Driver training method and system, and electronic device

Abstract

Disclosed in the present application are a driver training method and system, and an electronic device. The method comprises: acquiring traveling data and / or external environment data of a target vehicle, wherein the target vehicle is a vehicle which has been driven by a driver to be trained; on the basis of the traveling data and / or the external environment data, performing risk assessment on a driving behavior of said driver, so as to obtain a risk assessment result; when the risk assessment result indicates that there is a driving behavior requiring training, determining a training plan on the basis of the driving behavior requiring training; and on the basis of the training plan, training said driver. By means of the method, a reduction in a traffic accident occurrence probability is facilitated.

Description

Driver training methods, systems, and electronic equipment Technical Field

[0001] This application relates to the field of artificial intelligence technology, specifically to driver training methods, systems, electronic devices, and computer-readable storage media. Background Technology

[0002] With the increasing number of vehicles, traffic accidents involving vehicles are also on the rise, causing millions of injuries and property damage worldwide. Whether in cities or rural areas, traffic safety remains a major concern, and drivers' skills are directly related to the likelihood of accidents. To reduce the probability of traffic accidents, driver training is typically provided.

[0003] Existing methods for driver training typically employ static teaching materials and standardized training procedures. However, this approach is insufficient to effectively correct drivers' driving behaviors, and consequently, fails to effectively reduce the probability of traffic accidents.

[0004] Summary of the Invention

[0005] The purpose of this application is to provide driver training methods, systems, electronic devices, and computer-readable storage media, including but not limited to solving the problem that existing driver behavior training methods are difficult to effectively reduce the probability of traffic accidents.

[0006] The technical solution adopted in the embodiments of this application is:

[0007] Firstly, a driver training method is provided, including:

[0008] Acquire driving data and / or external environment data of the target vehicle, wherein the target vehicle is a vehicle driven by the driver to be trained;

[0009] A risk assessment is conducted on the driving behavior of the driver to be trained based on the driving data and / or the external environment data, and a risk assessment result is obtained.

[0010] When the risk assessment results indicate that there are driving behaviors that require training, a training plan is determined based on the driving behaviors that require training;

[0011] The drivers to be trained are trained in accordance with the training plan.

[0012] Secondly, a driver training system is provided, comprising:

[0013] A perception system is used to acquire driving data and / or external environment data of a target vehicle, wherein the target vehicle is a vehicle driven by a driver to be trained.

[0014] A risk assessment system is used to assess the driving behavior of the driver to be trained based on the driving data and / or the external environment data, and to obtain the risk assessment result.

[0015] A simulation training system is used to determine a training plan based on the driving behaviors requiring training when the risk assessment results indicate that such behaviors exist; and to train the driver to be trained according to the training plan.

[0016] Thirdly, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method as described in the first aspect.

[0017] Fourthly, a computer-readable storage medium is provided, the computer-readable storage medium storing a computer program that, when executed by a processor, implements the method as described in the first aspect.

[0018] Fifthly, a computer program product is provided that, when run on an electronic device, causes the electronic device to perform the method described in the first aspect.

[0019] The beneficial effects of the driver training method provided in this application are as follows:

[0020] In this embodiment, since the driving data and external environment data are data of the driver to be trained while driving the target vehicle, i.e., these data correspond to the driver to be trained, when a risk assessment is conducted on the driving behavior of the driver to be trained based on the driving data and / or external environment data, the risk assessment result can more accurately reflect the driving behavior of the driver to be trained. This makes the training plan determined according to the driving behavior to be trained more suitable for the driver to be trained. Consequently, after training the driver to be trained according to the training plan, the driving behavior of the driver to be trained can be effectively corrected, and the corrected driving behavior helps to reduce the probability of traffic accidents. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or exemplary technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 is a flowchart illustrating a driver training method according to an embodiment of this application;

[0023] Figure 2 is a schematic diagram of the structure of a driver training system provided in an embodiment of this application;

[0024] Figure 3 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0026] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0027] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0028] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] References to "one embodiment" or "some embodiments" in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized.

[0030] When a driver is driving a vehicle, the probability of the vehicle being involved in a traffic accident increases if the driver's driving behavior does not comply with traffic rules. For example, if a driver habitually maintains a close distance to the vehicle in front of them, the probability of their vehicle being involved in a rear-end collision is higher.

[0031] To reduce the probability of traffic accidents, drivers are typically trained. Specifically, static training materials and standardized training procedures are used. However, this approach places all drivers under the same standard, failing to consider the unique needs and individual differences of different drivers. Since drivers have varying skill levels, learning styles, and backgrounds, standardized training methods cannot meet the needs of individual differences, resulting in poor training effectiveness and ultimately failing to effectively reduce the probability of traffic accidents.

[0032] To effectively reduce the probability of traffic accidents, this application provides a driver training method. In this method, a risk assessment of the driver's driving behavior is performed by combining driving data and / or external environmental data obtained while the driver is driving the vehicle. When the risk assessment indicates that there are driving behaviors requiring training, a training plan is determined based on the required driving behaviors, and the driver is trained according to the training plan.

[0033] The driver training method provided in the embodiments of this application will now be described with reference to the accompanying drawings.

[0034] Figure 1 shows a flowchart of a driver training method provided in an embodiment of this application, which is described in detail below:

[0035] S11, Obtain driving data and / or external environment data of the target vehicle, wherein the target vehicle is a vehicle driven by the driver to be trained.

[0036] The target vehicle is the vehicle actually driven by the driver to be trained. The target vehicle can be a fixed vehicle (e.g., only one vehicle) or a variable vehicle (e.g., more than one vehicle). For example, if the driver to be trained, A, drives vehicle A and vehicle B, then vehicle A and vehicle B are both the target vehicles mentioned above.

[0037] The driving data here includes at least one of the following: target vehicle speed, target vehicle acceleration, and three-axis angular velocity. The target vehicle speed can be obtained through the target vehicle's Controller Area Network (CAN) bus, and the target vehicle's acceleration and three-axis angular velocity can be obtained through the target vehicle's Inertial Measurement Unit (IMU).

