Vehicle travel information determination method, device, equipment, medium and program product

Abstract

The application discloses a kind of determination method, device, equipment, medium and program product of vehicle driving information, applied to vehicle control technical field.The determination method includes: obtaining the navigation information and vehicle driving information of vehicle in first preset time period;Determine the driving environment factor and dynamics deviation of vehicle based on vehicle driving information, driving environment factor includes at least one of the following: first type driving environment factor, second type driving environment factor;According to the driving deviation calculation strategy corresponding to driving environment factor, dynamics deviation, vehicle driving information and preset driving direction, determine the driving deviation information of vehicle, and driving deviation information is used to adjust the driving of vehicle along preset driving direction.The method can solve the problem that the instability of GPS signal leads to the inability to accurately calculate the driving deviation of the vehicle, which in turn causes the vehicle to deviate from the preset driving direction.

Description

Technical Field

[0001] This application belongs to the field of vehicle control technology, and in particular relates to a method, device, equipment, medium and program product for determining vehicle driving information. Background Technology

[0002] Autonomous vehicles need to follow pre-set speeds and path plans to achieve autonomous driving. However, during actual driving, environmental factors such as road and weather conditions, as well as vehicle-specific factors such as vehicle dynamics and sensor accuracy, can cause deviations between the vehicle's actual driving direction and the preset driving direction.

[0003] In related technologies, vehicles receive Global Positioning System (GPS) signals from satellites and determine their actual driving direction based on these signals. This actual direction is then compared to a preset driving direction to calculate the vehicle's deviation. The vehicle's control parameters are then adjusted based on this deviation to ensure the vehicle travels along the preset direction. However, in some areas, such as tunnels, GPS signals can be unstable, making it difficult to accurately calculate the vehicle's deviation, which can cause the vehicle to stray from the intended path. Summary of the Invention

[0004] This application provides a method, apparatus, device, medium, and program product for determining vehicle driving information, which can solve the problem that the instability of GPS signals leads to the inability to accurately calculate the vehicle's driving deviation, thereby causing the vehicle to deviate from the preset driving direction.

[0005] In a first aspect, embodiments of this application provide a method for determining vehicle driving information, the method comprising:

[0006] Obtain the vehicle's navigation information and vehicle driving information within a first preset time period;

[0007] Based on vehicle driving information, determine the vehicle's driving environment factors and dynamic deviation. The driving environment factors include at least one of the following: a first type of driving environment factor and a second type of driving environment factor. The first type of driving environment factor is used to reflect the actual driving situation of the vehicle, and the second type of driving environment factor is used to reflect the vehicle's communication environment status. The dynamic deviation is used to characterize the degree of deviation between the actual driving direction of the vehicle and the preset driving direction in the navigation information caused by dynamic factors during driving.

[0008] Based on the driving deviation calculation strategy corresponding to the driving environment factors, the dynamic deviation amount, the vehicle driving information and the preset driving direction, the driving deviation information of the vehicle is determined. The driving deviation information includes information used to characterize the degree of deviation between the actual driving direction of the vehicle and the preset driving direction. The driving deviation information is used to adjust the vehicle to drive along the preset driving direction.

[0009] Secondly, embodiments of this application provide a vehicle driving information determining device, the vehicle driving information determining device comprising:

[0010] The first acquisition module is used to acquire the vehicle's navigation information and vehicle driving information within a first preset time period;

[0011] The first determining module is used to determine the vehicle's driving environment factors and dynamic deviation based on the vehicle's driving information. The driving environment factors include at least one of the following: a first type of driving environment factor and a second type of driving environment factor. The first type of driving environment factor is used to reflect the actual driving situation of the vehicle, and the second type of driving environment factor is used to reflect the vehicle's communication environment status. The dynamic deviation is used to characterize the degree of deviation between the actual driving direction of the vehicle and the preset driving direction in the navigation information caused by dynamic factors during driving.

[0012] The second determining module is used to determine the vehicle's driving deviation information based on the driving deviation calculation strategy corresponding to the driving environment factors, the dynamic deviation amount, the vehicle driving information, and the preset driving direction. The driving deviation information includes information used to characterize the degree of deviation between the vehicle's actual driving direction and the preset driving direction. The driving deviation information is used to adjust the vehicle to drive along the preset driving direction.

[0013] Thirdly, embodiments of this application provide a computer device, the computer device including: a processor and a memory storing computer program instructions; the processor executes the computer program instructions to implement a method for determining vehicle driving information as described in any of the first aspects.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement a method for determining vehicle driving information as described in any of the first aspects.

[0015] Fifthly, embodiments of this application provide a computer program product, which includes a computer program or instructions, and when the computer program or instructions are executed by a processor, implements a method for determining vehicle driving information as described in any of the first aspects.

[0016] The vehicle driving information determination method, apparatus, device, medium, and program product of this application divides driving environment factors into a first type of driving environment factors that reflect the actual driving situation of the vehicle and a second type of driving environment factors that reflect the communication environment status of the vehicle. Based on the different types of driving environment factors, corresponding driving deviation calculation strategies are formulated. This enables the vehicle to determine the dynamic deviation amount and driving environment factors using vehicle driving information when the GPS signal is poor. Then, based on the driving deviation calculation strategy corresponding to the driving environment factors, the driving deviation information of the vehicle can be accurately obtained, which effectively avoids the problem of the vehicle deviating from the expected driving path due to the inability to accurately calculate the driving deviation caused by unstable GPS signals. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart illustrating a method for determining vehicle driving information provided in some embodiments of this application is shown;

[0019] Figure 2 A flowchart illustrating a specific implementation of step 120 provided in some embodiments of this application is shown;

[0020] Figure 3 The diagram illustrates a specific implementation of step 130 provided in some embodiments of this application;

[0021] Figure 4 A flowchart illustrating another specific implementation of step 130 provided in some embodiments of this application is shown;

[0022] Figure 5 The diagram illustrates a flowchart of the step of determining the first weight in a method for determining vehicle driving information provided in some embodiments of this application.

[0023] Figure 6 The flowchart illustrating the step of determining the second weight in the method for determining vehicle driving information provided in some embodiments of this application is shown.

[0024] Figure 7 A flowchart illustrating a specific implementation of step 320 provided in some embodiments of this application is shown;

[0025] Figure 8 A flowchart illustrating another specific implementation of step 320 provided in some embodiments of this application is shown;

[0026] Figure 9 A flowchart illustrating another specific implementation of step 320 provided in some embodiments of this application is shown;

[0027] Figure 10 The present application provides a schematic diagram of the structure of a vehicle driving information determination device according to some embodiments;

[0028] Figure 11 A schematic diagram of the structure of a computer device provided in some embodiments of this application is shown. Detailed Implementation

[0029] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0030] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0031] It should be noted that the acquisition, storage, use, and processing of data in this application embodiment all comply with the relevant provisions of national laws and regulations.

[0032] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0033] To address the problems in the aforementioned related technologies, embodiments of this application provide a method, apparatus, device, medium, and program product for determining vehicle driving information. The following is a detailed description in conjunction with the appendix. Figure 1 To be continued Figure 9 The method for determining vehicle driving information provided in this application will be described in detail through specific embodiments and application scenarios.

[0034] Figure 1 A flowchart illustrating a method for determining vehicle driving information according to some embodiments of this application is shown. Figure 1 As shown, the method for determining the vehicle driving information may include steps 110 to 130.

[0035] Step 110: Obtain the vehicle's navigation information and vehicle driving information for the first preset time period.

[0036] Step 120: Based on the vehicle driving information, determine the vehicle's driving environment factors and dynamic deviation. The driving environment factors include at least one of the following: a first type of driving environment factor and a second type of driving environment factor. The first type of driving environment factor is used to reflect the actual driving situation of the vehicle, and the second type of driving environment factor is used to reflect the vehicle's communication environment status. The dynamic deviation is used to characterize the degree of deviation between the actual driving direction of the vehicle and the preset driving direction in the navigation information caused by dynamic factors during driving.

