Navigation route generation method and vehicle

Abstract

The application discloses a navigation route generation method and a vehicle, and relates to the technical field of intelligent cockpits, wherein the navigation route generation method comprises the following steps: acquiring at least one initial navigation route, each initial navigation route comprising a plurality of road segments; acquiring the overall vehicle load, the ambient temperature, the tire parameters and the road condition parameters of each road segment of the vehicle; determining the comprehensive risk coefficient of the vehicle on each road segment based on the overall vehicle load, the ambient temperature, the tire parameters and the road condition parameters; and determining the target navigation route according to the comprehensive risk coefficient of each road segment on the initial navigation route. The method can significantly improve the overall safety of route planning and realize the pre-positioning avoidance of driving risks.

Description

Technical Field

[0001] This application relates to the field of intelligent cockpit technology, and in particular to a navigation route generation method and a vehicle. Background Technology

[0002] In related technologies, navigation route planning typically prioritizes driving distance, traffic congestion level, or energy efficiency as the highest-priority constraints, while vehicle safety status serves only as a supplementary warning during the driving process. For example, single-parameter threshold modes such as overload alarms, abnormal tire pressure alarms, or high temperature warnings are used to push reminders to the driver when a risk is imminent.

[0003] Therefore, because the relevant technologies follow the inertial route planning logic that prioritizes distance, congestion, and energy consumption, and treats vehicle safety only as a supplementary warning after the fact, they cannot proactively avoid high-risk road sections in advance during the vehicle route planning stage. Under complex working conditions, the accuracy of risk identification is low, and it is impossible to achieve closed-loop safety control from source avoidance to process management. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in related technologies. To this end, the first objective of this application is to propose a navigation route generation method. This method acquires the vehicle's total load, ambient temperature, tire parameters, and road condition parameters for each road segment. Based on these parameters, it determines the vehicle's comprehensive risk coefficient for each road segment. Then, based on this comprehensive risk coefficient, it selects target navigation routes from the initial navigation routes. By using the vehicle's real-time safety status as a priority constraint for route generation, it can proactively eliminate candidate routes containing high-risk road segments during the planning stage. This effectively prevents the vehicle from traveling to dangerous road sections, significantly improves the overall safety of route planning, and achieves proactive avoidance of driving risks.

[0005] The second objective of this application is to propose a vehicle.

[0006] To achieve the above objectives, a first aspect of this application proposes a navigation route generation method, the method comprising: acquiring at least one initial navigation route, each initial navigation route comprising multiple road segments; acquiring vehicle load, ambient temperature, tire parameters, and road condition parameters for each road segment; determining a comprehensive risk coefficient for the vehicle in each road segment based on the vehicle load, ambient temperature, tire parameters, and road condition parameters; and determining a target navigation route based on the comprehensive risk coefficient of each road segment on the initial navigation route.

[0007] According to the navigation route generation method of this application embodiment, at least one initial navigation route is obtained, each initial navigation route including multiple road segments. The vehicle's total load, ambient temperature, tire parameters, and road condition parameters for each road segment are obtained. Based on the total load, ambient temperature, tire parameters, and road condition parameters, the comprehensive risk coefficient of the vehicle for each road segment is determined. The target navigation route is then determined based on the comprehensive risk coefficient of each road segment on the initial navigation route. Thus, this method combines the vehicle's real-time status with external road condition parameters to calculate the comprehensive driving risk of each road segment. Based on the initial navigation route, it selects a target navigation route with lower risk by combining risk screening. It no longer uses only distance length or travel time as the sole basis for route planning, but instead takes driving safety as the core constraint of route planning. It proactively eliminates high-risk route options during the planning stage, achieving proactive risk avoidance and effectively improving the overall safety of vehicle travel, thus better meeting users' needs for safe travel.

[0008] According to one embodiment of this application, the tire parameters include tire temperature, and the road condition parameters include road slope and road surface undulation frequency. Determining the comprehensive risk coefficient of the vehicle on multiple road sections based on the vehicle load, ambient temperature, tire parameters, and road condition parameters includes: determining a load risk factor based on the vehicle load and rated load; determining a temperature risk factor based on the ambient temperature, tire temperature, and the rate of temperature rise of the tire temperature; determining a road condition risk factor based on the road slope and the road surface undulation frequency; and determining the comprehensive risk coefficient based on the product of the load risk factor, the temperature risk factor, and the road condition risk factor.

[0009] According to one embodiment of this application, determining the target navigation route based on the comprehensive risk coefficient of each segment on the initial navigation route includes: if the comprehensive risk coefficient is greater than or equal to a first preset threshold, removing the initial navigation route corresponding to that segment to obtain a set of candidate navigation routes; and determining the target navigation route based on the set of candidate navigation routes.

[0010] According to one embodiment of this application, the method further includes: when the comprehensive risk coefficient is greater than or equal to a second preset threshold and less than a first preset threshold, obtaining the load risk factor of each segment in each candidate navigation route in the navigation route set; correcting the candidate navigation route set based on the load risk factor to obtain a target navigation route set; and determining the target navigation route based on the target navigation route set.

[0011] According to one embodiment of this application, the step of modifying the candidate navigation route set based on the load risk factor includes: removing the candidate navigation route corresponding to the road segment when the load risk factor is greater than a preset load risk factor.

[0012] According to one embodiment of this application, the method further includes: when the target navigation route is empty, determining one of the plurality of initial navigation routes as the target navigation route based on a preset rule.

[0013] According to one embodiment of this application, the method further includes: before the vehicle travels based on the target navigation route and enters a risky road segment, determining target control parameters based on the comprehensive risk coefficient, wherein the risky road segment is a road segment corresponding to a comprehensive risk coefficient greater than or equal to a preset threshold, and the target control parameters include at least one of the vehicle load, the ambient temperature, the tire parameters, and the road condition parameters; adjusting the corresponding controlled object based on the target control parameters, wherein the controlled object includes at least one of the tire pressure, suspension height, and thermal management function.

[0014] According to one embodiment of this application, before determining the comprehensive risk coefficient of a vehicle on each road segment, the method further includes: performing a compliance check, wherein the compliance check includes an overload compliance check and a tire safety status check; and issuing a corresponding warning if the compliance check fails.

[0015] According to one embodiment of this application, the method further includes: generating safe driving guidance instructions, the safe driving guidance instructions including safe speed suggestions, braking strategy suggestions and risk warning prompts for each segment of the target navigation route, the risk warning prompts being provided to the user via voice broadcast and / or display on the central control screen.

[0016] To achieve the above objectives, a vehicle is provided in the second aspect of this application, including a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the above-described navigation route generation method.

