Abnormal driving behavior detection system and method of vehicle and vehicle

Abstract

The invention provides an abnormal driving behavior detection system of a vehicle, an abnormal driving behavior detection method, the vehicle, computer equipment and a computer readable storage medium. The abnormal driving behavior detection system comprises: an acquisition module configured to acquire driving behavior information of a vehicle; the detection module is configured to detect and judge whether the vehicle has an abnormal driving behavior based on the driving behavior information; the execution module is configured to execute a response action under the condition that the vehicle has the abnormal driving behavior so as to correct the abnormal driving behavior, the driving behavior information comprises battery state information from a battery management system of the vehicle, and the obtaining module obtains the running state of the motor based on the battery state information. Whether the vehicle has the abnormal driving behavior or not is judged according to the battery state information from the battery management system of the vehicle, the running state of the motor can be obtained by means of the battery state information, and the abnormal driving behavior can be detected more accurately and timely.

Description

Technical Field

[0001] The present invention relates to the field of vehicles, and in particular, to a vehicle abnormal driving behavior detection system, an abnormal driving behavior detection method, a vehicle using the abnormal driving behavior detection system, a computer device for implementing the abnormal driving behavior detection method, and a computer-readable storage medium for executing the abnormal driving behavior detection method. Background Art

[0002] With technological advancements and social development, road traffic is becoming increasingly congested, the number of vehicles continues to increase, and the demand for road safety is becoming increasingly stringent. Abnormal vehicle driving is one of the main causes of traffic accidents. Currently, abnormal vehicle driving detection mainly utilizes various sensors installed on vehicles, such as inertial sensors, cameras, and lidar, to collect driving data. This data is analyzed to identify abnormal driving behavior. However, because sensor data may contain noise and other issues, current detection methods may mistakenly identify normal behavior as abnormal, resulting in false positives and other problems.

[0003] Therefore, it is necessary to improve the detection capability of abnormal driving behavior of vehicles and increase the detection accuracy of abnormal driving behavior, so as to reduce or avoid the occurrence of traffic accidents. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems existing in the above-mentioned prior art and to provide a vehicle abnormal driving behavior detection system to improve the detection capability of the vehicle abnormal driving behavior in a cost-effective and reliable manner.

[0005] To this end, according to one aspect of the present invention, a vehicle abnormal driving behavior detection system is provided, the abnormal driving behavior detection system comprising: an acquisition module, the acquisition module being configured to acquire driving behavior information of the vehicle; a detection module, the detection module being configured to detect and determine whether the vehicle has abnormal driving behavior based on the driving behavior information; and an execution module, the execution module being configured to execute a response action when the vehicle has abnormal driving behavior to correct the abnormal driving behavior, wherein the driving behavior information includes battery status information from a battery management system of the vehicle, and the acquisition module being configured to acquire the motor operating status of the vehicle based on the battery status information.

[0006] According to the above technical concept, the present invention may further include any one or more of the following optional forms.

[0007] In certain optional forms, the detection module includes a driving behavior prediction model configured to generate a driving behavior prediction signal based on the driving behavior information.

[0008] In some optional forms, the driving behavior prediction model includes a convolutional neural network unit configured to extract spatial features of the driving behavior information and generate a driving behavior feature map.

[0009] In some optional forms, the driving behavior prediction model includes a long short-term memory network unit, which is configured to perform temporal feature extraction based on the driving behavior feature graph and generate a driving behavior hidden state sequence.

[0010] In some optional forms, the driving behavior prediction model includes an attention unit configured to adjust the weight of the driving behavior hidden state sequence to generate a driving behavior prediction signal.

[0011] In some optional forms, the driving behavior prediction signal includes a driver intention prediction signal and an abnormal driving behavior signal.

[0012] In some optional forms, the acquisition module includes a preprocessing unit, which is configured to perform a preprocessing action on the driving behavior information, wherein the preprocessing action includes at least one of removing noise, removing outliers, and standardization.

[0013] In some optional forms, the acquisition module includes an extraction unit configured to perform data extraction on the preprocessed driving behavior information, wherein the data extraction includes at least one of acceleration or deceleration pattern extraction, acceleration or deceleration rate extraction, and driver fatigue behavior extraction.

