Security situation awareness method and system based on intelligent networked automobile

Abstract

The invention discloses a safety situation awareness method and system based on an intelligent networked automobile, relates to the technical field of driving safety, and solves the problems that related objects in a view blind area can be found in the first time and safety risk analysis can be carried out in real time. According to the method, dynamic data such as speeds and positions of a dangerous object and a vehicle are monitored in a short period, respective time intervals are accurately calculated, and cross comparison analysis is carried out on the basis; the early warning signal can be immediately generated at the critical moment (such as time interval crossing), a driver can be urged to brake and avoid emergently, a risk prompt can be sent out when potential risks exist, deceleration prevention is reminded, a sufficient reaction buffer space is provided for the driver, seamless connection from risk perception to coping and decision making is achieved, and driving safety is comprehensively protected.

Description

Technical Field

[0001] The present invention relates to the field of driving safety technology, and in particular to a safety situation perception method and system based on an intelligent networked vehicle. Background Art

[0002] Intelligent connected vehicles are the product of the deep integration of the automotive industry and information technology. They combine the characteristics of intelligence and networking, are reshaping the future mode of transportation, and represent the cutting-edge development trend of the automotive industry. From the perspective of intelligence, smart connected cars are equipped with advanced sensor systems, which are like the "sensory organs" of vehicles. The camera is like a sharp eye that can clearly capture the image information around the vehicle, whether it is road signs, pedestrian dynamics, or the driving trajectory of other vehicles, nothing can escape its "sight"; millimeter wave radar is like a precise detector, real-time monitoring of key data such as the distance and relative speed to surrounding objects, even in complex weather conditions, such as heavy rain and dense fog, it can still work stably and provide vehicles with reliable surrounding environment information.

[0003] The application with publication number CN113839904A discloses a security situation awareness method, system, electronic device and computer-readable storage medium based on intelligent networked vehicles. The method includes: obtaining target information of intelligent networked vehicles collected by preset probes; detecting whether there is abnormal information in the target information according to preset abnormality detection rules; if there is abnormal information, inputting the information to be detected in the target information into a trained abnormality detection model to obtain the abnormality detection result of the information to be detected in the target information; and performing information security status assessment based on the abnormal information and the abnormality detection result to obtain the security event situation of the intelligent networked vehicle.

