Commercial vehicle predictive vehicle speed torque control method and system based on curvature and medium

Abstract

The invention provides a commercial vehicle predictive vehicle speed torque control method and system based on curvature and a medium, and belongs to the technical field of vehicle automatic control. The method comprises the steps that when conditions are enabled, information of a road with a preset distance in front is obtained, and the attribute of a current road section and a nearest turning road section are recognized; identifying the curvature extreme value and position of the nearest turning road section according to the current road section attribute; the curvature radius is calculated according to the curvature extreme value of the nearest turning road section, and the turning guarantee vehicle speed is determined by combining a preset corresponding relation; according to the current position and the curvature extreme point position, calculating the remaining driving distance and the number of segments to obtain remaining road section information; based on a longitudinal kinetic equation and a kinematic equation of the vehicle, taking a turning guarantee vehicle speed as a terminal vehicle speed, and reversely calculating a predicted vehicle speed sequence section by section in combination with residual road section information; and comparing the current vehicle speed with the predicted vehicle speed sequence, and deciding to output a torque and a braking or sliding control instruction. Intelligent control over the vehicle speed and the torque is achieved, and fuel economy and driving safety are improved.

Description

Technical Field

[0001] This application belongs to the field of vehicle automatic control technology, specifically relating to a curvature-based predictive speed and torque control method, system, and medium for commercial vehicles. Background Technology

[0002] In intelligent driving of commercial vehicles, predictive cruise control utilizes information about the road ahead (such as slope and curvature) to pre-plan vehicle speed and gear, aiming to improve fuel economy, driving safety, and driving comfort. Existing predictive cruise control largely relies on GPS and electronic map data, using information such as road slope and curvature to plan vehicle speed based on preset algorithms. However, existing predictive cruise control has the following shortcomings: First, it lacks torque and gear control. Existing predictive cruise control focuses primarily on speed planning, failing to coordinate real-time control of torque and gear, resulting in limitations in balancing economy and dynamics. Second, it has poor curvature adaptability. On roads with significant curvature, existing methods struggle to adjust speed and torque accurately in real-time, easily causing driver panic and brake disengagement, affecting cruise continuity and driving experience. Third, it lacks real-time performance and adaptability. Relying on static map data and fixed algorithms, it struggles to cope with delays in road information updates and real-time traffic changes, limiting control flexibility. Finally, global economy optimization is insufficient, making it difficult to achieve globally optimal matching of speed, torque, and gear in complex road conditions.

[0003] In summary, this presents a predictive control method that can accurately, consistently, and efficiently address curved road conditions, improve overall cruise quality, and optimize energy consumption. Summary of the Invention

[0004] In a first aspect, embodiments of this application provide a curvature-based predictive speed-torque control method for commercial vehicles, comprising the following steps: S1. Determine whether the enable conditions for the curvature-based predictive speed and torque control function are met, and if so, obtain road information within a preset distance in front of the vehicle, identify the current road segment attributes and the nearest turning segment; S2. Based on the current road segment attributes, identify the curvature extrema and the location of the curvature extrema point of the nearest turning road segment; S3. Calculate the radius of curvature of the nearest turning segment based on the extreme value of curvature of the nearest turning segment, and determine the guaranteed turning speed based on the preset relationship between the radius of curvature and vehicle speed. S4. Based on the vehicle's current position and the location of the curvature extreme point, calculate the remaining driving distance and the number of segments to obtain the remaining road segment information; S5. Based on the vehicle's longitudinal dynamics equation and kinematic equation, with the guaranteed turning speed as the final speed, and combined with the remaining road segment information, the predicted speed sequence is calculated in reverse segment by segment. S6. Compare the current real-time vehicle speed with the predicted vehicle speed sequence, decide to output torque, braking or coasting control commands, and convert the corresponding control commands into torque, gear and braking requests and send them to the vehicle controller for execution.

[0005] Furthermore, the specific steps of step S1 are as follows: S11. Determine whether the vehicle is in cruise mode and whether the vehicle's map equipment can provide the vehicle controller with road information ahead in a normal manner; If satisfied, enable the curvature-based predictive speed-torque control function and proceed to step S12. If not satisfied, end; S12. Obtain the preset distance ahead of the vehicle's current position using a map device. The road information within the area, including a displacement array consisting of k points. Curvature array and slope array ; S13. Set the preset distance Divide the material into k equal segments, and assume that the curvature and slope of each segment remain constant; S14. Based on the curvature array The road segments are merged, dividing the road into straight-ahead, left-turn, and right-turn segments, and the attributes of the road segment where the vehicle is currently located are determined. And the nearest left turn segment to the current position. The nearest right turn section and the nearest straight section information.

