TWI712523B - Method and system for predicting dangerous vehicles - Google Patents

Abstract

A method for predicting dangerous vehicles is provided. The method is used in a system. The method includes: detecting, by RFID readers disposed on at least two street lamps, at least one vehicle with an RFID tag traveling between the street lamps, and transmitting a vehicle ID of the vehicle, the reading time when the vehicle passes the street lights and IDs of the street lamps to the cloud device; and receiving, by the cloud device, the vehicle ID, the reading time, and the IDs of the street lamps, identifying a vehicle type of the vehicle according to the vehicle ID and calculating an over speed data according to the reading time and the IDs of the street lamps, and determining whether to notify a user to perform an inventory check on the vehicle according to the vehicle type and the over speed data.

Description

Method and system for predicting dangerous vehicles

This disclosure relates to a method and system for predicting dangerous vehicles, and in particular to a method and system for predicting dangerous vehicles using RFID (Radio Frequency Identification) technology.

In view of the fact that the World Health Organization (WHO) released the "2018 Global Road Safety Status Report", the number of deaths due to road traffic in the world continues to rise, with the annual death toll reaching 1.35 million, and the proportion of drunk driving, drug driving, and terrorist attacks is increasing year by year. The vehicles on the road are already an unpredictable weapon.

In addition, the current management of general criminal vehicles adopts street image recognition methods. Due to the congenital problems of poor vision (heavy rain, thick fog, dirty lens, etc.) in image recognition, and the processing speed is too slow, the police can only do it. "Monitoring during the event" and "Review after the event". But this has caused casualties and broken countless families.

Therefore, there is a need for a method and system for predicting dangerous vehicles to reduce the risk of innocent casualties caused by dangerous vehicles.

The content disclosed below is only exemplary and is not meant to be restricted in any way. In addition to the described aspects, embodiments, and features, other aspects, embodiments, and features will also be apparent by referring to the drawings and the following specific embodiments. That is, the content disclosed below is provided to introduce the concepts, key points, benefits, and novel and non-obvious technical advantages described herein. The selected, not all, examples will be described in further detail below. Therefore, the content disclosed below is not intended to be an essential feature of the claimed subject matter, nor is it intended to be used in determining the scope of the claimed subject matter.

Therefore, the main purpose of the present disclosure is to provide a method and system for predicting dangerous vehicles to improve the aforementioned shortcomings.

This disclosure proposes a method for predicting dangerous vehicles, which is used in a system. The above method includes: detecting at least one vehicle with an RFID tag running between the street lights by RFID readers configured on at least two street lights, and transmitting a vehicle ID of the vehicle, and reading of the vehicle passing the street lights Time and the street light ID of the street light to a cloud device; and the cloud device receives the vehicle ID, the read time, and the street light ID, and distinguishes one of the vehicle types according to the vehicle ID and the read time and The street light ID calculates a speeding data, and determines whether to notify a user to check the vehicle based on the type of the vehicle and the speeding data.

In some embodiments, when one of the street lights is a traffic light, the method further includes: transmitting a red light cycle to the cloud device by the traffic light; receiving the red light cycle by the cloud device, according to the foregoing The red light cycle and the reading time determine whether the vehicle runs a red light; and when the vehicle runs a red light, the cloud device determines whether to notify the user to check the vehicle according to the type of the vehicle.

In some embodiments, a distance between the two consecutive street lights is defined as a segment. When the number of one of the street lights exceeds two or more, the method further includes: obtaining each street light according to the street light ID by the cloud device Calculate a distance of each road segment based on the GPS position, and obtain a speed of the vehicle on each road segment based on the distance and the read time; and use the cloud device to calculate the vehicle’s speed on each road segment The above-mentioned speed determines whether to notify the above-mentioned user to check the above-mentioned vehicle.

In some embodiments, when the number of one of the above-mentioned vehicles exceeds one or more, the above-mentioned method further includes: using the above-mentioned cloud device according to the above-mentioned vehicle ID of the above-mentioned vehicle driving on the above-mentioned road section, and the above-mentioned speed And the street light ID to calculate a disaster level of the road section; and when the disaster level exceeds a preset value, the cloud device determines a dangerous area to activate the warning light arranged on the street light in the area and notify a public security unit Information about the aforementioned vehicles.

In some embodiments, when the disaster level exceeds a preset value, the step of determining a dangerous area by the cloud device further includes: judging the driving speed of the vehicle by the cloud device according to the reading time and the street light ID, The driving direction and vehicle position; and the above-mentioned dangerous area is estimated based on the above-mentioned driving speed, the above-mentioned driving direction and the above-mentioned vehicle position, wherein the above-mentioned dangerous area is an area within a fixed time drive from the vehicle position.

This disclosure proposes a system for predicting dangerous vehicles, which includes at least: RFID readers configured on at least two street lights, detect at least one vehicle with an RFID tag running between the street lights, and transmit a vehicle ID of the vehicle , The vehicle passes the reading time of the street light and the street light ID of the street light to a cloud device; and the cloud device is coupled to the RFID reader to receive the vehicle ID, the reading time and the street light ID, according to the above The vehicle ID identifies one of the vehicle types of the above-mentioned vehicles, calculates a speeding data based on the read time and the street light ID, and determines whether to notify a user to check the vehicle based on the vehicle type and the speeding data.

