Vehicle operation condition adjusting method and device, dumper and storage medium
By acquiring scanning trajectories and desired parameters from open-pit coal mine dump trucks, and adjusting operating conditions in conjunction with pre-aiming trajectories and rule tables, the problems of unstable speed control and frequent switching were solved, enabling smooth tracking control of vehicles on unstructured roads, thus improving operational efficiency and fuel economy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHUZHOU CSR TIMES ELECTRIC CO LTD
- Filing Date
- 2024-10-30
- Publication Date
- 2026-05-01
AI Technical Summary
In open-pit coal mines, the speed control of dump trucks is unstable during heavy-load uphill and downhill operations, leading to frequent switching of working conditions and vehicle derailment, which affects work efficiency. Furthermore, traditional methods are not applicable on unstructured roads, resulting in low efficiency of autonomous driving.
By acquiring the scanning trajectory and desired vehicle operating parameters of autonomous vehicles, and combining the pre-aiming trajectory and rule table, the vehicle operating conditions are adjusted, including feedforward and feedback operating conditions, to avoid frequent switching of operating conditions and achieve smooth tracking control of vehicle speed.
It improves the operational efficiency of unmanned vehicles on unstructured roads, avoids the phenomenon of falling blocks, and ensures the control precision and fuel economy of vehicles.
Smart Images
Figure CN121956931A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of vehicle control technology, and in particular to a method, apparatus, dump truck, and storage medium for adjusting vehicle operating conditions. Background Technology
[0002] The entire process of open-pit coal mine dump truck operation involves loading and transporting coal / stone from the bottom of the pit to the topsoil. This process involves traversing numerous inclines and declines. On one hand, if speed control is unstable during heavy-load inclines and declines, frequent switching of operating conditions may occur. In addition, the actuator drive-to-brake delay can be as high as 2 seconds, causing the vehicle to drop pieces. On the other hand, following vehicles may identify the dropped pieces of the preceding vehicle as obstacles and stop. Heavy-load mine trucks are also extremely prone to dropping pieces when stopped on inclines. This creates a vicious cycle, which greatly reduces the efficiency of operation.
[0003] In traditional structured road systems, road slopes and smoothness are relatively continuous, and slope information can generally be retrieved in real time using inertial navigation or stored in a map. However, in unstructured mining areas, slopes and smoothness are irregular, and the actual map is constantly changing. Traditional methods for obtaining road slopes are not applicable to autonomous driving in mining areas, thus affecting operational efficiency. Summary of the Invention
[0004] This disclosure provides a method, device, dump truck, and storage medium for adjusting vehicle operating conditions, which can improve the accuracy of road information, smoothly track and control vehicle speed, achieve stable control of unstructured road slopes, and improve the operational efficiency of unmanned vehicles.
[0005] Firstly, this disclosure provides a method for adjusting vehicle operating conditions, wherein the vehicle operating conditions include any one of inertial operating conditions, driving operating conditions, and braking operating conditions; the adjustment method includes:
[0006] The scanning trajectory of the next target point of the autonomous vehicle and the expected vehicle operation parameters of each road point within the scanning trajectory are obtained; the scanning trajectory includes the expected trajectory planned based on the operation scenario and the target trajectory planned by scanning several meters ahead of the expected trajectory; the expected vehicle operation parameters include the expected speed and vehicle operating conditions.
[0007] The initial vehicle operating condition of the aiming point and the aiming vehicle operating condition that appears most frequently in the aiming trajectory are determined based on the expected vehicle operating parameters.
[0008] The initial vehicle operating conditions and the target vehicle operating conditions are input into a preset first operating condition filtering rule table to determine the feedforward operating conditions of the autonomous vehicle.
[0009] The feedback operating conditions of the autonomous vehicle are determined based on the actual speed of the current autonomous vehicle and the expected speed at the next waypoint.
[0010] Determine whether a lateral constraint point appears within the scan trajectory;
[0011] When no lateral constraint point appears or the duration of the lateral constraint point is less than the preset duration, the feedforward operation condition and the feedback operation condition are input into the preset second operation condition filtering rule table to select one of the vehicle operation conditions as the vehicle operation condition of the next way point of the autonomous vehicle.
[0012] In some embodiments, the adjustment method further includes:
[0013] When a lateral constraint point appears in the scanning trajectory and its duration exceeds a preset duration, the vehicle's operating conditions at the next point are adjusted based on the feedback operating conditions determined by the actual speed of the autonomous vehicle at its current operating point and the expected speed of the autonomous vehicle at the next point.
[0014] In some embodiments, the first working condition screening rule table includes:
[0015] When one of the two input vehicle operating conditions is an inertial condition, the inertial condition is determined to be the feedforward operating condition of the autonomous vehicle.
[0016] When both input vehicle operating conditions are driving conditions, the driving condition is determined to be the feedforward operating condition of the autonomous vehicle.
[0017] When both input vehicle operating conditions are braking conditions, the braking condition is determined to be the feedforward operating condition of the autonomous vehicle.
