Vehicle theft identification method and device, terminal equipment and storage medium

Abstract

The application discloses a vehicle theft identification method and device, a terminal device and a storage medium. The vehicle theft identification method comprises the following steps: acquiring vehicle state data and driver state data corresponding to a current vehicle, wherein the vehicle state data reflects the driving state of the current vehicle, and the driver state data reflects at least the posture of the head and hands of the driver of the current vehicle; determining whether a coercion event occurs based on the vehicle state data and the driver state data; and performing alarm processing based on the coercion event in the case where it is determined that the coercion event occurs. The application can improve the accuracy of vehicle theft identification.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, specifically to a vehicle theft identification method, device, terminal equipment, and computer-readable storage medium. Background Technology

[0002] With rapid economic development, vehicles are increasingly used in daily travel and logistics transportation, especially with the continuous expansion of logistics transportation networks and the growing number of logistics vehicles. However, as the number of logistics vehicles and other vehicles increases, criminals are targeting them for theft, resulting in the loss of vehicles and transported goods, and in serious cases, threatening the personal safety of drivers and passengers.

[0003] Currently, vehicle theft is usually determined by checking whether the vehicle doors have been subjected to force or by detecting unusual noises nearby. This method is prone to false alarms and can disrupt the normal use of the vehicle. Invention Overview

[0005] Technical issues

[0006] One of the objectives of this application is to provide a vehicle theft identification method, device, terminal equipment, and storage medium, which can improve the accuracy of vehicle theft identification.

[0007] Technical solutions

[0008] The technical solution adopted in the embodiments of this application is:

[0009] Firstly, a method for identifying vehicle theft is provided, including:

[0010] Obtain vehicle status data and driver status data corresponding to the current vehicle. The vehicle status data reflects the driving status of the current vehicle, and the driver status data reflects at least the head and hand posture of the driver of the current vehicle.

[0011] Determine whether a coercive event has occurred based on the vehicle status data and the driver status data;

[0012] If the coercive event is determined to have occurred, an alarm will be triggered based on the coercive event.

[0013] Secondly, a vehicle theft identification device is provided, comprising:

[0014] The data acquisition module is used to acquire the vehicle status data and driver status data corresponding to the current vehicle. The vehicle status data reflects the driving status of the current vehicle, and the driver status data reflects at least the head and hand posture of the driver of the current vehicle.

[0015] The coercion determination module is used to determine whether a coercion event has occurred based on the vehicle status data and the driver status data.

[0016] An alarm module is used to perform alarm processing based on the coercion event when it is determined that the coercion event has occurred.

[0017] Thirdly, a terminal device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the vehicle theft identification method described in the first aspect.

[0018] Fourthly, a computer-readable storage medium is provided, the computer storage medium storing a computer program, which, when executed by a processor, implements the steps of the vehicle theft identification method described in the first aspect.

[0019] Fifthly, a computer program product is provided, which, when run on a terminal device, causes the terminal device to execute the vehicle theft identification method described in the first aspect.

[0020] Beneficial effects

[0021] The beneficial effects of the method provided in the first aspect of this application are as follows: Since vehicle status data can reflect the current driving status of the vehicle, and driver status data can at least reflect the head and hand posture of the current driver, based on the vehicle's driving status and the driver's head and hand posture, it is possible to better determine whether the driver is in a coerced posture such as holding their head with both hands, which would prevent them from driving the vehicle normally. This allows for accurate determination of whether a coercion event has occurred, and thus enables timely alarm processing when a coercion event occurs. This improves the accuracy of vehicle theft identification, reduces misjudgments and false alarms, and enhances user experience and protects user safety. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or exemplary technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the vehicle theft identification method provided in this application embodiment;

[0024] Figure 2 This is a flowchart illustrating another vehicle theft identification method provided in this application embodiment;

[0025] Figure 3 This is a flowchart illustrating another vehicle theft identification method provided in this application embodiment;

[0026] Figure 4 This is a flowchart illustrating another vehicle theft identification method provided in this application embodiment;

[0027] Figure 5 This is a flowchart illustrating another vehicle theft identification method provided in this application embodiment;

[0028] Figure 6 This is a schematic diagram of the structure of a vehicle theft identification device provided in an embodiment of this application;

[0029] Figure 7 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application.

[0030] Embodiments of the present invention

[0031] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the scope of this application. However, those skilled in the art will recognize that this application can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted to avoid unnecessary detail that could obscure the description of this application.

[0032] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0033] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0034] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0035] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0036] To illustrate the technical solutions provided in this application, the following detailed description is provided in conjunction with specific drawings and embodiments.

[0037] Figure 1 A flowchart illustrating a vehicle theft identification method provided in an embodiment of this application is shown below, and details are as follows:

[0038] Step S101: Obtain the vehicle status data and driver status data corresponding to the current vehicle. The vehicle status data reflects the driving status of the current vehicle, and the driver status data reflects at least the head and hand posture of the driver of the current vehicle.

[0039] Optionally, the aforementioned current vehicle may be a vehicle that is in motion. Since logistics transport vehicles are more susceptible to theft, in some embodiments, the current vehicle may be a logistics transport vehicle that is in motion.

[0040] It should be noted that the vehicle theft identification method provided in this application embodiment can be applied to vehicle-mounted terminals. In this case, the current vehicle can be a logistics transportation vehicle or other vehicle equipped with a vehicle-mounted terminal. The vehicle theft identification method provided in this application embodiment can also be applied to management platforms such as logistics vehicle management platforms. In this case, the management platform can take the vehicle in motion among the vehicles it manages as the current vehicle, obtain the vehicle status data and driver status data corresponding to the current vehicle to determine the coercion event, that is, to identify vehicle theft.

[0041] Step S102: Determine whether a coercion event has occurred based on the aforementioned vehicle status data and driver status data.

