CN111524362B - Vehicle safety driving guarantee system and method based on multi-data fusion - Google Patents

Abstract

The invention provides a vehicle safe driving guarantee system and method based on multi-data fusion, comprising the following steps: firstly, information from the roadside sensor and information static data from the license plate snapshot camera are fused, then the fused static data are further subjected to dynamic and static fusion with dynamic data collected by a vehicle-mounted unit, and the data subjected to dynamic and static fusion are further fused with prestored high-precision map data; and marking abnormal events on the high-precision map, then tracking and positioning each vehicle in real time, when judging that vehicles, pedestrians or other things are about to enter or already enter an early warning area or a dangerous area, establishing a corresponding exclusive communication channel according to the type of a target object, and sending a matched safe driving and early warning guarantee scheme to terminal equipment bound with the vehicles in advance through the exclusive communication channel.

Description

Vehicle safety driving guarantee system and method based on multi-data fusion

Technical Field

The invention relates to the technical fields of behavior analysis, data fusion, target tracking and positioning, communication transmission, automatic control, high-precision map drawing and intelligent traffic, in particular to a method for multi-data fusion required by vehicle safety driving.

Background

Many car factories start to put automatic driving vehicles into small-scale mass production in 2019, and continuously make efforts for improving the intelligence, comfort and safety of the whole car. But still further improvements are needed in terms of single-vehicle autonomous driving safety. Many testing organizations are verifying the driving assistance function of some automobiles, but most of the evaluation results are disappointing, and a research team of champagne division of university of illinois, usa develops a fault assessment technology for automatic driving, and in the test of Baidu Apollo3.0 and Yingwei dedicated automatic driving system DriveAV, 561 key safety faults are discovered in as short as 4 hours! This research team is working on improving the safety of the autopilot technology through software and hardware improvements using artificial intelligence and machine learning. This team had previously analyzed all safety reports (covering 144 autodrive cars, running 1116605 miles cumulatively) submitted by the autopilot company from 2014 to 2017, concluding that the person had fallen the glasses too far: "human driving an automobile is 4000 times less likely to have an accident than an autonomous driving automobile, with the same mileage. The United states Tesla accident, the Uber test vehicle accident, the collision accident when the domestic automatic driving vehicle releases the meeting and the like all lead people to conjecture that the intelligent vehicle is not intelligent, and the vehicles have a common characteristic that pedestrians which suddenly appear in the dark can not be captured quickly, the whole road condition can not be judged accurately to adjust the driving state of the vehicles, the sudden accidents in 6 seconds can not be judged accurately, even the traffic environment, the traffic road condition, the traffic state and the road surface infrastructure such as temporary road closure, control and driving path change caused by road construction or traffic control are changed, the vehicle-mounted end high-precision map is not updated timely, so that the traffic accidents are all caused, and a plurality of accident-starting conditions show that the sensing system of the existing automatic driving vehicle has different visual angle dead zones, the sensing distance is short and the acquisition of real-time information is limited, therefore, the reason that the automatic driving automobile is unsafe is that the automobile self-perception system and the safety auxiliary information are not complete enough.

For the current situation of such automatic driving, the prior art provides a vehicle-road coordination system to provide more accurate real-time reliable road condition information for the automatic driving vehicle, and also to make the perception capability of the automatic driving vehicle infinitely prolonged, so as to further improve the safety of the automatic driving vehicle, and realize the high automation of the vehicle through the coordination operation between the vehicle and the road. Vehicle-road coordination is an interaction that allows the intent of the parties to the traffic to be interpreted very accurately, not just by guessing what the vehicle is going to behave, but rather knowing it accurately, so that an accurate decision can be made.

After the data of the access road cooperative system and the data of the vehicle sensing system are fused with each other, the road end fixed sensing equipment can provide enough decision basis and even instruction for the automatic driving vehicle, the complexity of the development of the automatic driving vehicle can be greatly reduced, and the cost can be greatly reduced. Autodrive commercialization can come in advance as well, since it does not require traversal of all scenes. Besides the sensing and communication facilities of the vehicle end and the road end, the traffic department also plans to intelligently modify the road so as to adapt to the requirement of automatic driving. Therefore, the vehicle-road cooperation has certain promotion effects on improvement of safety, cost reduction and the like of the automatic driving vehicle, and meanwhile, traffic jam can be solved, and the road utilization rate can be improved. Of course, vehicle-to-road coordination is also necessary to construct future cities. Development of vehicle-road coordination is therefore an effective way to achieve a high degree of automation. Just before the start of the intelligent automobile, intelligent road and vehicle road cooperation meeting the requirement of full automatic driving. Although the bicycle intelligence of Waymo and tesla, et al, matured, there was a considerable distance from full auto-driving, which was competing from the top half to the bottom half. The fields of vehicle-road coordination and automatic driving are undoubtedly the fastest-developing and most-valued technologies.

However, the vehicle-road cooperation is generally not recognized by the road traffic manager at the present stage, and the following problems exist: a

(1) The investment cost of the vehicle-road cooperative system is too high, and the main value of the vehicle-road cooperative system is to provide the communication work between the vehicle and the road condition sensing equipment to assist the driving assistance function of the fully-automatic driving vehicle or the semi-automatic driving vehicle.

(2) Many new technologies are still in a conceptual state and cannot well fall to the ground, even a small number of mature technologies can only be used in a test area with a clean environment and a single traffic state, and the technology verification and use for the automatic driving or semi-automatic driving test vehicle cannot be popularized in a large range.

(3) The existing technology can not ensure the safety of automatic driving vehicles and general driving vehicles in the current hybrid mode, and the traffic management and road smoothness become more difficult due to the hybrid mode.

(4) The existing system cannot improve the safety and comfort brought by intelligent traffic for drivers and passengers who really run on a common vehicle on a road.

(5) Although a vehicle-road cooperative system taking a road as a core is established on the basis of digital upgrading of highway infrastructure, a traffic system can sense various conditions of the road, vehicles and pedestrians in real time, and the people, the vehicles and the roads can be highly cooperative by developing the acquisition, filtering, analysis and processing capabilities of a road network while automobile intellectualization is developed, the road condition sensing equipment technology is still to be further improved, for example, laser radar sensing equipment, video sensing equipment, short-distance millimeter wave radar sensing equipment and the like cannot be used in all weather and multiple environments, so that the vehicle-road cooperative system is more limited. The most important thing is that the existing vehicle-road coordination system, road condition sensing equipment fixed on the road, and automatically-driven vehicles and semi-automatically-driven vehicles running on the road can not be effectively connected, so that three huge systems become isolated 'islands' in intelligent traffic and can not support the intellectualization of 'comprehensive' traffic systems.

It is anticipated that in the future 20-30 years, vehicles traveling on public-facing intelligent roads will be mixed mode, and vehicles traveling thereon at intelligent high speeds, for example, will include: fully autonomous vehicles, semi-autonomous assisted manually driven vehicles, fully manually driven vehicles, and are more complex for national and provincial and urban roads, including: more complex hybrid traffic modes such as pedestrians, electric vehicles, agricultural vehicles, livestock and the like. Rather than a single category vehicle transit. To push accurate information to vehicles traveling on a road, roadside sensing equipment is required to comprehensively sense the vehicles, continuously track and accurately acquire detailed information of each vehicle.