[0037] Step 130: Based on the driving deviation calculation strategy, dynamic deviation amount, vehicle driving information and preset driving direction corresponding to the driving environment factors, determine the vehicle's driving deviation information. The driving deviation information includes information used to characterize the degree of deviation between the vehicle's actual driving direction and the preset driving direction. The driving deviation information is used to adjust the vehicle to drive along the preset driving direction.

[0038] Therefore, by dividing driving environment factors into a first category reflecting the actual driving conditions of the vehicle and a second category reflecting the vehicle's communication environment, and formulating corresponding driving deviation calculation strategies based on different types of driving environment factors, the vehicle can determine the dynamic deviation and driving environment factors using vehicle driving information when the GPS signal is poor. Then, based on the driving deviation calculation strategies corresponding to the driving environment factors, the vehicle's driving deviation information can be accurately obtained, effectively avoiding the problem of the vehicle deviating from the expected driving path due to the inability to accurately calculate the driving deviation caused by unstable GPS signals.

[0039] The steps described above are explained in detail below.

[0040] First, regarding S110, the navigation information involved in this embodiment includes route planning information provided by the vehicle's navigation system, such as the vehicle's preset driving direction, so that the vehicle travels along the preset driving direction. Vehicle driving information includes vehicle speed, vehicle yaw rate, vehicle steering angle, vehicle lateral acceleration, GPS signal strength, and GPS information during vehicle driving.

[0041] For example, vehicle speed can be measured by speed sensors installed on vehicle wheels or drive shafts; vehicle yaw rate refers to the angular velocity of the vehicle rotating about its vertical axis, which can be measured by gyroscopes installed on the vehicle; vehicle steering angle can be measured by steering angle sensors installed on the vehicle; vehicle lateral acceleration can be measured by accelerometers installed on the vehicle; GPS information and GPS signal strength can be obtained through vehicle navigation systems or vehicle GPS receivers.

[0042] Secondly, regarding step 120, the first type of driving environment factors involved in this application embodiment are factors that reflect the actual driving conditions of the vehicle. Specifically, these may include environmental factors related to the type of road the vehicle is traveling on, environmental factors related to the vehicle's speed, environmental factors related to the vehicle's yaw rate, and environmental factors related to the vehicle's stability. The second type of driving environment factors are factors that reflect the vehicle's communication environment conditions, specifically, these may include environmental factors related to GPS signal strength.

[0043] For example, environmental factors related to the type of road the vehicle travels on may include, but are not limited to, straight roads, large-radius curves, bends, complex road conditions such as intersections, parking lots, and flat roads; environmental factors related to vehicle speed may include, but are not limited to, high-speed driving (vehicle speed greater than 20 m / s), medium-speed driving (vehicle speed between 3 and 20 m / s), and low-speed driving (vehicle speed less than 3 m / s); environmental factors related to vehicle yaw rate may include, but are not limited to, low yaw rate (less than or equal to 1° / s), medium yaw rate (between 1° / s and 5° / s), and high yaw rate (greater than 5° / s); environmental factors related to vehicle stability may include, but are not limited to, low sideslip (lateral acceleration less than or equal to 0.2g) and high sideslip (lateral acceleration greater than 0.2g), where g is the acceleration due to gravity, approximately 9.8 m / s². 2 Environmental factors related to GPS signal strength may include, but are not limited to, strong GPS signals (e.g., GPS signal strength greater than or equal to -75dBm), weak GPS signals (e.g., GPS signal strength between -90dBm and -75dBm), and unstable GPS signals (e.g., signal strength below -90dBm and fluctuating continuously within a preset time period).

[0044] In some embodiments of this application, determining the driving environment of a vehicle based on vehicle driving information may include determining environmental factors related to the road type of the vehicle by using the vehicle steering angle in the vehicle driving information. For example, if the vehicle steering angle remains at zero or fluctuates within a preset angle range for a preset time period, it is determined that the vehicle is driving on a straight road or a large-radius curve; if the steering angle deviates from zero and continues for a preset time period, such as more than 3 seconds, it is determined that the vehicle is driving on a curve.

[0045] In some other embodiments of this application, determining the vehicle's driving environment based on vehicle driving information may include determining environmental factors related to the vehicle's driving speed by analyzing changes in the vehicle's driving speed in the vehicle driving information.

[0046] In some other embodiments of this application, determining the vehicle's driving environment based on vehicle driving information may include determining environmental factors related to vehicle stability by using the vehicle's lateral acceleration in the vehicle driving information. For example, if the vehicle's lateral acceleration is less than or equal to 0.2g and lasts for a preset period of time, such as more than 3 seconds, it is determined that the vehicle is under an environmental factor with less sideslip.

[0047] The dynamic deviation involved in this application embodiment is the degree of deviation between the actual driving direction of the vehicle and the preset driving direction caused by the vehicle's dynamic characteristics. Specifically, the dynamic deviation takes into account the effect of lateral acceleration when the vehicle is turning. By determining this dynamic deviation, the vehicle's driving direction can be compensated based on it to improve the control accuracy of the vehicle. Based on this, as... Figure 2 As shown, determining the vehicle's dynamic deviation based on vehicle driving information in step 120 above may include steps 1201 to 1202.

[0048] Step 1201: Determine the initial dynamic deviation based on the vehicle's speed and yaw rate.

[0049] The initial dynamic deviation is a deviation that reflects the vehicle's steering tendency and the resulting tendency to deviate from the preset direction, obtained by multiplying the vehicle's speed and yaw rate.

[0050] Step 1202: Adjust the initial dynamic deviation according to the preset proportional coefficient to obtain the dynamic deviation.

[0051] The preset proportional coefficient is a pre-set value used to adjust the initial dynamic deviation so that the dynamic deviation can accurately reflect the deviation between the actual driving direction of the vehicle and the preset driving direction.

[0052] For example, when a vehicle turns, it generates lateral acceleration. This lateral acceleration causes the vehicle's actual direction of travel to deviate from the preset direction of travel. The magnitude of the deviation is proportional to the lateral acceleration, but the direction is opposite. For instance, when a vehicle turns left, the curvature is greater than 0, and the lateral acceleration is directed to the right, i.e., in the centrifugal direction. Based on this, the preset proportionality coefficient is negative, such as -0.006.

[0053] If the vehicle's speed is v (meters per second), its yaw rate is ω (radians per second), and the preset proportional coefficient is k, then the dynamic deviation δ pred It can be expressed by the following formula (1):

[0054] δ pred =k(v×ω) (1)

[0055] Therefore, by presetting the proportional coefficient, vehicle speed, and yaw rate, the vehicle's dynamic deviation can be determined relatively accurately, thus determining the degree of deviation between the vehicle's actual travel direction and the preset travel direction. Based on this dynamic deviation, the vehicle's steering, braking, and other control parameters can be adjusted in a timely manner, enabling the vehicle to travel more precisely along the preset travel direction.

[0056] Then, regarding step 130, the driving deviation calculation strategy involved in this embodiment is a strategy that calculates vehicle driving deviation information based on driving environment factors, dynamic deviation amount, vehicle driving information, and preset driving direction. It is understood that different driving deviation calculation strategies exist for different driving scenarios.

[0057] In addition, driving deviation information is used to characterize the degree of deviation between the actual driving direction of the vehicle and the preset driving direction. By processing this driving deviation information, the driving direction of the vehicle can be adjusted so that the vehicle is closer to the preset driving direction in subsequent driving.

[0058] For example, in autonomous driving scenarios, accurate deviation information allows autonomous vehicles to better follow preset routes and avoid deviations caused by environmental factors or the vehicle's own dynamic characteristics. In manual driving scenarios, accurate deviation information can also provide drivers with more precise directional guidance through the vehicle's driver assistance systems.