[0042] Specifically, since vehicle status data can reflect the current driving status of the vehicle, and driver status data can at least reflect the posture of the driver's hands and head, judgments based on vehicle status data and driver data can better analyze whether the current vehicle has experienced abnormal events such as sudden stops based on the current driving status. At the same time, based on the posture of the driver's head and hands, it can analyze whether the driver has made abnormal postures such as holding their head with both hands or raising their hands. That is, it can combine the abnormalities of the vehicle and the abnormalities of the driver's posture to comprehensively analyze whether a coercive event has occurred, thereby improving the accuracy of theft identification.

[0043] In some embodiments, driver status data also reflects the driver's facial expressions. Based on the driver's facial expressions, it is analyzed whether the driver is in a state of tension or fear. That is, by combining the abnormality of the vehicle, the abnormality of the driver's posture, and the abnormality of emotions, it is determined whether a coercive event has occurred, thereby further improving the accuracy of theft identification.

[0044] Step S103: If it is determined that the above-mentioned coercive event has occurred, an alarm is triggered based on the above-mentioned coercive event.

[0045] Optionally, when triggering an alarm based on a coercive event, the current vehicle location information can be obtained to determine the target location. Then, an alarm message can be generated based on the target location and the coercive event, and sent to a highway safety management office or the police. The alarm message can be used to call for help and ensure user safety.

[0046] In this embodiment, vehicle status data reflecting the current driving status of the vehicle is acquired, and driver status data reflecting at least the head and hand postures of the driver of the current vehicle is acquired. This allows for a better determination of whether the driver is in a coerced posture, such as holding their head with their hands, which would prevent them from driving the vehicle normally. This enables accurate determination of whether a coercion event has occurred, improves the accuracy of vehicle theft identification, reduces false alarms and misjudgments, and enhances user experience while ensuring user safety.

[0047] In some embodiments, prior to step S101 described above, the method further includes:

[0048] Determine whether the vehicle is in a dangerous environment based on current environmental data, which includes the current scene and / or traffic flow.

[0049] Correspondingly, step S101 above includes:

[0050] If it is determined that the current vehicle is in the aforementioned dangerous environment, the vehicle status data and driver status data corresponding to the current vehicle are obtained.

[0051] Since theft or robbery is less common in high-traffic areas like toll booths or gas stations, before identifying theft, it's advisable to first obtain environmental data about the vehicle's current location. This data can then be analyzed to determine if the vehicle is in a dangerous environment. If a dangerous environment is identified, vehicle and driver status data can then be used for theft detection, specifically to determine if a coercive event has occurred.

[0052] The current environmental data may include the current scene where the vehicle is located, such as a gas station, urban area, or traffic light intersection; or, the current environmental data may include the traffic flow at the current location of the vehicle; or, to further improve accuracy, the current environmental data may include the current scene and traffic flow, and the current scene and traffic flow may be used together to determine whether the vehicle is in a dangerous environment, thereby improving the accuracy of the judgment.

[0053] In some embodiments, when determining whether a vehicle is in a dangerous environment based on current environmental data, for the current scenario, it can be determined whether the current scenario is safe by whether it matches a preset safe scenario (such as a toll station). If the current scenario does not match the safe scenario, it can be determined that the current scenario is a dangerous scenario, that is, the vehicle is in a dangerous environment. For traffic flow, it can be analyzed whether the traffic flow is less than a preset traffic flow threshold (such as 10). If the traffic flow is less than the traffic flow threshold, it can be determined that the vehicle is in a dangerous environment.

[0054] It is understandable that when the current environmental data includes the current scene and traffic flow, the vehicle is determined to be in a dangerous environment only when the current scene is a dangerous scene and the traffic flow is less than the traffic flow threshold, in order to further improve the accuracy of dangerous environment judgment.

[0055] Through the above processing, it is determined whether the vehicle is in a dangerous environment based on the current environmental data. Theft identification is only performed when the vehicle is in a dangerous environment. If the vehicle is not in a dangerous environment, there is no need to obtain vehicle status data for theft identification. This reduces unnecessary theft identification, thereby reducing resource consumption and making it beneficial for applications in resource-limited devices such as in-vehicle terminals.

[0056] In some embodiments, prior to step S102 described above, the method further includes:

[0057] A1. Determine whether a vehicle insertion event has occurred.

[0058] A2. In the event that the above-mentioned vehicle insertion event has occurred, the speed data of the current vehicle in the first time period and whether there is a vehicle in front of the current vehicle are used to determine whether a forced stop event has occurred. The first time period is determined according to the time of occurrence of the above-mentioned vehicle insertion event.

[0059] Correspondingly, step S102 above includes:

[0060] In cases where the aforementioned vehicle insertion event and the aforementioned forced stop event have occurred, a determination is made as to whether a coercion event has occurred based on the aforementioned vehicle status data and the aforementioned driver status data.

[0061] Since criminals usually force vehicles to stop before stealing them, in order to further improve the accuracy of theft detection, it is possible to first detect whether any vehicles are cutting in front of the current vehicle to determine if a vehicle cutting in has occurred.

[0062] If a vehicle insertion event occurs, it indicates that a vehicle may be trying to force the current vehicle to stop. In this case, the first time period can be determined based on the time of the vehicle insertion event, the speed data of the current vehicle can be determined based on the speed data of the current vehicle in the first time period, and whether there is a vehicle in front of the current vehicle in the first time period to determine whether a forced stopping event has occurred. That is, the forced stopping event can be determined based on the speed change of the current vehicle in the first time period and whether there is a vehicle in front of the current vehicle.

[0063] like Figure 2 As shown, if a vehicle insertion event is determined to have occurred, and a forced stop event is also determined, then a coercion event is determined based on the vehicle status data and driver status data; if no vehicle insertion event is determined to have occurred, or if a forced stop event occurs after a vehicle insertion event, no further judgment is required, in order to reduce unnecessary resource consumption.

[0064] Optionally, when determining whether there is a vehicle in front of the current vehicle within the first time period, it can be determined whether there is still an inserting vehicle corresponding to the insertion event in front of the current vehicle within the first time period, thereby reducing interference from other vehicles and improving the accuracy of the forced stop event determination.