Disclosure of Invention

The object of the present invention is to solve at least one of the technical drawbacks mentioned.

Therefore, the invention aims to provide a vehicle safe driving guarantee system and method based on multi-data fusion.

In order to achieve the above object, an embodiment of the present invention provides a vehicle safe driving support system and method based on multi-data fusion, including the following steps:

the system comprises a plurality of road side sensors, a plurality of sensors and a controller, wherein each road side sensor is arranged beside a road at a preset interval and is used for acquiring dynamic information, characteristic information, road condition information and traffic state information of running vehicles in a target range;

the system comprises a plurality of vehicle-mounted units, a plurality of control units and a plurality of control units, wherein each vehicle-mounted unit is respectively arranged on a vehicle and is used for acquiring the dynamic information of the vehicle, the surrounding road conditions and the environmental information;

the license plate snapshot cameras are used for acquiring characteristic data of the vehicle; the vehicle characteristics can also be acquired through a synchronous acquisition and fusion association mechanism of the roadside sensor and the ETC roadside unit; the vehicle-mounted unit with the sensing and positioning function and the V2X drive test communication unit are acquired through a short-range communication identification technology; the information is acquired by adopting a 4G/5G or other special data transmission communication channel through a vehicle-mounted unit with a sensing and positioning function and a third-party service platform;

the roadside service platform is communicated with each roadside sensor, each vehicle-mounted unit and each license plate snapshot camera, and is used for firstly fusing static data of information from the roadside sensors and information from the license plate snapshot cameras, then further fusing the fused static data with dynamic data acquired by the vehicle-mounted units, and further fusing the data after dynamic and static fusion with pre-stored high-precision map data; and marking abnormal events on the high-precision map, then tracking and positioning each vehicle in real time, when judging that vehicles, pedestrians or other things are about to enter or already enter an early warning area or a dangerous area, establishing a corresponding exclusive communication channel according to the type of a target object, and sending a matched safe driving and early warning guarantee scheme to terminal equipment bound with the vehicles in advance through the exclusive communication channel.

Further, the roadside service platform performs first fusion on the dynamic information and the characteristic information of the vehicles, so that each vehicle carries complete data information including the dynamic information and the static information of the vehicle, then unique vehicle identity information and an ID identity number are generated in the system for each vehicle carrying the complete information by adopting a preset vehicle identity information compiling principle, the unique vehicle identity information and the ID identity number are recorded as D1 data, and the D1 data are static data;

then, the road side service platform performs bidirectional communication with unmanned vehicles, automatic driving vehicles and manual auxiliary driving vehicles on the road surface through a vehicle road cooperative road side communication channel, a 5G exclusive communication channel and a third party service platform communication channel to obtain attribute information of the unmanned vehicles, the automatic driving vehicles and the manual auxiliary driving vehicles, and the attribute information is marked as D2 data, and the D2 data is dynamic data;

finally, the roadside service platform performs second fusion on the D1 data and the D2 data, if the D2 data are data acquired through a vehicle-road cooperative roadside communication channel, a 5G exclusive communication channel and a third-party service platform communication channel and are successfully fused with the D1 data, the successfully fused vehicle data are recorded as D3 data, the D1 data are replaced by the D3 data, and the vehicle types corresponding to the D3 data are marked as a man-driven vehicle, an automatic driving vehicle and a manual auxiliary driving vehicle; and if the fusion fails, recording the vehicle data which fails in the fusion as D4 data, and marking the vehicle type corresponding to the D4 data as a common manual driving vehicle.

Furthermore, after the roadside service platform acquires various data collected by the roadside sensor and the vehicle-mounted unit sensor in real time and carries out real-time analysis and processing, whether abnormal events occur in the region can be effectively detected. If so, marking the abnormal event on a high-precision map, and forming a dangerous area and a safe avoidance driving path.

Further, the roadside service platform further fuses the data after the sound fuses with this regional high-precision map data of prestoring, includes: and a difference calculation method is adopted, the position information, longitude and latitude and type information of the abnormal event are used as judgment conditions, the overlapped part of contents in the two data sources are removed, the non-overlapped part of contents in the two data sources are mutually supplemented and perfected through a superposition enhancement calculation method, and the position information, lane information, type information, influence area, safety avoidance range and safety avoidance method of the abnormal event are superposed and fused with the high-precision map data of the prestored area to form the special high-precision map data information with the safety guidance form for traffic. Various prompt messages, alarm messages and control messages are issued to different clients through special communication channels of different vehicles, unmanned vehicles, automatic vehicles, manual auxiliary driving vehicles and full manual driving vehicles are prompted to change the current driving state, and collision and safe driving are avoided.

The invention also provides a vehicle safe driving guarantee method based on multi-data fusion, which comprises the following steps:

step S1, collecting dynamic information, characteristic information, road condition information and traffic state information of the running vehicles in a target range by using a plurality of road side sensors installed beside a road; acquiring self dynamic information, surrounding road conditions and environmental information by utilizing a plurality of vehicle-mounted units arranged on a vehicle; collecting characteristic data of a vehicle by using a license plate snapshot camera installed beside a road;

step S2, the roadside service platform performs data fusion on the dynamic information of each vehicle collected by the roadside sensor and the characteristic information collected by the license plate snapshot camera, then further performs fusion on the fused data and the data collected by the vehicle-mounted unit, and performs real-time processing and analysis on the fused data to obtain various abnormal event information on the road.

Step S3, the roadside service platform marks the abnormal events on the high-precision map, then tracks and positions each vehicle in real time, establishes a corresponding exclusive communication channel according to the type of a target object when judging that vehicles, pedestrians or other things are about to enter or enter an early warning area or a dangerous area, and sends a matched safe driving and early warning guarantee scheme to terminal equipment bound with the vehicles in advance through the exclusive communication channel.

Further, in step S2, the road side service platform further fuses the data after the dynamic and static fusion with the pre-stored high-precision map data, including: and removing overlapped contents in the two data sources by adopting a difference calculation method and taking the position information, longitude and latitude and type information of the abnormal event as judgment conditions, and mutually supplementing and perfecting the non-overlapped contents in the two data sources by adopting a superposition enhancement calculation method and taking the position information, longitude and latitude and type information of the abnormal event as judgment conditions to form the final abnormal event data information.

Further, in the step S2,

the traffic anomaly event comprises: abnormal vehicles, abnormal road conditions, abnormal traffic states, abnormal behaviors;

the final abnormal event data information comprises the type and the position of an abnormal event, wherein the type of the abnormal event comprises the following steps: abnormal vehicles, abnormal road conditions, abnormal traffic, abnormal areas; the position comprises a lane, longitude and latitude and a region.

Further, in step S3, the roadside service platform analyzes the abnormal behavior of the vehicle in the abnormal event, and marks a dangerous area and a driving path on a high-precision map.