[0006] Furthermore, the specific steps of step S2 are as follows: S21. Determine the current road segment attributes of the vehicle; If it is a left turn segment or a right turn segment, proceed to step S22; If it is a straight section, proceed to step S23; S22. Iterate through the curvature array of the current turning segment. Find the curvature extrema and curvature extrema in the curvature array Position coordinates in The current turning segment is taken as the nearest turning segment, and the process proceeds to step S3; S23. Compare the nearest left turn segment With the nearest right turn segment The distance from the starting point is used to determine the direction of the next upcoming turn. S24. Iterate through the curvature array of the next upcoming turning segment. Find the curvature extrema and curvature extrema in the curvature array Position coordinates in .

[0007] Furthermore, the specific steps of step S3 are as follows: S31. Based on curvature extrema The radius of curvature R of the nearest turning segment is calculated using the radius of curvature formula. The formula is as follows: ; S32. Based on the preset table of correspondence between radius of curvature and vehicle speed, query the guaranteed turning speed corresponding to radius of curvature R. .

[0008] Furthermore, the specific steps of step S4 are as follows: S41. Determine whether the vehicle is already in a turning section; If not, proceed to step S43; If so, proceed to step S42; S42. Determine whether the vehicle has passed the curvature extremum point based on its current position; If so, proceed to step S45; If not, proceed to step S44; S43. The vehicle is traveling on a straight section. Calculate the coordinates of the position from the vehicle's current position to the curvature extreme point using the following formula. Number of remaining road segments :

[0009] in, This represents the number of remaining segments in the current straight segment. These are the coordinates of the extreme point of curvature. Proceed to step S46; S44. Calculate the number of remaining road segment distances using the following formula. :

[0010] in, This represents the total number of segments in the current turning section. This represents the number of segments already traveled. The coordinates of the extreme point of curvature ; Proceed to step S46; S45. Number of segments to divide the remaining road segment distance Set to 0; S46. Determine the number of remaining road segments. Is it 0? If so, information on remaining road sections. Empty; If not, divide the road into segments based on the remaining distance. Extract the corresponding displacement and slope data from the road information array to generate information on the remaining road segments. .

[0011] Furthermore, the specific steps of step S5 are as follows: S51. From remaining road segment information In the process, the distance of each small segment is obtained sequentially from the curvature extremum point to the vehicle's current position. and slope ; S52. Based on the following vehicle longitudinal dynamics formula, calculate the acceleration of the vehicle when coasting in gear on the k-th road segment. :

[0012]

[0013]

[0014] in, This refers to engine torque. For the gearbox ratio, Main reduction ratio, Where r is the transmission mechanical efficiency, m is the tire radius, g is the vehicle weight, and f is the rolling resistance coefficient. Let A be the air resistance coefficient, ρ be the vehicle's frontal area, ρ be the air density, and v be the vehicle speed at the end of the k-th segment. δ is the vehicle rotational mass conversion factor; S53. Based on the following kinematic formula, use the speed at the end of segment k. acceleration and distance Calculate the starting speed of segment k by reverse calculation :

[0015] ; S54. Maintain speed while turning. Starting with the initial and final vehicle speeds, begin from the road segment where the curvature extremum is located, repeat steps S51 to S53, iterating backward segment by segment towards the vehicle's current position, to finally obtain the predicted vehicle speed sequence from the current position to the curvature extremum. .

[0016] Furthermore, in step S6, the vehicle's current real-time speed is... Compared with the predicted vehicle speed sequence Compare at least two graded vehicle speed thresholds selected in the data; Based on the comparison results, a hierarchical decision is made to output torque control commands, braking control commands, or coasting control commands.

[0017] Furthermore, in step S6, at least two graded vehicle speed thresholds include a first graded threshold and a second graded threshold. The first grading threshold is the first predicted value in the predicted vehicle speed sequence. ; The second-level threshold is the third predicted value in the predicted vehicle speed sequence. ; The specific steps of the hierarchical decision-making process include: If the current real-time vehicle speed If the value is greater than the first-level threshold, the decision outputs a control command to initiate emergency braking. If the current real-time vehicle speed If the value is less than or equal to the first grade threshold and greater than the second grade threshold, the decision output is used as a control command to enter geared coasting. If the current real-time vehicle speed If the value is less than or equal to the second-level threshold, the decision output is used to maintain the control command for normal drive.

[0018] Secondly, embodiments of this application also provide a curvature-based predictive speed-torque control system for commercial vehicles, comprising: The function enable and information acquisition module is used to determine whether the enable conditions of the curvature-based predictive speed and torque control function are met, and when they are met, to acquire road information within a preset distance in front of the vehicle, identify the current road segment attributes and the nearest turning segment. The curvature extremum recognition module is used to identify the curvature extremum and the location of the curvature extremum point of the nearest turning road segment based on the current road segment attributes. The turning safety speed determination module is used to calculate the radius of curvature of the nearest turning segment based on the extreme value of curvature of the nearest turning segment, and determine the turning safety speed based on the preset relationship between the radius of curvature and vehicle speed. The remaining road segment calculation module is used to calculate the remaining driving distance and the number of segments based on the vehicle's current position and the position of the curvature extreme point, and obtain the remaining road segment information; The predicted vehicle speed sequence calculation module is used to calculate the predicted vehicle speed sequence in reverse segment by segment based on the vehicle's longitudinal dynamics equation and kinematic equation, with the guaranteed turning speed as the final speed, and combined with the remaining road segment information. The driving behavior decision and execution module compares the current real-time vehicle speed with the predicted vehicle speed sequence, decides to output torque, braking or coasting control commands, and converts the corresponding control commands into torque, gear and braking requests and sends them to the vehicle controller for execution.

