminal's own dynamic traffic information includes the real-time positioning information, speed information, and direction information collected by the slow-moving traffic individual's smart phone, smart bracelet, and the vehicle-mounted intelligent terminal OBU;

[0021] The traffic global big data cloud center obtains dynamic data including road traffic signals, cycles, and traffic volume from the roadside unit RSU, and obtains static information related to spatial attributes from the traffic electronic map;

[0022] Step 2: According to the areas divided by the traffic global big data cloud center, or the roadside units RSUs in different blocks, based on the unified traffic electronic map information and coordinate system, various types of dynamic real-time traffic information are corrected and matched with map coordinates;

[0023] Step 3: Through the deduction and prediction module, the status and risk of various types of corrected traffic data are predicted, and warning or guidance information is generated.

[0024] Step 4: The warning or guidance information is released to all the smart terminals installed with the client APP and all the vehicle-mounted smart terminals OBU in each area that need to be reminded through the warning information release module, and reminders are given in the form of sound or graphics.

[0025] Preferably, step 2 is as follows:

[0026] Step 2.1, obtaining direction vector data of each traffic individual according to the OBU accelerometer, gyroscope and magnetometer of the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU;

[0027] And according to the OBU satellite positioning signals of the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU, the latitude and longitude scattered point information of each traffic individual is obtained;

[0028] Step 2.2, according to the latitude and longitude scatter information of each traffic individual, the OBU speed sensor of the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU is measured based on the positioning information to obtain speed vector data;

[0029] At the same time, the accuracy, continuity and reliability of the longitude and latitude data itself are corrected according to the longitude and latitude scattered point information of each traffic individual;

[0030] Step 2.3, according to the direction vector data of each traffic individual, and the speed vector data obtained by the OBU speed sensor of the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU, the result of correcting the accuracy, continuity and reliability of the longitude and latitude data itself according to the longitude and latitude scatter information of each traffic individual is verified;

[0031] Step 2.4, directly interpolate the verification results into the coordinate system of the high-definition electronic map;

[0032] Step 2.5: Based on the accurate positions of the calibration markers or fixed obstacles accumulated and calibrated in the electronic map, and the optimal traffic flow envelope channel fitted according to historical traffic flow statistics, the positions, directions, and speeds of the dynamic traffic individuals interpolated to the coordinate system of the high-definition electronic map are verified;

[0033] Step 2.6: Obtain the optimal position, direction and speed information of real-time traffic individuals, and then perform collision detection and prediction.

[0034] Preferably, in step 2.2, the method for correcting the accuracy, continuity and reliability of the longitude and latitude data itself according to the longitude and latitude scatter information of each traffic individual is: performing preliminary data correction processing on the longitude and latitude scatter information of the traffic individual to eliminate noise data and jump and drift points, and then correcting it by the following formula:

[0035] T′ i =T i ×A T′ ;

[0036] Where, T i is the original discrete data; T′ i is the predicted data; A T′ is the prediction transfer matrix of discrete data;

[0037]

[0038] COV lat It is the adjacent data lat in the dimension data sequence i and lat i-1 The covariance of represents the predicted increment;

[0039] COV lon is the adjacent data lon in the longitude data sequence i T″ i and lon i-1 The covariance of , represents the predicted increment.

[0040] More preferably, in step 2.3, the method for verifying the result of correcting the accuracy, continuity and reliability of the latitude and longitude data itself according to the latitude and longitude scatter information of each traffic individual is:

[0041] T″ i =T′ i ×A T″ ;

[0042] Among them, T″ i It is a further correction of the original data; A T″ is the predicted transfer matrix that is further corrected;

[0043]

[0044] Wherein, foreLat and foreLon are the speed measured by the software and chip of the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU, and the direction data separated into longitude and latitude;

[0045] The specific calculation is as follows:

[0046]

[0047] Where θ represents the direction vector determined by the traffic individual itself The angle of intersection with the latitude line; v represents the speed measured by the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU.