[0018] When the two input vehicle operating conditions are driving and braking respectively, the inertia condition is determined as the feedforward operating condition of the autonomous vehicle.
[0019] In some embodiments, the second working condition screening rule table includes:
[0020] When both input vehicle operating conditions are driving conditions, the driving condition is determined as the vehicle operating condition for the next waypoint of the autonomous vehicle.
[0021] When both input vehicle operating conditions are braking conditions, the braking condition is determined as the vehicle operating condition for the next destination of the autonomous vehicle.
[0022] When the two input vehicle operating conditions are driving condition and braking condition respectively, the inertia condition is determined as the vehicle operating condition of the next way point of the autonomous vehicle.
[0023] When the feedforward condition is an inertial condition, either the feedback condition or the feedforward condition is taken as the vehicle operation condition for the next waypoint of the autonomous vehicle.
[0024] In some embodiments, the adjustment method further includes:
[0025] When adjusting the vehicle operating conditions of the autonomous vehicle at the next path point using the second operating condition screening rule table, the expected speed of the autonomous vehicle at the next path point is adjusted to the average expected speed within the expected trajectory.
[0026] In some embodiments, determining the feedback operating condition of the autonomous vehicle based on the actual speed of the autonomous vehicle currently operating and the expected speed of the autonomous vehicle at the next waypoint includes:
[0027] The speed difference between the current actual speed of the autonomous vehicle and the expected speed of the autonomous vehicle at the next destination is determined;
[0028] Compare the speed difference with a preset speed threshold;
[0029] When the speed difference is greater than the speed threshold, the vehicle operating condition of the next waypoint of the autonomous vehicle is determined to be braking condition.
[0030] When the speed difference is less than the speed threshold, the vehicle operating condition of the next waypoint of the autonomous vehicle is determined to be the driving condition.
[0031] When the speed difference is equal to the speed threshold, the vehicle operating condition of the autonomous vehicle at the next waypoint remains unchanged.
[0032] In some embodiments, determining whether a lateral constraint point appears within the pre-aiming trajectory includes:
[0033] Based on the pre-aiming trajectory, the waypoint yaw angle of each waypoint within the pre-aiming trajectory and the distance between each waypoint and the end point of the pre-aiming trajectory are obtained;
[0034] The size of the lateral constraint point within the pre-aimed trajectory is calculated based on the yaw angle of the waypoint and the distance between the waypoint and the end point of the pre-aimed trajectory.
[0035] The size of the lateral constraint point within the pre-aiming trajectory determines whether a lateral constraint point exists within the pre-aiming trajectory.
[0036] In some embodiments, the formula for calculating the lateral constraint point is:
[0037]
[0038] In the formula, yaw is the waypoint yaw angle; n is the waypoint number; and s is the distance between the waypoint and the endpoint of the pre-aimed trajectory.
[0039] Secondly, this disclosure provides a device for adjusting the operating conditions of an autonomous vehicle, characterized in that it includes:
[0040] The planning module is used to obtain the scanning trajectory of the next target point of the autonomous vehicle and the expected vehicle operation parameters of each road point within the scanning trajectory;
[0041] The first generation module is used to determine the initial vehicle operating condition of the aiming point and the aiming vehicle operating condition that appears most frequently in the aiming trajectory based on the desired vehicle operating parameters, and input the initial vehicle operating condition and the aiming vehicle operating condition into a preset first operating condition filtering rule table to select one of the vehicle operating conditions as the feedforward operating condition of the autonomous vehicle.
[0042] The second generation module is used to determine the feedback operating conditions of the autonomous vehicle based on the actual speed of the current autonomous vehicle and the expected speed of the autonomous vehicle at the next waypoint.
[0043] The calculation and judgment module is used to determine whether a lateral constraint point appears within the scanning trajectory;
[0044] The operating condition adjustment module is used to determine the switching mode of the vehicle operating condition of the next way point of the autonomous vehicle based on the judgment result of the calculation and judgment module. When no lateral constraint point appears in the scanning trajectory or the duration of the lateral constraint point is less than the preset duration, the feedforward operating condition and the feedback operating condition are input into the preset second operating condition filtering rule table to determine one of the vehicle operating conditions as the vehicle operating condition of the next way point of the autonomous vehicle.
[0045] Thirdly, this disclosure provides a mining dump truck, including:
[0046] processor;
[0047] A memory for storing processor-executable instructions; wherein the processor is configured to implement the steps of the method described above.
[0048] Fourthly, this disclosure provides a computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the steps of the methods described in the above aspects.
[0049] Fifthly, this disclosure provides a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the methods described in the foregoing aspects.