Hereinafter, various aspects of the present disclosure will be described more fully with reference to the accompanying drawings. However, the present disclosure can be embodied in many different forms and should not be construed as being limited to any specific structure or function presented throughout the present disclosure. On the contrary, providing these aspects will make this disclosure comprehensive and complete, and this disclosure will fully convey the scope of this disclosure to those skilled in the art. Based on the content taught in this article, those skilled in the art should realize that no matter whether it is implemented alone or in combination with any other aspect of this disclosure, the scope of this disclosure is intended to cover any aspect disclosed in this article. For example, it can be implemented using any number of devices or execution methods proposed herein. In addition, in addition to the various aspects of the present disclosure set forth herein, the scope of the present disclosure is intended to cover devices or methods implemented using other structures, functions, or structures and functions. It should be understood that it can embody any aspect disclosed herein through one or more elements within the scope of the patent application.

The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any aspect of this disclosure or a design described herein as "exemplary" is not necessarily construed as being preferred or superior to other aspects of this disclosure or design. In addition, the same number indicates the same element in all the several figures, and unless otherwise specified in the description, the articles "a" and "above" include plural references.

It will be understood that when an element is referred to as being “connected” or “coupled” to another element, the element can be directly connected or coupled to the other element or intervening elements may be present. Conversely, when the element is said to be "directly connected" or "directly coupled" to another element, there are no intermediate elements. Other words used to describe the relationship between elements should be interpreted in a similar way (for example, "between" and "directly between", "adjacent" and "directly adjacent", etc.).

FIG. 1 shows a schematic diagram of a system 100 for predicting dangerous vehicles according to an embodiment of the present disclosure. The system 100 may include RFID readers 112A-112E, an RFID tag 122 and a cloud device 130 arranged on street lights 110A-110E in an area, wherein the RFID tag 122 is arranged on at least one vehicle 120. The cloud device 130 can be connected to the RFID readers 112A to 112E on the street lights 110A to 110E in this area through the network 150. The network 150 can be any type of network familiar to those skilled in the art, which can be used Any one of various protocols can be used to support data communication in various communications, including but not limited to TCP/IP, etc. For example, the network 150 may be a local area network (LAN), such as an Ethernet network, etc., a virtual network, including but not limited to a virtual private network (Virtual Private Network, VPN), Internet, wireless network and/or any combination of these and/or other networks.

The RFID readers 112A-112E can detect at least one vehicle 120 with an RFID tag 122 driving between the street lights 110A-110E, and transmit a vehicle ID of the vehicle 120, the reading time of the vehicle 120 passing the street lights 110A-110E, and the street lights The street light IDs of 110A to 110E are sent to the cloud device 130. In more detail, in this disclosure, the RFID readers 112A to 112E use Received Signal Strength Indication (RSSI) and Proximity detection modes to estimate when each vehicle passes by which street light Read the time, and upload the vehicle ID, the street light ID that the vehicle passes, and the read time to the cloud device 130.

In an embodiment, the street lights 110A to 110E may also be traffic lights. When the street light is a traffic light, the RFID reader configured on the traffic light not only transmits a vehicle ID of the vehicle 120, the reading time of the vehicle 120 through the traffic light, and the street light ID of the traffic light to the cloud device 130, but also transmits a red light The period reaches the aforementioned cloud device 130 for the cloud device 130 to determine whether the vehicle has a red light.

The cloud device 130 may have a database 132, which can be pre-built by the administrator for the map location of each street light and traffic light (for example, the GPS location of the street light and the traffic light, whether the area where the street light and the traffic light are located is a trouble-prone area, such as Large crowds, schools and other areas), relevant information of the corresponding vehicle ID (for example, whether the registered owner of this vehicle ID has a history of causing an accident or is a repeat offender of traffic violations, whether this vehicle ID is a stolen car or has been notified as a wanted vehicle, this vehicle Types of ID vehicles: motorcycles, passenger cars, trucks, buses, engineering vehicles, etc.). The administrator can input the above-mentioned data into the database 132 in advance.

In addition, each street light and/or can also be equipped with a warning light (not shown in the figure), and can be connected to the cloud device 130. The cloud device 130 can send an indication signal to control the warning light to emit light of different colors to warn drivers or pedestrians on this road section or this area.

To facilitate the description of the embodiment of the present disclosure, a distance between two consecutive street lights is defined as a road segment. As shown in Figure 1, the distance between street light 110A and street light 110B is road section A. The distance between the street lamp 110B and the street lamp 110C is road section B, and so on.

It should be understood that the cloud device 130 shown in FIG. 1 is an example of the architecture of the system 100 for predicting dangerous vehicles. Each element shown in FIG. 1 can be implemented by any type of electronic device, such as the electronic device 900 described with reference to FIG. 9, as shown in FIG.

FIG. 2 is a flowchart of a method 200 for predicting dangerous vehicles according to an embodiment of the disclosure. This method can be implemented in the system 100 for predicting dangerous vehicles as shown in FIG. 1.

In step S205, at least one vehicle with an RFID tag traveling between the street lights is detected by the RFID readers configured on at least two street lights, and a vehicle ID of the vehicle is transmitted, and the reading of the vehicle passing the street lights Get the time and the street light ID of the above street light to a cloud device.

In step S210, the cloud device receives the vehicle ID, the read time, and the street light ID, identifies one of the vehicle types according to the vehicle ID, and calculates a speeding data based on the read time and the street light ID, And according to the above-mentioned vehicle type and the above-mentioned speeding data, it is judged whether to notify a user to check the above-mentioned vehicle.

