A driving scoring method, device and electronic device based on vehicle-mounted DMS

Abstract

The present disclosure relates to a driving scoring method, device and electronic device based on an on-board DMS, the method comprising: determining improper driving behavior during driving and the duration corresponding to the improper driving behavior based on the on-board DMS; conducting a risk assessment on the improper driving behavior based on a driving assessment model to determine the risk coefficient corresponding to the improper driving behavior; and determining the driving score corresponding to the improper driving behavior according to the risk coefficient corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior. The embodiments of the present disclosure can evaluate the professional level of the driver, thereby assisting the driver in developing good driving habits.

Description

Technical Field

[0001] The present disclosure relates to the field of safe driving technology, and in particular to a driving scoring method, device and electronic equipment based on a vehicle-mounted DMS. Background Art

[0002] The Driver Monitor System (DMS) includes functions such as fatigue monitoring, distraction monitoring, dangerous behavior monitoring, expression recognition, and gesture recognition. The current traditional in-vehicle DMS only has a real-time reminder function. When improper driving behavior is detected during driving, it will generate corresponding sound alarms, voice alarms, instrument alarms and other reminders. It cannot play an effective role in the long-term development of driving habits and driving content feedback. Summary of the invention

[0003] The present disclosure proposes a technical solution of a driving scoring method, device and electronic equipment based on a vehicle-mounted DMS.

[0004] According to one aspect of the present disclosure, a driving scoring method based on a vehicle-mounted DMS is provided, comprising: determining improper driving behavior during driving and the duration corresponding to the improper driving behavior based on the vehicle-mounted DMS; performing a risk assessment on the improper driving behavior based on a driving assessment model to determine the risk coefficient corresponding to the improper driving behavior; and determining a driving score corresponding to the improper driving behavior based on the risk coefficient corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior.

[0005] In a possible implementation, the performing of a risk assessment on the improper driving behavior based on a driving assessment model and determining a risk coefficient corresponding to the improper driving behavior includes: determining an image to be assessed corresponding to the improper driving behavior; and performing a risk assessment on the image to be assessed based on the driving assessment model and determining a risk coefficient corresponding to the improper driving behavior.

[0006] In a possible implementation, determining the image to be evaluated corresponding to the improper driving behavior includes: determining an offset value between the image corresponding to the improper driving behavior and the image corresponding to the normal driving behavior; and determining the image to be evaluated from the image corresponding to the improper driving behavior according to the offset value.

[0007] In a possible implementation, performing risk assessment on the image to be assessed based on the driving assessment model to determine the risk coefficient corresponding to the improper driving behavior includes: inputting the image to be assessed into the driving assessment model to determine the risk coefficient corresponding to the image to be assessed; and determining the risk coefficient corresponding to the improper driving behavior based on the risk coefficient corresponding to the image to be assessed.

[0008] In a possible implementation, there are multiple images to be evaluated; and determining the risk coefficient corresponding to the improper driving behavior based on the risk coefficients corresponding to the images to be evaluated includes: determining an average risk coefficient value based on the risk coefficient corresponding to each of the images to be evaluated; and determining the average risk coefficient value as the risk coefficient corresponding to the improper driving behavior.

[0009] In a possible implementation, the method also includes: determining a sample image set, wherein the sample image set includes sample images corresponding to multiple different types of improper driving behaviors, and a labeled risk factor corresponding to each sample image; dividing the sample image set into a first training set, a verification set, and a test set according to a preset ratio; performing image enhancement processing on the sample images included in the first training set to determine a second training set.

[0010] In one possible implementation, the training method of the driving assessment model includes: based on the driving assessment model, performing a risk assessment on the sample images included in the second training set, and determining a predicted risk coefficient corresponding to the sample images included in the second training set; determining a first assessment loss based on the predicted risk coefficient corresponding to the sample images included in the second training set and the labeled risk coefficient corresponding to the sample images included in the second training set; and based on the first assessment loss, using an adaptive moment estimation algorithm to train the driving assessment model.

[0011] In a possible implementation, the method further includes: based on the driving assessment model, performing a risk assessment on the sample images included in the verification set, and determining a predicted risk coefficient corresponding to the sample images included in the verification set; determining a second assessment loss based on the predicted risk coefficient corresponding to the sample images included in the verification set and the labeled risk coefficient corresponding to the sample images included in the verification set; and training the driving assessment model by minimizing the second assessment loss.

[0012] In a possible implementation, the method further includes: based on the driving assessment model, performing a risk assessment on the sample images included in the test set, and determining a predicted risk coefficient corresponding to the sample images included in the test set; and evaluating the accuracy of the driving assessment model according to the predicted risk coefficient corresponding to the sample images included in the test set and the labeled risk coefficient corresponding to the sample images included in the test set.

[0013] According to one aspect of the present disclosure, a driving scoring device based on a vehicle-mounted DMS is provided, comprising: an acquisition module, for determining, based on the vehicle-mounted DMS, improper driving behavior during driving and a duration corresponding to the improper driving behavior; a risk assessment module, for performing a risk assessment on the improper driving behavior based on a driving assessment model and determining a risk coefficient corresponding to the improper driving behavior; and a driving scoring module, for determining a driving score corresponding to the improper driving behavior according to the risk coefficient corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior.

[0014] According to one aspect of the present disclosure, an electronic device is provided, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to call the instructions stored in the memory to execute the above method.

[0015] According to one aspect of the present disclosure, a computer-readable storage medium is provided, on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor.