[0048] More preferably, in step 2.5, the method for verifying the position, direction, and speed of the dynamic traffic entity interpolated to the high-definition electronic map coordinate system is as follows:

[0049] T″′ i =T″ i ×A T″′ +N;

[0050] Where, T″′ i is the result of further correction; A T″′ is the empirical trajectory transfer matrix; N is the rejection matrix of the exact coordinate point or obstacle;

[0051]

[0052] In the formula, ex lat is the empirical correction increment of the original dimensional data; ex lon is the empirical correction increment of the original longitude data;

[0053] ex lat =(lat i -lat 80％ ) / (lati -lat mid )

[0054] ex lon =(lon i -lon 80％ ) / (lon i -lon mid )

[0055] In the formula, lat i ,lon i is the original latitude and longitude data; lat 80％ ,lon 80％ It is the boundary envelope data of latitude and longitude in historical data statistics; lat mid ,lon mid It is the weighted average data of latitude and longitude in historical data statistics;

[0056]

[0057] Where N is the fixed obstacle influence parameter; 2×10 -8 The error is caused by the satellite chip of smart phones and smart bracelets; Lat j , Lon j is the fixed position of a fixed object obstacle j in the electronic map, including latitude and longitude); It is the latitude and longitude radius boundary of the exclusion range of a fixed object obstacle j in the electronic map.

[0058] Beneficial effects of the present invention:

[0059] The present invention is based on a real-time synchronization (access) system of mobile information (after desensitization processing) actively activated by smart devices, ensuring that all types of individuals (individuals, vehicles) entering the public transportation space can be discovered in advance and accurately, thereby improving the information transparency and operating efficiency of the transportation system. At the same time, it also provides corresponding feedback information (real-time information and conflict warnings of surrounding mobile transportation bodies) for the actively activated individuals, thereby improving traffic efficiency and travel safety.

[0060] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the accompanying drawings to fully understand the purpose, characteristics and effects of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 A schematic diagram showing a logical framework of a system according to an embodiment of the present invention.

[0062] Figure 2 The scope of the vehicle's own collected information of the first layer of information source acquired by the on-board intelligent terminal OBU in one embodiment of the present invention is shown.

[0063] Figure 3 The scope of the roadside RSU information of the second layer information source acquired by the roadside unit RSU in one embodiment of the present invention is shown.

[0064] Figure 4 The scope of the global traffic information of the third layer information source obtained by the smart terminal with the client APP installed in one embodiment of the present invention is shown.

[0065] Figure 5 A flow chart showing the correction processing and map coordinate matching of various types of dynamic real-time traffic information in step 2 of an embodiment of the present invention.

[0066] Figure 6 A schematic diagram showing an embodiment of the present invention for correcting the accuracy, continuity and reliability of longitude and latitude data according to longitude and latitude scattered point information of each traffic individual.

[0067] Figure 7 A schematic diagram showing the envelope distribution of historical discrete data of a certain road section according to an embodiment of the present invention is shown.

[0068] Figure 8 A schematic diagram showing a certain road section after completing step 2 according to an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0069] Example

[0070] like Figures 1 to 4 As shown in the figure, the traffic global safety warning system based on real-time safety information fusion technology includes a traffic global big data cloud center, an on-board intelligent terminal OBU, an intelligent terminal with a client APP installed, and a roadside unit RSU;

[0071] The vehicle-mounted intelligent terminal OBU is used to obtain the vehicle's own collected information as the first-level information source, including traffic information directly obtained through various sensors of the vehicle itself and the driver's vision;

[0072] The roadside unit RSU is used to obtain roadside RSU information as the second-layer information source, including surrounding traffic environment information provided by intelligent information devices in the road environment, specifically including road congestion, signal and road network geographical environment information collected by roadside monitoring equipment;

[0073] Smart terminals with client APP installed are used to obtain global traffic information as the third-layer information source, including non-aggregate traffic information resources provided by various traffic individuals for the global traffic environment through applications in smart terminals;

[0074] The traffic global big data cloud center calibrates the accuracy of dynamic traffic data including vehicle-collected information, roadside RSU information sources and global traffic information, and integrates them into a unified traffic electronic map.

[0075] like Figure 1 As shown, the graphics represent various terminals, the arrows represent data flows, and the text on the arrows represents key function processing.

