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

Abstract

The invention discloses a vehicle driving information determination method and device, equipment, a medium and a program product, and is applied to the technical field of vehicle control. The determination method comprises the following steps: acquiring navigation information and vehicle driving information of a vehicle in a first preset time period; based on the vehicle driving information, driving environment factors and a dynamic deviation value of the vehicle are determined, and the driving environment factors comprise at least one of a first type of driving environment factors and a second type of driving environment factors; according to a driving deviation calculation strategy corresponding to the driving environment factor, the dynamic deviation value, the vehicle driving information and a preset driving direction, driving deviation information of the vehicle is determined, and the driving deviation information is used for adjusting the vehicle to drive in the preset driving direction. According to the method, the problem that the vehicle deviates from the preset driving direction due to the fact that the driving deviation of the vehicle cannot be accurately calculated due to instability of the GPS signal can be solved.

Description

Technical Field

[0001] The present 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 Art

[0002] When driving, the self-driving vehicle must follow the pre-set driving speed and path planning to achieve autonomous driving. However, during the actual driving process, due to interference from environmental factors such as road conditions and weather conditions and vehicle factors such as vehicle dynamics and sensor accuracy, the actual driving direction of the vehicle may deviate from the preset driving direction.

[0003] In the related art, the vehicle receives the Global Positioning System (GPS) signal from the satellite, determines the actual driving direction of the vehicle based on the GPS signal, compares the actual driving direction with the preset driving direction, and calculates the driving deviation of the vehicle. Then, the control parameters of the vehicle are adjusted according to the driving deviation to ensure that the vehicle can travel along the preset driving direction. However, in some areas such as tunnels, the GPS signal is prone to instability, and the driving deviation of the vehicle cannot be accurately calculated, which causes the vehicle to deviate from the expected driving path. Summary of the invention

[0004] The embodiments of the present application provide a method, device, equipment, medium and program product for determining vehicle driving information, which can solve the problem that the unstable GPS signal makes it impossible to accurately calculate the vehicle's driving deviation, thereby causing the vehicle to deviate from the preset driving direction.

[0005] In a first aspect, an embodiment of the present application provides a method for determining vehicle driving information, the method for determining vehicle driving information comprising:

[0006] Obtaining navigation information and vehicle driving information of the vehicle in a first preset time period;

[0007] Based on the vehicle driving information, determining the driving environment factor and the dynamic deviation of the vehicle, the driving environment factor includes 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 condition of the vehicle, the second type of driving environment factor is used to reflect the communication environment condition of the vehicle, and the dynamic deviation is used to characterize the degree of deviation between the actual driving direction of the vehicle due to the dynamic factors during the driving process and the preset driving direction in the navigation information;

[0008] The vehicle's driving deviation information is determined based on the driving deviation calculation strategy, dynamic deviation, vehicle driving information and preset driving direction corresponding to the driving environment factors. 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 travel along the preset driving direction.

[0009] In a second aspect, an embodiment of the present application provides a device for determining vehicle driving information, the device for determining vehicle driving information comprising:

[0010] A first acquisition module, used to acquire navigation information and vehicle driving information of the vehicle in a first preset time period;

[0011] A first determination module is used to determine a driving environment factor and a dynamic deviation amount of the vehicle based on the vehicle driving information, wherein the driving environment factor includes at least one of the following: a first type of driving environment factor and a second type of driving environment factor, wherein the first type of driving environment factor is used to reflect the actual driving condition of the vehicle, and the second type of driving environment factor is used to reflect the communication environment condition of the vehicle, and the dynamic deviation amount is used to characterize the degree of deviation between the actual driving direction of the vehicle due to the dynamic factors during the driving process and the preset driving direction in the navigation information;

[0012] The second determination module is used to determine the vehicle's driving deviation information according to 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 travel along the preset driving direction.

[0013] In a third aspect, an embodiment of the present application provides a computer device, comprising: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, a method for determining vehicle driving information as described in any one of the first aspects is implemented.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having computer program instructions stored thereon. When the computer program instructions are executed by a processor, a method for determining vehicle driving information as described in any one of the first aspects is implemented.

[0015] In a fifth aspect, an embodiment of the present application provides a computer program product, which includes a computer program or instructions, and when the computer program or instructions are executed by a processor, a method for determining vehicle driving information as described in any one of the first aspects is implemented.

[0016] The vehicle driving information determination method, device, equipment, medium and program product of the embodiments of the present application divide the driving environment factors into a first type of driving environment factors reflecting the actual driving conditions of the vehicle and a second type of driving environment factors reflecting the communication environment conditions of the vehicle, and formulate corresponding driving deviation calculation strategies based on different types of driving environment factors. When the GPS signal of the vehicle is poor, the vehicle driving information can be used to determine the vehicle's dynamic deviation and driving environment factors, and then the vehicle's driving deviation information can be accurately obtained 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 due to unstable GPS signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 A schematic diagram showing a flow chart of a method for determining vehicle driving information provided by some embodiments of the present application;

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

[0020] Figure 3 A flowchart showing a specific implementation of step 130 provided in some embodiments of the present application is shown;

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

[0022] Figure 5 A schematic flow chart showing a step of determining a first weight in a method for determining vehicle driving information provided in some embodiments of the present application;

[0023] Figure 6 A schematic flow chart showing a step of determining a second weight in a method for determining vehicle driving information provided in some embodiments of the present application;

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

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

[0026] Fig. 9 A flowchart showing another specific implementation of step 320 provided in some embodiments of the present application is shown;

[0027] Fig.10 A schematic diagram showing the structure of a device for determining vehicle driving information provided by some embodiments of the present application;

[0028] Fig.11 A schematic diagram of the structure of a computer device provided in some embodiments of the present application is shown. DETAILED DESCRIPTION

[0029] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0030] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0031] It should be noted that the acquisition, storage, use and processing of data in the embodiments of the present application are in compliance with the relevant provisions of national laws and regulations.

