Vehicle rescue method, device and system, vehicle, medium and product
By obtaining driving information on the vehicle and performing edge calculations, automatically judging and sending rescue information, the problem of rescue delay when the vehicle faces safety risks is solved, and the timeliness of vehicle rescue is achieved.
Patent Information
- Application Number
- CN202411759245.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-29
- Publication Date
- 2025-04-04
AI Technical Summary
When vehicles face safety risks, the prior art will inevitably avoid rescue delays, resulting in users not being able to obtain rescue in time.
By acquiring the vehicle's driving information, performing edge calculations to determine whether the vehicle has a safety risk, and when there is a safety risk, rescue information is automatically sent through the communication unit on the vehicle, including sending rescue information when the wireless network signal is lost through the satellite communication unit.
It realizes the rapid judgment of safety risks locally and sends rescue information in a timely manner, avoids rescue delays and ensures the timeliness of vehicle rescue.
Smart Images
Figure CN119767284A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of vehicle technology, and in particular, to a vehicle rescue method, device, system, vehicle, medium and product. Background Art
[0002] With the development of vehicle technology and the continuous advancement of intelligent transportation systems, vehicles have brought many conveniences to people's daily travel. Vehicle safety is an issue that users are very concerned about. Especially when the vehicle faces safety risks, whether it can get timely and effective rescue is crucial for users. Summary of the invention
[0003] The purpose of the present disclosure is to provide a vehicle rescue method, device, system, vehicle, medium and product, which can avoid rescue delays and ensure the timeliness of vehicle rescue.
[0004] In order to achieve the above objectives, in a first aspect, the present disclosure provides a vehicle rescue method, the method comprising: Obtain vehicle driving information; Perform edge computing based on the driving information; In response to the edge computing result indicating that the vehicle is at risk of safety, rescue information is sent through the communication unit.
[0005] Optionally, before obtaining the driving information of the vehicle, the method further includes: Receiving first information, wherein the first information is used to indicate that the duration for which the vehicle cannot normally connect to the wireless network signal reaches a first preset duration; The step of sending rescue information through a communication unit in response to the edge computing result indicating that the vehicle has a safety risk includes: In response to the edge computing result indicating that the vehicle is at risk of safety, the rescue information is sent via a satellite communication unit.
[0006] Optionally, in response to the edge computing result indicating that the vehicle has a safety risk, sending the rescue information through a satellite communication unit includes: In response to the edge computing result indicating that the vehicle has a safety risk, outputting a prompt message through a prompt unit, wherein the prompt message is used for a user to confirm whether to send the rescue information through the satellite communication unit; If no confirmation instruction input by the user is received within a second preset time period from the prompt unit outputting the prompt information, or if a confirmation instruction input by the user indicating agreement to send the rescue information via the satellite communication unit is received within the second preset time period, the rescue information is sent via the satellite communication unit.
[0007] Optionally, performing edge computing according to the driving information includes: Determining, based on the driving information, whether the vehicle has a risk of vehicle failure and / or a risk of a traffic accident; When it is determined that the vehicle is at risk of vehicle failure and / or traffic accident, the edge computing result is obtained to characterize that the vehicle is at risk of safety.
[0008] Optionally, the driving information includes at least one of the following: driving state parameters of the vehicle and image information of the vehicle's surroundings; Whether the vehicle has a vehicle failure risk is determined by: Extracting features from the driving information to obtain first driving feature information; Inputting the first driving characteristic information into a fault prediction model to obtain a vehicle fault prediction result output by the fault prediction model; When the vehicle failure prediction result is greater than a first preset threshold, it is determined that the vehicle has a vehicle failure risk.
[0009] Optionally, the driving information includes at least one of the following: driving state parameters of the vehicle, vehicle perception information, and image information of the vehicle's surroundings; The method further comprises: Acquiring driving behavior information of the driver of the vehicle; Whether the vehicle has a risk of a traffic accident is determined in the following manner: Extracting features from the driving information and the driving behavior information respectively to obtain second driving feature information and driving behavior feature information; Inputting the second driving characteristic information and the driving behavior characteristic information into an accident prediction model to obtain a traffic accident prediction result output by the accident prediction model; When the traffic accident prediction result is greater than a second preset threshold, it is determined that the vehicle has a traffic accident risk.
[0010] Optionally, the method further comprises: In response to the edge computing result characterizing that the vehicle has a safety risk, the driving information and safety risk type information are sent through the satellite communication unit, and the safety risk type information is used to indicate that the vehicle has a vehicle failure risk and / or a traffic accident risk.
[0011] Optionally, after the step of performing edge computing according to the driving information, the method further includes: In response to receiving the second information, the edge computing process is terminated, wherein the second information is used to indicate that the duration for which the vehicle can normally connect to the wireless network signal reaches a third preset duration.
[0012] Optionally, before obtaining the driving information of the vehicle, the method further includes: receiving third information, wherein the third information is used to indicate that the duration for which the vehicle can normally connect to the wireless network signal reaches a fourth preset duration; The step of sending rescue information through a communication unit in response to the edge computing result indicating that the vehicle has a safety risk includes: In response to the edge computing result indicating that the vehicle is at safety risk, the rescue information is sent via a satellite communication unit and / or a wireless network communication unit.
[0013] In a second aspect, the present disclosure provides a vehicle rescue device, the device comprising: A processor is connected to the memory, and the processor is used to execute the computer program in the memory to implement the steps of the vehicle rescue method provided in the first aspect of the present disclosure.
[0014] In a third aspect, the present disclosure provides a vehicle rescue system, the system comprising a satellite communication unit and the vehicle rescue device provided in the second aspect of the present disclosure.