[0014] In some optional forms, the execution module includes an alarm unit, which is configured to execute an alarm action when the vehicle has abnormal driving behavior, and the alarm action includes triggering a real-time alarm and / or notifying the driver.

[0015] In certain optional forms, the execution module includes an action unit configured to perform a support action in response to the warning action, the support action including automatically braking or driving the vehicle to a safe location.

[0016] In some optional forms, the driving behavior information also includes: at least one of the vehicle's motion state information, control signal information, and driver state information.

[0017] In some optional forms, the motion state information includes at least one of the vehicle's speed, acceleration, deceleration, and position information, the control signal information includes at least one of the vehicle's braking system information, steering system information, and body stability control information, and the driver state information includes at least one of the driver's heart rate, body temperature, body posture, and movement.

[0018] In certain optional forms, the battery status information includes at least one of current, voltage, and power provided by a battery of the vehicle to a motor.

[0019] According to another aspect of the present invention, a method for detecting abnormal driving behavior of a vehicle is provided, the abnormal driving behavior detection method comprising: obtaining driving behavior information of the vehicle; determining whether the vehicle has abnormal driving behavior based on the driving behavior information; and performing a response action when the vehicle has abnormal driving behavior, wherein the driving behavior information includes battery status information from a battery management system of the vehicle, and the abnormal driving behavior detection method further comprises obtaining the motor operating status of the vehicle based on the battery status information.

[0020] In some optional forms, determining whether the vehicle has abnormal driving behavior based on the driving behavior information includes: generating a driving behavior prediction signal based on the driving behavior information.

[0021] In some optional forms, the abnormal driving behavior detection method includes: performing a pre-processing action on the driving behavior information.

[0022] In some optional forms, the abnormal driving behavior detection method includes: performing data extraction on the pre-processed driving behavior information.

[0023] In certain optional forms, executing a response action when the vehicle exhibits abnormal driving behavior includes executing an alarm action when the vehicle exhibits abnormal driving behavior.

[0024] In certain optional forms, performing a response action when the vehicle exhibits abnormal driving behavior includes performing a support action in response to the alert action.

[0025] According to another aspect of the present invention, a vehicle is provided, comprising a battery management system and the above-mentioned abnormal driving behavior detection system for the vehicle, wherein the abnormal driving behavior detection system is integrated with the battery management system.

[0026] According to another aspect of the present invention, a computer device is provided, comprising a memory, a processor, and instructions stored in the memory and executable by the processor, wherein the processor implements the above-mentioned method for detecting abnormal driving behavior of a vehicle when executing the instructions.

[0027] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium has computer-executable instructions stored thereon, and the computer-executable instructions are used to execute the above-mentioned abnormal driving behavior detection method for a vehicle.

[0028] The abnormal driving behavior detection system provided by the present invention uses battery status information from the vehicle's battery management system to detect whether the vehicle has abnormal driving behavior. The operating status of the motor can be obtained through the battery status information, and the driving status of the vehicle can be judged based on the operating status of the motor, thereby achieving more accurate and timely analysis of driving behavior, helping the abnormal driving behavior detection system to provide real-time feedback and reduce potential risks. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Other features and advantages of the present invention will be better understood through the following detailed description of the optional embodiments in conjunction with the accompanying drawings, in which:

[0030] Figure 1 A schematic diagram of a vehicle abnormal driving behavior detection system according to an embodiment of the present invention is shown;

[0031] Figure 2 A schematic diagram of a driving behavior prediction model according to an embodiment of the present invention is shown;

[0032] Figure 3 A schematic flow chart of a method for detecting abnormal driving behavior of a vehicle according to an embodiment of the present invention is shown;

[0033] Figure 4 A schematic flow chart showing a method for detecting abnormal driving behavior of a vehicle according to another embodiment of the present invention; and

[0034] Figure 5 A schematic diagram of a computer device according to one embodiment of the present invention is shown. DETAILED DESCRIPTION

[0035] The making and using of the embodiments are discussed in detail below. However, it should be understood that the specific embodiments discussed are merely illustrative of specific ways to make and use the invention, and are not intended to limit the scope of the invention.

