Vehicle automatic driving control method, device and equipment integrating watering data

Abstract

The present application provides a vehicle automatic driving control method, device and equipment integrating watering data, which relates to the field of automatic driving technology. The method includes: obtaining watering information, weather information and road information of the target road; calculating the watering amount slippery index of the target road according to the watering information; calculating the temperature and humidity slippery index of the target road according to the weather information; calculating the friction slippery index of the target road according to the road information; calculating the target slippery index of the target road by combining the watering amount slippery index, the temperature and humidity slippery index and the friction slippery index; generating and sending a control instruction to the automatic driving vehicle according to the target slippery index, so that the automatic driving vehicle adjusts the driving strategy according to the control instruction. The technical effect of the present application is: accurately assessing the slippery conditions of the road, so that the automatic driving vehicle can accurately make appropriate driving decisions.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a method, device and equipment for controlling an autonomous driving vehicle that integrates watering data. Background Art

[0002] With the rapid development of autonomous driving technology in mines, how to improve the safety of autonomous driving vehicles in complex mining road conditions has become an important technical issue. The mining environment has its own unique challenges, such as frequent road watering to suppress dust, complex and changeable weather conditions, and significant differences in road conditions in different regions. Especially after watering and dust reduction on mining roads, traditional autonomous driving systems are often unable to accurately assess the slipperiness of the road surface, thus affecting the driving safety and operating efficiency of mining vehicles.

[0003] Existing technologies usually use single sensor data, such as temperature and humidity sensors, to assess road slippery conditions. Although this method can assess road slippery conditions to a certain extent, due to the single or limited data source, it is difficult to accurately assess road slippery conditions using the above method, resulting in the inability of autonomous vehicles to accurately make appropriate driving decisions. Summary of the invention

[0004] The present application provides a vehicle automatic driving control method, device and equipment integrating watering data, which are used to accurately assess the slippery road conditions and enable the automatic driving vehicle to accurately make appropriate driving decisions.

[0005] In the first aspect, the present application provides a vehicle automatic driving control method that integrates watering data, the method comprising: obtaining watering information, weather information and road information of a target road; calculating a watering amount slippery index of the target road based on the watering information; calculating a temperature, humidity and slippery index of the target road based on the weather information; calculating a friction and slippery index of the target road based on the road information; calculating a target slippery index of the target road by combining the watering amount slippery index, the temperature, humidity and slippery index and the friction and slippery index; generating and sending a control instruction to the automatic driving vehicle based on the target slippery index, so that the automatic driving vehicle adjusts its driving strategy according to the control instruction.

[0006] By adopting the above technical solution, by obtaining multi-source data, including watering information, weather information and road information, a comprehensive data basis is provided for the subsequent slippery degree assessment. Secondly, this method calculates the slippery index of watering volume, the slippery index of temperature and humidity, and the slippery index of friction, respectively, and fully considers the various key factors affecting the slippery degree of the road. This multi-angle analysis method makes the assessment of the slippery degree of the road more comprehensive and accurate. By combining these three indexes to calculate the target slippery index, a comprehensive balance of multiple influencing factors is achieved, thereby accurately assessing the slippery conditions of the road and enabling the autonomous driving vehicle to accurately make appropriate driving decisions.

[0007] Optionally, the watering information includes the watering time, the watering amount and the watering area, and the calculating of the watering amount slippery index of the target road according to the watering information includes: calculating the difference between the watering time and the current time to obtain the watering duration; substituting the watering duration, the watering amount and the watering area into a first calculation formula to calculate the watering amount slippery index of the target road; wherein the first calculation formula is:

[0008] ;

[0009] In the formula, is the slippery index of watering volume, and is the weight coefficient, and To adjust the parameters, is the watering duration, is the watering amount, is the watering area, is the total area of ​​the target road.

[0010] By adopting the above technical solution, the solution comprehensively captures the impact of artificial watering on the slipperiness of the road by considering the three key parameters of watering time, watering amount and watering area. In particular, the watering duration is obtained by calculating the difference between the watering time and the current time, which effectively takes the time factor into consideration and reflects the dynamic change characteristics of the slipperiness of the road over time. By introducing detailed watering information and precise calculation formulas, the calculation accuracy of the watering amount slipperiness index is significantly improved, thereby further enhancing the reliability and adaptability of the entire autonomous driving control system.

[0011] Optionally, the weather information includes temperature and humidity, and calculating the temperature, humidity and slipperiness index of the target road according to the weather information includes: substituting the temperature and humidity into a second calculation formula to calculate the temperature, humidity and slipperiness index of the target road; wherein the second calculation formula is:

[0012] ;

[0013] In the formula, is the temperature, humidity and slipperiness index, , and is the adjustment factor, and is the adjustment factor, is the temperature, is the critical temperature, is the humidity.

[0014] By adopting the above technical solution, by introducing temperature and humidity information and using a refined calculation formula, the calculation accuracy of the temperature, humidity and slipperiness index is greatly improved, thereby further enhancing the adaptability of the autonomous driving control system to complex weather conditions. This technology based on detailed weather information and precise calculation methods greatly improves the adaptability of the autonomous driving system to complex weather conditions. It not only enhances the performance of the system in various climate environments, but also provides a reliable technical foundation for handling more complex weather conditions. The application of this method will significantly improve the safety and reliability of autonomous driving vehicles in various weather conditions.

