Vehicle safe driving method, control system and vehicle-mounted terminal

Abstract

The invention provides a vehicle safety driving method, a control system and a vehicle-mounted terminal. The vehicle safe driving method comprises the steps that the rainfall capacity of a target driving area is acquired to obtain a first safe speed, and primary safe lane prediction is carried out based on the rainfall capacity; performing secondary safe lane prediction based on the primary safe lane prediction result and the basic safety information of the vehicle in the target driving area, and obtaining a second safe speed according to the basic safety information of the vehicle; and based on the basic safety information of the vehicle, a target safety lane is obtained by combining the two safety lane prediction results, and a target safety speed is obtained by combining the first safety speed and the second safety speed, so that a safety driving strategy is formulated. According to the method, the calculation of the safe lane and the safe speed is more accurate through multi-source data fusion, the one-sidedness caused by a single data source is avoided, clearer and more accurate suggestions are provided for safe driving, and the driving safety of a driver under complex road conditions is effectively improved.

Description

Technical Field

[0001] The present application relates to the field of vehicle safety technology, and in particular to a vehicle safe driving method, a control system, and a vehicle-mounted terminal. Background Art

[0002] In daily traffic, wet roads and water accumulation have become one of the main potential threats to vehicle accidents. Current methods for dealing with road water accumulation have obvious limitations and generally only stay at the basic level of determining whether the road surface is flooded. However, such a simple judgment is of very limited practical help to drivers in ensuring driving safety. What drivers really need is more accurate and instructive information, such as which lane has relatively less water accumulation and is safer to drive, and what safe speed to drive at under different water accumulation conditions. However, the industry currently does not have a mature algorithm that can accurately guide and calculate water accumulation conditions, making it difficult to effectively improve driving safety for drivers in complex road conditions. Summary of the Invention

[0003] In order to solve the above technical problems, the present application provides a vehicle safe driving method, a control system and a vehicle-mounted terminal.

[0004] Specifically, the present application provides a method for safe vehicle driving, including: obtaining rainfall in a target driving area to obtain a first safe speed based on the rainfall, and performing a primary safe lane prediction based on the rainfall; performing a secondary safe lane prediction based on the primary safe lane prediction result and basic safety information of the vehicle in the target driving area, and obtaining a second safe speed based on the basic safety information of the vehicle; and, based on the basic safety information of the vehicle, obtaining a target safe lane in combination with the primary safe lane prediction result and the secondary safe lane prediction result, and obtaining a target safe speed in combination with the first safe speed and the second safe speed, so as to obtain and execute a safe driving strategy based on the target safe lane and the target safe speed.

[0005] In the above technical solution, different rainfall amounts have a direct impact on the slipperiness of the road surface and the depth of water accumulation; and the basic vehicle safety information includes at least real-time dynamic data such as the vehicle's position, speed, acceleration, etc. The fusion of multi-source data makes the calculation of safe lanes and safe speeds more accurate, avoiding the one-sidedness brought by a single data source, and thus providing clearer and more accurate suggestions for safe driving, effectively improving the driver's driving safety in complex road conditions.

[0006] Furthermore, obtaining the rainfall in the target driving area includes: judging in real time whether there is water accumulation in the target driving area through a water accumulation detection model; when the judgment result is that there is water accumulation, issuing a weather call instruction to obtain the rainfall in the target driving area from the cloud.

[0007] In the above technical solution, the water accumulation detection model is used to first determine whether there is water accumulation in the target driving area. Only when it is determined that there is water accumulation is a weather call instruction sent to the cloud to obtain the rainfall. This avoids unnecessary data requests when there is no water accumulation, reduces the occupation of cloud server resources, and reduces the network traffic consumption during data transmission, thereby improving the resource utilization efficiency of the overall system.

[0008] Furthermore, obtaining the first safe speed includes: obtaining a regional water film thickness based on the target driving area and the corresponding rainfall, and collecting vehicle parameters to calculate the first safe speed based on the vehicle parameters and the regional water film thickness through a modified LuGre friction model.

[0009] In the above technical solution, rainfall is a key factor affecting the formation of water film on the road surface. By accurately calculating the thickness of the water film, the actual condition of the road surface can be more scientifically reflected, providing a reliable basis for the subsequent calculation of the safe speed. The vehicle's own parameters have an important influence on the vehicle's driving performance on flooded roads. The modified LuGre friction model takes into account these factors and the influence of water film thickness on the friction between the tire and the road surface. It can more accurately simulate the driving mechanical characteristics of the vehicle on flooded roads, thereby calculating a safe speed that conforms to the actual situation.