[0059] In some embodiments of this application, the driving environment factors include a first type of driving environment factors; correspondingly, the driving deviation calculation strategy includes a first driving deviation calculation strategy; the driving deviation information includes a first type of driving deviation amount; and the first driving deviation calculation strategy includes a strategy for determining the first type of driving deviation amount based on the vehicle's driving speed and direction. Based on this, as... Figure 3As shown, step 130 above may include steps 210 to 230.

[0060] Step 210: Based on the first driving deviation calculation strategy, extract the vehicle driving speed direction corresponding to the first driving deviation calculation strategy from the vehicle driving information.

[0061] For example, the vehicle's speed is obtained from its speed sensor, and the vehicle's angle relative to a reference direction, such as true north, is obtained from its gyroscope. The direction of the vehicle's speed can be determined using the vehicle's speed and angle. For instance, if the speed is v and the angle is θ, in a Cartesian coordinate system, true north is the positive y-axis, and true east is the positive x-axis; the x-component of the vehicle's speed is v. x =cosθ, with the y-component being v. y =sinθ, based on this, the direction of the vehicle's velocity θ velocity It can be expressed by the following formula (2):

[0062] θ velocity =atan2(v y v x (2)

[0063] Here, atan2 is an arctangent function.

[0064] Step 220: Adjust the dynamic deviation amount according to the deviation between the vehicle's speed direction and the preset driving direction to obtain the first driving deviation amount of the vehicle in the first preset time period. The first driving deviation amount reflects the initial deviation degree of the vehicle's actual driving direction under the influence of the first type of driving environment factor from the preset driving direction in the first preset time period.

[0065] For example, the first driving deviation δ v1 It can be expressed by the following formula (3):

[0066] δ v1 =θ velocity -θ driving -δ pred (3)

[0067] Where, δ pred θ represents the dynamic deviation. velocity θ represents the direction of the vehicle's speed. driving Indicates the preset driving direction.

[0068] In this way, by considering the deviation between the vehicle's speed direction and the preset driving direction to adjust the dynamic deviation, the actual driving situation of the vehicle, as reflected by the speed direction, can be combined with the deviation caused by dynamic factors, so as to more accurately reflect the deviation between the vehicle's actual driving direction and the preset driving direction within the current first preset time period.

[0069] Step 230: Adjust the first driving deviation amount according to the second driving deviation amount of the vehicle in the second preset time period to obtain the first type of driving deviation amount. The first type of driving deviation amount reflects the degree of deviation of the actual driving direction of the vehicle under the influence of the first type of driving environmental factors from the preset driving direction in the first preset time period; wherein, the second preset time period is earlier than the first preset time period.

[0070] The second driving deviation is obtained by processing the vehicle driving information in the second preset time period through the method steps 220 and 230. It can be understood that the second driving deviation is the first type of driving deviation of the vehicle in the second preset time period.

[0071] For example, the first driving deviation can be adjusted based on the filter coefficient corresponding to the first driving deviation calculation strategy and the second driving deviation to obtain a first type of driving deviation, wherein the first type of driving deviation δ v It can be expressed by the following formula (4):

[0072] δ v =δ v2 +α v (δ v1 -δ v2 (4)

[0073] Where, δ v2 α represents the second driving deviation. v The filter coefficients represent the first type of driving deviation, used to smooth the δ output of the first driving deviation calculation strategy. v .

[0074] Here, α v This indicates that the first driving deviation within the first preset time period is updated in the first type of driving deviation δ. v The weight of time, α v The value of α can be determined based on the driving environment in which the vehicle is located. In a relatively stable driving environment, α... v The value is less than α when the vehicle is in an unstable driving environment. v value.

[0075] It is understandable that in real-world environments, the measured vehicle speed and direction may be affected by noise, such as sensor measurement errors and external interference. By introducing a filtering coefficient α... v These noises can be suppressed, reducing the first type of driving deviation δ v It is smoother and more stable.

[0076] Therefore, by calculating the first deviation amount based on the vehicle's speed and direction, the vehicle's deviation can be directly quantified from the perspective of its motion state, accurately reflecting the initial degree of deviation. The dynamic adjustment mechanism, which uses the second deviation amount to adjust the first deviation amount, helps reduce large adjustments during vehicle operation and improves the smoothness of the ride.

[0077] In some embodiments of this application, the driving environment factors include a first type of driving environment factor and a second type of driving environment factor; the driving deviation calculation strategy includes a first driving deviation calculation strategy and a second driving deviation calculation strategy; and the driving deviation information includes a first type of driving deviation amount and a second type of driving deviation amount. The first driving deviation calculation strategy includes a strategy for determining the first type of driving deviation amount based on the vehicle's driving speed and direction in the vehicle's driving information. The first type of driving deviation amount reflects the degree of deviation of the vehicle's actual driving direction relative to the preset driving direction under the influence of the first type of driving environment factor within a first preset time period. The second type of driving environment factor reflects that the vehicle's GPS signal strength is greater than a preset threshold. Correspondingly, when the environment in which the vehicle is located causes the GPS signal received by the vehicle to meet the condition that the strength reaches a certain standard, i.e., greater than the preset threshold, the second driving deviation calculation strategy is used to determine the vehicle's driving deviation amount. The preset threshold is an empirical value set after extensive experimental testing and analysis of the stability and availability of GPS signals under different driving scenarios. Based on this, such as... Figure 4 As shown, step 130 above may include steps 310 to 330.

[0078] Step 310: According to the second driving deviation calculation strategy, extract the Global Positioning System (GPS) information corresponding to the second driving deviation calculation strategy from the vehicle driving information.

[0079] The second driving deviation calculation strategy includes a strategy for determining the second type of driving deviation based on GPS information.

[0080] Step 320: Based on GPS information, dynamic deviation, vehicle driving information and preset driving direction, determine the second type of driving deviation of the vehicle. The second type of driving deviation reflects the degree of deviation of the actual driving direction of the vehicle under the influence of the second type of driving environmental factors from the preset driving direction within the first preset time period.

[0081] For example, by comprehensively considering GPS information, dynamic deviation, and vehicle driving information to determine the second type of driving deviation, it is possible to comprehensively reflect the actual driving deviation of the vehicle under the influence of GPS-related environmental factors, avoid the limitations of relying on a single factor to assess vehicle driving deviation, and improve the accuracy of vehicle driving deviation information assessment.

[0082] Step 330: Based on the first weight of the first type of driving deviation and the second weight of the second type of driving deviation, perform a weighted summation of the first type of driving deviation and the second type of driving deviation to obtain driving deviation information.

[0083] The first and second weights can be preset according to the actual situation. For example, in areas with good GPS signals, the second type of driving deviation is given a higher weight; while when the vehicle's driving status is relatively stable, the first type of driving deviation is given a higher weight.

[0084] Therefore, by using a weighted summation method to process the first type of driving deviation and the second type of driving deviation, the importance of the two types of driving deviation can be flexibly adjusted according to different driving environments and vehicle conditions, so as to obtain a driving deviation that comprehensively considers the actual driving conditions of the vehicle and the influence of GPS environmental factors.

[0085] In some embodiments of this application, prior to step 330 above, the method for determining the vehicle driving information may further include a step of determining a first weight, such as... Figure 5 As shown, this may specifically include steps 410 to 420.

[0086] Step 410: Determine the first quantity weight of the first category of driving environment factors as the proportion of the first quantity of the first category of driving environment factors to the total number of driving environment factors.

[0087] The total number of driving environment factors is the number of all driving environment factors that match the current vehicle's driving information, obtained by statistically analyzing all preset driving environment factors that may affect vehicle driving. The first number of the first category of driving environment factors is the number of driving environment factors belonging to the first category selected from all driving environment factors that match the current vehicle's driving information. The first quantity weight reflects the relative importance of the first category of driving environment factors in determining the overall driving deviation from a quantitative perspective.