[0015] Optionally, calculating the friction and slippery index of the target road based on the road information includes: determining the road surface friction of the target road based on the road information; and arithmetically multiplying the road surface friction by a preset parameter to obtain the friction and slippery index of the target road.

[0016] By adopting the above technical solution and introducing the calculation of friction slip index based on road information, the solution greatly improves the adaptability of the autonomous driving system to different types and conditions of roads. It not only enhances the performance of the system under complex road conditions, but also provides a reliable technical basis for handling more diverse road environments. This method significantly improves the safety and reliability of autonomous driving vehicles under various road conditions and promotes the development of autonomous driving technology to a higher level. Especially when facing complex and changing road networks, this method can help the autonomous driving system better cope with the differences between different road sections and achieve a smoother and safer driving experience.

[0017] Optionally, the target slippery index of the target road is calculated by combining the watering amount slippery index, the temperature and humidity slippery index and the friction slippery index, including: arithmetically multiplying the watering amount slippery index by a first weight coefficient to obtain a first index; arithmetically multiplying the temperature and humidity slippery index by a second weight coefficient to obtain a second index; arithmetically multiplying the friction slippery index by a third weight coefficient to obtain a third index; and arithmetically adding the first index, the second index and the third index to obtain the target slippery index of the target road.

[0018] By adopting the above technical solution, the three key indicators of watering slippery index, temperature and humidity slippery index and friction slippery index are cleverly integrated in a weighted summation manner, laying the foundation for deriving a comprehensive target slippery index. Through this comprehensive calculation method of weighted summation, the scheme greatly improves the accuracy of the autonomous driving system's assessment of the slipperiness of the road. It not only enhances the performance of the system in various complex environments, but also provides a reliable technical foundation for coping with more diverse road and weather conditions. The application of this method will significantly improve the safety and reliability of autonomous driving vehicles in various situations and promote the development of autonomous driving technology to a higher level. Especially in the face of complex and changing driving environments, this comprehensive evaluation method can help the autonomous driving system better weigh various factors and make more intelligent and safe driving decisions.

[0019] Optionally, based on the target slippery index, generating and sending a control instruction to the autonomous driving vehicle so that the autonomous driving vehicle adjusts its driving strategy according to the control instruction, includes: if the target slippery index is greater than a first set index, generating and sending a first control instruction to the autonomous driving vehicle so that the autonomous driving vehicle adjusts the vehicle according to the first control instruction; if the target slippery index is greater than a second set index but not greater than the set index, generating and sending a second control instruction to the autonomous driving vehicle so that the autonomous driving vehicle decelerates according to the second control instruction; if the target slippery index is not greater than the second set index, generating and sending a third control instruction to the autonomous driving vehicle so that the autonomous driving vehicle drives normally according to the third control instruction.

[0020] By adopting the above technical solution and introducing a hierarchical control strategy, the intelligent response of the autonomous driving vehicle to different degrees of slippery road is realized, which significantly improves the safety and adaptability of the system. The solution sets two key thresholds: the first set index and the second set index, and divides the target slippery index into three levels, corresponding to three different driving strategies. When the target slippery index exceeds the first set index, the system generates a first control instruction to instruct the vehicle to detour and effectively avoid high-risk sections. When the target slippery index is between the two set indexes, the system sends a second control instruction to require the vehicle to slow down, which improves safety in moderately slippery road conditions. When the target slippery index is lower than the second set index, the system allows the vehicle to drive normally, ensuring driving efficiency. The main technical effects of this hierarchical control strategy include improving the ability of the autonomous driving system to accurately respond to different degrees of slippery road conditions, and significantly enhancing driving safety through detours, deceleration and other measures.

[0021] Optionally, after generating and sending a control instruction to the autonomous driving vehicle according to the target slippery index, the method further includes: obtaining real-time driving information of the autonomous driving vehicle; judging, based on the real-time driving information, whether the actual driving state of the autonomous driving vehicle conforms to a preset driving state corresponding to the control instruction; if the actual driving state does not conform to the preset driving state, calculating a deviation value between the actual driving state and the preset driving state; generating a correction instruction based on the deviation value, and sending the correction instruction to the autonomous driving vehicle, so that the autonomous driving vehicle corrects the driving strategy according to the correction instruction until the actual driving state conforms to the preset driving state.

[0022] By adopting the above technical solution, a dynamic feedback correction mechanism is introduced, which significantly improves the precise control capability and adaptability of the autonomous driving system. First, by obtaining the real-time driving information of the autonomous driving vehicle, the system can monitor the actual driving state of the vehicle in real time. Then, the system compares the actual driving state with the preset driving state corresponding to the control instruction to determine whether the two are consistent. When it is found that the actual state does not match the preset state, the system calculates the deviation value between the two and generates a correction instruction based on this deviation value. This correction instruction is then sent to the autonomous driving vehicle to guide the vehicle to adjust its driving strategy in real time. This process will continue until the actual driving state matches the preset state. This dynamic correction mechanism improves the adaptability of the autonomous driving system to complex road conditions, enhances the stability and safety of vehicle driving, achieves rapid response to environmental changes, improves control accuracy, and provides the system with the ability to continuously optimize.

