Navigation route planning methods, devices, electronic equipment and storage media

Abstract

This application relates to a navigation route planning method, apparatus, electronic device, and storage medium. The navigation route planning method first determines a preliminary target road segment, then obtains the historical road condition scores of the target vehicle when it previously traveled the target road segment. The target vehicle includes the vehicle itself and / or other vehicles traveling on the target road segment. The historical road condition score is obtained by calculating the weighted variance of the vertical acceleration of the sampling points of the target road segment, which can effectively suppress the scoring inaccuracy caused by single-point anomalies. Next, based on the vehicle type threshold corresponding to the vehicle itself and the historical road condition score, it determines whether to travel on or detour around the target road segment. This can effectively determine whether the micro road conditions of the preliminary target road segment are suitable for the vehicle to travel on, providing users with better navigation routes, reducing vehicle damage, and improving traffic efficiency.

Description

Technical Field

[0001] This application relates to the field of vehicle navigation technology, and in particular to a navigation route planning method, device, electronic device and storage medium. Background Technology

[0002] Current vehicle navigation systems primarily rely on vehicle speed to determine road congestion. They cannot identify micro-road conditions such as road surface damage and potholes that affect vehicle comfort and safety during route planning. Furthermore, existing vehicles often rely on a single sensor to detect bumps, which is easily affected by vehicle load, differences in suspension systems, and driving behavior, resulting in a high misjudgment rate of micro-road conditions. Consequently, current vehicle navigation systems cannot provide users with more suitable navigation routes, impacting the user's driving experience. Summary of the Invention

[0003] This application provides a navigation route planning method, apparatus, electronic device, and storage medium to solve the technical problem of how to plan navigation routes based on microscopic road conditions.

[0004] Firstly, this application provides a navigation route planning method, the method comprising: Identify the target road sections in the preliminary plan; Obtain the historical traffic condition score of the target vehicle traveling on the target road segment; wherein, the target vehicle includes the vehicle itself and / or other vehicles traveling on the target road segment; the historical traffic condition score is obtained by calculating the weighted variance of the vertical acceleration of the sampling points of the target road segment; Based on the vehicle type threshold and the historical road condition score, it is determined whether to travel through or detour around the target road segment.

[0005] Optionally, obtain the historical traffic condition score of the target vehicle when traveling on the target road segment, including: Obtain the historical traffic condition score of the target vehicle when traveling on the target road segment from the cloud database; or, The system acquires raw data collected by the target vehicle at each sampling window when it travels through the target road segment; wherein, the sampling window includes M sampling points; and the raw data includes at least the vertical acceleration corresponding to each sampling point. The weighted variance of each sampling window is calculated based on the vertical acceleration. The historical traffic condition score of the target road segment is determined based on the weighted variances of each segment. The historical traffic condition scores are sent to a cloud database for storage.

[0006] Optionally, for any one of the sampling windows, the weighted variance of each sampling window is calculated based on the vertical acceleration, including: Calculate the absolute value of the vertical acceleration difference between every two adjacent sampling points within the sampling window; The abrupt change suppression weight of adjacent sampling points is determined based on the absolute value of the vertical acceleration difference and a preset coefficient; wherein, the abrupt change suppression weight is negatively correlated with the absolute value of the vertical acceleration difference and negatively correlated with the preset coefficient. Determine the mean value of the vertical acceleration within the sampling window; The weighted variance of the sampling window is calculated based on the window mean, the mutation suppression weight, and all the vertical accelerations within the sampling window.

[0007] Optionally, the historical traffic condition score of the target road segment is determined based on each of the weighted variances, including: The maximum weighted variance and the average weighted variance in the target road segment are determined based on each of the weighted variances. Get the preset window threshold; The number of windows greater than the preset window threshold is determined based on the weighted variances of each window. The turbulence index for the current event is determined based on the maximum weighted variance, the average weighted variance, the number of windows, and the preset weighting coefficients. The historical road condition score of the target road segment is determined based on the current bump index.

[0008] Optionally, the historical road condition score of the target road segment is determined based on the current bump index, including: Obtain the maximum historical bump index value in the target road segment; The historical road condition score of the target road segment is determined based on the current bump index and the maximum value of the historical bump index.