[0088] For example, the first quantity weight is obtained by dividing the first number of the first type of driving environment factors by the total number of driving environment factors. For instance, if the total number of driving environment factors is 10, and there are 3 first type driving environment factors, then the first quantity weight is 3 / 10 = 0.3.

[0089] Step 420: The product of the first quantity weight and the first basic weight corresponding to the first type of driving environment factor is determined as the first weight. The first basic weight is used to characterize the influence of the first type of driving deviation quantity corresponding to the first type of driving environment factor on the driving deviation information. The first weight is used to characterize the influence of the quantity of the first type of driving environment factor on the first basic weight.

[0090] The first basic weight is used to characterize the influence of the first type of driving deviation corresponding to the first type of driving environment factor on the driving deviation information. The first weight integrates the number of the first type of driving environment factors and the influence of the first type of driving environment factor itself on the driving deviation information, and comprehensively measures the impact of the first type of driving environment factor on the final driving deviation calculation in the entire process of determining the vehicle's driving deviation.

[0091] For example, referring to the example in step 410 above, if the first quantity weight is 0.3 and the first basic weight is 0.5, then the first weight is 0.3 × 0.5 = 0.15.

[0092] Therefore, by determining the first weight, we can comprehensively consider both the quantity and the degree of influence of the first type of driving environment factors on driving deviation information. This allows us to more reasonably weigh the influence of the first type of driving environment factors on driving deviation information when calculating vehicle driving deviation information in subsequent calculations, thereby improving the accuracy of driving deviation calculation.

[0093] In some embodiments of this application, prior to step 330 above, the method for determining vehicle driving information may further include a step of determining a second weight, such as... Figure 6 As shown, this may specifically include steps 510 to 520.

[0094] Step 510: Determine the proportion of the second quantity of the second type of driving environment factors to the total number of driving environment factors as the second quantity weight of the second type of driving environment factors.

[0095] The total number of driving environment factors is the number of all driving environment factors that match the current vehicle's driving information, obtained by statistically analyzing all preset driving environment factors that may affect vehicle driving. The second number of the second category of driving environment factors is the number of driving environment factors belonging to the second category selected from all driving environment factors that match the current vehicle's driving information. The second quantity weight reflects the relative importance of the second category of driving environment factors in determining the overall driving deviation from a quantitative perspective.

[0096] For example, the second quantity weight is obtained by dividing the second number of the second type of driving environment factors by the total number of driving environment factors. For instance, if the total number of driving environment factors is 10, and there are 7 second type driving environment factors, then the second quantity weight is 7 / 10 = 0.7.

[0097] Step 520: The product of the second quantity weight and the second basic weight corresponding to the second type of driving environment factor is determined as the second weight. The second basic weight is used to characterize the influence of the second type of driving deviation quantity corresponding to the second type of driving environment factor on the driving deviation information. The second weight is used to characterize the influence of the quantity of the second type of driving environment factor on the second basic weight.

[0098] The second basic weight is used to characterize the influence of the second type of driving deviation corresponding to the second type of driving environment factors on the driving deviation information. The second weight integrates the number of second type of driving environment factors and the influence of the second type of driving environment factors themselves on the driving deviation information, and comprehensively measures the impact of the second type of driving environment factors on the final driving deviation calculation in the entire process of determining the vehicle's driving deviation.

[0099] For example, referring to the example in step 520 above, if the second quantity weight is 0.7 and the second basic weight is 0.5, then the first weight is 0.7 × 0.5 = 0.35.

[0100] Therefore, by determining the second weight, we can comprehensively consider both the quantity and the degree of influence of the second type of driving environment factors on driving deviation information. This allows us to more reasonably weigh the influence of the second type of driving environment factors on driving deviation information when calculating vehicle driving deviation information in the future, thereby improving the accuracy of driving deviation calculation.

[0101] In some embodiments of this application, the second driving deviation calculation strategy mentioned above includes a first driving deviation calculation sub-strategy. Correspondingly, the first driving deviation calculation sub-strategy includes a sub-strategy for determining the second type of driving deviation amount based on a first reference driving direction determined by GPS information.

[0102] For example, the first reference driving direction can be obtained by processing the vehicle's GPS positioning data over a period of time. For instance, GPS location information at multiple consecutive time points can be acquired, and the actual driving direction of the vehicle, i.e., the first reference driving direction, can be determined by calculating the direction of the line connecting adjacent location points.

[0103] Based on this, such as Figure 7 As shown, step 320 can specifically include steps 610 and 620.

[0104] Step 610: Based on the first driving deviation calculation sub-strategy, adjust the dynamic deviation amount according to the deviation between the first reference driving direction and the preset driving direction, and determine the third driving deviation amount.

[0105] The deviation between the first reference driving direction and the preset driving direction can be represented by an angle difference. For example, the first reference driving direction and the preset driving direction can be represented as vectors using a vector calculation method, and then the angle between these two vectors can be calculated. This angle is the deviation between the first reference driving direction and the preset driving direction.

[0106] In this way, by combining the deviation in driving direction determined based on GPS information with the deviation caused by the vehicle's own dynamic characteristics, the third driving deviation quantity can more accurately represent the degree of deviation of the vehicle from the preset driving direction at the current moment.

[0107] For example, the third driving deviation δ h1 It can be expressed by the following formula (5):

[0108] δ h1 =θ driving -θ motion_1 -δ pred (5)

[0109] Where, θ driving Indicates the preset driving direction, θ motion_1 Indicates the first reference driving direction.

[0110] Step 620: Adjust the third driving deviation based on the fourth driving deviation of the vehicle within the second preset time period to obtain the second type of driving deviation; wherein the second preset time period is earlier than the first preset time period.

[0111] The fourth driving deviation can be calculated using the method described in step 610 above.

[0112] For example, the third driving deviation can be adjusted based on the filter coefficients corresponding to the first driving deviation calculation sub-strategy and the fourth driving deviation, to obtain the second type of driving deviation δ calculated based on the first driving deviation calculation sub-strategy. h Among them, the second type of driving deviation δ h It can be expressed by the following formula (6):

[0113] δ h =δ h2 +α h (δ h1 -δ h2 (6)

[0114] Where, δ h2α represents the fourth driving deviation. h This represents the filter coefficient for the second type of driving deviation calculated based on the first driving deviation calculation sub-strategy, used to smooth the δ output of the first driving deviation calculation sub-strategy. h .

[0115] Here, α h This indicates that the third driving deviation within the first preset time period is updated in relation to the second type of driving deviation δ. h The weight of time, α h The value of α can be determined based on the driving environment in which the vehicle is located. In a relatively stable driving environment, α... h The value is less than α when the vehicle is in an unstable driving environment. h value.

[0116] Therefore, by adjusting the dynamic deviation amount based on the difference between the first reference driving direction and the preset driving direction, the third driving deviation amount can be determined quickly and accurately. Then, the third driving deviation amount is adjusted based on the fourth driving deviation amount, taking into account the driving deviation of the vehicle in the second preset time period. This makes the second type of driving deviation amount closer to the actual vehicle driving deviation, avoiding the problem of inaccurate deviation estimation caused by only considering the data of the first preset time period, thereby improving the accuracy of driving deviation information.

[0117] In some embodiments of this application, the second driving deviation calculation strategy includes a second driving deviation calculation sub-strategy, which includes a sub-strategy for determining the second type of driving deviation amount based on the vehicle's lateral distance deviation determined by GPS information and the first reference driving direction. Based on this, as... Figure 8 As shown, step 320 can specifically include steps 710 and 720.