[0015] In a fourth aspect, the present disclosure provides a vehicle, comprising the vehicle rescue system provided in the third aspect of the present disclosure.
[0016] In a fifth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the vehicle rescue method provided in the first aspect of the present disclosure.
[0017] In a sixth aspect, the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the vehicle rescue method provided in the first aspect of the present disclosure.
[0018] Through the above technical solution, the driving information of the vehicle is obtained, and edge computing is performed based on the driving information. Through edge computing, it is possible to determine whether the vehicle has a safety risk based on the driving information locally in the vehicle, thereby ensuring the real-time nature of information processing and improving the efficiency of information processing. In response to the edge computing result indicating that the vehicle has a safety risk, it can be indicated that the vehicle needs rescue, and rescue information can be automatically sent through the communication unit on the vehicle to ensure the timeliness of the rescue information transmission, thereby avoiding rescue delays.
[0019] Other features and advantages of the present disclosure will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS
[0020] The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present disclosure but do not constitute a limitation of the present disclosure. In the accompanying drawings: Figure 1 The figure is a flow chart of a vehicle rescue method according to an exemplary embodiment.
[0021] Figure 2 The figure is a flow chart of a vehicle rescue method according to an exemplary embodiment.
[0022] Figure 3 The figure is a flow chart of a vehicle rescue method according to an exemplary embodiment.
[0023] Figure 4 The present invention is a flowchart showing a method for determining whether a vehicle has a vehicle failure risk according to an exemplary embodiment.
[0024] Figure 5 The present invention is a flowchart showing a method for determining whether a vehicle has a risk of a traffic accident according to an exemplary embodiment.
[0025] Figure 6 The figure is a flow chart of a vehicle rescue method according to an exemplary embodiment.
[0026] Figure 7 The figure is a block diagram of a vehicle rescue device according to an exemplary embodiment.
[0027] Figure 8 The invention is a block diagram of a vehicle rescue system according to an exemplary embodiment.
[0028] Fig. 9 The invention is a block diagram of a vehicle rescue system according to an exemplary embodiment. DETAILED DESCRIPTION
[0029] The specific implementation of the present disclosure is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described herein is only used to illustrate and explain the present disclosure, and is not used to limit the present disclosure.
[0030] It should be noted that all actions of acquiring signals, information or data in the present disclosure are carried out in compliance with the relevant data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.
[0031] Figure 1 is a flow chart of a vehicle rescue method according to an exemplary embodiment. The method may be applied to a vehicle rescue device. The vehicle rescue device may include a processor such as Figure 1As shown, the method may include steps 11 to 13.
[0032] In step 11, the driving information of the vehicle is obtained.
[0033] In step 12, edge computing is performed based on the driving information.
[0034] In step 13, in response to the edge computing result indicating that the vehicle is at risk of safety, rescue information is sent through the communication unit.
[0035] Among them, the vehicle's driving information may include, for example, the vehicle's driving status parameters, which include, for example, the vehicle's wheel speed, tire pressure, brake pedal opening and other information. The driving information may also include image information of the vehicle's surroundings, which may include images and videos. The image information may be collected, for example, by a camera or a driving recorder installed on the vehicle.
[0036] By way of example, the vehicle rescue device may be, for example, a vehicle-mounted edge computer or a vehicle-mounted edge computing platform. Edge computing refers to computing performed at or near the physical location of a user or data source. In the present disclosure, edge computing may be performed locally on the vehicle based on driving information to determine whether the vehicle poses a safety risk. Compared to the method of transmitting driving information to other devices to determine vehicle safety, additional communication overhead may be avoided. Furthermore, since there is no need to transmit driving information externally, but edge computing is performed based on driving information, communication delays may be avoided, thereby enabling a faster determination of whether the vehicle poses a safety risk. In response to the edge computing result indicating that the vehicle poses a safety risk, rescue information may be automatically sent through the communication unit on the vehicle, so that the rescue information may be sent out in a timely manner without the need for people in the vehicle to dial a rescue call.
[0037] Through the above technical solution, the driving information of the vehicle is obtained, and edge computing is performed based on the driving information. Through edge computing, it is possible to determine whether the vehicle has a safety risk based on the driving information locally in the vehicle, thereby ensuring the real-time nature of information processing and improving the efficiency of information processing. In response to the edge computing result indicating that the vehicle has a safety risk, it can be indicated that the vehicle needs rescue, and rescue information can be automatically sent through the communication unit on the vehicle to ensure the timeliness of the rescue information transmission, thereby avoiding rescue delays.
[0038] First, an implementation method of the present disclosure is introduced. At present, when a vehicle faces a safety risk, the user usually calls or sends a message to request rescue. However, if the vehicle travels to a remote area, such as deep mountains, forests, deserts and other areas where the network signal is not covered, or the area where the vehicle is currently located encounters a geological disaster and the network communication link is interrupted, in these cases, the user cannot get in touch with the outside world in time, and thus cannot get timely rescue, resulting in a delay in rescue. Based on this, in this implementation method, when the vehicle cannot connect to the wireless network signal, rescue information can be sent through the satellite communication unit on the vehicle.
[0039] Figure 2 is a flow chart of a vehicle rescue method according to an exemplary embodiment. Figure 2 As shown, before obtaining the driving information of the vehicle, step 14 may be further included, and step 13 may include step 131.
[0040] In step 14, first information is received.
[0041] In step 11, the driving information of the vehicle is obtained.
[0042] In step 12, edge computing is performed based on the driving information.
[0043] The first information is used to indicate that the duration for which the vehicle cannot normally connect to a wireless network signal reaches a first preset duration. The wireless network signal is, for example, a 4G or 5G network signal. For example, a signal monitoring unit may be provided in the vehicle to periodically monitor the signal connection status of the vehicle, and the signal connection status is used to indicate whether the vehicle can normally connect to the wireless network signal.