[0076] Through the built-in satellite positioning systems (Beidou / GPS), speed sensors, direction sensors, etc. of mobile phones, bracelets, vehicle-mounted OBUs, etc., the location, speed, direction and other information of pedestrians and motor vehicles are obtained, and the movement information of various types of traffic individuals is collected in different regions to carry out short-term situation simulation and propose a comprehensive traffic safety early warning system.

[0077] However, due to the poor positioning accuracy of the satellite positioning system (Beidou / GPS) of ordinary mobile phones and wristbands, when integrated into the intelligent transportation system, it is necessary to improve the positioning accuracy, direction accuracy, speed accuracy, etc. The present invention intends to improve the data accuracy and reliability through algorithm correction and map matching methods without increasing the investment in hardware equipment.

[0078] The present invention utilizes the satellite positioning information of existing mature mobile phones, bracelets, and vehicle-mounted OBUs as well as other information such as acceleration, direction, speed, etc., and integrates them into a unified traffic electronic map platform in an "active opening" mode, and enables (after desensitization processing) real-time conflict monitoring and early warning analysis between various types of traffic individuals, providing beyond-visual-range safety early warning services such as images and sounds for motor vehicles, non-motor vehicles, pedestrians, etc., thereby enhancing overall traffic safety.

[0079] For drivers and passengers, it provides a source of beyond-visual-range motion trajectory and direction judgment information covering all types of traffic individuals. It can not only predict various traffic conflicts in advance, expand the safety warning range of traditional line-of-sight or radar, but also provide global dynamic traffic information basic services for future intelligent driving or "autonomous driving" of vehicles.

[0080] For slow walkers (walking and cycling, etc.), a safety warning service based on sound reminders is provided (forecasting the distance, speed, direction, etc. of other traffic bodies), which improves the traffic safety factor at conflict nodes such as intersections and crossroads.

[0081] For traffic managers, by "collecting" information such as the movement status (position, speed, direction) of a wide range of traffic individuals and integrating the traffic information of roadside units (RSUs), the dynamic response speed and effect of existing intelligent traffic equipment can be upgraded and expanded; on this basis, the extended functions of the software APP can be used to further collect travel planning information such as the travel destinations and travel routes of drivers and slow walkers, and thus provide the society with richer and more accurate traffic guidance services and smart driving services (vehicle-road collaboration, autonomous driving, etc.) from the perspective of travel demand management.

[0082] In some embodiments, the various sensors of the car itself include radars and / or cameras; the roadside monitoring equipment includes cameras, coils and / or radars; and the smart terminals installed with the client APP include smart phones, tablet computers, smart watches and / or smart bracelets.

[0083] In some embodiments, the global traffic information is the desensitized Beidou / GPS information, movement direction information, and speed information uploaded by various types of traffic individuals, which are collected on the global traffic information platform.

[0084] In practical applications, the scope of global traffic information can cover all public transportation environments, providing the most abundant and reliable beyond-line-of-sight traffic information resources for intelligent decision-making on traffic information.

[0085] In some embodiments, the traffic global big data cloud center includes a processing module, a deduction and prediction module, and a warning information release module;

[0086] The processing module desensitizes the information uploaded by various types of traffic individuals by region, and processes the traffic movement data of various types of traffic individuals including location, speed, and direction;

[0087] The simulation and prediction module simulates and predicts short-term conflicts of various types of traffic individuals in different areas;

[0088] The warning information release module pushes various types of traffic conflict warning information by region.

[0089] In some embodiments, the workflow is as follows:

[0090] Step 1: All smart terminals with client APP installed and all vehicle-mounted smart terminals OBU upload their own dynamic traffic information to the traffic global big data cloud center;

[0091] Intelligent terminal self-dynamic traffic information includes real-time positioning information, speed information, and direction information collected by the smart phones, smart bracelets, and vehicle-mounted intelligent terminals OBU of slow-moving traffic individuals;

[0092] The traffic global big data cloud center obtains dynamic data including road traffic signals, cycles, and traffic volume from the roadside units (RSUs), and obtains static information related to spatial attributes from traffic electronic maps;

[0093] Step 2: According to the areas divided by the traffic global big data cloud center, or the roadside units RSUs in different blocks, based on the unified traffic electronic map information and coordinate system, various types of dynamic real-time traffic information are corrected and matched with map coordinates;

[0094] Step 3: Through the deduction and prediction module, the status and risk of various types of corrected traffic data are predicted, and warning or guidance information is generated.