[0004] In the current process of automobile safety situation awareness, situation awareness analysis is generally performed based on a smaller range around the vehicle, such as radar obstacle avoidance, active braking, etc. However, in the actual driving process, the vehicle must not only consider whether it will collide with others, but also whether others will collide with it. Especially at many forks in the road, there are a large number of non-motor vehicles driving. Because the driver in the car has the corresponding blind spots, it is impossible to immediately discover the existence of non-motor vehicles in the corresponding blind spots. If the driver is negligent in transferring ownership and does not give way to the corresponding non-motor vehicles, accidents are likely to occur. Although its radar system can brake quickly or actively avoid obstacles, the non-motor vehicle will still directly collide with the car, causing a safety accident. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a safety situation awareness method and system based on intelligent connected vehicles, which solves the problem of being able to detect relevant objects in the blind spot of the field of vision in the first time and perform safety risk analysis in real time.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A security situation awareness method based on an intelligent networked vehicle comprises the following steps: Step 1: Monitor the steering range and driving speed of the car in real time, and evaluate whether the steering range and driving speed monitored in real time meet the analysis conditions. If they meet the conditions, execute step 2. If not, monitor in real time. The specific method is as follows: The real-time monitored vehicle steering amplitude is calibrated as F i , the real-time monitored vehicle speed is calibrated as V i , where i represents different moments, identifying the monitored F i and V i Whether to satisfy: F i >Y1 and V i <Y2, where Y1 and Y2 are both preset values; Step 2: Based on the face position of the driver in the car, confirm the blind spot associated with the driver, monitor the blind spot in real time, and confirm the monitoring video in real time. Based on the current vehicle's driving direction and location, confirm the virtual driving area in real time in the on-board map, and analyze the monitoring video of the blind spot in real time to lock the dangerous object. The specific sub-steps are as follows: S21, based on the camera inside the car and the set face template, the face position of the driver is locked, based on the determined face position and the preset center point in the face template, the feature midpoint is confirmed, and then based on the car model associated with the car, the position of the A-pillar and the B-pillar is locked, and the car model is a preset model, which is set in advance in the car system; With the feature midpoint as the center, generate a set of circles with a radius of X1m from the top view of the car model, and then determine the area blocked by the corresponding edge contours based on the edge contours of the A-pillar and the B-pillar, where X1 is a preset value, and mark the confirmed blocked area as the blind spot; S22, based on the current steering amplitude of the vehicle, confirm the actual steering angle of the vehicle tire, generate a virtual driving route according to the actual steering angle, and mark the area covered by the virtual driving route within 5m as the virtual driving area; S23, real-time monitoring is performed on the determined blind area of ​​vision, and based on the progress of real-time monitoring, the moving objects existing in the blind area of ​​vision are confirmed: the previous frame of real-time monitoring is compared with the next frame of vision, the pixel features of the corresponding pixel points in the previous frame of vision are confirmed first, and then the same pixel points are locked in the next frame of vision based on the pixel features, and it is identified whether the spatial positions of two groups of the same pixel points in different frames of vision are the same, if they are the same, no calibration is performed, if they are not the same, they are calibrated as moving pixel points, and then the specific objects covered by the calibrated adjacent moving pixel points are calibrated as moving objects; Based on the spatial moving direction of the moving pixel point, the spatial moving direction of the moving object is confirmed, and whether the spatial moving direction intersects with the determined virtual driving area is identified. If so, the moving object is marked as a dangerous object, and if not, no marking is performed; Step 3: Based on the confirmed dangerous object, monitor the dangerous object in a short period, identify the speed data generated by the dangerous object in a short period, and then confirm the time. Then, based on the current driving speed of the car and the virtual driving area, confirm the time of the current car. Based on the time characteristics of the dangerous object and the current car, assess whether there is a safety risk. The specific sub-steps are: S31, defining a group of short periods, monitoring and confirming the speed data associated with the corresponding dangerous object in the short period, determining the minimum speed value and the maximum speed value from the several groups of speed data confirmed in the short period, thereby locking the speed range of the dangerous object; Based on the determined virtual driving area and the position of the corresponding dangerous object at the end of the short cycle, the shortest distance L1 between the dangerous object and the virtual driving area is determined. Based on L1 and the determined speed range, the time interval [T k min,T k max], whose T k min=L1÷maximum speed, its T k max = L1 ÷ minimum speed, where k represents different dangerous objects; S32, confirming the speed data generated by the current vehicle in a short period, and confirming the speed range of the current vehicle based on the minimum value and the maximum value in the speed data; Based on the remaining driving distance L2 of the virtual driving area and the length of the vehicle, the distance to be driven is determined, where the distance to be driven = L2 + the length of the vehicle. Based on the determined distance to be driven and speed range, the time interval [Qsmin, Qsmax] associated with the current vehicle is determined; S33, based on different time intervals associated with different dangerous objects [T k min,T kmax] and the time interval [Qsmin, Qsmax] associated with the current car, and identify whether the time interval of a dangerous object intersects with the time interval of the current car: If there is, a warning signal will be directly generated and displayed on the vehicle display terminal for the driver to view and take timely response measures; If it does not exist, identify whether it exists: |T k min-Qsmax|≤Y3 or |T k max-Qsmin|≤Y3: If there is a corresponding dangerous object that meets any of the above assessment conditions, a risk signal is generated and directly displayed on the vehicle display terminal; If there is no dangerous object that meets any of the above evaluation conditions, no signal is generated, which means that the corresponding car can pass normally.

[0007] Preferably, the blind area and the virtual driving area change in real time according to the driving process of the vehicle.

[0008] Preferably, the pixel features include the grayscale value of the current pixel and the grayscale values ​​of the neighboring pixels around the current pixel, and are sorted according to the positions of the current pixel and the neighboring pixels. The grayscale value of the current pixel is assumed to be A, and the grayscale values ​​of the neighboring pixels are H1-H8, that is, the sorted grayscale values ​​can be expressed as: .

[0009] Preferably, a safety situation awareness system based on an intelligent connected vehicle comprises: The vehicle data monitoring end monitors the steering range and driving speed of the vehicle in real time, and transmits the real-time monitored data to the process starting end; The process starter determines whether the steering amplitude and driving speed of the vehicle meet the analysis conditions based on the real-time monitoring of the steering amplitude and driving speed of the vehicle. If so, the subsequent road condition analysis is executed. If not, no processing is performed. The road condition analysis end confirms the blind spots associated with the driver based on the face position of the driver in the car, monitors the blind spots in real time, and confirms the surveillance video in real time. Based on the current vehicle's driving direction and location, the virtual driving area is confirmed in real time in the on-board map, and the surveillance video of the blind spots is analyzed in real time to lock in dangerous objects. On the safety risk analysis side, based on the confirmed dangerous object, the dangerous object is monitored in a short period of time, the speed data generated by the dangerous object in a short period is identified, and then the time is confirmed. Then, based on the current vehicle's driving speed and virtual driving area, the time of the current vehicle is confirmed. Based on the time characteristics of the dangerous object and the current vehicle, it is assessed whether there is a safety risk.