[0065] In some embodiments, the first time period can be determined based on a first preset duration (e.g., 10 seconds) and the time of occurrence of the vehicle insertion event. For example, assuming the first preset duration is 15 seconds, the first time period can be the time period corresponding to 15 seconds after the occurrence of the vehicle insertion event, or the first time period can be the time period corresponding to 15 seconds before and 15 seconds after the occurrence of the vehicle insertion event.

[0066] In this embodiment of the application, after determining that a vehicle insertion event has occurred, it is determined whether a forced stop event has occurred. If a forced stop event has occurred, a coercion event is then determined. By judging the events in a progressive manner, the accuracy of vehicle theft identification is further improved. Furthermore, if a certain event is not met, there is no need to perform subsequent judgments, thus avoiding continuous resource consumption.

[0067] In some embodiments, step A1 above includes:

[0068] Acquire lane line data and reference vehicle position information. The lane line data includes at least lane markings, and the reference vehicles include vehicles located behind the current vehicle in the current lane, as well as vehicles in adjacent lanes.

[0069] Based on the lane line data and location information above, determine whether the reference vehicle is located in front of the current vehicle.

[0070] If it is determined that the reference vehicle is in front of the current vehicle and the current vehicle is not changing lanes, then the vehicle insertion event is determined to have occurred.

[0071] The aforementioned lane line data includes at least lane markings. Optionally, the lane line data may also include one or more of the following: lane line fitting parameters (i.e., parameters of the lane line curve fitting equation), lane line confidence, and lane line curvature, to more accurately determine the lane position using lane markings and other data. It is understood that lane markings can be determined by methods such as target detection on images captured by the current vehicle in the lane; this application does not impose specific limitations on this.

[0072] To accurately determine whether a vehicle insertion event has occurred, the judgment process can begin by acquiring lane line data and the position information of a reference vehicle. Then, the position of each lane is determined based on the lane line data. Finally, the position of each lane and the position information of the reference vehicle are combined to determine whether the reference vehicle is located in front of the current vehicle in its current lane. Optionally, the proportion of the reference vehicle's body in the area in front of the current vehicle can be analyzed based on the position information of the reference vehicle and the lane position (i.e., the proportion of the reference vehicle's body in the area in front of the current vehicle to the total body of the reference vehicle). If the proportion of the reference vehicle's body in the area in front of the current vehicle is greater than or equal to a threshold (e.g., 30%), then it can be determined that the reference vehicle is in front of the current vehicle in its current lane.

[0073] If the reference vehicle is determined to be in front of the current vehicle, it indicates that the reference vehicle may cut in front of the current vehicle. In this case, it can be determined whether the current vehicle has changed lanes. If the current vehicle has not changed lanes, it means that the reference vehicle has cut in front of the current vehicle, causing the reference vehicle's position to change from an adjacent lane or behind the current vehicle to in front of the current vehicle. In this case, it can be determined that a vehicle cutting event has occurred. If the current vehicle has changed lanes, it means that the current vehicle's lane change has caused the reference vehicle to be in front of the current vehicle. In this case, it is determined that no vehicle cutting event has occurred.

[0074] It is understandable that if it is determined that the reference vehicle is not in front of the current vehicle in its lane, then there is no need to perform the subsequent judgment steps.

[0075] In some embodiments, in step A2 above, before determining whether a forced-stop event has occurred based on the vehicle speed data of the current vehicle in the first time period and whether there is a vehicle in front of the current vehicle, the method further includes:

[0076] Determine whether the forced-to-stop event occurred during the pursuit;

[0077] Correspondingly, step A2 above includes:

[0078] If the vehicle insertion event is determined to have occurred, and the forced stop event occurs during the chase, the coercion event is determined based on the vehicle status data and the driver status data.

[0079] To further improve the accuracy of vehicle theft identification, before judging a coercion event, if a vehicle insertion event is determined to have occurred, and a forced stop event occurs after the vehicle insertion event, it can be determined whether the forced stop event is a forced stop event that occurs during a chase. That is, it can be determined whether there is a situation where other vehicles, such as the vehicle that inserted the vehicle corresponding to the vehicle insertion event, chase the current vehicle and force it to stop.

[0080] Optionally, when determining whether a forced-to-stop event occurs during a chase, it can be first determined whether the current vehicle has experienced at least two violent driving events within a reference time period. If the current vehicle has experienced at least two violent driving events within the reference time period, it can be determined that the current vehicle is in a chase within the reference time period, and in this case, the reference time period can be used as the chase time period. Optionally, the aforementioned violent driving events include at least rapid acceleration events, sharp turning events, and rapid deceleration events.

[0081] It should be noted that this reference time period is a longer period than the first time period, determined based on the occurrence time of the vehicle insertion event, and includes the first time period. Optionally, the reference time period can be determined based on a preset reference duration (e.g., 80 seconds) and the occurrence time of the vehicle insertion event, wherein the preset reference duration is longer than the first preset duration.

[0082] For example, assuming the reference preset duration is 60 seconds, the first preset duration is 10 seconds, and assuming the current vehicle insertion event occurs at 12:30:00, then combined with the first preset duration, the first time period is determined to be 12:29:50-12:30:10, and the reference time period is 12:59:00-12:31:00.

[0083] If the vehicle experiences a sudden deceleration event A at 12:30:05, it can be determined that a forced stop event M occurred within the first time period (12:29:50-12:30:10). At this time, it is necessary to determine whether the vehicle experienced at least two violent driving events within the reference time period (12:59:00-12:31:00).

[0084] Assuming the vehicle experiences a sharp turn (event B) at 12:29:37, and considering the vehicle's sudden deceleration (event A), it can be determined that the vehicle experienced at least two violent driving events within a reference time period. This reference time period is the pursuit period; specifically, the vehicle experienced a forced-to-stop event (event M) during the pursuit. It should be noted that in some embodiments, the determination can also be made regarding whether the vehicle experienced at least two violent driving events within the reference time period, excluding sudden deceleration event A (the target sudden deceleration event, i.e., a sudden deceleration event occurring within the first time period, such as the first sudden deceleration event within the first time period).