[0095] Step 4: Use the warning information release module to release the warning or guidance information to all smart terminals with the client APP installed in each area that need to be reminded, as well as all vehicle-mounted smart terminals OBU, in the form of sound and graphics.

[0096] Since the satellite positioning data of mobile phones, wristbands, and vehicle-mounted OBUs are the most basic and reliable data sources (although the accuracy is not high), they can be used as the main basis for analysis. The speed measurement functions of various mobile phone software mostly calculate the corresponding space travel speed based on satellite positioning data, and this type of speed data can be extracted at the same time for verification.

[0097] In addition, mobile phones, bracelets, and vehicle-mounted OBUs also have chips such as accelerometers, gyroscopes, and magnetometers. Their data sources are independent of satellite positioning signals, and their relevant results can be used to verify and correct satellite positioning information results.

[0098] Based on the above ideas, the main ideas of this data processing model are:

[0099] First, the system corrects the accuracy, continuity and reliability of the longitude and latitude data based on the satellite positioning data reported by mobile phones, bracelets and vehicle-mounted OBUs.

[0100] Then, based on this, the correction output results are checked according to the speed and direction data measured by the mobile phone itself, and then the correction results are directly interpolated into the high-definition electronic map coordinate system.

[0101] Finally, based on the accurate position of the calibration markers (or fixed obstacles) accumulated and calibrated in the electronic map, and the "optimal traffic flow envelope channel" fitted according to historical traffic flow statistics, the position, direction, speed, etc. of the current dynamic traffic individuals are checked for the third time. Finally, the optimal position, direction, speed and other information of the real-time traffic individuals are obtained for "the integration of global traffic safety information and conflict warning".

[0102] In actual applications, first, various client apps upload their own dynamic traffic information to the traffic global big data cloud center, including real-time location, speed, direction and other information collected by mobile phones, bracelets and vehicle-mounted OBUs of slow-moving individuals. At the same time, the traffic global big data cloud center obtains dynamic data such as road traffic signals, cycles, and traffic volume from the roadside unit RSU, and obtains static information related to spatial attributes from the traffic electronic map.

[0103] Then, the relevant (regional) big data cloud center or roadside edge system, based on the unified traffic electronic map information and coordinate system, adopts the data processing model of the present invention to perform correction processing and map coordinate matching on various types of dynamic real-time traffic information.

[0104] Secondly, through the short-term simulation module, the status and risks of various types of corrected traffic data are predicted, and warning or guidance information is generated.

[0105] Finally, the information release module will release regional warning or guidance information to the traffic individual APPs in each area that need to be reminded, and remind relevant traffic movement individuals in the form of sound, graphics, etc., to improve traffic safety and operation efficiency.

[0106] like Figure 5 As shown, step 2 is as follows:

[0107] Step 2.1, obtain the direction vector data of each traffic individual according to the OBU accelerometer, gyroscope and magnetometer of the smart phone, smart bracelet and vehicle-mounted smart terminal OBU;

[0108] And according to the OBU satellite positioning signal of smart phones, smart bracelets and vehicle-mounted smart terminal OBU, the latitude and longitude scattered point information of each traffic individual is obtained;

[0109] Step 2.2, according to the latitude and longitude scattered point information of each traffic individual, the speed vector data is obtained by measuring the OBU speed sensor of the smart phone, smart bracelet and vehicle-mounted smart terminal OBU based on the positioning information;

[0110] At the same time, the accuracy, continuity and reliability of the longitude and latitude data itself are corrected according to the longitude and latitude scattered point information of each traffic individual;

[0111] Step 2.3, based on the direction vector data of each traffic individual and the speed vector data obtained from the OBU speed sensor of the smart phone, smart bracelet and vehicle-mounted smart terminal OBU, the result of correcting the accuracy, continuity and reliability of the longitude and latitude data itself according to the longitude and latitude scattered point information of each traffic individual is verified;

[0112] Step 2.4, directly interpolate the verification results into the coordinate system of the high-definition electronic map;

[0113] Step 2.5: Based on the accurate positions of the calibration markers or fixed obstacles accumulated and calibrated in the electronic map, and the optimal traffic flow envelope channel fitted according to historical traffic flow statistics, the positions, directions, and speeds of the dynamic traffic individuals interpolated to the coordinate system of the high-definition electronic map are verified;

[0114] Step 2.6: Obtain the optimal position, direction and speed information of real-time traffic individuals, and then perform collision detection and prediction.