Further, the position and the lane of the abnormal event are marked on the high-precision map, an alarm area and alarm information are marked, a correct vehicle running path and a correct vehicle running scheme of the vehicle in the area are planned according to the safe avoidance range, the safe avoidance method and the optimal running track of the vehicle, the avoidance risk of the vehicle is generated according to the real-time position information and the motion speed of the vehicle, a cooperative running scheme, early warning information, alarm information and vehicle control information are generated, various kinds of prompting information, alarm information and control information are sent to different clients through special communication channels of different vehicles, unmanned vehicles, automatic vehicles, manual auxiliary driving vehicles and full manual driving vehicles are prompted to change the current running state, and collision and safe running are avoided.

According to the invention, the dynamic information of the vehicle collected from the roadside sensing equipment and the static data of the vehicle collected from the vehicle-mounted unit are fused, so that the comprehensive sensing of the vehicle data can be realized, the sensing capability of the vehicle is prolonged, and traffic accidents and secondary accidents caused by the change of a driving path due to the fact that the roadside single sensing equipment cannot detect obstacles which are long in distance, small but harm to large obstacles, sprinklers, landslides and roadblocks and temporary traffic control are effectively avoided; the pedestrian or dangerous object in the blind area can not be detected by a single sensing device of the vehicle sensor, and the sensing capability and the distance of the vehicle can be extended infinitely. And the invention provides a customized safe driving guarantee scheme and an early warning and alarming guarantee scheme for each vehicle, each vehicle with different attributes acquires the surrounding traffic conditions through the sensing equipment of the vehicle, and wider and richer data information is sent by combining the road test vehicle and road coordination equipment, so that more clear decision and execution force can be provided for the automatic driving vehicle to ensure the driving safety of the vehicle and other vehicles.

Each vehicle can be accurately positioned and distinguished through multi-data fusion, and the exclusive communication channel of each vehicle can be acquired by combining the vehicle-road cooperative equipment to transmit effective and accurate data for each vehicle, but the communication bandwidth of the vehicle-road cooperative road test RSU communication unit is limited to only dozens of megabytes, so that the vehicle-road cooperative road test RSU communication unit faces huge data volume and numerous demanders. The communication capacity of the vehicle and the road is far from enough, and the calculation and processing capacity of the vehicle-mounted calculation unit is limited, so that huge data cannot be processed. Therefore, data needs to be further filtered and reduced, and the most useful, most effective and most refined data is pushed to the vehicle, so that the invention can adopt a safe data model established by layering, levels, regions, early warning, control and the like to simplify the data and achieve accurate point-to-point data pushing.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

fig. 1 is a flowchart of a method for generating a safe driving and early warning safeguard scheme based on multi-data fusion according to an embodiment of the present invention;

FIG. 2 is a schematic diagram of the layout and traffic status of the roadside sensor and the on-board unit according to the embodiment of the invention;

FIG. 3 is a schematic diagram of data fusion of a roadside sensor and an on-board unit to the same vehicle according to an embodiment of the invention;

fig. 4 is a schematic diagram of a safe driving and early warning safeguard scheme generation method based on multi-data fusion according to an embodiment of the present invention;

fig. 5 is a block diagram of a vehicle safety driving support system based on multi-data fusion according to an embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.

The method for generating the safe driving and early warning guarantee scheme based on multi-data fusion is described in detail below with reference to specific embodiments, so that vehicle data from multiple sources are fused for multiple times, the projection of each real vehicle in the whole system on an actual road is formed, and a corresponding communication mechanism is established.

As shown in fig. 1 and 4, a method for generating a safe driving and early warning safeguard scheme based on multi-data fusion according to an embodiment of the present invention is characterized by including the following steps:

step S1, collecting dynamic information, characteristic information, road condition information and traffic state information of the running vehicles in a target range by using a plurality of road side sensors installed beside a road; the method comprises the steps that a plurality of vehicle-mounted units mounted on a vehicle are used for collecting motion state information, surrounding road conditions and environment information of a running vehicle; and acquiring characteristic data of the vehicle by using a license plate snapshot camera arranged beside the road.

Referring to fig. 2, roadside sensors S1 are installed at every predetermined distance on one side of a road, and each roadside sensor S1 covers a vehicle passing through a certain area in front thereof. The roadside sensor S1 can be used to collect automatically driven vehicles, semi-automatic human-assisted driven vehicles (these two types of vehicles are denoted as vehicles of type S7) and ordinary human-driven vehicles (denoted as vehicles of type S7) passing on the road.

In an embodiment of the present invention, the dynamic information of the vehicle includes: the real-time movement speed, the movement direction, the longitude and latitude positions, the vehicle size, the vehicle type, the direction angle, the lane where the vehicle is located, the movement track, the unique ID serial number of the vehicle in the whole system, the existence of abnormal conditions of the vehicle, the occurrence of abnormal behaviors and the like of each vehicle.

It should be noted that the dynamic information of the vehicle is not limited to the above example, and may also include other types of dynamic data, and the type of the collected data is selected according to actual needs.

The roadside sensor S1 sends the collected vehicle dynamic information to the roadside service platform S2, and the roadside service platform S2 performs real-time analysis and processing on the data. The roadside service platform S2 is a data processing system combining software and hardware.

The method comprises the steps of collecting vehicle characteristic information of running vehicles by utilizing a plurality of license plate snapshot cameras or vehicle-mounted units arranged beside a road, and uploading the vehicle characteristic information to a road side service platform.

Referring to fig. 2, a license plate snapshot camera S4 is installed on one side of a road at intervals of a preset distance, and the license plate snapshot camera S4 may be used to obtain static information of a vehicle traveling in an area covered by the camera, including: vehicle characteristic information, surrounding road conditions and environmental information.

In addition, for a vehicle provided with an on-board unit (OBU), the complete characteristic information and data information of the vehicle can be acquired by identifying and reading the on-board unit (OBU) mounted on the vehicle through an ETC road-side antenna.

In an embodiment of the present invention, the vehicle characteristic information includes: color of vehicle, model, brand, trademark, license plate, driver information, category, year of production, etc. The surrounding road condition and environment information includes: the method comprises the following steps of judging whether abnormal accident vehicles exist or not and information of positions of the vehicles, and judging whether spilled objects, falling rocks, landslides and congestion exist on roads or not.

It should be noted that the characteristic information of the vehicle, the surrounding road condition and the environmental information are not limited to the above examples, and may also include other types of dynamic data, and the type of the collected data is selected according to actual needs.

The vehicle license plate snapshot camera and the vehicle-mounted unit send the acquired vehicle dynamic information to the road side service platform S2, and the road side service platform S2 performs real-time analysis processing on the data.

The dynamic information and the static information can be acquired through the steps. For unmanned vehicles, automatic vehicles and manual auxiliary driving vehicles, vehicle information is easy to obtain, and mutual communication with vehicle-mounted units mounted on the vehicles can be obtained through a vehicle road cooperative communication channel, a 5G exclusive communication channel and a third-party service platform communication channel to obtain identification information (including real-time dynamic information and vehicle static information) of the vehicles. For a normal vehicle, the vehicle-mounted unit such as a license plate snapshot camera or an ETC system may be acquired by some other auxiliary device.