[0118] Step 710: According to the second driving deviation calculation sub-strategy, the dynamic deviation is adjusted based on the difference between the first reference driving deviation and the second reference driving deviation to obtain the fifth driving deviation; wherein, the first reference driving deviation is determined based on the vehicle's lateral distance deviation and the vehicle's driving distance in the first preset time period, and the second reference driving deviation is determined based on the deviation between the first reference driving direction and the preset driving direction.

[0119] The lateral distance deviation of a vehicle refers to the distance deviation between the vehicle and a reference line or point perpendicular to the direction of travel during driving. For example, the lateral distance deviation can be obtained by comparing the lateral position of the vehicle at different times using GPS information. The distance traveled by the vehicle within a first preset time period can be obtained by continuously collecting GPS location data of the vehicle within that first preset time period, calculating the distances between adjacent location points, and accumulating the data.

[0120] Lateral distance deviation reflects whether the vehicle has deviated from the expected lateral position, while the travel distance takes into account the time factor and the overall driving state of the vehicle. The first reference travel deviation determined based on the lateral distance deviation and the travel distance reflects the vehicle's offset in lateral position and the travel distance in the first preset time period.

[0121] For example, the first reference driving deviation e h_est It can be expressed by the following formula (7):

[0122]

[0123] Among them, l t This indicates the lateral distance deviation of the vehicle at the current moment, l t-1 Δt represents the lateral distance deviation of the vehicle in the previous moment before the current moment, v represents the vehicle speed, and Δt represents the duration of the first preset time period.

[0124] Second reference driving deviation e h The fifth driving deviation δ is used to assess the degree of deviation between the vehicle's actual driving direction and the preset driving direction. Specifically, it can be calculated using the formula (5) above. Based on this, the fifth driving deviation δ l1 It can be calculated using the following formula (8):

[0125] δ l1 =e h_est -e h -δ pred (8)

[0126] Step 720: Adjust the fifth driving deviation based on the sixth driving deviation of the vehicle within the second preset time period to obtain the second type of driving deviation; wherein the second preset time period is earlier than the first preset time period.

[0127] For example, the fifth driving deviation can be adjusted based on the filter coefficients corresponding to the second driving deviation calculation sub-strategy and the sixth driving deviation, to obtain the second type of driving deviation δ calculated based on the second driving deviation calculation sub-strategy. l Among them, the second type of driving deviation δ l It can be expressed by the following formula (9):

[0128] δ l =δ l2 +α l (δ l1 -δ l2 (9)

[0129] Where, δ l2 α represents the sixth driving deviation.l This represents the filter coefficient for the second type of driving deviation calculated based on the first driving deviation calculation sub-strategy, used to smooth the δ output of the first driving deviation calculation sub-strategy. l .

[0130] Here, α l This indicates that the fifth driving deviation within the first preset time period is updated in relation to the second type of driving deviation δ. l The weight of time, α l The value of α can be determined based on the driving environment in which the vehicle is located. In a relatively stable driving environment, α... l The value is less than α when the vehicle is in an unstable driving environment. l value.

[0131] Therefore, by adjusting the dynamic deviation based on the difference between the first and second reference driving deviations, the lateral position change, directional deviation, and the vehicle's own dynamic factors can be combined. The second type of driving deviation, determined based on the fifth and sixth driving deviations obtained in this way, can more comprehensively and accurately reflect the actual deviation of the vehicle during driving, avoiding the problem of inaccurate deviation calculations caused by considering only a single factor.

[0132] In some embodiments of this application, the second driving deviation calculation strategy includes a third driving deviation calculation sub-strategy. This third driving deviation calculation sub-strategy includes a sub-strategy that determines a second type of driving deviation based on position changes determined by GPS information. The position changes include a first position change of the vehicle in a first direction and a second position change of the vehicle in a second direction. For example, the vehicle's position coordinates within a first preset time period can be obtained from GPS information, and the first position change of the vehicle in two different directions, such as the first direction (x-axis) and the second position change in the second direction (y-axis), can be calculated respectively.

[0133] Based on this, such as Figure 9 As shown, step 320 above may specifically include steps 810 and 820.

[0134] Step 810: According to the third driving deviation calculation sub-strategy, the dynamic deviation amount is adjusted based on the deviation between the second reference driving direction and the preset driving direction to determine the seventh driving deviation amount. The second reference driving direction is determined based on the first position change amount and the second position change amount.

[0135] For example, the second reference driving direction θ motion-2 It can be expressed by the following formula (10):

[0136] θ motion-2 =atan2(Δy, Δx) (10)

[0137] Where Δy represents the change in the second position and Δx represents the change in the first position.

[0138] Seventh driving deviation δ p1 It can be expressed by the following formula (11):

[0139] δ p1 =θ motion-2 -θ driving -δ pred (11)

[0140] Step 820: Adjust the seventh driving deviation based on the eighth driving deviation of the vehicle within the second preset time period to obtain the second type of driving deviation; wherein the second preset time period is earlier than the first preset time period.

[0141] The eighth driving deviation can be calculated using the method described in step 810 above.

[0142] For example, the seventh driving deviation can be adjusted using the filter coefficients corresponding to the third driving deviation calculation sub-strategy and the eighth driving deviation, to obtain the second type of driving deviation δ calculated based on the third driving deviation calculation sub-strategy. p Among them, the second type of driving deviation δ p It can be expressed by the following formula (12):

[0143] δ p =δ p2 +α p (δ p1 -δ p2 (12)

[0144] Where, δ p2 α represents the eighth driving deviation. p This represents the filter coefficient for the second type of driving deviation calculated based on the third driving deviation calculation sub-strategy, used to smooth the δ output of the third driving deviation calculation sub-strategy. p .

[0145] Here, α p This indicates that the seventh driving deviation within the first preset time period is updated in relation to the second type of driving deviation δ. p The weight of time, α p The value of α can be determined based on the driving environment in which the vehicle is located. In a relatively stable driving environment, α... p The value is less than α when the vehicle is in an unstable driving environment. p value.

[0146] Therefore, by determining the second reference driving direction based on the first and second position changes, the vehicle's driving direction can be accurately inferred from the actual position changes in two directions, such as lateral and longitudinal. Based on this second reference driving direction, the seventh and eighth driving deviations can be determined, allowing for a more precise quantification of the vehicle's deviations during driving.

[0147] In some embodiments of this application, the filtering coefficients of the various driving deviation calculation strategies described above can be represented by the following formula (13):

[0148]

[0149] Here, sampling_period represents the sampling period, and time_constant represents the time constant.

[0150] Here, in the data acquisition process related to the vehicle control system, the sampling period refers to the time interval between two consecutive samples, equivalent to the duration of the first or second preset time period mentioned above. The time constant, on the other hand, characterizes the dynamic response characteristics of the vehicle control system to driving deviations. Specifically, when the vehicle control system receives a driving deviation input, it does not immediately adjust the vehicle control parameters based on the deviation; instead, there is a transition process. The speed of this transition process is determined by the time constant. A smaller time constant means the system can respond to the driving deviation more quickly, bringing the vehicle's actual driving state closer to the new set value more rapidly. Conversely, a larger time constant results in a slower system response and a longer time required for the vehicle to reach the new set value.

[0151] In some embodiments of this application, the first driving deviation calculation strategy, the first driving deviation calculation sub-strategy, the second driving deviation calculation sub-strategy, and the third driving deviation calculation sub-strategy each correspond to a linear observer. These linear observers process and analyze navigation information and vehicle driving information during vehicle driving based on the methods set by the corresponding calculation strategy or calculation sub-strategy, so as to improve the accuracy and stability of determining vehicle driving deviation information.

[0152] It is understandable that after obtaining vehicle driving information and navigation information, the target observer corresponding to the driving environment factor can be obtained based on the association information of the preset driving environment factor and the preset observer. The output value of the target observer, i.e. the first type of driving deviation or the second type of driving deviation, is adaptively fused through the adaptive fusion strategy of the adaptive module to obtain the vehicle's driving deviation information.