[0009] Optionally, determining whether to travel through or detour around the target road segment based on the vehicle type threshold and the historical road condition score includes: The vehicle type threshold corresponding to the vehicle is determined based on the vehicle type and the preset vehicle type tolerance threshold mapping relationship; wherein, the vehicle type tolerance threshold mapping relationship is the mapping relationship between the vehicle type and the lowest road condition score that the vehicle type can tolerate; Route planning is performed based on the vehicle type threshold and the historical road condition score to determine whether to travel through or detour around the target road segment.

[0010] Optionally, determining whether to travel through or detour around the target road segment based on the vehicle type threshold and the historical road condition score includes: Obtain the historical weather and historical time corresponding to the historical traffic condition score; The attenuation coefficient is determined based on the interval between the historical time and the current time and the current weather. Determine weather influencing factors based on the current weather conditions; The current road condition score is determined based on the attenuation coefficient, the historical road condition score, the interval duration, and the weather influence factor. Based on the current road condition score and the vehicle type threshold, it is determined whether to travel through or detour around the target road segment.

[0011] Secondly, this application provides a navigation route planning device, the device comprising: The determination module is used to determine the target road segments in the preliminary plan; The acquisition module is used to acquire the historical traffic condition scores of the target vehicle traveling on the target road segment; wherein, the target vehicle includes its own vehicle and / or other vehicles traveling on the target road segment; the historical traffic condition scores are obtained by calculating the weighted variance of the vertical acceleration of the sampling points of the target road segment; The planning module is used to determine whether to travel through or detour around the target road segment based on the vehicle type threshold corresponding to its own vehicle and the historical road condition score.

[0012] Thirdly, this application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements the navigation route planning method described in any embodiment of the first aspect.

[0013] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the navigation route planning method as described in any embodiment of the first aspect.

[0014] Compared with the prior art, the technical solution provided in this application has the following advantages: The method provided in this application determines the initially planned target road segment; obtains the historical road condition score of the target vehicle passing through the target road segment; wherein, the target vehicle includes the vehicle itself and / or other vehicles passing through the target road segment; the historical road condition score is obtained by calculating the weighted variance of the vertical acceleration of the sampling points of the target road segment; and determines whether to pass through or detour through the target road segment based on the vehicle type threshold and the historical road condition score. This method can first determine the initially planned target road segment, then obtain the historical road condition score of the target vehicle when it previously passed through the target road segment. The historical road condition score is obtained by calculating the weighted variance of the vertical acceleration of the sampling points of the target road segment, which can effectively suppress the scoring inaccuracy caused by single-point anomalies. Next, it determines whether to pass through or detour through the target road segment based on the vehicle type threshold and the historical road condition score. This can effectively determine whether the micro road conditions of the initially planned target road segment are suitable for the vehicle to pass through, providing users with better navigation routes, reducing vehicle damage, and improving traffic efficiency. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0018] Figure 1 A system architecture diagram of a navigation route planning method provided in one embodiment of this application; Figure 2 A flowchart illustrating a navigation route planning method provided in one embodiment of this application; Figure 3 A schematic diagram of the structure of a navigation route planning device provided in one embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0021] To address the technical problem of how to plan navigation routes based on micro-road conditions in the prior art, this application provides a navigation route planning method, device, electronic device, and storage medium, which can effectively determine whether the micro-road conditions of the initially planned target road segment are suitable for the vehicle to pass through, provide users with better navigation routes, reduce vehicle damage, and improve traffic efficiency.

[0022] The first embodiment of this application provides a navigation route planning method, which can be applied to, for example... Figure 1 The system architecture shown includes at least vehicle 101 and server 102, which establish a communication connection.

[0023] This method can be applied to vehicle 101 or server 102 in the system architecture. The type of vehicle 101 is not limited, such as a fuel vehicle, a pure electric vehicle, a hybrid vehicle, or a fuel cell vehicle. The vehicle 101 using this method can be referred to as its own vehicle. The server 102 can be a local server, a cloud server, or a server cluster.

[0024] Next, based on this system architecture, the navigation route planning method will be explained in detail, such as... Figure 2 The navigation route planning method includes: Step 201: Determine the target road segment in the preliminary plan.

[0025] Preliminary route planning can be performed by the in-vehicle navigation system. For example, navigation planning can be based on the current location and the planned destination. Preliminary planning can consider factors such as short distance, short estimated travel time, few traffic lights, and low toll fees to design a planned route. The planned route consists of one or more road segments, each of which can be called the target road segment. After determining the preliminary target road segments, they can be evaluated to determine whether they are suitable for the current vehicle's passage.

