Non-motor vehicle converse running detection method and system

Abstract

The embodiment of the invention provides a non-motor vehicle retrograde driving detection method and system, and the method comprises the steps: responding to a riding vehicle, triggering retrograde driving detection, and obtaining a retrograde driving recognition strategy corresponding to the riding vehicle; wherein the retrograde motion identification strategy comprises at least one of Bluetooth signal identification, radar scanning identification and image identification; based on the converse driving identification strategy, determining whether an identification result of a running motor vehicle exists on the right side of the riding vehicle; and determining a converse running detection result of the riding vehicle based on the identification result.

Description

Technical Field

[0001] The present invention relates to the field of non-motor vehicle reverse driving detection, and in particular to a non-motor vehicle reverse driving detection method and system. Background Art

[0002] Non-motorized vehicles such as electric vehicles and shared bicycles play an important role in urban transportation. More and more users choose electric vehicles or shared bicycles to travel, but non-motorized vehicles traveling in the wrong direction not only increase the risk of accidents, but also pose a potential threat to the safety of other traffic participants.

[0003] Therefore, it is necessary to conduct reverse driving detection on users when using non-motor vehicles for travel. Summary of the invention

[0004] One or more embodiments of this specification provide a method for detecting wrong-way traffic of a non-motor vehicle. The method comprises: in response to a riding vehicle triggering wrong-way traffic detection, obtaining a wrong-way traffic identification strategy corresponding to the riding vehicle; wherein the wrong-way traffic identification strategy comprises at least one of Bluetooth signal identification, radar scanning identification, and image identification; based on the wrong-way traffic identification strategy, determining an identification result of whether there is a motor vehicle traveling to the right of the riding vehicle; based on the identification result, determining a wrong-way traffic detection result of the riding vehicle.

[0005] One or more embodiments of the present specification provide a non-motor vehicle wrong-traffic detection system, the system comprising: a detection module, for responding to a riding vehicle triggering wrong-traffic detection, and acquiring a wrong-traffic identification strategy corresponding to the riding vehicle; wherein the wrong-traffic identification strategy comprises at least one of Bluetooth signal identification, radar scanning identification, and image recognition; an identification module, for determining, based on the wrong-traffic identification strategy, an identification result of whether there is a moving motor vehicle on the right side of the riding vehicle; and a determination module, for determining, based on the identification result, a wrong-traffic detection result of the riding vehicle.

[0006] One or more embodiments of the present specification provide a non-motor vehicle wrong-way detection device, the device comprising: at least one storage medium storing computer instructions; at least one processor executing the computer instructions to implement the above-mentioned non-motor vehicle wrong-way detection method. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] This specification will be further described in the form of exemplary embodiments, which will be described in detail by the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein:

[0008] Figure 1 is a schematic diagram of an exemplary application scenario of a non-motor vehicle reverse driving detection system according to some embodiments of this specification;

[0009] Figure 2 is an exemplary flow chart of a non-motor vehicle reverse driving detection method according to some embodiments of this specification;

[0010] Figure 3 is an exemplary flow chart of determining recognition results according to some embodiments of this specification;

[0011] Figure 4 is an exemplary flow chart for determining a retrograde detection result according to some embodiments of this specification;

[0012] Figure 5 is an exemplary module diagram of a non-motor vehicle reverse driving detection system according to some embodiments of this specification;

[0013] Figure 6 is an exemplary schematic diagram of a riding scene according to some embodiments of this specification;

[0014] Figure 7 It is an exemplary schematic diagram of a method for detecting wrong-way traffic of a non-motor vehicle according to some embodiments of this specification. DETAILED DESCRIPTION

[0015] In order to more clearly illustrate the technical solutions of the embodiments of this specification, the following is a brief introduction to the drawings required for the description of the embodiments. Obviously, the drawings described below are only some examples or embodiments of this specification. For ordinary technicians in this field, this specification can also be applied to other similar scenarios based on these drawings without creative work. Unless it is obvious from the language environment or otherwise explained, the same reference numerals in the figures represent the same structure or operation.

[0016] It should be understood that the "system", "device", "unit" and / or "module" used herein are a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0017] As shown in this specification and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0018] Flowcharts are used in this specification to illustrate the operations performed by the system according to the embodiments of this specification. It should be understood that the preceding or following operations are not necessarily performed precisely in order. Instead, the steps may be processed in reverse order or simultaneously. At the same time, other operations may also be added to these processes, or one or more operations may be removed from these processes.