[0036] In addition, the flowcharts and block diagrams in the accompanying drawings illustrate possible implementations of the architecture, functionality, and operations of the methods and systems according to various embodiments of the present invention. It should be noted that the functions noted in the blocks may occur in a different order than that noted in the accompanying drawings. For example, two blocks shown in succession may actually be executed substantially in parallel, or they may sometimes be executed in the opposite order, depending on the functions involved.

[0037] During vehicle operation, various abnormal driving behaviors, such as sudden acceleration, deceleration, and sharp turns, can occur due to driver fatigue or misjudgment, posing a threat to road safety. The inventors have discovered that current methods for detecting abnormal driving behavior can generate false positives due to environmental fluctuations, sensor noise, and other issues. This means that normal driving behavior can be mistakenly identified as abnormal, thereby failing to detect truly abnormal driving behavior.

[0038] Reference Figure 1 , Figure 1 A schematic diagram of a vehicle abnormal driving behavior detection system according to an embodiment of the present invention is shown.

[0039] According to one embodiment of the present invention, the abnormal driving behavior detection system 100 of a vehicle generally includes an acquisition module 110, a detection module 120, and an execution module 130. Specifically, the acquisition module 110 is configured to acquire the driving behavior information of the vehicle, the detection module 120 is configured to receive the driving behavior information from the acquisition module 110, and determine whether the vehicle has abnormal driving behavior based on the driving behavior information, and the execution module 130 is configured to perform a response action when the detection module 120 detects that the vehicle has abnormal driving behavior, so as to correct the abnormal driving behavior. In this embodiment, the driving behavior information includes battery status information from the battery management system (BMS) of the vehicle. In this case, the acquisition module 110 acquires the motor operating status of the vehicle based on the battery status information. The acquisition module 110 can be, for example, the electric control unit (ECU) of the vehicle, which receives the battery status information from the battery management system and obtains the motor operating status based on the battery status information.

[0040] In this way, the abnormal driving behavior detection system of this embodiment uses the battery status information from the vehicle's battery management system to determine whether the vehicle has abnormal driving behavior, and the operating status of the motor can be obtained through the battery status information. Specifically, the battery status information can include the current (i.e., the high-voltage bus current, HV_BATT_Real_Curr), voltage (i.e., the high-voltage bus voltage, HV_BATT_Real_Volt), power (i.e., the high-voltage bus power, HV_BATT_Real_Power) provided by the battery to the motor. Through these battery status information, the operating status of the motor can be obtained. The operating status of the motor provides a real-time vehicle power system state, which can detect abnormal behaviors such as sudden acceleration, sudden braking, and sharp turns of the vehicle in a timely manner, thereby improving the response speed of the system. In addition, by using the battery status information for detection, the information provided by the vehicle's original battery management system can be directly used, reducing the number of modules of the abnormal driving behavior detection system 100, reducing the complexity of the system, and reducing manufacturing costs. For example, when the current provided to the motor suddenly increases, the vehicle will experience abnormal acceleration because more current is provided to the motor to generate greater torque, which promotes the acceleration of the vehicle. Therefore, using battery status information to determine whether the vehicle has abnormal driving behavior can achieve more accurate and timely analysis of driving behavior, helping the system provide real-time feedback, enabling the driver or vehicle control system to take timely measures to reduce potential risks. It can also help the system adapt to different driving conditions and environments, improving the safety and reliability of the vehicle in different environments. In addition, by obtaining the motor operating status through battery status information and understanding the data between the battery and the motor, it helps optimize the control strategy of the vehicle power system. By detecting abnormal driving conditions, the power system parameters (such as motor power, torque, speed, etc.) can be adjusted to improve the overall performance of the vehicle.