[0016] In the disclosed embodiment, based on the vehicle-mounted DMS, improper driving behavior during driving can be monitored, and the duration corresponding to the improper driving behavior can be recorded; based on the driving assessment model, the improper driving behavior can be automatically and efficiently evaluated for risk, and the risk coefficient corresponding to the improper driving behavior can be determined; according to the risk coefficient corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior, the driving score corresponding to the improper driving behavior can be determined. Since the driving score corresponding to the improper driving behavior can feedback the potential risks in the driving process, the driving score corresponding to the improper driving behavior can evaluate the driver's professional level, thereby assisting the driver to develop good driving habits.

[0017] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present disclosure. Other features and aspects of the present disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present disclosure and are used to illustrate the technical solutions of the present disclosure together with the specification.

[0019] Figure 1 A flowchart of a driving scoring method based on a vehicle-mounted DMS according to an embodiment of the present disclosure is shown;

[0020] Figure 2 A flowchart of a driving scoring system based on a vehicle-mounted DMS according to an embodiment of the present disclosure is shown;

[0021] Figure 3 A flowchart showing a method for training a driving assessment model according to an embodiment of the present disclosure;

[0022] Figure 4 A block diagram of a driving scoring device based on a vehicle-mounted DMS according to an embodiment of the present disclosure is shown;

[0023] Figure 5 A block diagram of an electronic device according to an embodiment of the present disclosure is shown;

[0024] Figure 6 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0025] Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the accompanying drawings represent elements with the same or similar functions. Although various aspects of the embodiments are shown in the accompanying drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0026] The word “exemplary” is used exclusively herein to mean “serving as an example, example, or illustration.” Any embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other embodiments.

[0027] The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the term "at least one" herein represents any combination of at least two of any one or more of a plurality of. For example, including at least one of A, B, and C can represent including any one or more elements selected from the set consisting of A, B, and C.

[0028] In addition, in order to better illustrate the present disclosure, numerous specific details are given in the following specific embodiments. It should be understood by those skilled in the art that the present disclosure can also be implemented without certain specific details. In some examples, methods, means, components and circuits well known to those skilled in the art are not described in detail in order to highlight the subject matter of the present disclosure.

[0029] Figure 1A flowchart of a driving scoring method based on a vehicle-mounted DMS according to an embodiment of the present disclosure is shown. The method can be executed by an electronic device such as a terminal device or a server. The terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. The method can be implemented by a processor calling a computer-readable instruction stored in a memory. Alternatively, the method can be executed by a server. Figure 1 As shown, the method includes:

[0030] In step S11, based on the vehicle-mounted DMS, improper driving behavior during driving and the duration corresponding to the improper driving behavior are determined.

[0031] After the vehicle is started, the vehicle DMS can be turned on until the vehicle is turned off. During the working period of the vehicle DMS, the vehicle DMS monitors improper driving behaviors during driving and records the duration of improper driving behaviors.

[0032] Among them, improper driving behavior can be various behaviors or actions that the driver exhibits during driving that affect driving safety. For example, improper driving behavior may include closing eyelids, yawning, deviation of sight from the road, deviation of face angle from the road, lowering the head to play with mobile phone, smoking, making phone calls, eating, picking up things, etc., and this disclosure does not make specific limitations on this.

[0033] Based on the vehicle-mounted DMS, the driver's facial features and body movements can be monitored in real time. When the vehicle-mounted DMS detects that the driver has specific behaviors or movements during driving, it determines that the driver has improper driving behavior. The specific process of the vehicle-mounted DMS monitoring and determining improper driving behavior can refer to the specific implementation methods of the vehicle-mounted DMS in the relevant technology, and this disclosure does not make specific limitations on this.

[0034] Furthermore, the improper driving behavior is continuously monitored until the improper driving behavior is detected to be over, and the duration corresponding to the improper driving behavior is recorded. Exemplarily, the duration can be in seconds, and the duration corresponding to each recorded improper driving behavior should be greater than one second. If the duration corresponding to a certain improper driving behavior is less than one second, the duration can be recorded as one second. The duration can also be in other time units, which is not specifically limited in the present disclosure.

[0035] In step S12, based on the driving evaluation model, a risk assessment is performed on the improper driving behavior to determine the risk coefficient corresponding to the improper driving behavior.

[0036] The driving evaluation model may be a pre-trained neural network model. For example, the driving evaluation model may be trained based on a facial feature detection model, or may be a neural network model of other network structures, which is not specifically limited in the present disclosure.

[0037] The specific process of training the driving evaluation model will be described in detail later in conjunction with possible implementation methods of the present disclosure, and will not be elaborated here.

[0038] The risk coefficient corresponding to improper driving behavior is used to indicate the degree of harm caused by improper driving behavior per second. The greater the harm that improper driving behavior may cause, the greater the corresponding risk coefficient. The value range of the risk coefficient corresponding to improper driving behavior can be [0.01-1] or other ranges, and this disclosure does not make specific limitations on this.

[0039] Based on the driving assessment model, the risk factor corresponding to improper driving behavior can be determined. The risk factor corresponding to improper driving behavior indicates the degree of danger caused by improper driving behavior per second, which is used to score improper driving behavior in the future.

[0040] The specific process of conducting risk assessment on improper driving behavior based on the driving assessment model and determining the risk coefficient corresponding to the improper driving behavior will be described in detail later in conjunction with possible implementation methods of the present disclosure and will not be elaborated here.

[0041] In step S13, a driving score corresponding to the improper driving behavior is determined according to the risk factor corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior.

[0042] Since the risk factor corresponding to improper driving behavior can indicate the degree of harm caused by improper driving behavior every second, the driving score corresponding to the improper driving behavior can be determined by combining the risk factor corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior. The driving score corresponding to the improper driving behavior can further reflect the harm that may be caused by this improper driving behavior and provide a basis for evaluating the driver's professional level.