In more detail, since the manager has pre-built the map location of each street light and traffic light, the cloud device can calculate the speed of the vehicle on a certain road section based on the distance and time difference between the two lamp posts. Then, the cloud device determines the speeding data of the vehicle on the road section according to the speed limit value of the road section, and determines whether to notify a user to interrogate the vehicle based on the vehicle type and the speeding data.

In another embodiment, when one of the street lights is a traffic light, the traffic light not only transmits the vehicle ID, the read time, and the traffic light ID, but also transmits a red light cycle to the cloud device. After receiving the red light period, the cloud device will determine whether the vehicle is running a red light based on the red light period and the read time. As an example, the cloud device can determine whether the vehicle is running a red light according to whether the vehicle is moving during a red light period. The time between the first reading time when the first traffic light reports the vehicle (that is, the time when the vehicle passes the first traffic light) and the second reading time when the second traffic light reports the vehicle (that is, the time when the vehicle passes the second traffic light) When it is less than the red light period, the cloud device determines that the vehicle is running a red light. Or, when the first traffic light reports the first reading time of the vehicle (that is, the time when the vehicle passes the first traffic light) and the second reading time when the next light reports the vehicle (that is, the time when the vehicle passes the next light) When the time is less than the red light period, the cloud device determines that the vehicle is running a red light. When it is determined that the vehicle is running a red light, the cloud device determines whether to notify the user to check the vehicle according to the type of the vehicle.

FIG. 3 is a flowchart of a method 300 for predicting dangerous vehicles according to an embodiment of the disclosure. This method can be implemented in the system 100 for predicting dangerous vehicles as shown in FIG. 1. The difference from Fig. 2 is that this method 300 is used for multi-segment situations where the number of street lamps exceeds two or more. In addition, the method 300 can be executed after the method 200 in FIG. 2 is continued.

In step S305, the cloud device obtains a GPS position of each street light according to the street light ID, calculates a distance of each road segment based on the GPS position, and obtains the vehicle's position on each road segment based on the distance and the read time. One speed.

In step S310, the cloud device is used to determine whether to notify the user to check the vehicle according to the speed of the vehicle on each road section.

In more detail, the cloud device can calculate the variable speed data between the two road sections according to the speed of the vehicle on each road section and the reading time. As shown in Figure 1, the variable speed data of the vehicle on road segment A and road segment B is defined as the speed of the vehicle on road segment B minus the speed of the vehicle on road segment A. The cloud device can determine the driver's disaster level (such as drunk driving, drug driving, etc.) of the vehicle driver based on the variable speed of several road sections, so as to decide whether to notify the user to conduct an interrogation of the above-mentioned vehicle.

FIG. 4 shows a flowchart of a method 400 for predicting dangerous vehicles according to an embodiment of the disclosure. This method can be implemented in the system 100 for predicting dangerous vehicles as shown in FIG. 1. Different from Fig. 3, this method 400 is used when the number of vehicles exceeds one. In addition, the method 400 can be executed after the method 300 in FIG. 3 is continued.

In step S405, the cloud device calculates a disaster level of the road section based on the vehicle ID of the vehicle traveling on the road section, the speed of the vehicle on each road section, and the street light ID. The detailed calculation of disaster levels will be explained later.

In step S410, when the disaster level exceeds a preset value, the cloud device determines a dangerous area to activate the warning lights arranged on the street lights in the area and notify a public security unit of information about the vehicle.

Before calculating the disaster level, the cloud device can first calculate the disaster factor for a single road section. To facilitate the description of the disclosed embodiments, each different traffic situation can be regarded as a disaster factor. for example. When the cloud device determines that the vehicle has run through a red light on this road section, it can be considered that this road section has a red light running factor. When the cloud device determines that the vehicle has exceeded the speed on this road section, it can be regarded as the road section with an overspeed factor. In addition, the administrator can also pre-define disaster factors related to historical data, as shown in Table 1. Disaster factors and application of historical data Car owner risk factor After the cloud device receives the vehicle ID, it can immediately determine whether the registered owner of the vehicle has a criminal record or is a repeat offender of a traffic violation. The cloud device will determine the disaster level of the vehicle based on these data. Vehicle offense factor After the cloud device receives the vehicle ID, it can immediately determine whether the vehicle is stolen or notified as a wanted vehicle. The cloud device will determine the disaster level of the vehicle based on these data Vehicle prior factor After the cloud device receives the vehicle ID, it can immediately determine whether the vehicle has a history of causing an accident or is a repeat offender of a traffic violation. The cloud device will determine the disaster level of the vehicle based on these data. Vehicle injury factor After receiving the vehicle ID, the cloud device can immediately determine what kind of vehicle the vehicle is: motorcycles, self-driving buses, trucks, buses, engineering vehicles, etc. The cloud device will determine the disaster level of the vehicle based on these data. Regional risk factor After the cloud device receives the vehicle ID, it can immediately determine whether this area is a trouble-prone area, such as a crowded area, a school, etc. The cloud device will determine the disaster level of this area based on these data. Table 1 should be noted that, although not explicitly defined in Table 1 Disaster factor score, but the administrator can pre-disaster factor to do to score defined. For example, the administrator can define different vehicle injury factor scores for different vehicle types. For example, the vehicle injury factor for motorcycles is one point, the vehicle injury factor for passenger cars is two points, the vehicle injury factor for trucks is three points, the vehicle injury factor for buses is four points, and the vehicle injury factor for engineering vehicles is five points. Wait. It should be noted that the disaster factors of the above historical data are not used to limit this disclosure, and those with ordinary knowledge in the relevant technical field can appropriately replace or adjust them according to this embodiment.