[0019] Non-motorized vehicles such as electric vehicles and bicycles play an important role in urban traffic. Non-motorized vehicles that drive in the wrong direction not only increase the risk of accidents, but also pose a potential threat to the safety of other traffic participants. This behavior not only endangers the safety of non-motorized vehicle riders themselves, but may also cause injuries or even death to pedestrians and motor vehicle drivers. In addition, non-motorized vehicles that drive in the wrong direction also cause road congestion, affect road traffic efficiency, and bring inconvenience to urban traffic.

[0020] In order to reduce or solve the problem of non-motor vehicles going against traffic, it is necessary to detect non-motor vehicles going against traffic efficiently and accurately to reduce the probability of traffic accidents and improve the safety and efficiency of road traffic. The existing detection methods currently rely more on urban road management systems and do not detect non-motor vehicles going against traffic. The reverse detection used on motor vehicles is based on the judgment of vehicles going in the forward direction of the lane. There are the following problems: the algorithm has a high error rate because there may be more reverse vehicles than forward vehicles, and the algorithm is prone to misjudgment. It is also easily disturbed by surrounding objects, and the algorithm has a high error rate, resulting in misjudgment.

[0021] Therefore, it is necessary to propose a method and system for detecting reverse driving of non-motor vehicles.

[0022] Figure 1 It is a schematic diagram of an exemplary application scenario of a non-motor vehicle wrong-way detection system according to some embodiments of this specification.

[0023] like Figure 1 As shown, the application scenario 100 of the non-motor vehicle wrong-way detection system may include a processing device 110 , a network 120 , a user terminal 130 , a storage device 140 and a riding vehicle 150 .

[0024] In some embodiments, the non-motor vehicle reverse detection system can detect whether the user is riding in the reverse direction by implementing the method and / or process disclosed in this application. The non-motor vehicle reverse detection system can be applied to a variety of application scenarios, such as reverse detection of shared bicycles, reverse detection of shared electric vehicles, etc.

[0025] In a typical application scenario, the processing device 110 obtains a wrong-traffic identification strategy corresponding to the riding vehicle in response to a riding vehicle triggering wrong-traffic detection; wherein the wrong-traffic identification strategy includes at least one of Bluetooth signal identification, radar scanning identification, and image recognition; based on the wrong-traffic identification strategy, determines whether there is a moving motor vehicle on the right side of the riding vehicle; based on the identification result, determines the wrong-traffic detection result of the riding vehicle.

[0026] In some embodiments, the processing device 110 can be used to process information and / or data related to non-motor vehicle reverse detection. In some embodiments, the processing device 110 can be a single server or a server group. The server group can be centralized or distributed (for example, the processing device 110 can be a distributed system). In some embodiments, the processing device 110 can be local or remote. For example, the processing device 110 can access information and / or data stored in the storage device 140 and the user terminal 130 through the network 120. For another example, the processing device 110 can be directly connected to the storage device 140 and the user terminal 130 to access the stored information and / or data. In some embodiments, the processing device 110 can be implemented on a cloud platform. As an example only, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, between clouds, multiple clouds, etc. or any combination of the above examples. In some embodiments, the processing device 110 can be integrated with the terminal 130.

[0027] The network 120 may connect the components of the system and / or connect the system with external resources. The network 120 enables communication between the components and with other components outside the system to facilitate the exchange of data and / or information. For example, the processing device 110 may obtain data (e.g., driving information, etc.) from the user terminal 130 and / or the storage device 140 through the network 120. In some embodiments, the network 120 may be any one or more of a wired network or a wireless network.

[0028] In some embodiments, the user terminal 130 may be a person, tool, or other entity related to riding. The user may be a service requester. For example, the user may scan the QR code on the shared bicycle / electric vehicle through the user terminal 130 to request a riding service. In this application, "user" and "user terminal" may be used interchangeably. In some embodiments, the user terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a vehicle-mounted device 130-3, and a laptop computer 130-4, etc., or any combination thereof. In some embodiments, the user terminal 130 may be a device with positioning technology for determining the location of the user terminal 130. In some embodiments, the user terminal 130 may be a computing device with computing capabilities to determine whether it is going against the flow during riding through the user terminal 130.