[0044] If the vehicle can connect to the wireless network signal normally, the vehicle can communicate with the base station device on the ground through the network, so that the base station device forwards the information to be sent. For example, when the vehicle is traveling in a tunnel, the vehicle may not be able to connect to the wireless network signal for a short period of time. In order to avoid misjudgment, in this disclosure, the duration of the vehicle's inability to connect to the wireless network signal is timed. When the timing reaches a first preset duration, it can be characterized that the vehicle may be in an area not covered by the wireless network signal, or an area where the wireless network signal is interrupted, and it can be determined that the vehicle is currently unable to send information out through the network.
[0045] In one embodiment, a timing module may be provided in the signal monitoring unit, and the timing module may start timing from the moment when the vehicle cannot normally connect to the wireless network signal. When the timing duration reaches a first preset duration, the signal monitoring unit may send a first message to the vehicle rescue device. In another embodiment, the signal monitoring unit may be connected to a signal processing unit provided in the vehicle, and the monitored signal connection status may be sent to the signal processing unit in real time. The signal processing unit may be provided with a timing module, and the timing module may start timing from the moment when the received signal connection status indicates that the vehicle cannot normally connect to the wireless network signal. When the timing duration reaches a first preset duration, the signal processing unit may send a first message to the vehicle rescue device.
[0046] In response to receiving the first information, the vehicle rescue device can start an edge computing process, obtain the vehicle's driving information, and perform edge computing based on the driving information to determine whether the vehicle poses a safety risk.
[0047] In step 131 , in response to the edge computing result indicating that the vehicle is at risk of safety, rescue information is sent via a satellite communication unit.
[0048] For example, if the edge computing result indicates that the vehicle has a safety risk, it may mean that the vehicle needs rescue. In the present disclosure, rescue information may be sent through the satellite communication unit on the vehicle. The rescue information may include, for example, information such as "please come to rescue in time". The rescue information may also include, for example, the current location information of the vehicle. The current location information of the vehicle may be located by the GPS navigation module on the vehicle. The GPS navigation module may locate the vehicle through navigation satellites. For example, the vehicle rescue device may trigger the satellite communication unit to send rescue information, or the vehicle rescue device may also send information that the vehicle has a safety risk to the vehicle controller, and the controller may trigger the satellite communication unit to send rescue information. The present disclosure does not limit this.
[0049] Among them, the satellite communication unit can autonomously search for satellites that can establish communication connections and send rescue information to satellites that can establish communication connections. After the satellite receives the rescue information in orbit, it can forward the rescue information to the vehicle rescue platform. In this way, rescue personnel can promptly learn that the vehicle needs rescue and take rescue measures in a timely manner.
[0050] Through the above technical solution, in response to receiving the first information, the driving information of the vehicle is obtained, and edge computing is performed according to the driving information, wherein the first information is used to indicate that the duration of the vehicle being unable to connect to the wireless network signal reaches a first preset duration. In the case where the duration of the vehicle being unable to connect to the wireless network signal reaches the first preset duration, it can be indicated that the vehicle is continuously unable to send information outward through the network. In the present disclosure, edge computing is performed according to the driving information of the vehicle. Through edge computing, it is possible to determine whether the vehicle has a safety risk based on the driving information locally in the vehicle, thereby ensuring the real-time nature of information processing and improving the efficiency of information processing. Edge computing also reduces the dependence on stable network connection. Even if the vehicle cannot connect to the wireless network signal, it is possible to determine whether the vehicle has a safety risk locally in the vehicle. In response to the edge computing result indicating that the vehicle has a safety risk, rescue information is sent through a satellite communication unit. In this way, when the vehicle cannot connect to the wireless network signal, long-distance communication and the sending of rescue information are realized through satellites, and there is no need to wait until the wireless network signal is connected to send rescue information, thereby avoiding rescue delays and ensuring the timeliness of rescue.
[0051] Figure 3 is a flow chart of a vehicle rescue method according to an exemplary embodiment. Figure 3 As shown, step 12 may include step 121 and step 122 , and step 131 may include step 1311 and step 1312 .
[0052] In step 121 , it is determined whether the vehicle has a risk of vehicle failure and / or a risk of a traffic accident based on the driving information.
[0053] In step 122, when it is determined that the vehicle is at risk of vehicle failure and / or traffic accident, an edge computing result is obtained to characterize the safety risk of the vehicle.
[0054] Among them, vehicle failure risk refers to the risk of vehicle failure, such as failure to ignite, lights not working, flat tires, etc., and driving accident risk refers to the risk of vehicle road traffic accidents, such as collisions, rollovers, etc. Vehicle failure risk and driving accident risk can also exist at the same time. For example, a serious accident may cause the vehicle to fail and become unusable.
[0055] For example, if the vehicle has a vehicle failure risk, it can be determined that the vehicle has a safety risk; if the vehicle has a traffic accident risk, it can be determined that the vehicle has a safety risk; if the vehicle has both a vehicle failure risk and a traffic accident risk, it can be determined that the vehicle has a safety risk.
[0056] Through the above scheme, when it is determined that the vehicle is at risk of vehicle failure and / or traffic accident, it can be determined that the vehicle is at safety risk. It can be determined whether the vehicle is at safety risk from two different dimensions, vehicle failure and traffic accident, which can improve the accuracy of the judgment of vehicle safety risks.
[0057] In the present disclosure, the driving information of the vehicle may include at least one of the following: a driving state parameter of the vehicle and image information of the vehicle's surroundings.