[0010] Furthermore, the rainfall amount includes at least real-time rainfall intensity and rainfall duration; the acquisition of regional water film thickness includes: acquiring road parameters in real time based on the target driving area; wherein the road parameters include at least road slope, pavement material drainage coefficient and lateral curvature radius; acquiring the real-time drainage capacity of the road based on the road parameters, and calculating the initial water film thickness according to the real-time drainage capacity of the road, the real-time rainfall intensity and rainfall duration; and obtaining the final regional water film thickness based on the initial water film thickness, the preset Kalman gain and the actual water film thickness.

[0011] In the above technical solution, comprehensive consideration is given to various factors such as rainfall and road conditions, so that the results more accurately reflect the actual situation. At the same time, the drainage capacity is dynamically corrected, taking into account the effects of slope and curvature radius on drainage. The Kalman gain is combined to dynamically adjust the weights according to the actual situation, thereby improving the reliability of the results.

[0012] Furthermore, the single-safe lane prediction includes: predicting the water accumulation thickness based on the rainfall through a preset water accumulation impact model to obtain the lane with the smallest water accumulation thickness as the first safe lane, and outputting a single-safe lane prediction result based on the first safe lane and current lane information.

[0013] In the above technical solution, the lane with the smallest water thickness is directly determined as the first safe lane. Under flooded road conditions, the smaller the water thickness, the lower the probability of dangerous situations such as skidding and loss of control while the vehicle is driving. This screening method is clear in purpose and fast. The current lane information is taken into account when outputting a safe lane prediction result, so that the prediction result is more in line with the driver's actual situation.

[0014] Furthermore, the secondary safe lane prediction includes: correcting the primary safe lane prediction result through the water accumulation detection model to obtain a second safe lane; and obtaining basic vehicle safety information in the target driving area, and performing a safety analysis based on the basic vehicle safety information to determine whether an ESP signal exists. When an ESP signal exists, position matching is performed based on the current lane information and the corresponding target lane information to output a secondary safe lane prediction result.

[0015] In the above technical solution, the first safe lane prediction is to screen the lanes by predicting the thickness of water accumulation based on rainfall, and the water accumulation detection model can provide more direct and accurate feedback on the actual water accumulation situation. Through this correction, the inaccuracy in the first prediction caused by model error or other factors can be avoided, making the determination of the second safe lane more accurate; judging whether there is an ESP (Electronic Stability Program) signal is an important part of ensuring driving safety. The appearance of the ESP signal means that the vehicle may be experiencing an unstable driving state, such as skidding, side sliding, etc. By monitoring the ESP signal, the system can detect potential dangers in time and match the position according to the current lane information and the corresponding target lane information, providing the driver with safer lane suggestions, avoiding the vehicle from falling into further dangerous conditions, and reducing the probability of accidents.

[0016] Furthermore, the acquiring the second safe speed includes: based on the basic vehicle safety information, using the real-time vehicle speed when the ESP signal appears for the first time as the second safe speed.

[0017] In the above technical solution, ESP will be triggered when the vehicle is in an unstable driving state such as skidding or side sliding. The first time the ESP signal appears, it means that the vehicle has approached or reached the friction limit that the road surface can provide at the real-time speed under the current road conditions; using the speed at this time as the second safe speed can accurately reflect the limit of the vehicle's driving speed imposed by the actual road conditions in the current target driving area, and provide the driver with a speed reference that fits the actual road conditions.

[0018] Furthermore, obtaining the target safe lane and target safe speed includes: determining whether the ESP signals of each lane in the target driving area are the same based on the basic safety information of the vehicle; wherein, if the ESP signals of each lane are the same, determining whether the water thickness of each lane is the same based on the secondary safe lane prediction result; if they are the same, obtaining the target safe lane based on the primary safe lane prediction result, and using the first safe speed as the target safe speed; otherwise, obtaining the target safe lane based on the secondary safe lane prediction result, and using the first safe speed as the target safe speed; if the ESP signals of each lane are not exactly the same, obtaining the lane with the least ESP signal based on the secondary safe lane prediction result as the target safe lane, and using the second safe speed as the target safe speed.

[0019] This technical solution comprehensively considers multiple factors, including the ESP signal, primary and secondary safe lane predictions, and lane water depth, as part of the vehicle's basic safety information. Through a layered, step-by-step judgment logic, it comprehensively and meticulously analyzes the actual conditions of different lanes, ultimately selecting the most appropriate target safe lane and target safe speed for the vehicle.

[0020] Furthermore, based on the same concept, the present application also provides a vehicle control system, comprising:

[0021] The acquisition module is used to acquire a safe driving strategy using the vehicle safe driving method described above.

[0022] A control module is used to control the target vehicle to perform automatic driving based on the safe driving strategy, and / or control the multimedia system of the target vehicle to perform safe broadcasting based on the safe driving strategy.