[0115] Ordinary civilian satellite positioning systems (Beidou / GPS on mobile phones and wristbands, etc.) have poor positioning accuracy and are prone to position and direction deviations, which will have a significant impact on the integration and warning effects. Without increasing the hardware investment in existing equipment (which is expensive), it is necessary to first calibrate the accuracy of various types of dynamic traffic data and integrate them into a unified traffic electronic map, thereby providing an accurate data foundation for traffic warnings.

[0116] In some embodiments, in step 2.2, the method for correcting the accuracy, continuity and reliability of the longitude and latitude data itself according to the longitude and latitude scattered point information of each traffic individual is:

[0117] T′ i =T i ×A T′ ;

[0118] Where, T i is the original discrete data; T′ i is the predicted data; A T′ is the prediction transfer matrix of discrete data;

[0119]

[0120] COV lat It is the adjacent data lat in the dimension data sequence i and lat i-1 The covariance of represents the predicted increment;

[0121] COV lon is the adjacent data lon in the longitude data sequence i T″ i and lon i-1 The covariance of , represents the predicted increment.

[0122] like Figure 6 As shown, in actual applications, the positioning accuracy of ordinary civilian satellite positioning systems (Beidou / GPS of mobile phones and bracelets, etc.) is poor, and position and direction deviations are prone to occur. It is a discrete data information. The above-mentioned technical means are used for preliminary data correction processing, which can preliminarily eliminate noise data and jump and drift points.

[0123] In some embodiments, in step 2.3, the method for verifying the accuracy, continuity and reliability of the latitude and longitude data itself according to the latitude and longitude scattered point information of each traffic individual is:

[0124] T″ i =T′ i ×A T″ ;

[0125] Among them, T″ i It is a further correction of the original data; A T″ is the predicted transfer matrix that is further corrected;

[0126]

[0127] Where foreLat and foreLon are the speed measured by the software and chip of smart phones, smart bracelets and vehicle-mounted smart terminals OBU, and the direction data broken down into longitude and latitude;

[0128] The specific calculation is as follows:

[0129]

[0130] Where θ represents the direction vector determined by the traffic individual itself The angle of intersection with the latitude line; v represents the speed measured by the smart phone, smart bracelet and vehicle-mounted smart terminal OBU.

[0131] In practical applications, the accelerometers, gyroscopes, magnetometers and other chips in mobile phones, bracelets and OBUs can independently predict and point the movement direction of traffic individuals, and can therefore be used to further verify the prediction results.

[0132] In some embodiments, in step 2.5, the method for verifying the position, direction, and speed of the dynamic traffic entity interpolated to the high-definition electronic map coordinate system is as follows:

[0133] T″′ i =T″ i ×A T″′ +N;

[0134] Where, T″′ i is the result of further correction; A T″′ is the empirical trajectory transfer matrix; N is the rejection matrix of the exact coordinate point or obstacle;

[0135]

[0136] In the formula, ex latis the empirical correction increment of the original dimensional data; ex lon is the empirical correction increment of the original longitude data;

[0137] ex lat =(lat i -lat 80％ ) / (lat i -lat mid )

[0138] ex lon =(lon i -lon 80％ ) / (lon i -lon mid )

[0139] In the formula, lat i ,lon i is the original latitude and longitude data; lat 80％ ,lon 80％ It is the boundary envelope data of latitude and longitude in historical data statistics; lat mid ,lon mid It is the weighted average data of latitude and longitude in historical data statistics;

[0140]

[0141] Where N is the fixed obstacle influence parameter; 2×10 -8 The error is caused by the satellite chip of smart phones and smart bracelets; Lat j , Lon j is the fixed position of a fixed object obstacle j in the electronic map, including latitude and longitude); It is the latitude and longitude radius boundary of the exclusion range of a fixed object obstacle j in the electronic map.