[0010] The present invention provides a safety situation awareness method and system based on intelligent networked vehicles. Compared with the prior art, it has the following beneficial effects: The present invention accurately locates the blind spots formed by the A-pillar and the B-pillar. As the vehicle travels, the blind spot changes in real time, and it is always closely monitored. With the help of advanced pixel feature comparison technology, the moving object is locked, and then the dangerous object is screened out through cross-judgment with the virtual driving area. The potential threat of the blind spot that was originally difficult for the driver to perceive is successfully visualized, turning the passive into the active, filling the key gap in the driving field of vision, and greatly reducing the probability of collision accidents caused by blind spots. By monitoring the speed, position and other dynamic data of dangerous objects and the vehicle itself in a short period of time, the respective time intervals are accurately calculated, and cross-comparison analysis is carried out based on this. This can not only generate early warning signals in real time at critical moments (such as time interval intersections) to urge the driver to brake and evade, but also issue risk warnings when there are potential risks, reminding the driver to slow down for prevention, and giving the driver sufficient reaction buffer space, thus achieving seamless connection from risk perception to response decision-making, and protecting driving safety in all aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 It is a schematic diagram of the process of the present invention; Figure 2 It is a schematic diagram for determining the blind area of ​​the visual field of the present invention. DETAILED DESCRIPTION