[0029] The storage device 140 may store data and / or instructions. In some embodiments, the storage device 140 may store data obtained / acquired by the user terminal 140. In some embodiments, the storage device 140 may store data and / or instructions used by the processing device 110 to execute or use to complete the exemplary methods described in this application. In some embodiments, the storage device 140 may include a large capacity memory, a removable memory, a volatile read-write memory, a read-only memory (ROM), etc. or any combination thereof. In some embodiments, the storage device 140 may be implemented on a cloud platform.

[0030] In some embodiments, the storage device 140 may be connected to the network 120 to communicate with one or more components (e.g., the processing device 110, the user terminal 130) in or outside the application scenario 100 (e.g., the cloud platform, etc.). One or more components in the application scenario 100 may access data or instructions stored in the storage device 140 through the network 120. In some embodiments, the storage device 140 may be directly connected or communicated with one or more components (e.g., the processing device 110, the user terminal 130, etc.) in the application scenario 100. In some embodiments, the storage device 140 may be part of the processing device 110.

[0031] Figure 2 is an exemplary flow chart of a non-motor vehicle reverse driving detection method according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps. In some embodiments, the process 200 can be executed by a processing device or a non-motor vehicle wrong-way detection system.

[0032] Step 202 , in response to a riding vehicle triggering a wrong-way detection, obtaining a wrong-way identification strategy corresponding to the riding vehicle. In some embodiments, step 202 may be performed by the detection module 510 .

[0033] Reverse detection refers to the monitoring and detection of bicycle / electric vehicle users' behavior of driving in the opposite direction on the road. When the user chooses to ride in the opposite direction of the specified vehicle driving direction, certain technical means are used to detect the reverse behavior.

[0034] In some embodiments, the non-motor vehicle reverse detection system (hereinafter referred to as the system) can automatically trigger reverse detection after a user starts riding for a certain period of time. For example, after a user scans a QR code on a shared bicycle to request a ride, the system can determine whether the vehicle is riding through the positioning device on the bicycle, and automatically trigger reverse detection when the vehicle is riding.

[0035] In some embodiments, the system may also periodically trigger retrograde detection, for example, triggering retrograde detection once every 1 minute, 2 minutes, etc.

[0036] The reverse traffic identification strategy is a method / means for detecting and distinguishing whether the user's riding vehicle is driving in the opposite direction on the road. In some embodiments, the reverse traffic identification strategy includes at least one of Bluetooth signal identification, radar scanning identification, and image recognition.

[0037] Bluetooth signal recognition refers to the method of using Bluetooth technology to detect and identify whether a riding vehicle is driving against the flow of traffic on the road. Bluetooth signal recognition detects the Bluetooth device signals around it, such as the Bluetooth signals emitted by other shared bicycles and motor vehicles, to analyze their location and direction, and determine whether they are driving against the flow of traffic.

[0038] Radar scanning recognition refers to the use of radar technology to detect and identify whether a cyclist is driving against the flow of traffic on the road. Radar scanning recognition transmits radio waves and receives their reflected signals to obtain information such as the distance, direction and speed of the target object, and analyzes this data to determine whether there is any wrong-way behavior.

[0039] Image recognition refers to the use of computer vision technology to analyze and determine the wrong-way behavior of a cyclist by identifying images. Image recognition extracts features from video or image data and classifies these features using machine learning algorithms to determine whether there is wrong-way behavior.

[0040] In some embodiments, for different riding vehicles, it is necessary to select a suitable reverse traffic identification strategy according to the configuration of the vehicle. For example, for some bicycles, it is equipped with a Bluetooth device and can receive Bluetooth signals, but it may not have the conditions for radar scanning and image recognition (for example, it is not equipped with a radar device and an imaging device), then the reverse traffic identification strategy corresponding to the vehicle is Bluetooth signal recognition. For another example, for some bicycles, it is equipped with a Bluetooth device, a radar device and an imaging device at the same time, then the reverse traffic identification strategy corresponding to the vehicle includes Bluetooth signal recognition, radar scanning recognition and image recognition.