[0032] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0033] In order to solve the problems in the above-mentioned related technologies, the embodiments of the present application provide a method, device, equipment, medium and program product for determining vehicle driving information. Figure 1 To Attachment Fig. 9 , the method for determining vehicle driving information provided by the embodiment of the present application is described in detail through specific embodiments and their application scenarios.

[0034] Figure 1 FIG. 1 is a flow chart showing a method for determining vehicle driving information provided by some embodiments of the present application. Figure 1 As shown, the method for determining vehicle driving information may include steps 110 to 130.

[0035] Step 110, obtaining navigation information and vehicle driving information of the vehicle in a 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 condition of the vehicle, and the second type of driving environment factor is used to reflect the vehicle's communication environment condition. The dynamic deviation is used to characterize the degree of deviation between the actual driving direction of the vehicle due to dynamic factors during driving and the preset driving direction in the navigation information.

[0037] Step 130, 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, determine the driving deviation information of the vehicle, 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, and the driving deviation information is used to adjust the vehicle to travel along the preset driving direction.

[0038] Therefore, by dividing the driving environment factors into a first type of driving environment factors that reflect the actual driving conditions of the vehicle and a second type of driving environment factors that reflect the communication environment conditions of the vehicle, and formulating corresponding driving deviation calculation strategies based on different types of driving environment factors, the vehicle can use the vehicle driving information to determine the vehicle's dynamic deviation and driving environment factors when the GPS signal is poor, and then 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 due to unstable GPS signals.

[0039] The above steps are described in detail below, as shown below.

[0040] First, in S110, the navigation information involved in the embodiment of the present application 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. The vehicle driving information includes the vehicle's driving speed, the vehicle's yaw rate, the vehicle's steering angle, the vehicle's lateral acceleration, the GPS signal strength, and the global positioning system GPS information during the vehicle's driving process.

[0041] Exemplarily, the vehicle speed can be measured by a speed sensor installed on the vehicle wheel or drive shaft; the vehicle yaw angular velocity refers to the angular velocity of the vehicle's rotation around the vertical axis, which can be measured by a gyroscope installed on the vehicle; the vehicle steering angle can be measured by a steering angle sensor installed on the vehicle; the vehicle lateral acceleration can be measured by an accelerometer installed on the vehicle; GPS information and GPS signal strength can be obtained through the vehicle navigation system or the vehicle GPS receiver.

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

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

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

[0045] In other embodiments of the present application, determining the driving environment of the vehicle based on the vehicle driving information may include determining an environmental factor of the vehicle related to the vehicle driving speed through a change in the vehicle driving speed in the vehicle driving information.

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

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

[0048] Step 1201, determining an initial dynamic deviation according to the vehicle's driving speed and the vehicle's yaw rate.

[0049] The initial dynamic deviation is obtained based on the product of the vehicle's travel speed and the vehicle's yaw rate, and is a deviation that can reflect the vehicle's turning tendency and the resulting tendency to deviate from a preset direction.

[0050] Step 1202, adjusting the initial dynamic deviation amount according to a preset proportional coefficient to obtain a dynamic deviation amount.

[0051] The preset proportionality coefficient is a preset 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 will generate lateral acceleration, which will cause the actual driving direction of the vehicle to deviate from the preset driving direction. The deviation is proportional to the lateral acceleration, but in the opposite direction. For example, when the vehicle turns left, the curvature is greater than 0, and the lateral acceleration is toward the right, i.e., the centrifugal direction. Based on this, the preset proportional coefficient takes a negative value, such as -0.006.

[0053] If the vehicle speed is v (unit: m / s), the vehicle yaw rate is ω (unit: radians / s), 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, the dynamic deviation of the vehicle can be determined more accurately by presetting the proportional coefficient, the vehicle speed and the vehicle yaw rate to determine the degree of deviation between the actual driving direction of the vehicle and the preset driving direction. In this way, the steering, braking and other control parameters of the vehicle can be adjusted in time according to the dynamic deviation, so that the vehicle can travel more accurately along the preset driving direction.

[0056] Then, step 130 is involved. The driving deviation calculation strategy involved in the embodiment of the present application is a strategy for calculating the vehicle driving deviation information based on driving environment factors, dynamic deviation, vehicle driving information and preset driving direction. It is understandable that different driving deviation calculation strategies are used in different driving scenarios.

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

[0058] For example, in the autonomous driving scenario of an autonomous vehicle, accurate driving deviation information can enable the autonomous vehicle to better follow the preset driving route and avoid deviations from the route due to environmental factors or the vehicle's own dynamic characteristics. In the manual driving scenario of a driving vehicle, accurate driving deviation information can also provide the driver with more accurate driving direction prompts through the vehicle's assisted driving system.