[0023] In a second aspect, the present application provides a vehicle automatic driving control device that integrates watering data, and the device includes: an acquisition module, a first calculation module, a second calculation module, a third calculation module, a combination module and a generation module; wherein the acquisition module is used to acquire the watering information, weather information and speed limit information of the target road; the first calculation module is used to calculate the watering amount and slippery index of the target road according to the watering information; the second calculation module is used to calculate the temperature, humidity and slippery index of the target road according to the weather information; the third calculation module is used to calculate the friction and slippery index of the target road according to the speed limit information; the combination module is used to calculate the target slippery index of the target road by combining the watering amount and slippery index, the temperature, humidity and slippery index and the friction and slippery index; the generation module is used to generate and send control instructions to the automatic driving vehicle according to the target slippery index, so that the automatic driving vehicle adjusts its driving strategy according to the control instructions.

[0024] In the third aspect, the present application provides an electronic device, adopting the following technical solution: including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes a computer program such as any one of the above-mentioned vehicle automatic driving control methods integrating watering data.

[0025] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: a computer program is stored which can be loaded by a processor and execute any of the above-mentioned vehicle automatic driving control methods integrating watering data.

[0026] In summary, the present application includes at least one of the following beneficial technical effects:

[0027] By acquiring multi-source data, including watering information, weather information, and road information, a comprehensive data foundation is provided for the subsequent slippery degree assessment. Secondly, the method calculates the slippery index of watering volume, the slippery index of temperature and humidity, and the slippery index of friction, respectively, fully considering the various key factors affecting the slippery degree of the road. This multi-angle analysis method makes the assessment of the slippery degree of the road more comprehensive and accurate. By combining these three indexes to calculate the target slippery index, a comprehensive balance of multiple influencing factors is achieved, thereby accurately assessing the slippery conditions of the road and enabling the autonomous driving vehicle to accurately make appropriate driving decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of a vehicle automatic driving control method integrating watering data provided in an embodiment of the present application;

[0029] Figure 2 It is a structural schematic diagram of a vehicle automatic driving control device integrating watering data provided in an embodiment of the present application;

[0030] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application.

[0031] Description of reference numerals: 1000, electronic device; 1001, processor; 1002, communication bus; 1003, user interface; 1004, network interface; 1005, memory. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0033] In the description of the embodiments of the present application, words such as "illustrative", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "illustrative", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "illustrative", "for example" or "for example" is intended to present related concepts in a concrete way.

[0034] Figure 1 is a flow chart of a vehicle automatic driving control method integrating watering data provided in an embodiment of the present application, such as Figure 1 As shown, the method includes S101-S106.

[0035] S101, obtaining watering information, weather information and road information of a target road.

[0036] In one example, a real-time communication interface is established with a city management system or a sprinkler dispatching system. This can be achieved through an API connection to obtain the sprinkler information of the target road. Environmental sensing equipment such as high-definition cameras, millimeter-wave radars, etc. can also be deployed to monitor the operation of sprinkler trucks; and combined with on-board sensors to detect the waterlogging on the road and estimate the sprinkler information. The sprinkler information may include detailed data such as sprinkler time, sprinkler volume, and sprinkler area.

[0037] Weather information includes temperature and humidity. Relevant data can be directly collected through the on-board weather sensors equipped on autonomous vehicles; or a connection can be established with a third-party meteorological data service provider to obtain real-time weather parameters in the target road area. Special weather monitoring equipment such as thermometers and hygrometers can also be deployed on both sides of the road to collect local micro-meteorological data. Multi-source data such as satellite remote sensing and numerical weather forecast model outputs can also be integrated for comprehensive acquisition.

[0038] Road information reflects the material and status of the target road, and can be obtained by real-time scanning and detection of the road using on-board laser radar and millimeter-wave radar. It can also analyze road images taken by on-board cameras to identify road types and status. It can also be connected to the city's intelligent road system to obtain recorded basic road data. Or it can use on-board sensor fusion calculations to estimate road information such as the friction coefficient of the target road.

[0039] S102, calculating the watering amount and slippery index of the target road according to the watering information.

[0040] In one example, calculating the watering amount and slippery index of the target road based on the watering information is one of the key steps in evaluating the slipperiness of the road. As a human factor, watering directly affects the slipperiness of the road surface, so accurate calculation of the watering amount and slippery index is crucial for the subsequent adjustment of the driving strategy of the autonomous vehicle.

[0041] In the specific implementation, the watering information of the target road is first obtained, including the watering time, watering amount and watering area. The watering duration is obtained by calculating the difference between the watering time and the current time. This step takes into account the influence of time factors on the slipperiness of the road, because as time goes by, the moisture on the road surface will gradually evaporate or penetrate, and the slipperiness will decrease accordingly.

[0042] Substitute the watering duration, watering amount and watering area into the first calculation formula to calculate the watering amount slippery index of the target road; wherein the first calculation formula is: ;

[0043] In the formula, is the slippery index of watering volume, and is the weight coefficient, and To adjust the parameters, For the duration of watering, is the amount of watering, For the sprinkler area, is the total area of ​​the target road.