On the basis of acquiring the vehicle information, the vehicle information is accurately sent to each vehicle, so that the sensing capability of the vehicle is infinitely prolonged, the sensing function is stronger, and the decision mechanism is more accurate. If the comprehensive sensing, the full information acquisition and the accurate point-to-point data pushing are to be achieved, more data fusion of the data must be achieved. Based on the method, the following steps are adopted to realize two times of data fusion. The self identification data of the unmanned vehicle, the automatic driving vehicle and the manual auxiliary driving vehicle, which are acquired by the vehicle-road cooperative two-way communication, are integrated together through the fusion mechanism provided by the invention.

Step S2, the roadside service platform performs data fusion on the dynamic information of each vehicle collected by the roadside sensor and the characteristic information collected by the license plate snapshot camera, then further performs fusion on the fused data and the data collected by the vehicle-mounted unit, and performs real-time processing and analysis on the fused data to obtain various abnormal event information on the road.

Static data fusion is carried out on the information of the roadside sensor of the roadside service platform S2 and the information from the license plate snapshot camera, the first fusion is carried out, therefore, each vehicle carries complete data information including dynamic information of the vehicle and characteristic information of the vehicle, then a preset vehicle identity information compiling principle is adopted to generate unique vehicle identity information and an ID identity number in the system, and the unique vehicle identity information and the ID identity number are recorded as D1 data. The D1 data is static data.

Referring to fig. 3, the roadside service platform S2 performs bidirectional communication with the autonomous vehicle and the semi-autonomous vehicle assisted by human beings on the road surface through the V2X vehicle-road cooperative roadside communication unit (RSU) S3, and obtains attribute information of the autonomous vehicle, the autonomous vehicle and the vehicle assisted by human beings (i.e., vehicle information embedded in an on-board unit (OBU) of the vehicle-road cooperative system in which the vehicle is installed with V2X), which is recorded as D2 data. The D2 data is dynamic data. The D2 data is, for example: the color, model, brand, trademark, license plate, machine coding information, real-time longitude and latitude positioning information and the like of the vehicle.

Then, the roadside service platform performs second fusion on the D1 data and the D2 data, records the successfully fused vehicle data as D3 data if the D2 data is successfully fused with the D1 data through a vehicle-road cooperative roadside communication channel, and replaces the D1 data with the D3 data.

Specifically, the roadside service platform S2 performs second fusion on the D2 data of the unmanned vehicle, the autonomous vehicle, and the human-assisted vehicle acquired by the V2X vehicle road in cooperation with the roadside communication unit S3 and the vehicle data D1 with the unique vehicle identity information and the ID identity number, and the system further perfects the data of each vehicle after the fusion to form data D3. If the fusion is successful, the system will replace the newly generated D3 data with the originally generated D1 data, and if the D2 data is not a vehicle characteristic attribute obtained through a dedicated communication channel during the secondary fusion process, and the D1 data is not fused with the D2 data or the fusion is unsuccessful, the data for these vehicles will be modified to D4 data. Through the two-time fusion mode, each vehicle actually running on the road forms data projection in the roadside service platform, and after all tracked vehicle data are fused, the vehicles forming brand-new D3 data types and the vehicles forming D4 data types can be continuously concerned, tracked and positioned by the roadside service platform in real time.

The following is a description of a specific form of fusion of the present invention.

It is first explained that the accuracy of various data collected by the roadside sensor S1 is lost due to the problems of installation process, geographical environment, road condition, vehicle or object occlusion, communication delay, clock synchronization, etc. In addition, real-time longitude and latitude information (meter level or sub-meter level) acquired by a positioning module (such as Beidou, Galileo and GPS positioning modules) of the unmanned vehicle, the automatic vehicle and the manual auxiliary driving vehicle which are provided with the V2X vehicle-mounted unit cannot be completely consistent or coincident with the longitude and latitude information (centimeter level) acquired by the roadside sensor S1, because the target positioning accuracy between the two devices is different, even because of the deviation caused in the installation and construction process. Resulting in a second data fusion and data transfer failure. In order to solve the above problems, the present invention adopts the following two fusion mechanisms to realize data fusion, thereby overcoming the above problems.

In embodiments of the present invention, the first fusion and the second fusion may be performed in two forms:

(1) mechanism for fusing through vehicle characteristic information

Through a roadside sensor S1, a license plate snapshot camera S4 and a roadside service platform (edge calculation server) S2, vehicle characteristic data (such as license plates, vehicle types, colors, brands, trademarks and the like) in D1 data with unique vehicle identity information and ID identity numbers are jointly generated, the vehicle characteristic data in the D1 data and the vehicle characteristic data in the D2 data are compared one by one, and if the three conditions of the license plates, the vehicle types and the colors are identical, correlation fusion is carried out.

That is, the vehicle characteristic data in the D1 data and the vehicle characteristic data in the D2 data are compared one by one, and the association fusion can be performed as long as the three conditions of the license plate, the vehicle type, and the color are the same. Other items in the data are used as reference items, but not necessary items, and the other items do not influence the association fusion even if the other items are different.

(2) Shadow adjoint fusion mechanism.

Referring to fig. 2, the principle of the shadow-adjoint fusion target association mechanism is that when a vehicle enters a system and a detection area S4 (also called a target activity range) is set according to actual road conditions, vehicle data collected by a roadside sensor S1 generates complete vehicle data S5 (i.e., D1 data) through a roadside service platform S2, where the data includes: longitude and latitude information, trace information, track information, motion direction information, speed information and lane information of the vehicle are extracted to be used in association with a target.

The vehicle characteristic information which is not contained in the S6(D2 data) acquired by the road-side service platform S2 through the V2X vehicle-road cooperation road-side communication unit S3 only contains dynamic information of the vehicle and other information data to be extracted, and the information includes, but is not limited to, longitude and latitude information, trace information, track information, motion direction information, speed information, lane information and other data of the vehicle, and is ready for target association.

Due to the fact that the working principle of the road side sensor S1 is different from that of the V2X vehicle road cooperative road side communication unit S3, dynamic information of the same vehicle obtained by two ways is completely the same and is directly fused and associated due to different obtained data modes, errors in tracking and positioning accuracy of the same vehicle and the like. However, both devices acquire data in real time for the same target, so that the dynamic data change generated by the vehicle generates the same change such as ghost or shadow in the data acquired by both devices, and therefore, a phenomenon similar to ghost or shadow occurs in the detection range set by the system S4, at this time, the system takes the target data with complete information tracked vehicle data information S5(D1 data) as a real target or a main target, and then targets the vehicle data information S6(D2 data) as a false target or an auxiliary target.

The present invention sets an associated target range S7 centering on the real target. For example, a circle with a real target as a center and a radius of 5 meters (the value can be adjusted according to actual conditions, the greater the value is, the higher the correlation precision is, but the lower the correlation chance is, or vice versa) is taken as a target correlation range, all targets in the range are listed as valid correlation target objects, targets exceeding the range are listed as invalid correlation target objects, the system is not concerned about the invalid correlation target objects, the system compares the correlation objects with the real-time motion speed, the target motion direction, the longitude and latitude, the vehicle size, the vehicle type and the lane where the targets are located of the target as references, and the system takes the preset correlation combination value as a reference value and the number M of continuous repeated occurrences of the point trace according with the rule as a judgment condition for judging whether to perform correlation fusion or not (M is an adjustable value and is selected as an integer in the range of 1-10).