[0041] In some embodiments, the driving behavior information may also include: vehicle motion state information, control signal information, driver state information, etc. The vehicle motion state information may include the vehicle's speed, acceleration, deceleration, position information, etc. The control signal information may include the vehicle's braking system information (such as brake pedal status, brake hydraulic pressure, brake system temperature, etc.), steering system information (such as steering wheel position, vehicle steering angle, steering power status light, etc.), body stability control information (such as the vehicle's side swing angle, yaw angle, etc.), etc. The driver state information may include the driver's heart rate, body temperature, body posture and movements, etc. In this way, the abnormal driving behavior detection system 100 can use input data from different sources to detect driving behavior, so that the detection system can obtain information from different angles and more comprehensively understand the status of the vehicle and the surrounding environment, thereby improving the system's robustness to various different environments and driving conditions.

[0042] like Figure 1 As shown, the detection module 120 may include a driving behavior prediction model 121. This driving behavior prediction model 121 may receive driving behavior information from the acquisition module 110 and generate a driving behavior prediction signal based on the driving behavior information. The driving behavior prediction signal may then be used to determine whether the vehicle is in an abnormal driving state. This provides real-time vehicle behavior assessment, allowing the system to promptly detect abnormal driving states, thereby enabling earlier action and reducing the occurrence of traffic accidents.

[0043] Reference Figure 2 , Figure 2 A schematic diagram of a driving behavior prediction model according to an embodiment of the present invention is shown.

[0044] In this embodiment, the driving behavior prediction model 121 may include a convolutional neural network unit 122, a long short-term memory network unit 123, and an attention unit 124. The convolutional neural network unit 122 includes an input layer 125 and a convolutional neural network layer 126. The input layer 125 is used to specify the format of the driving behavior information to unify input data of different formats. This format may, for example, include the batch size, time steps, and feature dimensions of the data. In some embodiments, the batch size can be set to 1, the time step can be set to t, and the feature dimension can be set to n. Then, an input data can be represented as a real number sequence matrix R t×n , remember X i R t×n The convolutional neural network layer 126 can extract the spatial features of the driving behavior information, that is, the spatial connection between different feature values. After the driving behavior information processed by the input layer 125 is input into the convolutional neural network layer 126, convolution, pooling and node expansion (dimensionality reduction) operations are performed in sequence. In this embodiment, the convolutional neural network layer 126 can adopt a one-dimensional convolution, that is, the convolution kernel is convolved in a single time domain direction. In some embodiments, the number of convolution kernels is set to r and the size is set to k, then X i:i+k-1 is the real sequence matrix R t×n The real number sequence matrix from the i-th time step to the i+k-1-th time step. The weight matrix W1 is a k×n real number matrix. A feature extraction is performed on the sequence vector of each k time step to obtain the feature o i , the calculation formula is as follows:

[0045]

[0046] Where f is a nonlinear activation function and b1∈R (i.e. b1 is a real number) is used as a bias. After a convolution kernel extracts a sequence of input data, a feature map o with a shape of (t-k+1)×1 is obtained. The calculation formula is as follows:

[0047] o=[o1,o2,…,o t-k+1 ] T

[0048] Since the convolutional neural network layer 126 has a total of r convolution kernels, r feature maps will be obtained. After the convolution in the convolutional neural network layer 126, a pooling operation will be performed. The pooling size can be set to 2, and the sliding step size can be set to 2. R feature maps o of the shape [(t-k+1) / 2]×1 will be obtained. The calculation formula is as follows:

[0049] o=max{o i ,o i+1}

[0050] Wherein, i=1, 3, 5, ..., tk, and the r feature maps here are the driving behavior feature maps extracted by the convolutional neural network layer 126.

[0051] After obtaining the driving behavior feature graph, its dimensionality is reduced to a real vector of length r×(t-k+1) / 2, which preserves the spatial connections between different eigenvalues ​​in the input data. This real vector is then input into the long short-term memory network unit 123, which performs temporal feature extraction, specifically extracting temporal variation information, and generates a driving behavior hidden state sequence. The driving behavior hidden state sequence is the sequence of hidden states output at each time step. Hidden states refer to the sequence information retained by the long short-term memory network unit 123 during data processing, which is used to capture long-term dependencies in the sequence, i.e., correlations between data at widely separated time intervals. In certain embodiments, the driving behavior prediction model 121 may include multiple long short-term memory network units 123, enabling the driving behavior prediction model 121 to understand the input data at multiple levels, improving the model's ability to understand data at different time intervals within the time series.