[0044] The formula consists of two parts, the first part is: ;

[0045] Used to adjust the time and watering amount. To adjust the parameters, used to control the rate of time decay, is the watering duration, that is, the time difference from the watering time to the current time, Used to indicate the decay of slippery degree over time. The value of will gradually decrease; middle, It is to adjust the parameters to control the influence of the amount of water spraying on the degree of slipperiness. When the amount of water sprayed is higher, The larger the value is, the greater the corresponding watering amount and slippery index is.

[0046] The second part is: ;

[0047] Used to adjust the impact of the sprinkler area ratio. This ratio reflects the proportion of the area covered by sprinklers to the entire road.

[0048] The formula takes into account the three main factors of time, watering amount and watering area, which are the key factors affecting the slipperiness of the road. By using exponential and power functions, the formula reflects the nonlinear relationship between these factors and the slipperiness. , , α and β can be optimized according to different road conditions, climate characteristics and other factors to make it more suitable for specific application scenarios. The structure of the formula allows other influencing factors to be added when needed, and has good scalability. Through this formula, multiple qualitative factors can be converted into a quantitative slippery index, providing an objective basis for autonomous driving decisions.

[0049] S103, calculating the temperature, humidity and slipperiness index of the target road according to the weather information.

[0050] In one example, calculating the temperature, humidity and slipperiness index of the target road based on weather information is another key step in assessing the slipperiness of the road. Temperature and humidity, as important environmental factors, directly affect the moisture state and slipperiness of the road surface. Therefore, accurately calculating the temperature, humidity and slipperiness index is crucial for comprehensively assessing road conditions and optimizing the driving strategy of autonomous vehicles.

[0051] In the specific implementation, the weather information of the target road is first obtained, mainly including temperature and humidity data. This data can be obtained in real time through meteorological monitoring equipment on the road, or obtained from the meteorological department. Changes in temperature and humidity will directly affect the evaporation rate and condensation of water on the road surface, and then affect the slipperiness of the road. For example, under high temperature and low humidity conditions, the water on the road evaporates faster and the slipperiness may be lower; while under low temperature and high humidity conditions, the water on the road evaporates slowly, and condensation may even occur, resulting in an increase in slipperiness.

[0052] Substitute the temperature and humidity into the second calculation formula to calculate the temperature, humidity and slipperiness index of the target road; wherein the second calculation formula is: ;

[0053] In the formula, is the temperature, humidity and slipperiness index, , and is the adjustment factor, and is the adjustment factor, is the temperature, is the critical temperature, For humidity.

[0054] This formula takes into account the complex effects of temperature and humidity on road slipperiness. It is an S-shaped function used to simulate the nonlinear effect of temperature on slipperiness, especially when approaching a critical temperature. Rapid changes in time. The term reflects the direct impact of humidity on the degree of slipperiness. The higher the humidity, the greater the value of this term. and The term further adjusts the linear and nonlinear effects of humidity and temperature. , and , γ and δ and other parameters can be used to optimize the calculation results according to the climate characteristics and road properties of different regions.

[0055] The advantage of this calculation method is that it can dynamically reflect the impact of temperature and humidity changes on road slipperiness. For example, when the temperature is close to 0°C, even if the humidity is not high, the slipperiness may increase sharply due to ice; while under high temperature conditions, even if the humidity is high, the road surface may not be particularly slippery. This refined calculation provides more comprehensive and accurate basic data for subsequent driving strategy adjustments.

[0056] S104: Calculate the friction slip index of the target road according to the road information.

[0057] In one example, calculating the friction slippery index of the target road based on road information is the third key step in assessing the slipperiness of the road. The inherent characteristics of the road, especially the road friction, are fundamental factors affecting vehicle driving safety, so incorporating it into the calculation of the slippery index is crucial for comprehensively assessing road conditions and optimizing the driving strategy of autonomous vehicles.

[0058] When implementing this, we first need to obtain road information of the target road. This information may include road material, road texture, road age, maintenance status, and other aspects. These factors together determine the basic friction characteristics of the road. For example, newly laid asphalt roads usually have good friction, while old or severely worn roads may have low friction; similarly, roads with special anti-skid designs will have higher friction than ordinary roads.

[0059] Next, based on the acquired road information, the road friction of the target road is determined through a pre-established database. This friction can be expressed as a friction coefficient, which reflects the basic friction characteristics of the road surface in a dry state. The friction coefficient is usually a value between 0 and 1, and the larger the value, the stronger the friction.

[0060] Then, the determined road friction is arithmetically multiplied by the preset parameter to obtain the friction slip index of the target road. This preset parameter can be understood as an adjustment coefficient, which is used to convert the friction into a numerical range comparable to other slip indexes (such as the water sprinkling slip index and the temperature and humidity slip index). The specific calculation formula can be expressed as: F=k×μ, where F represents the friction slip index, k is the preset parameter (adjustment coefficient), and μ is the road friction (friction coefficient).

[0061] The advantage of this calculation method is that it can incorporate the inherent characteristics of the road into the assessment of the slippery degree. For example, even under the same watering conditions and weather conditions, a road surface with higher friction may be safer than a road surface with lower friction. By introducing the friction slippery index, the autonomous driving system can more comprehensively evaluate the road conditions and make more accurate judgments.