[0058] For example, the image information of the vehicle's surroundings may be an image, a video, or both. The vehicle's driving state parameters may include at least one of the following: the vehicle's tire pressure information, brake information, wheel speed information, current information, voltage information, gearbox information, driving time, mileage, fuel consumption, emissions, vibration information, and operating sound information. Among them, the tire pressure information may be detected by a tire pressure sensor, the brake information may be, for example, the brake pedal opening, which may be collected by a position sensor installed on the brake pedal, the wheel speed information may be collected by a wheel speed sensor, the current information may be detected by a current sensor, the voltage information may be detected by a voltage sensor, the vibration information may be collected by a vibration sensor, and the operating sound information may be collected by a sound sensor.
[0059] In one embodiment, whether the vehicle is at risk of vehicle failure can be determined by Figure 4 Determined in the manner shown.
[0060] In step 41, feature extraction is performed on the driving information to obtain first driving feature information.
[0061] In step 42, the first driving characteristic information is input into the fault prediction model to obtain a vehicle fault prediction result output by the fault prediction model.
[0062] In step 43 , when the vehicle failure prediction result is greater than a first preset threshold, it is determined that the vehicle has a vehicle failure risk.
[0063] In step 44 , when the vehicle failure prediction result is less than or equal to the first preset threshold, it is determined that the vehicle does not have a vehicle failure risk.
[0064] Among them, the method of extracting features from driving information can refer to relevant technologies, such as extracting features through a feature extraction model, and features refer to attributes or metrics used to describe data. If the driving information includes driving state parameters, the first driving feature information obtained includes driving state parameter feature information. If the driving information includes image information of the vehicle's surroundings, the first driving feature information obtained includes image feature information. Preferably, the driving information may include driving state parameters and image information, and the first driving feature information obtained includes driving state parameter feature information and image feature information, so that a comprehensive judgment can be made by combining the vehicle's driving state parameters and the surrounding environment image at the same time, thereby improving the accuracy and reliability of vehicle fault prediction.
[0065] For example, a trained fault prediction model may be pre-configured in the vehicle rescue device. The fault prediction model may be trained based on the driving information of the vehicle with the fault and the image information of the surrounding environment when the vehicle has the fault, as well as the driving information of the normal vehicle and the image information of the surrounding environment when the normal vehicle is driving. The fault prediction model may be, for example, a neural network model. The first driving characteristic information is input into the fault prediction model. The fault prediction model may make a prediction based on the first driving characteristic information and output a vehicle fault prediction result. The vehicle fault prediction result may represent the possibility of the vehicle having a fault. The larger the vehicle fault prediction result output by the fault prediction model, the greater the possibility of the vehicle having a fault. The smaller the vehicle fault prediction result output by the fault prediction model, the smaller the possibility of the vehicle having a fault.
[0066] If the vehicle fault prediction result is greater than the first preset threshold, it can be determined that the vehicle is at risk of vehicle failure. When it is determined that the vehicle is at risk of failure, the vehicle fault level can also be determined based on the vehicle fault prediction result, such as a first-level fault warning, a second-level fault warning, etc. For example, the vehicle fault level can be determined based on the corresponding relationship between the fault prediction result and the fault level, as well as the vehicle fault prediction result output by the fault prediction model.
[0067] By adopting the above technical scheme, a vehicle fault prediction result can be obtained through a fault prediction model. When the vehicle fault prediction result is greater than a first preset threshold, it can be determined that the vehicle is at risk of vehicle failure. When the vehicle fault prediction result is less than or equal to the first preset threshold, it can be determined that the vehicle is not at risk of vehicle failure. By setting the first preset threshold, the false alarm rate of faults can be reduced and the accuracy of vehicle fault prediction can be improved.
[0068] In the present disclosure, the driving information of the vehicle may include at least one of the following: driving state parameters of the vehicle, vehicle perception information, and image information of the vehicle's surrounding environment.
[0069] Among them, the driving state parameters and image information have been described above. The driving state parameters used to determine whether there is a traffic accident and the driving state parameters used to determine whether the vehicle has a fault can be the same or different, without limitation. The vehicle perception information may include at least one of the following: information collected by a ranging sensor installed on the vehicle, information collected by a radar sensor, and information collected by a body stability and control system sensor. There is no limitation on the number of sensors such as ranging sensors and radar sensors.
[0070] The vehicle rescue method provided by the present disclosure may further include: obtaining driving behavior information of the driver of the vehicle. The driving behavior information may be information stored in the vehicle's memory for judging the driver's driving habits, such as the driving mode used by the driver, the average speed of the vehicle, the frequency of lane changes, whether a sharp turn is made, etc. The driving behavior information may be collected during the driver's previous driving process and stored in the vehicle's memory.
[0071] In one embodiment, whether the vehicle is at risk of a traffic accident can be determined by Figure 5 Determined in the manner shown.
[0072] In step 51, feature extraction is performed on the driving information and the driving behavior information respectively to obtain second driving feature information and driving behavior feature information.
[0073] In step 52, the second driving characteristic information and the driving behavior characteristic information are input into the accident prediction model to obtain the driving accident prediction result output by the accident prediction model.
[0074] In step 53, when the traffic accident prediction result is greater than the second preset threshold, it is determined that the vehicle has a traffic accident risk.
[0075] In step 54 , when the traffic accident prediction result is less than or equal to the second preset threshold, it is determined that the vehicle does not have a traffic accident risk.
[0076] The driving information may include one or more of driving state parameters, vehicle perception information, and image information. Preferably, the driving information may include driving state parameters, vehicle perception information, and image information at the same time, and the second driving characteristic information may include driving state parameter characteristic information, vehicle perception characteristic information, and image characteristic information, so that a comprehensive judgment can be made by combining the vehicle's driving state parameters, vehicle perception information, and surrounding environment images at the same time, thereby improving the accuracy and reliability of traffic accident prediction.
