Unmanned vehicle control method and device on bumpy road surface, unmanned vehicle and storage medium

Abstract

The invention relates to the technical field of unmanned driving, automatic driving and unmanned vehicles, and discloses an unmanned vehicle control method and device on a bumpy road surface, an unmanned vehicle and a storage medium. The method comprises the following steps: determining a driving control strategy of the unmanned vehicle according to the bumping degree of a current road surface; under the condition that the driving control strategy represents that the unmanned vehicle needs to be controlled to decelerate, a vertical bumping characteristic signal set representing the bumping degree within a preset distance range is obtained; calculating a longitudinal acceleration constraint corresponding to each vertical bumping characteristic signal in the vertical bumping characteristic signal set based on a linear mapping relationship between the longitudinal acceleration and the vertical bumping characteristic signal, and screening a maximum longitudinal acceleration constraint; and based on the maximum longitudinal acceleration constraint, determining a target vehicle speed after the unmanned vehicle is decelerated, and controlling the unmanned vehicle. According to the invention, intelligent recognition and self-adaptive speed control of the unmanned vehicle on the bumpy road surface are realized, so that the vehicle can automatically adjust the driving speed according to the road surface bumpy degree detected in real time.

Description

Technical Field

[0001] The present application relates to the fields of unmanned driving, automatic driving, and unmanned vehicles, and in particular to a control method and device for an unmanned vehicle on a bumpy road, an unmanned vehicle, and a storage medium. Background Art

[0002] In complex working environments such as mining areas, unmanned vehicles often face undesirable road conditions such as ups and downs, bumps, and potholes. Traditional unmanned driving control methods mainly rely on pre-planned trajectories and speeds for passive control, and lack the ability to perceive and respond to road conditions in real time. When the road conditions are poor, the vehicle will still travel at the preset speed, causing severe vehicle bumps and increasing the risk of vehicle damage and accidents. When it is found on site that the road surface is poor and needs to be rectified, a one-size-fits-all approach is often used to implement a unified speed limit requirement for all vehicles. This approach is too mechanical and cannot be flexibly adjusted according to actual road conditions.

[0003] The main problem with the related technology is that the vehicle does not have the ability to identify the degree of road bumps in real time and cannot adaptively adjust the driving speed according to the road conditions. This causes the vehicle to vibrate violently when passing through bumpy sections, which not only affects transportation efficiency, but also aggravates the wear of vehicle parts, increases failure rate and maintenance costs. At the same time, due to the lack of an intelligent speed control mechanism, the existing speed limit method is too extensive, which can neither guarantee safety nor cause unnecessary efficiency loss. Summary of the invention

[0004] The present application provides a method and device for controlling an unmanned vehicle on a bumpy road, an unmanned vehicle and a storage medium, which are used to realize intelligent recognition and adaptive speed control of bumpy roads by the unmanned vehicle, so that the vehicle can automatically adjust the driving speed according to the degree of road bumpiness detected in real time.

[0005] In the first aspect, the present application provides a method for controlling an unmanned vehicle on a bumpy road, the method comprising: determining a driving control strategy for the unmanned vehicle based on the bumpiness of a current road surface; when the driving control strategy indicates that the unmanned vehicle needs to be controlled to slow down, obtaining a set of vertical bump characteristic signals that characterize the bumpiness within a preset distance range; based on a linear mapping relationship between longitudinal acceleration and vertical bump characteristic signals, calculating the longitudinal acceleration constraint corresponding to each vertical bump characteristic signal in the vertical bump characteristic signal set and screening the maximum longitudinal acceleration constraint; based on the maximum longitudinal acceleration constraint, determining a target vehicle speed after slowing down the unmanned vehicle, and controlling the unmanned vehicle.

[0006] In a second aspect, the present application provides a control device for an unmanned vehicle on a bumpy road, the control device for an unmanned vehicle on a bumpy road comprising:

[0007] The acquisition module is used to determine the driving control strategy of the unmanned vehicle according to the bumpiness of the current road surface;

[0008] A collision zone analysis module, configured to obtain a vertical bump feature signal set representing the bump degree within a preset distance range when the driving control strategy indicates that the unmanned vehicle needs to be controlled to slow down;

[0009] A position relationship determination module, used for calculating the longitudinal acceleration constraint corresponding to each vertical bump characteristic signal in the vertical bump characteristic signal set and screening the maximum longitudinal acceleration constraint based on the linear mapping relationship between the longitudinal acceleration and the vertical bump characteristic signal;

[0010] A decision module is used to determine a target vehicle speed after decelerating the unmanned vehicle based on the maximum longitudinal acceleration constraint, and to control the unmanned vehicle.

[0011] A third aspect of the present application provides an unmanned vehicle, comprising: an unmanned vehicle control device for bumpy roads as described in the second aspect.

[0012] The fourth aspect of the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, it enables the computer to execute the unmanned vehicle control method on a bumpy road as described in the first aspect.