[0041] It should be noted that the above embodiments are for illustrative purposes only, and in some other embodiments, other reverse traffic identification methods may also be used. For example, by identifying the geographic location, combined with the global positioning system (GPS) or other positioning technologies, the real-time location data of the riding vehicle is obtained, and the movement trajectory and direction of the riding vehicle are analyzed to determine whether there is a reverse traffic situation.

[0042] In some embodiments, the processing device can obtain the configuration information of the riding vehicle from the system or database, for example, whether a Bluetooth device, a radar device, and an imaging device are provided, and determine the reverse traffic recognition strategy corresponding to the provided module. For example, when a Bluetooth device is provided, Bluetooth signal recognition can be determined, when a radar device is provided, radar scanning recognition can be determined, and when an imaging device is provided, image recognition can be determined. In some embodiments, when multiple modules are provided at the same time, the recognition method corresponding to one or more modules can be selected, and this embodiment does not limit this.

[0043] In some embodiments, a wrong-traffic recognition strategy may be preset and stored for each non-motor vehicle, and the processing device may obtain the wrong-traffic recognition strategy by reading the stored wrong-traffic recognition strategy.

[0044] Step 204 , based on the wrong-way identification strategy, determine whether there is a moving motor vehicle to the right of the riding vehicle. In some embodiments, step 204 may be performed by the identification module 520 .

[0045] The recognition result refers to the judgment result corresponding to the reverse traffic recognition strategy. For example, the recognition results include the presence of a moving motor vehicle, the absence of a moving motor vehicle, and the absence of the recognition strategy. In some embodiments, the presence of a moving motor vehicle can be represented by a value of 1, the absence of a moving motor vehicle can be represented by a value of 2, and the absence of the recognition strategy can be represented by a value of 0. Among them, the absence of the recognition strategy may be that the riding vehicle does not have the corresponding equipment. For example, the riding vehicle is only equipped with a Bluetooth device, but not equipped with a radar device and an imaging device. The recognition results of the radar scanning recognition corresponding to the radar device and the image recognition corresponding to the imaging device are 0, and the result of the Bluetooth signal recognition can be 1 or 2.

[0046] In some embodiments, Figure 6 As shown, Figure 6 It is an exemplary schematic diagram of a riding scene according to some embodiments of this specification. Figure 6The hollow arrow in the figure indicates the normal driving direction of the road. Whether the riding vehicle is going against the flow can be determined by judging whether there is a moving motor vehicle on the right side of the riding vehicle. For example, 610 indicates a riding vehicle, and the figure shows the situation on both sides of the riding vehicle when riding against the flow. The left side is the green plants on the edge of the road, 620 indicates the against-flow identification strategy, and the right area 640 is the motor vehicle lane. If there is a moving motor vehicle 630 in the right motor vehicle lane, it means that the riding vehicle is going against the flow. It can be assumed that the riding direction of the riding vehicle is normal, and the motor vehicle in the motor vehicle lane should be on the left side of the riding vehicle.

[0047] It is understandable that since parking spaces may be provided at the edge of some roads, that is, when a cycling vehicle is driving normally, there may be motor vehicles on its right side, but the motor vehicles in the parking spaces are stationary. Therefore, by judging whether the motor vehicle on the right side of the cycling vehicle is in motion, it is also possible to accurately judge whether the cycling vehicle is driving in the wrong direction.

[0048] In some embodiments, for different wrong-way identification strategies, the processing device may determine whether there is a moving motor vehicle through corresponding identification methods.

[0049] For Bluetooth signal recognition, multiple Bluetooth scans can be performed to collect Bluetooth signals of motor vehicles within the scanning range. The scanning results are checked based on whether the increase and decrease amplitudes of the multiple Bluetooth signal strengths match the speed of the riding vehicle itself. If they match, it can be considered that the motor vehicle is stationary. Otherwise, it is considered that the motor vehicle corresponding to the Bluetooth signal is moving. For example, assuming that the distance between a motor vehicle and a cycling vehicle is 10 meters, the Bluetooth signal of the motor vehicle is collected through multiple scans. The increase or decrease in the strength of the Bluetooth signal and the speed of the cycling vehicle can be used to determine whether the cycling vehicle is approaching or moving away from the motor vehicle. If the cycling vehicle is traveling in the opposite direction, the motor vehicle may have two states, one is stationary and the other is driving. When the motor vehicle is stationary, it means that only the position of the cycling vehicle is changing. Then the increase or decrease in the Bluetooth signal of the motor vehicle should match the driving speed of the cycling vehicle. For example, the change in the Bluetooth signal should be equal to the driving distance. On the contrary, when the motor vehicle is in driving state, since the motor vehicle and the cycling vehicle are traveling in opposite directions, the relative speed between the two will be greater than the driving speed of the cycling vehicle, and the change in the Bluetooth signal will exceed the speed of the cycling vehicle, that is, it does not match the driving speed of the cycling vehicle. At this time, it can be considered that the cycling vehicle is traveling in the opposite direction.