[0085] In some embodiments, it can be determined whether a violent driving event has occurred in the current vehicle within a reference time period based on the current vehicle's six-axis data and vehicle speed data. The six-axis data refers to the data collected by the six-axis inertial sensors installed on the current vehicle, at least the acceleration of the current vehicle along the x-axis and y-axis, the directions of which are the horizontal and vertical axes of the current vehicle. By using the acceleration in different directions and the vehicle speed, it is possible to more accurately determine whether a violent driving event has occurred.

[0086] In this embodiment, when a vehicle is stolen by criminals, the driver usually tries to escape by accelerating, while the criminals chase the vehicle. That is, during the theft, the current vehicle and the criminal's vehicle usually chase each other. Therefore, in the case of vehicle insertion event and forced stop event, the forced stop event is first determined based on the time of occurrence of the vehicle insertion event. If the forced stop event occurs during the chase, the coercion event is then determined, which further improves the accuracy of theft identification.

[0087] In some embodiments, in step A2 above, determining whether a forced-stop event has occurred based on the vehicle speed data of the current vehicle during the first time period and whether there is a vehicle in front of the current vehicle includes:

[0088] A21. Determine the insertion type corresponding to the above vehicle insertion event to obtain the target insertion type.

[0089] A22. In the case where the target insertion type is overtaking insertion by a following vehicle, determine whether the current vehicle experienced a sudden deceleration event and / or a stopping event during the first time period based on the vehicle speed data.

[0090] A23. If it is determined that the current vehicle experienced the aforementioned sudden deceleration event and / or the aforementioned stopping event during the aforementioned first time period, and if there is a vehicle in front of the current vehicle, then it is determined that the aforementioned forced-to-stop event has occurred.

[0091] To reduce interference caused by vehicles changing lanes from positions such as the side and front, the insertion type corresponding to the occurring vehicle insertion event can be determined first to obtain the target insertion type. The insertion type of the vehicle insertion event can include forward vehicle insertion, side vehicle insertion, and rear vehicle insertion. It should be noted that in this embodiment, when the inserting vehicle is a vehicle to the side or rear of the current vehicle (i.e., a vehicle located behind the current vehicle in an adjacent lane) or a vehicle behind it, the corresponding vehicle insertion event can be determined as a following vehicle overtaking and inserting.

[0092] If the target insertion type of the vehicle insertion event is overtaking insertion by a following vehicle, then determine whether the current vehicle experienced a sudden deceleration event within the first time period based on the vehicle speed data; or, determine whether the current vehicle experienced a stopping event within the first time period based on the vehicle speed data; or, determine whether the current vehicle experienced both a sudden deceleration event and a stopping event within the first time period based on the vehicle speed data.

[0093] like Figure 3As shown, if the target insertion type is not a following vehicle overtaking insertion, it indicates that the vehicle insertion event may be caused by a lane change by a vehicle to the side or in front. In this case, subsequent judgment of emergency deceleration and / or stopping events is not required. In some embodiments, to further ensure the accuracy of the forced-stop event judgment, when the target insertion type is not a following vehicle overtaking insertion, it is possible to analyze whether the inserting vehicle (i.e., the vehicle corresponding to the vehicle insertion event) was always located to the side or in front of the current vehicle during a preset time period before the occurrence of the vehicle insertion event (such as the time period corresponding to 1 minute before the occurrence of the vehicle insertion event). If the inserting vehicle was always located to the side or in front of the current vehicle during the preset time period, it can be considered that the inserting vehicle is a normal lane change insertion, and subsequent judgment of emergency deceleration and / or stopping events is not required. If the inserting vehicle was not always located to the side or in front of the current vehicle during the preset time period, the inserting vehicle may not be a normal lane change insertion. In this case, judgment of emergency deceleration and / or stopping events can be performed to determine whether a forced-stop event has occurred.

[0094] Optionally, when determining whether a sudden deceleration event has occurred within the first time period based on vehicle speed data, the current vehicle acceleration can be analyzed based on the vehicle speed data, and the determination of whether a sudden deceleration event has occurred based on the acceleration can be made.

[0095] For example, assuming the first time period corresponds to a duration of 10 seconds, the acceleration can be calculated every 2 seconds based on the vehicle speed data, resulting in 5 accelerations. If the value of any of the 5 accelerations is greater than or equal to the acceleration threshold (such as 7), it can be determined that the current vehicle has experienced a sudden deceleration event within the first time period.

[0096] If it is determined that the current vehicle experienced a sudden deceleration or stopping event within the first time period, or both, it can be determined whether there is a vehicle in front of the current vehicle. If there is a vehicle in front of the current vehicle, it is determined that a forced-to-stop event has occurred. If there is no vehicle in front of the current vehicle, it means that the current vehicle may have decelerated and / or stopped for other reasons. In this case, it can be determined that no forced-to-stop event has occurred.

[0097] In some embodiments, if it is determined that the current vehicle has experienced the above-mentioned sudden deceleration event and / or the above-mentioned stopping event during the first time period, and the distance between the current vehicle and the vehicle in front is less than or equal to a distance threshold (e.g., 2 meters), it can be determined that the above-mentioned forced stopping event has occurred. That is, when the current vehicle experiences a sudden deceleration event and / or a stopping event during the first time period, the distance between the current vehicle and the vehicle in front can be combined to more accurately determine whether the current vehicle has been forced to stop.

[0098] In this embodiment, when the vehicle insertion event is a following vehicle overtaking insertion, subsequent judgments are made to reduce interference caused by normal vehicle insertion. Simultaneously, a forced-stop event is only determined when the current vehicle experiences a sudden deceleration event and / or a stopping event within the first time period, and there is a vehicle in front of it, ensuring the accuracy of forced-stop event judgment.