[0023] In the above technical solution, the vehicle safe driving method comprehensively considers multiple factors that affect driving safety, such as rainfall, water depth, basic vehicle safety information, ESP signals, etc. Through precise analysis of multiple steps such as primary safe lane prediction and secondary safe lane prediction, it can tailor a safe driving plan for the vehicle to adapt to different road conditions and vehicle states, so that the vehicle can avoid dangerous areas during driving and travel at a safe and reasonable speed, fundamentally reducing the probability of accidents; in addition to realizing automatic driving control, the control module can also control the multimedia system of the target vehicle based on the safe driving strategy to perform safety broadcasts. The multimedia system can broadcast current safe driving information to passengers in the car in clear and easy-to-understand voice or text information, such as recommended driving lanes, safe speeds, road conditions ahead, etc. This allows passengers to understand the vehicle's driving conditions and potential risks in a timely manner, enhancing the passengers' sense of security and control over the journey.

[0024] Furthermore, based on the same concept, the present application also provides a vehicle-mounted terminal, which includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the above-mentioned vehicle safe driving method.

[0025] Compared with the prior art, the present invention has the following advantages:

[0026] This application first obtains the rainfall in the target driving area to obtain a first safe speed based on the rainfall, and performs a safe lane prediction based on the rainfall; then performs a secondary safe lane prediction based on the primary safe lane prediction result and the basic safety information of the vehicle in the target driving area, and obtains a second safe speed based on the basic safety information of the vehicle; further based on the basic safety information of the vehicle, the target safe lane is obtained by combining the primary safe lane prediction result and the secondary safe lane prediction result, and the target safe speed is obtained by combining the first safe speed and the second safe speed, so as to obtain a safe driving strategy based on the target safe lane and the target safe speed. This application makes the calculation of safe lanes and safe speeds more accurate through multi-source data fusion, avoids the one-sidedness brought by a single data source, and thus provides clearer and more accurate suggestions for safe driving, effectively improving the driving safety of drivers in complex road conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a flow chart of the vehicle safe driving method described in this application.

[0028] Figure 2 Schematic diagram of the simulation results of the preset water accumulation impact model described in this application.

[0029] Figure 3 This is a framework diagram of the vehicle control system described in this application. DETAILED DESCRIPTION

[0030] The following is a further detailed description of a vehicle safe driving method, control system and vehicle-mounted terminal of the present application in conjunction with specific embodiments and drawings.

[0031] For details, see Figure 1 , the present application provides a vehicle safe driving method, including the following steps S100-S300.

[0032] In a preferred embodiment, the water accumulation detection model of the roadside edge computing device is used to determine whether there is water accumulation in the target driving area. If there is water accumulation, the rainfall in the target driving area is obtained from the cloud, and then the regional water film thickness is calculated based on the target driving area and the corresponding rainfall. Further, based on the regional water film thickness and the collected vehicle parameters, the critical hydroplaning speed, that is, the first safe speed, is calculated by modifying the LuGre friction model; then, combined with the rainfall in the target driving area, the preset water accumulation impact model is obtained through simulation to realize the prediction of the flooded lane, and then the lane with the least water accumulation is obtained as the first safe lane, and a safe lane prediction result is obtained based on the current lane information and the first safe lane, which includes a preliminary safe driving strategy.

[0033] Furthermore, based on the first safe lane, the water accumulation detection model is called to determine whether the first safe lane is actually the lane with the least water accumulation, and then adjustments are made; and then basic safety information of vehicles in the target driving area is further obtained, so that when an ESP signal is present, the current lane where the target vehicle is located is matched with the target lane where the ESP signal is present to determine the lane to which it belongs, and the speed of the target vehicle when the first ESP signal appears is used as the second safe speed.

[0034] Furthermore, based on the above prediction results, the lane with the least ESP signal is taken as the target safe lane, and the speed when the ESP signal appears is taken as the target safe speed, and then a safe driving strategy is output.

[0035] Alternatively, when the ESP signals are the same, based on the secondary safe lane prediction results, the lane with the least water accumulation is obtained as the target safe lane, and the first safe speed obtained in the primary safe lane prediction is used as the target safe speed, and then a safe driving strategy is output.

[0036] Alternatively, when the ESP signals are the same and the amount of water in each lane in the secondary safe lane prediction results is basically the same, the first safe lane in the primary safe lane prediction results is obtained as the target safe lane, and the corresponding first safe speed is used as the target safe speed, and then a safe driving strategy is output.

[0037] Next, steps S100 to S300 are described in detail.

[0038] Step S100: Obtaining rainfall in a target driving area, obtaining a first safe speed according to the rainfall, and performing a safe lane prediction based on the rainfall.

[0039] Furthermore, obtaining the rainfall in the target driving area includes: judging in real time whether there is water accumulation in the target driving area through a water accumulation detection model; when the judgment result is that there is water accumulation, issuing a weather call instruction to obtain the rainfall in the target driving area from the cloud.