[0059] In some embodiments of the present application, the driving environment factor includes a first type of driving environment factor, and 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 driving speed direction. Based on this, Figure 3As shown, the above step 130 may include steps 210 to 230 .

[0060] Step 210: extracting the vehicle driving speed direction corresponding to the first driving deviation calculation strategy from the vehicle driving information according to the first driving deviation calculation strategy.

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

[0062] θ velocity =atan2(v y , v x ) (2)

[0063] Among them, atan2 is an inverse tangent function.

[0064] Step 220, according to the deviation between the vehicle's driving speed direction and the preset driving direction, the dynamic deviation is adjusted to obtain a first driving deviation of the vehicle within a first preset time period. The first driving deviation reflects the initial deviation degree of the actual driving direction of the vehicle under the influence of the first type of driving environment factors relative to the preset driving direction within the first preset time period.

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

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

[0067] Among them, δ pred represents the dynamic deviation, θ velocity Indicates the direction of vehicle speed, θ driving Indicates the preset direction of travel.

[0068] In this way, by considering the deviation between the vehicle's driving speed direction and the preset driving direction to adjust the dynamic deviation, the vehicle's current actual driving condition reflected by the driving speed direction can be combined with the deviation caused by dynamic factors, thereby more accurately reflecting the deviation between the vehicle's actual driving direction and the preset driving direction in the current first preset time period.

[0069] Step 230, according to the second driving deviation amount of the vehicle in the second preset time period, adjust the first driving deviation amount to obtain a 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 environment factors 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.

[0070] The second driving deviation is obtained by processing the vehicle driving information of the second preset time period through the method steps of step 220 and step 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 amount can be adjusted according to the filter coefficient corresponding to the first driving deviation calculation strategy and the second driving deviation amount to obtain a first type of driving deviation amount, wherein the first type of driving deviation amount δ v It can be expressed by the following formula (4):

[0072] δ v =δ v2 +α v (δ v1 -δ v2 ) (4)

[0073] Among them, δ v2 represents the second driving deviation, α v The filter coefficient representing the first type of driving deviation is used to smooth the delta output by the first driving deviation calculation strategy. v .

[0074] Here, α v Indicates that the first driving deviation amount within the first preset time period is updating the first type of driving deviation amount δ v The weight of v The value of can be determined according to the driving environment of the vehicle. When the vehicle is in a relatively stable driving environment, α v The value is smaller than α when the vehicle is in an unstable driving environment. v value.

[0075] It is understandable that in actual environments, the measured vehicle speed direction may be affected by noise, such as sensor measurement errors, external interference, etc. By introducing the filter coefficient α v , these noises can be suppressed so that the first type of driving deviation δ v Smoother and more stable.

[0076] Therefore, by calculating the first driving deviation amount through the vehicle's driving speed direction, the vehicle's driving deviation can be directly quantified from the perspective of the vehicle's motion state, accurately reflecting the initial deviation degree of the vehicle. The dynamic adjustment mechanism of adjusting the first driving deviation amount using the second driving deviation amount helps to reduce large adjustments during vehicle driving and improve the smoothness of vehicle driving.

[0077] In some embodiments of the present application, 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, and the driving deviation information includes a first type of driving deviation amount and a second type of driving deviation amount, wherein the first driving deviation calculation strategy includes a strategy for determining the first type of driving deviation amount based on the vehicle driving speed 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 relative to the preset driving direction within a first preset time period. The second type of driving environment factors reflects that the GPS signal strength of the vehicle 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, that is, is greater than the preset threshold, the second driving deviation calculation strategy is used to determine the driving deviation amount of the vehicle. The preset threshold is an empirical value set after a large number of experimental tests and analysis of the stability and availability of GPS signals in different driving scenarios. Based on this, Figure 4 As shown, the above step 130 may include steps 310 to 330 .

[0078] Step 310 : extracting global positioning system GPS information corresponding to the second driving deviation calculation strategy from the vehicle driving information according to the second driving deviation calculation strategy.

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

[0080] Step 320, 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, wherein 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 environment factors relative to 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 can comprehensively reflect the actual driving deviation of the vehicle under the influence of GPS-related environmental factors, avoid the limitation of relying solely on a single factor to evaluate the vehicle driving deviation, and improve the accuracy of the vehicle driving deviation information evaluation.

[0082] Step 330 , performing weighted sum processing on 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.

[0083] Among them, the first weight and the second weight can be pre-set according to actual conditions. For example, in an area with good GPS signals, a higher weight is given to the second type of driving deviation; and when the vehicle driving state is relatively stable, a higher weight is given to the first type of driving deviation.

[0084] Therefore, the weighted summation method is adopted to process the first type of driving deviation and the second type of driving deviation, which can flexibly adjust the importance of the two types of driving deviation 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 the present application, before the above step 330, the above method for determining vehicle driving information may further include a step of determining a first weight, such as Figure 5 As shown, it may specifically include steps 410 to 420.

[0086] Step 410: determine the ratio of the first quantity of the first type of driving environment factors in the driving environment factors to the total quantity of the driving environment factors as the first quantity weight of the first type of driving environment factors.