[0026] Step 202: Obtain the historical traffic condition score of the target vehicle traveling on the target road segment; wherein, the target vehicle includes its own vehicle and / or other vehicles traveling on the target road segment; the historical traffic condition score is obtained by calculating the weighted variance of the vertical acceleration of the sampling points of the target road segment.

[0027] Historical traffic condition scores can be determined by the vehicle itself based on raw data such as vertical acceleration collected during its last passage through the target road segment, or they can be obtained from a cloud database. Historical traffic condition scores in the cloud database can be determined and uploaded by the vehicle itself based on raw data such as vertical acceleration collected during its last passage through the target road segment, or they can be determined and uploaded by vehicles of the same model based on raw data such as vertical acceleration collected during their last passage through the target road segment. To avoid scoring inaccuracies caused by single-point anomalies, historical traffic condition scores are designed to be obtained by calculating the weighted variance of the vertical acceleration at the sampling points of the target road segment.

[0028] In one embodiment, obtaining the historical traffic condition score of the target vehicle traversing the target road segment includes at least two of the following cases: In the first scenario, historical traffic condition scores for the target vehicle's travel on the target road segment are obtained from a cloud database.

[0029] The historical traffic rating in the cloud database can be determined and uploaded by the vehicle itself based on raw data such as vertical acceleration collected when it last passed through the target road segment, or it can be determined and uploaded by other vehicles passing through the target road segment based on raw data such as vertical acceleration collected when they last passed through the target road segment.

[0030] Vertical acceleration data can be acquired through wheel acceleration sensors. For example, an acceleration sensor mounted on a suspension component near the wheel measures the acceleration of the unsprung mass. Wheel acceleration sensors can detect rapid wheel bounce caused by uneven road surfaces. Because wheel acceleration sensors are susceptible to interference from factors such as vehicle load and differences in suspension systems, the target vehicle can be one with a small load deviation from the vehicle itself, or one with the same or similar suspension system, such as a vehicle of the same or similar model.

[0031] In the second scenario, the raw data collected by the target vehicle at each sampling window when passing through the target road segment is obtained; wherein, the sampling window includes M sampling points; the raw data includes at least the vertical acceleration corresponding to each sampling point; the weighted variance of each sampling window is calculated based on the vertical acceleration; the historical road condition score of the target road segment is determined based on each weighted variance; and the historical road condition score is sent to the cloud database for storage.

[0032] In this embodiment, the sampling window can be a fixed duration, such as 1 second or 0.5 seconds. Within each sampling window, sampling can be performed according to a sampling frequency. In the following embodiment, a sampling window of 0.5 seconds and a sampling frequency of 100Hz are used as examples. One sampling window includes 50 sampling points, meaning sampling is performed once every 10ms. At each sampling point, the sampling point number, sampling time, and vertical acceleration are recorded. The vertical acceleration can be obtained by acquiring the Z-axis acceleration using an onboard triaxial accelerometer, and can be represented by a_z(i), where i represents the sampling point number. On a normal, stable road surface, a_z is typically around -0.3 m / s². 2 up to 0.3m / s 2 Within this range, the instantaneous acceleration on bumpy road sections can reach ±8m / s². 2 Or even higher. Table 1 below shows some data collected when the vehicle passed through a bumpy road section.

[0033] Table 1

[0034] The system can calculate the weighted variance of each sampling window, then determine the historical road condition score for the target road segment based on each weighted variance, and send the historical road condition score to a cloud database for storage. This allows vehicles of the same type or model to be evaluated based on this historical road condition score when passing through the target road segment in the future. The historical road condition score obtained by calculating the weighted variance of the vertical acceleration of the sampling points on the target road segment avoids scoring inaccuracies caused by single-point anomalies.

[0035] In one embodiment, for any given sampling window, the weighted variance of each sampling window is calculated based on the vertical acceleration, including: calculating the absolute value of the difference in vertical acceleration between every two adjacent sampling points within the sampling window; determining the mutation suppression weight of adjacent sampling points based on the absolute value of the vertical acceleration difference and a preset coefficient; wherein the mutation suppression weight is negatively correlated with the absolute value of the vertical acceleration difference and negatively correlated with the preset coefficient; determining the window mean of the vertical acceleration within the sampling window; and calculating the weighted variance of the sampling window based on the window mean, the mutation suppression weight, and all vertical accelerations within the sampling window.