[0038] The external environment data here includes at least one of the following: external image data (such as image data including lane lines, and / or image data including traffic lights, and / or image data including pedestrians or vehicles), radar data (used to indicate the distance and / or speed and / or angle between the target vehicle and external objects), and GPS data (such as the target vehicle's location information (longitude and latitude), the upper limit speed and lower limit speed of the road segment where the target vehicle is currently located).

[0039] In this embodiment, the identity information of the driver to be trained can be obtained, and driving data and / or external environment data of vehicles driven by the driver to be trained within a preset time period (i.e., target vehicles) can be obtained based on the identity information. The preset time period is the duration between the current moment and a previous moment. For example, if the preset driving time period is 10 days and the current moment is May 10, 2024, then driving data and / or external environment data of vehicles driven by the driver to be trained from May 1, 2024 to May 10, 2024 can be obtained.

[0040] S12, based on the above driving data and / or the above external environment data, conduct a risk assessment on the driving behavior of the driver to be trained, and obtain the risk assessment result.

[0041] Optionally, a risk assessment of the driver's driving behavior can be conducted based on driving data. For example, the driving behavior of the driver to be trained can be assessed based on the target vehicle speed included in the driving data. If the target vehicle speed is too high, the driving behavior is considered to have a high risk.

[0042] Optionally, risk assessment of the driver's driving behavior can be conducted based on external environmental data. For example, risk assessment of the driving behavior of the driver to be trained can be conducted based on radar data included in the external environmental data. If the target vehicle is too close to an object outside the vehicle, the driving behavior is considered to have a high risk.

[0043] Optionally, a risk assessment of the driver's driving behavior can be conducted based on driving data and external environmental data. For example, the risk assessment result can be determined based on the target vehicle speed included in the driving data and the upper limit speed included in the external environmental data.

[0044] In this embodiment, one or more driving behaviors can be determined based on driving data and / or external environment data. When there is only one driving behavior, a risk assessment is performed on that driving behavior to obtain a corresponding risk assessment result. When there are more than one driving behavior, a risk assessment is performed on each of the more than one driving behavior to obtain a corresponding assessment value, and then a corresponding risk assessment result is determined based on each assessment value.

[0045] Optionally, the risk assessment results can be expressed as a score or as descriptive information such as text. When the risk assessment results are expressed as a score, a higher score can be assigned to a higher risk, and a higher risk indicates a greater probability that the driving behavior requires training.

[0046] S13, When the above risk assessment results indicate that there are driving behaviors that require training, a training plan shall be determined based on the above driving behaviors that require training.

[0047] For example, if the driving behavior requiring training is following other vehicles, the training plan is determined based on the following other vehicles. This training plan includes training the driver to be trained to maintain a certain distance from other vehicles when driving.

[0048] For example, when the driving behavior requiring training is lane keeping ability, a training plan is determined based on lane keeping ability, which includes training the driver to be trained not to cross lanes while driving the vehicle.

[0049] For example, when the driving behavior that needs training is speed control ability, the training plan is determined based on speed control ability. The training plan includes training the driver to be trained to decelerate and / or accelerate quickly while driving the vehicle.

[0050] In addition, driving behaviors that require training may also include braking reaction time, etc., which will not be elaborated here.

[0051] Optionally, the training program can be an application to a virtual cockpit.

[0052] Optionally, to enhance the realism of the training process for drivers, the virtual cockpit can simulate various driving scenarios, including different weather conditions (such as rain, snow, fog, and haze), traffic conditions (such as peak traffic and traffic jams), emergencies (such as emergency braking and obstacle avoidance), and even road types (such as urban roads to highways). Because it can simulate various driving scenarios, drivers can experience different driving conditions in a virtual environment during training, increasing their driving experience and ability to handle unexpected situations.

[0053] S14, Train the aforementioned drivers to be trained in accordance with the above training plan.

[0054] Specifically, the system records the operational data of the driver under training. Further, it analyzes the recorded data to determine if the driver requires further training. If so, a new training plan is generated, allowing for continued training until it is determined that further training is no longer needed. For example, if a driver's driving behavior A improves after training, but driving behavior B does not, the generated new training plan will no longer include corrections for driving behavior A, but will include corrections for driving behavior B, thus achieving automatic and accurate adjustment of the training plan.

[0055] In this embodiment, since the driving data and external environment data are data of the driver to be trained while driving the target vehicle, i.e., these data correspond to the driver to be trained, when a risk assessment is conducted on the driving behavior of the driver to be trained based on the driving data and / or external environment data, the risk assessment result can more accurately reflect the driving behavior of the driver to be trained. This makes the training plan determined according to the driving behavior to be trained more suitable for the driver to be trained. Consequently, after training the driver to be trained according to the training plan, the driving behavior of the driver to be trained can be effectively corrected, and the corrected driving behavior helps to reduce the probability of traffic accidents.

[0056] In some embodiments, when determining the risk assessment result based on external environmental data, the above-mentioned S12 includes:

[0057] A1. Based on the above external environment data, determine the deviation of the target vehicle from the lane, and conduct a lane departure risk assessment on the driving behavior of the driver to be trained based on the above deviation to obtain the lane departure assessment value.

[0058] Specifically, the position of the lane lines in the image is identified based on the lane line image data included in the external environment data. Considering that when the target vehicle is on the lane line, the lane line is essentially directly in front of the camera, and the lane line is usually not distorted in the image at this time, the degree of distortion of the lane line can be determined based on its position in the image, and then the lane departure assessment value can be determined based on the degree of distortion. Alternatively, the lane departure assessment value can also be determined by calculating the distance between the target vehicle's forward centerline (i.e., the straight line where the vehicle's center position is located) and the lane line, which will not be elaborated here.

[0059] For example, considering that the lane departure assessment value is positively correlated with the deviation of the target vehicle from the lane, assuming that the lane departure assessment value uses a Score... 车道偏离 This indicates that the deviation of the target vehicle from the lane is expressed in μ. 偏离度The following formula can be used to calculate the lane departure assessment value: Score 车道偏离 =μ 偏离度 ×30.

[0060] A2. Determine the above risk assessment results based on the lane departure assessment values.