[0043] For example, based on the vehicle-mounted DMS, improper driving behavior A is detected during driving, and the duration corresponding to improper driving behavior A is determined to be 60 seconds. Based on the driving assessment model, a risk assessment is performed on improper driving behavior A, and the risk coefficient corresponding to improper driving behavior A is determined to be 0.5. By multiplying the risk coefficient corresponding to improper driving behavior A, 0.5, by the duration corresponding to improper driving behavior A, 60 seconds, it can be determined that the driving score corresponding to improper driving behavior A is 30.

[0044] In the same driving process, improper driving behaviors may occur multiple times, and the driving score corresponding to each improper driving behavior can be calculated separately. According to the driving scores corresponding to multiple improper driving behaviors, the total score corresponding to this driving process can be determined.

[0045] For example, in a driving process, a total of n improper driving behaviors occur. According to the above method, the risk coefficient corresponding to the i-th improper driving behavior can be determined as Ai. At this time, the total score S corresponding to the driving process can be determined according to the following formula (1):

[0046]

[0047] Among them, t i is the duration corresponding to the i-th improper driving behavior.

[0048] In addition to the above-described method, other methods may be used to determine the total score of the driving process according to actual conditions, and the present disclosure does not make any specific limitations on this.

[0049] Optionally, after the driving process is finished and the total score corresponding to the driving process is determined, the total score corresponding to the driving process can be displayed to the driver based on a display device on the vehicle or a mobile terminal connected to the vehicle, or the total score corresponding to the driving process can be voice broadcast to the driver based on a voice broadcast device on the vehicle or a mobile terminal connected to the vehicle. Thus, the total score corresponding to the driving process can be used to objectively evaluate the driver's professional level in the driving process, thereby helping the driver to develop good driving habits.

[0050] For example, the total score corresponding to the current driving process can be announced by voice through the vehicle-mounted DMS or the voice broadcast device of the vehicle system, or the total score corresponding to the current driving process can be displayed on the display screen of the vehicle system. The present disclosure does not make specific limitations on this.

[0051] Optionally, the total score corresponding to each driving process within a preset time period can be counted to generate a driving report corresponding to the preset time period, for example, a weekly driving report for each week, or a monthly driving report for each month. Furthermore, based on specific usage requirements, the driving report corresponding to the preset time period can be displayed to the driver. The display method is the same as the method for displaying the total score corresponding to the driving process mentioned above, which will not be repeated here. The driving report corresponding to the preset time period can also be sent to a third-party scoring system. Thus, through the driving report corresponding to the preset time period, the long-term tracking of the driver's business ability can be achieved, and the driver's business ability can be further evaluated in combination with the third-party system.

[0052] Figure 2 FIG. 1 is a flow chart of a driving scoring system based on a vehicle-mounted DMS according to an embodiment of the present disclosure. Figure 2 As shown in the figure, the driving scoring system is based on the functions of fatigue driving monitoring, distracted driving monitoring and dangerous action monitoring of the vehicle-mounted DMS. According to the above driving scoring method, the total score corresponding to each driving process of the driver within the preset time period is determined. In addition to fatigue driving monitoring, distracted driving monitoring and dangerous action monitoring, other improper driving behavior monitoring can also be performed. Figure 2 They are no longer listed one by one.

[0053] like Figure 2 As shown, for ordinary vehicles, such as family cars, personal commercial vehicles, etc., a driving report corresponding to the preset time period can be generated directly according to the total score corresponding to each driving process in the preset time period, thereby assisting the driver to develop good driving habits.

[0054] For operating vehicles, such as buses, taxis, and freight trucks, the total score corresponding to each driving process of the operating vehicles within a preset time period can be counted, and the statistical results can be output and saved. Then, the statistical results can be voice-broadcasted through the on-board DMS or the voice broadcasting device of the vehicle system; or, the statistical results can be displayed on the display screen of the vehicle system, so that the drivers of the operating vehicles can understand their driving status and business capabilities in the preset time period. According to the statistical results, a driving report corresponding to the preset time period can also be generated, and the driving report corresponding to the preset time period can be sent to the scoring system of the corresponding operating company, so that the corresponding operating company can summarize and archive the driving reports corresponding to the driver in the preset time period, and further evaluate the driver's business capabilities in combination with the scoring system of the operating company.

[0055] In the disclosed embodiment, based on the vehicle-mounted DMS, improper driving behavior during driving can be monitored, and the duration corresponding to the improper driving behavior can be recorded; based on the driving assessment model, the improper driving behavior can be automatically and efficiently evaluated for risk, and the risk coefficient corresponding to the improper driving behavior can be determined; according to the risk coefficient corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior, the driving score corresponding to the improper driving behavior can be determined. Since the driving score corresponding to the improper driving behavior can feedback the potential risks in the driving process, the driving score corresponding to the improper driving behavior can evaluate the driver's professional level, thereby assisting the driver to develop good driving habits.

[0056] In a possible implementation, the method also includes: determining a sample image set, wherein the sample image set includes sample images corresponding to a plurality of different types of improper driving behaviors and a labeled risk factor corresponding to each sample image; dividing the sample image set into a first training set, a verification set, and a test set according to a preset ratio; performing image enhancement processing on the sample images included in the first training set to determine a second training set.

[0057] In order to determine the driving assessment model, it is necessary to pre-build a sample image set for training the driving assessment model. The sample image set may include sample images corresponding to various types of improper driving behaviors, and annotated risk factors corresponding to each sample image. The sample images may be images corresponding to improper driving behaviors acquired based on the vehicle-mounted DMS, or images corresponding to improper driving behaviors acquired by other means, which is not specifically limited in the present disclosure.