[0019] Thirdly, embodiments of this application also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the curvature-based predictive speed and torque control method for commercial vehicles as described in the first aspect.

[0020] As can be seen from the above technical solutions, this application has the following advantages: The curvature-based predictive speed and torque control method, system, and medium for commercial vehicles provided in this application achieve intelligent driving control of commercial vehicles under complex road conditions by dynamically acquiring road information ahead and combining it with a vehicle dynamics model. It can optimize vehicle speed, torque, and gear in real time, improve the fuel economy, driving safety, and driving comfort of commercial vehicles, and can cope with different road conditions and traffic conditions, showing broad application prospects. Attached Figure Description

[0021] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the curvature-based predictive speed and torque control method for commercial vehicles according to the present invention.

[0023] Figure 2 This is a schematic diagram of the curvature-based predictive speed and torque control system for commercial vehicles according to the present invention. Detailed Implementation

[0024] The various embodiments of this disclosure will be described more fully in the following detailed description of the specific steps of the curvature-based predictive speed-torque control method for commercial vehicles. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0025] This embodiment provides a curvature-based predictive speed and torque control method for commercial vehicles. Through dynamic programming and predictive control, it achieves coordinated optimization of vehicle speed, torque, and gear position, thereby improving fuel economy, driving safety, and driving comfort, and adapting to complex road conditions.

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figure 1 The diagram shows a flowchart of a curvature-based predictive speed-torque control method for commercial vehicles in a specific embodiment. The method includes the following steps: S1. Determine whether the enable conditions for the curvature-based predictive speed and torque control function are met, and if so, obtain road information within a preset distance in front of the vehicle, identify the current road segment attributes and the nearest turning segment; It should be noted that determining the enabling conditions of the function and obtaining road information ensures that the control method is activated under appropriate conditions and provides road data support for subsequent steps. S2. Based on the current road segment attributes, identify the curvature extrema and the location of the curvature extrema point of the nearest turning road segment; It should be noted that identifying the curvature extrema and their location of the nearest turning section provides crucial curve characteristic information for vehicle speed planning; by accurately identifying curvature extrema, the danger level of the curve can be predicted in advance, providing a basis for adjusting vehicle speed and improving driving safety. S3. Calculate the radius of curvature of the nearest turning segment based on the extreme value of curvature of the nearest turning segment, and determine the guaranteed turning speed based on the preset relationship between the radius of curvature and vehicle speed. It should be noted that calculating the radius of curvature and determining the safe turning speed achieves the goal of dynamically adjusting the vehicle speed based on the geometric characteristics of the curve; it can ensure that the vehicle maintains a safe speed when driving on a curve, avoid driving risks caused by excessive speed, and at the same time take into account fuel economy. S4. Based on the vehicle's current position and the location of the curvature extreme point, calculate the remaining driving distance and the number of segments to obtain the remaining road segment information; It should be noted that calculating the remaining driving distance and the number of segments generates remaining road segment information, providing accurate road segment data for the calculation of predicted vehicle speed sequences; this ensures the accuracy and real-time performance of vehicle speed planning and can effectively cope with complex road conditions. S5. Based on the vehicle's longitudinal dynamics equation and kinematic equation, with the guaranteed turning speed as the final speed, and combined with the remaining road segment information, the predicted speed sequence is calculated in reverse segment by segment. It should be noted that the vehicle speed sequence is predicted by back-calculation based on dynamics and kinematic equations, achieving globally optimal planning of vehicle speed; by comprehensively considering vehicle dynamics characteristics, road gradient and distance, the vehicle speed curve can be optimized, improving fuel economy and driving comfort. S6. Compare the current real-time vehicle speed with the predicted vehicle speed sequence, decide to output torque, braking or coasting control commands, and convert the corresponding control commands into torque, gear and braking requests and send them to the vehicle controller for execution; It should be noted that by comparing the current vehicle speed with the predicted vehicle speed sequence and outputting control commands, real-time monitoring and adjustment of the vehicle's driving status are achieved. Through hierarchical decision-making logic, braking, coasting, or normal driving can be flexibly selected, improving the stability of the control system and the smoothness of driving.

[0028] This embodiment achieves intelligent optimization of vehicle speed, torque, and gear by dynamically acquiring road information ahead and combining it with vehicle dynamics models. This improves the fuel economy, driving safety, and driving comfort of commercial vehicles, while reducing driver intervention and enhancing cruise continuity.