[0061] In this embodiment, the higher the degree to which the target vehicle deviates from its lane, the larger the corresponding lane departure assessment value. That is, the magnitude of the lane departure assessment value represents the degree to which the target vehicle deviates from its lane. Since traffic rules require vehicles to travel within lane lines, a high degree of lane deviation during driving will affect vehicles in other lanes, thereby increasing the probability of traffic accidents. Therefore, in this embodiment, determining the risk assessment result based on the lane departure assessment value helps improve the accuracy of the obtained risk assessment result.

[0062] In some embodiments, considering that the time required for the target vehicle to respond to driving commands also affects vehicle safety, i.e., the probability of a traffic accident, the risk assessment result can be determined by combining the time required for the target vehicle to respond to driving commands. In this case, S12 above includes:

[0063] B1. Determine the influencing factors of the target vehicle mentioned above. These influencing factors reflect the length of time required for the target vehicle to respond to driving commands.

[0064] Optionally, the influence factors of the target vehicle can be determined in the following ways:

[0065] The influence factor of the target vehicle is determined based on at least one of the following: the vehicle type, the vehicle quality, and the environmental complexity of the road segment where the target vehicle is currently located.

[0066] The vehicle types mentioned here include any of the following: trucks, buses, and minibuses. Specifically, the larger the vehicle, the greater the impact on the vehicle.

[0067] The mass of the target vehicle can be either its unloaded or fully loaded mass. Specifically, the greater the mass of the vehicle, the greater its inertia, which limits the vehicle's response time to commands. For example, after receiving a braking command, a vehicle with greater inertia will take longer to achieve full braking. Therefore, the greater the mass of the vehicle, the greater its impact on the vehicle.

[0068] The greater the environmental complexity, the greater the impact on vehicles. The environmental complexity of a road segment is typically related to the number of pedestrians and vehicles. For example, a road segment with more pedestrians has a higher environmental complexity than one with fewer pedestrians. Optionally, the environmental complexity of a road segment can also be related to climate, terrain, etc. In this case, weight values ​​can be set for pedestrians, vehicles, climate, and terrain, and the environmental complexity of the road segment can be determined based on the combination of these weight values.

[0069] In this embodiment of the application, when the number of factors affecting the target vehicle is greater than 1, the final factors can be determined based on the corresponding weight values ​​pre-assigned to the target vehicle's model, mass, and environmental complexity of the road segment where the target vehicle is currently located. The sum of the weight values ​​involved in the calculation can be set to 1.

[0070] B2. Based on the above driving data and / or the above external environment data, and based on the above influencing factors, conduct a risk assessment on the driving behavior of the above-mentioned drivers to be trained, and obtain the risk assessment results.

[0071] Specifically, considering that the longer the response time to driving commands is, the lower the vehicle's safety—for example, the longer the response time to a braking command is, the higher the probability of a traffic accident—it is beneficial to consider the impact factors of the target vehicle when conducting risk assessments to improve the accuracy of the subsequent risk assessment results.

[0072] In some embodiments, considering that there are many factors affecting vehicle safety, such as vehicle speed and traffic flow, a risk assessment of driving behavior can be conducted by combining these factors. In this case, B2 above includes:

[0073] (1) Based on the aforementioned influencing factors, the target vehicle speed included in the aforementioned driving data, and the upper limit speed included in the aforementioned external environment data, an overspeed risk assessment is conducted on the driving behavior of the aforementioned drivers to be trained, and an overspeed assessment value is obtained. Specifically, the overspeed risk assessment can be conducted by calculating the target vehicle speed exceeding the upper limit speed of the current road segment (i.e., the upper limit speed parsed from the external environment data) or by calculating the ratio of the target vehicle speed to the upper limit speed, and then combining the influencing factors.

[0074] Optionally, considering that the risk of speeding only exists when the target vehicle's speed exceeds the speed limit, it can be determined whether the target vehicle's speed exceeds the speed limit before calculating the speeding assessment value. In this case, based on the aforementioned influencing factors, the target vehicle speed included in the aforementioned driving data, and the speed limit included in the aforementioned external environment data, the speeding risk assessment of the driving behavior of the driver to be trained is performed to obtain the speeding assessment value, including:

[0075] If it is determined that the speed of the target vehicle included in the aforementioned driving data is greater than the upper speed limit included in the aforementioned external environment data, then based on the first preset evaluation value, the aforementioned influencing factor, the speed of the target vehicle included in the aforementioned driving data, and the upper speed limit included in the aforementioned external environment data, a speeding risk assessment is performed on the driving behavior of the driver to be trained, resulting in a speeding evaluation value. When the speeding evaluation value is less than or equal to the aforementioned first preset evaluation value, it indicates that the speeding of the target vehicle is within an acceptable range. For example, assuming the first preset evaluation value is set to 50 (of course, it can also be set to other values ​​according to the acceptable range of speeding, such as 40, which is not limited here), and the influencing factor is γ... 车型 This indicates that the target vehicle's speed is expressed in V. t This indicates that the upper limit speed is expressed in V. 上限 If the above is true, the overspeed assessment score can be calculated using the following formula. 超速 :

[0076] In the above formula, when the target vehicle's speed is less than or equal to the upper speed limit, the speeding assessment value is set to 0; otherwise, it is set to "50" and... Choose the smaller value.

[0077] And / or, (2) based on the above-mentioned influencing factors, the target vehicle speed contained in the above-mentioned driving data, and the lower limit speed contained in the above-mentioned external environment data, conduct an idling risk assessment on the driving behavior of the above-mentioned driver to be trained, and obtain an idling assessment value. Specifically, the idling risk assessment can be conducted by calculating the target vehicle speed being less than the lower limit speed of the current road segment (i.e., the lower limit speed parsed from the external environment data) or by calculating the ratio of the target vehicle speed to the lower limit speed, and then combining the influencing factors.