[0058] The annotated risk factor corresponding to the sample image can be determined by professionals performing manual risk assessment on the sample image.

[0059] Exemplarily, referring to Table 1, an initial risk coefficient is set for each type of improper driving behavior according to the type of improper driving behavior. Sample images with initial risk coefficients are randomly assigned to professionals participating in manual risk assessment. Professionals fine-tune the initial risk coefficient based on the details of the sample image to determine the annotated risk coefficient for each sample image. For example, for the improper driving behavior of sight deviation from the road, when the angle of the driver's sight deviation from the road is the maximum value, the corresponding initial risk coefficient is 1. In sample image A, the angle of the driver's sight deviation from the road is 80% of the maximum angle. Therefore, it can be determined that the annotated risk coefficient corresponding to sample image A is 0.8.

[0060]

[0061]

[0062] Table 1

[0063] In addition to the above-described method, other methods may be used to determine the annotated risk factor corresponding to the sample image according to actual conditions, and the present disclosure does not make any specific limitation on this.

[0064] After determining the sample image set, the sample image set can be divided into a first training set, a validation set, and a test set according to a preset ratio. Among them, the first training set is used to train the driving evaluation model; the validation set is used to optimize the driving evaluation model trained based on the first training set, and optimize the error loss of the driving evaluation model to a minimum; the test set is used to test the accuracy of the driving evaluation model. The preset ratio for dividing the sample image set can be determined according to actual conditions. For example, the preset ratio between the first training set, the validation set, and the test set can be 8:1:1, or other ratios, and the present disclosure does not make specific limitations on this.

[0065] Figure 3 A flow chart of a method for training a driving evaluation model according to an embodiment of the present disclosure is shown. Figure 3As shown, a sample image set is determined, and the sample image set is divided into a first training set, a validation set, and a test set according to a preset ratio.

[0066] After determining the first training set, in order to expand the data volume of the training samples and enhance the diversity of the training samples so as to improve the accuracy of the driving assessment model determined by the training, the sample images included in the first training set can be subjected to image enhancement processing to obtain a second training set with a larger data volume and greater diversity. The annotated risk coefficient corresponding to each sample image included in the second training set is the same as the annotated risk coefficient of the corresponding sample image before the image enhancement processing.

[0067] With the above Figure 3 For example, Figure 3 As shown, image enhancement processing is performed on the sample images included in the first training set to obtain the second training set.

[0068] The image enhancement processing method may include changing the contrast, brightness, saturation and other parameters of the sample image, randomly cropping the sample image, upsampling and downsampling the sample image, randomly distorting the sample image, etc., and the present disclosure does not make specific limitations on this.

[0069] In one possible implementation, the training method of the driving assessment model includes: based on the driving assessment model, performing a risk assessment on the sample images included in the second training set, and determining the predicted risk coefficients corresponding to the sample images included in the second training set; determining a first assessment loss based on the predicted risk coefficients corresponding to the sample images included in the second training set and the labeled risk coefficients corresponding to the sample images included in the second training set; and based on the first assessment loss, using an adaptive moment estimation algorithm to train the driving assessment model.

[0070] The sample images included in the second training set are input into the driving assessment model, and the predicted risk coefficients corresponding to the sample images included in the second training set can be determined. According to the predicted risk coefficients corresponding to the sample images included in the second training set and the labeled risk coefficients corresponding to the sample images included in the second training set, a first assessment loss can be determined, and the first assessment loss can reflect the error of the driving assessment model in assessing the risk of the sample images.

[0071] Exemplarily, the first evaluation loss may be determined based on a preset loss function. The preset loss function may be an L2 loss function determined based on a mean square error (MSE), that is, the first evaluation loss MSE may be determined according to the following formula (2):

[0072]

[0073] Among them, Y i is the predicted risk coefficient corresponding to the i-th sample image included in the second training set, is the labeled risk coefficient corresponding to the i-th sample image included in the second training set.

[0074] After determining the first evaluation loss, the network parameters of the driving evaluation model may be adjusted using an adaptive moment estimation algorithm (Adam).

[0075] By iteratively executing the above steps, iterative training of the driving evaluation model using the second training set is achieved until the preset training conditions are met and the training is terminated.

[0076] With the above Figure 3 For example, Figure 3 As shown, the driving evaluation model is trained based on the second training set to obtain a driving evaluation model trained based on the second training set.

[0077] In one possible implementation, the method also includes: based on the driving assessment model, performing a risk assessment on the sample images included in the verification set, and determining the predicted risk coefficients corresponding to the sample images included in the verification set; determining a second assessment loss based on the predicted risk coefficients corresponding to the sample images included in the verification set and the labeled risk coefficients corresponding to the sample images included in the verification set; and training the driving assessment model by minimizing the second assessment loss.

[0078] In order to further optimize the evaluation performance of the driving evaluation model, after obtaining the driving evaluation model trained based on the second training set, the driving evaluation model can be iteratively trained again using the comprehensive validation set.

[0079] By inputting the sample images included in the validation set into the driving assessment model trained based on the second training set, the predicted risk coefficient corresponding to the sample images included in the validation set can be determined. According to the predicted risk coefficient corresponding to the sample images included in the validation set and the labeled risk coefficient corresponding to the sample images included in the validation set, the second assessment loss can be determined. The method for determining the second assessment loss is the same as the method for determining the first assessment loss mentioned above, and will not be repeated here. By minimizing the second assessment loss and iteratively training the driving assessment model by comprehensively utilizing the second training set and the validation set, the assessment performance of the driving assessment model can be optimized.