[0050] This disclosure provides a method, apparatus, dump truck, and storage medium for adjusting vehicle operating conditions. The adjustment method includes: acquiring the scanning trajectory of the next aiming point of the autonomous driving vehicle and the desired vehicle operating parameters of each road point within the scanning trajectory; the scanning trajectory includes a desired trajectory planned based on the work scenario and a aiming trajectory planned by scanning several meters further ahead of the desired trajectory; the desired vehicle operating parameters include desired speed and vehicle operating conditions; determining the initial vehicle operating conditions of the aiming point and the aiming vehicle operating conditions that appear most frequently in the aiming trajectory based on the desired vehicle operating parameters; and then... The initial vehicle operating condition and the pre-aimed vehicle operating condition are input into a preset first operating condition filtering rule table to determine the feedforward operating condition of the autonomous vehicle; the feedback operating condition of the autonomous vehicle is determined based on the actual speed of the current autonomous vehicle and the expected speed of the next path point; it is determined whether a lateral constraint point appears within the scanning trajectory; when no lateral constraint point appears or the duration of the lateral constraint point is less than a preset duration, the feedforward operating condition and the feedback operating condition are input into a preset second operating condition filtering rule table to select one of the vehicle operating conditions as the vehicle operating condition of the next path point of the autonomous vehicle. By further scanning the pre-aimed trajectory after the desired trajectory, the scanning trajectory of the vehicle's next pre-aimed point can be pre-aimed. This, combined with the first rule table to determine the feedforward operating condition, and in conjunction with the feedback operating condition and the second rule table, allows for adjustments to the vehicle's operating condition when there are no lateral constraint points or the duration of lateral constraint points is short. This avoids frequent operating condition switching, achieving smooth tracking control of vehicle speed, preventing the vehicle from slipping during the entire operation, improving the efficiency of autonomous driving, enhancing the accuracy of road information, and enabling stable control of vehicle speed on unstructured road slopes, thus improving the operational efficiency of autonomous driving. Simultaneously, if a lateral constraint point appears and its duration exceeds the preset duration, the feedback operating condition is used to adjust the vehicle's operating condition, ensuring control accuracy. This allows the vehicle's operating condition switching strategy to simultaneously meet both operational efficiency and fuel economy requirements. Attached Figure Description
[0051] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:
[0052] Figure 1 An exemplary flowchart of a vehicle operating condition adjustment method provided in Embodiment 1 of this disclosure;
[0053] Figure 2 An exemplary flowchart illustrating another determination result of the vehicle operating condition adjustment method provided in this embodiment of the disclosure;
[0054] Figure 3 This is an exemplary diagram corresponding to the first operating condition screening rule table in the vehicle operating condition adjustment method of this disclosure embodiment;
[0055] Figure 4 The embodiments of this disclosure correspond to Figure 1 An exemplary flowchart of S4 in the middle;
[0056] Figure 5 The embodiments of this disclosure correspond to Figure 1 An exemplary flowchart for speed comparison in S4;
[0057] Figure 6 The embodiments of this disclosure correspond to Figure 1 An exemplary flowchart of S5 in China;
[0058] Figure 7 This is an exemplary diagram corresponding to the second operating condition screening rule table in the vehicle operating condition adjustment method of this disclosure embodiment;
[0059] Figure 8 This is an exemplary structural diagram of the vehicle operating condition adjustment device provided in Embodiment 2 of this disclosure;
[0060] Figure 9 This is a schematic block diagram of a computer device provided in an embodiment of this disclosure;
[0061] Figure 10 This is a schematic diagram of a readable storage medium provided in an embodiment of this disclosure.
[0062] In the accompanying drawings, the same parts are referred to by the same reference numerals, and the drawings are not drawn to scale. Detailed Implementation
[0063] To enable those skilled in the art to better understand the technical solutions of this disclosure, and to fully understand and implement the process of how this disclosure applies technical means to solve technical problems and achieve corresponding technical effects, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, not all embodiments. The embodiments of this disclosure and the various features within them can be combined with each other without conflict, and the resulting technical solutions are all within the protection scope of this disclosure. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of this disclosure.
[0064] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0065] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.
[0066] The entire process of open-pit coal mine dump truck operation involves loading and transporting coal / stone from the bottom of the pit to the topsoil. This process involves traversing numerous inclines and declines. On one hand, if speed control is unstable during heavy-load inclines and declines, frequent switching of operating conditions may occur. In addition, the actuator drive-to-brake delay can be as high as 2 seconds, causing the vehicle to drop pieces. On the other hand, following vehicles may identify the dropped pieces of the preceding vehicle as obstacles and stop. Heavy-load mine trucks are also extremely prone to dropping pieces when stopped on inclines. This creates a vicious cycle, which greatly reduces the efficiency of operation.
[0067] In traditional structured road systems, road slopes and smoothness are relatively continuous, and slope information can generally be retrieved in real time using inertial navigation or stored in a map. However, in unstructured mining areas, slopes and smoothness are irregular, and the actual map is constantly changing, making traditional methods for obtaining road slopes unsuitable for autonomous driving in mining areas.
[0068] The technical problem solved by this embodiment is to overcome the shortcomings of traditional structured road methods and provide a working condition adjustment method based on road preview + rule table. This method solves the problem of achieving stable speed tracking control of mining dump trucks in the case of insufficient road information such as unstructured roads and feedforward slopes, and avoids the mining dump trucks from dropping blocks during the entire operation process, thereby improving the efficiency of unmanned operation of mining dump trucks.