[0077] For example, a trained accident prediction model may be pre-configured in the vehicle rescue device. The accident prediction model may be trained based on the driving information and vehicle perception information of the vehicle involved in the accident, as well as the image information of the vehicle's surrounding environment when the accident occurs. The accident prediction model may be, for example, a neural network model. The second driving characteristic information and the driving behavior characteristic information are input into the accident prediction model. The accident prediction model may make predictions based on the second driving characteristic information and the driving behavior characteristic information, and output a driving accident prediction result. The driving accident prediction result may characterize the possibility of a driving accident for the vehicle. The larger the driving accident prediction result output by the accident prediction model, the greater the possibility of a driving accident for the vehicle. The smaller the driving accident prediction result output by the accident prediction model, the smaller the possibility of a driving accident for the vehicle.
[0078] If the traffic accident prediction result is greater than the second preset threshold, it can be determined that the vehicle is at risk of a traffic accident. When it is determined that the vehicle is at risk of a traffic accident, the traffic accident level can also be determined based on the vehicle traffic accident prediction result, such as level one accident warning, level two accident warning, etc. For example, the traffic accident level can be determined based on the correspondence between the accident prediction result and the accident level, as well as the traffic accident prediction result output by the accident prediction model.
[0079] By adopting the above technical scheme, a traffic accident prediction result can be obtained through the accident prediction model. When the traffic accident prediction result is greater than the second preset threshold, it can be determined that the vehicle is at risk of a traffic accident. When the traffic accident prediction result is less than or equal to the second preset threshold, it can be determined that the vehicle is not yet at risk of a traffic accident. By setting the second preset threshold, the accident false alarm rate can be reduced and the accuracy of vehicle traffic accident prediction can be improved.
[0080] like Figure 3 As shown, in step 1311, in response to the edge computing result indicating that the vehicle has a safety risk, a prompt message is output through a prompt unit.
[0081] The prompt information is used by the user to confirm whether to send rescue information via the satellite communication unit, and the prompt information can be used as the first prompt information. The first prompt information can be, for example, "the vehicle currently has a safety risk, whether to send rescue information via satellite".
[0082] For example, the prompt unit can be a display screen arranged in the vehicle, and the first prompt information can be displayed on the display screen. The prompt unit can also be a voice playback device arranged in the vehicle, and the first prompt information can be played through the voice playback device. The prompt unit can also include a display screen and a voice playback device, and the prompt information can be output by displaying on the display screen and playing through the voice playback device at the same time.
[0083] For example, if the prompt unit includes a display screen, two buttons "Send" and "Don't Send" may be displayed on the display screen. If the user clicks the "Send" button, it may indicate that a confirmation instruction entered by the user indicating that the user agrees to send the rescue information through the satellite communication unit has been received. If the user clicks the "Don't Send" button, it may indicate that a confirmation instruction entered by the user indicating that the user disagrees to send the rescue information has been received. For another example, the user may not be able to click a button on the display screen, and the user's intention may be determined by the voice spoken by the user. If it is recognized that the voice spoken by the user includes, for example, "request rescue", it may indicate that a confirmation instruction entered by the user indicating that the user agrees to send the rescue information through the satellite communication unit has been received. If it is recognized that the voice spoken by the user includes "no rescue needed", it may indicate that a confirmation instruction entered by the user indicating that the user disagrees to send the rescue information has been received.
[0084] The present disclosure does not limit the output method of the first prompt information and the method of obtaining the user's confirmation instruction.
[0085] In step 1312, if no confirmation instruction input by the user is received within the second preset time period from the prompt unit outputting the prompt information, or if a confirmation instruction input by the user indicating agreement to send rescue information via the satellite communication unit is received within the second preset time period, the rescue information is sent via the satellite communication unit.
[0086] The second preset time period can be preset. If a confirmation instruction input by the user indicating the user agrees to send the rescue information is received within the second preset time period, the rescue information can be sent through the satellite communication unit. If a confirmation instruction input by the user is not received within the second preset time period from the prompt unit outputting the prompt information, that is, a confirmation instruction indicating the user agrees to send the rescue information is not received, and a confirmation instruction indicating the user disagrees to send the rescue information is not received, the user may not be able to make a choice at this time. In order to ensure the timeliness of the rescue, the rescue information can be sent directly through the satellite communication unit.
[0087] Through the above technical scheme, prompt information can be output through the prompt unit, and the prompt information is used by the user to confirm whether to send rescue information through the satellite communication unit. In this way, the user can decide whether to send rescue information. If the user confirms that the rescue information does not need to be sent, unnecessary satellite communication can be avoided. If the user confirms that the rescue information needs to be sent, or the user's confirmation instruction is not received, the rescue information can be sent out through the satellite communication unit in time to ensure the timeliness of the rescue.
[0088] In one embodiment, the prompt unit may also output safety risk type information, which is used to indicate that the vehicle is at risk of vehicle failure and / or traffic accident risk, and if it is determined that the vehicle is at risk of vehicle failure, the prompt unit may also output the vehicle failure level, and if it is determined that the vehicle is at risk of traffic accident, the prompt unit may also output the traffic accident level, so that the user can fully understand the current safety status of the vehicle and make a decision whether to send rescue information through the satellite communication unit.
[0089] The vehicle rescue method provided by the present disclosure may also include: In response to the edge computing result indicating that the vehicle is subject to a safety risk, driving information and safety risk type information are sent via a satellite communication unit, where the safety risk type information is used to indicate that the vehicle is subject to a vehicle failure risk and / or a traffic accident risk.