And performing association fusion on the real target and the false target which meet the requirements, taking the false target dynamic information as the standard for the fused target dynamic information, marking the target vehicle as an automatic driving vehicle or a semi-automatic manual auxiliary driving vehicle, and correcting the ID identity number in the target vehicle to complete the association fusion work of the two data. If not successful in the data fusion association process, this action is continued until a new shadow like a change in vehicle dynamics is found and the fusion is successful.

By adopting the shadow adjoint fusion mode, longitude and latitude information and trace point track information respectively acquired by the same vehicle are analyzed and compared in real time through the road side sensor S1 and the V2X vehicle-road cooperative road side communication unit S3, and vehicle data conforming to the shadow adjoint rule are found for fusion. The shadow adjoint type fusion mechanism is adopted for further fusion, so that the condition that the secondary data fusion is lost can be ensured.

After the fusion is completed, the vehicle types are divided according to the fusion result. Specifically, the vehicle type corresponding to the D3 data is marked as an unmanned vehicle, an automatic driving vehicle and a manual auxiliary driving vehicle; and if the fusion fails, recording the vehicle data which fails in the fusion as D4 data, and marking the vehicle type corresponding to the D4 data as a common manual driving vehicle.

(1) Unmanned vehicle, automatic driving vehicle and manual auxiliary driving vehicle

For unmanned vehicles, automatic driving vehicles and manual auxiliary driving vehicles, V2X vehicle paths are adopted to cooperate with roadside short-range communication channels to communicate and interact data with the vehicles.

(2) Common manual driving vehicle

For a common manually driven vehicle, a third-party cloud service platform is adopted to communicate and interact data with the vehicle; or point-to-point accurate information prompt is carried out through a variable information board installed on the road side, so that the communication mechanism of each vehicle is established.

Therefore, the dynamic data of each vehicle acquired by the road side sensor and the vehicle characteristic data acquired by the license plate snapshot camera/vehicle-mounted unit are accurately fused to form finished vehicle data information, so that the vehicle data can be comprehensively sensed, and the accurate data of the vehicle can be acquired. Through the accurate data that acquire the vehicle, can realize the accurate judgement to vehicle state and the place ahead road condition to for unmanned vehicle, automatic driving vehicle, artifical supplementary driving vehicle, ordinary artifical driving vehicle generate corresponding guarantee scheme of traveling respectively, and then can all provide effectual safety guarantee for unmanned vehicle, automatic driving vehicle, artifical supplementary driving vehicle, ordinary artifical driving vehicle, guarantee safety of traveling, the purpose of trip safety.

According to the invention, after the complete vehicle information acquired by the road side sensor is accurately fused with the position information in the self identification data of the automatic driving vehicle or the semi-automatic auxiliary manual driving vehicle and the client (mobile phone navigation and tablet computer) information of the common vehicle, the vehicle which needs to push data and is ready to receive data can be found from the mass data acquired by the road side sensor. Only when the vehicle can find the position of the vehicle in the vast car sea and can acquire the sensing data beyond the range of the sensor of the vehicle, the safety of the automatic driving vehicle can be really guaranteed, and various prompting messages sent to the semi-automatic auxiliary manual driving vehicle and the full-automatic auxiliary manual driving vehicle can be meaningful.

In an embodiment of the invention, the traffic anomaly event comprises: abnormal vehicles, abnormal road conditions, abnormal traffic conditions, abnormal behaviors.

The abnormal vehicle includes: accident vehicles, vehicles traveling in the wrong direction, overspeed vehicles, slow-moving vehicles, illegal lane-changing vehicles and the like.

The abnormal road conditions include: the normal affairs of vehicles on the road are influenced by the appearance of throwing objects, falling rocks, landslides, pedestrians, animals and the like on the road.

The abnormal traffic state includes: and the vehicle runs with information such as congestion, jam, queue and the like.

The abnormal behavior includes: the behavior phenomenon that one or a large number of vehicles run in an unstable running path and a traffic direction at a certain position or a certain area of a road is indicated. For example, a large number of vehicles suddenly decelerate, accelerate, change lane and the like in a certain area or position, or the vehicles run on an emergency lane for a long time and large trucks occupy express lanes for a long time.

Through the abnormal behavior analysis, the impending or hidden serious traffic hidden trouble can be found, and the hidden trouble is a phenomenon or thing which cannot be timely found by the roadside sensor S1 and the vehicle S7 (unmanned vehicle, automatic vehicle and manual auxiliary driving vehicle). The unmanned vehicle, the automatic vehicle and the manual auxiliary driving vehicle can dynamically sense the abnormal environment, abnormal objects and dangerous objects of a road through various sensors of the unmanned vehicle, the automatic vehicle and the manual auxiliary driving vehicle, and the road side service platform S2 respectively sends the abnormal information to V2X vehicle-road cooperative road side communication equipment to be uploaded to a road side service platform or to be uploaded to a third party service platform through a third party communication channel after the abnormal information is superposed with longitude and latitude information or position information, so that the abnormal information is used for later data fusion processing. The data can also be obtained through a third-party navigation service platform, such as: map service providers such as high, Baidu, Google, etc. obtain more data sources.

The roadside service platform system S2 processes and analyzes the data sent by the roadside sensor S1 to obtain abnormal event data information, and fuses the abnormal event data information acquired by the sensor itself with the unmanned vehicle, the autonomous vehicle, and the manual assistant driving vehicle. Specifically, a difference calculation method is adopted, and according to the position information, longitude and latitude and type information of the abnormal event, the overlapped part of contents in the two data sources is removed. And then mutually supplementing and perfecting the contents of the non-overlapped parts in the two data sources by a superposition enhanced calculation method according to the position information, longitude and latitude and type information of the abnormal event as judgment conditions, performing analysis processing on the data obtained from various sensors in a road side sensor and a vehicle-mounted unit, finding whether the abnormal event occurs on a detection road, if so, positioning the abnormal event on a high-precision map, marking the type, the position, the influence range and the content attribute of the abnormal event, and forming an optimal traffic scheme and a traffic path according to a vehicle safety driving principle, and then sending the information together with the data with the abnormal event information to different clients and vehicles for use on a terminal through special communication channels of different clients.

In the embodiment of the invention, the final abnormal event data information comprises the type and the position of the abnormal event, wherein the type of the abnormal event comprises the following steps: abnormal vehicles, abnormal road conditions, abnormal traffic, abnormal areas; the position comprises a lane, longitude and latitude and a region.

And S4, the roadside service platform marks the abnormal events on the high-precision map, then tracks and positions each vehicle in real time, establishes a corresponding exclusive communication channel according to the type of a target object when judging that vehicles, pedestrians or other things are about to enter or enter an early warning area or a dangerous area, and sends a matched safe driving and early warning guarantee scheme to terminal equipment bound with the vehicles in advance through the exclusive communication channel.