[0050] For radar scanning, the vehicle-mounted radar installed on the riding vehicle can be used for scanning. Vehicle-mounted radar is a perception technology commonly used on vehicles, which can help vehicles detect and track surrounding vehicles. Through the vehicle-mounted radar, it is possible to identify whether there is a moving motor vehicle on the right side in the following ways.

[0051] 1. Emitting radar waves: Vehicle-mounted radar transmits radio waves, such as microwaves or millimeter waves, which propagate into the surrounding environment.

[0052] 2. Reflection of waves: When these waves encounter surrounding objects, such as other vehicles, they are reflected back to the radar system.

[0053] 3. Receiving and processing: The radar system receives the reflected signals and analyzes their time delay, frequency change, and amplitude change. This information can provide data about surrounding objects, such as the distance between the surrounding objects and the riding vehicle, its moving speed, etc.

[0054] 4. Distance and speed calculation: By analyzing the time delay of the reflected signal, the radar system can calculate the distance of the object. At the same time, by analyzing the frequency change of the reflected radar signal, the speed of the object can be measured.

[0055] 5. After measuring the speed, the relative speed between the object and the riding vehicle can be analyzed. By comparing the speed with the speed of the riding vehicle, it can be determined whether there are any moving motor vehicles around the riding vehicle.

[0056] For image recognition, the surrounding environment can be photographed through an imaging device installed on the riding vehicle, such as a camera, and then the displacement and direction of surrounding vehicles can be calculated in multiple frames of images through image recognition, target tracking and other technologies to determine whether there is a moving motor vehicle on the right.

[0057] Step 206 , based on the recognition result, determine the wrong-way detection result of the riding vehicle. In some embodiments, step 206 may be performed by the determination module 530 .

[0058] The reverse detection result refers to the result of determining whether the riding vehicle is reverse driving. For example, the reverse detection result includes whether the riding vehicle is reverse driving or not (or driving normally).

[0059] In some embodiments, the processing device can directly determine the wrong-way detection result of the riding vehicle based on the recognition result. For example, if the recognition result is that there is a motor vehicle on the right side of the riding vehicle, the wrong-way detection result is determined to be wrong-way; if the recognition result is that there is no motor vehicle on the right side of the riding vehicle, the wrong-way detection result is determined to be not wrong-way.

[0060] In some embodiments, the processing device can further comprehensively determine the wrong-way detection result based on the recognition result and the surrounding environment information of the riding vehicle. For more detailed description, please refer to Figure 4 The description is not repeated here.

[0061] In some embodiments, when the wrong-way detection result reflects that the riding vehicle is going in the wrong direction, a wrong-way handling strategy is executed.

[0062] The reverse traffic handling strategy refers to the way or method of handling when the riding vehicle is in the reverse traffic. In some embodiments, the reverse traffic handling strategy includes reverse traffic prompts and / or prohibition of riding. For example, when the user is detected riding in the reverse traffic for the first time, the APP can promptly remind the user to pay attention to riding safety and not to ride in the reverse traffic. When the same user is detected riding in the reverse traffic for multiple times or the reverse traffic time is too long (for example, exceeding the preset time limit, such as 5 minutes, 10 minutes, etc.), the user can be prompted to lock the riding vehicle and prohibit riding.

[0063] In some embodiments of this specification, based on the principle that vehicles should drive on the right side of the road traffic rules, and the characteristics of non-motor vehicles driving on both sides of the road, by judging whether there is a motor vehicle driving on the right side of the vehicle, it is determined whether the riding vehicle is driving in the opposite direction, which can greatly improve the accuracy and detection cost of reverse detection. By combining sensor technology to collect data and analyze it, it is possible to quickly identify reverse non-motor vehicles and take appropriate measures to reduce potential dangers and the occurrence of reverse behavior, thereby improving the overall quality of road traffic and improving the safety and smoothness of urban road traffic.