[0013] In the technical solution provided by the present application, firstly, by acquiring a set of vertical bump characteristic signals within a preset distance range, accurate perception of road conditions is achieved. The data collection method based on the preset distance range not only ensures the real-time nature of the data, but also avoids the misjudgment that may be caused by single-point data, making the road condition assessment more comprehensive and reliable; secondly, the longitudinal acceleration constraint is calculated using a linear mapping relationship between the longitudinal acceleration and the vertical bump characteristic signal, and the maximum longitudinal acceleration constraint is screened. This mapping method converts the abstract bumpiness into a specific acceleration constraint, providing a clear quantitative basis for subsequent speed control. At the same time, by screening the maximum constraint value, it ensures that the control strategy can effectively cope with the most unfavorable road conditions; finally, the target vehicle speed is determined and controlled based on the maximum longitudinal acceleration constraint, realizing a closed-loop control from road condition perception to speed regulation. The entire process is fully automated and does not require human intervention, greatly improving the intelligence level and operating efficiency of unmanned vehicles. In addition, the control process of this method is smooth and reliable, avoiding the potential safety hazards caused by sudden braking, protecting the vehicle from damage caused by severe bumps, and ensuring the safety of transported goods. At the same time, due to the use of an adaptive control strategy based on actual road conditions, it is more flexible and efficient than the traditional unified speed limit method, and can maximize transportation efficiency while ensuring safety. Through this intelligent road condition perception and speed control solution, the adaptability and reliability of unmanned vehicles in complex road conditions have been significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0015] Figure 1 A schematic diagram of an embodiment of a method for controlling an unmanned vehicle on a bumpy road in an embodiment of the present application;

[0016] Figure 2 This is a schematic diagram of an embodiment of a control device for an unmanned vehicle on a bumpy road in an embodiment of the present application. DETAILED DESCRIPTION

[0017] Embodiments of the present application provide a method, device, unmanned vehicle and storage medium for controlling an unmanned vehicle on a bumpy road. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 , an embodiment of the method for controlling an unmanned vehicle on a bumpy road in the embodiment of the present application includes:

[0019] Step S101: Determine the driving control strategy of the unmanned vehicle according to the current road bumpiness;

[0020] Step S102: when the driving control strategy indicates that the unmanned vehicle needs to be controlled to slow down, a vertical bump characteristic signal set representing the bump degree within a preset distance range is obtained;

[0021] Step S103, based on the linear mapping relationship between the longitudinal acceleration and the vertical bump characteristic signal, calculating the longitudinal acceleration constraint corresponding to each vertical bump characteristic signal in the vertical bump characteristic signal set and screening the maximum longitudinal acceleration constraint;

[0022] Step S104: determining a target vehicle speed after decelerating the unmanned vehicle based on the maximum longitudinal acceleration constraint, and controlling the unmanned vehicle.

[0023] It is understandable that the execution subject of the present application can be a control device for an unmanned vehicle on a bumpy road, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0024] Optionally, first collect vertical acceleration data through the vehicle-mounted sensor. Vertical acceleration data reflects the vibration of the vehicle in the vertical direction, and the data collection frequency is usually set to once every 0.1 seconds. The collected data is processed to calculate the characteristic value reflecting the degree of bumpiness. For example, when the vehicle is driving, the average vertical acceleration collected for 10 consecutive seconds exceeds 2.5m / s 2 , it indicates that the current road surface is quite bumpy and the speed reduction control strategy needs to be activated. When it is determined that the unmanned vehicle needs to be controlled to slow down, the system will obtain a set of vertical bump feature signals that characterize the degree of bumpiness within a preset distance range. This preset distance range is divided into two parts: a fast response segment and a slow response segment. The fast response segment is set to within 5 meters in front of the vehicle to promptly detect sudden severe bumps; the slow response segment is set to within 20 meters to evaluate the overall bumpiness of the road section. Vertical acceleration data is collected in these two ranges and features are extracted. For example, when the vehicle is traveling at a speed of 20 km / h, about 25 data points are collected within a range of 5 meters and about 100 data points are collected within a range of 20 meters. The degree of bumpiness is characterized by calculating the root mean square value of these data.

[0025] For the acquired vertical bump characteristic signal set, each vertical bump characteristic signal is converted into a corresponding longitudinal acceleration constraint value through a pre-established mapping relationship. This mapping relationship takes into account the physical characteristics of the vehicle and road conditions and is formulated based on actual test data. For example, when the vertical acceleration root mean square value within a range of 5 meters is detected to be 2.8m / s 2 When the longitudinal acceleration constraint value is converted to 1.4m / s 2 ; When the vertical acceleration within 20 meters is 2.5m / s 2 When the constraint value is converted to 1.2m / s 2 . The maximum value is selected from these constraint values ​​as the longitudinal acceleration constraint.

[0026] Finally, based on the maximum longitudinal acceleration constraint selected, the target vehicle speed after deceleration is determined. The determination of the target vehicle speed needs to take into account the current vehicle speed, road conditions and safety factors. For example, when an unmanned mining vehicle is traveling at a speed of 30km / h, if the vertical acceleration root mean square values ​​detected within the front 5 meters and 20 meters are 2.8m / s respectively2 and 2.5m / s 2 , after mapping calculation, the maximum longitudinal acceleration constraint is 1.4m / s 2 , the control system will smoothly reduce the vehicle speed to 22km / h within 3 seconds to ensure that the vehicle can safely pass the bumpy road. During the deceleration process, the control system will continuously monitor the road conditions and dynamically adjust the control parameters to ensure that the deceleration process is smooth and controllable. The entire process ensures driving safety and avoids the impact caused by sudden braking.