[0080] With the above Figure 3 For example, Figure 3 As shown, the second evaluation loss is determined based on the validation set, and it is judged whether the second evaluation loss is less than a preset value. If the second evaluation loss is not less than the preset value, the driving evaluation model is retrained based on the second training set; if the second evaluation loss is less than the preset value, the training is terminated to obtain the trained driving evaluation model.

[0081] In a possible implementation, the method also includes: based on the driving assessment model, performing a risk assessment on the sample images included in the test set, and determining the predicted risk coefficients corresponding to the sample images included in the test set; and evaluating the accuracy of the driving assessment model according to the predicted risk coefficients corresponding to the sample images included in the test set and the labeled risk coefficients corresponding to the sample images included in the test set.

[0082] After the training of the driving assessment model is completed by combining the second training set and the validation set, the sample images included in the test set are input into the trained driving assessment model, and the predicted risk coefficient corresponding to the sample images included in the test set can be determined. The predicted risk coefficient corresponding to the sample images included in the test set is compared with the labeled risk coefficient corresponding to the sample images included in the test set to determine the accuracy of the driving assessment model. Based on the accuracy of the driving assessment model, it can be determined whether the trained driving assessment model determined by the aforementioned iterative training can be applied to the risk assessment of improper driving behavior.

[0083] With the above Figure 3 For example, Figure 3 As shown, the trained driving evaluation model is tested using the test set to determine the accuracy of the driving evaluation model.

[0084] Exemplarily, when the error between the predicted risk coefficient corresponding to the sample image included in the test set and the labeled risk coefficient corresponding to the sample image included in the test set is small, it can be determined that the predicted risk coefficient corresponding to the sample image determined by the driving assessment model is more accurate. At this time, it can be determined that after the training of the driving assessment model is completed by combining the second training set and the validation set, it can be applied to the risk assessment of improper driving behavior.

[0085] When the error between the predicted risk factor corresponding to the sample image included in the test set and the labeled risk factor corresponding to the sample image included in the test set is large, it can be determined that the predicted risk factor corresponding to the sample image determined by the driving assessment model is inaccurate. At this time, it is necessary to continue training the driving assessment model until its accuracy meets the standard.

[0086] In addition to the above-described method, other methods may be used to determine the accuracy of the driving evaluation model according to actual conditions, and the present disclosure does not make any specific limitation on this.

[0087] After obtaining a driving assessment model that meets the accuracy requirements, it can be used to conduct risk assessment of improper driving behaviors during driving.

[0088] In a possible implementation, based on a driving assessment model, a risk assessment is performed on improper driving behavior to determine a risk coefficient corresponding to the improper driving behavior, including: determining an image to be assessed corresponding to the improper driving behavior; based on the driving assessment model, a risk assessment is performed on the image to be assessed to determine a risk coefficient corresponding to the improper driving behavior.

[0089] Since improper driving behavior is time-persistent, after the vehicle-mounted DMS detects improper driving behavior, it can continuously collect images corresponding to the improper driving behavior, and then determine the image to be evaluated from the images corresponding to the improper driving behavior. The image to be evaluated corresponding to the improper driving behavior is input into the driving evaluation model, so that the driving evaluation model performs a risk assessment on the image to be evaluated and determines the risk coefficient corresponding to the improper driving behavior.

[0090] In a possible implementation, determining an image to be evaluated corresponding to the improper driving behavior includes: determining an offset value between an image corresponding to the improper driving behavior and an image corresponding to the normal driving behavior; and determining the image to be evaluated from the images corresponding to the improper driving behavior according to the offset value.

[0091] By comparing any image collected after the on-board DMS detects improper driving behavior with the image corresponding to the normal driving behavior, the similarity between the image corresponding to the improper driving behavior and the image corresponding to the normal driving behavior can be determined, and then based on the similarity between the image corresponding to the improper driving behavior and the image corresponding to the normal driving behavior, the offset value between the image corresponding to the improper driving behavior and the image corresponding to the normal driving behavior can be determined.

[0092] The greater the similarity between the image corresponding to the improper driving behavior and the image corresponding to the normal driving behavior, the smaller the offset value between the image corresponding to the improper driving behavior and the image corresponding to the normal driving behavior; the smaller the similarity between the image corresponding to the improper driving behavior and the image corresponding to the normal driving behavior, the greater the offset value between the image corresponding to the improper driving behavior and the image corresponding to the normal driving behavior.

[0093] According to the offset value between the image corresponding to the improper driving behavior and the image corresponding to the normal driving behavior, the image to be evaluated can be determined from the images corresponding to the improper driving behavior.

[0094] In a possible implementation, a preset threshold for screening images to be evaluated can be set according to the requirements of driving behavior evaluation. Based on the comparison result of the offset value between the image corresponding to the improper driving behavior and the image corresponding to the normal driving behavior and the preset threshold, the image to be evaluated is determined in the images corresponding to the improper driving behavior.

[0095] Exemplarily, the images corresponding to the improper driving behavior B include image a, image b, image c, and image d. The images a, image b, image c, and image d are respectively compared with the image e corresponding to the normal driving behavior to determine the offset values ​​between the images a, image b, image c, image d and image e. When the offset values ​​between the images a, image b and image e are greater than a preset threshold, and the offset values ​​between the images c, image d and image e are less than the preset threshold, it means that the similarity between the images a, image b and image e is low, that is, the improper driving behaviors at the corresponding moments of the images a and image b deviate greatly from the normal driving behaviors. At this time, the images a and image b can be determined as the images to be evaluated.