[0078] Optionally, considering that idling risk only exists when the target vehicle speed is below the lower limit speed, it can be determined whether the target vehicle speed is below the lower limit speed before calculating the idling risk assessment value. In this case, based on the aforementioned influencing factors, the target vehicle speed included in the aforementioned driving data, and the lower limit speed included in the aforementioned external environment data, the idling risk assessment value is obtained by evaluating the driving behavior of the driver to be trained, including:

[0079] If it is determined that the speed of the target vehicle included in the above driving data is less than the lower limit speed included in the above external environment data, an idling risk assessment is performed on the driving behavior of the driver to be trained based on the second preset assessment value, the above influencing factor, the speed of the target vehicle included in the above driving data, and the lower limit speed included in the above external environment data, to obtain an idling assessment value. When the above idling assessment value is less than or equal to the above second preset assessment value, it indicates that the idling speed of the target vehicle is within an acceptable range.

[0080] For example, assuming the second preset evaluation value is set to 50 (of course, it can also be set to other values ​​according to the acceptable range of idling speed, such as 40, which is not limited here), the influence factor is γ. 车型 This indicates that the target vehicle's speed is expressed in V. t This indicates that the lower limit speed is expressed as V. 下限 The idle speed score can be calculated using the following formula. 怠速 :

[0081] In the above formula, when the target vehicle speed is greater than or equal to the lower speed limit, the overspeed assessment value is set to 0.

[0082] And / or, (3) Determine the speed of the vehicle in front of the target vehicle based on the above-mentioned external environment data and the target vehicle speed contained in the above-mentioned driving data. Based on the speed of the vehicle in front, the above-mentioned influencing factors, and the target vehicle speed contained in the above-mentioned driving data, conduct a speed risk assessment on the driving behavior of the driver to be trained and obtain a speed assessment value. Among them, the vehicle in front of the target vehicle refers to the vehicle in front of the target vehicle, which usually refers to the vehicle in front of the target vehicle and closest to the target vehicle in different lanes. At this time, the maximum number of vehicles in front of the target vehicle is the same as the maximum number of lanes in the current road segment where the target vehicle is located. In the embodiments of this application, the speed of the vehicle in front can be detected by millimeter-wave radar, or the distance between the target vehicle and the vehicle in front can be determined by analyzing the size of the image frames of the same vehicle in two adjacent frames. Then, the speed of the vehicle in front can be estimated based on the speed of the target vehicle itself and the interval between the image frames of the two adjacent frames.

[0083] Optionally, considering that a rear-end collision can only occur if the target vehicle's speed is greater than or equal to the speed of the vehicle in front, the vehicle speed assessment value can be calculated only after it is determined that the target vehicle's speed is greater than or equal to the speed of the vehicle in front; otherwise, it can be either not calculated or set to 0. Assume the vehicle speed assessment value uses a Score. 车速 This indicates that the target vehicle's speed is expressed in V. t This indicates that the speed of the vehicle in front is expressed in V. 前车 The impact factor is expressed as γ. 车型 If so, the score can be calculated in the following way. 车速 :

[0084] And / or, (4) Determine the position information of vehicles in the lane based on the above-mentioned external environment data, and conduct a traffic flow risk assessment on the driving behavior of the driver to be trained based on the above-mentioned position information of vehicles in the lane and the above-mentioned influencing factors to obtain a traffic flow assessment value. Specifically, the external environment data refers to the environmental data outside the target vehicle, so the position information of the vehicle determined based on the external environment data is the position information of the vehicles around the target vehicle. Since the target vehicle may collide with the vehicles around it during driving, such as the vehicle that is driving in the same direction as the target vehicle and is closest to the target vehicle, combining the position information of vehicles in the lane when calculating the traffic flow assessment value is beneficial to improving the accuracy of the obtained traffic flow assessment value.

[0085] Optionally, based on the vehicle's position information in the lane and the aforementioned influencing factors, a traffic flow risk assessment is performed on the driving behavior of the driver to be trained, resulting in a traffic flow assessment value, including:

[0086] C1. If the location information of the vehicles in the aforementioned lanes indicates that there are no vehicles in the lane where the target vehicle is located, or in any other lanes, then the traffic flow assessment value is set to 0. For example, suppose there are three lanes on the road segment where the target vehicle is currently located: lane 1, lane 2, and lane 3, and the target vehicle is in lane 1. Then lane 1 is the "lane where the target vehicle is located," while lanes 2 and 3 are "other lanes." That is, the "other lanes" mentioned above refer to all lanes on the road segment where the target vehicle is located that are not in the lane where the target vehicle is located. When only the target vehicle is traveling in each lane, it indicates that there is no traffic flow risk. In this case, setting the traffic flow assessment value to 0 is more consistent with the actual situation, thus improving the accuracy of the obtained traffic flow assessment value.

[0087] C2. When the location information of the vehicles in the lane indicates that there are only vehicles in the target lane, the traffic flow assessment value is determined according to the first traffic flow weight value and the above-mentioned influencing factors. The target lane is: the lane other than the lane where the target vehicle is located.

[0088] C3. When the location information of the vehicles on the lane indicates that there are only vehicles in the lane where the target vehicle is located, the traffic flow assessment value is determined based on the second traffic flow weight value and the above-mentioned influencing factors.

[0089] C4. If the location information of the vehicles in the lane indicates that the target vehicle is in the lane and there are vehicles in both the target lane, the traffic flow assessment value is determined based on the third traffic flow weight value and the influencing factors.

[0090] Among them, the first traffic flow weight value is less than the second traffic flow weight value and the third traffic flow weight value.

[0091] For example, suppose the traffic flow assessment value is set to Score. 车流 Then the score 车流 The following formula can be used to calculate it:

[0092] In the above formula, the weight of the first traffic flow is 5, the weight of the second traffic flow is 10, and the weight of the third traffic flow is 20. Of course, in actual situations, other values ​​can be set, which are not limited here.

[0093] And / or, (5) Determine the distance between the target vehicle and other vehicles based on the above external environment data, and conduct a distance risk assessment on the driving behavior of the driver to be trained based on the above distance, the above influencing factors, and the target vehicle speed contained in the above driving data to obtain a distance assessment value. Specifically, after analyzing the presence of vehicles based on image data, the distance information in the radar data can be combined to match the distance between the target vehicle and other vehicles (i.e., the distance between vehicles). Since the probability of traffic accidents between different vehicles at different speeds is usually different when the distance is the same, calculating the distance assessment value based on the distance, influencing factors, and target vehicle speed is beneficial to improving the accuracy of the obtained distance assessment value.