FIGS. 5A to 5B are flowcharts of the method 500 for predicting dangerous vehicles according to an embodiment of the present disclosure. This method can be implemented in the system 100 for predicting dangerous vehicles as shown in FIG. 1. The difference from Figure 2 is that this method 500 is a flow chart for the cloud device to determine the disaster factor score of a single road section in more detail.

In step S505, the cloud device receives the vehicle ID, street light and/or traffic light reading time, street light ID, and red light cycle on the road section. In step S510, the cloud device judges the vehicle damage factor according to the vehicle ID (for example, the vehicle damage factor of a motorcycle is one point, the vehicle damage factor of a small passenger car is two points, the vehicle damage factor of a truck is three points, and the vehicle damage factor of a bus is three points. The injury factor is four points, and the vehicle injury factor of engineering vehicles is five points, etc.).

In step S515, the cloud device determines whether the vehicle is a stolen vehicle or notified as a wanted vehicle according to the vehicle ID. When the cloud device determines that the vehicle is a stolen vehicle or is notified as a wanted vehicle ("Yes" in step S515), in step S520, the cloud device determines that the vehicle's current crime factor is one point. In step S525, the cloud device may notify a user or a patrolman to check the vehicle. When the cloud device determines that the vehicle is not a stolen car or is notified as a wanted vehicle ("No" in step S515), in step S570, the cloud device accumulates the disaster factor.

In step S530, the cloud device determines whether the vehicle runs a red light according to the reading time and the red light cycle. When the cloud device determines that the vehicle has run through a red light ("Yes" in step S530), in step S535, the cloud device determines that the vehicle has run through a red light factor as one point. In step S540, the cloud device determines whether the score of the vehicle injury factor is greater than or equal to 3 points according to the vehicle ID. When the cloud device determines that the score of the vehicle damage factor is greater than or equal to 3 points ("Yes" in step S540), in step S545, the cloud device may notify a user or a patrolman to check the vehicle. When the cloud device determines that the vehicle did not run a red light ("No" in step S530) or the score of the vehicle damage factor is not greater than or equal to 3 points ("No" in step S540), in step S570, the cloud device Accumulate disaster factors.

In step S550, the cloud device determines whether the vehicle is speeding according to the reading time. When the cloud device determines that the vehicle has exceeded the speed limit ("Yes" in step S550), in step S555, the cloud device can determine the speed factor score of the vehicle according to the amount of speeding (for example, the speed limit is more than 10 kilometers) One point, two points for more than 20 kilometers, three points for more than 30 kilometers, four points for more than 40 kilometers, five points for more than 50 kilometers, etc.). In step S560, the cloud device determines whether the score of the overspeed factor is greater than or equal to 4 points. When the cloud device determines that the vehicle speeding factor score is greater than or equal to 4 minutes ("Yes" in step S560), in step S565, the cloud device may notify a user or a patrolman to check the vehicle. When the cloud device determines that the vehicle is not speeding ("No" in step S550) or the score of the vehicle's speeding factor is not greater than or equal to 4 points ("No" in step S560), in step S570, the cloud device Accumulate disaster factors.

FIGS. 6A to 6E show a flowchart of a method 600 for predicting dangerous vehicles according to an embodiment of the present disclosure. This method can be implemented in the system 100 for predicting dangerous vehicles as shown in FIG. 1. Different from FIGS. 3 and 4, this method 600 is a flow chart for the cloud device to determine the disaster factor scores of multiple road sections in more detail. In addition, the method 600 can be executed after the method 500 in FIG. 5 is continued.

In step S602, the cloud device can calculate the variable speed data between multiple road sections according to the speed of the vehicle on each road section and the reading time, and obtain the variable speed factor. In more detail, the administrator can define the score of the variable speed factor in advance. For example, if the change of variable speed exceeds the speed of more than 20 kilometers per hour, the score is one point, the change of variable speed exceeds the speed of 30 kilometers per hour or more as two points, and the change of variable speed exceeds the speed of 40 kilometers per hour or more as three points. The score for variable speed changes exceeding 50 kilometers per hour is four points, and the score for variable speed changes exceeding 60 kilometers per hour is five points. Give an example to illustrate how the variable speed factor is calculated. Taking Figure 1 as an example, the speed of the "drug driving" vehicle on section A is 60 kilometers per hour, and the speed on section B is changed to 10 kilometers per hour. However, the vehicle speed in section C is 60 kilometers per hour, the vehicle speed in section D is changed to 10 kilometers per hour, and the vehicle speed in section E is 60 kilometers per hour. The cloud device calculates the variable speed of the road section A～E, and can get -50km/h, +50km/h, -50km/h, +50km/h, so the accumulated value of the variable speed factor in the road section A～E Is 16.

In step S604, the cloud device determines whether the score of the variable speed factor is greater than or equal to 4. When the cloud device determines that the score of the variable speed factor is greater than or equal to 4 ("Yes" in step S604), in step S606, the cloud device determines whether the number of consecutive road sections exceeds a threshold, where the continuous road section refers to the case of variable speed Continue to occur in continuous sections. When the cloud device determines that the number of consecutive road sections exceeds a threshold ("Yes" in step S606), in step S608, the cloud device may notify a user or a patrolman to check the vehicle. When the cloud device determines that the score of the variable speed factor is not greater than or equal to 4 ("No" in step S604) or that the number of consecutive road segments does not exceed the threshold ("No" in step S606), in step S610, the cloud The device accumulates the disaster factors, and the process continues to step S652.