[0040] In some embodiments, a roadside Multi-access Edge Computing (MEC) device is deployed along the road. It possesses data processing and communication capabilities and is equipped with a visual waterlogging detection model. This device is responsible for real-time monitoring of road waterlogging and interacting with the cloud. The waterlogging detection model, installed on the MEC, captures road images via a camera and uses image processing and machine learning algorithms to determine whether waterlogging exists in the target driving area. The cloud device also stores and manages extensive weather data, providing a query interface for real-time rainfall information.

[0041] Specifically, the camera on the MEC collects images of the target driving area at a set interval (such as 5 seconds). The water accumulation detection model processes the collected images in real time and determines whether there is water accumulation in the area by analyzing the color, texture, reflection and other features in the image. For example, the water accumulation area usually appears as a darker color with obvious reflection in the image. If water accumulation is detected, the model outputs the judgment result as "water accumulation exists", otherwise it outputs "no water accumulation".

[0042] Furthermore, when the water accumulation detection model determines that there is water accumulation in the target driving area, MEC immediately generates a real-time weather call interface instruction (i.e., the weather call instruction) for the road test edge computing device. The instruction includes the geographic location information (such as longitude and latitude) of the target driving area so that the cloud can accurately provide weather data for the area. MEC sends the weather call instruction to the cloud device through a pre-established network connection.

[0043] Furthermore, after the cloud receives the weather call command sent by MEC, it parses the geographic location information in the command, queries the real-time rainfall data of the area from its database based on the geographic location information, organizes and formats the queried data, generates response data that complies with the interface specification, and returns the response data to MEC through the network.

[0044] Furthermore, obtaining the first safe speed includes: obtaining a regional water film thickness based on the target driving area and the corresponding rainfall, and collecting vehicle parameters to calculate the first safe speed based on the vehicle parameters and the regional water film thickness through a modified LuGre friction model.

[0045] The vehicle parameters include at least the vertical force on the tire, tire pressure, tire width, wheel footprint length, tire radius, wheel footprint equivalent radius and wheel footprint width.

[0046] Furthermore, the collected vehicle parameters and the calculated regional water film thickness are used as inputs of the modified LuGre friction model. The modified LuGre friction model is used in combination with factors such as the vehicle's braking performance and safe braking distance to calculate the first safe speed under current meteorological conditions and vehicle status, namely the critical hydroplaning speed.

[0047] In the above technical solution, rainfall is a key factor affecting the formation of water film on the road surface. By accurately calculating the thickness of the water film, the actual condition of the road surface can be more scientifically reflected, providing a reliable basis for the subsequent calculation of the safe speed. The vehicle's own parameters have an important influence on the vehicle's driving performance on flooded roads. The modified LuGre friction model takes into account these factors and the influence of water film thickness on the friction between the tire and the road surface. It can more accurately simulate the driving mechanical characteristics of the vehicle on flooded roads, thereby calculating a safe speed that conforms to the actual situation.

[0048] Furthermore, the rainfall includes at least real-time rainfall intensity R (mm / h) and rainfall duration T (min); the acquisition of regional water film thickness includes: acquiring road parameters in real time based on the target driving area; wherein the road parameters include at least road slope S (%), pavement material drainage coefficient C d (dimensionless, ranging from 0 to 1, 0.8 to 0.95 for asphalt pavement, and can be set by those skilled in the art according to actual application) and the lateral curvature radius R c (m); Obtain the real-time drainage capacity Q of the road based on the road parameters drain , and according to the real-time drainage capacity Q of the road drain , real-time rainfall intensity R and rainfall duration T are used to calculate the initial water film thickness h eq ; and, based on the initial water film thickness h eq , preset Kalman gain K and actual water film thickness h radar Obtain the final regional water film thickness.

[0049] In some embodiments, the road's real-time drainage capability Among them, the slope will accelerate drainage. The smaller the curvature radius, the sharper the bend and the lower the drainage efficiency. The improved Kinematic Wave Equation model is used to consider the balance between rainfall accumulation and drainage process, and the water film thickness that changes with time. Among them, α = 0.25, β = 0.1 are obtained by fitting historical waterlogging data, R(t) is the rainfall intensity that changes with time, and A is the roughness coefficient of the road per unit area.

[0050] In order to simplify the real-time calculation, the equivalent steady-state thickness model is introduced

[0051] Furthermore, the road test edge computing equipment monitors the water surface ripple characteristics in real time and inverts the actual water film thickness h radar , h calculated by the model is converted into eq With h radar Fusion, output the final regional water film thickness h final =K·h eq +(1-K)·h radar .

[0052] Among them, the preset Kalman gain K is dynamically adjusted according to the confidence of the road test edge computing device, such as reducing the weight when visibility is low on rainy days.