[0094] Optionally, considering that a driver's better visual perception while driving corresponds to a lower probability of traffic accidents, and that visual perception and vehicle distance are usually not linearly related, different vehicle distance assessment strategies can be selected based on visual perception to calculate the vehicle distance assessment value. In this case, the aforementioned vehicle distance risk assessment is performed on the driving behavior of the driver to be trained based on the aforementioned vehicle distance, the aforementioned influencing factors, and the target vehicle speed included in the aforementioned driving data, to obtain the vehicle distance assessment value, including:

[0095] D1. Determine the visual perception value of the driver to be trained based on the above vehicle distance, the above influencing factors, and the target vehicle speed included in the above driving data. The higher the visual perception value, the better the driver to be trained's perception.

[0096] Specifically, considering that the visual perception of the driver to be trained is positively correlated with the target vehicle he is driving, that is, the larger the influencing factor, the better the visual perception of the driver to be trained (e.g., the greater the mass of the target vehicle, the better the visual perception), in addition, the visual perception of the driver to be trained is positively correlated with the distance between the target vehicle and other vehicles, but negatively correlated with the speed of the target vehicle, the visual perception value can be determined based on the above positive and negative correlations.

[0097] For example, the visual perception value can be calculated using the following formula: γ 车型 .D t / V t , where Dt Indicates the distance between vehicles.

[0098] D2. Select the corresponding vehicle distance assessment strategy based on the range of the visual perception values ​​of the drivers to be trained.

[0099] Specifically, multiple vehicle distance assessment strategies can be set, for example, four vehicle distance assessment strategies can be set. Of course, other numbers of vehicle distance assessment strategies can also be set according to the actual situation, which is not limited here.

[0100] The visual perception values ​​for different vehicle distance assessment strategies have different ranges, which can be determined based on experience.

[0101] D3. Based on the selected distance assessment strategy, assess the distance to the driving behavior of the drivers to be trained and obtain the distance assessment value.

[0102] For example, when the number of vehicle distance assessment strategies is 4, these strategies can be represented by the following formula:

[0103] In the above formula, the vehicle distance evaluation value uses the Score. 车距 This indicates that "min" represents taking the smaller value. In the above formula, when The smaller the value, the smaller the weight of the vehicle distance involved in the "min" calculation (e.g., "5", "50", "70").

[0104] In this embodiment, since more than one vehicle distance assessment strategy is set to calculate the vehicle distance assessment value, and visual perception and vehicle distance are usually not linearly related, calculating the vehicle distance assessment value in the above manner is beneficial to improving the accuracy of the obtained vehicle distance assessment value.

[0105] And / or, (6) Based on the acceleration of the target vehicle contained in the above driving data and the above influencing factors, conduct an acceleration risk assessment on the driving behavior of the above-mentioned driver to be trained, and obtain an acceleration assessment value.

[0106] Optionally, considering that acceleration can be positive or negative, different calculation methods can be used to calculate the acceleration evaluation value based on whether the acceleration is positive or negative, in order to improve the accuracy of the obtained acceleration evaluation value.

[0107] Among them, the aforementioned influencing factors and acceleration are all positively correlated with acceleration risk. Therefore, the above acceleration assessment value can be calculated using the following formula:

[0108] The acceleration weight values ​​in the above formula (such as "75 / 2" and "18") can be set according to the actual situation, and are not limited here.

[0109] After obtaining one or more of the above assessment values, the above risk assessment result is obtained based on at least one of the above speeding assessment value, the above idling assessment value, the above vehicle speed assessment value, the above traffic flow assessment value, the above vehicle distance assessment value, and the above acceleration assessment value.

[0110] Specifically, if the calculated assessment value includes the speeding assessment value, then the speeding assessment value can be used in conjunction with the risk assessment result; similarly, if the calculated assessment value includes both the speeding assessment value and the idling assessment value, then the speeding assessment value and the idling assessment value can be used in conjunction with the risk assessment result, which will not be elaborated here.

[0111] In some embodiments, assuming the calculated assessment values ​​include lane departure assessment value, speeding assessment value, idling assessment value, vehicle speed assessment value, traffic flow assessment value, distance assessment value, and acceleration assessment value, the above-mentioned risk assessment results can be calculated based on the lane departure assessment value, speeding assessment value, idling assessment value, vehicle speed assessment value, traffic flow assessment value, distance assessment value, and acceleration assessment value, and the maximum value of each assessment value is not greater than 100. In this case, the risk assessment result can be calculated according to the following formula: Score sum =min(Score) 车道偏离 +Score 超速 +Score 怠速 +Score 车速 +Score 车流 + Score 车距 +Score 加速度 ,100).

[0112] In some embodiments, considering factors other than vehicle-related factors, such as terrain, road type (highway, provincial road, or other type of road), weather, and visibility, traffic accidents can be caused by other factors besides vehicle-related factors. Therefore, these factors can also be considered when calculating the risk assessment results. It is assumed that the assessment values ​​corresponding to these factors are expressed using a Score. 其他 In other words, Score sum =min(Score) 车道偏离 +Score 超速 +Score 怠速 +Score 车速 +Score 车流 + Score 车距 +Score 加速度 +Score 其他 ,100).

[0113] It should be noted that the better the terrain, the higher the score. 其他 The smaller the score, the higher the road type (i.e., the more vehicle-friendly the road, such as a highway).其他 The smaller the score, the better the weather. 其他 The smaller the value, the higher the visibility. 其他 The smaller.

[0114] In this embodiment of the application, since other factors affecting traffic safety are also considered, the final risk assessment result is more accurate.

[0115] In some embodiments, considering that the risk assessment results can indicate whether there are driving behaviors requiring training, in order to provide timely feedback to drivers awaiting training, the following method is added after S12:

[0116] Based on the above risk assessment results, decide whether to provide feedback to the aforementioned drivers awaiting training.

[0117] Specifically, when the risk assessment results indicate that there are driving behaviors that require training, it means that the driver to be trained has driving behaviors that need to be corrected. In this case, it is selected to provide feedback to the driver to be trained, and the feedback includes information on the driving behaviors that require training. Otherwise, it is selected not to provide feedback to the driver to be trained.