And the roadside service platform analyzes the abnormal behaviors of the vehicles in the abnormal events and marks dangerous areas and driving paths on the high-precision map.

And then tracking and positioning each vehicle in real time, when judging that vehicles, pedestrians or other things are about to enter or already enter the early warning area or the dangerous area, establishing a corresponding exclusive communication channel according to the type of the target object, and sending a matched safe driving and early warning guarantee scheme to terminal equipment bound with the vehicle in advance through the exclusive communication channel.

In this step, corresponding exclusive communication channels are respectively established for different types of unmanned vehicles, automatic vehicles, manual auxiliary driving vehicles, full-manual driving vehicles, pedestrians or other things.

In the embodiment of the invention, the safe driving and early warning guarantee scheme comprises the following steps: when an abnormal event is detected, the special communication channel issues early warning, warning information, a traffic scheme and a control instruction to the terminal equipment which is bound with the vehicle in advance to ensure the safe driving of the vehicle, assist and ensure the safety of the vehicle, pedestrians or other things, reduce the occurrence of event accidents and improve the traffic efficiency of roads.

As shown in fig. 5, the vehicle safety driving support system based on multiple data fusion according to the embodiment of the present invention includes: the vehicle-mounted road side capturing system comprises a plurality of road side sensors 1, a plurality of vehicle-mountedunits 2, a plurality of licenseplate capturing cameras 3 and a roadside service platform 4.

Specifically, each of the roadside sensors 1 is installed beside a road at a preset interval, and is used for collecting dynamic information, characteristic information, road condition information and traffic state information of running vehicles within a target range. And each vehicle-mountedunit 2 is respectively arranged on the vehicle and used for acquiring the motion state information, the surrounding road conditions and the environmental information of the running vehicle. And the plurality of licenseplate snapshot cameras 3 are used for acquiring the characteristic data of the vehicle.

The roadside service platform 4 is communicated with each road side sensor 1, each vehicle-mountedunit 2 and each licenseplate snapshot camera 3, and is used for fusing static data of information from the road side sensors 1 and information from the licenseplate snapshot cameras 3. And then, further performing dynamic and static fusion on the fused static data and the dynamic data acquired by the vehicle-mountedunit 2, and further fusing the data subjected to dynamic and static fusion with pre-stored high-precision map data.

Specifically, theroadside service platform 4 performs first fusion on the dynamic information and the static information of the vehicle, so that each vehicle carries complete data information including the dynamic information and the static information of the vehicle, and then generates unique vehicle identity information and an ID identity number in the system, which are recorded as D1 data, for each vehicle carrying the complete information by adopting a preset vehicle identity information compilation principle. The D1 data is static data.

Then, theroadside service platform 4 performs bidirectional communication with the unmanned vehicle, the autonomous vehicle, and the manual assistant driving vehicle on the road surface through the vehicle/road cooperative roadside communication unit, and acquires information of the unmanned vehicle, the autonomous vehicle, and the manual assistant driving vehicle, which is recorded as D2 data. The D2 data is dynamic data.

Theroadside service platform 4 performs second fusion on the D1 data and the D2 data, if the D2 data acquire the data through a special communication channel and the fusion is successful, records the vehicle data successfully fused as D3 data, replaces the D1 data with the D3 data, and marks the vehicle type corresponding to the D3 data as information of an unmanned vehicle, an automatic driving vehicle and a manual auxiliary driving vehicle; and otherwise, recording the vehicle data as D4 data, and marking the vehicle type corresponding to the D4 data as a common manual driving vehicle.

Theroadside service platform 4 further fuses the data after the sound fusion with the high-precision map data of prestoring, includes: and removing overlapped contents in the two data sources by adopting a difference calculation method and taking the position information, longitude and latitude and type information of the abnormal event as judgment conditions, and mutually supplementing and perfecting the non-overlapped contents in the two data sources by adopting a superposition enhancement calculation method and taking the position information, longitude and latitude and type information of the abnormal event as judgment conditions to form final abnormal event data information. Theroadside service platform 4 marks the abnormal event on a high-precision map.

In the embodiment of the invention, theroadside service platform 4 is used for analyzing the abnormal behavior of the vehicle in the abnormal event and marking a dangerous area and a driving path on a high-precision map.

And then tracking and positioning each vehicle in real time, when judging that vehicles, pedestrians or other things are about to enter or already enter the early warning area or the dangerous area, establishing a corresponding exclusive communication channel according to the type of the target object, and sending a matched safe driving and early warning guarantee scheme to terminal equipment bound with the vehicle in advance through the exclusive communication channel.

According to the invention, the static data acquired from the roadside sensing equipment and the dynamic data acquired from the vehicle-mounted unit are fused, so that the comprehensive sensing of the vehicle data can be realized, the sensing capability of the vehicle is prolonged, and traffic accidents and secondary accidents caused by the change of a driving path due to the fact that the roadside single sensing equipment cannot detect a small obstacle, a large spill object, a collapse and a road block and temporary traffic control; the pedestrian or dangerous object in the blind area can not be detected by a single sensing device of the vehicle sensor, and the sensing capability and the distance of the vehicle can be extended infinitely. And the invention provides a customized safe driving guarantee scheme and an early warning and alarming guarantee scheme for each vehicle, each vehicle with different attributes acquires the surrounding traffic conditions through the sensing equipment of the vehicle, and wider and richer data information is sent by combining the road test vehicle and road coordination equipment, so that more clear decision and execution force can be provided for the automatic driving vehicle to ensure the driving safety of the vehicle and other vehicles.

Each vehicle can be accurately positioned and distinguished through multi-data fusion, and the exclusive communication channel of each vehicle can be acquired by combining the vehicle-road cooperative equipment to transmit effective and accurate data for each vehicle, but the communication bandwidth of the vehicle-road cooperative road test RSU communication unit is limited to only dozens of megabytes, so that the vehicle-road cooperative road test RSU communication unit faces huge data volume and numerous demanders. The communication capacity of the vehicle and the road is far from enough, and the calculation and processing capacity of the vehicle-mounted calculation unit is limited, so that huge data cannot be processed. Therefore, data needs to be further filtered and reduced, and the most useful, most effective and most refined data is pushed to the vehicle, so that the invention can adopt a safe data model established by layering, levels, regions, early warning, control and the like to simplify the data and achieve accurate point-to-point data pushing.