[0012] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example

[0013] See also Figure 1 , the present application provides a security situation awareness method based on an intelligent connected vehicle, comprising the following steps: Step 1: monitor the steering amplitude and driving speed of the car in real time, and evaluate whether the steering amplitude and driving speed monitored in real time meet the analysis conditions. If they meet, execute step 2. If they do not meet, monitor in real time. Specifically, when the corresponding vehicle passes the corresponding intersection, the steering amplitude of the vehicle will be gradually increased, and the corresponding driving speed will be reduced simultaneously. Therefore, when both sets of conditions are met, it is necessary to execute the corresponding analysis process to specifically analyze whether there is a safety risk in this process; The specific sub-steps of the assessment are: The real-time monitored vehicle steering amplitude is calibrated as F i , the real-time monitored vehicle speed is calibrated as V i , where i represents different moments, identifying the monitored F i and V i Whether to satisfy: F i >Y1 and V i <Y2, where Y1 and Y2 are preset values, and their specific values ​​are determined by the operator based on experience, and Y1 is generally 45°, and Y2 is generally 10km / h; Step 2: Based on the face position of the driver in the car, confirm the blind spot associated with the driver (the specific blind spot blocked by the A-pillar and the B-pillar), monitor the blind spot in real time, and confirm the monitoring video in real time. Based on the current vehicle's driving direction and location, confirm the virtual driving area in real time in the on-board map, and analyze the monitoring video of the blind spot in real time to lock the dangerous object. Specifically, when the vehicle is driving to an intersection or entering a basement, it will generally intersect with the non-motor vehicle lane. Since non-motor vehicles are all driving at high speeds, when the vehicle quickly enters this intersection, the non-motor vehicle will be involved by the car, due to untimely braking or slow reaction (for the next generation), which can easily cause an accident. Because the corresponding B-pillar is blocked, it is difficult for the on-board personnel to find the non-motor vehicle behind the right when turning. If the driver neglects such a situation, an accident is likely to occur; Among them, the specific sub-steps of locking the dangerous object are: S21, based on the camera inside the car and the set face template, the face position of the driver is locked (the method of recognizing the face by the face template is relatively common in the prior art, so it will not be described in detail here), based on the determined face position and the preset center point in the face template, the feature midpoint is confirmed, and the feature midpoint is located on the face of the driver, and then based on the car model associated with the car, the position of the A-pillar and the B-pillar is locked, and the car model is a preset model, which is set in advance in the system of the car; With the feature midpoint as the center, generate a set of circles with a radius of X1m from the top view of the car model. Then, based on the edge contours of the A-pillar and the B-pillar, determine the area blocked by the corresponding edge contours. X1 is a preset value, generally 15m (it can be regarded as rotating the feature midpoint with light and shadow, and the circle with a radius of 15m is rotated. The shadow area in the circle is the corresponding blocked area. Figure 2 , according to the specific positions of the corresponding A-pillar and B-pillar, the corresponding related blind area is confirmed, and the confirmed blocked area is marked as the blind area, and the blind area changes in real time according to the driving process of the vehicle; S22, based on the current steering amplitude of the vehicle, confirm the actual steering angle of the vehicle tire (the relevant parameters can be directly obtained from the vehicle system), generate a virtual driving route based on the actual steering angle, and mark the area covered by the virtual driving route within 5m as the virtual driving area (representing the corresponding area that the vehicle will definitely travel when it continues to travel according to this angle in the future, here is when the current wheel angle of the vehicle is this value, when it continues to travel according to this value, the route it travels is the determined virtual driving route, after traveling 5m along the virtual driving route, the specific area covered by the corresponding route is the corresponding virtual driving area), and the virtual driving area changes in real time according to the driving process of the vehicle; S23, real-time monitoring is performed on the determined blind area of ​​vision, and based on the progress of real-time monitoring, the moving objects existing in the blind area of ​​vision are confirmed: the previous frame of real-time monitoring is compared with the next frame of vision, the pixel features of the corresponding pixel points in the previous frame of vision are confirmed first, and then the same pixel points are locked in the next frame of vision based on the pixel features, and it is identified whether the spatial positions of two groups of the same pixel points in different frames of vision are the same, if they are the same, no calibration is performed, if they are not the same, they are calibrated as moving pixel points, and then the specific objects covered by the calibrated adjacent moving pixel points are calibrated as moving objects; Based on the spatial moving direction of the moving pixel point, the spatial moving direction of the moving object is confirmed, and whether the spatial moving direction intersects with the determined virtual driving area is identified. If so, the moving object is marked as a dangerous object, and if not, no marking is performed; Its pixel features include the grayscale value of the current pixel and the grayscale values ​​of the neighboring pixels around the current pixel, and are sorted according to the positions of the current pixel and the neighboring pixels. Under normal circumstances, there are eight neighboring pixels around a pixel. The grayscale value of the current pixel is A, and the grayscale values ​​of the surrounding pixels are H1-H8 respectively. Then the sorted grayscale values ​​can be expressed as: , then this sequence is the corresponding pixel feature, and the same point A can be confirmed based on this pixel feature; Step 3: Based on the confirmed dangerous object, monitor the dangerous object in a short period, identify the speed data generated by the dangerous object in a short period, and then confirm the time. Then, based on the current driving speed of the car and the virtual driving area, confirm the time of the current car. Based on the time characteristics of the dangerous object and the current car, assess whether there is a safety risk. The specific sub-steps of the assessment are: S31. Define a group of short periods, the specific value of which is prepared in advance by relevant operators, and is generally 2 seconds or 3 seconds, etc. Monitor and confirm the speed data associated with the corresponding dangerous object within the short period (the speed confirmation can be performed based on the moving distance and moving time of the moving pixel points inside the corresponding dangerous object), determine the minimum speed value and the maximum speed value from several groups of speed data confirmed within the short period, and thus lock the speed range of the dangerous object; Based on the determined virtual driving area and the position of the corresponding dangerous object at the end of the short cycle, the shortest distance L1 between the dangerous object and the virtual driving area is determined. Based on L1 and the determined speed range, the time interval [T k min,T k max], whose T k min=L1÷maximum speed, its T k max = L1 ÷ minimum speed, where k represents different dangerous objects; S32, confirming the speed data generated by the current vehicle in a short period, and confirming the speed range of the current vehicle based on the minimum value and the maximum value in the speed data; Based on the remaining driving distance L2 of the virtual driving area and the length of the vehicle, the distance to be driven is determined, where the distance to be driven = L2 + the length of the vehicle. Based on the determined distance to be driven and speed range, the time interval [Qsmin, Qsmax] associated with the current vehicle is determined; S33, based on different time intervals associated with different dangerous objects [T k min,T k max] and the time interval [Qsmin, Qsmax] associated with the current car, to identify whether the time interval of a certain dangerous object intersects with the time interval of the current car: if so, a warning signal is directly generated and displayed on the vehicle display terminal for the driver to view and take timely response measures, indicating that the vehicle needs to stop and allow related non-motor vehicles to pass by; If it does not exist, identify whether it exists: |T k min-Qsmax|≤Y3 or |T kmax-Qsmin|≤Y3, if there is a corresponding dangerous object that meets any of the above assessment conditions, a risk signal is generated and directly displayed on the vehicle display terminal, indicating that there is a safety risk, prompting the corresponding driver to slow down to avoid accidents; If there is no dangerous object that meets any of the above assessment conditions, no signal will be generated, which means that the corresponding car can pass normally and its blind spot is safe.