[0118] Optionally, when the risk assessment result is expressed as a score, it can be compared with a preset score threshold. If the risk assessment result is greater than the preset score threshold, it indicates that there is a driving behavior requiring training. In this case, an assessment value that meets the target condition can be selected from the various assessment values ​​used to calculate the risk assessment result, and corresponding feedback content can be generated based on the assessment value that meets the target condition. Then, feedback is given to the driver to be trained based on the generated feedback content. Optionally, the target condition includes: the largest assessment value among the various assessment values, or includes assessment values ​​among the various assessment values ​​that are greater than the preset assessment value threshold.

[0119] For example, suppose the preset score threshold is 60, and the target condition is the highest evaluation value among all evaluation values. If the risk assessment result is 70, and the evaluation values ​​used to calculate this risk assessment result are: lane departure evaluation value, speeding evaluation value, idling evaluation value, vehicle speed evaluation value, traffic flow evaluation value, distance evaluation value, and acceleration evaluation value. Since 70 is greater than 60, it indicates that there is a driving behavior that needs training. In this case, the highest evaluation value (let's say the distance evaluation value) is determined from the lane departure evaluation value, speeding evaluation value, idling evaluation value, vehicle speed evaluation value, traffic flow evaluation value, distance evaluation value, and acceleration evaluation value. Then, feedback content is generated based on the distance evaluation value, such as "Current driving distance is too close, please pay attention to maintaining distance." The driver to be trained is then given feedback based on this feedback content. For example, the feedback content can be broadcast to the driver to be trained via voice. Of course, feedback methods can also be through voice broadcast, text prompts, etc., which are not limited here.

[0120] In this embodiment of the application, when the risk assessment results indicate that there is a driving behavior that requires training, feedback is provided to the driver to be trained. This allows the driver to receive timely feedback, which helps to correct the wrong driving behavior in a timely manner and thus helps to reduce the probability of traffic accidents.

[0121] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0122] Corresponding to the driver training method described in the above embodiments, Figure 2 shows a structural block diagram of the driver training system provided in the embodiments of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0123] Referring to Figure 2, the driver training system 2 includes: a perception system 21, a risk assessment system 22, and a simulation training system 23. Wherein:

[0124] The perception system 21 is used to acquire driving data and / or external environment data of the target vehicle, which is the vehicle driven by the driver to be trained.

[0125] The risk assessment system 22 is used to assess the driving behavior of the driver to be trained based on the above-mentioned driving data and / or the above-mentioned external environment data, and to obtain the risk assessment results.

[0126] The simulation training system 23 is used to determine a training plan based on the driving behaviors requiring training when the risk assessment results indicate that such behaviors exist; and to train the drivers to be trained according to the training plan.

[0127] In this embodiment, since the driving data and external environment data are data of the driver to be trained while driving the target vehicle, i.e., these data correspond to the driver to be trained, when a risk assessment is conducted on the driving behavior of the driver to be trained based on the driving data and / or external environment data, the risk assessment result can more accurately reflect the driving behavior of the driver to be trained. This makes the training plan determined according to the driving behavior to be trained more suitable for the driver to be trained. Consequently, after training the driver to be trained according to the training plan, the driving behavior of the driver to be trained can be effectively corrected, and the corrected driving behavior helps to reduce the probability of traffic accidents.

[0128] In some embodiments, the risk assessment system 22 includes:

[0129] The influencing factor determination module is used to determine the influencing factor of the target vehicle, which reflects the length of time required for the target vehicle to respond to driving commands.

[0130] The risk assessment result calculation module is used to conduct a risk assessment on the driving behavior of the driver to be trained based on the above driving data and / or the above external environment data and the above influencing factors, and obtain the risk assessment result.

[0131] In some embodiments, the above-mentioned impact factor determination module is specifically used for:

[0132] The influence factor of the target vehicle is determined based on at least one of the following: the vehicle type, the vehicle quality, and the environmental complexity of the road segment where the target vehicle is currently located.

[0133] In some embodiments, the risk assessment result calculation module includes:

[0134] The speeding assessment unit is used to assess the speeding risk of the driver to be trained based on the above-mentioned influencing factors, the target vehicle speed included in the above-mentioned driving data, and the upper limit speed included in the above-mentioned external environment data, and to obtain the speeding assessment value.

[0135] And / or,

[0136] The idle speed assessment value calculation unit is used to conduct an idle speed risk assessment on the driving behavior of the driver to be trained based on the above-mentioned influencing factors, the target vehicle speed contained in the above-mentioned driving data, and the lower limit speed contained in the above-mentioned external environment data, and to obtain an idle speed assessment value.

[0137] And / or,

[0138] The vehicle speed assessment unit is used to determine the speed of the vehicle in front of the target vehicle based on the external environment data and the target vehicle speed contained in the driving data, and to conduct a vehicle speed risk assessment on the driving behavior of the driver to be trained based on the speed of the vehicle in front, the influencing factors and the target vehicle speed contained in the driving data, and to obtain a vehicle speed assessment value.

[0139] And / or,

[0140] The traffic flow assessment value calculation unit is used to determine the position information of vehicles in the lane based on the above-mentioned external environment data, and to conduct a traffic flow risk assessment on the driving behavior of the driver to be trained based on the above-mentioned position information of vehicles in the lane and the above-mentioned influencing factors, so as to obtain a traffic flow assessment value.

[0141] And / or,

[0142] The vehicle distance assessment unit is used to determine the distance between the target vehicle and other vehicles based on the above-mentioned external environment data, and to conduct a vehicle distance risk assessment on the driving behavior of the driver to be trained based on the above-mentioned vehicle distance, the above-mentioned influencing factors and the target vehicle speed included in the above-mentioned driving data, and to obtain the vehicle distance assessment value.

[0143] And / or,

[0144] The acceleration assessment value calculation unit is used to conduct an acceleration risk assessment on the driving behavior of the driver to be trained based on the acceleration of the target vehicle contained in the above driving data and the above influencing factors, and obtain an acceleration assessment value.