In step S612, the cloud device determines whether all vehicle IDs in the above-mentioned multiple road segments are stolen vehicles or are notified as wanted vehicles (that is, whether the score of the vehicle's current crime factor has increased). When the cloud device determines that there is a stolen vehicle or is notified as a wanted vehicle in the above-mentioned multiple road segments ("Yes" in step S612), in step S614, the cloud device accumulates the disaster factor. Then, in step S616, the cloud device will notify a public security unit (for example, the anti-crime unit) about the information about the vehicle, and the process continues to step S652. When the cloud device determines that there is no vehicle ID as a stolen car or is notified as a wanted vehicle in the above-mentioned multiple road segments ("No" in step S612), the cloud device does not perform any action, and the flow continues to step S652.

In step S618, the cloud device determines whether the number of vehicles running red lights in the above-mentioned multiple road segments is greater than one. When the cloud device determines that the number of vehicles running red lights in the above-mentioned multiple road segments is greater than 1 ("Yes" in step S618), in step S620, the cloud device accumulates the disaster factor, and the flow continues to step S652. When the number of vehicles running a red light on the above-mentioned multi-way segment by the cloud device is not greater than 1 ("No" in step S618), the cloud device does not perform any action, and the flow continues to step S652.

In step S622, the cloud device determines whether the number of speeding vehicles in the above-mentioned multiple road segments is greater than one. When the cloud device determines that the number of speeding vehicles in the above-mentioned multiple road segments is greater than 1 ("Yes" in step S622), in step S624, the cloud device accumulates the disaster factor. Next, in step S626, the cloud device determines whether the score of the overspeed factor is greater than 2 points. For the algorithm of overspeed factor score, please refer to step S555 in Figure 5. When the cloud device judges that the score of the speeding factor is greater than 2 points ("Yes" in step S626), in step S628, the cloud device will notify a security unit (for example, the anti-crime unit) about the above-mentioned vehicle information, and activate For the warning lights arranged on the above-mentioned street lights in this area, the process continues to step S652. When the cloud device determines that the number of speeding vehicles in the above-mentioned multiple road segments is not greater than 1 ("No" in step S622) or the score of the speeding factor is not greater than 2 points ("No" in step S626), the cloud device No action is performed, and the flow continues to step S652.

In step S630, the cloud device determines whether the number of vehicles with variable speeds in the multiple road segments is greater than one. When the cloud device determines that the number of vehicles with variable speeds in the above-mentioned multiple road segments is greater than one ("Yes" in step S630), in step S632, the cloud device accumulates the disaster factor. Then, in step S634, the cloud device may notify a user or a patrolman to check the above-mentioned vehicle, and the process continues to step S652. When the cloud device determines that the number of vehicles with variable speeds in the above-mentioned multiple road segments is not greater than 1 ("No" in step S630), the cloud device does not perform any action, and the flow continues to step S652.

In step S636, the cloud device determines whether the number of vehicle injury factors of the vehicles in the above-mentioned multiple road segments is greater than one. For the algorithm of vehicle injury factor score, please refer to step S510 in Figure 5. When the cloud device determines that the number of vehicle injury factors of the vehicles in the above-mentioned multiple road segments is greater than 1 ("Yes" in step S636), in step S638, the cloud device accumulates the disaster factors, and the flow continues to step S652. When the cloud device determines that the number of vehicle injury factors of the vehicle in the above-mentioned multiple road segments is not greater than 1 ("No" in step S636), the cloud device does not perform any action, and the flow continues to step S652.

In step S640, the cloud device determines whether the number of vehicle history factors of the vehicle in the above-mentioned multiple road segments is greater than one. Refer to Table 1 for vehicle history factors. When the cloud device determines that the number of vehicle history factors of the vehicle in the above-mentioned multiple road segments is greater than 1 ("Yes" in step S640), in step S642, the cloud device accumulates the disaster factors, and the flow continues to step S652. When the cloud device determines that the number of vehicle history factors of the vehicle in the above-mentioned multiple road segments is not greater than 1 ("No" in step S640), the cloud device does not perform any action, and the flow continues to step S652.

In step S644, the cloud device determines whether the number of risk factors of the vehicle owner is greater than one in the above-mentioned multiple road segments. Refer to Table 1 for the owner’s risk factors. When the cloud device determines that the number of risk factors of the vehicle owner is greater than 1 in the above-mentioned multiple road segments ("Yes" in step S644), in step S646, the cloud device accumulates the disaster factors, and the flow continues to step S652. When the cloud device determines that the number of risk factors of the vehicle owner in the above-mentioned multiple road segments is not greater than 1 ("No" in step S644), the cloud device does not perform any action, and the flow continues to step S652.

In step S648, the cloud device determines whether the number of regional risk factors in the above-mentioned multiple road sections is greater than one. Refer to Table 1 for regional risk factors. When the cloud device determines that the number of regional risk factors in the above-mentioned multiple road sections is greater than 1 ("Yes" in step S648), in step S650, the cloud device accumulates the disaster factors, and the flow continues to step S652. When the cloud device determines that the number of regional risk factors in the above-mentioned multiple road sections is not greater than 1 ("No" in step S648), the cloud device does not perform any action, and the flow continues to step S652.