[0069] This embodiment provides a method for adjusting vehicle operating conditions, such as... Figure 1As shown, the process includes: acquiring the scanning trajectory of the next pre-aiming point of the autonomous vehicle and the expected vehicle operating parameters of each road point within the scanning trajectory; the scanning trajectory includes the expected trajectory planned based on the work scenario and the pre-aiming trajectory planned by scanning several meters further ahead of the expected trajectory; the expected vehicle operating parameters include the expected speed and vehicle operating conditions; determining the initial vehicle operating conditions of the pre-aiming point and the pre-aiming vehicle operating conditions that appear most frequently in the pre-aiming trajectory based on the expected vehicle operating parameters; inputting the initial vehicle operating conditions and the pre-aiming vehicle operating conditions into a preset first operating condition filtering rule table to determine the feedforward operating conditions of the autonomous vehicle; determining the feedback operating conditions of the autonomous vehicle based on the actual speed of the current operation of the autonomous vehicle and the expected speed of the next road point; determining whether a lateral constraint point appears within the scanning trajectory; when no lateral constraint point appears or the duration of the lateral constraint point is less than a preset duration, inputting the feedforward operating conditions and the feedback operating conditions into a preset second operating condition filtering rule table to select one of the vehicle operating conditions as the vehicle operating conditions of the next road point of the autonomous vehicle. By further scanning the pre-aiming trajectory after the desired trajectory, the scanning trajectory of the vehicle's next pre-aiming point can be pre-aimed. After determining the feedforward operating condition by combining the first rule table, and with the feedback operating condition and the second rule table, the vehicle's operating condition can be adjusted using the second rule table when there are no lateral constraint points or the lateral constraint points appear for a short time. This more frequent switching of operating conditions enables smooth tracking and control of the vehicle speed, avoids the problem of the vehicle dropping blocks during the entire operation, and improves the efficiency of the vehicle's autonomous driving.
[0070] Example 1
[0071] Figure 1 This is a flowchart illustrating a method for adjusting vehicle operating conditions according to an embodiment of this disclosure. Figure 1 and Figure 2 As shown, a method for adjusting vehicle operating conditions includes steps S1-S7, specifically including:
[0072] S1. Obtain the scanning trajectory of the next target point of the autonomous vehicle and the expected vehicle operation parameters of each road point within the scanning trajectory; the scanning trajectory includes the expected trajectory planned based on the operation scenario and the target trajectory planned by scanning several meters ahead of the expected trajectory; the expected vehicle operation parameters include the expected speed and vehicle operating conditions.
[0073] In some embodiments, after planning the global desired trajectory according to the work scenario, the vehicle scans the desired trajectory forward by using the next road point as a preview point to obtain information such as speed, operating conditions, and curvature of each road point ahead. Then, a preview trajectory of several meters is further scanned, allowing the vehicle to acquire a preview trajectory several meters further forward based on the desired trajectory, thereby improving the accuracy of road information judgment. In this embodiment, the vehicle operating parameters also include curvature, so as to calculate whether lateral constraint points appear through curvature calculation.
[0074] S2. Determine the initial vehicle operating condition of the aiming point and the aiming vehicle operating condition that appears most frequently in the aiming trajectory based on the expected vehicle operating parameters.
[0075] In some embodiments, the pre-aiming trajectory further scanned also contains several waypoints, wherein the vehicle operating conditions of each waypoint have been initially planned, and the vehicle operating conditions that occur most frequently (i.e. the conditions with the highest weight) are determined as the vehicle operating conditions of the pre-aiming trajectory by using statistical methods.
[0076] S3. Input the initial vehicle operating conditions and the target vehicle operating conditions into a preset first operating condition filtering rule table to determine the feedforward operating conditions of the autonomous vehicle.
[0077] In some embodiments, the first working condition screening rule table includes:
[0078] When one of the two input vehicle operating conditions is an inertial condition, the inertial condition is determined to be the feedforward operating condition of the autonomous vehicle.
[0079] When both input vehicle operating conditions are driving conditions, the driving condition is determined to be the feedforward operating condition of the autonomous vehicle.
[0080] When both input vehicle operating conditions are braking conditions, the braking condition is determined to be the feedforward operating condition of the autonomous vehicle.
[0081] When the two input vehicle operating conditions are driving and braking respectively, the inertia condition is determined as the feedforward operating condition of the autonomous vehicle.
[0082] The specific table is as follows: Figure 3 As shown, B represents the initial vehicle operating condition at the aiming point, and C represents the aiming vehicle operating condition, thus facilitating the determination of the feedforward operating condition.
[0083] S4. Determine the feedback operating conditions of the autonomous vehicle based on the actual speed of the current autonomous vehicle and the expected speed of the next waypoint.
[0084] In some embodiments, step S4, such as Figure 4 As shown, it includes:
[0085] S41. Determine the speed difference between the current actual speed of the autonomous vehicle and the expected speed of the autonomous vehicle at the next destination.