[0099] In some embodiments, step A23, before determining whether the current vehicle experienced a sudden deceleration event and / or a stopping event during the first time period based on the vehicle speed data, further includes:

[0100] The time interval between the occurrence time of the inserted vehicle corresponding to the above vehicle insertion event and the occurrence time of the above vehicle insertion event is determined to obtain the target time interval. The occurrence time of the inserted vehicle is determined based on the time when the inserted vehicle is detected.

[0101] Determine whether the target time interval is less than or equal to the interval threshold.

[0102] Correspondingly, step A23 above includes:

[0103] If the target insertion type is overtaking insertion and the target time interval is less than or equal to the interval threshold, the vehicle speed data is used to determine whether the current vehicle experienced a sudden deceleration event and / or a stopping event during the first time period.

[0104] Specifically, in order to further improve the accuracy of the judgment of forced stop events, before judging whether a sudden deceleration event and / or a stopping event has occurred, the occurrence time of the vehicle corresponding to the vehicle insertion event can be determined first, and then the target time interval can be determined based on the time interval between the occurrence time of the vehicle insertion event and the occurrence time of the vehicle insertion event.

[0105] It is understandable that during the current vehicle's operation, vehicles around the current vehicle can be detected by means of video data captured by the vehicle's rearview camera or other camera devices, or by sensor detection. The time of the inserted vehicle's appearance is the time when the inserted vehicle is detected, and this appearance time is earlier than the time when the vehicle insertion event occurs.

[0106] If the target time interval is less than or equal to the interval threshold (e.g., 15 seconds), it indicates that the vehicle is suddenly overtaking and may be forced to stop. In this case, the vehicle speed data can be used to determine whether the vehicle has experienced a sudden deceleration and / or stopping event within the first time period to determine whether a forced stopping event has occurred. If the target time interval is greater than the interval threshold, the vehicle can be considered to be overtaking normally and no further judgment is required.

[0107] In this embodiment, the time interval between the appearance time of the inserted vehicle and the time when it enters the front of the current vehicle is used to determine whether the inserted vehicle is making a normal overtaking insertion. If it is determined that the insertion is not a normal overtaking insertion, further judgment is made, thereby reducing the interference caused by vehicles making normal overtaking insertions and further improving the accuracy of the judgment of forced stopping events.

[0108] In some embodiments, step S102 includes:

[0109] B1. Based on the above driver status data, determine whether the driver's current posture matches the target posture.

[0110] B2. If the current posture matches the target posture, determine whether the current vehicle is stationary based on the vehicle status data.

[0111] B3. If it is determined that the vehicle is currently in the above-mentioned stationary state, it is determined that the above-mentioned coercion event has occurred.

[0112] To improve the accuracy of theft detection, the driver's current posture can be determined first based on driver status data that reflects the driver's hand and head posture. Then, it can be judged whether the current posture matches the target posture (such as a raised hand posture or a squatting posture with hands behind the head).

[0113] If the driver's current posture matches the target posture, the system determines whether the vehicle is stationary based on vehicle status data that reflects the vehicle's driving status. If the driver's current posture does not match the target posture, no further determination is required.

[0114] If the vehicle is determined to be stationary, it can be considered that the vehicle has stopped moving and the driver has been coerced, i.e., a coercion event can be determined to have occurred. If the vehicle is determined to be moving, i.e., the vehicle is not stationary, it can be determined that no coercion event has occurred, and no alarm processing is required.

[0115] In this embodiment, the determination of whether a coercion event has occurred is based on a comprehensive assessment of the driver's current posture and the vehicle's stationary state, thereby ensuring the accuracy of the coercion event determination. At the same time, if it is determined that the driver's current posture does not match the target posture, no further judgment is required, which can reduce unnecessary processing steps, thereby reducing resource consumption and saving resources.

[0116] In some embodiments, the driver state data includes image data captured of the driver, the target posture includes a hand-raised posture, and step B1 includes:

[0117] Based on the above image data, the positions of the driver's hands and head were determined.

[0118] The current posture is determined based on the relative positional relationship between the hand position and the head position.

[0119] Determine whether the current posture matches the hand-raised posture.

[0120] Specifically, in situations where criminals possess weapons, drivers may raise their hands or cover their heads with their hands to avoid harm, thus indicating surrender. Therefore, to accurately determine whether the driver's current posture matches the raised hand posture, the driver's hand and head positions can be determined first based on image data captured from the driver. Then, the relative positional relationship between the hand position and the synchronous position can be analyzed. Based on this relative positional relationship, the driver's current posture can be determined, thereby enabling a better assessment of whether the driver's current posture matches the raised hand posture (i.e., the target posture).

[0121] Optionally, when determining the driver's current posture based on the relative position of the hands and head, the height of the driver's hands can be determined based on the hand position, and the height of the driver's head can be determined based on the synchronization position. If the height of both of the driver's hands is the same as the height of the head, the driver's current posture can be determined to be a raised hand posture; if the height of either of the driver's hands is not the same as the height of the head, the driver's current posture can be determined to be a lowered hand posture.

[0122] In this embodiment of the application, the target posture includes a hand-raised posture. When determining whether the driver's current posture matches the hand-raised posture, the driver's current posture is determined based on the characteristics of the hand-raised posture and the relative positional relationship between the driver's hands and head. This allows for quick and accurate judgment when determining whether the driver's current posture matches the hand-raised posture, thereby ensuring the accuracy of the judgment result.

[0123] In some embodiments, step B2 above includes:

[0124] B21. If it is determined that the current posture matches the target posture, determine the duration of the current posture.

[0125] B22. If the duration of the above-mentioned holding period is greater than or equal to the duration threshold, determine whether the current vehicle is in the above-mentioned stationary state based on the above-mentioned vehicle status data.

[0126] To further improve the accuracy of coercion event judgment, when determining whether the driver's current posture matches the target posture, the duration for which the driver maintains the current posture can be determined based on the target video data. The duration of the maintenance is then compared with a duration threshold (e.g., 3 seconds).