In step S652, the cloud device accumulates the scores of all disaster factors to obtain a disaster level (that is, the disaster level is the total score of the disaster factors). Then, in step S654, the cloud device can determine whether the disaster level is greater than a first preset value (for example, 30). When the cloud device determines that the disaster level is greater than a first preset value ("Yes" in step S654), in step S656, the cloud device determines whether the disaster level is greater than a second preset value (for example, 50). When the cloud device determines that the disaster level is greater than the second preset value ("Yes" in step S656), in step S658, the cloud device will notify a security unit (for example, the anti-crime unit) about the information about the vehicle, and activate The warning lights on the above street lights are arranged in this area.

When the cloud device determines that the disaster level is not greater than the first preset value ("No" in step S654), the process ends. When the cloud device determines that the disaster level is greater than the second preset value ("No" in step S656), in step S660, the cloud device may notify a user or a patrolman to interrogate the vehicle.

FIG. 7 shows a flowchart of a processing method 700 for high-risk vehicles according to an embodiment of the disclosure. This method can be implemented in the system 100 for predicting dangerous vehicles as shown in FIG. 1. The processing method 700 may be a detailed processing flow of step S658 in FIG. 6.

Before the process starts, it is assumed that the cloud device has obtained relevant information about dangerous vehicles on a certain road section (for example, vehicle ID, reading time, street light ID, vehicle speed, driving direction and vehicle location, disaster level, etc.). In step S705, the cloud device estimates the dangerous area based on the driving speed, the driving direction, and the vehicle position, where the dangerous area is an area within a fixed time drive from the vehicle position. For example, the cloud device can estimate all street light IDs in a first dangerous area within a 2-minute drive and all street light IDs in a second dangerous area within a 1-minute drive from the cloud device based on information related to dangerous vehicles. FIG. 8 is a schematic diagram showing the dangerous area according to an embodiment of the present disclosure. As shown in the figure, the arrow 810 indicates the location of the dangerous vehicle and the driving direction. The first dangerous area 820 is an area within a 2-minute drive of the vehicle. The second dangerous area 830 is an area within a 1-minute drive of the vehicle.

In step S710, the cloud device can activate warning lights of different colors in different dangerous areas to inform passers-by or vehicles to pay attention to their own safety, and notify the security unit belonging to the dangerous area for processing according to the disaster level. In addition, the cloud device can continuously detect the location of dangerous vehicles in a fixed period of time (for example, 10 seconds), and continuously report the location, driving direction, and related information (for example, vehicle ID, vehicle type, vehicle Color and other information) to facilitate the round-up of the security unit.

As mentioned above, through the method and system for predicting dangerous vehicles in the present disclosure, the RFID reader is used to obtain the relative data of each vehicle, and then predict which vehicles are highly dangerous vehicles, and activate the warning signal of the current dangerous road section or area. Inform passers-by, vehicles and patrol officers to go to dangerous vehicles of concern. This disclosure can effectively achieve "pre-deterrence" to avoid unfortunate traffic incidents and improve the safety of vehicles and passersby.

For the described embodiments of the present disclosure, the following describes an exemplary operating environment in which the embodiments of the present disclosure can be implemented. Refer specifically to FIG. 9, which shows an exemplary operating environment for implementing the embodiments of the present disclosure, which can generally be regarded as an electronic device 900. The electronic device 900 is only an example of a suitable computing environment, and is not intended to imply any limitation on the use or functional scope of the present disclosure. The electronic device 900 should not be interpreted as having any dependency or requirement related to any one or combination of the illustrated elements.

This disclosure can be executed by computer program code or machine using instructions. The instructions can be computer executable instructions of program modules, and the program modules are executed by computers or other machines, such as personal digital assistants or other portable devices. . Generally speaking, program modules include routines, programs, objects, components, data structures, etc. Program modules refer to program codes that perform specific tasks or implement specific abstract data types. This disclosure can be implemented in various system configurations, including portable devices, consumer electronics, general-purpose computers, and more professional computing devices. The disclosure can also be implemented in a distributed computing environment to process devices connected by a communication network.

Refer to Figure 9. The electronic device 900 includes a bus 910 directly or indirectly coupled to the following devices, a memory 912, one or more processors 914, one or more display elements 916, an input/output (I/O) port 918, and an input/output (I/O) port 918, Output (I/O) element 920 and illustrative power supply 922. The bus 910 represents a component that can be one or more buses (for example, an address bus, a data bus, or a combination thereof). Although the blocks in Figure 9 are shown with lines for brevity, in fact, the boundaries of the various components are not specific. For example, the presentation components of the display device can be regarded as I/O components; the processor can have a memory. .

The electronic device 900 generally includes various computer readable media. The computer-readable media can be any available media that can be accessed by the electronic device 900, and the media includes both volatile and non-volatile media, removable and non-removable media. For example, but not limited to, computer-readable media may include computer storage media and communication media. Computer-readable media includes both volatile and non-volatile media implemented in any method or technology used to store information such as computer-readable instructions, data structures, program modules, or other data, etc. Removable and non-removable media. Computer storage media include but are not limited to RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disc (DVD) or other optical disc storage devices, floppy disk, floppy disk, floppy disk storage A device or other magnetic storage device, or any other medium that can be used to store required information and that can be accessed by the electronic device 900. The computer storage medium itself does not include signals.

Communication media generally include computer-readable instructions, data structures, program modules, or other data in the form of modular data signals such as carrier waves or other transmission mechanisms, and include any information transmission media. The term "modular data signal" refers to a signal that has one or more feature sets or is modified in one of the ways to encode information in the signal. For example, but not limited to, communication media include wired media and wireless media such as wired networks or direct wired connections, such as audio, radio frequency, infrared, and other wireless media. The combination of the above media is included in the range of computer readable media.