[0052] After obtaining the hidden state sequence of driving behavior, the attention unit 124 will adjust the weight of the hidden state sequence, that is, adjust the weighted average sum of the hidden state sequence. The attention unit 124 may include an attention mechanism layer 127, a fully connected layer 128, and an output layer 129. The attention mechanism layer 127 receives the hidden state sequence output by the long short-term memory network unit 123 and converts it into attention weights through the fully connected layer 128. Then, the attention weights output by the fully connected layer 128 are normalized using the softmax function to obtain the assigned weights for each hidden state sequence. The weight calculation formula is as follows:

[0053] S i =tanh(WHi +b i )

[0054] α i =softmax(S i )

[0055] Then use the weights to calculate the weighted average sum of the driving behavior hidden state sequence. The calculation formula is as follows:

[0056]

[0057] Among them, S i The score of each hidden state sequence, that is, the score of its importance or contribution, H i is the output of the long short-term memory network unit 123, α i is the weight coefficient, C i is the result of weighted summation, and softmax is the activation function.

[0058] The output layer 129 sets the prediction time step o t , finally, the output layer 129 will output o t The prediction result of the next step, that is, the prediction result at a certain moment in the future, is the driving behavior prediction signal.

[0059] In some embodiments, the driving behavior prediction signal may include a driver intention (e.g., acceleration intention, deceleration intention, steering intention, etc.) prediction signal and an abnormal driving behavior signal (e.g., abnormal acceleration signal, abnormal deceleration signal, abnormal steering signal, etc.), etc., to improve the interaction capability between the vehicle and the driver, obtain the driver's personalized driving mode, improve the detection accuracy of abnormal driving behavior, and further improve the safety and reliability of the vehicle.

[0060] Furthermore, the driving behavior prediction model 121 can store the generated driving behavior prediction signals and further analyze them to train and improve the driving behavior prediction model 121, thereby improving the accuracy of the detection system. Furthermore, after analyzing the driving behavior prediction signals, the driving behavior prediction model 121 can generate a corresponding report to analyze the driver's driving habits. For example, the generated report can include the driver's driving speed range in urban areas or at traffic lights, typical driving times, and commonly used driving routes.

[0061] like Figure 1As shown, the acquisition module 110 may include a pre-processing unit 111, which is used to perform pre-processing actions on the driving behavior information, wherein the pre-processing actions may include noise removal, outlier removal, standardization, etc. In this way, the processing of irrelevant information by the driving behavior prediction model 121 can be reduced, the processing speed of the model can be improved, and it can be ensured that different information data have similar scales, that is, similar value ranges, which helps to improve the stability of the model. In some embodiments, a digital filter can be used to remove high-frequency or low-frequency noise, a statistical method can be used to remove outliers, and the input data can be standardized by subtracting the mean of the input data and dividing by the standard deviation. It will be understood that the pre-processing actions performed on the driving behavior information are not limited to this and can be changed as needed.

[0062] Additionally, the acquisition module 110 may further include an extraction unit 112, which may receive the driving behavior information from the preprocessing unit 111 and perform data extraction on the preprocessed driving behavior information to obtain driving behavior information for configuring the driving behavior prediction model 121. This helps provide more accurate input data for the driving behavior prediction model 121, thereby improving the model's accuracy. In certain embodiments, the data extraction may include acceleration or deceleration pattern extraction, acceleration or deceleration rate extraction, driver fatigue behavior extraction, and the like.

[0063] exist Figure 1 In the embodiment, the execution module 130 may include an alarm unit 131, which may execute an alarm action when the detection module 120 detects abnormal driving behavior of the vehicle, so that the driver or the vehicle control system can take timely response. In some embodiments, the alarm action may be to trigger a real-time alarm and / or notify the driver. Specifically, triggering a real-time alarm may include sending an alarm message to a traffic management department, sending an alarm message to an emergency service department, sending an alarm message to the driver's family or emergency contacts, etc. Notifying the driver may include causing the vehicle to emit a sound alarm, or vibrating the seat, steering wheel, seat belt, etc. to make the driver feel the abnormal situation, or displaying the alarm message through the vehicle's infotainment system or instrument panel, etc.