[0142] like Figure 7 and Figure 8 As shown in the figure, based on the historical data of the movement trajectories of various types of traffic individuals collected by mobile phones, bracelets, and OBUs on a certain road section or in a certain scene, the probability envelope distribution of the traffic flow on that road section can be statistically calculated, and then the scatter points of the real-time data can be corrected and optimized.

[0143] In addition, the electronic map coordinate system contains a series of accurate coordinate points (obstacles or reference points), which can be used to correct abnormal nodes in real-time data.

[0144] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A traffic safety warning system based on real-time safety information fusion technology; its characteristics are: It includes the traffic global big data cloud center, the on-board intelligent terminal OBU, the intelligent terminal with client APP and the roadside unit RSU; The vehicle-mounted intelligent terminal OBU is used to obtain the vehicle's own collected information as the first layer of information source, including traffic information directly obtained through various sensors of the vehicle itself and the driver's vision; The roadside unit RSU is used to obtain roadside RSU information as a second-layer information source, including surrounding traffic environment information provided by intelligent information devices in the road environment, specifically including road congestion, signal and road network geographical environment information collected by roadside monitoring equipment; The smart terminal with the client APP installed is used to obtain global traffic information as the third-layer information source, including non-aggregate traffic information resources provided by various traffic individuals for the global traffic environment through the application in the smart terminal; The traffic global big data cloud center corrects the accuracy of dynamic traffic data including vehicle self-collected information, roadside RSU information sources and global traffic information, and integrates them into a unified traffic electronic map.

2. The traffic global safety warning system based on real-time safety information fusion technology according to claim 1 is characterized in that: The various sensors of the automobile include radars and / or cameras; the roadside monitoring equipment includes cameras, coils and / or radars; the smart terminals installed with the client APP include smart phones, tablet computers, smart watches and / or smart bracelets.

3. The traffic global safety warning system based on real-time safety information fusion technology according to claim 2 is characterized in that: The global traffic information is the desensitized Beidou / GPS information, movement direction information, and speed information uploaded by various types of traffic individuals, which are collected on the global traffic information platform.

4. The traffic global safety warning system based on real-time safety information fusion technology according to claim 1 is characterized in that: The traffic global big data cloud center includes a processing module, a deduction and prediction module, and a warning information release module; The processing module performs desensitization processing on the information uploaded by various types of traffic individuals by region, and processes the traffic movement data including location, speed and direction of various types of traffic individuals; The deduction and prediction module deduces and predicts short-term conflicts of various types of traffic individuals in different regions; The warning information release module pushes various types of traffic conflict warning information by region.

5. The traffic global safety warning system based on real-time safety information fusion technology according to claim 1 is characterized in that: The specific workflow is as follows: Step 1: All the smart terminals installed with the client APP and all the on-board smart terminals OBU upload the smart terminal's own dynamic traffic information to the traffic global big data cloud center; The intelligent terminal's own dynamic traffic information includes the real-time positioning information, speed information, and direction information collected by the slow-moving traffic individual's smart phone, smart bracelet, and the vehicle-mounted intelligent terminal OBU; The traffic global big data cloud center obtains dynamic data including road traffic signals, cycles, and traffic volume from the roadside unit RSU, and obtains static information related to spatial attributes from the traffic electronic map; Step 2: According to the areas divided by the traffic global big data cloud center, or the roadside units RSUs in different blocks, based on the unified traffic electronic map information and coordinate system, various types of dynamic real-time traffic information are corrected and matched with map coordinates; Step 3: Through the deduction and prediction module, the status and risk of various types of corrected traffic data are predicted, and warning or guidance information is generated. Step 4: The warning or guidance information is released to all the smart terminals installed with the client APP and all the vehicle-mounted smart terminals OBU in each area that need to be reminded through the warning information release module, and reminders are given in the form of sound or graphics.