[0087] The total number of driving environment factors is the number of all driving environment factors matching the vehicle driving information of the current vehicle obtained by counting all preset driving environment factors that may affect the vehicle driving. The first number of first-category driving environment factors is obtained by counting the driving environment factors belonging to the first category of driving environment factors selected from all driving environment factors matching the vehicle driving information of the current vehicle. The first quantity weight reflects the relative importance of the first category of driving environment factors in determining the overall driving deviation amount from a quantity perspective.

[0088] Exemplarily, the first quantity of the first type of driving environment factors obtained is divided by the total number of driving environment factors, and the obtained ratio is the first quantity weight. For example, if the total number of driving environment factors is 10, of which 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 degree of influence of the first type of driving deviation amount corresponding to the first type of driving environment factor on the driving deviation information, and the first weight is used to characterize the degree of 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 amount corresponding to the first type of driving environment factor on the driving deviation information. The first weight combines the number of the first type of driving environment factors and the influence of the first type of driving environment factors themselves on the driving deviation information, and comprehensively measures the influence of the first type of driving environment factors on the final driving deviation amount calculation in the entire vehicle driving deviation amount determination process.

[0091] Exemplarily, referring to the example in the above step 410, 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, it is possible to comprehensively consider both the quantity and the degree of influence of the first type of driving environment factors on the driving deviation information, so that when subsequently calculating the vehicle driving deviation information, it is possible to more reasonably weigh the degree of influence of the first type of driving environment factors on the driving deviation information, thereby improving the accuracy of the calculation of the driving deviation amount.

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

[0094] Step 510: Determine the ratio of the second number of the second type of driving environment factors in the driving environment factors to the total number of the driving environment factors as the second number 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 matching the vehicle driving information of the current vehicle obtained by counting all preset driving environment factors that may affect the vehicle driving. The second number of second-category driving environment factors is obtained by counting the driving environment factors belonging to the second category of driving environment factors selected from all driving environment factors matching the vehicle driving information of the current vehicle. The second quantity weight reflects the relative importance of the second category of driving environment factors in determining the overall driving deviation amount from a quantity perspective.

[0096] Exemplarily, the second quantity of the second type of driving environment factors obtained is divided by the total number of driving environment factors, and the obtained ratio is the second quantity weight. For example, if the total number of driving environment factors is 10, of which there are 7 second type of 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 amount corresponding to the second type of driving environment factor on the driving deviation information, and 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 amount corresponding to the second type of driving environment factor on the driving deviation information. The second weight combines the number of the 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 influence of the second type of driving environment factors on the final driving deviation amount calculation in the entire vehicle driving deviation amount determination process.

[0099] Exemplarily, referring to the example in the above step 520, 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, it is possible to comprehensively consider both the quantity and the degree of influence of the second type of driving environment factors on the driving deviation information, so that when subsequently calculating the vehicle driving deviation information, it is possible to more reasonably weigh the degree of influence of the second type of driving environment factors on the driving deviation information, thereby improving the accuracy of the driving deviation amount calculation.

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

[0102] For example, the first reference driving direction can be obtained by processing GPS positioning data of the vehicle within a period of time. For example, GPS location information at multiple time points is continuously obtained, and the actual driving direction of the vehicle, i.e., the first reference driving direction, is determined by calculating the connecting direction of adjacent location points.

[0103] Based on this, Figure 7 As shown, the above step 320 may specifically include step 610 and step 620.

[0104] Step 610: According to 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 a 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 by a vector calculation method, and then the angle between the two vectors is calculated, and the angle is the deviation between the first reference driving direction and the preset driving direction.

[0106] In this way, by combining the driving direction deviation determined based on GPS information and the deviation caused by the vehicle's own dynamic characteristics, the third driving deviation amount more accurately represents the degree of deviation of the vehicle relative to the preset driving direction at the current moment.

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

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

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

[0110] Step 620, adjusting the third driving deviation amount according to the fourth driving deviation amount of the vehicle in the second preset time period to obtain a second type of driving deviation amount; wherein the second preset time period is earlier than the first preset time period.

[0111] The fourth driving deviation amount may be calculated by the method of step 610 above.

[0112] For example, the third driving deviation amount can be adjusted according to the filter coefficient corresponding to the first driving deviation calculation sub-strategy and the fourth driving deviation amount to obtain the second type of driving deviation amount δ calculated based on the first driving deviation calculation sub-strategy h , where 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] Among them, δ h2represents the fourth driving deviation, α h represents the filter coefficient of the second type of driving deviation calculated based on the first driving deviation calculation sub-strategy, which is used to smooth the delta output by the first driving deviation calculation sub-strategy h .

[0115] Here, α h Indicates that the third driving deviation amount within the first preset time period is updating the second type of driving deviation amount δ h The weight of h The value of can be determined according to the driving environment of the vehicle. When the vehicle is in a relatively stable driving environment, α h The value is smaller than α when the vehicle is in an unstable driving environment. h value.

[0116] Therefore, by adjusting the dynamic deviation amount through the difference between the first reference driving direction and the preset driving direction, the third driving deviation amount can be determined quickly and accurately, and 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, so that the obtained second type of driving deviation amount is 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 the present application, 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. Figure 8 As shown, the above step 320 may specifically include step 710 and step 720.