[0029] Furthermore, as a refinement and extension of the specific implementation methods of the above embodiments, in order to fully illustrate the specific implementation process of this embodiment, another curvature-based predictive speed-torque control method for commercial vehicles is provided, taking a heavy commercial vehicle (vehicle weight m=18000kg, tire radius r=0.52m, rolling resistance coefficient f=0.008, air resistance coefficient...) as an example. =0.65, vehicle frontal area A=7.2m 2 The air density ρ = 1.225 kg / m³ 3 Rotational mass conversion factor δ=1.05, gearbox transmission ratio =1.8, final drive ratio =4.3, transmission mechanical efficiency =0.92) is the controlled object. The vehicle is equipped with a high-precision map device (update frequency 10Hz, positioning accuracy ±1m), which supports real-time output of road displacement, curvature and slope data within a range of 2000m ahead; Control function enable conditions are met: the vehicle is in cruise mode (set cruise speed 80km / h), the map device communicates normally with the vehicle controller (VCU), there are no fault alarm messages, and the driver has not intervened in braking or shifting operations. The method includes the following steps: S1. Determine whether the enabling conditions for the curvature-based predictive speed-torque control function are met. If met, acquire road information within a preset distance ahead of the vehicle, and identify the current road segment attributes and the nearest turning segment. The specific steps of step S1 are as follows: S11. Determine whether the vehicle is in cruise mode and whether the vehicle's map equipment can provide the vehicle controller with road information ahead in a normal manner; If satisfied, enable the curvature-based predictive speed-torque control function and proceed to step S12. If not satisfied, end; For example, the vehicle is currently cruising stably, with the engine speed at 1500 rpm and the vehicle speed at 78 km / h. The map device continuously sends road data ahead to the VCU via the CAN bus, meeting the enable conditions for the curvature-based predictive speed and torque control function, and proceeds to the next step. S12. Obtain the preset distance ahead of the vehicle's current position using a map device. The road information within the area, including a displacement array consisting of k points. Curvature array and slope array ; For example, road information within 1000m ahead of the vehicle's current location (preset distance Dis=1000m) is obtained through a map device, generating a dataset consisting of k=100 points, including: Displacement array Dis=[0m,10m,20m,...,1000m] (each segment is 10m apart); The curvature array Curvt (unit: 1 / m) has some key data as follows: [0,0,...,0 (first 50 segments, straight segments),0.01,0.015,0.02,0.025,0.02 (last 50 segments, right-turn segments)]; The slope array Slop (unit: rad) represents the overall gentle slope of the road segment, with Slop=[0,0,...,0.005 (local slight slope),...,0]; S13. Set the preset distance Divide the material into k equal segments, and assume that the curvature and slope of each segment remain constant; For example, a preset distance of 1000m is divided into 100 segments, each segment having a length of s=10m, assuming that the curvature and slope remain constant within each segment; S14. Based on the curvature array The road segments are merged, dividing the road into straight-ahead, left-turn, and right-turn segments, and the attributes of the road segment where the vehicle is currently located are determined. And the nearest left turn segment to the current position. The nearest right turn section and the nearest straight section information; For example, based on the curvature array, the road segments are merged. The first 50 segments (0-500m) have a curvature of 0 and are identified as straight segments; the next 50 segments (500-1000m) have a curvature greater than 0 and are in the same direction and are identified as right-turn segments; the current vehicle is in a straight segment (position coordinate x=300m, corresponding to the 30th segment), the starting position of the nearest right-turn segment is 500m (the 50th segment), and the nearest left-turn segment is 1500m away from the current position, so the nearest turning segment is a right-turn segment; S2. Based on the current road segment attributes, identify the curvature extrema and the location of the curvature extrema point of the nearest turning segment; the specific steps of step S2 are as follows: S21. Determine the current road segment attributes of the vehicle; If it is a left turn segment or a right turn segment, proceed to step S22; If it is a straight section, proceed to step S23; For example, if the vehicle is currently in a straight section, proceed to step S23; S22. Iterate through the curvature array of the current turning segment. Find the curvature extrema and curvature extrema in the curvature array Position coordinates in The current turning segment is taken as the nearest turning segment, and the process proceeds to step S3; S23. Compare the nearest left turn segment With the nearest right turn segment The distance from the starting point is used to determine the direction of the next upcoming turn. For example, the starting point distances of the nearest right turn segment (starting distance 200m) and the nearest left turn segment (starting distance 1200m) are compared to determine the next turning segment to be entered as the right turn segment; S24. Iterate through the curvature array of the next upcoming turning segment. Find the curvature extrema and curvature extrema in the curvature array Position coordinates in ; For example, traverse the curvature array of the right-turn segments (segments 50-100) to find the curvature extrema. =0.025 1 / m, the corresponding extreme point coordinates are the 75th segment (750m displacement); S3. Calculate the radius of curvature of the nearest turning segment based on its curvature extreme value, and determine the guaranteed turning speed based on the preset relationship between the radius of curvature and vehicle speed; the specific steps of step S3 are as follows: S31. Based on curvature extrema The radius of curvature R of the nearest turning segment is calculated using the radius of curvature formula. The formula is as follows: ; For example, according to the radius of curvature formula Substitute =0.025 1 / m, therefore R=1 / 0.025=40m; S32. Based on the preset table of correspondence between radius of curvature and vehicle speed, query the guaranteed turning speed corresponding to radius of curvature R. ; Among them, the vehicle speed value in the preset curve radius and vehicle speed correspondence table can be set and adjusted by the user; For example, a preset table showing the relationship between radius of curvature and vehicle speed (which can be customized by the user) is shown in Table 1 below: Table 1