[0127] Since the vehicle is usually stationary when the driver is in a coerced state while holding their hand up, in order to accurately determine whether a coercion event has occurred, if the duration of the driver holding their hand up is greater than or equal to a duration threshold, the vehicle status data is used to determine whether the vehicle is stationary.

[0128] like Figure 4 As shown, if the duration is less than the duration threshold, it can be considered that the current posture matching the target posture is a coincidence, and there is no need to determine the stationary state of the vehicle.

[0129] In other embodiments, when the duration of the held posture is determined to be less than a duration threshold, a target time period (e.g., one minute before and one minute after the posture occurrence time) can be determined based on the occurrence time of the current posture matching the target posture. Then, the driver's current posture within the target time period is detected, and the number of occurrences of the current posture matching the target posture within the target time period is determined. If the number of occurrences is greater than or equal to a threshold (greater than 1, such as 3), the vehicle can be judged as stationary; if the number of occurrences is less than the threshold, the vehicle is not judged as stationary. That is, when the duration of the held posture matching the target posture does not meet the requirements, the driver is further judged as to whether he / she is being coerced based on the number of occurrences of the current posture matching the target posture within the time period, so as to reduce the possibility of misjudgment and better protect user safety.

[0130] In this embodiment of the application, when the driver's current posture matches the target posture, it is determined whether it is necessary to judge the vehicle's stationary state based on the duration of the current posture, thereby reducing interference caused by coincidence when the current posture matches the target posture and further improving the accuracy of the judgment of coercion events.

[0131] In some embodiments, step B22 above includes:

[0132] If the duration of the above-mentioned holding period is greater than or equal to the threshold of the holding period, it is determined from the above-mentioned vehicle status data whether the current vehicle is in the above-mentioned stationary state within the time period corresponding to the above-mentioned holding period.

[0133] Correspondingly, step B23 above includes:

[0134] If it is determined that the current vehicle is in the stationary state during the time period corresponding to the aforementioned holding duration, the aforementioned coercion event is determined to have occurred.

[0135] Optionally, when determining whether the current vehicle is stationary, the time period corresponding to the duration of the driver's hand posture (matching the target posture) can be determined first. Then, the target vehicle state data corresponding to the time period can be determined based on the vehicle state data. The determination of whether the current vehicle is stationary within the time period can be made based on the target vehicle state data, without having to make a judgment based on the complete vehicle state data. This can reduce the amount of computation and improve efficiency.

[0136] It is understandable that the driver's current posture is maintained in a posture that matches the target posture during the holding period. If the vehicle remains stationary during this holding period, the driver can be considered to have been coerced, and a coercion event can be determined. If the vehicle is not stationary during this holding period, it indicates that a coercion event may not have occurred. In this case, whether or not a distress signal from the driver is received can be used to further determine whether a coercion event has occurred.

[0137] In this embodiment, whether a coercion event has occurred is determined based on whether the vehicle is stationary during the holding period corresponding to the holding duration. That is, if the driver's current posture is maintained in a posture that matches the target posture during the holding period and the vehicle remains stationary, then a coercion event is determined to have occurred. This avoids interference from various coincidences and improves the accuracy of the judgment.

[0138] In some embodiments, step S103, prior to the alarm processing based on the aforementioned coercive event, further includes:

[0139] C1. Obtain the target video data corresponding to the second time period. The target video data includes at least the video data captured on the driver of the current vehicle. The second time period is determined based on the time of occurrence of the coercive event.

[0140] C2. Based on the above target video data, determine whether the current vehicle has experienced a driver change event.

[0141] Correspondingly, S103 above includes:

[0142] If it is determined that the aforementioned coercion event has occurred, and the aforementioned driver change event has occurred in the current vehicle, an alarm will be triggered based on the aforementioned coercion event.

[0143] To reduce false alarms, when a coercion event is determined based on vehicle and driver status data, a second time period can be determined based on the occurrence time of the coercion event, and then the target video data corresponding to this second time period can be acquired. Optionally, the second time period can be determined based on the occurrence time of the coercion event and a second preset duration (e.g., 90 seconds). For example, assuming the second preset duration is 100 seconds, the time period corresponding to the 100 seconds before and after the occurrence time of the coercion event can be used as the second time period.

[0144] To further confirm whether the vehicle has been robbed, the target video data includes at least video data captured on the driver of the vehicle. Optionally, the target video data may also include video data captured by the vehicle's cameras on the external environment (such as the area in front of the vehicle and outside the driver's side door), so as to analyze whether there is any danger in the external environment based on the video data reflecting the external environmental conditions, thereby enabling a more accurate determination of whether an alarm should be triggered.

[0145] like Figure 5 As shown, after obtaining the target video data, the system analyzes whether the driver of the current vehicle has been replaced based on the target video data. If the driver of the current vehicle is also replaced after the coercion event is determined to have occurred, i.e., a driver replacement event is sent, it indicates that the driver has been coerced and the current vehicle has been robbed. An alarm can be directly triggered based on the coercion event.

[0146] If a coercion event is determined to have occurred, but no driver change event has occurred in the current vehicle, in order to further improve user safety and reduce the occurrence of false alarms, the external environment of the current vehicle can be further analyzed to determine whether the driver is in danger and whether an alarm should be triggered. Alternatively, the need to trigger an alarm can be determined by whether an alarm signal from the driver or a traffic light is received.

[0147] In this embodiment of the application, after determining that a coercion event has occurred, video data of the current vehicle's driver taken within a target time period related to the occurrence time of the coercion event is first obtained. After obtaining the target video data, it is determined whether the current vehicle's driver has been changed based on the target video data. If the driver has been changed, an alarm is triggered to reduce the occurrence of false alarms, thereby improving the accuracy of vehicle theft identification.

[0148] In some embodiments, step C2 above includes:

[0149] Based on the aforementioned target video data, determine whether the driver got out of the vehicle.

[0150] If the driver gets out of the vehicle as described above, it is determined whether the driver gets back into the vehicle based on the video data corresponding to the third time period in the target video data. The time corresponding to the third time period is later than the time when the driver gets back into the vehicle.