The memory 912 includes computer storage media in the form of volatile and non-volatile memory. The memory can be removable, non-movable, or a combination of the two. Exemplary hardware devices include solid-state memory, hard disk drives, optical disk drives, and the like. The electronic device 900 includes one or more processors that read data from entities such as the memory 912 or the I/O element 920. The display element 916 displays data instructions to the user or other devices. Exemplary display elements include display devices, speakers, printing elements, vibration elements, and the like.

The I/O port 918 allows the electronic device 900 to be logically connected to other devices including the I/O element 920, some of which are built-in devices. Exemplary components include microphones, joysticks, gaming stations, satellite dish receivers, scanners, printers, wireless devices, etc. The I/O element 920 can provide a natural user interface for processing user-generated gestures, sounds, or other physiological inputs. In some instances, these inputs can be sent to an appropriate network component for further processing. The electronic device 900 may be equipped with a depth camera, such as a stereo camera system, an infrared camera system, an RGB camera system and a combination of these systems, to detect and identify objects. In addition, the electronic device 900 may be equipped with sensors (such as radar, LiDAR) to periodically sense the surrounding environment within a sensing range, and generate sensor information indicating that it is associated with the surrounding environment. Furthermore, the electronic device 900 may be equipped with an accelerometer or a gyroscope for detecting motion. The output of the accelerometer or gyroscope may be provided to the electronic device 900 for display.

In addition, the processor 914 in the electronic device 900 can also execute programs and instructions in the memory 912 to present the actions and steps described in the above embodiments, or other descriptions in the specification.

Any specific sequence or hierarchical steps of the procedure disclosed herein is purely an example. Based on design preferences, it must be understood that any specific sequence or hierarchical steps in the procedure can be rearranged within the scope disclosed in this document. The accompanying method claims present elements of various steps in an exemplary order, and therefore should not be limited by the specific order or hierarchy shown here.

The use of ordinal numbers such as "first", "second", and "third" used to modify elements in the scope of the patent application does not imply any priority, priority, order between elements, or execution of methods The order of the steps is only used as an identification to distinguish different elements with the same name (with different ordinal numbers).

Although this disclosure has been disclosed as above with implementation examples, it is not intended to limit this case. Anyone familiar with this technique can make some changes and modifications without departing from the spirit and scope of this disclosure. Therefore, the scope of protection of this case should Subject to the definition of the scope of patent application attached.

100: System 110A~110E: street light 112A～112E: RFID reader 120: Vehicle 122: RFID tag 130: Cloud device 132: Database 150: Network 200: method S205, S210: steps 300: method S305, S310: steps 400: method S405, S410: steps 500: method S505, S510, S515, S520, S525, S530, S535, S540, S545, S550, S555, S560, S565, S570: steps 600: method S602, S604, S606, S608, S610, S612, S614, S616, S618, S620, S622, S624, S626, S628, S630, S632, S634, S636, S638, S640, S642, S644, S646, S648, S650, S652, S654, S656, S658, S660: steps 700: method S705, S710: steps 810: Arrow 820: The first danger zone 830: second danger zone 900: Electronic device 910: Bus 912: memory 914: processor 916: display element 918: I/O port 920: I/O components 922: power supply

Fig. 1 is a schematic diagram showing a system for predicting dangerous vehicles according to an embodiment of the disclosure. FIG. 2 is a flowchart showing the method for predicting dangerous vehicles according to an embodiment of the present disclosure. FIG. 3 is a flowchart showing the method for predicting dangerous vehicles according to an embodiment of the present disclosure. FIG. 4 is a flowchart of the method for predicting dangerous vehicles according to an embodiment of the present disclosure. FIGS. 5A to 5B are flowcharts showing the method of predicting dangerous vehicles according to an embodiment of the present disclosure. FIGS. 6A to 6E are flowcharts showing the method for predicting dangerous vehicles according to an embodiment of the present disclosure. FIG. 7 is a flowchart showing the processing method for high-risk vehicles according to an embodiment of the present disclosure. FIG. 8 is a schematic diagram showing the dangerous area according to an embodiment of the present disclosure. Figure 9 shows an exemplary operating environment for implementing the embodiments of the present disclosure.

200: method

S205, S210: steps

Claims (12)