[0118] Step 710, according to the second driving deviation calculation sub-strategy, the dynamic deviation is adjusted according to the difference between the first reference driving deviation and the second reference driving deviation, so as to obtain a 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 within a 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 the vehicle refers to the distance deviation between the vehicle and a reference line or reference point perpendicular to the driving direction during the driving process. For example, the lateral distance deviation of the vehicle can be obtained by obtaining the lateral position of the vehicle at different times through GPS information and comparing them. The driving distance of the vehicle in the first preset time period can be obtained by continuously collecting the GPS position data of the vehicle in the first preset time period, calculating the distance between adjacent position points and accumulating them.

[0120] The lateral distance deviation reflects whether the vehicle deviates from the expected lateral position, while the driving distance takes into account the time factor and the overall driving state of the vehicle. The first reference driving deviation amount determined based on the lateral distance deviation and the driving distance reflects the deviation of the vehicle in the lateral position and the driving distance in the first preset time period.

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

[0122]

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

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

[0125] δ l1 =e h_est -e h -δ pred (8)

[0126] Step 720, adjusting the fifth driving deviation amount according to the sixth driving deviation amount of the vehicle in the second preset time period to obtain a second type of driving deviation amount; wherein the second preset time period is earlier than the first preset time period.

[0127] For example, the fifth driving deviation amount can be adjusted according to the filter coefficient corresponding to the second driving deviation calculation sub-strategy and the sixth driving deviation amount to obtain the second type of driving deviation amount δ calculated based on the second driving deviation calculation sub-strategy l , where 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] Among them, δ l2 represents the sixth driving deviation, αl represents the filter coefficient of the second type of driving deviation calculated based on the first driving deviation calculation sub-strategy, which is used to smooth the delta output by the first driving deviation calculation sub-strategy l .

[0130] Here, α l Indicates that the fifth driving deviation amount within the first preset time period is updating the second type driving deviation amount δ l The weight of l The value of can be determined according to the driving environment of the vehicle. When the vehicle is in a relatively stable driving environment, α l The value is smaller than α when the vehicle is in an unstable driving environment. l value.

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

[0132] In some embodiments of the present application, the second driving deviation calculation strategy includes a third driving deviation calculation sub-strategy, and the third driving deviation calculation sub-strategy includes a sub-strategy for determining a second type of driving deviation amount based on a position change amount determined by GPS information, and the position change amount includes a first position change amount of the vehicle in a first direction and a second position change amount of the vehicle in a second direction. Exemplarily, the vehicle position coordinates of the vehicle within a first preset time period can be obtained from the GPS information, and the first position change amount of the vehicle in two different directions, such as the first direction, i.e., the x-axis, and the second position change amount of the vehicle in the second direction, i.e., the y-axis, can be calculated respectively.

[0133] Based on this, Fig. 9 As shown, the above step 320 may specifically include step 810 and step 820.

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

[0135] Exemplarily, 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] Wherein, Δy represents the second position change, and Δx represents the first position change.

[0138] Seventh running deviation amount 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, adjusting the seventh driving deviation amount according to the eighth driving deviation amount of the vehicle in the second preset time period to obtain a second type of driving deviation amount; wherein the second preset time period is earlier than the first preset time period.

[0141] The eighth driving deviation amount may be calculated by the method of step 810 above.

[0142] For example, the seventh driving deviation amount can be adjusted by the filter coefficient corresponding to the third driving deviation calculation sub-strategy and the eighth driving deviation amount to obtain the second type of driving deviation amount δ calculated based on the third driving deviation calculation sub-strategy p , where 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] Among them, δ p2 represents the eighth driving deviation, α p represents the filter coefficient of the second type of driving deviation calculated based on the third driving deviation calculation sub-strategy, used to smooth the delta output by the third driving deviation calculation sub-strategy p .

[0145] Here, α p Indicates that the seventh driving deviation amount in the first preset time period is updating the second type driving deviation amount δ p The weight of p The value of can be determined according to the driving environment of the vehicle. When the vehicle is in a relatively stable driving environment, α p The value is smaller 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 position change amount and the second position change amount, the driving direction of the vehicle can be accurately inferred from the actual position change of the vehicle in two directions, such as the lateral and longitudinal directions. The seventh driving deviation amount and the eighth driving deviation amount determined based on such a second reference driving direction can more accurately quantify the deviation of the vehicle during driving.

[0147] In some embodiments of the present application, the filter coefficients of the above-mentioned driving deviation calculation strategies can be expressed by the following formula (13):

[0148]

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

[0150] Here, in the data collection process related to the vehicle control system, the sampling period refers to the time interval between two consecutive samples, which is equivalent to the length of the first preset time period or the second preset time period mentioned above. The time constant is used to characterize the dynamic response characteristics of the vehicle control system to the driving deviation. Specifically, when the vehicle control system receives the input of the driving deviation, the system will not immediately adjust the vehicle control parameters according to the driving deviation, but there is a transition process. The speed of this transition process is determined by the time constant. The smaller the time constant, the faster the system can respond to the driving deviation and make the actual driving state of the vehicle approach the new set value faster; conversely, the larger the time constant, the slower the system response speed, and the longer it takes for the vehicle to reach the new set value.