[0064] In addition, the execution module 130 may further include an action unit 132, which may receive an alarm action from the alarm unit 131 and, in response to the alarm action, execute a support action to correct the abnormal state of the vehicle. In some embodiments, the support action may include, for example, automatically braking the vehicle through a vehicle control system or driving the vehicle to a safe location by the driver.

[0065] Reference Figure 3 , Figure 3A schematic flow chart of a method for detecting abnormal driving behavior of a vehicle according to an embodiment of the present invention is shown.

[0066] A method for detecting abnormal driving behavior of a vehicle according to an embodiment of the present invention includes the following steps.

[0067] Step S101: Acquire vehicle driving behavior information.

[0068] In step S101 , the driving behavior information includes battery status information from a battery management system of the vehicle. After obtaining the battery status information, the detection method will obtain the motor operating status of the vehicle based on the battery status information.

[0069] Step S102: Determine whether the vehicle has abnormal driving behavior based on the driving behavior information.

[0070] Step S103: Execute a response action when the vehicle has abnormal driving behavior.

[0071] The technical effect of the abnormal driving behavior detection method of the vehicle according to this embodiment can be found in the Figure 1 The description is not repeated here.

[0072] Reference Figure 4 , Figure 4 A schematic flowchart of a method for detecting abnormal driving behavior of a vehicle according to another embodiment of the present invention is shown.

[0073] A method for detecting abnormal driving behavior of a vehicle according to another embodiment of the present invention includes the following steps.

[0074] Step S201: Acquire vehicle driving behavior information.

[0075] Step S202: performing pre-processing on the driving behavior information.

[0076] Step S203: extracting data from the pre-processed driving behavior information.

[0077] Step S204: Generate a driving behavior prediction signal based on the driving behavior information.

[0078] Step S205: Determine whether the vehicle has abnormal driving behavior based on the driving behavior prediction signal.

[0079] Step S206: Execute an alarm action when the vehicle has abnormal driving behavior.

[0080] Step S207: Execute a support action in response to the alert action.

[0081] The technical effect of the abnormal driving behavior detection method of the vehicle according to this embodiment can be found in the Figure 1 and Figure 2 The description is not repeated here.

[0082] Reference Figure 5 , Figure 5 A schematic diagram of a computer device according to one embodiment of the present invention is shown.

[0083] The present invention also provides a computer device 200, such as Figure 5 As shown, the computer device 200 may include a memory 210 and a processor 220. Instructions 211 may be stored in the memory 210, and the instructions 211 may be executed by the processor 220. When the processor 220 executes the instructions 211, it implements the abnormal driving behavior detection method of the vehicle according to the above embodiment.

[0084] The computer device 200 of this embodiment can be a notebook, a desktop computer, a cloud server, etc. It is understood that the components included in the computer device 200 are not limited to the memory 210 and the processor 220, and can vary depending on different needs. For example, the computer device 200 can also include multiple components connected to its input / output interface ( Figure 5 (not shown) including but not limited to: input units, such as a keyboard, a mouse, etc.; output units, such as a display, a speaker, etc.; storage units, such as a semiconductor storage device, a magnetic surface storage device, an optical storage device, etc.; and communication units, such as a network card, a wireless communication transceiver, etc.

[0085] In some embodiments, the memory 210 may include, for example, a random access memory (RAM) or a read-only memory (ROM). The memory 210 may be used to store instructions, programs, codes, and other programs and data required by the computer device 200, but is not limited thereto.

[0086] In addition, the processor 220 can be a central processing unit (CPU) or other general-purpose processors, such as a digital signal processing (DSP), a field programmable gate array (FPGA), a programmable logic array (PLA), etc.

[0087] In an exemplary embodiment of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium has computer-executable instructions stored thereon. The computer-executable instructions are used to execute the abnormal driving behavior detection method for a vehicle according to the above embodiment.