[0153] For example, the adaptive fusion strategy includes at least one of the following:

[0154] When the first type of environmental factor exhibits at least one of the characteristics of high-speed driving, flat road surface, and small sideslip, the weight of the observer output value corresponding to the first driving deviation calculation strategy will account for a larger proportion compared with the output values ​​of other observers.

[0155] When the first type of environmental factor exhibits at least one of the characteristics of straight-line driving, large-radius curve driving, high-speed driving, and small yaw rate, and the second type of environmental factor indicates a strong GPS signal, the weight of the observer output value corresponding to the first driving deviation calculation sub-strategy will account for a larger proportion compared to the output values ​​of other observers.

[0156] When the first type of environmental factor exhibits at least one of the characteristics of cornering, medium speed driving, and yaw rate, and the second type of environmental factor indicates a strong GPS signal, the weight of the observer output value corresponding to the second driving deviation calculation sub-strategy will account for a larger proportion compared to the output values ​​of other observers.

[0157] When the first type of environmental factor represents low-speed driving or complex road conditions, and the second type of environmental factor represents weak or unstable GPS signals, the weight of the observer output value corresponding to the third driving deviation calculation sub-strategy will account for a larger proportion compared to the output values ​​of other observers.

[0158] Based on the vehicle driving information determination method provided in the above embodiments, this application also provides specific implementation methods of the vehicle driving information determination device. Please refer to the following embodiments.

[0159] See Figure 10 The vehicle driving information determination device 900 provided in this application embodiment includes:

[0160] The first acquisition module 910 is used to acquire the vehicle's navigation information and vehicle driving information within a first preset time period;

[0161] The first determining module 920 is used to determine the vehicle's driving environment factors and dynamic deviation based on the vehicle's driving information. The driving environment factors include at least one of the following: a first type of driving environment factor and a second type of driving environment factor. The first type of driving environment factor is used to reflect the actual driving situation of the vehicle, and the second type of driving environment factor is used to reflect the vehicle's communication environment status. The dynamic deviation is used to characterize the degree of deviation between the actual driving direction of the vehicle and the preset driving direction in the navigation information caused by dynamic factors during driving.

[0162] The second determining module 930 is used to determine the vehicle's driving deviation information based on the driving deviation calculation strategy corresponding to the driving environment factors, the dynamic deviation amount, the vehicle driving information, and the preset driving direction. The driving deviation information includes information used to characterize the degree of deviation between the vehicle's actual driving direction and the preset driving direction. The driving deviation information is used to adjust the vehicle to drive along the preset driving direction.

[0163] Therefore, by dividing driving environment factors into a first type of driving environment factor reflecting the actual driving situation of the vehicle and a second type of driving environment factor reflecting the vehicle's communication environment status, and formulating corresponding driving deviation calculation strategies based on different types of driving environment factors, the vehicle can accurately determine the dynamic deviation and driving environment factors of the vehicle using the first determining module 920 based on the vehicle driving information obtained by the first acquiring module 910 when the GPS signal is poor. Then, the second determining module 930 can accurately obtain the vehicle's driving deviation information based on the driving deviation calculation strategy corresponding to the driving environment factors, effectively avoiding the problem of the vehicle deviating from the expected driving path due to the inability to accurately calculate the driving deviation caused by unstable GPS signals.

[0164] In some embodiments of this application, the second determining module 930 described above may include:

[0165] The first extraction submodule is used to extract the vehicle speed direction corresponding to the first driving deviation calculation strategy from the vehicle driving information according to the first driving deviation calculation strategy when the driving environment factors include a first type of driving environment factors, the driving deviation calculation strategy includes a first driving deviation calculation strategy, and the driving deviation information includes a first type of driving deviation amount.

[0166] The first adjustment submodule is used to adjust the dynamic deviation amount according to the deviation between the vehicle's driving speed direction and the preset driving direction, so as to obtain the first driving deviation amount of the vehicle in the first preset time period. The first driving deviation amount reflects the initial deviation degree of the vehicle's actual driving direction under the influence of the first type of driving environment factor from the preset driving direction in the first preset time period.

[0167] The second adjustment submodule is used to adjust the first driving deviation based on the second driving deviation of the vehicle within a second preset time period to obtain a first type of driving deviation. The first type of driving deviation reflects the degree of deviation of the actual driving direction of the vehicle under the influence of a first type of driving environmental factor from the preset driving direction within the first preset time period; wherein, the second preset time period is earlier than the first preset time period.

[0168] In some embodiments of this application, the second determining module 930 described above may include:

[0169] The second extraction submodule is used to extract GPS information corresponding to the second driving deviation calculation strategy from the vehicle driving information, given that the driving environment factors include a first type of driving environment factors and a second type of driving environment factors, the driving deviation calculation strategy includes a first driving deviation calculation strategy and a second driving deviation calculation strategy, the driving deviation information includes a first type of driving deviation amount and a second type of driving deviation amount, the first driving deviation calculation strategy includes a strategy of determining the first type of driving deviation amount based on the vehicle driving speed and direction in the vehicle driving information, and the first type of driving deviation amount reflects the degree of deviation of the actual driving direction of the vehicle under the influence of the first type of driving environment factors from the preset driving direction within a first preset time period;

[0170] The first determining submodule is used to determine the second type of driving deviation of the vehicle based on GPS information, dynamic deviation, vehicle driving information and preset driving direction. The second type of driving deviation reflects the degree of deviation of the actual driving direction of the vehicle under the influence of the second type of driving environmental factors from the preset driving direction within a first preset time period.

[0171] The second determining submodule is used to perform weighted summation of the first type of driving deviation and the second type of driving deviation based on the first weight of the first type of driving deviation and the second weight of the second type of driving deviation to obtain driving deviation information.

[0172] In some embodiments of this application, the first determining submodule described above may include:

[0173] The first adjustment unit is used to adjust the dynamic deviation amount according to the first driving deviation calculation sub-strategy and the deviation between the first reference driving direction and the preset driving direction, and determine the third driving deviation amount when the second driving deviation calculation strategy includes a first driving deviation calculation sub-strategy and the first driving deviation calculation sub-strategy includes a sub-strategy that determines the second type of driving deviation amount based on the first reference driving direction determined by GPS information.

[0174] The second adjustment unit is used to adjust the third driving deviation based on the fourth driving deviation of the vehicle within the second preset time period to obtain the second type of driving deviation; wherein the second preset time period is earlier than the first preset time period.

[0175] In some embodiments of this application, the first determining submodule described above may include:

[0176] The third adjustment unit is used to adjust the dynamic deviation amount according to the second driving deviation calculation strategy, based on the difference between the first reference driving deviation amount and the second reference driving deviation amount, to obtain the fifth driving deviation amount, when the second driving deviation calculation strategy includes a second driving deviation calculation sub-strategy, and the second driving deviation calculation sub-strategy includes a sub-strategy for determining the second type of driving deviation amount based on the vehicle lateral distance deviation determined by GPS information and the first reference driving direction; wherein, the first reference driving deviation amount is determined based on the vehicle lateral distance deviation and the vehicle's driving distance in a first preset time period, and the second reference driving deviation amount is determined based on the deviation between the first reference driving direction and the preset driving direction;

[0177] The fourth adjustment unit is used to adjust the fifth driving deviation based on the sixth driving deviation of the vehicle within the second preset time period to obtain the second type of driving deviation; wherein the second preset time period is earlier than the first preset time period.