[0014] Among them, when the car is always in the determined virtual driving area, the analysis processes in step 2 and step 3 continue to exist. When the car passes through this virtual driving area, the corresponding analysis process will not be performed. When the corresponding analysis conditions (step 1) are met next time, the corresponding analysis process will be executed again. Example

[0015] A safety situation awareness system based on an intelligent connected vehicle, comprising: The vehicle data monitoring end monitors the steering range and driving speed of the vehicle in real time, and transmits the real-time monitored data to the process starting end; The process starter determines whether the steering amplitude and driving speed of the vehicle meet the analysis conditions based on the real-time monitoring of the steering amplitude and driving speed of the vehicle. If so, the subsequent road condition analysis is executed. If not, no processing is performed. The road condition analysis end confirms the blind spots associated with the driver based on the face position of the driver in the car, monitors the blind spots in real time, and confirms the surveillance video in real time. Based on the current vehicle's driving direction and location, the virtual driving area is confirmed in real time in the on-board map, and the surveillance video of the blind spots is analyzed in real time to lock in dangerous objects. On the safety risk analysis side, based on the confirmed dangerous object, the dangerous object is monitored in a short period of time, the speed data generated by the dangerous object in a short period is identified, and then the time is confirmed. Then, based on the current vehicle's driving speed and virtual driving area, the time of the current vehicle is confirmed. Based on the time characteristics of the dangerous object and the current vehicle, it is assessed whether there is a safety risk.

[0016] Some of the data in the above formulas are numerically calculated by removing their dimensions. Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0017] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A security situation awareness method based on intelligent connected vehicles, characterized in that: The following steps are involved: Step 1: monitor the steering range and driving speed of the car in real time, and evaluate whether the steering range and driving speed monitored in real time meet the analysis conditions. If they meet, execute step 2; if not, monitor in real time; Step 2: Based on the face position of the driver in the car, confirm the blind spot associated with the driver, monitor the blind spot in real time, and confirm the monitoring video in real time. Based on the current driving direction and location of the vehicle, confirm the virtual driving area in real time in the on-board map, and analyze the monitoring video of the blind spot in real time to lock the dangerous object; Step 3: Based on the confirmed dangerous object, conduct short-term monitoring of the dangerous object, identify the speed data generated by the dangerous object in a short period, and then confirm the time. Based on the current vehicle's driving speed and virtual driving area, confirm the time of the current vehicle. Based on the time characteristics of the dangerous object and the current vehicle, assess whether there is a safety risk.

2. A security situation awareness method based on intelligent networked vehicles according to claim 1, characterized in that: In step 1, the specific method of evaluating whether the steering amplitude and driving speed monitored in real time meet the analysis conditions is: The steering range of the car monitored in real time is calibrated as F i , the real-time monitored vehicle speed is calibrated as V i , where i represents different moments, identifying the monitored F i and V i Whether to satisfy: F i >Y1 and V i <Y2, where Y1 and Y2 are both preset values.

3. A security situation awareness method based on intelligent networked vehicles according to claim 1, characterized in that: In step 2, the specific sub-steps of locking the dangerous object are: S21, based on the camera inside the car and the set face template, the face position of the driver is locked, based on the determined face position and the preset center point in the face template, the feature midpoint is confirmed, and then based on the car model associated with the car, the position of the A-pillar and the B-pillar is locked, and the car model is a preset model, which is set in advance in the car system; With the feature midpoint as the center, generate a set of circles with a radius of X1m from the top view of the car model, and then determine the area blocked by the corresponding edge contours based on the edge contours of the A-pillar and the B-pillar, where X1 is a preset value, and mark the confirmed blocked area as the blind spot; S22, based on the current steering amplitude of the vehicle, confirm the actual steering angle of the vehicle tire, generate a virtual driving route according to the actual steering angle, and mark the area covered by the virtual driving route within 5m as the virtual driving area; S23, real-time monitoring is performed on the determined blind area of ​​vision, and based on the progress of real-time monitoring, the moving objects existing in the blind area of ​​vision are confirmed: the previous frame of real-time monitoring is compared with the next frame of vision, the pixel features of the corresponding pixel points in the previous frame of vision are confirmed first, and then the same pixel points are locked in the next frame of vision based on the pixel features, and it is identified whether the spatial positions of two groups of the same pixel points in different frames of vision are the same, if they are the same, no calibration is performed, if they are not the same, they are calibrated as moving pixel points, and then the specific objects covered by the calibrated adjacent moving pixel points are calibrated as moving objects; Based on the spatial moving direction of the moving pixel point, the spatial moving direction of the moving object is confirmed, and it is identified whether this spatial moving direction intersects with the determined virtual driving area. If so, the moving object is marked as a dangerous object. If not, no calibration is performed.