[0062] Calculating the friction slippery index in this way can provide important basic data for the subsequent calculation of the comprehensive slippery index. This not only improves the accuracy of the slippery degree assessment, but also enables the autonomous driving system to better adapt to different types of roads. For example, on roads with low friction, even if the weather conditions are good, the system may choose to reduce the speed or increase the distance between vehicles; while on roads with good anti-skid performance, the system may allow a higher driving speed to be maintained in wet conditions.

[0063] S105, calculating a target slippery index of the target road by combining the watering amount slippery index, the temperature and humidity slippery index, and the friction slippery index.

[0064] In one example, the core step of the entire method is to calculate the target slippery index of the target road by combining the watering slippery index, the temperature and humidity slippery index, and the friction slippery index. Multiple key factors that affect the slipperiness of the road are considered comprehensively to obtain a comprehensive and accurate evaluation result. This comprehensive evaluation is crucial for autonomous vehicles to make safe and efficient driving decisions.

[0065] In the specific implementation, it is first necessary to obtain the watering slippery index, temperature and humidity slippery index, and friction slippery index calculated in the previous steps. These three indexes reflect the impact of artificial watering, weather conditions, and road characteristics on the slippery degree. Next, the weighted summation method is used to calculate the target slippery index, which can flexibly adjust the relative importance of each factor.

[0066] The calculation process is as follows: First, multiply the watering slippery index by the first weight coefficient to obtain the first index. Then, multiply the temperature and humidity slippery index by the second weight coefficient to obtain the second index. Next, multiply the friction slippery index by the third weight coefficient to obtain the third index. Finally, add these three indexes to obtain the target slippery index of the target road.

[0067] Based on the above embodiment, as an optional implementation, in S105, combining the watering amount slippery index, the temperature and humidity slippery index, and the friction slippery index, calculating the target slippery index of the target road specifically includes:

[0068] The first index is obtained by arithmetically multiplying the watering amount slippery index by the first weight coefficient; the second index is obtained by arithmetically multiplying the temperature and humidity slippery index by the second weight coefficient; the third index is obtained by arithmetically multiplying the friction slippery index by the third weight coefficient; the first index, the second index and the third index are arithmetically added to obtain the target slippery index of the target road.

[0069] In one example, it is first necessary to obtain the watering slippery index, temperature and humidity slippery index, and friction slippery index calculated in the previous steps. Next, three weight coefficients are introduced: the first weight coefficient, the second weight coefficient, and the third weight coefficient, which correspond to the watering slippery index, temperature and humidity slippery index, and friction slippery index, respectively. The setting of these weight coefficients can be adjusted according to specific geographical locations, seasonal changes, road characteristics, and other factors to adapt to different environmental conditions.

[0070] The calculation process is as follows: First, the watering slippery index is arithmetically multiplied by the first weight coefficient to obtain the first index. This step takes into account the impact of artificial watering on the slippery degree of the road. Then, the temperature and humidity slippery index is arithmetically multiplied by the second weight coefficient to obtain the second index, which reflects the impact of current weather conditions on the slippery degree of the road. Next, the friction slippery index is arithmetically multiplied by the third weight coefficient to obtain the third index, which takes into account the impact of the road's own characteristics on the slippery degree. Finally, these three indices are arithmetically added to obtain the target slippery index of the target road.

[0071] The advantage of this calculation method is that it can flexibly adjust the weight of each factor. For example, in arid areas, the weight of the slippery index of watering volume may be increased; in areas with changeable climates, the weight of the slippery index of temperature and humidity may be increased; and in areas with complex road conditions, the weight of the slippery index of friction may be increased. By adjusting these weight coefficients, the target slippery index can be better adapted to the characteristics of different regions and environments.

[0072] S106: Generate and send a control instruction to the autonomous driving vehicle based on the target slippery index, so that the autonomous driving vehicle adjusts its driving strategy based on the control instruction.

[0073] In one example, different slippery degree thresholds need to be set first, and these thresholds divide the target slippery index into different levels, each level corresponding to a different driving strategy. In this embodiment, two key thresholds are set: a first set index and a second set index. According to the comparison results between the target slippery index and the two thresholds, the system will generate corresponding control instructions.

[0074] When the target slippery index is greater than the first set index, it means that the road is highly slippery and there is a great safety hazard. At this time, the system will generate and send a first control instruction to the autonomous driving vehicle, instructing the vehicle to take a detour. This strategy can effectively avoid high-risk sections and maximize driving safety.

[0075] When the target slippery index is greater than the second set index but not greater than the first set index, it means that the road is moderately slippery. In this case, the system will generate and send a second control instruction to the autonomous driving vehicle, instructing the vehicle to slow down. By reducing the speed, the vehicle's handling stability can be increased and the risk of skidding and loss of control can be reduced.

[0076] When the target slippery index is not greater than the second set index, it means that the road slipperiness is within an acceptable range. At this time, the system will generate and send a third control instruction to the autonomous driving vehicle, allowing the vehicle to drive normally. In this case, the vehicle can maintain normal driving speed and behavior, which ensures efficiency while not ignoring potential safety risks.