[0090] Among them, in order to enable rescue personnel to judge the safety of the vehicle in time and estimate the situation on the vehicle site, driving information and safety risk type information can also be sent through the satellite communication unit to enable rescue personnel to quickly judge the scope and urgency of the fault and / or accident, which helps rescue personnel to quickly locate the problem and take corresponding rescue measures, thereby improving the effectiveness of the rescue.
[0091] In the present disclosure, after the step of performing edge computing according to driving information, the vehicle rescue method may further include: In response to receiving the second information, the edge computing process is terminated, wherein the second information is used to indicate that the duration for which the vehicle can normally connect to the wireless network signal reaches a third preset duration.
[0092] The method of monitoring and timing the vehicle's wireless network signal has been introduced above. Among them, if the vehicle rescue device receives the second information, it can indicate that the vehicle can connect to the wireless network signal normally and stably, that is, the vehicle has restored the communication function with the base station equipment on the ground, and can send information outward through the network. In one embodiment, in order to avoid the consumption of local computing resources of the vehicle, the edge computing process can be terminated. In this case, for example, the driving information can be sent to the server through the wireless network communication unit, and the server will judge whether the vehicle has a safety risk. In one embodiment, after terminating the edge computing process, the vehicle rescue device can also cache information such as the duration of edge computing, the end time of edge computing, vehicle location information, vehicle status information, and whether the vehicle has a safety risk as a log record.
[0093] Through the above technical solution, even if the vehicle cannot connect to the wireless network signal, edge computing can be used to determine whether the vehicle has safety risks locally. When the vehicle cannot connect to the wireless network signal, long-distance communication and rescue information can be sent through satellites. There is no need to wait until the vehicle is connected to the wireless network signal to send rescue information, which avoids rescue delays and ensures the timeliness of rescue.
[0094] Another embodiment of the present disclosure is described below. In this embodiment, in order to ensure the real-time nature of information processing and avoid communication delays, if the vehicle can normally connect to the wireless network signal, edge computing can also be performed locally in the vehicle to determine whether the vehicle is at risk of safety, and rescue information can be sent through the wireless network communication unit and / or satellite communication unit on the vehicle.
[0095] Figure 6 is a flow chart of a vehicle rescue method according to an exemplary embodiment. Figure 6 As shown, before obtaining the driving information of the vehicle, step 115 may be further included, and step 13 may include step 132.
[0096] In step 15, third information is received, wherein the third information is used to indicate that the duration for which the vehicle can normally connect to the wireless network signal reaches a fourth preset duration.
[0097] In step 11, the driving information of the vehicle is obtained.
[0098] In step 12, edge computing is performed based on the driving information.
[0099] In step 132 , in response to the edge computing result indicating that the vehicle is at risk of safety, rescue information is sent via a satellite communication unit and / or a wireless network communication unit.
[0100] Among them, the method of monitoring and timing the signal connection status of the vehicle has been introduced above. In response to receiving the third information, the vehicle rescue device can obtain the driving information of the vehicle, and perform edge computing based on the driving information to determine whether the vehicle has a safety risk, wherein the third information is used to indicate that the duration for which the vehicle can normally connect to the wireless network signal reaches a fourth preset duration, which can indicate that the vehicle has not experienced a network connection interruption within the fourth preset duration, that is, the wireless network signal currently connected to the vehicle is relatively stable. Among them, the method of performing edge computing based on driving information can refer to the above introduction.
[0101] In this embodiment, since the wireless network signal to which the vehicle is currently connected is relatively stable, the vehicle can send information to the outside through the wireless network communication unit or through the satellite communication unit. For example, in response to the edge computing result indicating that the vehicle is at safety risk, a second prompt message can be output through the prompt unit, and the second prompt message is used for the user to confirm whether to send the rescue information through the wireless network communication unit or through the satellite communication unit. If a confirmation instruction input by the user indicating that the rescue information is agreed to be sent through the wireless network communication unit is received, the rescue information can be sent through the wireless network communication unit. If a confirmation instruction input by the user indicating that the rescue information is agreed to be sent through the satellite communication unit is received, the rescue information can be sent through the satellite communication unit. If the user wants to further ensure the timeliness of the rescue, he or she can also choose to send the rescue information through the wireless network communication unit and the satellite communication unit at the same time. In this way, it can be further ensured that the rescue information can be sent out in a timely and effective manner, so that the user can be rescued in a timely manner.
[0102] In this embodiment, in response to the edge computing results indicating that the vehicle is at a safety risk, driving information and safety risk type information may be sent via a wireless network communication unit and / or a satellite communication unit. The safety risk type information is used to indicate that the vehicle is at risk of vehicle failure and / or traffic accident, so that rescue personnel can quickly determine the scope and urgency of the failure and / or accident, helping rescue personnel to quickly locate the problem and take appropriate rescue measures, thereby improving the effectiveness of the rescue.
[0103] Through the above technical solution, the third information is used to indicate that the duration for which the vehicle can normally connect to the wireless network signal reaches a fourth preset duration, which can indicate that the vehicle has not experienced a network connection interruption within the fourth preset duration, that is, the wireless network signal to which the vehicle is currently connected is relatively stable. Edge computing is performed based on the vehicle's driving information, and it is possible to determine whether the vehicle has a safety risk based on the driving information locally in the vehicle, thereby ensuring the real-time nature of information processing and improving information processing efficiency. When the vehicle is able to normally connect to the wireless network signal, rescue information can be sent through a satellite communication unit and / or a wireless network communication unit to ensure that the rescue information can be sent out in a timely and effective manner, so that the user can receive timely rescue.