[0086] S42. Compare the speed difference with a preset speed threshold value;
[0087] S43. When the speed difference is greater than the speed threshold, the feedback operating condition of the autonomous vehicle is determined to be braking condition.
[0088] S44. When the speed difference is less than the speed threshold, the feedback operating condition of the autonomous vehicle is determined to be the driving condition.
[0089] S45. When the speed difference is equal to the speed threshold, it is determined that the feedback operating condition of the autonomous vehicle is consistent with the vehicle operating condition at the previous moment.
[0090] Specifically, such as Figure 5 As shown, V represents the actual vehicle speed, Vdes represents the desired speed, and Vthershold represents the speed threshold, thus enabling the calculation of the speed threshold. Figure 5 The flowchart shows how to quickly determine the vehicle's operating conditions based on the speed difference.
[0091] By using speed difference to determine feedback conditions, the feedback conditions of each waypoint can be accurately defined, reducing speed errors. This facilitates the determination of the next waypoint's condition switching method in conjunction with feedforward operating conditions, thus meeting the requirements of operational efficiency and fuel economy.
[0092] S5. Determine whether a lateral constraint point appears within the scan trajectory.
[0093] In some embodiments, step S5, such as Figure 6 As shown, it includes:
[0094] S51. Based on the pre-aiming trajectory, obtain the waypoint yaw angle of each waypoint in the pre-aiming trajectory and the distance between each waypoint and the end point of the pre-aiming trajectory.
[0095] S52. Calculate the size of the lateral constraint point in the pre-aiming trajectory based on the waypoint yaw angle and the distance between the waypoint and the end point of the pre-aiming trajectory.
[0096] S53. Determine whether a lateral constraint point appears in the pre-aiming trajectory based on the size of the lateral constraint point within the pre-aiming trajectory.
[0097] Specifically, the formula for calculating the lateral constraint points is as follows:
[0098]
[0099] In the formula, yaw is the waypoint yaw angle; n is the waypoint number; and s is the distance between the waypoint and the endpoint of the pre-aimed trajectory.
[0100] This method calculates the changes in lateral constraint points, and then determines whether a lateral constraint point exists within the pre-aimed trajectory based on the size of the lateral constraint point. Specifically, when the calculated value of the lateral constraint point is 0, it indicates that no lateral constraint point exists; otherwise, it indicates that a lateral constraint point exists.
[0101] S6. When no lateral constraint point appears or the duration of the lateral constraint point is less than the preset duration, the feedforward operation condition and the feedback operation condition are input into the preset second operation condition filtering rule table to select one of the vehicle operation conditions as the vehicle operation condition of the next way point of the autonomous vehicle.
[0102] In some embodiments, the second working condition screening rule table in step S6 specifically includes:
[0103] When both input vehicle operating conditions are driving conditions, the driving condition is determined as the vehicle operating condition for the next waypoint of the autonomous vehicle.
[0104] When both input vehicle operating conditions are braking conditions, the braking condition is determined as the vehicle operating condition for the next destination of the autonomous vehicle.
[0105] When the two input vehicle operating conditions are driving condition and braking condition respectively, the inertia condition is determined as the vehicle operating condition of the next way point of the autonomous vehicle.
[0106] When the feedforward condition is an inertial condition, either the feedback condition or the feedforward condition is taken as the vehicle operation condition for the next waypoint of the autonomous vehicle.
[0107] Specifically, such as Figure 7 As shown in the figure, T1 represents the feedforward operating condition; T2 represents the feedback operating condition; and T3 represents the vehicle operating condition at the next waypoint of the determined autonomous vehicle. D represents T1, and A represents T2.
[0108] The second filtering rule table determines the vehicle operating condition for the next road point when no lateral constraint point appears or the duration of the lateral constraint point is less than the preset duration. This is achieved by combining feedforward and feedback operating conditions. The vehicle operating condition for the corresponding road point determined by the desired trajectory scanning is switched to ensure stable vehicle operation and improve work efficiency.
[0109] Furthermore, when adjusting the vehicle operating conditions of the autonomous vehicle at the next point using the second operating condition screening rule table, the expected speed of the autonomous vehicle at the next point is adjusted to the average expected speed within the expected trajectory, so as to facilitate smooth vehicle operation.
[0110] S7. When a lateral constraint point appears in the scanning trajectory and the duration of the occurrence exceeds the preset duration, the vehicle operation condition of the autonomous vehicle at the next point is adjusted according to the feedback operation condition determined by the actual speed of the current operation of the autonomous vehicle and the expected speed of the autonomous vehicle at the next point.
[0111] In some embodiments, the preset duration is set to 300ms to facilitate analysis of whether the lateral constraint point clearly meets the requirements for lateral switching. When a lateral constraint point appears in the scanning trajectory and the duration exceeds the preset duration, the vehicle operating condition of the next path point is determined by using the speed difference between the actual speed and the expected speed. This makes the vehicle speed switching stable, avoids continuous vehicle speed control, prevents the vehicle from dropping blocks, and improves work efficiency.