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

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

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

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

[0155] When the first type of environmental factor presents at least one of the characteristics of straight-line driving, large-radius curve driving, high-speed driving, and low yaw angular velocity, and the second type of environmental factor indicates that the GPS signal is strong, the weight of the observer output value corresponding to the first driving deviation calculation sub-strategy will occupy a larger proportion than the output values ​​of other observers;

[0156] When the first type of environmental factor presents at least one of the characteristics of curve driving, medium speed driving, and medium yaw rate, and the second type of environmental factor indicates that the GPS signal is strong, the weight of the observer output value corresponding to the second driving deviation calculation sub-strategy will occupy a larger proportion than the output values ​​of other observers;

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

[0158] Based on the method for determining vehicle driving information provided in the above embodiment, the present application also provides a specific implementation of a device for determining vehicle driving information. Please refer to the following embodiment.

[0159] See also Fig.10 The vehicle driving information determination device 900 provided in the embodiment of the present application includes:

[0160] A first acquisition module 910 is used to acquire navigation information and vehicle driving information of the vehicle in a first preset time period;

[0161] A first determination module 920 is used to determine a driving environment factor and a dynamic deviation amount of the vehicle based on the vehicle driving information, where the driving environment factor includes at least one of the following: a first type of driving environment factor and a second type of driving environment factor, where the first type of driving environment factor is used to reflect the actual driving condition of the vehicle, and the second type of driving environment factor is used to reflect the communication environment condition of the vehicle, and the dynamic deviation amount is used to characterize the degree of deviation between the actual driving direction of the vehicle due to dynamic factors during driving and the preset driving direction in the navigation information;

[0162] The second determination module 930 is used to determine the vehicle's driving deviation information according to 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 travel along the preset driving direction.

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

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

[0165] a first extraction submodule, configured to extract a vehicle driving 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 factor includes a first type of driving environment factor, 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] A 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 a first driving deviation amount of the vehicle in a first preset time period, wherein the first driving deviation amount reflects the initial deviation degree of the actual driving direction of the vehicle under the influence of the first type of driving environment factor relative to the preset driving direction in the first preset time period;

[0167] The second adjustment submodule is used to adjust the first driving deviation amount according to the second driving deviation amount of the vehicle in the 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 environment factors 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.

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

[0169] A second extraction submodule is used to extract, from the vehicle driving information, the global positioning system GPS information corresponding to the second driving deviation calculation strategy, when the driving environment factor includes the first driving environment factor and the second driving environment factor, 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 driving deviation amount and the second driving deviation amount, the first driving deviation calculation strategy includes a strategy for determining the first driving deviation amount based on the vehicle driving speed direction in the vehicle driving information, and the first driving deviation amount reflects the degree of deviation of the actual driving direction of the vehicle under the influence of the first driving environment factor relative to the preset driving direction within a first preset time period;

[0170] A first determination submodule is used to determine a second type of driving deviation of the vehicle according to GPS information, a dynamic deviation, vehicle driving information and a preset driving direction, wherein the second type of driving deviation reflects a degree of deviation of an actual driving direction of the vehicle under the influence of a second type of driving environment factor relative to the preset driving direction within a first preset time period;

[0171] The second determining submodule is used to perform weighted sum processing on 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.

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

[0173] a first adjustment unit, configured 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 to determine a third driving deviation amount when the second driving deviation calculation strategy includes the first driving deviation calculation sub-strategy and the first driving deviation calculation sub-strategy includes a sub-strategy for determining the second type of driving deviation amount based on the first reference driving direction determined based on GPS information;

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

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

[0176] a third adjustment unit, for adjusting the dynamic deviation amount according to the second driving deviation calculation sub-strategy and the difference between the first reference driving deviation amount and the second reference driving deviation amount to obtain a 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 a second type of driving deviation amount based on a vehicle lateral distance deviation determined based on GPS information and a first reference driving direction; wherein the first reference driving deviation amount is determined based on the vehicle lateral distance deviation and a driving distance of the vehicle within a first preset time period, and the second reference driving deviation amount is determined based on a deviation between the first reference driving direction and a preset driving direction;

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

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

[0179] a fifth adjustment unit, configured to adjust the dynamic deviation amount according to a deviation between the second reference driving direction and the preset driving direction according to the third driving deviation calculation sub-strategy to determine a seventh driving deviation amount when 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 a second type of driving deviation amount based on a position change amount determined based on GPS information, and the position change amount includes a first position change amount of the vehicle in the first direction and a second position change amount of the vehicle in the second direction, the second reference driving direction being 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 amount according to the eighth driving deviation amount of the vehicle in a second preset time period to obtain a second type of driving deviation amount; wherein the second preset time period is earlier than the first preset time period.