[0064] Figure 3 is an exemplary flow chart of determining recognition results according to some embodiments of this specification. Figure 3 As shown, the process 300 includes the following steps. In some embodiments, the process 300 can be executed by a processing device or a non-motor vehicle wrong-way detection system.

[0065] Step 302: Acquire motor vehicle identification data corresponding to the wrong-way identification strategy.

[0066] Vehicle identification data refers to data / signals collected by a device installed on a riding vehicle for identifying a vehicle. For example, vehicle identification data includes Bluetooth signals, radar signals, images, and videos.

[0067] In some embodiments, the motor vehicle identification data is collected from the right side area of ​​the riding vehicle. Based on the principle that vehicles should drive on the right side of road traffic rules, and the characteristics of non-motor vehicles driving on both sides of the road, it is possible to determine whether the riding vehicle is driving in the wrong direction by judging whether there is a driving motor vehicle on the right side of the vehicle. Therefore, when collecting data, collecting from the right side area of ​​the riding vehicle can reduce the amount of data collected and improve the recognition efficiency of data collection and wrong direction detection. At the same time, collecting data only from the right side area can reduce the collection of interference information, thereby improving the accuracy of wrong direction detection.

[0068] In some embodiments, for the collection of Bluetooth signals in the right area, a directional antenna may be configured on the Bluetooth device to accurately lock onto the Bluetooth signals in the right area.

[0069] In some embodiments, for the collection of radar signals in the right area, a radar device configured on the vehicle may be configured to transmit radar signals to the right area and receive returned radar signals.

[0070] In some embodiments, the image of the right area may be captured by setting the direction of the imaging device configured on the vehicle to capture the right area.

[0071] Step 304: Determine the recognition result based on the motor vehicle recognition data.

[0072] In some embodiments, the processing device may process the collected motor vehicle identification data in a corresponding identification manner based on the data type, for example, Bluetooth signal, radar signal or image, and then determine the identification result. Figure 2 The relevant description will not be repeated here.

[0073] In some embodiments, in order to obtain more accurate wrong-traffic detection results, the wrong-traffic detection results of the riding vehicle can also be determined based on the recognition results in combination with the AI ​​algorithm.

[0074] For example, the processing device can determine the wrong-way detection result of the riding vehicle based on the recognition result using a preset wrong-way detection model.

[0075] The wrong-way detection model may be a pre-trained machine learning model. The processing device can use the recognition result as input data of the wrong-way detection model, input the recognition result into the wrong-way detection model for processing, and the wrong-way detection model outputs the wrong-way detection result of the riding vehicle.

[0076] Figure 4 FIG. 1 is an exemplary flow chart of determining a retrograde detection result according to some embodiments of this specification. Figure 4As shown, the process 400 includes the following steps. In some embodiments, the process 400 can be executed by a processing device or a non-motor vehicle wrong-way detection system.

[0077] Step 402, obtaining driving information related to the riding vehicle.

[0078] Driving information refers to various types of information related to the current riding vehicle. In some embodiments, the driving information may include the speed, acceleration, and surrounding environment information of the riding vehicle. For example, weather (rainy, sunny, snowy), time, etc.

[0079] In some embodiments, the speed and acceleration of the riding vehicle can be obtained through a speed sensor installed on the vehicle. The weather, time, etc. can be sent to the riding vehicle through the system, or obtained from an external device through a processing device or from other devices by calling an interface. This embodiment does not limit this.

[0080] Step 404: Use the preset wrong-traffic detection model to process the recognition result and the driving information to determine the wrong-traffic detection result of the riding vehicle.

[0081] In some embodiments, the processing device may input the driving information and the recognition result into a preset wrong-traffic detection model, and the wrong-traffic detection model may output the wrong-traffic detection result of the riding vehicle.

[0082] In some embodiments, the preset retrograde detection model may be a neural network model, a classification model, or other combination models, such as an XGBoost model, etc., which is not limited in this embodiment.