[0104] Based on the same inventive concept, the present disclosure also provides a vehicle rescue device, Figure 7 is a block diagram of a vehicle rescue device according to an exemplary embodiment. Figure 7 As shown, the device 70 may include: A first acquisition module 71 is used to acquire the driving information of the vehicle; An edge computing module 72, configured to perform edge computing according to the driving information; The trigger module 73 is used to send rescue information through the communication unit in response to the edge computing result indicating that the vehicle has a safety risk.
[0105] Optionally, the device 70 further includes: A first receiving module, configured to receive first information before the first acquiring module 71 acquires the driving information of the vehicle, wherein the first information is used to indicate that the duration for which the vehicle cannot normally connect to the wireless network signal reaches a first preset duration; The trigger module 73 is used for sending the rescue information through a satellite communication unit in response to the edge computing result indicating that the vehicle has a safety risk.
[0106] Optionally, the trigger module 73 is used to: In response to the edge computing result indicating that the vehicle has a safety risk, outputting a prompt message through a prompt unit, wherein the prompt message is used for a user to confirm whether to send the rescue information through the satellite communication unit; If no confirmation instruction input by the user is received within a second preset time period from the prompt unit outputting the prompt information, or if a confirmation instruction input by the user indicating agreement to send the rescue information via the satellite communication unit is received within the second preset time period, the rescue information is sent via the satellite communication unit.
[0107] Optionally, the edge computing module 72 includes: A first determination submodule, configured to determine whether the vehicle has a vehicle failure risk and / or a traffic accident risk according to the driving information; The second determination submodule obtains the edge computing result for characterizing that the vehicle has a safety risk when it is determined that the vehicle has a vehicle failure risk and / or a traffic accident risk.
[0108] Optionally, the driving information includes at least one of the following: driving state parameters of the vehicle and image information of the vehicle's surroundings; Whether the vehicle has a vehicle failure risk is determined by the first determining submodule in the following manner: Extracting features from the driving information to obtain first driving feature information; Inputting the first driving characteristic information into a fault prediction model to obtain a vehicle fault prediction result output by the fault prediction model; When the vehicle failure prediction result is greater than a first preset threshold, it is determined that the vehicle has a vehicle failure risk.
[0109] Optionally, the driving information includes at least one of the following: driving state parameters of the vehicle, vehicle perception information, and image information of the vehicle's surroundings; The device 70 further comprises: A second acquisition module is used to acquire driving behavior information of the driver of the vehicle; Whether the vehicle has a traffic accident risk is determined by the first determination submodule in the following manner: Extracting features from the driving information and the driving behavior information respectively to obtain second driving feature information and driving behavior feature information; Inputting the second driving characteristic information and the driving behavior characteristic information into an accident prediction model to obtain a traffic accident prediction result output by the accident prediction model; When the traffic accident prediction result is greater than a second preset threshold, it is determined that the vehicle has a traffic accident risk.
[0110] Optionally, the device 70 further includes: A communication trigger module is used to respond to the edge computing result characterizing that the vehicle has a safety risk, and to send the driving information and safety risk type information through the satellite communication unit, wherein the safety risk type information is used to indicate that the vehicle has a vehicle failure risk and / or a traffic accident risk.
[0111] Optionally, the device 70 is used to: after the edge computing module 72 performs edge computing based on the driving information, in response to receiving second information, end the edge computing process, wherein the second information is used to indicate that the duration for which the vehicle can normally connect to the wireless network signal reaches a third preset duration.
[0112] Optionally, the device 70 further includes: A second receiving module, configured to receive third information before the first acquiring module 71 acquires the driving information of the vehicle, wherein the third information is used to indicate that the duration for which the vehicle can normally connect to the wireless network signal reaches a fourth preset duration; The trigger module 73 is used for sending the rescue information via a satellite communication unit and / or a wireless network communication unit in response to the edge computing result indicating that the vehicle has a safety risk.
[0113] The present disclosure also provides a vehicle rescue device, the device comprising: A processor is connected to a memory, and the processor is used to execute a computer program in the memory to implement the steps of the vehicle rescue method provided in any of the above embodiments of the present disclosure.
[0114] The present disclosure also provides a vehicle rescue system, comprising a communication unit and the vehicle rescue device provided by any one of the above embodiments. Figure 8 is a block diagram of a vehicle rescue system according to an exemplary embodiment. Figure 8 As shown, the vehicle rescue system 80 may include a communication unit 81 and a vehicle rescue device 82. The vehicle rescue device 82 is used to implement the vehicle rescue method provided by any of the above embodiments of the present disclosure, and the communication unit 81 may be a satellite communication unit for sending rescue information.
[0115] Fig. 9 is a block diagram of a vehicle rescue system according to an exemplary embodiment. Fig. 9 As shown, the vehicle rescue system 90 may include a satellite communication unit 91, a vehicle rescue device 92, a signal monitoring unit 93, a signal processing unit 94, an information collection unit 95, and a prompt unit 96. The vehicle rescue device 92 may include a data preprocessing module 921, a fault prediction module 922, and an accident prediction module 923.
[0116] Taking the case where the vehicle cannot connect to the wireless network signal, sending rescue information through the satellite communication unit on the vehicle as an example, the signal monitoring unit 93 is used to monitor the signal connection status of the vehicle, and send the monitored signal connection status to the signal processing unit 94 in real time. The signal processing unit 94 can start timing from the moment when the received signal connection status indicates that the vehicle cannot normally connect to the wireless network signal, and send the first information to the vehicle rescue device 92 when the timing duration reaches the first preset duration. The information collection unit 95 may include a module for collecting driving information, such as a tire pressure sensor, a wheel speed sensor, a current sensor, a voltage sensor, a sound sensor, a vibration sensor, a radar sensor, a distance sensor, a camera, etc. The information collection unit 95 can send the driving information to the vehicle rescue device 92, or the information collection unit 95 can also send the driving information to the event data recorder in the vehicle, and the event data recorder packages the driving information and sends it to the vehicle rescue device 92.