[0017] The vehicle according to the embodiments of this application can significantly improve the overall safety of route planning by executing the above-described navigation route generation method, thereby achieving proactive avoidance of driving risks.

[0018] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0019] Figure 1This is a flowchart of a navigation route generation method according to an embodiment of this application.

[0020] Figure 2 This is a flowchart of a navigation route generation method according to a specific embodiment of this application.

[0021] Figure 3 This is a flowchart of a navigation route generation method according to another specific embodiment of this application.

[0022] Figure 4 This is a flowchart illustrating a specific example of a navigation route generation method according to this application.

[0023] Figure 5 This is a block diagram of a vehicle according to an embodiment of this application. Detailed Implementation

[0024] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0025] In the route planning process of vehicle navigation, to achieve the common goal of efficiently and conveniently navigating users from their starting point to their destination, related technologies typically use the shortest distance, shortest travel time (i.e., avoiding congestion), or lowest energy consumption as core optimization objectives. They calculate and recommend one or more candidate navigation routes using road network data and real-time traffic information provided by map service providers. This approach is widely adopted primarily to maximize travel efficiency and minimize user time costs.

[0026] However, when this solution is applied to scenarios with extremely high vehicle safety requirements, such as fully loaded commercial vehicles driving on undulating mountain roads in high summer temperatures, or passenger cars encountering complex road conditions during holidays when fully loaded, its performance is less than ideal. The solution's inherent reactive warning design logic—optimizing traffic efficiency or energy consumption—inevitably compromises its ability to proactively avoid potential major safety risks by only issuing warnings for exceeding single parameter limits after route planning is complete. For example, in the combined conditions of high temperatures, full loads, and continuous bumpy roads, this problem can lead to vehicles unknowingly entering extremely high-risk environments. Specifically, the route planning system completely ignores the coupled amplification effect of the road itself on tire fatigue temperature rise and load fluctuations until the tire temperature exceeds the safety threshold, at which point the system issues a single warning that the predetermined route cannot be changed. By then, the vehicle is already in danger, and serious safety accidents such as tire blowouts and loss of control cannot be prevented at their source.

[0027] In-depth analysis revealed that the relevant technologies treat vehicle driving safety merely as a local problem that can be solved by independent threshold alarms during driving, rather than a holistic constraint that requires global pre-assessment during the route planning stage. As a result, the priority of safety parameters is seriously underestimated, and their role is limited to post-event supplementation and independent judgment. They cannot be coupled with the route generation process, let alone become the core basis for route selection.

[0028] To address the aforementioned issues, this application proposes a navigation route generation method. By constructing a comprehensive risk coefficient that quantifies the complex coupling risks of multiple factors and using this coefficient as the highest priority constraint for route planning, the method fundamentally changes the route generation logic without significantly sacrificing traffic efficiency. This transforms the approach from reactive, post-event warnings to proactive, source-level avoidance, solving the problem that related technologies cannot proactively avoid high-risk road sections caused by the coupling of multiple factors during the planning stage. This significantly improves driving safety.

[0029] In one embodiment of this application, the navigation route generation method can be applied to a vehicle equipped with an in-vehicle navigation system, a vehicle body domain controller, and a series of sensors for sensing the vehicle's own state and external environment. These sensors include, but are not limited to, seat pressure sensors or in-vehicle weighing sensors for acquiring vehicle load, exterior temperature sensors for acquiring ambient temperature, tire pressure monitoring systems for acquiring tire pressure and temperature, and in-vehicle navigation system and advanced driver assistance system perception modules for acquiring road gradient and road surface undulation frequency. The vehicle body domain controller, as the core processing unit, is electrically connected to the aforementioned sensors and navigation system via a controller area network bus or in-vehicle Ethernet, and is responsible for receiving and processing data, running the core algorithm, and generating navigation commands.

[0030] The navigation route generation method and vehicle proposed in this application are described below with reference to the accompanying drawings.

[0031] Figure 1 This is a flowchart of a navigation route generation method according to an embodiment of this application.

[0032] like Figure 1 As shown, the navigation route generation method in this application embodiment may include the following steps: S1, Obtain at least one initial navigation route, each initial navigation route including multiple road segments.

[0033] Specifically, after the user sets the start point, destination, and possible intermediate stops for their trip through the in-vehicle navigation system or mobile terminal, the processor first calls upon the full road network data of the navigation map to generate all passable routes that comply with traffic rules, forming an initial set of navigation routes. To ensure the comprehensiveness of the planning results and the feasibility of subsequent filtering, the number of initial selectable routes generated for a single trip is preferably no less than four.

[0034] Therefore, at least one initial navigation route can be obtained from the initial route set, and each initial navigation route includes multiple segments. That is, each initial navigation route is broken down into continuous segments according to the smallest unit (such as 1 kilometer), resulting in multiple segments, so as to carry out a detailed segment-by-segment risk assessment later.

[0035] As a specific implementation, the processor can read the latitude and longitude point sequence of each route through the application programming interface of map data, and segment it according to a preset segmentation strategy (such as fixed distance or road attribute change points). This approach transforms the global route planning problem into a local, quantifiable road segment risk assessment problem, providing a structured data foundation for subsequent refined safety screening.

[0036] S2 acquires the vehicle's total load, ambient temperature, tire parameters, and road condition parameters for each road segment.

[0037] Specifically, after acquiring the initial route set and completing the road segment division, the processor immediately activates multiple data acquisition channels. This allows the acquisition of vehicle load, ambient temperature, tire parameters, and road condition parameters for each road segment. The vehicle load is not a static rated load capacity, but a dynamic vehicle mass calculated in real-time by weighing sensors on the vehicle (such as seat pressure sensors and air suspension height sensors) in conjunction with the vehicle's domain controller. The calculation formula can include: Vehicle Load = Original Curb Weight + Weight of Passengers and Cargo + Remaining Fuel and Battery Weight + Suspension Deformation Correction Value. Ambient temperature can be directly obtained from the vehicle's external temperature sensor. Tire parameters include at least real-time tire temperature data, tire pressure data, and tire wear data provided by the tire monitoring system. Road condition parameters are extracted for each road segment from the navigation map and the ADAS (Advanced Driving Assistance System) perception module, and include, but are not limited to, road gradient, curvature, frequency of road surface undulations, and bump amplitude. These parameters are the most basic and critical inputs for subsequent risk calculations.