[0178] In some embodiments of this application, the first determining submodule described above may include:

[0179] The fifth adjustment unit is used to adjust the dynamic deviation amount according to the deviation between the second reference driving direction and the preset driving direction in the case where the second driving deviation calculation strategy includes a third driving deviation calculation sub-strategy, the third driving deviation calculation sub-strategy includes a sub-strategy for determining the second type of driving deviation amount based on the position change amount determined by GPS information, and the position change amount includes the first position change amount of the vehicle in the first direction and the second position change amount of the vehicle in the second direction, and to determine the seventh driving deviation amount. The second reference driving direction is determined based on the first position change amount and the second position change amount.

[0180] The sixth adjustment unit is used to adjust the seventh driving deviation based on the eighth driving deviation of the vehicle within the second preset time period to obtain the second type of driving deviation; wherein the second preset time period is earlier than the first preset time period.

[0181] In some embodiments of this application, the vehicle driving information determining device 900 may further include:

[0182] The third determining module is used to determine the first quantity weight of the first type of driving environment factors as the proportion of the first quantity of the first type of driving environment factors to the total number of driving environment factors before performing the step of weighted summation of the first type of driving deviation and the second type of driving deviation based on the first weight of the first type of driving deviation and the second weight of the second type of driving deviation to obtain driving deviation information.

[0183] The fourth determining module is used to determine the first weight by multiplying the first quantity weight and the first basic weight corresponding to the first type of driving environment factor. The first basic weight is used to characterize the influence of the first type of driving deviation quantity corresponding to the first type of driving environment factor on the driving deviation information. The first weight is used to characterize the influence of the quantity of the first type of driving environment factor on the first basic weight.

[0184] In some embodiments of this application, the vehicle driving information determining device 900 may further include:

[0185] The fifth determining module is used to determine the proportion of the second quantity of the second type of driving environment factors to the total number of driving environment factors as the second quantity weight of the second type of driving environment factors before performing the step of weighted summation of the first type of driving deviation amount and the second type of driving deviation amount according to the first weight of the first type of driving deviation amount and the second weight of the second type of driving deviation amount to obtain driving deviation information.

[0186] The sixth determining module is used to determine the second weight by multiplying the second quantity weight and the second basic weight corresponding to the second type of driving environment factor. The second basic weight is used to characterize the influence of the second type of driving deviation quantity corresponding to the second type of driving environment factor on the driving deviation information. The second weight is used to characterize the influence of the quantity of the second type of driving environment factor on the second basic weight.

[0187] In some embodiments of this application, the first determining module 920 may specifically include:

[0188] The third determination submodule is used to determine the initial dynamic deviation based on the vehicle speed and yaw rate when the vehicle driving information includes the vehicle speed and yaw rate.

[0189] The fourth determination submodule is used to adjust the initial dynamic deviation according to the preset proportional coefficient to obtain the dynamic deviation.

[0190] The various modules of the vehicle driving information determination device 900 provided in this application embodiment can achieve... Figures 1 to 9 The functions of each step in the method for determining vehicle driving information, and the corresponding technical effects they achieve, will not be elaborated here for the sake of brevity.

[0191] Figure 11 The illustration shows a schematic diagram of the hardware structure of a computer device provided in some embodiments of this application.

[0192] The computer device may include a processor 1101 and a memory 1102 storing computer program instructions. The memory stores a computer program, and when the processor executes the computer program, it implements the steps in any of the above embodiments of the method for determining vehicle driving information.

[0193] Specifically, the processor 1101 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0194] For example, the computer device may be a terminal, specifically including but not limited to vehicles and other means of transportation. The processor 1101 described above may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits that may be configured to implement the embodiments of this application.

[0195] Memory 1102 may include mass storage for data or instructions. For example, and not limitingly, memory 1102 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 1102 may include removable or non-removable (or fixed) media. Where appropriate, memory 1102 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 1102 is non-volatile solid-state memory.

[0196] In a particular embodiment, memory 1102 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory 1102 includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the data processing method according to the first aspect of this application.

[0197] The processor 1101 reads and executes computer program instructions stored in the memory 1102 to implement any of the vehicle driving information determination methods in the above embodiments.

[0198] In one example, the computer device may also include a communication interface 1103 and a bus 1110. Wherein, as... Figure 11 As shown, the processor 1101, memory 1102, and communication interface 1103 are connected through bus 1110 and complete communication with each other.

[0199] The communication interface 1103 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0200] Bus 1110 includes hardware, software, or both, that couples components of a computer device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 1110 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, any suitable bus or interconnect is contemplated herein.

[0201] The computer device can execute the vehicle driving information determination method in the embodiments of this application, thereby achieving the combination of Figures 1 to 10 The method and apparatus described herein are for determining vehicle driving information.

[0202] Furthermore, in conjunction with the vehicle driving information determination method in the above embodiments, this application embodiment can provide a computer-readable storage medium for implementation. This computer-readable storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the vehicle driving information determination methods in the above embodiments. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, etc.

[0203] Furthermore, in conjunction with the vehicle driving information determination method in the above embodiments, this application embodiment can provide a computer program product to implement this method. This program product is stored in a storage medium and may specifically include a computer program or instructions. When executed by a processor, the computer program or instructions implement any of the vehicle driving information determination methods in the above embodiments. This program product is executed by at least one processor to implement the various processes as described in the above data processing method embodiments, and can achieve the same technical effects. To avoid repetition, further details are omitted here.

[0204] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0205] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0206] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0207] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0208] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for determining vehicle driving information, characterized in that, include: Obtain the vehicle's navigation information and vehicle driving information within a first preset time period; Based on the vehicle driving information, the driving environment factors and dynamic deviation of the vehicle are determined. The driving environment factors include at least one of the following: a first type of driving environment factor and a second type of driving environment factor. The first type of driving environment factor is used to reflect the actual driving situation of the vehicle, and the second type of driving environment factor is used to reflect the communication environment status of the vehicle. The dynamic deviation is used to characterize the degree of deviation between the actual driving direction of the vehicle and the preset driving direction in the navigation information due to dynamic factors during driving. Based on the driving deviation calculation strategy corresponding to the driving environment factors, the dynamic deviation amount, the vehicle driving information, and the preset driving direction, the driving deviation information of the vehicle is determined. The driving deviation information includes information used to characterize the degree of deviation between the actual driving direction of the vehicle and the preset driving direction. The driving deviation information is used to adjust the vehicle to drive along the preset driving direction. Wherein, the driving environment factors include the first type of driving environment factors, the driving deviation calculation strategy includes the first driving deviation calculation strategy, and the driving deviation information includes the first type of driving deviation amount; The step of determining the vehicle's driving deviation information based on the driving deviation calculation strategy corresponding to the driving environment factors, the dynamic deviation amount, the vehicle driving information, and the preset driving direction includes: According to the first driving deviation calculation strategy, the vehicle driving speed direction corresponding to the first driving deviation calculation strategy is extracted from the vehicle driving information; the dynamic deviation is adjusted according to the deviation between the vehicle driving speed direction and the preset driving direction to obtain the first driving deviation of the vehicle in a first preset time period. The first driving deviation reflects the initial degree of deviation of the actual driving direction of the vehicle under the influence of the first type of driving environmental factor relative to the preset driving direction in the first preset time period; the first driving deviation is adjusted according to the second driving deviation of the vehicle in a second preset time period to obtain the first type of driving deviation, which reflects the degree of deviation of the actual driving direction of the vehicle under the influence of the first type of driving environmental factor relative to the preset driving direction in the first preset time period; wherein, the second preset time period is earlier than the first preset time period.