[0053] Taking the highway section as an example, assuming that the current real-time rainfall intensity R = 50 mm / h, rainfall duration T = 15 min, Cd = 0.9, S = 2%, Rc = 500; calculate h eq ≈2.3mm. Further, h radar =2.1mm, after Kalman filtering h final =2.2mm. After inputting this value into the modified LuGre friction model, the first safe speed of the current vehicle model is calculated, that is, the critical hydroplaning speed, such as 82km / h.

[0054] Furthermore, the single-safe lane prediction includes: predicting the water accumulation thickness based on the rainfall through a preset water accumulation impact model to obtain the lane with the smallest water accumulation thickness as the first safe lane, and outputting a single-safe lane prediction result based on the first safe lane and current lane information.

[0055] In some embodiments, MEC is used to interact with the cloud to obtain the current rainfall data of the target driving area; at the same time, a preset waterlogging impact model is obtained in advance through simulation, which reflects the distribution pattern of waterlogging thickness in each lane of the road under different rainfall intensities, such as Figure 2 As shown in the figure, assuming that the simulation results show that when the rainfall intensity is relatively low, the peak thickness of the water is near the right edge of the middle lane. As the rainfall intensity increases, the peak gradually moves to the left side of the middle lane. When the rainfall intensity reaches 5 mm per minute, the water is mainly distributed in the outer lane near the center line, and the water on the inner lane decreases with the increase of the distance from the middle of the road.

[0056] Using the above method, the acquired rainfall is input into the preset water accumulation impact model. The model will predict the water accumulation thickness of each lane in the target driving area based on the correspondence between different rainfall intensities and water accumulation distribution stored in itself. For example, if the current rainfall is small, the model will predict that the water accumulation near the right edge of the middle lane will be thicker, while the water accumulation in other parts will be relatively thinner according to the rules obtained from the previous simulation.

[0057] Furthermore, based on the water depth prediction results, the lane with the least water depth is identified and designated as the first safe lane. For example, if the prediction results show that the inner lane will have less water overall under the current rainfall, then the inner lane will be selected as the first safe lane.

[0058] Furthermore, the information of the first safe lane and the information of the lane in which the vehicle is currently located are combined to output a safe lane prediction result. If the vehicle is currently in the first safe lane, the prediction result can prompt the driver to continue driving in that lane. If the vehicle is not in the first safe lane, the prediction result can suggest the driver to change to the first safe lane under safe conditions.

[0059] In the above technical solution, the lane with the smallest water thickness is directly determined as the first safe lane. Under flooded road conditions, the smaller the water thickness, the lower the probability of dangerous situations such as skidding and loss of control while the vehicle is driving. This screening method is clear in purpose and fast. The current lane information is taken into account when outputting a safe lane prediction result, so that the prediction result is more in line with the driver's actual situation.

[0060] Step S200: performing a secondary safe lane prediction based on the primary safe lane prediction result and basic vehicle safety information in the target driving area, and obtaining a second safe speed according to the basic vehicle safety information.

[0061] Furthermore, the secondary safe lane prediction includes: correcting the primary safe lane prediction result through the water accumulation detection model to obtain a second safe lane; and obtaining basic vehicle safety information in the target driving area, and performing a safety analysis based on the basic vehicle safety information to determine whether an ESP signal exists. When an ESP signal exists, position matching is performed based on the current lane information and the corresponding target lane information to output a secondary safe lane prediction result.

[0062] In some embodiments, based on the results of a single safe lane prediction, the water accumulation detection model of the road test edge computing device is invoked to determine the actual water accumulation status of each lane. This is then used to determine whether the first safe lane is the lane with the least water accumulation. If not, the lane is corrected and the lane with the least water accumulation is selected as the second safe lane based on the actual water accumulation status. For example, in some driving areas, all three lanes are straight lanes, but two lanes are prone to water accumulation, while one lane is not. In this case, the judgment result is inconsistent with the result obtained by the preset water accumulation impact model, and the characteristics of the flooded lane need to be adjusted.

[0063] It should be noted that the "first" and "second" in the first safety lane and the second safety lane are only used to distinguish the two and do not imply a higher or lower level of safety.

[0064] Furthermore, the road test edge computing device communicates with vehicles in the target driving area to collect basic safety messages (BSM information) of the vehicles. This information includes dynamic data such as the vehicle's speed, acceleration, driving direction, steering angle, as well as static data such as the vehicle's size and type. The collected BSM information is analyzed, focusing on detecting whether there is an ESP signal. ESP is an active safety system used to improve vehicle driving stability. When the vehicle is in an unstable condition such as skidding or side sliding, the ESP system will be activated and send a corresponding signal. For example, if the vehicle's yaw angular velocity, lateral acceleration and other parameters are detected to be out of the normal range, and are accompanied by adjustment signals of the braking system or engine torque, it can be determined that an ESP signal exists, which means that the vehicle may currently be in an unstable driving state.