[0038] It should be noted that in cases leading to major safety accidents such as tire blowouts and loss of control, the vehicle load is the direct source of force on tire deformation and rolling resistance, and also the fundamental driving force for heat accumulation inside the tire. Ambient temperature and tire parameters jointly determine the thermodynamic state of the tire material, while the rate of temperature rise is a direct indicator of tire fatigue accumulation. Road condition parameters (such as gradient and frequency of undulations) are external excitation sources, accelerating mechanical fatigue and heat accumulation in the tire by increasing the magnitude or frequency of load fluctuations. In other words, these parameters comprehensively cover the core factors affecting tire safety from three dimensions: load input, material state, and external excitation. This avoids errors caused by relying solely on a single parameter to calculate risk, providing a more accurate basis for subsequent route risk assessment. Specifically, this application incorporates all the above parameters into a unified quantitative framework, achieving a complete capture of the risk amplification effect of the coupled "load-temperature-road condition" factors. This allows the route planning system to transform from a single-parameter fixed threshold report to a multi-parameter dynamically coupled risk quantity, improving the accuracy of risk identification in high-risk scenarios.

[0039] S3 determines the comprehensive risk coefficient of a vehicle on each road segment based on vehicle load, ambient temperature, tire parameters, and road condition parameters.

[0040] Specifically, after obtaining vehicle load, ambient temperature, tire parameters, and road condition parameters, the comprehensive risk coefficient of the vehicle for each road segment can be determined based on these parameters. For example, when the vehicle load exceeds a preset threshold, the ambient temperature exceeds the normal temperature range, the tire temperature or tire pressure is abnormal, the tire wear exceeds the warning value, or the road slope, curvature, or bump amplitude exceeds the safe range, the corresponding parameters will contribute a corresponding proportion of the risk score. By weighted summing of the risk scores of all parameters, the comprehensive risk coefficient of that road segment can be obtained. The weight values ​​can be pre-calibrated and stored according to the degree of influence of different parameters on driving safety.

[0041] S4 determines the target navigation route based on the comprehensive risk coefficient of each segment on the initial navigation route.

[0042] Specifically, after determining the comprehensive risk coefficient of each road segment, the target navigation route can be determined based on the comprehensive risk coefficient of each road segment on the initial navigation route. For example, different risk thresholds can be pre-set, and the comprehensive risk coefficients of all road segments in the initial navigation route can be iterated. If the comprehensive risk coefficient of at least one road segment in a given initial route exceeds the set risk threshold, the entire route will be directly removed from the candidate set. After this round of safety screening, the remaining routes constitute the set of candidate navigation routes that meet all safety requirements. This process ensures that all output routes are safe from the outset. Alternatively, if the comprehensive risk coefficients of all road segments in the initial navigation route are higher than the highest risk threshold, the route with the lowest average comprehensive risk coefficient among multiple available routes can be selected as the target navigation route output based on the average comprehensive risk coefficient of all road segments in the initial navigation route.

[0043] Finally, when multiple target navigation routes are available, the processor also selects the optimal target navigation route from the set of candidate safe routes and outputs it to the user based on the user's travel needs (such as choosing the shortest time priority mode, or the balanced mode that combines the best time and distance).

[0044] Therefore, by first breaking down road segments and then assessing safety risks segment by segment, this method breaks the limitations of traditional navigation that only uses distance and time as filtering dimensions. It incorporates the real-time status of the vehicle and the characteristics of road conditions into the filtering logic of the navigation route, which can effectively avoid high-risk road segments when the vehicle is in poor condition. This significantly improves driving safety in scenarios such as long-distance self-driving and heavy-load travel, and avoids safety accidents such as tire blowouts and brake failures caused by mismatch between vehicle status and road conditions, thus better meeting the needs of users for safe travel.

[0045] Figure 2 This is a flowchart of a navigation route generation method according to an embodiment of this application.

[0046] According to one embodiment of this application, tire parameters include tire temperature, and road condition parameters include road gradient and road surface undulation frequency. When determining the comprehensive risk coefficient of a vehicle across multiple road segments based on vehicle load, ambient temperature, tire parameters, and road condition parameters, such as... Figure 2 As shown.

[0047] S1, Obtain at least one initial navigation route, each initial navigation route including multiple road segments.

[0048] S2 acquires the vehicle's total load, ambient temperature, tire temperature, and the road gradient and frequency of road surface undulations for each road segment.

[0049] S31 determines the load risk factor based on the vehicle load and rated load.

[0050] S32 determines the temperature risk factor based on ambient temperature, tire temperature, and the rate of temperature rise of the tire.

[0051] S33 determines road condition risk factors based on road slope and the frequency of road surface undulations.

[0052] S34 determines the comprehensive risk coefficient based on the product of load risk factor, temperature risk factor, and road condition risk factor.

[0053] Specifically, in a preferred embodiment, the aforementioned parameters can be more precisely defined and quantified to address the problem that related technologies can only independently determine risks through a single parameter, ignoring the nonlinear risk amplification effect among load, temperature, and road conditions, leading to inaccurate identification of complex high-risk scenarios.

[0054] First, after obtaining at least one initial navigation route, each route comprising multiple road segments, and acquiring the vehicle's total load, ambient temperature, tire temperature, and the frequency of road gradient and undulation for each road segment, a load risk factor can be determined based on the total vehicle load and rated load. Total vehicle load refers to the dynamic total vehicle mass, while rated load refers to the vehicle's permissible load capacity. For example, the load risk factor F1 can be determined based on the ratio of dynamic total vehicle mass to rated load capacity, calibrated on a real vehicle, and ranges from 1.00 to 3.50. For instance, when the vehicle is unloaded, F1 is 1.00; when the vehicle is fully loaded, F1 is 1.80; and when the vehicle is overloaded by 50%, F1 reaches 3.50. This quantification is based on real vehicle test data, which shows that for every 10% increase in load, tire rolling resistance increases by 8%, and the risk of tire blowout increases by 15%.

[0055] Tire parameters include tire temperature, and a temperature risk factor can be determined based on ambient temperature, tire temperature, and the rate of temperature rise. Tire temperature is obtained in real-time from the tire pressure monitoring system, while the rate of temperature rise is calculated based on the rate of change of tire temperature over time. For example, a pre-defined relationship can be used to determine the load risk factor. This relationship can be established beforehand between ambient temperature, tire temperature, the rate of temperature rise, and the temperature risk factor. Once these parameters are determined, the temperature risk factor can be obtained simply by applying this relationship.

[0056] For example, the temperature risk factor F2 ranges from 1.00 to 4.00. For instance, assuming the tire temperature and the rate of temperature rise remain constant, F2 is 1.00 at a normal temperature of 25°C; it is 1.60 at a high temperature of 40°C; and when the tire temperature exceeds the safety threshold of 80°C, the system determines the risk to be extremely high, and F2 is directly assigned a value of 4.00.