[0030] According to Table 1, the guaranteed turning speed corresponding to a radius of curvature R=40m is 45km / h; S4. Based on the vehicle's current position and the location of the curvature extreme point, calculate the remaining driving distance and the number of segments to obtain the remaining road segment information; the specific steps of step S4 are as follows: S41. Determine whether the vehicle is already in a turning section; If not, proceed to step S43; If so, proceed to step S42; For example, if the vehicle is currently in a straight section (not yet entering a turning section), proceed to step S43; S42. Determine whether the vehicle has passed the curvature extremum point based on its current position; If so, proceed to step S45; If not, proceed to step S44; S43. The vehicle is traveling on a straight section. Calculate the coordinates of the position from the vehicle's current position to the curvature extreme point using the following formula. Number of remaining road segments :

[0031] in, This represents the number of remaining segments in the current straight segment. These are the coordinates of the extreme point of curvature. Proceed to step S46; For example, the number of remaining segments in the current straight segment. =Curvature extreme point location coordinates-Current segment coordinates=75-30=45 segments, each segment length is 10m, the remaining driving distance is 45×10=450m; S44. Calculate the number of remaining road segment distances using the following formula. :

[0032] in, This represents the total number of segments in the current turning section. This represents the number of segments already traveled. The coordinates of the extreme point of curvature ; Proceed to step S46; S45. Number of segments to divide the remaining road segment distance Set to 0; S46. Determine the number of remaining road segments. Is it 0? If so, information on remaining road sections. Empty; If not, divide the road into segments based on the remaining distance. Extract the corresponding displacement and slope data from the road information array to generate information on the remaining road segments. ; For example, the number of remaining road segments is 45≠0. The displacement (310m-750m) and slope data (both 0.005rad) of segments 31-75 are extracted from the road information array to generate the remaining road segment information. S5. Based on the vehicle's longitudinal dynamics equations and kinematic equations, and taking the guaranteed turning speed as the final vehicle speed, combined with the remaining road segment information, the predicted vehicle speed sequence is calculated segment by segment in reverse. The specific steps of step S5 are as follows: S51. From remaining road segment information In the process, the distance of each small segment is obtained sequentially from the curvature extremum point to the vehicle's current position. and slope ; For example, the distance s=10m and the slope θ=0.005rad for each segment are extracted sequentially from the curvature extremum point (segment 75) to the current position (segment 30); S52. Based on the following vehicle longitudinal dynamics formula, calculate the acceleration of the vehicle when coasting in gear on the k-th road segment. :

[0033]

[0034]

[0035] in, This is the engine torque (considered as 0 during coasting). For the gearbox ratio, Main reduction ratio, Where r is the transmission mechanical efficiency, m is the tire radius, g is the vehicle weight, and f is the rolling resistance coefficient. Let A be the air resistance coefficient, ρ be the vehicle's frontal area, ρ be the air density, and v be the vehicle speed at the end of the k-th segment. δ is the vehicle rotational mass conversion factor; For example, based on the vehicle longitudinal dynamics formula, the engine torque during coasting =0, substitute the parameters to calculate the acceleration of the k-th segment:

[0036] Taking segment 75 (with the final speed being the guaranteed turning speed of 45km / h = 12.5m / s) as an example, let's substitute the data:

[0037] Calculated ≈-0.23m / s² (the negative sign indicates deceleration); S53. Based on the following kinematic formula, use the speed at the end of segment k. acceleration and distance Calculate the starting speed of segment k by reverse calculation :

[0038] ; For example, according to the kinematic formula Calculate the starting speed of segment k; taking segment 75 as an example, calculate the ending speed. =12.5m / s², acceleration =-0.23m / s², distance s=10m, therefore: , (45.65km / h); S54. Maintain speed while turning. Starting with the initial and final vehicle speeds, begin from the road segment where the curvature extremum is located, repeat steps S51 to S53, iterating backward segment by segment towards the vehicle's current position, to finally obtain the predicted vehicle speed sequence from the current position to the curvature extremum. ; For example, taking a guaranteed turning speed of 45 km / h as the final speed, starting from segment 75, the calculation is performed in reverse iteratively up to segment 30, and some key data of the predicted speed sequence are shown in Table 2 below: Table 2