[0065] Furthermore, when an ESP signal is detected, the system obtains the current lane information (i.e., the lane the target vehicle is traveling in) and simultaneously determines the target lane information (i.e., the lane where the ESP signal is present). The current lane information is then matched with the target lane information to determine the vehicle's current lane and the surrounding lanes. For example, if an ESP signal is detected in the right lane and there are fewer vehicles in the left lane at a suitable distance, the prediction result can prompt the driver to change lanes left.

[0066] The vehicle's location information can be obtained through a vehicle-mounted global positioning system (GPS) device or a roadside high-precision positioning system.

[0067] In the above technical solution, the first safe lane prediction is to screen the lanes by predicting the thickness of water accumulation based on rainfall, and the water accumulation detection model can provide more direct and accurate feedback on the actual water accumulation situation. Through this correction, the inaccuracy in the first prediction due to model error or other factors can be avoided, making the determination of the second safe lane more accurate; judging whether there is an ESP (Electronic Stability Program) signal is an important part of ensuring driving safety. By monitoring the ESP signal, the system can detect potential dangers in a timely manner and match the position based on the current lane information and the corresponding target lane information, providing the driver with safer lane suggestions, avoiding the vehicle from falling into further dangerous conditions, and reducing the probability of accidents.

[0068] Furthermore, the acquiring the second safe speed includes: based on the basic vehicle safety information, using the real-time vehicle speed when the ESP signal appears for the first time as the second safe speed.

[0069] In the above technical solution, ESP will be triggered when the vehicle is in an unstable driving state such as skidding or side sliding. The first time the ESP signal appears, it means that the vehicle has approached or reached the friction limit that the road surface can provide at the real-time speed under the current road conditions; using the speed at this time as the second safe speed can accurately reflect the limit of the vehicle's driving speed imposed by the actual road conditions in the current target driving area, and provide the driver with a speed reference that fits the actual road conditions.

[0070] Step S300: Based on the basic safety information of the vehicle, a target safe lane is obtained by combining the first safe lane prediction result and the second safe lane prediction result. At the same time, a target safe speed is obtained by combining the first safe speed and the second safe speed, so as to obtain and execute a safe driving strategy according to the target safe lane and the target safe speed.

[0071] Furthermore, obtaining the target safe lane and the target safe speed includes: judging whether ESP signals of lanes in the target driving area are the same based on the basic vehicle safety information.

[0072] Among them, if the ESP signals of each lane are the same, the water thickness of each lane is determined to be the same based on the secondary safe lane prediction result. If they are the same, the target safe lane is obtained based on the primary safe lane prediction result, and the first safe speed is used as the target safe speed; otherwise, the target safe lane is obtained based on the secondary safe lane prediction result, and the first safe speed is used as the target safe speed.

[0073] In some embodiments, the ESP signals of each lane in the target driving area are analyzed and determined to determine whether the ESP signals of each lane are identical. If the ESP signals of each lane are identical, the water thickness of each lane is further determined based on the secondary safe lane prediction results. If the water thickness of each lane is identical, since the primary safe lane prediction selects the safe lane based on the water thickness, the target safe lane is determined based on the primary safe lane prediction results, and the first safe speed is used as the target safe speed. For example, if the primary safe lane prediction results indicate that lane 3 has the smallest water thickness and is the first safe lane, then lane 3 is designated as the target safe lane, and the first safe speed, such as 60 km / h, is used as the target safe speed.

[0074] If the depth of water accumulation in each lane varies, the secondary safe lane prediction result can more accurately reflect the actual safety status of each lane. Therefore, the lane with the least water accumulation based on the secondary safe lane prediction result is selected as the target safe lane, and the first safe speed is still used as the target safe speed. For example, if the secondary safe lane prediction result shows that lane 2 is relatively more conducive to safe driving due to water accumulation, lane 2 becomes the target safe lane, and the first safe speed of 60 km / h is still used as the target safe speed.

[0075] If the ESP signals of the lanes are not completely the same, the lane with the least ESP signal is obtained as the target safe lane based on the secondary safe lane prediction result, and the second safe speed is used as the target safe speed.

[0076] In some embodiments, when the ESP signals in each lane are not identical, a low ESP signal indicates greater vehicle stability and fewer safety hazards in that lane. Therefore, based on the secondary safe lane prediction results, the lane with the lowest ESP signal is selected as the target safe lane, and the second safe speed is used as the target safe speed. Assuming the secondary safe lane prediction finds that lane 1 has the lowest ESP signal, lane 1 is designated as the target safe lane, and the second safe speed, such as 50 km / h, is used as the target safe speed.

[0077] Furthermore, a safe driving strategy is formulated based on the determined target safe lane and target safe speed, combined with the results of the primary safe lane prediction or the secondary safe lane prediction. If the target safe lane is obtained based on the primary safe lane prediction and the target safe speed is 60 km / h, the safe driving strategy is the strategy obtained from the primary safe lane prediction. For example, based on the current lane information, the driver may be reminded to change lanes as quickly as possible to lane 2, corresponding to the primary safe lane, if safe, and adjust the speed to approximately 60 km / h while maintaining a safe distance and driving with caution. The reverse is also true.