[0057] Road condition parameters include road gradient and road surface undulation frequency. Road gradient can be calculated from the elevation data of the navigation route, while road surface undulation frequency is the number of times the road surface elevation changes beyond a preset threshold per unit path length, which can be statistically obtained from road elevation data. Road condition risk factors can then be determined based on road gradient and road surface undulation frequency, and can also be directly retrieved through a preset correspondence. Road surface undulation frequency is a newly introduced core parameter used to characterize the degree of continuous bumpiness on a road segment. The road condition risk factor F3 ranges from 1.00 to 5.00. For example, on paved flat roads, F3 is 1.00; on long downhill sections, F3 is 2.50; and on high-frequency continuous undulation sections, F3 is directly assigned a value of 5.00. Test data shows that on continuously undulating and bumpy roads, the tire temperature rise rate is more than 6 times that on flat roads, and the risk of tire blowout increases exponentially.

[0058] Finally, the final comprehensive risk coefficient R can be determined based on the product of load risk factor F1, temperature risk factor F2, and road condition risk factor F3. Those skilled in the art will understand that calculation methods to achieve the same purpose also include the introduction of other correlation coefficients or weighting factors, but the above-described direct product method is the core method that best reveals the coupling amplification effect.

[0059] For example, the processor calculates the load risk factor F1 (e.g., 1.00 for unloaded and 1.80 for fully loaded) within a calibrated range of 1.00-3.50 based on the ratio of the vehicle load to the rated load; it calculates the temperature risk factor F2 (e.g., 1.00 for 25℃ and 1.60 for 40℃) within a range of 1.00-4.00 based on ambient temperature, tire temperature, and their rate of temperature rise; and it calculates the road condition risk factor F3 (e.g., 1.00 for flat roads and 5.00 for continuously undulating roads) within a range of 1.00-5.00 based on road gradient and the frequency of road surface undulations. Then, the comprehensive risk coefficient can be obtained using the formula R=F1×F2×F3. This product operation mathematically simulates the coupled amplification effect of multiple risk factors. For example, when the vehicle is fully loaded (F1=1.8), at high temperature (F2=1.6), and on a continuously undulating road (F3=5.0), the R value is 14.4, which accurately reflects that the risk of tire blowout is 14.4 times that of a flat road with no load at normal temperature. This solves the defect of related technologies that can only provide independent alarms and cannot identify compound risks.

[0060] This allows for the precise quantification of the risk amplification effect under complex high-risk scenarios such as full load, high temperature, and continuously undulating roads, thereby significantly improving the accuracy of risk identification and providing a solid data foundation for subsequent precise route selection. Compared to the simple superposition of single risk factors, the comprehensive risk coefficient obtained through product calculation can effectively reflect the nonlinear coupling amplification effect of load, temperature, and road conditions on tire safety. It can more accurately identify complex high-risk road sections, avoid missing actual high-risk scenarios, and provide an accurate and reliable risk assessment basis for generating safer navigation routes.

[0061] According to one embodiment of this application, when determining the target navigation route based on the comprehensive risk coefficient of each segment on the initial navigation route, such as... Figure 3 As shown.

[0062] S41. If the comprehensive risk coefficient is greater than or equal to the first preset threshold, proceed to step S42; otherwise, proceed to step S44.

[0063] S42, the initial navigation route corresponding to this road segment is removed to obtain a set of candidate navigation routes.

[0064] S43, determine the target navigation route based on the candidate navigation route set. The first preset threshold can be determined according to the actual situation.

[0065] Specifically, to further optimize the logic of determining the target navigation route in the above embodiments, making it more automated and clear, and able to effectively handle road segments with various risk levels, this application also provides the following preferred solution. When determining the target navigation route based on the comprehensive risk coefficient of each road segment on the initial navigation route, the relationship between the comprehensive risk coefficient and a first preset threshold can first be judged. If the comprehensive risk coefficient is greater than or equal to the first preset threshold, it indicates that the road segment belongs to a composite high-risk road segment, and the probability of safety accidents such as tire blowouts is much higher than that of ordinary road segments, which does not meet the requirements of safe navigation. Therefore, the initial navigation route containing this road segment can be directly removed from the candidate routes, and the remaining initial navigation routes constitute the candidate navigation route set.

[0066] After determining the candidate navigation route set, the target navigation route can be determined from the candidate route set. For example, the final target navigation route can be selected by combining the user's navigation preferences (such as prioritizing the shortest distance, prioritizing traffic speed, etc.), which can meet the user's basic navigation needs while minimizing the avoidance of road sections with high tire blowout risk and improving vehicle driving safety.

[0067] It should be noted that the first preset threshold is the boundary value for defining high risk or extremely high risk, and its specific value can be calibrated according to the vehicle's safety redundancy strategy. For example, in a specific embodiment, when R ≥ 4.0 (the first preset threshold), the system determines the road segment as high risk (prohibited from passage); when R ≥ 8.0, it is determined as extremely high risk (severe danger). For these two risk levels, regardless of the vehicle's load status (empty, fully loaded, or overloaded), as long as there is at least one road segment in the initial route that meets this condition, the entire initial route will be forcibly removed from the set of selectable routes to ensure that all road segments of each route in the remaining candidate route set are safe and passable. For example, during the verification process, if a road segment of an initial route is found to have an R value of 5.5, this route will be excluded as a whole, even if other road segments are safe. Subsequently, the final target navigation route is determined from the filtered candidate navigation route set based on the user's preset travel needs (such as time priority or balanced mode). By setting a clear first preset threshold, high-risk road sections are automatically and forcibly removed from the entire road, ensuring the safety baseline of the candidate route set and providing a high-quality safe candidate set for subsequent selection of the optimal route.

[0068] Therefore, by pre-screening high-risk routes with a first preset threshold, the probability of safety accidents such as tire blowouts during vehicle operation is reduced from the root. There is no need to change the vehicle's route or increase the user's driving burden. Without affecting the efficiency of regular navigation, the safety foundation for vehicle travel is strengthened. At the same time, the candidate routes retained after screening can still be selected according to the user's navigation preferences, taking into account both driving safety and the user's personalized navigation needs.

[0069] According to one embodiment of this application, such as Figure 3 As shown, the navigation route generation method also includes: S44. If the comprehensive risk coefficient is greater than or equal to the second preset threshold and less than the first preset threshold, proceed to step S45; otherwise, proceed to step S41.

[0070] S45, obtain the load risk factor for each segment of each candidate navigation route in the navigation route set.