[0039] S6. Compare the current real-time vehicle speed with the predicted vehicle speed sequence, decide whether to output torque, braking, or coasting control commands, and convert the corresponding control commands into torque, gear, and braking requests, sending them to the vehicle controller for execution; in step S6, the vehicle's current real-time speed is... Compared with the predicted vehicle speed sequence Compare at least two graded vehicle speed thresholds selected in the data; Based on the comparison results, a graded decision is made to output a torque control command, a braking control command, or a coasting control command; step S6 includes at least two graded vehicle speed thresholds, including a first graded threshold and a second graded threshold. The first grading threshold is the first predicted value in the predicted vehicle speed sequence. ; The second-level threshold is the third predicted value in the predicted vehicle speed sequence. ; The specific steps of the hierarchical decision-making process include: If the current real-time vehicle speed If the value is greater than the first-level threshold, the decision outputs a control command to initiate emergency braking. If the current real-time vehicle speed If the value is less than or equal to the first grade threshold and greater than the second grade threshold, the decision output is used as a control command to enter geared coasting. If the current real-time vehicle speed If the value is less than or equal to the second-level threshold, the decision output is used to maintain the control command for normal drive. It should be noted that the first predicted value This represents the speed of the vehicle at the nearest planned point (such as the first road segment point) to its current location; this value directly reflects the requirements for real-time vehicle operation; if the current speed exceeds this value (i.e., > This means that the vehicle will exceed the safe planned speed in a very short distance / time, and the risk is imminent, so an emergency braking command must be triggered for rapid intervention. Third predicted value This represents the vehicle speed at a planned point relatively far from the vehicle's current location (such as the third road segment point); this value reflects the requirements for the vehicle's phased objectives. First predicted value. Compared with the third predicted value The difference between these values ​​essentially defines a buffer distance for smooth speed adjustment; if the current vehicle speed is within this range (i.e., ... < < This indicates that although the vehicle needs to decelerate, there is still sufficient distance margin, so a more economical and smooth coasting method in gear can be adopted to achieve deceleration. In the hierarchical control logic of this invention, the second predicted value The corresponding road location is less urgent than the nearest point, and its clarity as a phased speed adjustment target is less than that of the third predicted value. Increasing the threshold to an independent value would unnecessarily subdivide the buffers, potentially leading to issues with the first predicted value. Compared with the second predicted value Between, second predicted value Compared with the third predicted value Frequent or oscillating switching between control modes (such as braking and coasting) is detrimental to the stability of the control system and driving smoothness; therefore, the second predicted value is not selected. As an independent grading threshold, it is a design consideration for optimizing control strategies and simplifying decision-making logic, rather than ignoring the data; second predicted value The numerical information is still fully contained in the prediction sequence, serving the overall vehicle speed planning curve; For example, the classification thresholds are selected as follows: the first classification threshold (first predicted value) is the predicted vehicle speed of segment 31, which is 75.8 km / h, and the second classification threshold (third predicted value) is the predicted vehicle speed of segment 33, which is 74.2 km / h. Vehicle speed comparison and decision: The current real-time vehicle speed is 78km / h, which is greater than the first level threshold of 75.8km / h. The decision is to output an emergency braking control command.

[0040] Command conversion and execution: Convert emergency braking command into braking request (brake pressure 0.6MPa) and torque request (engine torque 0N). The system sends a request for a gear hold (current gear 6) to the vehicle controller. Upon the controller's response, the vehicle begins to brake smoothly, gradually reducing its speed until it reaches the curvature extreme point at a speed of 45 km / h.

[0041] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0042] like Figure 2 As shown, the following are embodiments of a curvature-based predictive speed and torque control system for commercial vehicles provided in this disclosure. This system belongs to the same inventive concept as the curvature-based predictive speed and torque control methods for commercial vehicles described above. For details not described in detail in the embodiments of the curvature-based predictive speed and torque control system for commercial vehicles, please refer to the embodiments of the curvature-based predictive speed and torque control methods for commercial vehicles described above.

[0043] The system includes: The function enable and information acquisition module is used to determine whether the enable conditions of the curvature-based predictive speed and torque control function are met, and when they are met, to acquire road information within a preset distance in front of the vehicle, identify the current road segment attributes and the nearest turning segment. The curvature extremum recognition module is used to identify the curvature extremum and the location of the curvature extremum point of the nearest turning road segment based on the current road segment attributes. The turning safety speed determination module is used to calculate the radius of curvature of the nearest turning segment based on the extreme value of curvature of the nearest turning segment, and determine the turning safety speed based on the preset relationship between the radius of curvature and vehicle speed. The remaining road segment calculation module is used to calculate the remaining driving distance and the number of segments based on the vehicle's current position and the position of the curvature extreme point, and obtain the remaining road segment information; The predicted vehicle speed sequence calculation module is used to calculate the predicted vehicle speed sequence in reverse segment by segment based on the vehicle's longitudinal dynamics equation and kinematic equation, with the guaranteed turning speed as the final speed, and combined with the remaining road segment information. The driving behavior decision and execution module compares the current real-time vehicle speed with the predicted vehicle speed sequence, decides to output torque, braking or coasting control commands, and converts the corresponding control commands into torque, gear and braking requests and sends them to the vehicle controller for execution.