2. The determination method according to claim 1, characterized in that, The driving environment factors include the first type of driving environment factors and the second type of driving environment factors. The driving deviation calculation strategy includes the first driving deviation calculation strategy and the second driving deviation calculation strategy. The driving deviation information includes the first type of driving deviation amount and the second type of driving deviation amount. The first driving deviation calculation strategy includes a strategy of determining the first type of driving deviation amount based on the vehicle driving speed and direction in the vehicle driving information. The first type of driving deviation amount reflects the degree of deviation of the actual driving direction of the vehicle under the influence of the first type of driving environment factors from the preset driving direction within a first preset time period. The step of determining the vehicle's driving deviation information based on the driving deviation calculation strategy corresponding to the driving environment factors, the dynamic deviation amount, the vehicle driving information, and the preset driving direction includes: According to the second driving deviation calculation strategy, extract the Global Positioning System (GPS) information corresponding to the second driving deviation calculation strategy from the vehicle driving information; Based on the GPS information, the dynamic deviation, the vehicle driving information, and the preset driving direction, a second type of driving deviation of the vehicle is determined. The second type of driving deviation reflects the degree of deviation of the actual driving direction of the vehicle from the preset driving direction under the influence of the second type of driving environmental factors within a first preset time period. Based on the first weight of the first type of driving deviation and the second weight of the second type of driving deviation, the first type of driving deviation and the second type of driving deviation are weighted and summed to obtain the driving deviation information.

3. The determination method according to claim 2, characterized in that, The second driving deviation calculation strategy includes a first driving deviation calculation sub-strategy, which includes a sub-strategy for determining the second type of driving deviation amount based on a first reference driving direction determined by the GPS information; The step of determining the second type of driving deviation of the vehicle based on the GPS information, the dynamic deviation, the vehicle driving information, and the preset driving direction includes: Based on the first driving deviation calculation sub-strategy, the dynamic deviation amount is adjusted according to the deviation between the first reference driving direction and the preset driving direction to determine the third driving deviation amount; Based on the fourth driving deviation of the vehicle within the second preset time period, the third driving deviation is adjusted to obtain the second type of driving deviation; wherein the second preset time period is earlier than the first preset time period.

4. The determination method according to claim 2, characterized in that, The second driving deviation calculation strategy includes a second driving deviation calculation sub-strategy, which includes a sub-strategy for determining the second type of driving deviation amount based on the vehicle lateral distance deviation determined by the GPS information and the first reference driving direction; The step of determining the second type of driving deviation of the vehicle based on the second driving deviation calculation strategy, the GPS information, the dynamic deviation, the vehicle driving information, and the preset driving direction includes: According to the second driving deviation calculation sub-strategy, the dynamic deviation is adjusted based on the difference between the first reference driving deviation and the second reference driving deviation to obtain the fifth driving deviation; wherein, the first reference driving deviation is determined based on the vehicle lateral distance deviation and the vehicle's driving distance in the first preset time period, and the second reference driving deviation is determined based on the deviation between the first reference driving direction and the preset driving direction. Based on the sixth driving deviation of the vehicle within the second preset time period, the fifth driving deviation is adjusted to obtain the second type of driving deviation; wherein the second preset time period is earlier than the first preset time period.

5. The determination method according to claim 2, characterized in that, The second driving deviation calculation strategy includes a third driving deviation calculation sub-strategy, which includes a sub-strategy for determining the second type of driving deviation based on the position change determined by the GPS information, wherein the position change includes the first position change of the vehicle in the first direction and the second position change of the vehicle in the second direction; The step of determining the second type of driving deviation of the vehicle based on the second driving deviation calculation strategy, the GPS information, the dynamic deviation, the vehicle driving information, and the preset driving direction includes: According to the third driving deviation calculation sub-strategy, the dynamic deviation amount is adjusted based on the deviation between the second reference driving direction and the preset driving direction to determine the seventh driving deviation amount. The second reference driving direction is determined based on the first position change amount and the second position change amount. Based on the eighth driving deviation of the vehicle within a second preset time period, the seventh driving deviation is adjusted to obtain the second type of driving deviation; wherein the second preset time period is earlier than the first preset time period.

6. The determining method according to any one of claims 2 to 5, characterized in that, Before obtaining the driving deviation information by performing a weighted summation of the first type of driving deviation and the second type of driving deviation based on the first weight and the second weight of the second type of driving deviation, the method further includes: The proportion of the first quantity of the first type of driving environment factors to the total number of driving environment factors is determined as the first quantity weight of the first type of driving environment factors. The product of the first quantity weight and the first basic weight corresponding to the first type of driving environment factor is determined as the first weight. The first basic weight is used to characterize the influence of the first type of driving deviation quantity corresponding to the first type of driving environment factor on the driving deviation information. The first weight is used to characterize the influence of the quantity of the first type of driving environment factor on the first basic weight.

7. The determining method according to any one of claims 2 to 5, characterized in that, Before obtaining the driving deviation information by performing a weighted summation of the first type of driving deviation and the second type of driving deviation based on the first weight and the second weight of the second type of driving deviation, the method further includes: The proportion of the second quantity of the second type of driving environment factors to the total number of driving environment factors is determined as the second quantity weight of the second type of driving environment factors. The product of the second quantity weight and the second basic weight corresponding to the second type of driving environment factor is determined as the second weight. The second basic weight is used to characterize the influence of the second type of driving deviation quantity corresponding to the second type of driving environment factor on the driving deviation information. The second weight is used to characterize the influence of the quantity of the second type of driving environment factor on the second basic weight.

8. The determining method according to any one of claims 1 to 5, characterized in that, The vehicle driving information includes vehicle speed and vehicle yaw rate; Determining the dynamic deviation of the vehicle based on the vehicle driving information includes: The initial dynamic deviation is determined based on the vehicle's travel speed and yaw rate. The initial dynamic deviation is adjusted according to a preset proportional coefficient to obtain the dynamic deviation.

9. A device for determining vehicle driving information, characterized in that, The device includes: The first acquisition module is used to acquire the vehicle's navigation information and vehicle driving information within a first preset time period; The first determining module is used to determine the vehicle's driving environment factors and dynamic deviation based on the vehicle's driving information. The driving environment factors include at least one of the following: a first type of driving environment factor and a second type of driving environment factor. The first type of driving environment factor is used to reflect the actual driving situation of the vehicle, and the second type of driving environment factor is used to reflect the vehicle's communication environment status. The dynamic deviation is used to characterize the degree of deviation between the actual driving direction of the vehicle and the preset driving direction in the navigation information caused by dynamic factors during driving. The second determining module is used to determine the vehicle's driving deviation information based on the driving deviation calculation strategy corresponding to the driving environment factors, the dynamic deviation amount, the vehicle driving information, and the preset driving direction. The driving deviation information includes information characterizing the degree of deviation between the vehicle's actual driving direction and the preset driving direction. The driving deviation information is used to adjust the vehicle to drive along the preset driving direction. Wherein, the driving environment factors include the first type of driving environment factors, the driving deviation calculation strategy includes the first driving deviation calculation strategy, and the driving deviation information includes the first type of driving deviation amount; The second determining module is specifically used for: extracting the vehicle speed direction corresponding to the first driving deviation calculation strategy from the vehicle driving information according to the first driving deviation calculation strategy; adjusting the dynamic deviation amount according to the deviation between the vehicle speed direction and the preset driving direction to obtain a first driving deviation amount of the vehicle in a first preset time period, wherein the first driving deviation amount reflects the initial degree of deviation of the actual driving direction of the vehicle under the influence of the first type of driving environmental factor relative to the preset driving direction in the first preset time period; adjusting the first driving deviation amount according to the second driving deviation amount of the vehicle in a second preset time period to obtain a first type of driving deviation amount, wherein the first type of driving deviation amount reflects the degree of deviation of the actual driving direction of the vehicle under the influence of the first type of driving environmental factor relative to the preset driving direction in the first preset time period; wherein the second preset time period is earlier than the first preset time period.

10. A computer device, characterized in that, The computer device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the method for determining vehicle driving information as described in any one of claims 1-8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the method for determining vehicle driving information as described in any one of claims 1-8.

12. A computer program product, characterized in that, Includes a computer program, which, when executed, implements the method for determining vehicle driving information as described in any one of claims 1-8.

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