[0151] If it is determined that the aforementioned driver boarding behavior has occurred, and the driver corresponding to the aforementioned driver boarding behavior is different from the driver corresponding to the aforementioned driver alighting behavior, then the aforementioned driver change event is determined to have occurred.

[0152] Specifically, since changing drivers usually requires the original driver to get out of the vehicle first and the new driver to get in, to improve the accuracy of judging driver change events, the judgment process can first analyze the target video data to determine whether the driver got out of the vehicle. If the driver got out of the vehicle, a third time period later than the time of the driver getting out of the vehicle is determined based on the time of the driver getting out of the vehicle (getting out time). Then, the video data corresponding to the third time period in the target video data is analyzed to determine whether the driver got in the vehicle, without having to analyze the driver getting in the vehicle based on the entire target video data, thus reducing the amount of calculation in the judgment process.

[0153] If it is determined that a driver got out of the vehicle, and then a driver got back in the vehicle after that, it can be determined whether the driver for the driver who got out of the vehicle is the same driver as the driver who got back in the vehicle. If they are different, then a driver change event is determined to have occurred; if they are the same, then the driver has not been changed, and in this case, it can be determined that no driver change event has occurred.

[0154] In some embodiments, a third time period can be determined based on a third preset duration (e.g., 25 seconds) and the alighting time. The third preset duration is shorter than the second preset duration. For example, assuming the third preset duration is 30 seconds, the time period corresponding to the 30 seconds following the alighting time can be used as the third time period.

[0155] It is understandable that when the driver gets out of the car late, it may not be possible to obtain complete video data corresponding to the third time period based on the target video data. In this case, the video data corresponding to the third time period can be directly obtained from the video data taken of the driver to ensure the completeness of the video data corresponding to the third time period, thereby ensuring the accuracy of the judgment of the driver's behavior of getting out of the car.

[0156] Optionally, when determining whether a driver has gotten out of or into the vehicle, the driver's body movement trajectory can be analyzed, and the determination can be made based on the driver's body movement trajectory.

[0157] In this embodiment, the system first determines whether a driver has exited the vehicle based on the target video data. If a driver exits the vehicle, it then determines whether a driver has re-entered the vehicle based on video data following the exit. This eliminates the need to directly analyze the entire target video data for both driver entry and exit, reducing computational complexity. Furthermore, by incorporating the characteristics of driver change events, the system determines whether a driver re-enters the vehicle after the exit. Only when the driver exiting and re-entering are not the same driver is a driver change event determined, ensuring the accuracy of the results and thus guaranteeing the accuracy of vehicle theft detection.

[0158] Corresponding to the vehicle theft identification method provided in the above embodiments, Figure 6 This diagram illustrates the structure of a vehicle theft detection device according to some embodiments of this application. For ease of explanation, only the parts relevant to the embodiments of this application are shown. (Refer to...) Figure 6 The device includes: a data acquisition module 61, a coercion judgment module 62, and an alarm module 63. Among them,

[0159] The data acquisition module 61 is used to acquire the vehicle status data and driver status data corresponding to the current vehicle. The vehicle status data reflects the driving status of the current vehicle, and the driver status data reflects at least the head and hand posture of the driver of the current vehicle.

[0160] The coercion determination module 62 is used to determine whether a coercion event has occurred based on the aforementioned vehicle status data and driver status data.

[0161] The alarm module 63 is used to perform alarm processing based on the aforementioned coercion event when it is determined that the coercion event has occurred.

[0162] In this embodiment, vehicle status data reflecting the current driving status of the vehicle is acquired, and driver status data reflecting at least the head and hand postures of the driver of the current vehicle is acquired. This allows for a better determination of whether the driver is in a coerced posture, such as holding their head with their hands, which would prevent them from driving the vehicle normally. This enables accurate determination of whether a coercion event has occurred, improves the accuracy of vehicle theft identification, reduces false alarms and misjudgments, and enhances user experience while ensuring user safety.

[0163] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0164] Figure 7 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Figure 7 As shown, the terminal device 7 of this embodiment includes: at least one processor 70 ( Figure 7 The diagram shows only one processor, a memory 71, and a computer program 72 stored in the memory 71 and executable on the at least one processor 70, which, when executed, performs the steps in any of the above-described method embodiments.

[0165] The terminal device 7 can be a desktop computer, laptop, handheld computer, or cloud server, etc. This terminal device may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will understand that... Figure 7 The example of terminal device 7 is merely an illustration and does not constitute a limitation on terminal device 7. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0166] The processor 70 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0167] In some embodiments, the memory 71 may be an internal storage unit of the terminal device 7, such as a hard disk or memory of the terminal device 7. In other embodiments, the memory 71 may be an external storage device of the terminal device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device 7. Furthermore, the memory 71 may include both internal and external storage units of the terminal device 7. The memory 71 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 71 can also be used to temporarily store data that has been output or will be output.

[0168] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0169] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the above-described method embodiments.

[0170] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0171] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for identifying vehicle theft, characterized in that, include: Obtain vehicle status data and driver status data corresponding to the current vehicle. The vehicle status data reflects the driving status of the current vehicle, and the driver status data reflects at least the head and hand posture of the driver of the current vehicle. Determine whether a coercive event has occurred based on the vehicle status data and the driver status data; Acquire target video data corresponding to the second time period, wherein the target video data includes at least video data captured on the driver of the current vehicle, and the second time period is determined based on the occurrence time of the coercive event; Based on the target video data, determine whether a driver change event has occurred in the current vehicle; If the coercion event is determined to have occurred, and the driver change event occurs in the current vehicle, an alarm is triggered based on the coercion event.

2. The vehicle theft identification method as described in claim 1, characterized in that, Before determining whether a coercive event has occurred based on the vehicle status data and the driver status data, the method further includes: Determine if a vehicle insertion event has occurred; If the vehicle insertion event is determined to have occurred, a stop-forcing event is determined based on the vehicle speed data of the current vehicle in a first time period and whether there is a vehicle in front of the current vehicle. The first time period is determined according to the occurrence time of the vehicle insertion event. Correspondingly, determining whether a coercive event has occurred based on the vehicle status data and the driver status data includes: If the vehicle insertion event and the forced stop event are both determined to have occurred, the coercion event is determined based on the vehicle status data and the driver status data.