[0044] This embodiment achieves coordinated optimization of commercial vehicle speed, torque, and gear through dynamic planning and predictive control by interacting and coordinating the function enable and information acquisition module, curvature extreme value identification module, turning guarantee speed determination module, remaining road segment calculation module, predicted speed sequence calculation module, and driving behavior decision and execution module. This improves fuel economy, driving safety, and driving comfort, and adapts to complex road conditions.

[0045] The storage medium provided in this application stores a program product capable of implementing a curvature-based predictive speed and torque control method for commercial vehicles.

[0046] The curvature-based predictive speed-torque control method for commercial vehicles includes: determining whether the enabling conditions for the curvature-based predictive speed-torque control function are met; if so, acquiring road information within a preset distance ahead of the vehicle, identifying the current road segment attributes and the nearest turning segment; identifying the curvature extremum and the location of the curvature extremum point of the nearest turning segment based on the current road segment attributes; calculating the curvature radius of the nearest turning segment based on the curvature extremum, and determining the guaranteed turning speed based on a preset relationship between the curvature radius and vehicle speed; calculating the remaining driving distance and the number of segments based on the vehicle's current position and the location of the curvature extremum point, obtaining the remaining road segment information; calculating the predicted speed sequence segment by segment in reverse, based on the vehicle's longitudinal dynamics equation and kinematic equation, with the guaranteed turning speed as the endpoint speed, and combining the remaining road segment information; comparing the current real-time vehicle speed with the predicted speed sequence, deciding to output torque, braking, or coasting control commands, and converting the corresponding control commands into torque, gear, and braking requests and sending them to the vehicle controller for execution.

[0047] In some possible implementations, the curvature-based predictive speed-torque control method for commercial vehicles disclosed herein can be implemented as a program product comprising program code that, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0048] The storage medium disclosed herein may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0049] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A curvature-based predictive speed-torque control method for commercial vehicles, characterized in that, Includes the following steps: S1. Determine whether the enable conditions for the curvature-based predictive speed and torque control function are met, and if so, obtain road information within a preset distance in front of the vehicle, identify the current road segment attributes and the nearest turning segment; S2. Based on the current road segment attributes, identify the curvature extrema and the location of the curvature extrema point of the nearest turning road segment; S3. Calculate the radius of curvature of the nearest turning segment based on the extreme value of curvature of the nearest turning segment, and determine the guaranteed turning speed based on the preset relationship between the radius of curvature and vehicle speed. S4. Based on the vehicle's current position and the location of the curvature extreme point, calculate the remaining driving distance and the number of segments to obtain the remaining road segment information; S5. Based on the vehicle's longitudinal dynamics equation and kinematic equation, with the guaranteed turning speed as the final speed, and combined with the remaining road segment information, the predicted speed sequence is calculated in reverse segment by segment. S6. Compare the current real-time vehicle speed with the predicted vehicle speed sequence, decide to output torque, braking or coasting control commands, and convert the corresponding control commands into torque, gear and braking requests and send them to the vehicle controller for execution.

2. The curvature-based predictive speed and torque control method for commercial vehicles according to claim 1, characterized in that, The specific steps of step S1 are as follows: S11. Determine whether the vehicle is in cruise mode and whether the vehicle's map equipment can provide the vehicle controller with road information ahead in a normal manner; If satisfied, enable the curvature-based predictive speed-torque control function and proceed to step S12. If not satisfied, end; S12. Obtain the preset distance ahead of the vehicle's current position using a map device. The road information within the area, including a displacement array consisting of k points. Curvature array and slope array ; S13. Set the preset distance Divide the material into k equal segments, and assume that the curvature and slope of each segment remain constant; S14. Based on the curvature array The road segments are merged, dividing the road into straight-ahead, left-turn, and right-turn segments, and the attributes of the road segment where the vehicle is currently located are determined. And the nearest left turn segment to the current position. The nearest right turn section and the nearest straight section information.

3. The curvature-based predictive speed and torque control method for commercial vehicles according to claim 2, characterized in that, The specific steps of step S2 are as follows: S21. Determine the current road segment attributes of the vehicle; If it is a left turn segment or a right turn segment, proceed to step S22; If it is a straight section, proceed to step S23; S22. Iterate through the curvature array of the current turning segment. Find the curvature extrema and curvature extrema in the curvature array Position coordinates in The current turning segment is taken as the nearest turning segment, and the process proceeds to step S3; S23. Compare the nearest left turn segment With the nearest right turn segment The distance from the starting point is used to determine the direction of the next upcoming turn. S24. Iterate through the curvature array of the next upcoming turning segment. Find the curvature extrema and curvature extrema in the curvature array Position coordinates in .

4. The curvature-based predictive speed and torque control method for commercial vehicles according to claim 3, characterized in that, The specific steps of step S3 are as follows: S31. Based on curvature extrema The radius of curvature R of the nearest turning segment is calculated using the radius of curvature formula. The formula is as follows: ； S32. Based on the preset table of correspondence between radius of curvature and vehicle speed, query the guaranteed turning speed corresponding to radius of curvature R. .