6. The traffic global safety warning system based on real-time safety information fusion technology according to claim 1 is characterized in that: Step 2 is as follows: Step 2.1, obtaining direction vector data of each traffic individual according to the OBU accelerometer, gyroscope and magnetometer of the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU; And according to the OBU satellite positioning signals of the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU, the latitude and longitude scattered point information of each traffic individual is obtained; Step 2.2, according to the latitude and longitude scatter information of each traffic individual, the OBU speed sensor of the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU is measured based on the positioning information to obtain speed vector data; At the same time, the accuracy, continuity and reliability of the longitude and latitude data itself are corrected according to the longitude and latitude scattered point information of each traffic individual; Step 2.3, according to the direction vector data of each traffic individual, and the speed vector data obtained by the OBU speed sensor of the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU, the result of correcting the accuracy, continuity and reliability of the longitude and latitude data itself according to the longitude and latitude scatter information of each traffic individual is verified; Step 2.4, directly interpolate the verification results into the coordinate system of the high-definition electronic map; Step 2.5: Based on the accurate positions of the calibration markers or fixed obstacles accumulated and calibrated in the electronic map, and the optimal traffic flow envelope channel fitted according to historical traffic flow statistics, the positions, directions, and speeds of the dynamic traffic individuals interpolated to the coordinate system of the high-definition electronic map are verified; Step 2.6: Obtain the optimal position, direction and speed information of real-time traffic individuals, and then perform collision detection and prediction.

7. The traffic global safety warning system based on real-time safety information fusion technology according to claim 1 is characterized in that: In step 2.2, the method for correcting the accuracy, continuity and reliability of the latitude and longitude data itself according to the latitude and longitude scatter information of each traffic individual is: performing preliminary data correction processing on the latitude and longitude scatter information of the traffic individual to eliminate noise data and jump and drift points, and then correcting it by the following formula: T′ i =T i ×A T′ ; Where, T i is the original discrete data; T′ i is the predicted data; A T′ is the prediction transfer matrix of discrete data; COV lat is the adjacent data lat in the dimension data sequence i and lat i-1 The covariance of represents the predicted increment; COV lon is the adjacent data lon in the longitude data sequence i T″ i and lon i-1 The covariance of , represents the predicted increment.

8. The traffic global safety warning system based on real-time safety information fusion technology according to claim 7 is characterized in that: In step 2.3, the method for verifying the result of correcting the accuracy, continuity and reliability of the latitude and longitude data itself according to the latitude and longitude scattered point information of each traffic individual is as follows: T″ i =T′ i ×A T″ ; Among them, T″ i It is a further correction of the original data; A T″ is the predicted transfer matrix that is further corrected; Wherein, foreLat and foreLon are the speed measured by the software and chip of the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU, and the direction data separated into longitude and latitude; The specific calculation is as follows: Where θ represents the direction vector determined by the traffic individual itself The angle of intersection with the latitude line; v represents the speed measured by the smart phone, the smart bracelet and the vehicle-mounted smart terminal OBU.

9. The traffic global safety warning system based on real-time safety information fusion technology according to claim 8 is characterized in that: In step 2.5, the method for verifying the position, direction, and speed of the dynamic traffic entity interpolated to the high-definition electronic map coordinate system is as follows: T″′ i =T″ i ×A T″′ +N; Where, T″′ i is the result of further correction; A T″′ is the empirical trajectory transfer matrix; N is the rejection matrix of the exact coordinate point or obstacle; In the formula, ex lat is the empirical correction increment of the original dimensional data; ex lon is the empirical correction increment of the original longitude data; ex lat =(years i -years 80％ ) / (years i -years mid ) ex lon =(lon i -lon 80％ ) / (lon i -lon mid ) In the formula, lat i ,lon i is the original latitude and longitude data; lat 80％ ,lon 80％ It is the boundary envelope data of latitude and longitude in historical data statistics; lat mid ,lon mid It is the weighted average data of latitude and longitude in historical data statistics; Where N is the fixed obstacle influence parameter; 2×10 -8 The error is caused by the satellite chip of smart phones and smart bracelets; Lat j , Lon j is the fixed position of a fixed object obstacle j in the electronic map, including latitude and longitude); It is the latitude and longitude radius boundary of the exclusion range of a fixed object obstacle j in the electronic map.

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