[0096] In one possible implementation, the image with the largest offset value between the image corresponding to the normal driving behavior and the image frame sequence continuously acquired by the vehicle-mounted DMS for the improper driving behavior can be determined, and then the image and a preset number of adjacent frame images before and after the image can be selected as the images to be evaluated.

[0097] In addition to the above-described method, other methods may be used to determine the image to be evaluated from the images corresponding to the improper driving behavior according to actual conditions, and the present disclosure does not make any specific limitation on this.

[0098] In a possible implementation, based on a driving assessment model, a risk assessment is performed on the image to be assessed to determine the risk factor corresponding to the improper driving behavior, including: inputting the image to be assessed into the driving assessment model to determine the risk factor corresponding to the image to be assessed; and determining the risk factor corresponding to the improper driving behavior based on the risk factor corresponding to the image to be assessed.

[0099] After the image to be evaluated is determined, the image to be evaluated is input into the driving evaluation model, and the image to be evaluated is evaluated based on the driving evaluation model to determine the risk factor corresponding to the image to be evaluated. Since the image to be evaluated can indicate improper driving behavior, the risk factor corresponding to the improper driving behavior can be determined based on the risk factor corresponding to the image to be evaluated.

[0100] Exemplarily, when the number of images to be evaluated is one, the risk factor corresponding to the image to be evaluated is directly determined as the risk factor corresponding to the improper driving behavior.

[0101] Exemplarily, the image to be evaluated corresponding to the improper driving behavior C is image f. After determining that the risk factor corresponding to image f is 0.6 based on the driving evaluation model, it can be determined that the risk factor corresponding to the improper driving behavior C is 0.6.

[0102] In a possible implementation, there are multiple images to be evaluated; the risk coefficient corresponding to the improper driving behavior is determined according to the risk coefficient corresponding to the images to be evaluated, including: determining an average risk coefficient value according to the risk coefficient corresponding to each image to be evaluated; and determining the average risk coefficient value as the risk coefficient corresponding to the improper driving behavior.

[0103] When there are multiple images to be evaluated, the risk factor corresponding to each image to be evaluated can be determined respectively, and then the average risk factor is calculated according to the risk factor corresponding to each image to be evaluated, and the average risk factor is used as the risk factor corresponding to the improper driving behavior.

[0104] Exemplarily, the images to be evaluated corresponding to the improper driving behavior D include image g, image h, and image j. Based on the driving evaluation model, after determining that the risk coefficient corresponding to image g is 0.5, the risk coefficient corresponding to image h is 0.6, and the risk coefficient corresponding to image j is 0.4, the average risk coefficient is further determined to be 0.5. At this time, it can be determined that the risk coefficient corresponding to the improper driving behavior D is 0.5.

[0105] In the disclosed embodiment, based on the vehicle-mounted DMS, improper driving behavior during driving can be monitored, and the duration corresponding to the improper driving behavior can be recorded; based on the driving assessment model, the improper driving behavior can be automatically and efficiently evaluated for risk, and the risk coefficient corresponding to the improper driving behavior can be determined; according to the risk coefficient corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior, the driving score corresponding to the improper driving behavior can be determined. Since the driving score corresponding to the improper driving behavior can feedback the potential risks of the driving process, the driving score corresponding to the improper driving behavior can objectively evaluate the driver's professional level, thereby assisting the driver to develop good driving habits.

[0106] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not repeat them. It can be understood by those skilled in the art that in the above-mentioned method of the specific implementation method, the specific execution order of each step should be determined according to its function and possible internal logic.

[0107] In addition, the present disclosure also provides a driving scoring device based on a vehicle-mounted DMS, an electronic device, a computer-readable storage medium, and a program, all of which can be used to implement any driving scoring method based on a vehicle-mounted DMS provided by the present disclosure. The corresponding technical solutions and descriptions are referred to the corresponding records in the method part and will not be repeated here.

[0108] Figure 4 FIG. 2 shows a block diagram of a driving scoring device based on a vehicle-mounted DMS according to an embodiment of the present disclosure. Figure 4As shown, the driving scoring device 400 includes:

[0109] The acquisition module 401 is used to determine the improper driving behavior during driving and the duration corresponding to the improper driving behavior based on the vehicle-mounted DMS;

[0110] The risk assessment module 402 is used to perform risk assessment on improper driving behavior based on the driving assessment model and determine the risk coefficient corresponding to the improper driving behavior;

[0111] The driving score module 403 is used to determine a driving score corresponding to the improper driving behavior according to the risk factor corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior.

[0112] In a possible implementation, the risk assessment module 402 is specifically used to: determine an image to be assessed corresponding to the improper driving behavior; and perform risk assessment on the image to be assessed based on a driving assessment model to determine a risk coefficient corresponding to the improper driving behavior.

[0113] In a possible implementation, the risk assessment module 402 is further used to: determine an offset value between an image corresponding to the improper driving behavior and an image corresponding to the normal driving behavior; and determine an image to be assessed from the images corresponding to the improper driving behavior according to the offset value.

[0114] In a possible implementation, the risk assessment module 402 is further used to: input the image to be assessed into the driving assessment model to determine the risk factor corresponding to the image to be assessed; and determine the risk factor corresponding to the improper driving behavior according to the risk factor corresponding to the image to be assessed.

[0115] In a possible implementation, there are multiple images to be evaluated; the risk assessment module 402 is further used to: determine an average risk factor value based on the risk factor corresponding to each image to be evaluated; and determine the average risk factor value as the risk factor corresponding to the improper driving behavior.