A method for predicting dangerous vehicles, used in a system, includes: detecting at least one vehicle with an RFID tag driving between the street lights by RFID readers configured on at least two street lights, and transmitting the information of the vehicle A vehicle ID, the reading time of the vehicle passing the street light and the street light ID of the street light to a cloud device; and the cloud device receives the vehicle ID, the reading time, and the street light ID, and distinguishes the vehicle according to the vehicle ID One of the vehicle types and the calculation of speeding data based on the above-mentioned reading time and the above-mentioned street light ID, and determining whether to notify a user to interrogate the above-mentioned vehicle based on the above-mentioned vehicle type and the above-mentioned speeding data; A distance is defined as a road segment. When the number of one of the street lights exceeds two or more, the above method further includes: obtaining a GPS position of each street light according to the street light ID through the cloud device, and calculating each road segment based on the GPS position And obtain a speed of the vehicle on each road segment based on the distance and the read time; and the cloud device determines whether to notify the user of the vehicle on each road segment based on the speed of the vehicle on each road segment. Check. The method for predicting dangerous vehicles as described in item 1 of the scope of patent application, wherein when one of the street lights is a traffic light, the method further includes: transmitting a red light cycle to the cloud device through the traffic light; The cloud device receives the red light period, and determines whether the vehicle runs a red light based on the red light period and the read time; and when the vehicle runs a red light, the cloud device determines whether to notify the user to check the vehicle according to the type of the vehicle . The method for predicting dangerous vehicles as described in the first item of the scope of patent application, wherein when the number of one of the above-mentioned vehicles exceeds one or more, the above-mentioned method further includes: using the above-mentioned cloud device according to the above-mentioned vehicles on the above-mentioned road section ID, the above-mentioned speed of the vehicle on each road section, and the above-mentioned street light ID to calculate a disaster level of the above-mentioned road section; and when the disaster level exceeds a preset value, the cloud device determines a dangerous area to activate the configuration in the area The warning lights on the above street lights also notify a public security unit of the information about the above vehicles. For the method for predicting dangerous vehicles as described in item 3 of the scope of patent application, when the disaster level exceeds a preset value, the step of determining a dangerous area by the cloud device further includes: using the cloud device according to the reading time And the street light ID to determine the driving speed, driving direction, and vehicle position of the vehicle; and to estimate the dangerous area based on the driving speed, driving direction, and vehicle position, where the dangerous area is a fixed time drive from the vehicle position Within the area. The method for predicting dangerous vehicles as described in item 3 of the scope of patent application, wherein the cloud device is used to calculate the vehicle ID according to the vehicle ID of the vehicle driving on the road section, the speed of the vehicle on each road section, and the street light ID. The step of a disaster level of a road section further includes: accumulating disaster factors according to the vehicle ID, the speed of the vehicle on each road section, and the street light ID; and obtaining a score of the accumulated disaster factors to obtain the disaster level. Such as the method for predicting dangerous vehicles as described in item 5 of the scope of patent application, Among them, the above-mentioned disaster factors include at least the red light factor, the speeding factor, the variable speed factor, the owner's risk factor, the vehicle's current crime factor, the vehicle history factor, the vehicle injury factor and the regional risk factor. A system for predicting dangerous vehicles includes at least: RFID readers arranged on at least two street lights, detecting at least one vehicle with an RFID tag running between the street lights, and transmitting a vehicle ID of the vehicle and the vehicle passing by The reading time of the street light and the street light ID of the street light are connected to a cloud device; and the cloud device is coupled to the RFID reader to receive the vehicle ID, the reading time, and the street light ID, and distinguish according to the vehicle ID One of the vehicle types of the above-mentioned vehicles and the above-mentioned reading time and the above-mentioned street lamp ID to calculate a speeding data, and according to the above-mentioned vehicle type and the above-mentioned speeding data to determine whether to notify a user to interrogate the above-mentioned vehicle; wherein the two consecutive street lights in the above-mentioned street light The distance between each street light is defined as a segment. When the number of one of the street lights exceeds two or more, the above system is further implemented: the cloud device obtains a GPS position of each street light according to the street light ID, and calculates each street light according to the above GPS position. A distance of a road section, and a speed of the vehicle in each road section is obtained based on the distance and the reading time; and the cloud device determines whether to notify the user of the above-mentioned speed according to the speed of the vehicle in each road section. Vehicles are checked. For example, the system for predicting dangerous vehicles described in item 7 of the scope of patent application, wherein when one of the street lights is a traffic light, the system further executes: transmitting a red light cycle to the cloud device through the traffic light; The cloud device receives the red light period, and according to the red light period and the upper The reading time judges whether the vehicle runs a red light; and when the vehicle runs a red light, the cloud device judges whether to notify the user to check the vehicle according to the type of the vehicle. For example, the system for predicting dangerous vehicles as described in item 7 of the scope of patent application, wherein when the number of one of the above-mentioned vehicles exceeds one or more, the above-mentioned system is further executed: the above-mentioned cloud device is used according to the above-mentioned vehicles on the above-mentioned road section. ID, the above-mentioned speed of the vehicle on each road section, and the above-mentioned street light ID to calculate a disaster level of the above-mentioned road section; and when the disaster level exceeds a preset value, the cloud device determines a dangerous area to activate the configuration in the area The warning lights on the above street lights also notify a public security unit of the information about the above vehicles. For example, in the system for predicting dangerous vehicles as described in item 9 of the scope of patent application, when the disaster level exceeds a preset value, the step of determining a dangerous area by the cloud device further includes: using the cloud device according to the reading time And the street light ID to determine the driving speed, driving direction, and vehicle position of the vehicle; and to estimate the dangerous area based on the driving speed, driving direction, and vehicle position, where the dangerous area is a fixed time drive from the vehicle position Within the area. The system for predicting dangerous vehicles as described in item 9 of the scope of the patent application, wherein the cloud device calculates the road segment based on the vehicle ID of the vehicle driving on the road segment, the speed of the vehicle on each road segment, and the street light ID The steps of a disaster level further include: according to the vehicle ID, the speed of the vehicle on each road section, and the street light ID accumulates the disaster factor; and obtains a score of the accumulated disaster factor to obtain the disaster level. The system for predicting dangerous vehicles as described in item 11 of the scope of patent application, wherein the above-mentioned hazard factors include at least red light factor, overspeed factor, variable speed factor, vehicle owner's risk factor, vehicle current crime factor, vehicle history factor, vehicle injury factor and area risk factor.

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