[0077] This hierarchical control strategy based on the target slippery index has many advantages. First, it can dynamically adjust the driving strategy according to the actual road conditions, avoiding the problem of over-conservatism or over-risking that may be caused by fixed strategies. Second, by setting different thresholds and corresponding control instructions, the system can achieve more refined driving control and achieve a better balance between safety and efficiency. Furthermore, this method is highly flexible and scalable, and the thresholds and control strategies can be adjusted according to the characteristics of different regions and different models.

[0078] The effect of implementing this control method is significant. On highly slippery roads, the risk of accidents can be greatly reduced by choosing to take a detour; on moderately slippery roads, appropriate deceleration can ensure safety without excessively affecting driving efficiency; under normal road conditions, normal driving can maintain good traffic efficiency. This dynamic adjustment capability enables autonomous vehicles to better cope with complex and changing road environments, improving overall driving safety and user experience.

[0079] After generating and sending control instructions to the autonomous driving vehicle according to the target slippery index, it also includes:

[0080] Acquire real-time driving information of the autonomous driving vehicle; determine, based on the real-time driving information, whether the actual driving state of the autonomous driving vehicle conforms to the preset driving state corresponding to the control instruction; if the actual driving state does not conform to the preset driving state, calculate the deviation value between the actual driving state and the preset driving state; generate a correction instruction based on the deviation value, and send the correction instruction to the autonomous driving vehicle, so that the autonomous driving vehicle corrects the driving strategy according to the correction instruction until the actual driving state conforms to the preset driving state.

[0081] In one example, it is necessary to first obtain real-time driving information of the autonomous vehicle. This information may include data on vehicle speed, driving direction, acceleration, tire slip, etc. This real-time data is collected by various sensors on the vehicle and transmitted to the control system through the vehicle communication system.

[0082] Next, the system will determine whether the actual driving state of the autonomous vehicle is consistent with the preset driving state corresponding to the control command based on the real-time driving information obtained. The preset driving state is the ideal state determined based on the control command sent previously (such as detour driving, deceleration driving or normal driving). The purpose of this step is to detect whether the actual behavior of the vehicle is consistent with the expected behavior.

[0083] If the system finds that the actual driving state does not conform to the preset driving state, it will proceed to the next step: calculating the deviation between the actual driving state and the preset driving state. This deviation may include speed difference, path deviation, acceleration difference, etc. The purpose of calculating the deviation is to quantify the gap between vehicle behavior and expectations, and provide a basis for subsequent corrections.

[0084] Based on the calculated deviation, the system generates corresponding corrective instructions. The content and intensity of the corrective instructions depend on the nature and size of the deviation. For example, if the vehicle speed is higher than a preset value, the corrective instruction may require further deceleration; if the path deviates from the planned route, the corrective instruction may include instructions to adjust the direction.

[0085] After the correction instruction is generated, the system will immediately send it to the autonomous vehicle. After the vehicle receives the correction instruction, it will adjust the current driving strategy accordingly. This process is continuous, and the system will continue to repeat the cycle of obtaining real-time information, judging the status, calculating the deviation, generating and sending correction instructions until the actual driving state meets the preset driving state.

[0086] For example, in a large open-pit coal mine, in order to improve the safety and transportation efficiency of autonomous driving trucks used in mines, the mine management department decided to implement a vehicle autonomous driving control method that integrates watering data. The specific implementation steps of this method are as follows:

[0087] Information acquisition: The mining area intelligent management system obtains the following information of the target transportation road in real time:

[0088] Watering information: includes the most recent watering time, watering volume, and watering area. For example, a 1-km-long transport road was watered 10 minutes ago with a watering volume of 2,000 liters, covering the entire width of the road.

[0089] Weather information: The current temperature obtained from the mine area weather station is 28°C and the relative humidity is 60%.

[0090] Road information: This section of road is a gravel road with a slope of 8%. According to historical data, its standard friction coefficient is 0.6.

[0091] Calculate the slippery index of the watering amount: If the calculated slippery index of the watering amount is 0.65.

[0092] Calculate the temperature, humidity and slippery index: If the calculated temperature and slippery index is 0.42.

[0093] Calculate the friction and slip index: If the calculated friction and slip index is 0.75.

[0094] Calculate the target slippery index: The final target slippery index is: 0.4×0.65+0.3×0.42+0.3×0.75=0.611

[0095] Generate control instructions: Assume that the first setting index is 0.8 and the second setting index is 0.5.

[0096] Since 0.611 is greater than 0.5 but less than 0.8, the system generates a second control instruction, requiring the autonomous driving mining truck to slow down.

[0097] Send control instructions: The system sends a deceleration instruction to the autonomous mining truck approaching the road section. After receiving the instruction, the truck limits the maximum speed to 70% of the original plan and increases the safety distance from the vehicle in front.

[0098] Real-time monitoring and adjustment: The system continuously monitors the actual driving status of the truck. Assuming that the truck is still driving at a speed close to the original speed, the system calculates that the speed deviation is 15%. Based on this deviation, the system generates a correction instruction, requiring the truck to further reduce speed and increase the sensitivity of the braking system. The system continues this process until the actual driving status of the truck meets the expected safety parameters.