The invention provides more reliable, accurate and timely important data information such as early warning information, traffic schemes, control instructions and the like for unmanned vehicles, automatic driving vehicles, manual auxiliary driving vehicles, full manual driving vehicles and clients required by third parties, so as to guarantee the driving safety and the traveling safety. Important data such as early warning information, traffic schemes, control instructions and the like formed in a multi-system, multi-aspect and multi-integration mode are higher in reliability than high-precision map data generated by single-side equipment or single data sources. The invention can timely reflect the road condition state and the traffic state change, abnormal traffic accidents on the road and the danger of the driving safety caused by obstacles, quickly generate corresponding auxiliary information such as early warning information, a traffic scheme, a control instruction and the like, and can avoid serious traffic accidents caused by self-unmanned vehicles, automatic vehicles, manual auxiliary driving vehicles and full-manual driving vehicles and avoid secondary accidents. By the method, the large-range cooperative running of all types of vehicles is realized, and the traffic efficiency of the whole road is further improved. The data obtained by the system and the method can form a vehicle safe driving model to guide all running vehicles on the road to safely drive and avoid danger. The data obtained by the system and the method can form a danger early warning model, can warn and prompt illegal vehicles with abnormal driving and abnormal behaviors, immediately output abnormal behavior warning information once triggering a relevant judgment mechanism, and prompt and obtain point-to-point information for the corresponding vehicles. The data obtained by the method can enable the road sensing equipment and the edge computing equipment to be combined with each other to form a safety guarantee system with larger functions, provide enough decision basis and even instruction for unmanned vehicles, automatic vehicles and manual auxiliary driving vehicles, and improve the driving safety of all vehicles in essence; the complexity of development of unmanned vehicles, automatic vehicles and manual auxiliary driving vehicles can be greatly reduced, and the cost is also greatly reduced. Unmanned, autonomous commercialization can also come in advance because it does not need to traverse all scenes.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (8)

1. A vehicle safety driving guarantee system based on multidata fusion is characterized by comprising:

the system comprises a plurality of road side sensors, a plurality of sensors and a controller, wherein each road side sensor is arranged beside a road at a preset interval and is used for acquiring dynamic information, characteristic information, road condition information and traffic state information of running vehicles in a target range;

the system comprises a plurality of vehicle-mounted units, a plurality of control units and a plurality of control units, wherein each vehicle-mounted unit is respectively arranged on a vehicle and is used for acquiring the dynamic information of the vehicle, the surrounding road conditions and the environmental information;

the license plate snapshot cameras are used for acquiring characteristic data of the vehicle; the vehicle characteristics can also be acquired through a synchronous acquisition and fusion association mechanism of the roadside sensor and the ETC roadside unit; the vehicle-mounted unit with the sensing and positioning function and the V2X drive test communication unit are acquired through a short-range communication identification technology; the information is acquired by adopting a 4G/5G or other special data transmission communication channel through a vehicle-mounted unit with a sensing and positioning function and a third-party service platform;

the roadside service platform is communicated with each roadside sensor, each vehicle-mounted unit and each license plate snapshot camera, and is used for firstly fusing static data of information from the roadside sensors and information from the license plate snapshot cameras, then further fusing the fused static data with dynamic data acquired by the vehicle-mounted units, and further fusing the data after dynamic and static fusion with pre-stored high-precision map data; marking abnormal events on the high-precision map, then tracking and positioning each vehicle in real time, when judging that vehicles, pedestrians or other things are about to enter or already enter an early warning area or a dangerous area, establishing a corresponding exclusive communication channel according to the type of a target object, and sending a matched safe driving and early warning guarantee scheme to terminal equipment bound with the vehicles in advance through the exclusive communication channel;

the roadside service platform performs first fusion on the dynamic information and the characteristic information of the vehicles, so that each vehicle carries complete data information comprising the dynamic information and the static information of the vehicle, then unique vehicle identity information and ID identity numbers are generated in the system for each vehicle carrying the complete information by adopting a preset vehicle identity information compiling principle, the unique vehicle identity information and ID identity numbers are recorded as D1 data, and the D1 data are static data;

then, the road side service platform performs bidirectional communication with unmanned vehicles, automatic driving vehicles and manual auxiliary driving vehicles on the road surface through a vehicle road cooperative road side communication channel, a 5G exclusive communication channel and a third party service platform communication channel to obtain attribute information of the unmanned vehicles, the automatic driving vehicles and the manual auxiliary driving vehicles, and the attribute information is marked as D2 data, and the D2 data is dynamic data;

finally, the roadside service platform performs second fusion on the D1 data and the D2 data, if the D2 data are data acquired through a vehicle-road cooperative roadside communication channel, a 5G exclusive communication channel and a third-party service platform communication channel and are successfully fused with the D1 data, the successfully fused vehicle data are recorded as D3 data, the D1 data are replaced by the D3 data, and the vehicle types corresponding to the D3 data are marked as unmanned vehicles, automatic vehicles and manual auxiliary driving vehicles; if the fusion fails, recording the vehicle data which fails in the fusion as D4 data, and marking the vehicle type corresponding to the D4 data as a common manual driving vehicle;

wherein, the first fusion and the second fusion can be completed by adopting the following two forms:

(1) mechanism for fusing through vehicle characteristic information

The vehicle characteristic data in the D1 data with unique vehicle identity information and ID identity numbers are generated through a road side sensor, a license plate snapshot camera and a road side service platform, the vehicle characteristic data in the D1 data and the vehicle characteristic data in the D2 data are compared one by one, and if the conditions of license plates, vehicle types and colors are the same, correlation fusion is carried out;

(2) the shadow-adjoint fusion mechanism is,

the shadow adjoint fusion target association mechanism principle is that when a vehicle enters a system and a detection area is set according to actual road conditions, vehicle data collected by a road side sensor generates complete vehicle data D1 data through a road side service platform; the road side service platform only comprises dynamic information of vehicles and other information data to be extracted, wherein the vehicle characteristic information which is not contained in the D2 data acquired by the V2X vehicle-road cooperation road side communication unit; taking the target data with complete information and real-time tracked vehicle data information D1 data as a real target or a main target, and then taking the vehicle data information D2 data target as a false target or an auxiliary target; setting a correlation target range S7 by taking a real target as a center, wherein all targets in the range are listed as effective correlation target objects, targets exceeding the range are listed as invalid correlation target objects, and the real-time movement speed, the target movement direction, the longitude and latitude, the vehicle size, the vehicle type and the lane where the target is located of the target are taken as reference to compare the correlation objects, and the preset correlation combination value is taken as a reference value and the continuous repeated occurrence times M of the regular point traces are taken as judgment conditions for judging whether to correlate and fuse; and (3) performing association fusion on the real target and the false target which meet the requirements, wherein the fused target dynamic information is subject to the false target dynamic information, marking the target vehicle as an automatic driving vehicle or a semi-automatic manual auxiliary driving vehicle, correcting the ID identity number in the target vehicle, completing the association fusion work of the two data, and if the association process of the data fusion is not successful, continuing the action until a new shadow like the change of the vehicle dynamic information is found and the fusion is successful.

2. The method for generating a safe driving and early warning safeguard scheme based on multiple data fusion as claimed in claim 1, wherein the roadside service platform is capable of effectively detecting whether an abnormal event occurs in the area after acquiring various data collected from a roadside sensor and an on-board unit sensor in real time and performing real-time analysis and processing, and if so, marking the abnormal event on a high-precision map and forming a dangerous area and a safe avoidance driving path.