[0145] The risk assessment result calculation unit is used to obtain the risk assessment result based on at least one of the above-mentioned speeding assessment value, the above-mentioned idle speed assessment value, the above-mentioned vehicle speed assessment value, the above-mentioned traffic flow assessment value, the above-mentioned vehicle distance assessment value, and the above-mentioned acceleration assessment value.

[0146] In some embodiments, the above-mentioned overspeed assessment value calculation unit is specifically used for:

[0147] If it is determined that the speed of the target vehicle contained in the above driving data is greater than the upper limit speed contained in the above external environment data, the driving behavior of the driver to be trained is assessed for speeding risk based on the first preset evaluation value, the above influencing factor, the speed of the target vehicle contained in the above driving data and the upper limit speed contained in the above external environment data, and a speeding evaluation value is obtained. When the speeding evaluation value is less than or equal to the first preset evaluation value, it indicates that the speeding of the target vehicle is within an acceptable range.

[0148] In some embodiments, the idle speed evaluation value calculation unit described above is specifically used for:

[0149] If it is determined that the speed of the target vehicle included in the above driving data is less than the lower limit speed included in the above external environment data, an idling risk assessment is performed on the driving behavior of the driver to be trained based on the second preset assessment value, the above influencing factor, the speed of the target vehicle included in the above driving data, and the lower limit speed included in the above external environment data, to obtain an idling assessment value. When the above idling assessment value is less than or equal to the above second preset assessment value, it indicates that the idling speed of the target vehicle is within an acceptable range.

[0150] In some embodiments, the traffic flow assessment value calculation unit described above is specifically used for:

[0151] If the location information of the vehicles in the lane indicates that there are no vehicles in the lane where the target vehicle is located or in other lanes, the traffic flow assessment value is set to 0.

[0152] When the location information of the vehicles in the lane indicates that there are only vehicles in the target lane, the traffic flow assessment value is determined based on the first traffic flow weight value and the above-mentioned influencing factors. The target lane is: a lane other than the lane where the target vehicle is located.

[0153] If the location information of the vehicles in the lane indicates that there are only vehicles in the lane where the target vehicle is located, the traffic flow assessment value is determined based on the second traffic flow weight value and the above-mentioned influencing factors.

[0154] In the case where the location information of the vehicles in the lane indicates that the target vehicle is in the lane and there are vehicles in both the target lane, the traffic flow assessment value is determined based on the third traffic flow weight value and the influencing factors.

[0155] Among them, the first traffic flow weight value is less than the second traffic flow weight value and the third traffic flow weight value.

[0156] In some embodiments, the vehicle distance evaluation value calculation unit is specifically used for:

[0157] The visual perception value of the driver to be trained is determined based on the above-mentioned vehicle distance, the above-mentioned influencing factors, and the target vehicle speed included in the above-mentioned driving data. The higher the visual perception value, the better the perception of the driver to be trained.

[0158] Select the corresponding vehicle distance assessment strategy based on the range of the visual perception values ​​of the drivers to be trained.

[0159] Based on the selected distance assessment strategy, the driving behavior of the drivers to be trained is assessed to obtain distance assessment values.

[0160] In some embodiments, the risk assessment system 22 described above is specifically used for:

[0161] Based on the aforementioned external environment data, the deviation of the target vehicle from the lane is determined. Based on the aforementioned deviation, the driving behavior of the driver to be trained is assessed for lane departure risk, and a lane departure assessment value is obtained.

[0162] The risk assessment results are determined based on the lane departure assessment values ​​mentioned above.

[0163] In some embodiments, the driver training system provided in this application further includes:

[0164] The feedback system is used to determine whether to provide feedback to the drivers to be trained based on the risk assessment results obtained above.

[0165] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0166] Figure 3 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. As shown in Figure 3, the electronic device 3 of this embodiment includes: at least one processor 30 (only one processor is shown in Figure 3), a memory 31, and a computer program 32 stored in the memory 31 and executable on the at least one processor 30. When the processor 30 executes the computer program 32, it implements the steps in any of the above-described method embodiments.

[0167] The electronic device 3 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. This electronic device may include, but is not limited to, a processor 30 and a memory 31. Those skilled in the art will understand that Figure 3 is merely an example of the electronic device 3 and does not constitute a limitation on the electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, etc.

[0168] The processor 30 may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0169] In some embodiments, the memory 31 may be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. In other embodiments, the memory 31 may be an external storage device of the electronic device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 3. Furthermore, the memory 31 may include both internal and external storage units of the electronic device 3. The memory 31 is used to store the operating system, applications, bootloader, data, and other programs, such as the program code of the computer program. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0170] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0171] This application also provides a network device, which includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the steps in any of the above method embodiments.

[0172] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps in the above-described method embodiments.

[0173] This application provides a computer program product that, when run on a mobile terminal, enables the mobile terminal to implement the steps described in the various method embodiments.

[0174] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographic device / electronic device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0175] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0176] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0177] In the embodiments provided in this application, it should be understood that the disclosed apparatus / network devices and methods can be implemented in other ways. For example, the apparatus / network device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0178] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0179] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method of training a driver, characterized by, The method comprises: obtaining driving data and / or external environment data of a target vehicle, the target vehicle being a vehicle driven by a driver to be trained; performing risk assessment on the driving behavior of the driver to be trained according to the driving data and / or the external environment data, to obtain a risk assessment result; when the risk assessment result indicates that there is a driving behavior that needs to be trained, determining a training plan according to the driving behavior that needs to be trained; training the driver to be trained according to the training plan.

2. The driver training method according to claim 1, characterized in that, The method comprises: determining an influence factor of the target vehicle, the influence factor reflecting the length of time required for the target vehicle to respond to a driving instruction; performing risk assessment on the driving behavior of the driver to be trained according to the driving data and / or the external environment data and according to the influence factor, to obtain a risk assessment result.

3. The driver training method according to claim 2, characterized in that, The method comprises: determining the influence factor of the target vehicle according to at least one of the vehicle model of the target vehicle, the mass of the target vehicle, and the environmental complexity of the road section currently traveled by the target vehicle.