[0112] The vehicle operating condition adjustment method provided in this embodiment scans a pre-aiming trajectory after the desired trajectory, enabling the scanning trajectory of the vehicle's next pre-aiming point to be pre-aimed. This, combined with a first rule table to determine the feedforward operating condition, and then with a feedback operating condition and a second rule table, allows for adjustments to the vehicle's operating condition when there are no lateral constraint points or the duration of lateral constraint points is short. This more frequent operating condition switching achieves smooth tracking control of the vehicle speed, preventing the vehicle from slipping during the entire operation and improving the efficiency of autonomous driving. Simultaneously, if a lateral constraint point appears and its duration exceeds a preset duration, the feedback operating condition is used to adjust the vehicle's operating condition, ensuring control accuracy and allowing the vehicle's operating condition switching strategy to simultaneously meet operational efficiency and fuel economy requirements. This embodiment addresses the issue of whether lateral constraint points occur by employing two operating condition adjustment methods in combination, thus resolving the problems of operational efficiency and fuel economy.
[0113] Example 2
[0114] Based on the above embodiments, this embodiment provides a device for adjusting the operating conditions of an autonomous vehicle, such as... Figure 8 As shown, the adjustment device includes a planning module 11, a first generation module 12, a second generation module 13, a calculation and judgment module 14, and a working condition adjustment module 15. Among them,
[0115] Planning module 11 is used to obtain the scanning trajectory of the next target point of the autonomous vehicle and the expected vehicle operation parameters of each road point within the scanning trajectory;
[0116] The first generation module 12 is used to determine the initial vehicle operating condition of the aiming point and the aiming vehicle operating condition that appears most frequently in the aiming trajectory according to the expected vehicle operating parameters, and input the initial vehicle operating condition and the aiming vehicle operating condition into a preset first operating condition filtering rule table to filter one of the vehicle operating conditions as the feedforward operating condition of the autonomous vehicle.
[0117] The second generation module 13 is used to determine the feedback operating conditions of the autonomous vehicle based on the actual speed of the current operation of the autonomous vehicle and the expected speed of the next way point of the autonomous vehicle.
[0118] The calculation and judgment module 14 is used to determine whether a lateral constraint point appears within the scanning trajectory;
[0119] The operating condition adjustment module 15 is used to determine the switching mode of the vehicle operating condition of the next path point of the autonomous vehicle based on the judgment result of the calculation and judgment module. When no lateral constraint point appears in the scanning trajectory or the duration of the lateral constraint point is less than the preset duration, the feedforward operating condition and the feedback operating condition are input into the preset second operating condition filtering rule table to determine one of the vehicle operating conditions as the vehicle operating condition of the next path point of the autonomous vehicle.
[0120] Specifically, the planning module 11 is located on the vehicle and is used to scan the information in front of the vehicle in real time to determine the expected trajectory and vehicle operating parameters. The first generation module 12, the second generation module 13, and the calculation and judgment module 14 are all connected to the planning module 11. The operating condition adjustment module 15 is connected to the calculation and judgment module 14, the first generation module 12, and the second generation module 13, so as to facilitate the determination of the operating condition adjustment method of the next route point based on the judgment result of the calculation and judgment module, so as to make adaptive adjustments, ensure the stability of vehicle speed, and at the same time meet the requirements of work efficiency and fuel economy.
[0121] Example 3
[0122] Based on the above embodiments, this embodiment provides an application example, applying the adjustment method to a mining dump truck. The method specifically includes the following steps:
[0123] (1) The planning module plans the global expected trajectory based on the operation scenario, obtains the expected trajectory, and performs a forward expected trajectory scan, scanning the speed, working conditions, curvature, and other information of each road point in the road ahead S meters;
[0124] (2) Scan forward M meters based on the desired trajectory scan, and calculate whether a lateral constraint point appears based on the lateral curvature of the waypoint.
[0125] (3) According to rule table 1, the working condition with the highest proportion is selected as feedforward working condition T1 using statistical methods.
[0126] (4) A working condition is defined as feedback working condition T2 based on the actual speed and the expected speed;
[0127] (5) According to the rule table established in this patent, input the feedforward working condition and the feedback working condition to obtain a working condition T3;
[0128] (6) If the time of the lateral constraint point is greater than 300ms, the final working condition switching method is T2, and the speed control with small error is achieved by using single-point pre-aiming to reduce the lateral error.
[0129] (7) If no lateral constraint point occurs within 300ms, the final working condition switching method is T3, and speed error is allowed to ensure continuous working condition.
[0130] (8) The controllers are designed using a feedforward + PI feedback strategy. If the T3 working condition switching mode is used, the expected speed is the average expected speed of the scanning segment S, and the ramp is the average ramp. Finally, different control parameters are selected according to the load conditions.
[0131] (9) Repeat steps (1)-(8).
[0132] In step (1), the desired trajectory S = t1 * v is scanned, where t1 is the aiming time and v is the actual speed.
[0133] The scanning distance M in step (2) is M = t2 * v, where t2 is the pre-aiming time and v is the actual velocity. The formula for calculating the lateral constraint point is:
[0134]
[0135] (Use 0 for the first 5 points, and use the curvature change rate of the 6th point for the last 5 points), where is the waypoint yaw angle, is the waypoint distance from the destination, and n is the waypoint number.