[0088] Alternatively, the computer-readable storage medium according to the present embodiment may be a ROM, a RAM, a semiconductor storage device, a magnetic surface storage device, an optical storage device, and the like.

[0089] The present invention provides a vehicle abnormal driving behavior detection system, which uses battery status information from the vehicle's battery management system as input data, obtains the operating status of the motor through the battery status information, and determines whether the vehicle has abnormal driving behavior based on the operating status of the motor through a detection module. When abnormal driving behavior is detected, a response action is taken through an execution module, thereby achieving more accurate and timely detection of driving behavior, reducing the risk of false alarms or missed alarms, and being able to immediately execute a response when the vehicle is driving abnormally, thereby reducing or avoiding traffic accidents.

[0090] The abnormal driving behavior detection system of the present invention can be integrated with the vehicle's onboard sensors, the vehicle's automatic diagnostic system (OBD), smartphone sensors, etc., so that the abnormal driving behavior detection system can adapt to different hardware configurations to detect other abnormal situations. For example, when the abnormal driving behavior detection system is integrated with smartphone sensors, the built-in sensors of the mobile phone, such as accelerometers, gyroscopes, GPS, etc., can be used to collect data related to user behavior. The detection system can be used to monitor the behavior of mobile phone users, such as walking, running, cycling, etc. By analyzing this data, the detection system can detect abnormal or dangerous behavior and provide relevant warnings or feedback.

[0091] It should be understood that the embodiment shown in the figure only shows an optional configuration of the abnormal driving behavior detection system of a vehicle according to the present invention. However, it is only illustrative and not restrictive. Other configurations may also be adopted without departing from the spirit and scope of the present invention.

[0092] The technical content and technical features of the present invention have been disclosed above. However, it is understood that, based on the creative ideas of the present invention, those skilled in the art may make various changes and improvements to the above-disclosed concepts, all of which fall within the scope of protection of the present invention. The description of the above embodiments is illustrative and not restrictive. The scope of protection of the present invention is determined by the claims.

Claims

1. A vehicle abnormal driving behavior detection system, characterized in that: The abnormal driving behavior detection system (100) comprises: an acquisition module (110), the acquisition module (110) being configured to acquire driving behavior information of the vehicle; a detection module (120), the detection module (120) being configured to detect and determine whether the vehicle has abnormal driving behavior based on the driving behavior information; an execution module (130), wherein the execution module (130) is configured to execute a response action in the event that the vehicle has abnormal driving behavior, so as to correct the abnormal driving behavior, The driving behavior information includes battery status information from a battery management system of the vehicle, and the acquisition module (110) is configured to acquire the motor operating status of the vehicle based on the battery status information.

2. The abnormal driving behavior detection system for a vehicle according to claim 1, characterized in that: The detection module (120) includes a driving behavior prediction model (121), and the driving behavior prediction model (121) is configured to generate a driving behavior prediction signal based on the driving behavior information.

3. The abnormal driving behavior detection system for a vehicle according to claim 2, characterized in that: The driving behavior prediction model (121) includes a convolutional neural network unit (122), and the convolutional neural network unit (122) is configured to extract spatial features of the driving behavior information and generate a driving behavior feature map.

4. The abnormal driving behavior detection system for a vehicle according to claim 3, characterized in that: The driving behavior prediction model (121) includes a long short-term memory network unit (123), and the long short-term memory network unit (123) is configured to extract temporal features based on the driving behavior feature graph and generate a driving behavior hidden state sequence.

5. The abnormal driving behavior detection system for a vehicle according to claim 4, characterized in that: The driving behavior prediction model (121) includes an attention unit (124) configured to adjust the weight of the driving behavior hidden state sequence to generate the driving behavior prediction signal.

6. The abnormal driving behavior detection system for a vehicle according to claim 5, characterized in that: The driving behavior prediction signal includes a driver intention prediction signal and an abnormal driving behavior signal.