[0099] Through this method, the mine autonomous driving system can accurately assess road conditions and make corresponding safety adjustments. This not only improves transportation efficiency, but more importantly, significantly enhances the safety of mining trucks in complex road conditions. For example, in this embodiment, the system successfully avoids the risks of skidding or insufficient braking that may occur on slippery roads, ensuring the safety of mining operations.

[0100] Based on the above method, the present application also discloses a vehicle automatic driving control device integrating watering data, such as Figure 2 As shown, Figure 2 It is a structural schematic diagram of a vehicle automatic driving control device that integrates watering data provided in an embodiment of the present application.

[0101] A vehicle automatic driving control device integrating watering data comprises: an acquisition module, a first calculation module, a second calculation module, a third calculation module, a combination module and a generation module; wherein the acquisition module is used to acquire the watering information, weather information and speed limit information of the target road; the first calculation module is used to calculate the watering amount slippery index of the target road according to the watering information; the second calculation module is used to calculate the temperature and humidity slippery index of the target road according to the weather information; the third calculation module is used to calculate the friction slippery index of the target road according to the speed limit information; the combination module is used to calculate the target slippery index of the target road by combining the watering amount slippery index, the temperature and humidity slippery index and the friction slippery index; the generation module is used to generate and send a control instruction to the automatic driving vehicle according to the target slippery index, so that the automatic driving vehicle adjusts the driving strategy according to the control instruction.

[0102] In one example, the first calculation module is further used to calculate the difference between the watering time and the current time to obtain the watering duration; the watering duration, watering amount and watering area are substituted into the first calculation formula to calculate the watering amount slippery index of the target road; wherein the first calculation formula is: ;

[0103] In the formula, is the slippery index of watering volume, and is the weight coefficient, and To adjust the parameters, For the duration of watering, is the amount of watering, For the sprinkler area, is the total area of ​​the target road.

[0104] In one example, the second calculation module is further used to substitute the temperature and humidity into the second calculation formula to calculate the temperature, humidity and slipperiness index of the target road; wherein,

[0105] The second calculation formula is: ;

[0106] In the formula, is the temperature, humidity and slipperiness index, , and is the adjustment factor, and is the adjustment factor, is the temperature, is the critical temperature, For humidity.

[0107] In one example, the third calculation module is further used to determine the road friction of the target road based on the road information; and arithmetically multiply the road friction by a preset parameter to obtain the friction slippery index of the target road.

[0108] In one example, the combination module is also used to arithmetically multiply the watering amount slippery index by the first weight coefficient to obtain a first index; arithmetically multiply the temperature and humidity slippery index by the second weight coefficient to obtain a second index; arithmetically multiply the friction slippery index by the third weight coefficient to obtain a third index; and arithmetically add the first index, the second index and the third index to obtain a target slippery index for the target road.

[0109] In one example, the generation module is also used to generate and send a first control instruction to the autonomous driving vehicle if the target slippery index is greater than a first set index, so that the autonomous driving vehicle adjusts the vehicle according to the first control instruction; if the target slippery index is greater than a second set index and not greater than the set index, generate and send a second control instruction to the autonomous driving vehicle, so that the autonomous driving vehicle decelerates according to the second control instruction; if the target slippery index is not greater than the second set index, generate and send a third control instruction to the autonomous driving vehicle, so that the autonomous driving vehicle drives normally according to the third control instruction.

[0110] In one example, the above-mentioned device is also used to obtain real-time driving information of the autonomous driving vehicle; based on the real-time driving information, determine whether the actual driving state of the autonomous driving vehicle conforms to the preset driving state corresponding to the control instruction; if the actual driving state does not conform to the preset driving state, calculate the deviation value between the actual driving state and the preset driving state; generate a correction instruction based on the deviation value, and send the correction instruction to the autonomous driving vehicle, so that the autonomous driving vehicle corrects the driving strategy according to the correction instruction until the actual driving state conforms to the preset driving state.

[0111] It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0112] See also Figure 3 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 3 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0113] The communication bus 1002 is used to realize the connection and communication between these components.

[0114] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0115] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0116] Among them, the processor 1001 may include one or more processing cores. The processor 1001 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 1005, and calling data stored in the memory 1005. Optionally, the processor 1001 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 1001 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 1001, and it can be implemented separately through a chip.

[0117] Among them, the memory 1005 may include a random access memory (Random Access Memory, RAM) and may also include a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may optionally also be at least one storage device located away from the aforementioned processor 1001. As Figure 3 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program of a vehicle automatic driving control method integrating watering data.

[0118] exist Figure 3In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 1001 can be used to call an application program stored in the memory 1005 for a vehicle automatic driving control method that integrates watering data. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

[0119] An electronic device readable storage medium stores instructions, which, when executed by one or more processors, enable the electronic device to execute one or more of the methods described in the above embodiments.