[0136] Step (3) can be summarized as follows: first, count the frequency of each working condition in the scanning segment; then, calculate the frequency of each working condition; finally, find the working condition with the highest frequency, obtain the working condition of the pre-aiming point, and use the statistical table 1 to obtain working condition 1, where 0: inertia, 1: driving, and 2: braking. Figure 3 As shown, this indicates that the feedforward operating conditions are determined by combining the vehicle operating conditions at the pre-aiming point with the pre-aiming vehicle operating conditions using the first rule filtering table.
[0137] The calculation method for step (4) T2 is as follows, specifically as follows: Figure 5As shown, by comparing the actual speed with the desired speed, a speed difference is formed, which facilitates the adjustment of operating conditions by combining the speed difference with the preset speed threshold value.
[0138] The rule table for step (5) is as follows: Figure 7 As shown, 0 represents inertia, 1 represents driving, and 2 represents braking; T1 in the figure represents the feedforward operating condition; T2 represents the feedback operating condition; and T3 represents the vehicle operating condition at the next way point of the determined autonomous vehicle; where D represents T1 and A represents T2.
[0139] The method for calculating the duration of the lateral constraint point in steps (6) and (7) is to multiply the number of durations by the control cycle to obtain the duration.
[0140] By repeating the above steps, the accuracy of road information extraction for mining dump trucks can be improved, and stable speed control of the vehicle can be achieved, avoiding frequent switching of working conditions, thus improving work efficiency and fuel economy.
[0141] Example 4
[0142] Based on the above embodiments, this disclosure provides a mining dump truck, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: implement the steps of any one of the methods in Embodiment 1.
[0143] Example 5
[0144] Based on the above embodiments, this embodiment provides an electronic device, such as... Figure 9 As shown, the system includes a memory 21, a processor 22, and a computer program stored on the memory 21. The processor 22 executes the computer program to implement the steps of the method described in the above embodiments.
[0145] In some embodiments of this example, a computer-readable storage medium is provided, such as... Figure 10 As shown, a computer program 31 is stored thereon, which, when executed by a processor, implements the steps of the method described in the above embodiments.
[0146] In some embodiments of this example, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the method described in the above embodiments.
[0147] The processor may include, but is not limited to, one or more processors or microprocessors. Each processor may be implemented as an Application Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), controller, microcontroller, microprocessor, or other electronic component, for executing the methods described in the above embodiments.
[0148] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof. Computer-readable storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (e.g., hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).
[0149] Computer-readable storage media may also store at least one computer-executable program / instruction, such as computer-readable instructions. Computer-readable storage media include, but are not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Computer-readable storage media may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the computer-readable storage medium, the various methods described above can be performed.
[0150] In addition, the computer device may include (but is not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (e.g., keyboard, mouse, speakers, etc.).
[0151] The processor can communicate with external devices via the I / O bus through wired or wireless networks.
[0152] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product / computer program product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.
[0153] In the embodiments provided in this disclosure, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.
[0154] It should be noted that, in this disclosure, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element limited by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0155] While the embodiments disclosed herein are as described above, the foregoing content is merely for the purpose of facilitating understanding of this disclosure and is not intended to limit this disclosure. Any person skilled in the art to which this disclosure pertains may make any modifications and changes in form and detail of the implementation without departing from the spirit and scope of this disclosure; however, the scope of patent protection of this disclosure shall still be determined by the scope defined in the appended claims.
Claims
1. A method for adjusting vehicle operating conditions, wherein the vehicle operating conditions include any one of an inertial operating condition, a driving operating condition, and a braking operating condition; characterized in that, The adjustment method includes: The scanning trajectory of the next target point of the autonomous vehicle and the expected vehicle operation parameters of each road point within the scanning trajectory are obtained; the scanning trajectory includes the expected trajectory planned based on the operation scenario and the target trajectory planned by scanning several meters ahead of the expected trajectory; the expected vehicle operation parameters include the expected speed and vehicle operating conditions. The initial vehicle operating condition of the aiming point and the aiming vehicle operating condition that appears most frequently in the aiming trajectory are determined based on the expected vehicle operating parameters. The initial vehicle operating conditions and the target vehicle operating conditions are input into a preset first operating condition filtering rule table to determine the feedforward operating conditions of the autonomous vehicle. The feedback operating conditions of the autonomous vehicle are determined based on the actual speed of the current autonomous vehicle and the expected speed at the next waypoint. Determine whether a lateral constraint point appears within the scan trajectory; When no lateral constraint point appears or the duration of the lateral constraint point is less than the preset duration, the feedforward operation condition and the feedback operation condition are input into the preset second operation condition filtering rule table to select one of the vehicle operation conditions as the vehicle operation condition of the next way point of the autonomous vehicle.