[0181] In some embodiments of the present application, the above-mentioned vehicle driving information determination device 900 may also include:

[0182] a third determining module, for determining, before executing the step of performing weighted sum processing on 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 the driving deviation information, a ratio of the first quantity of the first type of driving environment factors in the driving environment factors to the total quantity of the driving environment factors as the first quantity weight of the first type of driving environment factors;

[0183] The fourth determination module is used to determine the product of the first quantitative weight and the first basic weight corresponding to the first type of driving environment factor as the first weight, the first basic weight is used to characterize the influence of the first type of driving deviation amount corresponding to the first type of driving environment factor on the driving deviation information, and 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 the present application, the above-mentioned vehicle driving information determination device 900 may also include:

[0185] a fifth determining module, configured to determine, before executing the step of performing weighted sum processing on 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 the driving deviation information, a ratio of the second number of the second type of driving environment factors in the driving environment factors to the total number of the driving environment factors as the second number weight of the second type of driving environment factors;

[0186] The sixth determination module is used to determine the product of the second quantitative weight and the second basic weight corresponding to the second type of driving environment factor as the second weight, the second basic weight is used to characterize the influence of the second type of driving deviation amount corresponding to the second type of driving environment factor on the driving deviation information, and 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 the present application, the first determining module 920 may specifically include:

[0188] A third determination submodule is used to determine the initial dynamic deviation amount according to the vehicle driving speed and the vehicle yaw angular velocity when the vehicle driving information includes the vehicle driving speed and the vehicle yaw angular velocity;

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

[0190] Each module of the vehicle driving information determination device 900 provided in the embodiment of the present application can realize Figures 1 to 9 The functions of each step of the method for determining vehicle driving information provided, and the ability to achieve its corresponding technical effects, are not repeated here for the sake of brevity.

[0191] Fig.11 A schematic diagram of the hardware structure of a computer device provided in some embodiments of the present application is shown.

[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, the steps in any one of the above-mentioned vehicle driving information determination method embodiments are implemented.

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

[0194] Exemplarily, the computer device may be a terminal, and specifically may include but is not limited to vehicles and other transportation vehicles. The processor 1101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0195] The memory 1102 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 1102 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. In appropriate cases, the memory 1102 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 1102 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 1102 is a non-volatile solid-state memory.

[0196] In a particular embodiment, the memory 1102 may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Therefore, generally, the memory 1102 includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) 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 the present application.

[0197] The processor 1101 implements any one of the vehicle driving information determination methods in the above embodiments by reading and executing computer program instructions stored in the memory 1102 .

[0198] In one example, the computer device may further include a communication interface 1103 and a bus 1110. Fig.11 As shown, the processor 1101, the memory 1102, and the communication interface 1103 are connected via a bus 1110 and communicate with each other.

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

[0200] Bus 1110 includes hardware, software or both, and the parts of computer equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 1110 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0201] The computer device can execute the method for determining vehicle driving information in the embodiment of the present application, thereby realizing the combination Figures 1 to 10 A method and device for determining vehicle driving information are described.

[0202] In addition, in combination with the method for determining vehicle driving information in the above-mentioned embodiment, the embodiment of the present application may provide a computer-readable storage medium for implementation. The computer-readable storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the methods for determining vehicle driving information in the above-mentioned embodiments is implemented. Examples of computer-readable storage media include non-transitory computer-readable storage media, such as portable disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, etc.

[0203] In addition, in combination with the method for determining vehicle driving information in the above embodiment, the embodiment of the present application may provide a computer program product for implementation. The program product is stored in a storage medium, and may specifically include a computer program or instruction. When the computer program or instruction is executed by a processor, any one of the methods for determining vehicle driving information in the above embodiment is implemented. The program product is executed by at least one processor to implement the various processes of the above data processing method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0204] It should be clear that the present application is not limited to the specific configuration and processing described above and shown in the figures. For the sake of simplicity, a detailed description of the known method is omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between the steps after understanding the spirit of the present 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, it can be, for example, an electronic circuit, an application specific integrated circuit (ASIC), appropriate firmware, a plug-in, a function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in 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, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0206] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. 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 embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0207] Aspects of the present disclosure are described above with reference to the flowchart and / or block diagram of the method, device (system) and computer program product according to the embodiment of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer 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 device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes of the flowchart and / or block diagram. 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 can also be understood that each box in the block diagram and / or flowchart and the combination of boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs a specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0208] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in this application, and these modifications or replacements should be included in the protection scope of this application.

Claims

1. A method for determining vehicle driving information, characterized in that: include: Obtaining navigation information and vehicle driving information of the vehicle in a first preset time period; Based on the vehicle driving information, determining a driving environment factor and a dynamic deviation amount of the vehicle, the driving environment factor comprising 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 being used to reflect the actual driving condition of the vehicle, the second type of driving environment factor being used to reflect the communication environment condition of the vehicle, and the dynamic deviation amount being used to characterize the degree of deviation between the actual driving direction of the vehicle due to dynamic factors during driving and the preset driving direction in the navigation information; The driving deviation information of the vehicle is determined based on the driving deviation calculation strategy corresponding to the driving environment factor, 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 actual driving direction of the vehicle and the preset driving direction. The driving deviation information is used to adjust the vehicle to travel along the preset driving direction.

2. The determination method according to claim 1, characterized in that: The driving environment factor includes the first type of driving environment factor, 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; The determining of the driving deviation information of the vehicle according to the driving deviation calculation strategy corresponding to the driving environment factor, the dynamic deviation amount, the vehicle driving information and the preset driving direction includes: extracting the vehicle driving speed direction corresponding to the first driving deviation calculation strategy from the vehicle driving information according to the first driving deviation calculation strategy; The dynamic deviation is adjusted according to the deviation between the driving speed direction of the vehicle and the preset driving direction to obtain a first driving deviation of the vehicle in a first preset time period, wherein the first driving deviation reflects the initial deviation degree of the actual driving direction of the vehicle under the influence of the first type of driving environment factors relative to the preset driving direction in the first preset time period; According to the second driving deviation amount of the vehicle within the second preset time period, the first driving deviation amount is adjusted to obtain the first type of driving deviation amount, which reflects the degree of deviation of the actual driving direction of the vehicle under the influence of the first type of driving environment factors relative to the preset driving direction within the first preset time period; wherein the second preset time period is earlier than the first preset time period.