[0120] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

[0121] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0122] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0123] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0124] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0125] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

[0126] The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the specification and practicing the disclosure here, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field not recorded in the present disclosure. The description and examples are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A vehicle automatic driving control method integrating watering data, characterized in that: The method comprises: Obtain watering information, weather information and road information of the target road; According to the watering information, the watering amount and slippery index of the target road are calculated; the watering information includes the watering time, the watering amount and the watering area, and the watering amount and slippery index of the target road are calculated according to the watering information, including: calculating the difference between the watering time and the current time to obtain the watering duration; substituting the watering duration, the watering amount and the watering area into a first calculation formula to calculate the watering amount and slippery index of the target road; wherein the first calculation formula is: Where S is the watering slip index, k1 and k2 are weight coefficients, α and β are adjustment parameters, ΔT is the watering duration, V is the watering amount, A is the watering area, and A t is the total area of ​​the target road; Calculating the temperature, humidity and slipperiness index of the target road according to the weather information; Calculating the friction slip index of the target road according to the road information; Calculating a target slippery index of the target road by combining the watering amount slippery index, the temperature and humidity slippery index, and the friction slippery index; Based on the target slippery index, a control instruction is generated and sent to the autonomous driving vehicle, so that the autonomous driving vehicle adjusts its driving strategy according to the control instruction.

2. The vehicle automatic driving control method integrating watering data according to claim 1 is characterized in that: The weather information includes temperature and humidity, and the temperature, humidity and slipperiness index of the target road is calculated according to the weather information, including: substituting the temperature and humidity into a second calculation formula to calculate the temperature, humidity and slipperiness index of the target road; wherein, The second calculation formula is: Wherein, W is the temperature and humidity slip index, c1, c2 and c3 are adjustment coefficients, γ and δ are adjustment coefficients, T is the temperature, T0 is the critical temperature, and H is the humidity.

3. The vehicle automatic driving control method integrating watering data according to claim 1 is characterized in that: Calculating the friction slip index of the target road according to the road information includes: Determining the road friction of the target road according to the road information; The road surface friction force is arithmetically multiplied by a preset parameter to obtain a friction slip index of the target road.

4. The vehicle automatic driving control method integrating watering data according to claim 1 is characterized in that: The step of calculating the target slippery index of the target road by combining the watering amount slippery index, the temperature and humidity slippery index, and the friction slippery index includes: The watering slip index is arithmetically multiplied by the first weight coefficient to obtain a first index; the temperature and humidity slip index is arithmetically multiplied by the second weight coefficient to obtain a second index; the friction slip index is arithmetically multiplied by the third weight coefficient to obtain a third index; The first index, the second index and the third index are arithmetically added to obtain a target slippery index of the target road.

5. The vehicle automatic driving control method integrating watering data according to claim 1 is characterized in that: The step of generating and sending a control instruction to the autonomous driving vehicle according to the target slippery index, so that the autonomous driving vehicle adjusts the driving strategy according to the control instruction, includes: If the target slippery index is greater than a first set index, generating and sending a first control instruction to the autonomous driving vehicle, so that the autonomous driving vehicle performs vehicle adjustment according to the first control instruction; If the target slippery index is greater than a second set index and not greater than the first set index, generating and sending a second control instruction to the autonomous driving vehicle, so that the autonomous driving vehicle decelerates according to the second control instruction; If the target slippery index is not greater than the second set index, a third control instruction is generated and sent to the autonomous driving vehicle, so that the autonomous driving vehicle drives normally according to the third control instruction.

6. The vehicle automatic driving control method integrating watering data according to claim 1 is characterized in that: After generating and sending a control instruction to the automatic driving vehicle according to the target slippery index, the method further includes: Acquiring real-time driving information of the autonomous driving vehicle; According to the real-time driving information, determining whether the actual driving state of the automatic driving vehicle conforms to the preset driving state corresponding to the control instruction; If the actual driving state does not conform to the preset driving state, calculating a deviation value between the actual driving state and the preset driving state; A correction instruction is generated according to the deviation value, and the correction instruction is sent to the autonomous driving vehicle, so that the autonomous driving vehicle corrects the driving strategy according to the correction instruction until the actual driving state meets the preset driving state.

7. A vehicle automatic driving control device integrating watering data, characterized in that: The device comprises: an acquisition module, a first calculation module, a second calculation module, a third calculation module, a combination module and a generation module; wherein, The acquisition module is used to acquire watering information, weather information and speed limit information of the target road; The first calculation module is used to calculate the watering amount slippery index of the target road according to the watering information; the watering information includes the watering time, the watering amount and the watering area, and the calculation of the watering amount slippery index of the target road according to the watering information includes: calculating the difference between the watering time and the current time to obtain the watering duration; substituting the watering duration, the watering amount and the watering area into the first calculation formula to calculate the watering amount slippery index of the target road; wherein the first calculation formula is: Where S is the watering slip index, k1 and k2 are weight coefficients, α and β are adjustment parameters, ΔT is the watering duration, V is the watering amount, A is the watering area, and A t is the total area of ​​the target road; The second calculation module is used to calculate the temperature, humidity and slipperiness index of the target road according to the weather information; The third calculation module is used to calculate the friction slip index of the target road according to the speed limit information; The combining module is used to combine the watering amount slippery index, the temperature and humidity slippery index and the friction slippery index to calculate the target slippery index of the target road; The generation module is used to generate and send a control instruction to the autonomous driving vehicle according to the target slippery index, so that the autonomous driving vehicle adjusts its driving strategy according to the control instruction.

8. An electronic device, characterized in that: It includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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