[0078] This technical solution comprehensively considers multiple factors, including the ESP signal, primary and secondary safe lane predictions, and lane water depth, as part of the vehicle's basic safety information. Through a layered, step-by-step judgment logic, it comprehensively and meticulously analyzes the actual conditions of different lanes, ultimately selecting the most appropriate target safe lane and target safe speed for the vehicle.

[0079] Further, based on the same concept, see Figure 3 , the present application also provides a vehicle control system, comprising:

[0080] The acquisition module is used to acquire a safe driving strategy using the vehicle safe driving method described above.

[0081] A control module is used to control the target vehicle to perform automatic driving based on the safe driving strategy, and / or control the multimedia system of the target vehicle to perform safe broadcasting based on the safe driving strategy.

[0082] In some embodiments, the obtained safe driving strategy is assumed to be "There is water ahead, please change lanes to lane 2 corresponding to the first safe lane, and adjust the speed to about 60km / h, while maintaining a safe distance and driving carefully."

[0083] Furthermore, when the target vehicle supports the automatic driving function, the control module controls the vehicle's automatic driving system according to the safe driving strategy obtained by the acquisition module; the control module sends corresponding instructions to the vehicle's steering system, power system and braking system; among them, the steering system will accurately adjust the vehicle's driving direction so that the vehicle can change lanes smoothly to lane 2; the power system will adjust the engine's output power according to the target speed to maintain the vehicle at a speed of 60km / h; the braking system will perform braking operations when necessary to ensure the safety and stability of the vehicle's driving.

[0084] In other embodiments, the control module may also control the target vehicle's multimedia system to broadcast safety announcements based on the safe driving policy. For example, if the safe driving policy indicates that there is water on the road ahead and the vehicle needs to slow down and maintain a safe distance, the control module will send a command to the multimedia system, which will broadcast the relevant safety information to the driver via the vehicle's speakers. Simultaneously, the multimedia system may also display a text prompt on the vehicle's display screen, such as "Water on the road ahead, please slow down," to enhance the driver's attention and understanding of the safety information.

[0085] In the above technical solution, the vehicle safe driving method comprehensively considers multiple factors that affect driving safety, such as rainfall, water depth, basic vehicle safety information, ESP signals, etc. Through precise analysis of multiple steps such as primary safe lane prediction and secondary safe lane prediction, it can tailor a safe driving plan for the vehicle to adapt to different road conditions and vehicle states, so that the vehicle can avoid dangerous areas during driving and travel at a safe and reasonable speed, fundamentally reducing the probability of accidents; in addition to realizing automatic driving control, the control module can also control the multimedia system of the target vehicle based on the safe driving strategy to perform safety broadcasts. The multimedia system can broadcast current safe driving information to passengers in the car in clear and easy-to-understand voice or text information, such as recommended driving lanes, safe speeds, road conditions ahead, etc. This allows passengers to understand the vehicle's driving conditions and potential risks in a timely manner, enhancing the passengers' sense of security and control over the journey.

[0086] Furthermore, based on the same concept, the present application also provides a vehicle-mounted terminal, which includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the above-mentioned vehicle safe driving method.

[0087] In some embodiments, the memory and processor are interconnected via a bus; the processor may be one or more CPUs. When the processor is a CPU, the CPU may be a single-core CPU or a multi-core CPU. The processor is used to control various functional modules of the vehicle terminal and process signals.

[0088] The memory includes but is not limited to RAM (Random Access Memory), ROM (Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), and CD-ROM (Compact Disc Read-Only Memory). The memory is used to store computer programs, operating systems, various applications and data, such as storing computer programs for implementing the vehicle safe driving method.

[0089] In summary, the present application provides a vehicle safe driving method, control system and vehicle-mounted terminal; first, the rainfall in the target driving area is obtained to obtain a first safe speed based on the rainfall, and a safe lane prediction is performed based on the rainfall; then, a secondary safe lane prediction is performed based on the primary safe lane prediction result and the basic safety information of the vehicle in the target driving area, and a second safe speed is obtained based on the basic safety information of the vehicle; further, based on the basic safety information of the vehicle, the target safe lane is obtained by combining the primary safe lane prediction result and the secondary safe lane prediction result, and the target safe speed is obtained by combining the first safe speed and the second safe speed, so as to obtain a safe driving strategy based on the target safe lane and the target safe speed. The present application makes the calculation of safe lanes and safe speeds more accurate through multi-source data fusion, avoids the one-sidedness brought by a single data source, and thus provides clearer and more accurate suggestions for safe driving, effectively improving the driving safety of drivers in complex road conditions.