3. The vehicle theft identification method according to claim 2, characterized in that, The method of determining whether a forced-stop event has occurred based on the vehicle speed data of the current vehicle within a first time period and whether there is a vehicle in front of the current vehicle includes: Determine the insertion type corresponding to the vehicle insertion event to obtain the target insertion type; When the target insertion type is a following vehicle overtaking insertion, the vehicle speed data is used to determine whether the current vehicle has experienced a sudden deceleration event and / or a stopping event during the first time period; If it is determined that the current vehicle has experienced the sudden deceleration event and / or the stopping event during the first time period, and there is a vehicle in front of the current vehicle, then it is determined that the forced stop event has occurred.

4. The vehicle theft identification method according to claim 3, characterized in that, Before determining whether the current vehicle experienced a sudden deceleration event and / or a stopping event within the first time period based on the vehicle speed data, the method further includes: The time interval between the occurrence time of the inserted vehicle corresponding to the vehicle insertion event and the occurrence time of the vehicle insertion event is determined to obtain the target time interval, wherein the occurrence time of the inserted vehicle is determined based on the time when the inserted vehicle is detected; Determine whether the target time interval is less than or equal to the interval threshold; Correspondingly, when the target insertion type is a following vehicle overtaking insertion, determining whether the current vehicle experienced a sudden deceleration event and / or a stopping event within the first time period based on the vehicle speed data includes: If the target insertion type is a following vehicle overtaking insertion and the target time interval is less than or equal to the interval threshold, the vehicle speed data is used to determine whether the current vehicle has experienced a sudden deceleration event and / or a stopping event within the first time interval.

5. The vehicle theft identification method according to claim 2, characterized in that, The determination of whether a vehicle insertion event has occurred includes: Acquire lane line data and reference vehicle position information. The lane line data includes at least lane markings, and the reference vehicles include vehicles located behind the current vehicle in the current lane, as well as vehicles in adjacent lanes. Based on the lane line data and the location information, it is determined whether the reference vehicle is located in front of the current vehicle; If it is determined that the reference vehicle is in front of the current vehicle and the current vehicle is not changing lanes, then the vehicle insertion event is determined to have occurred.

6. The vehicle theft identification method as described in claim 2, characterized in that, Before determining whether a coercive event has occurred based on the vehicle status data and the driver status data, the method further includes: Determine whether the forced-to-stop event occurred during the pursuit; Correspondingly, the step of determining whether a coercion event has occurred based on the vehicle state data and the driver state data when it is determined that the vehicle insertion event has occurred and the forced stop event has occurred includes: If the vehicle insertion event is determined to have occurred, and the forced stop event occurs during the chase, the coercion event is determined based on the vehicle status data and the driver status data.

7. The vehicle theft identification method according to claim 1, characterized in that, The step of determining whether a coercive event has occurred based on the vehicle status data and the driver status data includes: Based on the driver's state data, determine whether the driver's current posture matches the target posture; If the current posture matches the target posture, it is determined whether the current vehicle is stationary based on the vehicle status data. If it is determined that the current vehicle is in the stationary state, the coercion event is determined to have occurred.

8. The vehicle theft identification method according to claim 7, characterized in that, When it is determined that the current posture matches the target posture, determining whether the current vehicle is stationary based on the vehicle state data includes: If the current posture is determined to match the target posture, the duration of maintaining the current posture is determined. If the holding time is greater than or equal to the holding time threshold, it is determined whether the current vehicle is in the stationary state based on the vehicle state data.

9. The vehicle theft identification method according to claim 7, characterized in that, The driver state data includes image data captured from the driver, and the target posture includes a hand-raised posture. The step of determining whether the driver's current posture matches the target posture based on the driver state data includes: The driver's hand and head positions are determined based on the image data; The current posture is determined based on the relative positional relationship between the hand position and the head position; Determine whether the current posture matches the hand-raised posture.

10. The vehicle theft identification method according to claim 1, characterized in that, The step of determining whether a driver change event has occurred in the current vehicle based on the target video data includes: Determine whether the driver got out of the vehicle based on the target video data; If it is determined that the driver got out of the vehicle, it is determined whether the driver got back into the vehicle based on the video data corresponding to the third time period in the target video data, where the time corresponding to the third time period is later than the time when the driver got back into the vehicle. If it is determined that the driver boarding behavior has occurred, and the driver corresponding to the driver boarding behavior is different from the driver alighting behavior, then the driver change event is determined to have occurred.

11. The vehicle theft identification method according to any one of claims 1 to 10, characterized in that, Before obtaining the vehicle status data and driver status data corresponding to the current vehicle, the method further includes: Determine whether the vehicle is in a dangerous environment based on current environmental data, where the current environmental data includes the current scene and / or traffic flow. Correspondingly, obtaining the vehicle status data and driver status data corresponding to the current vehicle includes: If it is determined that the current vehicle is in the dangerous environment, the vehicle status data and the driver status data corresponding to the current vehicle are obtained.

12. A vehicle theft identification device, characterized in that, include: The data acquisition module is used to acquire the vehicle status data and driver status data corresponding to the current vehicle. The vehicle status data reflects the driving status of the current vehicle, and the driver status data reflects at least the head and hand posture of the driver of the current vehicle. The coercion determination module is used to determine whether a coercion event has occurred based on the vehicle status data and the driver status data. An alarm module is used to acquire target video data corresponding to a second time period, the target video data including at least video data captured of the driver of the current vehicle, the second time period being determined based on the occurrence time of the coercion event; and to determine whether a driver change event has occurred in the current vehicle based on the target video data. If the coercion event is determined to have occurred, and the driver change event occurs in the current vehicle, an alarm is triggered based on the coercion event.

13. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 11.

14. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 11.

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