[0116] In a possible implementation, the driving scoring device 400 further includes: a driving assessment model training module, which is used to: determine a sample image set, wherein the sample image set includes sample images corresponding to multiple different types of improper driving behaviors, and a labeled risk factor corresponding to each sample image; divide the sample image set into a first training set, a verification set, and a test set according to a preset ratio; and perform image enhancement processing on the sample images included in the first training set to determine a second training set.

[0117] In one possible implementation, the driving assessment model training module is also used to: perform risk assessment on the sample images included in the second training set based on the driving assessment model, and determine the predicted risk coefficients corresponding to the sample images included in the second training set; determine the first assessment loss based on the predicted risk coefficients corresponding to the sample images included in the second training set and the labeled risk coefficients corresponding to the sample images included in the second training set; and train the driving assessment model based on the first assessment loss using an adaptive moment estimation algorithm.

[0118] In one possible implementation, the driving assessment model training module is also used to: perform risk assessment on sample images included in a validation set based on the driving assessment model, and determine predicted risk coefficients corresponding to the sample images included in the validation set; determine a second assessment loss based on the predicted risk coefficients corresponding to the sample images included in the validation set and the labeled risk coefficients corresponding to the sample images included in the validation set; and train the driving assessment model by minimizing the second assessment loss.

[0119] In one possible implementation, the driving assessment model training module is further used to: perform risk assessment on the sample images included in the test set based on the driving assessment model, and determine the predicted risk coefficients corresponding to the sample images included in the test set; and evaluate the accuracy of the driving assessment model according to the predicted risk coefficients corresponding to the sample images included in the test set and the labeled risk coefficients corresponding to the sample images included in the test set.

[0120] In some embodiments, the functions or modules included in the device provided by the embodiments of the present disclosure can be used to execute the method described in the above method embodiments. The specific implementation can refer to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0121] The embodiment of the present disclosure also provides a computer-readable storage medium on which computer program instructions are stored, and the computer program instructions implement the above method when executed by a processor. The computer-readable storage medium can be a volatile or non-volatile computer-readable storage medium.

[0122] An embodiment of the present disclosure further proposes an electronic device, comprising: a processor; and a memory for storing instructions executable by the processor; wherein the processor is configured to call the instructions stored in the memory to execute the above method.

[0123] The embodiments of the present disclosure also provide a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above method.

[0124] The electronic device may be provided as a terminal, a server, or a device in other forms.

[0125] Figure 5 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Figure 5 The electronic device 800 may be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or other terminal device.

[0126] Reference Figure 5 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output interface 812 , a sensor component 814 , and a communication component 816 .

[0127] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the above-mentioned method. In addition, the processing component 802 may include one or more modules to facilitate the interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate the interaction between the multimedia component 808 and the processing component 802.

[0128] The memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0129] The power supply component 806 provides power to the various components of the electronic device 800. The power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.

[0130] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0131] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), and when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 804 or sent via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.

[0132] The input / output interface 812 provides an interface between the processing component 802 and the peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0133] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of the components, such as the display and keypad of the electronic device 800, and the sensor assembly 814 can also detect the position change of the electronic device 800 or a component of the electronic device 800, the presence or absence of contact between the user and the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and the temperature change of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 may also include an optical sensor, such as a complementary metal oxide semiconductor (CMOS) or a charge coupled device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0134] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as a wireless network (Wi-Fi), a second generation mobile communication technology (2G), a third generation mobile communication technology (3G), a fourth generation mobile communication technology (4G), a long-term evolution (LTE) of a universal mobile communication technology, a fifth generation mobile communication technology (5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0135] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.

[0136] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions, which can be executed by a processor 820 of an electronic device 800 to perform the above method.

[0137] Figure 6 A block diagram of an electronic device according to an embodiment of the present disclosure is shown. Figure 6 , the electronic device 1900 can be provided as a server or a terminal device. Figure 6 , the electronic device 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by a memory 1932 for storing instructions executable by the processing component 1922, such as an application. The application stored in the memory 1932 may include one or more modules, each corresponding to a set of instructions. In addition, the processing component 1922 is configured to execute instructions to perform the above method.

[0138] The electronic device 1900 may also include a power supply component 1926 configured to perform power management of the electronic device 1900, a wired or wireless network interface 1950 configured to connect the electronic device 1900 to a network, and an input / output interface 1958. The electronic device 1900 may operate based on an operating system stored in the memory 1932, such as a Microsoft Server operating system (Windows Server 2003). TM ), a graphical user interface operating system launched by Apple (Mac OS X TM ), a multi-user, multi-process computer operating system (Unix TM ), a free and open source Unix-like operating system (Linux TM ), an open source Unix-like operating system (FreeBSD TM ) or similar.

[0139] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions, which can be executed by the processing component 1922 of the electronic device 1900 to perform the above method.

[0140] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0141] Computer readable storage medium can be a tangible device that can hold and store instructions used by an instruction execution device. Computer readable storage medium can be, for example, (but not limited to) an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (non-exhaustive list) of computer readable storage medium include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, for example, a punch card or a convex structure in a groove on which instructions are stored, and any suitable combination thereof. The computer readable storage medium used here is not interpreted as a transient signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagated by a waveguide or other transmission medium (for example, a light pulse by an optical fiber cable), or an electrical signal transmitted by a wire.

[0142] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0143] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0144] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0145] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0146] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0147] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

[0148] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK) and the like.

[0149] The above description of various embodiments tends to emphasize the differences between the various embodiments. The same or similar aspects can be referenced to each other, and for the sake of brevity, they will not be repeated herein.