[0071] S46. Based on the load risk factor, the candidate navigation route set is corrected to obtain the target navigation route set.

[0072] S47, determine the target navigation route based on the target navigation route set. The second preset threshold can be determined according to the actual situation.

[0073] Specifically, to further refine the route selection logic and adapt to vehicle safety requirements under different load conditions, this application also provides the following preferred solution. Building upon the previous method of eliminating high-risk routes using a first preset threshold (e.g., R≥4.0) to obtain a candidate navigation route set, this method further includes a risk correction step.

[0074] The relationship between the comprehensive risk coefficient and the second preset threshold is assessed. If the comprehensive risk coefficient is greater than or equal to the second preset threshold but less than the first preset threshold, it indicates that the overall risk of the current route is at a medium level. Simply eliminating all routes is no longer sufficient for accurate screening. In this case, it is necessary to delve deeper into the road segment level, and to explore the risk differences of each road segment in conjunction with the current load conditions of the vehicles. In other words, when the comprehensive risk coefficient R is within a specific range, that is, greater than or equal to the second preset threshold (e.g., 2.0) and less than the first preset threshold (e.g., 4.0), the road segment is defined as medium risk (drive with caution). For these medium-risk road segments, the risk level has not yet reached the point of direct rejection, but it is closely related to the specific load conditions of the vehicles.

[0075] To address this issue, the processor obtains the load risk factor F1 for each segment of each candidate navigation route in the navigation route set. Then, based on the load risk factor, the candidate navigation route set is further refined to obtain the final target navigation route set. Finally, the target navigation route is determined based on this target navigation route set. The logic behind this refinement is that a medium-risk road segment may be safe for empty or partially loaded vehicles, but may be too dangerous for fully loaded or overloaded vehicles. For example, when the vehicle's current load exceeds the preset load threshold for that road segment, the load risk factor F1 for that segment is assigned a coefficient greater than 1, further amplifying the overall risk of that segment. If the overall risk coefficient of the entire route increases to no less than the first preset threshold after refinement, the candidate route is removed from the candidate set. When the vehicle's current load does not exceed the preset load threshold for that road segment, the load risk factor F1 for that segment is assigned a coefficient no greater than 1, which can appropriately reduce the risk proportion of that segment, retaining candidate routes that still meet the requirements for safe passage. This segment-level correction for medium-risk routes can improve the safety of navigation routes without excessively filtering out candidate routes, while taking into account the actual operating status of vehicles, thus balancing traffic efficiency and risk management needs.

[0076] Once the target navigation route set is determined, the target navigation route can be determined based on the target navigation route set. For example, the route with the lowest overall risk coefficient in the target navigation route set can be selected as the final output target navigation route. Alternatively, it can be combined with the user's preset preferences (such as prioritizing the shortest distance or prioritizing travel time) to match the target navigation route that meets the user's needs from the routes that meet the safety threshold requirements.

[0077] Therefore, by segmenting and tiering risk screening and correction, high-risk routes that seriously threaten driving safety can be eliminated in advance, while medium-risk routes can be finely adjusted based on the actual load status of the vehicle. This avoids the problems of either missing risky routes or over-filtering usable routes under a uniform screening standard, effectively improving the matching degree between the generated navigation route and the vehicle's current state. While ensuring driving safety, it also retains the most efficient alternative routes, improving the user navigation experience. For heavy-load vehicles, it can avoid additional traffic risks caused by vehicle load in advance, reducing the possibility of accidents caused by mismatch between road conditions and load. For scenarios such as logistics and transportation that require frequent route planning, it can provide more reliable technical support for vehicle driving safety.

[0078] According to one embodiment of this application, the candidate navigation route set is modified based on a load risk factor, including: removing the candidate navigation route corresponding to a road segment if the load risk factor is greater than a preset load risk factor. The preset load risk factor can be determined according to actual conditions.

[0079] Specifically, when revising the candidate navigation route set based on the load risk factor, the relationship between the load risk factor and the preset load risk factor can be judged. If the load risk factor is greater than the preset load risk factor, it means that the current risk level of the road segment has exceeded the safe range that the current vehicle load can bear. Continuing to pass through will greatly increase the probability of an accident. Therefore, the candidate navigation route containing the road segment is directly removed from the candidate set and will no longer be included in the subsequent route selection range.

[0080] It should be noted that the preset load risk factor can be determined according to actual conditions. For example, it can be adaptively adjusted by the vehicle system based on historical vehicle traffic data and safety accident statistics under different loads. Alternatively, it can be manually set by the user based on their driving experience and actual transportation needs. It can also be uniformly updated and pushed through a cloud server combining real-time traffic conditions and regional road characteristics. Different values ​​can be set for different scenarios to match different safety requirements. For example, the preset load risk factor can be calibrated as the F1 value corresponding to a fully loaded state, i.e., 1.80.

[0081] Therefore, through this round of screening, the final candidate route set not only avoids absolutely high-risk road sections but also eliminates medium-risk road sections that are unsuitable for travel under full load conditions, thus obtaining a more stringent and safer target navigation route set. Through the aforementioned secondary evaluation based on load risk factors, it is possible to more precisely adapt to vehicle safety requirements under different load conditions, especially in fully loaded and overloaded scenarios, further improving the safety and applicability of route planning.

[0082] According to one embodiment of this application, the navigation route generation method further includes: when the target navigation route is empty, determining one of a plurality of initial navigation routes as the target navigation route based on preset rules.

[0083] Specifically, to address the extreme scenario where all candidate routes are eliminated due to safety risks, and to ensure the continuity and robustness of the navigation system's functionality, this application also provides the following preferred solution. After the multi-layered screening process described above, an extreme situation may arise where all candidate navigation routes are eliminated due to the presence of high-risk road sections or medium-risk road sections under full load conditions, resulting in an empty target navigation route set. In this case, the vehicle will lose safe routes for navigation, and the system cannot fall into an infinite loop or cease operation. To solve this technical problem, this method also includes a fallback strategy.

[0084] If the target navigation route set is determined to be empty, the processor will directly select one of the original initial navigation routes as the final target navigation route based on preset rules. These preset rules can be based on the principle of minimizing risk to ensure driving safety. For example, from all the eliminated initial routes, the route with the lowest overall risk coefficient value will be selected; or the route with the shortest total mileage of high-risk sections will be selected; or the route calculated based on conventional navigation algorithms (such as shortest distance) will be directly selected, while simultaneously issuing the highest level of warning to the user: "High risk along the entire route, please drive with caution."