[0083] In some embodiments, the preset wrong-traffic detection model can be obtained by training based on training samples. The training samples include historical recognition results and historical driving information, and the label is whether the riding vehicle is wrong-traffic. The preset wrong-traffic detection model can be obtained by training the initial wrong-traffic detection model through a model training method such as a gradient descent method.

[0084] In this embodiment, by using the recognition result and the driving information as the input of the wrong-traffic detection model and combining it with an intelligent algorithm to judge whether the riding vehicle is going in the wrong direction, the accuracy of the wrong-traffic detection can be further improved.

[0085] Figure 5 is an exemplary module diagram of a non-motor vehicle reverse driving detection system according to some embodiments of this specification. Figure 5 As described above, the system 500 includes a detection module 510 , an identification module 520 , and a determination module 530 .

[0086] The detection module 510 is used to obtain a wrong-traffic identification strategy corresponding to the riding vehicle in response to the wrong-traffic detection being triggered by the riding vehicle; wherein the wrong-traffic identification strategy includes at least one of Bluetooth signal recognition, radar scanning recognition and image recognition.

[0087] The identification module 520 is used to determine the identification result of whether there is a motor vehicle on the right side of the riding vehicle based on the wrong-way identification strategy. In some embodiments, the identification module 520 is further used to: obtain motor vehicle identification data corresponding to the wrong-way identification strategy; wherein the motor vehicle identification data is collected from the right side area of ​​the riding vehicle; and determine the identification result based on the motor vehicle identification data.

[0088] The determination module 530 is used to determine the wrong-way detection result of the riding vehicle based on the recognition result. In some embodiments, the determination module 530 is further used to: determine the wrong-way detection result of the riding vehicle based on the recognition result using a preset wrong-way detection model. In some embodiments, the determination module 530 is further used to: obtain driving information related to the riding vehicle; use the preset wrong-way detection model to process the recognition result and the driving information to determine the wrong-way detection result of the riding vehicle.

[0089] Figure 7 It is an exemplary schematic diagram of a method for detecting wrong-way traffic of a non-motor vehicle according to some embodiments of this specification.

[0090] like Figure 7 As shown, the user triggers the reverse traffic detection during riding.

[0091] The wrong-traffic detection system obtains and executes the wrong-traffic recognition strategy, for example, by identifying the location information of the riding vehicle through Bluetooth signal recognition, radar scanning recognition and image recognition. For example, it identifies whether there is a moving vehicle on the right side of the riding vehicle.

[0092] After the position information of the motor vehicle is identified, the wrong-way detection is performed. For example, the position information of the motor vehicle is input into a wrong-way detection model, and the wrong-way detection model determines whether the riding vehicle is wrong-way or not.

[0093] When it is determined to be not against the flow, the user can continue riding normally. When it is determined to be against the flow, a against the flow warning is triggered, prompting the user not to go against the flow or prohibiting the user from riding.

[0094] about Figure 7 For a more detailed description, see Figures 2 to 4 The description is not repeated here.

[0095] It should be understood that Figure 5The system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in a processor control code, such as a carrier medium such as a disk, CD or DVD-ROM, a programmable memory such as a read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. Such code is provided on the system and its modules of this specification. Not only can the hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. be implemented, it can also be implemented with software such as executed by various types of processors, and it can also be implemented by a combination of the above hardware circuits and software (for example, firmware).

[0096] It should be noted that the above description of the non-motor vehicle reverse traffic detection system and its modules is only for the convenience of description and cannot limit this specification to the scope of the embodiments. It is understandable that for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem to connect with other modules without deviating from this principle. In some embodiments, Figure 5 The detection module 510, identification module 520 and determination module 530 disclosed in the specification can be different modules in a system, or a module can realize the functions of two or more modules. For example, each module can share a storage module, or each module can have its own storage module. Such variations are within the protection scope of this specification.

[0097] The basic concepts have been described above. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of this specification. Although not explicitly stated here, those skilled in the art may make various modifications, improvements and corrections to this specification. Such modifications, improvements and corrections are suggested in this specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of this specification.

[0098] At the same time, this specification uses specific words to describe the embodiments of this specification. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of this specification. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or more in different positions in this specification does not necessarily refer to the same embodiment. In addition, certain features, structures or characteristics in one or more embodiments of this specification can be appropriately combined.