5. The curvature-based predictive speed and torque control method for commercial vehicles according to claim 4, characterized in that, The specific steps of step S4 are as follows: S41. Determine whether the vehicle is already in a turning section; If not, proceed to step S43; If so, proceed to step S42; S42. Determine whether the vehicle has passed the curvature extremum point based on its current position; If so, proceed to step S45; If not, proceed to step S44; S43. The vehicle is traveling on a straight section. Calculate the coordinates of the position from the vehicle's current position to the curvature extreme point using the following formula. Number of remaining road segments : in, This represents the number of remaining segments in the current straight segment. The coordinates of the extreme point of curvature; Proceed to step S46; S44. Calculate the number of remaining road segment distances using the following formula. : in, This represents the total number of segments in the current turning section. This represents the number of segments already traveled. The coordinates of the extreme point of curvature ; Proceed to step S46; S45. Number of segments to divide the remaining road segment distance Set to 0; S46. Determine the number of remaining road segments. Is it 0? If so, information on remaining road sections. Empty; If not, divide the road into segments based on the remaining distance. Extract the corresponding displacement and slope data from the road information array to generate information on the remaining road segments. .

6. The curvature-based predictive speed and torque control method for commercial vehicles according to claim 5, characterized in that, The specific steps of step S5 are as follows: S51. From remaining road segment information In the process, the distance of each small segment is obtained sequentially from the curvature extremum point to the vehicle's current position. and slope ; S52. Based on the following vehicle longitudinal dynamics formula, calculate the acceleration of the vehicle when coasting in gear on the k-th road segment. : in, This refers to engine torque. For the gearbox ratio, Main reduction ratio, Where r is the transmission mechanical efficiency, m is the tire radius, g is the vehicle weight, and f is the rolling resistance coefficient. Let A be the air resistance coefficient, ρ be the vehicle's frontal area, ρ be the air density, and v be the vehicle speed at the end of the k-th segment. δ is the vehicle rotational mass conversion factor; S53. Based on the following kinematic formula, use the speed at the end of segment k. acceleration and distance Calculate the starting speed of segment k by reverse calculation : ； S54. Maintain speed while turning. Starting with the initial destination speed, begin from the road segment where the curvature extremum is located, repeat steps S51 to S53, iterating backward segment by segment towards the vehicle's current position, and finally obtain the predicted speed sequence from the current position to the curvature extremum. .

7. The curvature-based predictive speed and torque control method for commercial vehicles according to claim 6, characterized in that, In step S6, the vehicle's current real-time speed is... Compared with the predicted vehicle speed sequence The at least two graded vehicle speed thresholds selected in the data are compared. Based on the comparison results, a hierarchical decision is made to output torque control commands, braking control commands, or coasting control commands.

8. The curvature-based predictive speed and torque control method for commercial vehicles according to claim 7, characterized in that, In step S6, at least two graded vehicle speed thresholds include a first graded threshold and a second graded threshold; The first grading threshold is the first predicted value in the predicted vehicle speed sequence. ; The second-level threshold is the third predicted value in the predicted vehicle speed sequence. ; The specific steps of the hierarchical decision-making process include: If the current real-time vehicle speed If the value is greater than the first-level threshold, the decision outputs a control command to initiate emergency braking. If the current real-time vehicle speed If the value is less than or equal to the first grade threshold and greater than the second grade threshold, the decision output is used as a control command to enter geared coasting. If the current real-time vehicle speed If the value is less than or equal to the second-level threshold, the decision output is used to maintain the control command for normal drive.

9. A curvature-based predictive speed and torque control system for commercial vehicles, characterized in that, include: The function enable and information acquisition module is used to determine whether the enable conditions of the curvature-based predictive speed and torque control function are met, and when they are met, to acquire road information within a preset distance in front of the vehicle, identify the current road segment attributes and the nearest turning segment. The curvature extremum recognition module is used to identify the curvature extremum and the location of the curvature extremum point of the nearest turning road segment based on the current road segment attributes. The turning safety speed determination module is used to calculate the radius of curvature of the nearest turning segment based on the extreme value of curvature of the nearest turning segment, and determine the turning safety speed based on the preset relationship between the radius of curvature and vehicle speed. The remaining road segment calculation module is used to calculate the remaining driving distance and the number of segments based on the vehicle's current position and the position of the curvature extreme point, and obtain the remaining road segment information; The predicted vehicle speed sequence calculation module is used to calculate the predicted vehicle speed sequence in reverse segment by segment based on the vehicle's longitudinal dynamics equation and kinematic equation, with the guaranteed turning speed as the final speed, and combined with the remaining road segment information. The driving behavior decision and execution module is used to compare the current real-time vehicle speed with the predicted vehicle speed sequence, decide to output torque, braking or coasting control commands, and convert the corresponding control commands into torque, gear and braking requests and send them to the vehicle controller for execution.

10. A storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the curvature-based predictive speed and torque control method for commercial vehicles as described in any one of claims 1 to 8.

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