2. The method for adjusting vehicle operating conditions according to claim 1, characterized in that, The adjustment method further includes: When a lateral constraint point appears in the scanning trajectory and its duration exceeds a preset duration, the vehicle's operating conditions at the next point are adjusted based on the feedback operating conditions determined by the actual speed of the autonomous vehicle at its current operating point and the expected speed of the autonomous vehicle at the next point.
3. The method for adjusting vehicle operating conditions according to claim 1, characterized in that, The first working condition screening rule table includes: When one of the two input vehicle operating conditions is an inertial condition, the inertial condition is determined to be the feedforward operating condition of the autonomous vehicle. When both input vehicle operating conditions are driving conditions, the driving condition is determined to be the feedforward operating condition of the autonomous vehicle. When both input vehicle operating conditions are braking conditions, the braking condition is determined to be the feedforward operating condition of the autonomous vehicle. When the two input vehicle operating conditions are driving and braking respectively, the inertia condition is determined as the feedforward operating condition of the autonomous vehicle.
4. The method for adjusting vehicle operating conditions according to claim 1, characterized in that, The second working condition screening rule table includes: When both input vehicle operating conditions are driving conditions, the driving condition is determined as the vehicle operating condition for the next waypoint of the autonomous vehicle. When both input vehicle operating conditions are braking conditions, the braking condition is determined as the vehicle operating condition for the next destination of the autonomous vehicle. When the two input vehicle operating conditions are driving condition and braking condition respectively, the inertia condition is determined as the vehicle operating condition of the next way point of the autonomous vehicle. When the feedforward condition is an inertial condition, either the feedback condition or the feedforward condition is taken as the vehicle operation condition for the next waypoint of the autonomous vehicle.
5. The method for adjusting vehicle operating conditions according to claim 1, characterized in that, The adjustment method further includes: When adjusting the vehicle operating conditions of the autonomous vehicle at the next path point using the second operating condition screening rule table, the expected speed of the autonomous vehicle at the next path point is adjusted to the average expected speed within the expected trajectory.
6. A method for adjusting vehicle operating conditions according to claim 1 or 2, characterized in that, The step of determining the feedback operating condition of the autonomous vehicle based on the actual speed of the current operation of the autonomous vehicle and the expected speed of the autonomous vehicle at the next waypoint includes: The speed difference between the current actual speed of the autonomous vehicle and the expected speed of the autonomous vehicle at the next destination is determined; Compare the speed difference with a preset speed threshold; When the speed difference is greater than the speed threshold, the vehicle operating condition of the next waypoint of the autonomous vehicle is determined to be braking condition. When the speed difference is less than the speed threshold, the vehicle operating condition of the next waypoint of the autonomous vehicle is determined to be the driving condition. When the speed difference is equal to the speed threshold, the vehicle operating condition of the autonomous vehicle at the next waypoint remains unchanged.
7. The method for adjusting vehicle operating conditions according to claim 1, characterized in that, The step of determining whether a lateral constraint point appears within the pre-aimed trajectory includes: Based on the pre-aiming trajectory, the waypoint yaw angle of each waypoint within the pre-aiming trajectory and the distance between each waypoint and the end point of the pre-aiming trajectory are obtained; The size of the lateral constraint point within the pre-aimed trajectory is calculated based on the yaw angle of the waypoint and the distance between the waypoint and the end point of the pre-aimed trajectory. The size of the lateral constraint point within the pre-aiming trajectory determines whether a lateral constraint point exists within the pre-aiming trajectory.
8. The method for adjusting vehicle operating conditions according to claim 7, characterized in that, The formula for calculating the lateral constraint point is: In the formula, yaw is the waypoint yaw angle; n is the waypoint number; and s is the distance between the waypoint and the endpoint of the pre-aimed trajectory.
9. A device for adjusting vehicle operating conditions, characterized in that, include: The planning module is used to obtain the scanning trajectory of the next target point of the autonomous vehicle and the expected vehicle operation parameters of each road point within the scanning trajectory; The first generation module is used to determine the initial vehicle operating condition of the aiming point and the aiming vehicle operating condition that appears most frequently in the aiming trajectory based on the desired vehicle operating parameters, and input the initial vehicle operating condition and the aiming vehicle operating condition into a preset first operating condition filtering rule table to select one of the vehicle operating conditions as the feedforward operating condition of the autonomous vehicle. The second generation module is used to determine the feedback operating conditions of the autonomous vehicle based on the actual speed of the current autonomous vehicle and the expected speed of the autonomous vehicle at the next waypoint. The calculation and judgment module is used to determine whether a lateral constraint point appears within the scanning trajectory; The operating condition adjustment module is used to determine the switching mode of the vehicle operating condition of the next way point of the autonomous vehicle based on the judgment result of the calculation and judgment module. When no lateral constraint point appears in the scanning trajectory or the duration of the lateral constraint point is less than the preset duration, the feedforward operating condition and the feedback operating condition are input into the preset second operating condition filtering rule table to determine one of the vehicle operating conditions as the vehicle operating condition of the next way point of the autonomous vehicle.
10. A mining dump truck, characterized in that, include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to implement the steps of the method according to any one of claims 1-8.
11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-8.