[0036] In this embodiment, the weight in the weighted variance refers to the mutation suppression weight, which is negatively correlated with the absolute value of the vertical acceleration difference and negatively correlated with the preset coefficient. Specifically, the absolute value of the vertical acceleration difference between every two adjacent sampling points within the sampling window can be calculated first, i.e., Δa = |a_z(i)-a_z(i-1)|. Taking the preset coefficient k as an example, the mutation suppression weight w_i = 1 / (1 + k·|a_z(i) - a_z(i-1)|). If k is 0.3, then w_i = 1 / (1+0.3×Δa). Next, the window mean μ is calculated, μ = the sum of the number of a_z points / the number of points. For example: μ = (a_z(450)+a_z(451)+...+a_z(499)) / 50 = (-0.2-0.3+3.5+8.2-4.1-....) / 50 = 0.15m / s 2 Table 2 shows the calculation results of Δa and w_i for some sampling points, as follows: Table 2

[0037] Next, calculate the weighted variance σ of all 50 sampling points. 2 _w: Molecular = Σ[w_i×(a_z(i)-μ) 2 =1.00×(-0.2-0.15) 2 +0.97×(-0.3-0.15) 2 +0.47×(3.5-0.15) 2 +...=0.1225+0.195+5.26+13.62+3.78+...= 42.8 Denominator=Σw_i=1.00+0.97+0.47+0.41+0.21+...=38.5 Therefore, the weighted variance σ of this sampling window 2 _w=42.8 / 38.5=1.11(m / s 2 ) 2 If we only calculate the conventional variance of the absolute value of the vertical acceleration difference between every two adjacent sampling points, then the conventional variance σ 2 _unweighted=5.83(m / s 2 ) 2 Therefore, the weighted variance of this application embodiment can be reduced by (5.83-1.11) / 5.83=81%, which can effectively suppress the excessive punishment of a single event and avoid scoring inaccuracies caused by single point anomalies.

[0038] In one embodiment, determining the historical road condition score of the target road segment based on each weighted variance includes: determining the maximum weighted variance and the average weighted variance in the target road segment based on each weighted variance; obtaining a preset window threshold; determining the number of windows greater than the preset window threshold based on each weighted variance; determining the current bump index based on the maximum weighted variance, the average weighted variance, the number of windows, and a preset weight coefficient; and determining the historical road condition score of the target road segment based on the current bump index.

[0039] In this embodiment, the maximum and average weighted variances of the target road segment are determined based on the weighted variances; a preset window threshold is obtained; the number of windows exceeding the preset window threshold is determined based on the weighted variances; the current bump index is determined based on the maximum weighted variance, the average weighted variance, the number of windows, and a preset weighting coefficient; and the historical road condition score of the target road segment is determined based on the current bump index. Specifically, taking a road segment with 12 consecutive sampling windows as an example, the weighted variances calculated for each sampling window are: [0.08, 0.12, 1.11, 0.95, 0.32, 0.15, 0.09, 0.11, 0.87, 1.05, 0.21, 0.13], where windows 3, 4, 9, and 10 show significant bumps. Next, the maximum weighted variance σ²_w_max, the average weighted variance σ²_w_avg, and the number of windows exceeding the preset window threshold N_spike are determined based on these 12 weighted variances.

[0040] σ²_w_avg=(0.08+0.12+1.11+...+0.13) / 12 =0.43 σ²_w_max = max(all windows) = 1.11 (the largest value is the third one). N_spike = Number of windows satisfying σ²_w > 0.8 = 4 (threshold 0.8 is the preset window threshold) The current bump index is determined based on the maximum weighted variance, the average weighted variance, the number of windows, and preset weighting coefficients. For example, the current bump index RI = α·σ²_w_avg + β·σ²_w_max + γ·N_spike = 0.4×0.43 + 0.4×1.11 + 0.2×4 = 0.172 + 0.444 + 0.8 = 1.416. Here, α, β, and γ are preset weighting coefficients, and their sum is one. The historical road condition score of the target road segment is determined based on the current bump index.