3. 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 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 for determining the first type of driving deviation amount based on the vehicle driving speed 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 relative to the preset driving direction within a first preset time period; The determining of the driving deviation information of the vehicle according to the driving deviation calculation strategy corresponding to the driving environment factor, the dynamic deviation amount, the vehicle driving information and the preset driving direction includes: extracting, according to the second driving deviation calculation strategy, global positioning system GPS information corresponding to the second driving deviation calculation strategy from the vehicle driving information; Determining a second type of driving deviation of the vehicle according to the GPS information, the dynamic deviation, the vehicle driving information and the preset driving direction, wherein 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 environment factor relative to the preset driving direction within a first preset time period; According to 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-summed to obtain the driving deviation information.

4. The determination method according to claim 3, characterized in that: 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 for determining the second type of driving deviation amount based on a first reference driving direction determined by the GPS information; The determining of the second type of driving deviation of the vehicle according to the GPS information, the dynamic deviation, the vehicle driving information and the preset driving direction includes: According to 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 a third driving deviation amount; The third driving deviation is adjusted according to the fourth driving deviation of the vehicle in 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.

5. The determination method according to claim 3, characterized in that: 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 the GPS information and the first reference driving direction; The determining of the second type of driving deviation of the vehicle according to 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 according to the difference between the first reference driving deviation and the second reference driving deviation to obtain a fifth driving deviation; wherein the first reference driving deviation is determined based on the vehicle lateral distance deviation and the driving distance of the vehicle within 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; The fifth driving deviation is adjusted according to the sixth driving deviation of the vehicle in 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.

6. The determination method according to claim 3, characterized in that: The second driving deviation calculation strategy includes a third driving deviation calculation sub-strategy, and the third driving deviation calculation sub-strategy includes a sub-strategy for determining the second type of driving deviation amount based on a position change amount determined by the GPS information, the position change amount including a first position change amount of the vehicle in the first direction and a second position change amount of the vehicle in the second direction; The determining of the second type of driving deviation of the vehicle according to 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 according to the deviation between the second reference driving direction and the preset driving direction to determine a seventh driving deviation amount, wherein the second reference driving direction is determined based on the first position change amount and the second position change amount; According to the eighth driving deviation amount of the vehicle in the second preset time period, the seventh driving deviation amount is adjusted to obtain the second type of driving deviation amount; wherein the second preset time period is earlier than the first preset time period.

7. The determination method according to any one of claims 3 to 6, characterized in that: Before performing weighted sum processing on 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 the driving deviation information, the method further includes: determining a ratio of a first quantity of the first type of driving environment factors in the driving environment factors to a total quantity of the driving environment factors as a 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 degree of influence of the first type of driving deviation amount corresponding to the first type of driving environment factor on the driving deviation information. The first weight is used to characterize the degree of influence of the quantity of the first type of driving environment factor on the first basic weight.

8. The determination method according to any one of claims 3 to 6, characterized in that: Before performing weighted sum processing on 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 the driving deviation information, the method further includes: determining a ratio of a second number of the second type of driving environment factors in the driving environment factors to a total number of the driving environment factors as a second number 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 amount 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.

9. The determination method according to any one of claims 1 to 6, characterized in that: The vehicle driving information includes a vehicle driving speed and a vehicle yaw rate; and determining the dynamic deviation of the vehicle based on the vehicle driving information includes: determining an initial dynamic deviation amount according to the vehicle driving speed and the vehicle yaw rate; The initial dynamic deviation is adjusted according to a preset proportional coefficient to obtain the dynamic deviation.

10. A device for determining vehicle driving information, characterized in that: The device comprises: A first acquisition module, used to acquire navigation information and vehicle driving information of the vehicle in a first preset time period; a first determination module, for determining a driving environment factor and a dynamic deviation amount of the vehicle based on the vehicle driving information, wherein the driving environment factor includes at least one of the following: a first type of driving environment factor and a second type of driving environment factor, wherein the first type of driving environment factor is used to reflect the actual driving condition of the vehicle, and the second type of driving environment factor is used to reflect the communication environment condition of the vehicle, and the dynamic deviation amount is used to characterize the degree of deviation between the actual driving direction of the vehicle due to dynamic factors during driving and the preset driving direction in the navigation information; The second determination module is used to determine the driving deviation information of the vehicle according to the driving deviation calculation strategy corresponding to the driving environment factor, the dynamic deviation, the vehicle driving information and the preset driving direction, the driving deviation information including information used to characterize the degree of deviation between the actual driving direction of the vehicle and the preset driving direction, and the driving deviation information is used to adjust the vehicle to travel along the preset driving direction.

11. A computer device, characterized in that: The computer device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for determining vehicle driving information as described in any one of claims 1-9 is implemented.

12. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for determining vehicle driving information according to any one of claims 1 to 9 is implemented.

13. A computer program product, characterized in that It includes a computer program, which, when executed, implements the method for determining vehicle driving information as described in any one of claims 1 to 9.

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