[0090] Although example embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above example embodiments are merely illustrative and are not intended to limit the scope of the present application. Various changes and modifications may be made therein by those skilled in the art without departing from the scope and spirit of the present application. All such changes and modifications are intended to be included within the scope of the present application as required by the appended claims.

[0091] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply the existence of any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or device comprising the element.

[0092] Although the present application is described in conjunction with the above specific embodiments, it is obvious that those skilled in the art can make many substitutions, modifications and variations based on the above content. Therefore, all such substitutions, improvements and variations are included in the spirit and scope of the appended claims.

Claims

1. A vehicle safe driving method, characterized in that: The following steps are involved: Obtaining rainfall in a target driving area, obtaining a first safe speed according to the rainfall, and performing a safe lane prediction based on the rainfall; Performing a secondary safe lane prediction based on the primary safe lane prediction result and basic safety information of the vehicle in the target driving area, and obtaining a second safe speed based on the basic safety information of the vehicle; Furthermore, based on the basic safety information of the vehicle, a target safe lane is obtained by combining the first safe lane prediction result and the second safe lane prediction result, and a target safe speed is obtained by combining the first safe speed and the second safe speed, so as to obtain and execute a safe driving strategy according to the target safe lane and the target safe speed.

2. The vehicle safe driving method according to claim 1, characterized in that: The obtaining of the rainfall in the target driving area includes: The water accumulation detection model is used to determine in real time whether there is water accumulation in the target driving area. When the judgment result is that there is water accumulation, a weather call instruction is issued to obtain the rainfall in the target driving area from the cloud.

3. The vehicle safe driving method according to claim 2, characterized in that: The obtaining of the first safe speed includes: obtaining a regional water film thickness based on the target driving area and the corresponding rainfall, and collecting vehicle parameters to calculate the first safe speed based on the vehicle parameters and the regional water film thickness using a modified LuGre friction model.

4. The vehicle safe driving method according to claim 3, characterized in that: The rainfall amount at least includes real-time rainfall intensity and rainfall duration; The obtaining of the regional water film thickness includes: Acquiring road parameters in real time based on the target driving area; wherein the road parameters include at least road slope, pavement material drainage coefficient, and lateral curvature radius; Acquiring a real-time drainage capacity of the road based on the road parameters, and calculating an initial water film thickness according to the real-time drainage capacity of the road, the real-time rainfall intensity, and the rainfall duration; The final regional water film thickness is obtained based on the initial water film thickness, the preset Kalman gain and the actual water film thickness.

5. The vehicle safe driving method according to claim 3, characterized in that: The primary safe lane prediction includes: predicting the water accumulation thickness based on the rainfall through a preset water accumulation impact model to obtain the lane with the smallest water accumulation thickness as the first safe lane, and outputting a primary safe lane prediction result based on the first safe lane and current lane information.

6. The vehicle safe driving method according to claim 5, characterized in that: The secondary safe lane prediction includes: correcting the first safe lane prediction result using the water accumulation detection model to obtain a second safe lane; Obtain basic vehicle safety information in the target driving area and perform a safety analysis based on the basic vehicle safety information to determine whether an ESP signal exists. If an ESP signal exists, position matching is performed based on the current lane information and the corresponding target lane information to output a secondary safe lane prediction result.

7. The vehicle safe driving method according to claim 6, characterized in that: The acquiring the second safe speed includes: based on the basic vehicle safety information, taking the real-time vehicle speed when the ESP signal appears for the first time as the second safe speed.

8. The vehicle safe driving method according to claim 7, characterized in that: The obtaining of the target safe lane and target safe speed includes: determining whether the ESP signals of each lane in the target driving area are the same based on the basic vehicle safety information; If the ESP signals of each lane are the same, the second safe lane prediction result is used to determine whether the water depth in each lane is the same. If so, the target safe lane is determined based on the first safe lane prediction result, and the first safe speed is used as the target safe speed. Otherwise, the target safe lane is determined based on the second safe lane prediction result, and the first safe speed is used as the target safe speed. If the ESP signals of the lanes are not completely the same, the lane with the least ESP signal is obtained as the target safe lane based on the secondary safe lane prediction result, and the second safe speed is used as the target safe speed.

9. A vehicle control system, characterized in that: include: an acquisition module, configured to acquire a safe driving strategy using the vehicle safe driving method according to any one of claims 1 to 8; A control module is used to control the target vehicle to perform automatic driving based on the safe driving strategy, and / or control the multimedia system of the target vehicle to perform safe broadcasting based on the safe driving strategy.

10. A vehicle-mounted terminal, characterized in that: The vehicle-mounted terminal includes a processor and a memory, and the memory stores at least one instruction, at least one program, a code set or an instruction set. The at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the vehicle safe driving method as described in any one of claims 1 to 8.

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