[0041] In one embodiment, determining the historical road condition score of a target road segment based on the current bump index includes: obtaining the maximum historical bump index in the target road segment; and determining the historical road condition score of the target road segment based on the current bump index and the maximum historical bump index.

[0042] In this embodiment, the historical road condition score can be calculated according to the following formula, for example: Score=100-min(100,RI / RI_max×100)=100-min(100,1.416 / 5.0×100)=100-28.32=71.68≈72 points.

[0043] Step 203: Determine whether to travel through or detour around the target road segment based on the vehicle type threshold and historical road condition score corresponding to the vehicle itself.

[0044] This method first determines the initially planned target road segment, then obtains the historical road condition scores of the target vehicle when it previously traveled the target road segment. The historical road condition score is obtained by calculating the weighted variance of the vertical acceleration of the sampling points of the target road segment, which can effectively suppress the scoring inaccuracy caused by single-point anomalies. Next, based on the vehicle type threshold and the historical road condition score, it determines whether to travel through or detour around the target road segment. This can effectively determine whether the micro road conditions of the initially planned target road segment are suitable for the vehicle to travel, providing users with better navigation routes, reducing vehicle damage, and improving traffic efficiency.

[0045] In one embodiment, determining whether to travel through or detour around a target road segment based on the vehicle type threshold and historical road condition score includes: determining the vehicle type threshold corresponding to the vehicle based on the vehicle type and a preset vehicle type tolerance threshold mapping relationship; wherein, the vehicle type tolerance threshold mapping relationship is a mapping relationship between the vehicle type and the lowest road condition score that the vehicle type can tolerate; and performing route planning based on the vehicle type threshold and historical road condition score to determine whether to travel through or detour around the target road segment.

[0046] In this embodiment, different vehicle thresholds for tolerable road conditions can be configured for different vehicle models. Different vehicle models have different minimum tolerable road condition scores due to different usage scenarios or user groups. For example, vehicles with high ground clearance (high chassis) have a significantly lower risk of collisions on bumpy roads compared to vehicles with low ground clearance (low chassis). Therefore, the minimum tolerable road condition scores for trucks, SUVs, and pickup trucks can be set relatively high, while the minimum tolerable road condition scores for sedans, sports cars, and new energy vehicles with battery packs mounted on the chassis need to be set relatively low. Alternatively, the minimum tolerable road condition score can be set based on the vehicle's value, without restriction. Specifically, for example, the higher the ground clearance, the lower the minimum tolerable road condition score. For instance, a luxury sedan might be configured with a minimum tolerable road condition score of 75 points, while a truck might be configured with a minimum tolerable road condition score of 40 points. This allows for different route planning based on the adaptability of different vehicle models to road conditions. When the historical road condition score is higher than the vehicle type threshold, the target route can be chosen. When the historical road condition score is lower than the vehicle type threshold, the target route can be detoured. For example, if the historical road condition score of the target route is 60, the route planning for trucks is to pass through the target route, while the planning for luxury cars is to detour. Of course, when the historical road condition score is lower than the vehicle type threshold, it can also be determined whether there is a suitable alternative route, such as whether there is an alternative route that increases the distance by ≤15%. If so, the detour is recommended. If no alternative route exists, the vehicle must proceed, but a warning is required, such as reminding the vehicle to limit its speed. Route planning can provide text or voice prompts, such as: "Construction detected on the A Street ahead, road conditions are poor (score 17 / 100). It is recommended to detour via B Road → C Road (adding 1.2km, estimated additional time 3 minutes), or continue straight, but reduce speed to 20km / h, high risk of bumps."

[0047] In one embodiment, determining whether to travel through or detour around a target road segment based on the vehicle type threshold and historical road condition score includes: obtaining historical weather and historical time corresponding to the historical road condition score; determining an attenuation coefficient based on the interval between the historical time and the current time and the current weather; determining a weather influence factor based on the current weather; determining the current road condition score based on the attenuation coefficient, historical road condition score, interval, and weather influence factor; and determining whether to travel through or detour around the target road segment based on the current road condition score and the vehicle type threshold.