[0099] In addition, unless explicitly stated in the claims, the order of the processing elements and sequences described in this specification, the use of alphanumeric characters, or the use of other names are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some invention embodiments that are currently considered useful through various examples, it should be understood that such details are only for illustrative purposes, and the attached claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the essence and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0100] Similarly, it should be noted that in order to simplify the description disclosed in this specification and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this specification, multiple features are sometimes combined into one embodiment, figure or description thereof. However, this disclosure method does not mean that the features required by the subject matter of this specification are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0101] In some embodiments, numbers describing the number of components and attributes are used. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise specified, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximate values, which may change according to the required features of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the setting of such numerical values ​​is as accurate as possible within the feasible range.

[0102] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, documents, etc., cited in this specification are hereby incorporated by reference in their entirety. Except for application history documents that are inconsistent with or conflicting with the contents of this specification, documents that limit the broadest scope of the claims of this specification (currently or later attached to this specification) are also excluded. It should be noted that if the descriptions, definitions, and / or use of terms in the materials attached to this specification are inconsistent or conflicting with the contents described in this specification, the descriptions, definitions, and / or use of terms in this specification shall prevail.

[0103] Finally, it should be understood that the embodiments described in this specification are only used to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, as an example and not a limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly introduced and described in this specification.

Claims

1. A method for detecting a non-motor vehicle driving in the wrong direction, the method comprising: In response to a riding vehicle triggering a wrong-traffic detection, acquiring a wrong-traffic identification strategy corresponding to the riding vehicle; wherein the wrong-traffic identification strategy includes at least one of Bluetooth signal identification, radar scanning identification, and image recognition; Based on the wrong-way identification strategy, determining whether there is a moving motor vehicle on the right side of the riding vehicle; Based on the recognition result, a wrong-way detection result of the riding vehicle is determined.

2. The method according to claim 1, wherein the step of determining whether there is a moving motor vehicle on the right side of the riding vehicle based on the wrong-way recognition strategy comprises: Acquire motor vehicle identification data corresponding to the wrong-way identification strategy; wherein the motor vehicle identification data is collected from the right side area of ​​the riding vehicle; Based on the motor vehicle identification data, the identification result is determined.

3. The method according to claim 1, wherein determining the wrong-way detection result of the riding vehicle based on the recognition result comprises: Based on the recognition result, a preset wrong-travel detection model is used to determine the wrong-travel detection result of the riding vehicle.

4. The method according to claim 3, wherein based on the recognition result, determining the wrong-way detection result of the riding vehicle using a preset wrong-way detection model comprises: Acquiring driving information related to the riding vehicle; The preset wrong-traffic detection model is used to process the recognition result and the driving information to determine the wrong-traffic detection result of the riding vehicle.

5. The method according to claim 1, further comprising: When the wrong-way detection result reflects that the riding vehicle is going in the wrong direction, a wrong-way processing strategy is executed.

6. A non-motor vehicle reverse driving detection system, the system comprising: A detection module, configured to obtain a wrong-traffic recognition strategy corresponding to the riding vehicle in response to a wrong-traffic detection being triggered by the riding vehicle; wherein the wrong-traffic recognition strategy includes at least one of Bluetooth signal recognition, radar scanning recognition, and image recognition; an identification module, configured to determine, based on the wrong-way identification strategy, whether there is a moving motor vehicle on the right side of the riding vehicle; A determination module is used to determine a wrong-way detection result of the riding vehicle based on the recognition result.

7. The system according to claim 6, wherein the identification module is further configured to include: Acquire motor vehicle identification data corresponding to the wrong-way identification strategy; wherein the motor vehicle identification data is collected from the right side area of ​​the riding vehicle; Based on the motor vehicle identification data, the identification result is determined.

8. The system according to claim 6, wherein the determining module is further configured to: Based on the recognition result, a preset wrong-travel detection model is used to determine the wrong-travel detection result of the riding vehicle.

9. The system according to claim 8, wherein the determining module is further configured to: Acquiring driving information related to the riding vehicle; The preset wrong-traffic detection model is used to process the recognition result and the driving information to determine the wrong-traffic detection result of the riding vehicle.

10. A non-motor vehicle reverse driving detection device, the device comprising: at least one storage medium storing computer instructions; At least one processor executes the computer instructions to implement the method according to any one of claims 1 to 5.

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