7. The abnormal driving behavior detection system for a vehicle according to claim 1, characterized in that: The acquisition module (110) includes a preprocessing unit (111), and the preprocessing unit (111) is configured to perform a preprocessing action on the driving behavior information, wherein the preprocessing action includes at least one of noise removal, outlier removal, and standardization processing.

8. The abnormal driving behavior detection system for a vehicle according to claim 7, characterized in that: The acquisition module (110) includes an extraction unit (112), and the extraction unit (112) is configured to perform data extraction on the pre-processed driving behavior information, wherein the data extraction includes at least one of acceleration or deceleration pattern extraction, acceleration or deceleration rate extraction, and driver fatigue behavior extraction.

9. The abnormal driving behavior detection system for a vehicle according to claim 1, characterized in that: The execution module (130) includes an alarm unit (131), which is configured to execute an alarm action when the vehicle has abnormal driving behavior, the alarm action including triggering a real-time alarm and / or notifying the driver.

10. The abnormal driving behavior detection system for a vehicle according to claim 9, characterized in that: The execution module (130) includes an action unit (132) configured to execute a support action in response to the warning action, the support action including automatically braking or driving the vehicle to a safe position.

11. The abnormal driving behavior detection system for a vehicle according to any one of claims 1 to 10, characterized in that: The driving behavior information includes at least one of the vehicle's motion state information, control signal information, and driver state information.

12. The abnormal driving behavior detection system for a vehicle according to claim 11, characterized in that: The motion state information includes at least one of the vehicle's speed, acceleration, deceleration, and position information; the control signal information includes at least one of the vehicle's braking system information, steering system information, and body stability control information; and the driver state information includes at least one of the driver's heart rate, body temperature, body posture, and movement.

13. The abnormal driving behavior detection system for a vehicle according to any one of claims 1 to 10, characterized in that: The battery status information includes at least one of current, voltage, and power provided by a battery of the vehicle to a motor.

14. A method for detecting abnormal driving behavior of a vehicle, characterized in that: The abnormal driving behavior detection method comprises: Acquiring driving behavior information of the vehicle (S101; S201); Determining whether the vehicle has abnormal driving behavior based on the driving behavior information (S102); If the vehicle has abnormal driving behavior, a response action is performed (S103). The driving behavior information includes battery status information from a battery management system of the vehicle, and the abnormal driving behavior detection method further includes obtaining a motor operating status of the vehicle based on the battery status information.

15. The method for detecting abnormal driving behavior of a vehicle according to claim 14, wherein: Determining whether the vehicle has abnormal driving behavior based on the driving behavior information includes generating a driving behavior prediction signal based on the driving behavior information ( S204 ).

16. The method for detecting abnormal driving behavior of a vehicle according to claim 14, wherein: The abnormal driving behavior detection method includes: performing a pre-processing action on the driving behavior information (S202).

17. The method for detecting abnormal driving behavior of a vehicle according to claim 16, wherein: The abnormal driving behavior detection method includes: extracting data from the pre-processed driving behavior information (S203).

18. The method for detecting abnormal driving behavior of a vehicle according to claim 14, wherein: Executing a response action when the vehicle has abnormal driving behavior includes: executing an alarm action when the vehicle has abnormal driving behavior ( S206 ).

19. The method for detecting abnormal driving behavior of a vehicle according to claim 18, wherein: Executing a response action when the vehicle has abnormal driving behavior includes: executing a support action in response to the alarm action (S207).

20. A vehicle, characterized in that: The vehicle includes a battery management system, and the abnormal driving behavior detection system for a vehicle according to any one of claims 1 to 13, the abnormal driving behavior detection system being integrated with the battery management system.

21. A computer device, characterized in that: The computer device (200) includes a memory (210), a processor (220), and instructions (211) stored in the memory (210) and executable by the processor (220), wherein when the processor (220) executes the instructions (211), the abnormal driving behavior detection method of a vehicle according to any one of claims 14 to 19 is implemented.

22. A computer-readable storage medium, characterized in that The computer-readable storage medium has computer-executable instructions stored thereon, and the computer-executable instructions are used to execute the abnormal driving behavior detection method for a vehicle according to any one of claims 14 to 19.

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