[0048] In this embodiment, to avoid static scores failing to reflect the timeliness of road condition information, an attenuation coefficient and weather correction are introduced. Specifically, the attenuation coefficient is determined based on the interval between historical and current times and the current weather, and the weather influence factor is determined based on the current weather. For example, if the original data for calculating the historical road condition score was collected at 14:30 on January 29, 2026, and the weather was light rain, the historical road condition score calculated based on the original data would be 72 points. The weather influence factor corresponding to light rain would be δ = -15 points (the weather at that time and the weather influence factor can be determined according to a preset mapping relationship, without restriction). If the user needs to drive through the target road segment 24 hours later, i.e., January 30, 2026... At 14:30, the initial navigation route planning still proceeds through the target road segment. The weather at this time is sunny, so the attenuation coefficient λ = 0.2 (λ can be set to 0.05 on dry roads; data attenuation is faster in rainy weather). Therefore, the current road condition score Valid_Score = Score_0 × e^(-λ·Δt) + δ·Weather_Factor = 72 × e^(-0.2 × 24) + (-15) × 0 (currently sunny, Weather_Factor = 0) = 72 × e^(-4.8) + 0 = 72 × 0.0082 = 0.59 ≈ 1 point. It can be seen that after 24 hours, the historical road condition score is almost invalid. At this point, the system can automatically request new data from the cloud or mark the target road segment as having unknown road conditions. Compared to static scoring, this embodiment, which introduces an attenuation coefficient and weather correction, avoids leading users to target road segments with unknown conditions. For example, if the query is performed 4 hours later (i.e., route planning is done at 18:30 on 2026-01-29, assuming the weather is still light rain), Valid_Score=72×e^(-0.2×4)+(-15) (continuous light rain, Weather_Factor=1)=72×0.449-15=32.3-15=17.3≈17 points. At this time, the score drops to 17 points, the road conditions are poor, and the navigation route planning will suggest detours.

[0049] In this embodiment, by using weighted variance to detect continuous abrupt changes, the influence of a single bump event is not exaggerated by traditional single variance. The attenuation coefficient and weather correction avoid the problem that static scoring cannot reflect timeliness. The number of windows N_spikes greater than the preset window threshold can effectively identify the density of damage in the road, and can effectively distinguish between a single pothole and continuous road damage. Thus, when planning navigation routes, it can effectively determine whether the micro road conditions of the initially planned target road segment are suitable for the vehicle to pass, providing users with better navigation routes, reducing vehicle damage, and improving traffic efficiency.

[0050] Based on the same technical concept, the second embodiment of this application provides a navigation route planning device, such as... Figure 3 The device includes: Module 301 is used to determine the target road segment in the preliminary plan; The acquisition module 302 is used to acquire the historical traffic condition score of the target vehicle passing through the target road segment; wherein, the target vehicle includes its own vehicle and / or other vehicles passing through the target road segment; the historical traffic condition score is obtained by calculating the weighted variance of the vertical acceleration of the sampling points of the target road segment; The planning module 303 is used to determine whether to travel through or detour around the target road segment based on the vehicle type threshold corresponding to its own vehicle and the historical road condition score.

[0051] The device first determines the initially planned target road segment, then obtains the historical road condition score of the target vehicle when it previously traveled the target road segment. The historical road condition score is obtained by calculating the weighted variance of the vertical acceleration of the sampling points of the target road segment, which can effectively suppress the scoring inaccuracy caused by single-point anomalies. Next, based on the vehicle type threshold and the historical road condition score, it determines whether to travel through or detour around the target road segment. This can effectively determine whether the micro road conditions of the initially planned target road segment are suitable for the vehicle to travel, providing users with better navigation routes, reducing vehicle damage, and improving traffic efficiency.

[0052] like Figure 4 As shown in the figure, this application provides an electronic device, including a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114. Memory 113 is used to store computer programs; In one embodiment of this application, the processor 111, when executing the program stored in the memory 113, implements the navigation route planning method provided in any of the foregoing method embodiments.

[0053] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus can be divided into address bus, data bus, control bus, etc. For ease of illustration, only one thick line is used to represent it in the diagram, but this does not mean that there is only one bus or one type of bus.

[0054] The communication interface is used for communication between the aforementioned terminal and other devices.

[0055] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0056] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0057] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the navigation route planning method provided in any of the foregoing method embodiments.

[0058] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0059] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0060] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0061] It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. In the description, suffixes such as "module," "part," or "unit" used to denote elements are used solely for illustrative purposes and have no specific meaning in themselves. Therefore, "module," "part," or "unit" may be used interchangeably.