[0085] Therefore, by setting a clear preset rule as a fallback solution, the system avoids the situation where there is no available safe route and its function is interrupted, ensuring the stability and continuity of the navigation service and reflecting the comprehensive consideration of the system design under extreme conditions.

[0086] According to one embodiment of this application, the navigation route generation method further includes: before the vehicle travels based on the target navigation route and enters a risky road segment, determining target control parameters based on a comprehensive risk coefficient, wherein the risky road segment is a road segment with a comprehensive risk coefficient greater than or equal to a preset threshold, and the target control parameters include at least one of vehicle load, ambient temperature, tire parameters, and road condition parameters; adjusting the corresponding controlled object based on the target control parameters, wherein the controlled object includes at least one of tire pressure, suspension height, and thermal management function. The preset threshold can be determined according to actual conditions.

[0087] Specifically, to combine risk avoidance during the planning phase with active safety control during driving to form a full-link active safety guarantee, this application also provides the following preferred solutions. After determining a safe target navigation route in the preceding embodiments, the method further includes a step of actively adjusting the vehicle state. That is, before the vehicle enters a risky road segment based on the target navigation route, the system determines the target control parameters based on the comprehensive risk coefficient R of the road segment. For example, if the road segment is a slippery downhill section, the risk comes from insufficient road surface grip, and the target control parameter is standard tire pressure, the system will automatically adjust the tire pressure to the standard value to improve tire grip; if the road segment is an undulating and bumpy section, the risk comes from excessive vehicle body undulation and easy loss of control, and the target control parameter is to raise the suspension height, the system will automatically adjust the suspension height to improve vehicle stability; if the road segment is a long continuous curve, the risk comes from the braking system working continuously and easily overheating, and the target control parameter is to activate brake thermal management cooling, the system will activate the thermal management function in advance to pre-cool the braking system and avoid heat fade affecting braking performance. By proactively adjusting the vehicle's status before entering high-risk road sections, driving risks can be further reduced, and safety during navigation can be improved. High-risk road sections are defined as those with a comprehensive risk coefficient R greater than or equal to a preset threshold (e.g., 2.0). Target control parameters include at least one of the following: vehicle load, ambient temperature, tire parameters, and road condition parameters. Determining these parameters is for the purpose of generating specific physical adjustment commands.

[0088] Therefore, based on these target control parameters, the system adjusts the corresponding controlled objects. The controlled objects include at least one of tire pressure, suspension height, and thermal management functions. For example, for vehicles equipped with active tire pressure control systems, the system can make a fine-tuning of the tire pressure within a safe range of 0.1 to 0.2 atmospheres 3 kilometers before entering a high-risk road section to optimize tire contact pattern and reduce temperature rise. For vehicles equipped with air suspension, the suspension stiffness can be increased in advance before entering a continuously undulating and bumpy road section to reduce vehicle body vibration and tire load fluctuations. For high-temperature environments, the efficiency of tire cooling or braking system cooling can be improved in advance.

[0089] Therefore, by directly converting the risk data generated during the route planning stage into proactive physical adjustment actions before driving, this preferred solution achieves a leap from planned avoidance to proactive adaptation, enabling vehicles to enter high-risk road sections in an optimized physical state, thereby forming a preventative, closed-loop proactive safety guarantee.

[0090] According to one embodiment of this application, before determining the comprehensive risk coefficient of a vehicle on each road segment, the navigation route generation method further includes: performing a compliance check, wherein the compliance check includes an overload compliance check and a tire safety status check; and issuing a corresponding warning prompt if the compliance check fails.

[0091] Specifically, to establish a robust safety and compliance baseline during the route planning process and prevent vehicles from being guided to high-risk scenarios due to illegal or abnormal vehicle conditions, this application also provides the following preferred solution. Before determining the comprehensive risk coefficient of the vehicle for each road segment, a preliminary compliance verification step is included. This step comprises two core components: overload compliance verification and tire safety status verification.

[0092] For example, during overload compliance checks, the processor compares the dynamic vehicle weight with the vehicle's rated load capacity. If overload is detected (i.e., the vehicle weight exceeds the rated load capacity), an overload violation alert is immediately sent to the user. Simultaneously, the system locks the highest level of safety constraints, forcibly planning a route to avoid all high-risk road sections (e.g., long downhill slopes, undulating roads, highways, etc.). During tire safety status checks, the system compares real-time tire pressure, tire temperature data, and potential tire wear data with safety thresholds set by national standards or the manufacturer. If tire pressure or temperature exceeds the safe range, or tire wear reaches its limit, the system immediately sends an anomaly alert to the user and simultaneously increases the safety redundancy threshold, forcibly avoiding high-risk road sections such as those with high temperatures and bumpy surfaces in subsequent route planning. In cases where these preliminary checks fail (i.e., overload or abnormal tire condition), the system issues corresponding warnings, informing the user that the current vehicle status is non-compliant.

[0093] Therefore, by setting up this pre-emptive dual verification mechanism, it is ensured that all route planning decisions are made under the premise that the vehicle status is legal and compliant and initially safe, thus cutting off the possibility of entering an inappropriate route due to abnormal vehicle status, and improving the safety baseline and legal compliance of the entire system.

[0094] According to one embodiment of this application, the navigation route generation method further includes: generating safe driving guidance instructions, which include safe speed suggestions, braking strategy suggestions, and risk warning prompts for each segment of the target navigation route, and the risk warning prompts are provided to the user through voice broadcast and / or display on the central control screen.

[0095] Specifically, to extend the safety value of route planning to the driver's behavioral level, forming a complete "planning-guidance" closed loop and assisting the driver in operating the vehicle more safely, this application also provides the following preferred solutions. After generating and outputting a safe target navigation route, a step can also be taken to generate safe driving guidance instructions. These instructions are not simple directional prompts, but rather scenario-based guidance that deeply integrates route risk information. Among them, the safe driving guidance instructions include at least safe speed suggestions, braking strategy suggestions, and risk warning prompts for each segment of the target navigation route.

[0096] For example, when a vehicle is about to enter a long, high-risk downhill section, the system can issue a voice warning: "A long, continuous downhill slope lies ahead for 3km. The current fully loaded and high-temperature condition poses a high risk. It is recommended to maintain a speed of 60km / h and use engine braking to avoid frequent braking that could cause tire overheating." Simultaneously, the central control screen will display information such as the safe speed range and optimal braking strategy for that section. These risk warnings are primarily provided to users through voice announcements or central control screen displays, or both. Furthermore, when entering high-risk sections, the system can automatically enhance the sensitivity of driver assistance functions such as forward collision warning and lane keeping assist to help users avoid unexpected risks. For instance, when a vehicle is about to enter a winding, narrow mountain road, the system will adjust its sensitivity before entering the section, allowing driver assistance functions to identify lane departure risks and potential oncoming vehicles earlier, and to issue warnings or intervene more quickly when the user makes a driving error, reducing the probability of an accident.