4. The driver training method according to claim 2, characterized by, The method comprises: performing overspeed risk assessment on the driving behavior of the driver to be trained according to the influence factor, the target vehicle speed contained in the driving data, and the upper limit speed contained in the external environment data, to obtain an overspeed evaluation value; and / or, performing idling speed risk assessment on the driving behavior of the driver to be trained according to the influence factor, the target vehicle speed contained in the driving data, and the lower limit speed contained in the external environment data, to obtain an idling speed evaluation value; and / or, determining the speed of the preceding vehicle of the target vehicle according to the external environment data and the target vehicle speed contained in the driving data, and performing vehicle speed risk assessment on the driving behavior of the driver to be trained according to the speed of the preceding vehicle, the influence factor, and the target vehicle speed contained in the driving data, to obtain a vehicle speed evaluation value; and / or, determining the position information of vehicles on the lane according to the external environment data, and performing traffic flow risk assessment on the driving behavior of the driver to be trained according to the position information of the vehicles on the lane and the influence factor, to obtain a traffic flow evaluation value; and / or, determining the distance between the target vehicle and other vehicles according to the external environment data, and performing distance risk assessment on the driving behavior of the driver to be trained according to the distance, the influence factor, and the target vehicle speed contained in the driving data, to obtain a distance evaluation value; and / or, performing acceleration risk assessment on the driving behavior of the driver to be trained according to the acceleration of the target vehicle contained in the driving data and the influence factor, to obtain an acceleration evaluation value. ​ The risk assessment result is obtained according to at least one of the overspeed evaluation value, the idling speed evaluation value, the vehicle speed evaluation value, the vehicle flow evaluation value, the vehicle distance evaluation value and the acceleration evaluation value.

5. The driver training method according to claim 4, characterized in that, The overspeed risk of the driving behavior of the driver to be trained is evaluated according to the influence factor, the target vehicle speed contained in the driving data and the upper limit speed contained in the off-road environment data, and an overspeed evaluation value is obtained, including: In a case where it is judged that the target vehicle speed contained in the driving data is greater than the upper limit speed contained in the off-road environment data, the overspeed risk of the driving behavior of the driver to be trained is evaluated according to a first preset evaluation value, the influence factor, the target vehicle speed contained in the driving data and the upper limit speed contained in the off-road environment data, and an overspeed evaluation value is obtained, wherein when the overspeed evaluation value is less than or equal to the first preset evaluation value, it is indicated that the overspeed of the target vehicle is in an acceptable range.

6. The driver training method according to claim 4, characterized by, The idling speed risk of the driving behavior of the driver to be trained is evaluated according to the influence factor, the target vehicle speed contained in the driving data and the lower limit speed contained in the off-road environment data, and an idling speed evaluation value is obtained, including: In a case where it is judged that the target vehicle speed contained in the driving data is less than the lower limit speed contained in the off-road environment data, the idling speed risk of the driving behavior of the driver to be trained is evaluated according to a second preset evaluation value, the influence factor, the target vehicle speed contained in the driving data and the lower limit speed contained in the off-road environment data, and an idling speed evaluation value is obtained, wherein when the idling speed evaluation value is less than or equal to the second preset evaluation value, it is indicated that the idling speed of the target vehicle is in an acceptable range.

7. The driver training method according to claim 4, characterized by, The vehicle flow risk of the driving behavior of the driver to be trained is evaluated according to the position information of the vehicle on the lane and the influence factor, and a vehicle flow evaluation value is obtained, including: In a case where the position information of the vehicle on the lane indicates that there is no vehicle on the lane where the target vehicle is located and other lanes, the vehicle flow evaluation value is set to 0; In a case where the position information of the vehicle on the lane indicates that there is only vehicle on the target lane, the vehicle flow evaluation value is determined according to a first vehicle flow weight value and the influence factor, and the target lane is a lane other than the lane where the target vehicle is located; In a case where the position information of the vehicle on the lane indicates that there is only vehicle on the lane where the target vehicle is located, the vehicle flow evaluation value is determined according to a second vehicle flow weight value and the influence factor; In a case where the position information of the vehicle on the lane indicates that there is vehicle on the lane where the target vehicle is located and the target lane, the vehicle flow evaluation value is determined according to a third vehicle flow weight value and the influence factor; The first vehicle flow weight value < the second vehicle flow weight value < the third vehicle flow weight value.

8. The driver training method according to claim 4, characterized by, The distance risk assessment of the driving behavior of the driver to be trained is performed according to the vehicle distance, the influence factor, and the target vehicle speed contained in the driving data, and a vehicle distance assessment value is obtained, including: The visual perception value of the driver to be trained is determined according to the vehicle distance, the influence factor, and the target vehicle speed contained in the driving data, and the higher the visual perception value, the better the perception of the driver to be trained; The corresponding vehicle distance assessment strategy is selected according to the interval range of the visual perception value of the driver to be trained; The driving behavior of the driver to be trained is assessed according to the selected vehicle distance assessment strategy, and a vehicle distance assessment value is obtained.

9. The driver training method according to claim 1, characterized by, The risk assessment of the driving behavior of the driver to be trained is performed according to the driving data and / or the off-vehicle environment data, and a risk assessment result is obtained, including: The deviation degree of the target vehicle from the lane is determined according to the off-vehicle environment data, the driving behavior of the driver to be trained is assessed for lane deviation risk according to the deviation degree, and a lane deviation assessment value is obtained; The risk assessment result is determined according to the lane deviation assessment value.

10. The driver training method according to any one of claims 1 to 9, characterized in that, After the risk assessment result is obtained, it further includes: It is determined whether to feed back to the driver to be trained according to the risk assessment result.

11. A driver training system characterized by, It includes: A perception system is configured to obtain driving data and / or off-vehicle environment data of a target vehicle, the target vehicle being a vehicle driven by a driver to be trained; A risk assessment system is configured to perform risk assessment of the driving behavior of the driver to be trained according to the driving data and / or the off-vehicle environment data, and obtain a risk assessment result; A simulation training system is configured to determine a training plan according to the driving behavior that needs to be trained when the risk assessment result indicates that there is driving behavior that needs to be trained; And configured to train the driver to be trained according to the training plan.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to realize the method of any one of claims 1 to 10.

13. A computer-readable storage medium, the computer-readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to realize the method of any one of claims 1 to 10.

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