[0062] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A navigation route planning method characterized by, The method comprises: determining a preliminary planned target section; obtaining a historical road condition score of a target vehicle passing through the target section; wherein the target vehicle comprises the ego vehicle and / or other vehicles passing through the target section; the historical road condition score is obtained based on a weighted variance of vertical acceleration of sampling points of the target section; determining to pass through or bypass the target section based on a vehicle type threshold corresponding to the ego vehicle and the historical road condition score.

2. The method of claim 1, wherein, Obtaining a historical road condition score of a target vehicle passing through the target section comprises: obtaining the historical road condition score of the target vehicle passing through the target section from a cloud database; or, obtaining original data collected by the target vehicle in each sampling window when passing through the target section; wherein the sampling window comprises M sampling points; the original data at least comprises vertical acceleration corresponding to each sampling point; calculating a weighted variance of each sampling window according to the vertical acceleration; determining the historical road condition score of the target section according to each weighted variance; sending the historical road condition score to a cloud database for storage.

3. The method of claim 2, wherein, For any one of the sampling windows, calculating a weighted variance of each sampling window according to the vertical acceleration comprises: calculating an absolute value of a vertical acceleration difference of each two adjacent sampling points in the sampling window; determining a mutation suppression weight of adjacent sampling points according to the absolute value of the vertical acceleration difference and a preset coefficient; wherein the mutation suppression weight is negatively correlated with the absolute value of the vertical acceleration difference, and the mutation suppression weight is negatively correlated with the preset coefficient; determining a window mean of the vertical acceleration in the sampling window; calculating the weighted variance of the sampling window according to the window mean, the mutation suppression weight and all the vertical accelerations in the sampling window.

4. The method of claim 2, wherein, Determining the historical road condition score of the target section according to each weighted variance comprises: determining a maximum weighted variance and an average weighted variance in the target section according to each weighted variance; obtaining a preset window threshold; determining a window number greater than the preset window threshold according to each weighted variance; determining a current jounce index according to the maximum weighted variance, the average weighted variance, the window number and a preset weight coefficient; determining the historical road condition score of the target section according to the current jounce index.

5. The method of claim 4, wherein, Determining the historical road condition score of the target section according to the current jounce index comprises: obtaining a maximum historical jounce index in the target section; determining the historical road condition score of the target section according to the current jounce index and the maximum historical jounce index.

6. The method of claim 1, wherein, Determining to pass through or bypass the target section based on a vehicle type threshold corresponding to the ego vehicle and the historical road condition score comprises: determining the vehicle type threshold corresponding to the ego vehicle according to a vehicle type of the ego vehicle and a preset vehicle type tolerance threshold mapping relationship; wherein the vehicle type tolerance threshold mapping relationship is a mapping relationship between a vehicle type and a lowest road condition score tolerated by the vehicle type. According to the vehicle type threshold and the historical road condition score, a route is planned to determine whether to pass through or bypass the target road section.

7. The method of claim 1, wherein, Based on the vehicle type threshold corresponding to the ego vehicle and the historical road condition score, it is determined whether to pass through or bypass the target road section, including: Obtaining historical weather and historical time corresponding to the historical road condition score; According to the interval length between the historical time and the current time and the current weather, a decay coefficient is determined; According to the current weather, a weather influence factor is determined; According to the decay coefficient, the historical road condition score, the interval length and the weather influence factor, a current road condition score is determined; According to the current road condition score and the vehicle type threshold, it is determined whether to pass through or bypass the target road section.

8. A navigation route planning apparatus characterized by comprising: The device comprises: A determination module is configured to determine a preliminarily planned target road section; An acquisition module is configured to acquire a historical road condition score of a target vehicle passing through the target road section; wherein the target vehicle comprises the ego vehicle and / or other vehicles passing through the target road section; the historical road condition score is obtained based on a weighted variance of a vertical acceleration of a sampling point of the target road section; A planning module is configured to determine whether to pass through or bypass the target road section based on a vehicle type threshold corresponding to the ego vehicle and the historical road condition score.

9. An electronic device, comprising: The device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; The memory is configured to store a computer program; The processor is configured to execute the program stored on the memory, and implement the navigation route planning method in any one of claims 1-7.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the navigation route planning method in any one of claims 1-7.

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