[0150] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

[0151] If the technical solution of this application involves personal information, the product using the technical solution of this application has clearly informed the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution of this application involves sensitive personal information, the product using the technical solution of this application has obtained the individual's separate consent before processing the sensitive personal information, and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, clear and prominent signs are set to inform that the personal information collection scope has been entered and personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that he or she agrees to the collection of his or her personal information; or on the device that processes personal information, the personal information processing rules are notified by obvious signs / information, and the individual's authorization is obtained through pop-up information or by asking the individual to upload his or her personal information; among them, the personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

[0152] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A driving scoring method based on vehicle-mounted DMS, characterized in that: include: Determine, based on the vehicle-mounted DMS, improper driving behavior during driving and the duration corresponding to the improper driving behavior; Based on the driving assessment model, a risk assessment is performed on the improper driving behavior to determine a risk coefficient corresponding to the improper driving behavior, wherein the risk coefficient corresponding to the improper driving behavior is used to indicate the degree of harm caused by the improper driving behavior per unit time; Determining a driving score corresponding to the improper driving behavior according to the risk factor corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior; The determining, according to the risk factor corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior, a driving score corresponding to the improper driving behavior includes: Determine the driving score corresponding to the improper driving behavior by multiplying the risk coefficient corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior; The step of performing risk assessment on the improper driving behavior based on the driving assessment model and determining a risk coefficient corresponding to the improper driving behavior includes: Determining an image to be evaluated corresponding to the improper driving behavior; Based on the driving assessment model, performing risk assessment on the image to be assessed, and determining a risk coefficient corresponding to the improper driving behavior; The step of performing risk assessment on the image to be assessed based on the driving assessment model to determine a risk factor corresponding to the improper driving behavior includes: Inputting the image to be evaluated into the driving evaluation model to determine the risk factor corresponding to the image to be evaluated, wherein different images to be evaluated under the same type of improper driving behavior have different risk factors corresponding to them; The risk factor corresponding to the improper driving behavior is determined according to the risk factor corresponding to the image to be evaluated.

2. The method according to claim 1, characterized in that The step of determining the image to be evaluated corresponding to the improper driving behavior includes: Determining an offset value between an image corresponding to the improper driving behavior and an image corresponding to a normal driving behavior; The image to be evaluated is determined from the images corresponding to the improper driving behavior according to the offset value.

3. The method according to claim 1, characterized in that The number of the images to be evaluated is multiple; The determining, according to the risk factor corresponding to the image to be evaluated, the risk factor corresponding to the improper driving behavior includes: Determining an average value of the risk factor according to the risk factor corresponding to each of the images to be evaluated; The average value of the risk factors is determined as the risk factor corresponding to the improper driving behavior.

4. The method according to claim 1 or 2, characterized in that: The method further comprises: Determine a sample image set, wherein the sample image set includes sample images corresponding to a plurality of different types of the improper driving behaviors, and a labeled risk factor corresponding to each of the sample images; According to a preset ratio, the sample image set is divided into a first training set, a validation set and a test set; Perform image enhancement processing on the sample images included in the first training set to determine a second training set.

5. The method according to claim 4, characterized in that The training method of the driving evaluation model includes: Based on the driving assessment model, performing risk assessment on the sample images included in the second training set, and determining a predicted risk coefficient corresponding to the sample images included in the second training set; Determining a first assessment loss according to a predicted risk coefficient corresponding to the sample image included in the second training set and a labeled risk coefficient corresponding to the sample image included in the second training set; Based on the first evaluation loss, the driving evaluation model is trained using an adaptive moment estimation algorithm.

6. The method according to claim 5, characterized in that The method further comprises: Based on the driving assessment model, performing risk assessment on the sample images included in the validation set, and determining a predicted risk coefficient corresponding to the sample images included in the validation set; Determining a second assessment loss according to the predicted risk coefficient corresponding to the sample image included in the validation set and the labeled risk coefficient corresponding to the sample image included in the validation set; The driving evaluation model is trained by minimizing the second evaluation loss.

7. The method according to claim 6, characterized in that The method further comprises: Based on the driving assessment model, performing risk assessment on the sample images included in the test set, and determining a predicted risk coefficient corresponding to the sample images included in the test set; The accuracy of the driving assessment model is evaluated based on the predicted risk coefficients corresponding to the sample images included in the test set and the labeled risk coefficients corresponding to the sample images included in the test set.

8. A driving scoring device based on a vehicle-mounted DMS, characterized in that: include: An acquisition module, used to determine improper driving behavior during driving and a duration corresponding to the improper driving behavior based on the vehicle-mounted DMS; A risk assessment module, used to perform risk assessment on the improper driving behavior based on a driving assessment model, and determine a risk coefficient corresponding to the improper driving behavior, wherein the risk coefficient corresponding to the improper driving behavior is used to indicate the degree of harm caused by the improper driving behavior per unit time; A driving scoring module, used to determine a driving score corresponding to the improper driving behavior according to a risk factor corresponding to the improper driving behavior and a duration corresponding to the improper driving behavior; The driving scoring module is specifically used for: Determine the driving score corresponding to the improper driving behavior by multiplying the risk coefficient corresponding to the improper driving behavior and the duration corresponding to the improper driving behavior; The risk assessment module is specifically used to: determine the image to be assessed corresponding to the improper driving behavior; perform risk assessment on the image to be assessed to determine the risk coefficient corresponding to the improper driving behavior; The risk assessment module is further used to: input the image to be assessed into the driving assessment model to determine the risk coefficient corresponding to the image to be assessed, wherein different images to be assessed under the same type of improper driving behavior have different corresponding risk coefficients; and determine the risk coefficient corresponding to the improper driving behavior based on the risk coefficient corresponding to the image to be assessed.

9. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to call the instructions stored in the memory to execute the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images