[0097] Thus, by transforming risk information into specific, intuitive, and actionable operational suggestions, a closed loop from static route planning to dynamic driving behavior guidance is achieved. This enables drivers to anticipate risks in advance and take optimal countermeasures, thereby fully leveraging the value of safe route planning and improving overall safety and controllability during driving.

[0098] Furthermore, to continuously improve the accuracy of risk identification and the rationality of route planning, and to achieve self-evolution of the system throughout its entire lifecycle, this application also provides a data-driven model iteration optimization scheme. For example, with user authorization, the system collects and analyzes key data during actual vehicle operation, including but not limited to tire temperature rise curves under different road conditions, records of triggered risk events (such as tire pressure or temperature exceeding limits), and detailed road condition characteristics of the actual driving sections. Based on this high-fidelity data, the system continuously optimizes the calibration values ​​of load risk factors, temperature risk factors, and road condition risk factors through methods such as machine learning or regression analysis, thereby improving the accuracy of the comprehensive risk coefficient calculation. For example, if a large amount of data indicates that there is a systematic deviation between the actual tire temperature rise and the model prediction value under a certain road surface type, the system will automatically adjust the calculation formula or value parameters of the corresponding road condition risk factors to make the model more closely match the real physical scenario. Secondly, some users tend to choose shorter routes when unloaded, while preferring smoother routes when fully loaded. Based on this historical data, the system automatically optimizes the safety redundancy threshold and guidance logic for route planning for the user, thereby achieving personalized safety strategies for different vehicles. This adaptation allows the same algorithm to exhibit differentiated characteristics across different user groups, better suited to their needs and risk assessment preferences, thus improving the user experience.

[0099] Secondly, since road conditions themselves change over time (e.g., road maintenance, road surface aging, seasonal slipperiness), by aggregating a large amount of anonymized driving records (including tire temperature rise, vibration amplitude, frequency of risk events, etc.), it is possible to identify risky road sections that are not marked in the map data or have changed, especially blind spots on remote roads and unpaved roads. This updated road network risk data will be incorporated into the calculation in the next round of route planning, thereby achieving risk coverage across the entire road network and all scenarios. Silent upgrades can be performed using over-the-air (OTA) technology after the vehicle is connected to the network. The upgrade process does not affect the offline operation of core functions; users do not need to go to a 4S store or interrupt their current journey. After the system completes data packet download, verification, and model parameter updates in the background, the latest optimization results can be applied in the next navigation planning. Through OTA upgrades, this solution can quickly and cost-effectively cover all existing vehicle models, enabling continuous iteration of technology in mass-produced vehicles and extending the value of the technology throughout its lifecycle.

[0100] In a further preferred embodiment, to respect user autonomy and meet personalized needs, this application also reserves the highest level of intervention authority for the user. Specifically, all route planning, vehicle status active adjustment, and safe driving guidance actions automatically generated by the system can be turned off by the user with a single click via a unified switch or independent option on the vehicle's infotainment interface. When the user chooses to turn off these functions, the system will immediately switch to the vehicle's factory-installed conventional navigation mode, planning routes according to common logic such as distance, congestion, and energy consumption, and will no longer perform safety screening and vehicle active adjustment based on comprehensive risk coefficients. This design allows the system to provide the highest level of safety protection in the default state while fully complying with the user's final will, avoiding user discomfort or trust crisis caused by automatic intervention. At the same time, all user operation records, including the time and scenario of their selection to turn off functions, as well as manually selected route preferences, will be synchronously retained by the system for model iteration and personalized adaptation. By preserving the user's highest level of intervention authority, this preferred solution achieves an excellent balance between safety protection and user autonomy, improving the system's user acceptance and practical usability.

[0101] In summary, let's take a specific application scenario as an example. Consider a family of five on a long-distance road trip in the sweltering summer heat. The vehicle is fully loaded with luggage, and the family sets off in 38°C conditions. After entering their destination into the in-car navigation system, the user starts navigation. At this point, the vehicle processor first obtains the vehicle load value through the seat pressure sensor and the body domain controller, confirming that the vehicle is fully loaded. Simultaneously, the outside temperature sensor shows an ambient temperature as high as 38°C. The system performs a double check beforehand, and the results show that the vehicle is not overloaded, the tires are in normal condition, and the process continues.

[0102] Subsequently, the system read the road network data between the starting point and the destination, generating three initial navigation routes. The processor broke down each route into segments of 1 kilometer each, and extracted road condition parameters such as road gradient and frequency of road surface undulations from the map data for each segment. For one segment, the system identified it as a continuously undulating mountain road with a long downhill section. The processor began calculating the comprehensive risk coefficient of this segment: based on the full load condition, the load risk factor F1 was assigned a value of 1.8; based on the ambient temperature of 38°C and the real-time tire temperature and temperature rise rate, the temperature risk factor F2 was assigned a value of 1.6; based on the high-frequency undulation characteristics of this segment, the road condition risk factor F3 was assigned a value of 5.0. Ultimately, the comprehensive risk coefficient R of this segment was 1.8 × 1.6 × 5.0 = 14.4, far exceeding the first preset threshold (4.0), and was judged as extremely high risk. According to the safety screening filtering rules, if any such segment exists in a route, the entire route is eliminated. Initial routes 1 and 2, which included this section, were therefore eliminated. Route 3, although slightly longer by 10 minutes, had an R-value of less than 2.0 for all sections and was deemed safe throughout, ultimately being output to the user as the target navigation route.

[0103] During the journey, when the vehicle approached a medium-risk (R=2.5) continuous bumpy section on Route 3, the system generated a vehicle status adjustment command, increasing the tire pressure by 0.15 bar 3 kilometers in advance and improving the stiffness of the air suspension. Simultaneously, the system reminded the user via voice: "A continuous bumpy section is approaching in 2 kilometers. It is recommended to maintain a speed of 50 km / h for optimal driving stability." The entire driving process was smooth, with tire temperature consistently kept below the safe threshold. The user completely avoided high-risk mountain roads and arrived at their destination safely and comfortably. In this scenario, the invention enables users to avoid major safety risks such as tire blowouts and loss of control from the very beginning of route generation, providing a significantly enhanced safety experience that surpasses the "post-event warning" capabilities of related technologies.

[0104] The following is combined Figure 4 The method described in this application is used to describe the method.