4. A security situation awareness method based on intelligent networked vehicles according to claim 3, characterized in that: The blind area of ​​vision and the virtual driving area change in real time according to the driving process of the vehicle.

5. The method for security situation awareness based on intelligent networked vehicles according to claim 3 is characterized in that: The pixel features include the grayscale value of the current pixel and the grayscale values ​​of the neighboring pixels around the current pixel, and are sorted according to the positions of the current pixel and the neighboring pixels. The grayscale value of the current pixel is assumed to be A, and the grayscale values ​​of the surrounding pixels are H1-H8, that is, the sorted grayscale values ​​can be expressed as: .

6. A security situation awareness method based on intelligent networked vehicles according to claim 3, characterized in that: In step 3, the specific sub-steps for assessing whether there is a security risk are: S31, defining a group of short periods, monitoring and confirming the speed data associated with the corresponding dangerous object in the short period, determining the minimum speed value and the maximum speed value from the several groups of speed data confirmed in the short period, thereby locking the speed range of the dangerous object; Based on the determined virtual driving area and the position of the corresponding dangerous object at the end of the short cycle, the shortest distance L1 between the dangerous object and the virtual driving area is determined. Based on L1 and the determined speed range, the time interval [T k min,T k max], whose T k min=L1÷maximum speed, its T k max = L1 ÷ minimum speed, where k represents different dangerous objects; S32, confirming the speed data generated by the current vehicle in a short period, and confirming the speed range of the current vehicle based on the minimum value and the maximum value in the speed data; Based on the remaining driving distance L2 of the virtual driving area and the length of the vehicle, the distance to be driven is determined, where the distance to be driven = L2 + the length of the vehicle. Based on the determined distance to be driven and speed range, the time interval [Qsmin, Qsmax] associated with the current vehicle is determined; S33, based on different time intervals associated with different dangerous objects [T k min,T k max] and the time interval [Qsmin, Qsmax] associated with the current car, and identify whether the time interval of a dangerous object intersects with the time interval of the current car: If there is, a warning signal will be directly generated and displayed on the vehicle display terminal for the driver to view and take timely response measures; If it does not exist, identify whether it exists: |T k min-Qsmax|≤Y3 or |T k max-Qsmin|≤Y3: If there is a corresponding dangerous object that meets any of the above assessment conditions, a risk signal is generated and directly displayed on the vehicle display terminal; If there is no dangerous object that meets any of the above evaluation conditions, no signal is generated, which means that the corresponding car can pass normally.

7. A security situation awareness system based on an intelligent networked vehicle, the awareness system operates according to a security situation awareness method based on an intelligent networked vehicle according to any one of claims 1 to 6, characterized in that: include: The vehicle data monitoring end monitors the steering range and driving speed of the vehicle in real time, and transmits the real-time monitored data to the process starting end; The process starter determines whether the steering amplitude and driving speed of the vehicle meet the analysis conditions based on the real-time monitoring of the steering amplitude and driving speed of the vehicle. If so, the subsequent road condition analysis is executed. If not, no processing is performed. The road condition analysis end confirms the blind spots associated with the driver based on the face position of the driver in the car, monitors the blind spots in real time, and confirms the surveillance video in real time. Based on the current vehicle's driving direction and location, the virtual driving area is confirmed in real time in the on-board map, and the surveillance video of the blind spots is analyzed in real time to lock in dangerous objects. On the safety risk analysis side, based on the confirmed dangerous object, the dangerous object is monitored in a short period of time, the speed data generated by the dangerous object in a short period is identified, and then the time is confirmed. Then, based on the current vehicle's driving speed and virtual driving area, the time of the current vehicle is confirmed. Based on the time characteristics of the dangerous object and the current vehicle, it is assessed whether there is a safety risk.

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