[0117] The data preprocessing module 921 in the vehicle rescue device 92 can preprocess the driving information, such as denoising, cleaning, and standardization. The fault prediction module 922 can be integrated with a fault prediction model. The fault prediction module 922 can call the driving information preprocessed by the data preprocessing module 921, and predict vehicle faults through the fault prediction model. The accident prediction module 923 can be integrated with an accident prediction model. The accident prediction module 923 can call the driving information preprocessed by the data preprocessing module 921, and predict traffic accidents through the accident prediction model.
[0118] In one example, the prompt unit 96 and the satellite communication unit 91 may be integrated into a vehicle computer; in another example, the prompt unit 96 and the satellite communication unit 91 may be separately provided on the vehicle, which is not limited in the present disclosure.
[0119] The present disclosure also provides a vehicle, comprising the vehicle rescue system provided by any embodiment of the present disclosure.
[0120] The present disclosure also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the vehicle rescue method provided in any of the above-mentioned embodiments are implemented.
[0121] The present disclosure also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the vehicle rescue method provided in any of the above embodiments.
[0122] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.
[0123] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.
[0124] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.
Claims
1. A vehicle rescue method, characterized in that: The method comprises: Obtain vehicle driving information; Perform edge computing based on the driving information; In response to the edge computing result indicating that the vehicle is at risk of safety, rescue information is sent through the communication unit.
2. The method according to claim 1, characterized in that Before obtaining the driving information of the vehicle, the method further includes: Receiving first information, wherein the first information is used to indicate that the duration for which the vehicle cannot normally connect to the wireless network signal reaches a first preset duration; The step of sending rescue information through a communication unit in response to the edge computing result indicating that the vehicle has a safety risk includes: In response to the edge computing result indicating that the vehicle is at risk of safety, the rescue information is sent via a satellite communication unit.
3. The method according to claim 2, characterized in that In response to the edge computing result indicating that the vehicle has a safety risk, sending the rescue information through a satellite communication unit includes: In response to the edge computing result indicating that the vehicle has a safety risk, outputting a prompt message through a prompt unit, wherein the prompt message is used for a user to confirm whether to send the rescue information through the satellite communication unit; If no confirmation instruction input by the user is received within a second preset time period from the prompt unit outputting the prompt information, or if a confirmation instruction input by the user indicating agreement to send the rescue information via the satellite communication unit is received within the second preset time period, the rescue information is sent via the satellite communication unit.
4. The method according to claim 2, characterized in that: The performing edge computing according to the driving information includes: Determining, based on the driving information, whether the vehicle has a risk of vehicle failure and / or a risk of a traffic accident; When it is determined that the vehicle is at risk of vehicle failure and / or traffic accident, the edge computing result is obtained to characterize that the vehicle is at risk of safety.
5. The method according to claim 4, characterized in that The driving information includes at least one of the following: driving state parameters of the vehicle and image information of the vehicle's surroundings; Whether the vehicle has a vehicle failure risk is determined by: Extracting features from the driving information to obtain first driving feature information; Inputting the first driving characteristic information into a fault prediction model to obtain a vehicle fault prediction result output by the fault prediction model; When the vehicle failure prediction result is greater than a first preset threshold, it is determined that the vehicle has a vehicle failure risk.
6. The method according to claim 4, characterized in that The driving information includes at least one of the following: driving state parameters of the vehicle, vehicle perception information, and image information of the vehicle's surrounding environment; The method further comprises: Acquiring driving behavior information of the driver of the vehicle; Whether the vehicle has a risk of a traffic accident is determined in the following manner: Extracting features from the driving information and the driving behavior information respectively to obtain second driving feature information and driving behavior feature information; Inputting the second driving characteristic information and the driving behavior characteristic information into an accident prediction model to obtain a traffic accident prediction result output by the accident prediction model; When the traffic accident prediction result is greater than a second preset threshold, it is determined that the vehicle has a traffic accident risk.
7. The method according to claim 4, characterized in that The method further comprises: In response to the edge computing result characterizing that the vehicle has a safety risk, the driving information and safety risk type information are sent through the satellite communication unit, and the safety risk type information is used to indicate that the vehicle has a vehicle failure risk and / or a traffic accident risk.
8. The method according to claim 2, characterized in that: After the step of performing edge computing according to the driving information, the method further includes: In response to receiving the second information, the edge computing process is terminated, wherein the second information is used to indicate that the duration for which the vehicle can normally connect to the wireless network signal reaches a third preset duration.
9. The method according to claim 1, characterized in that: Before obtaining the driving information of the vehicle, the method further includes: receiving third information, wherein the third information is used to indicate that the duration for which the vehicle can normally connect to the wireless network signal reaches a fourth preset duration; The step of sending rescue information through a communication unit in response to the edge computing result indicating that the vehicle has a safety risk includes: In response to the edge computing result indicating that the vehicle is at safety risk, the rescue information is sent via a satellite communication unit and / or a wireless network communication unit.
10. A vehicle rescue device, characterized in that: The device comprises: A processor, the processor is connected to the memory, and the processor is used to execute the computer program in the memory to implement the steps of the vehicle rescue method according to any one of claims 1 to 9.
11. A vehicle rescue system, characterized in that: The system comprises a communication unit and the vehicle recovery device according to claim 10.
12. A vehicle, characterized in that: The vehicle includes the vehicle recovery system of claim 11.
13. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the vehicle rescue method according to any one of claims 1 to 9 are implemented.
14. A computer program product, characterized in that It comprises a computer program, which, when executed by a processor, implements the steps of the vehicle rescue method described in any one of claims 1 to 9.