[0105] As a specific example, the navigation route generation method of this application may include the following steps: S101, Obtain at least one initial navigation route, each initial navigation route including multiple road segments.

[0106] S102, acquire the vehicle's total load, ambient temperature, tire parameters, and road condition parameters for each road segment.

[0107] S103 determines the load risk factor based on the vehicle load and rated load, the temperature risk factor based on the ambient temperature, tire temperature and the rate of temperature rise of the tire temperature, and the road condition risk factor based on the road slope and the frequency of road surface undulations.

[0108] S104 determines the comprehensive risk coefficient based on the product of load risk factor, temperature risk factor, and road condition risk factor.

[0109] S105, determine whether the comprehensive risk coefficient is greater than or equal to the first preset threshold. If yes, proceed to step S106; if no, proceed to step S107.

[0110] S106, the initial navigation route corresponding to this road segment is removed to obtain a set of candidate navigation routes.

[0111] S107, determine whether the comprehensive risk coefficient is greater than or equal to the second preset threshold and less than the first preset threshold. If yes, proceed to step S108; if no, proceed to step S114.

[0112] S108, obtain the load risk factor for each segment of each candidate navigation route in the navigation route set.

[0113] S109, Based on the load risk factor, the candidate navigation route set is corrected to obtain the target navigation route set, and the target navigation route is determined based on the target navigation route set.

[0114] S110, when the target navigation route is empty, determine one of the multiple initial navigation routes as the target navigation route based on preset rules.

[0115] S111 controls the vehicle to travel based on the target navigation route and generates safe driving guidance instructions.

[0116] S112, Adjust the corresponding controlled object based on the target control parameters, wherein the controlled object includes at least one of tire pressure, suspension height and thermal management function.

[0117] S113, Adjust the corresponding controlled object based on the target control parameters.

[0118] S114: Based on preset rules, determine one of the candidate navigation routes as the target navigation route, control the vehicle to drive based on the target navigation route, and generate safe driving guidance instructions.

[0119] In summary, the navigation route generation method according to the embodiments of this application obtains at least one initial navigation route, each initial navigation route including multiple road segments, acquires the vehicle's total load, ambient temperature, tire parameters, and road condition parameters for each road segment, determines the vehicle's comprehensive risk coefficient for each road segment based on the total load, ambient temperature, tire parameters, and road condition parameters, and determines the target navigation route based on the comprehensive risk coefficient of each road segment on the initial navigation route. Therefore, this method can significantly improve the overall safety of route planning and achieve proactive avoidance of driving risks.

[0120] Corresponding to the above embodiments, this application also proposes a vehicle.

[0121] like Figure 5 As shown, the vehicle 200 in this embodiment may include: a memory 210, a processor 220, and a program stored in the memory 210 and executable on the processor 220. When the processor 220 executes the program, it implements the above-described navigation route generation method.

[0122] The vehicle according to the embodiments of this application can significantly improve the overall safety of route planning by executing the above-described navigation route generation method, thereby achieving proactive avoidance of driving risks.

[0123] It should be noted that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0124] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0125] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0126] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0127] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0128] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for generating navigation routes, characterized in that, The method includes: Obtain at least one initial navigation route, each of which includes multiple road segments; Obtain the vehicle's total load, ambient temperature, tire parameters, and road condition parameters for each road segment; The comprehensive risk coefficient of the vehicle in each road segment is determined based on the vehicle load, the ambient temperature, the tire parameters, and the road condition parameters. The target navigation route is determined based on the comprehensive risk coefficient of each segment on the initial navigation route.

2. The navigation route generation method according to claim 1, characterized in that, The tire parameters include tire temperature, and the road condition parameters include road gradient and road surface undulation frequency. The determination of the vehicle's comprehensive risk coefficient across multiple road sections based on the vehicle load, ambient temperature, tire parameters, and road condition parameters includes: The load risk factor is determined based on the vehicle load and rated load. A temperature risk factor is determined based on the ambient temperature, the tire temperature, and the rate of temperature rise of the tire. Road condition risk factors are determined based on the road slope and the frequency of road surface undulations; The comprehensive risk coefficient is determined based on the product of the load risk factor, the temperature risk factor, and the road condition risk factor.

3. The navigation route generation method according to claim 2, characterized in that, The step of determining the target navigation route based on the comprehensive risk coefficient of each segment on the initial navigation route includes: If the comprehensive risk coefficient is greater than or equal to the first preset threshold, the initial navigation route corresponding to the road segment is removed to obtain a set of candidate navigation routes. The target navigation route is determined based on the set of candidate navigation routes.

4. The navigation route generation method according to claim 3, characterized in that, The method further includes: If the comprehensive risk coefficient is greater than or equal to the second preset threshold and less than the first preset threshold, the load risk factor of each segment in each candidate navigation route in the navigation route set is obtained. The candidate navigation route set is corrected based on the load risk factor to obtain the target navigation route set; The target navigation route is determined based on the target navigation route set.

5. The navigation route generation method according to claim 4, characterized in that, The step of correcting the candidate navigation route set based on the load risk factor includes: If the load risk factor is greater than the preset load risk factor, the candidate navigation route corresponding to that road segment will be eliminated.

6. The navigation route generation method according to claim 5, characterized in that, The method further includes: If the target navigation route is empty, one of the multiple initial navigation routes is determined as the target navigation route based on preset rules.

7. The navigation route generation method according to claim 6, characterized in that, The method further includes: Before the vehicle travels based on the target navigation route and enters a risky road segment, target control parameters are determined based on the comprehensive risk coefficient. The risky road segment is the road segment corresponding to a comprehensive risk coefficient greater than or equal to a preset threshold. The target control parameters include at least one of the vehicle load, the ambient temperature, the tire parameters, and the road condition parameters. The corresponding controlled object is adjusted based on the target control parameters, wherein the controlled object includes at least one of tire pressure, suspension height, and thermal management function.

8. The navigation route generation method according to claim 2, characterized in that, Before determining the overall risk coefficient of a vehicle on each road segment, the method further includes: Conduct compliance verification, which includes overload compliance verification and tire safety status verification; If the compliance check fails, a corresponding warning message will be issued.

9. The navigation route generation method according to claim 1, characterized in that, The method further includes: Generate safe driving guidance instructions, which include safe speed suggestions, braking strategy suggestions, and risk warning prompts for each segment of the target navigation route. The risk warning prompts are provided to the user through voice broadcast and / or display on the central control screen.

10. A vehicle, characterized in that, include: The system includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the navigation route generation method according to any one of claims 1-9.

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