3. The method for generating a safe driving and early warning safeguard scheme based on multiple data fusion of claim 1, wherein the roadside service platform further fuses the data after the dynamic and static fusion with the pre-stored high-precision map data of the region, and the method comprises the following steps: the method comprises the steps of adopting a differential calculation method and taking position information, longitude and latitude and type information of an abnormal event as judgment conditions, removing overlapped part of contents in two data sources, mutually supplementing and perfecting the non-overlapped part of contents in the two data sources through a superposition enhancement calculation method, superposing and fusing the position information, lane information, type information, an influence area, a safety avoidance range and a safety avoidance method of the abnormal event with high-precision map data in a pre-stored area to form special high-precision map data information for traffic in a safety guidance form, issuing various prompt information, alarm information and control information to different clients through special communication channels of different vehicles, and prompting unmanned vehicles, automatic vehicles, manual auxiliary driving vehicles and full manual driving vehicles to change the current driving state, avoid collision and run safely.

4. A vehicle safe driving guarantee method based on multi-data fusion is characterized by comprising the following steps:

step S1, collecting dynamic information, characteristic information, road condition information and traffic state information of the running vehicles in a target range by using a plurality of road side sensors installed beside a road; acquiring self dynamic information, surrounding road conditions and environmental information by utilizing a plurality of vehicle-mounted units arranged on a vehicle; collecting characteristic data of a vehicle by using a license plate snapshot camera installed beside a road;

step S2, a road side service platform performs data fusion on the dynamic information of each vehicle collected by the road side sensor and the characteristic information collected by the license plate snapshot camera, then the fused data is further fused with the data collected by the vehicle-mounted unit, and the fused data is processed and analyzed in real time to obtain various abnormal event information on the road; the roadside service platform performs first fusion on the dynamic information and the characteristic information of the vehicles, so that each vehicle carries complete data information comprising the dynamic information and the static information of the vehicle, then unique vehicle identity information and ID identity numbers are generated in the system for each vehicle carrying the complete information by adopting a preset vehicle identity information compiling principle, the unique vehicle identity information and ID identity numbers are recorded as D1 data, and the D1 data are static data;

then, the road side service platform performs bidirectional communication with unmanned vehicles, automatic driving vehicles and manual auxiliary driving vehicles on the road surface through a vehicle road cooperative road side communication channel, a 5G exclusive communication channel and a third party service platform communication channel to obtain attribute information of the unmanned vehicles, the automatic driving vehicles and the manual auxiliary driving vehicles, and the attribute information is marked as D2 data, and the D2 data is dynamic data;

finally, the roadside service platform performs second fusion on the D1 data and the D2 data, if the D2 data are data acquired through a vehicle-road cooperative roadside communication channel, a 5G exclusive communication channel and a third-party service platform communication channel and are successfully fused with the D1 data, the successfully fused vehicle data are recorded as D3 data, the D1 data are replaced by the D3 data, and the vehicle types corresponding to the D3 data are marked as a man-driven vehicle, an automatic driving vehicle and a manual auxiliary driving vehicle; if the fusion fails, recording the vehicle data which fails in the fusion as D4 data, and marking the vehicle type corresponding to the D4 data as a common manual driving vehicle; wherein, the first fusion and the second fusion can be completed by adopting the following two forms:

(1) mechanism for fusing through vehicle characteristic information

The vehicle characteristic data in the D1 data with unique vehicle identity information and ID identity numbers are generated through a road side sensor, a license plate snapshot camera and a road side service platform, the vehicle characteristic data in the D1 data and the vehicle characteristic data in the D2 data are compared one by one, and if the conditions of license plates, vehicle types and colors are the same, correlation fusion is carried out;

(2) shadow adjoint fusion mechanism

The shadow adjoint fusion target association mechanism principle is that when a vehicle enters a system and a detection area is set according to actual road conditions, vehicle data collected by a road side sensor generates complete vehicle data D1 data through a road side service platform; the road side service platform only comprises dynamic information of vehicles and other information data to be extracted, wherein the vehicle characteristic information which is not contained in the D2 data acquired by the V2X vehicle-road cooperation road side communication unit; taking the target data with complete information and real-time tracked vehicle data information D1 data as a real target or a main target, and then taking the vehicle data information D2 data target as a false target or an auxiliary target; setting a correlation target range S7 by taking a real target as a center, wherein all targets in the range are listed as effective correlation target objects, targets exceeding the range are listed as invalid correlation target objects, and the real-time movement speed, the target movement direction, the longitude and latitude, the vehicle size, the vehicle type and the lane where the target is located of the target are taken as reference to compare the correlation objects, and the preset correlation combination value is taken as a reference value and the continuous repeated occurrence times M of the regular point traces are taken as judgment conditions for judging whether to correlate and fuse; carrying out association fusion on a real target and a false target which meet the requirements, wherein the fused target dynamic information is subject to the false target dynamic information, marking the target vehicle as an automatic driving vehicle or a semi-automatic manual auxiliary driving vehicle, correcting the ID identity number in the target vehicle, completing association fusion work of two data, and if the association process of the data fusion is not successful, continuing the action until a new shadow like the change of the vehicle dynamic information is found and the fusion is successful;

step S3, the roadside service platform marks the abnormal events on the high-precision map, then tracks and positions each vehicle in real time, establishes a corresponding exclusive communication channel according to the type of a target object when judging that vehicles, pedestrians or other things are about to enter or enter an early warning area or a dangerous area, and sends a matched safe driving and early warning guarantee scheme to terminal equipment bound with the vehicles in advance through the exclusive communication channel.

5. The vehicle safety driving support method based on multiple data fusion of claim 4, wherein in step S2, the road side service platform further fuses the dynamic and static fused data with the pre-stored high-precision map data, comprising: and removing overlapped contents in the two data sources by adopting a difference calculation method and taking the position information, longitude and latitude and type information of the abnormal event as judgment conditions, and mutually supplementing and perfecting the non-overlapped contents in the two data sources by adopting a superposition enhancement calculation method and taking the position information, longitude and latitude and type information of the abnormal event as judgment conditions to form final abnormal event data information.

6. The multiple data fusion-based vehicle safe driving ensuring method according to claim 5, wherein in the step S2,

the exception event comprises: abnormal vehicles, abnormal road conditions, abnormal traffic states, abnormal behaviors;

the final abnormal event data information comprises the type and the position of an abnormal event, wherein the type of the abnormal event comprises the following steps: abnormal vehicles, abnormal road conditions, abnormal traffic, abnormal areas; the position comprises a lane, longitude and latitude and a region.

7. The vehicle safety driving support method based on multiple data fusion of claim 4, wherein in step S3, the roadside service platform analyzes abnormal behavior of the vehicle in an abnormal event, and marks a dangerous area and a driving path on a high-precision map.

8. The method for guaranteeing safe driving of a vehicle based on multi-data fusion as claimed in claim 4, wherein the position and lane of the abnormal event are marked on the high-precision map, and the warning area and the warning information are marked, the correct vehicle driving path and the right driving scheme of the vehicle in the area are planned according to the safe avoidance range, the safe avoidance method and the optimal driving track of the vehicle, and the avoidance risk of the vehicle is generated according to the real-time position information and the moving speed of the vehicle, so as to generate the cooperative driving scheme, the early warning information, the warning information and the vehicle control information, and the various kinds of the warning information, the warning information and the control information are issued to different clients through the dedicated communication channels of different vehicles, and the unmanned vehicle, the automatic vehicle, the manual assistant driving vehicle and the full manual driving vehicle are prompted to change the current driving state, avoid collision and run safely.

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