[0027] It should be noted that the maximum longitudinal acceleration constraint refers to the maximum deceleration limit in the longitudinal direction of the vehicle set to ensure vehicle safety when driving on bumpy roads. This constraint directly determines the amplitude and rate of vehicle deceleration. The determination of the maximum longitudinal acceleration constraint requires comprehensive consideration of multiple factors such as the degree of road bumps, vehicle status and safety limits. The specific calculation process is as follows: First, obtain the vertical bump characteristic signal within the preset distance range (5-meter fast ring and 20-meter middle ring). For the 5-meter fast ring, the on-board sensor collects vertical acceleration data at a sampling frequency of 100Hz. When the vehicle speed is 25km / h, about 72 data points can be collected; for the 20-meter middle ring, about 288 data points can be collected under the same conditions. These raw data are calculated by root mean square to obtain the vertical bump characteristic signal.

[0028] The acquired vertical bump characteristic signal is converted into the longitudinal acceleration constraint value through linear mapping. The mapping process takes into account the vehicle's own characteristics (such as vehicle weight, suspension characteristics, etc.) and safety margin. For example, when the vertical acceleration root mean square value within a range of 5 meters is detected to be 2.8m / s 2 When the longitudinal acceleration constraint value is 1.4m / s, the linear mapping can be used to obtain 2 ; When the vertical acceleration within 20 meters is 2.5m / s 2 When , the constraint value is 1.2m / s 2 The maximum value is selected from all calculated longitudinal acceleration constraint values ​​as the final maximum longitudinal acceleration constraint. The reason for selecting the maximum value instead of the minimum value is that the maximum constraint value corresponds to the most severe turbulence, and stronger speed reduction measures need to be taken. This can ensure safety while avoiding excessive speed reduction affecting operational efficiency.

[0029] Take a certain type of unmanned mining truck as an example: when the vehicle is fully loaded with 120 tons and travels at a speed of 30km / h on a soft road, the sensor detects that the root mean square value of the vertical acceleration within 5 meters in front is 2.8m / s 2 , 2.5m / s within 20m 2 After mapping calculation, the longitudinal acceleration constraint values ​​are 1.4m / s2 and 1.2m / s 2 , take the maximum value of 1.4m / s2 As the final maximum longitudinal acceleration constraint. Based on this constraint value, the control system will gradually reduce the vehicle speed to 22km / h while ensuring safety. The entire deceleration process takes about 3 seconds and is smooth and controllable. The setting of the maximum longitudinal acceleration constraint fully considers the actual working conditions: it is necessary to deal with bumpy road conditions by slowing down in time to protect the safety of vehicles and goods; it is also necessary to avoid adverse conditions such as violent shaking of goods or skidding of vehicles due to excessive braking. This control method based on real-time adaptive adjustment of road conditions can effectively balance the requirements of safety and operational efficiency.

[0030] In the embodiment of the present application, firstly, by acquiring a set of vertical bump characteristic signals within a preset distance range, accurate perception of road conditions is achieved. The data collection method based on the preset distance range not only ensures the real-time nature of the data, but also avoids the misjudgment that may be caused by single-point data, making the road condition assessment more comprehensive and reliable; secondly, the longitudinal acceleration constraint is calculated using a linear mapping relationship between the longitudinal acceleration and the vertical bump characteristic signal, and the maximum longitudinal acceleration constraint is screened. This mapping method converts the abstract degree of bumpiness into a specific acceleration constraint, providing a clear quantitative basis for subsequent speed control. At the same time, by screening the maximum constraint value, it ensures that the control strategy can effectively cope with the most unfavorable road conditions; finally, the target vehicle speed is determined and controlled based on the maximum longitudinal acceleration constraint, realizing a closed-loop control from road condition perception to speed regulation. The entire process is fully automated and does not require human intervention, greatly improving the intelligence level and operating efficiency of the unmanned vehicle. In addition, the control process of this method is smooth and reliable, avoiding the potential safety hazards caused by sudden braking, protecting the vehicle from damage caused by severe bumps, and ensuring the safety of transported goods. At the same time, due to the use of an adaptive control strategy based on actual road conditions, it is more flexible and efficient than the traditional unified speed limit method, and can maximize transportation efficiency while ensuring safety. Through this intelligent road condition perception and speed control solution, the adaptability and reliability of unmanned vehicles in complex road conditions have been significantly improved.

[0031] In an optional embodiment, the linear mapping relationship between the longitudinal acceleration and the vertical turbulence characteristic signal is characterized by the following formula:

[0032]

[0033] Among them, a x is the longitudinal target acceleration, K a is the acceleration mapping slope coefficient, is a vertical bump characteristic signal, which is determined based on the vertical acceleration of the unmanned vehicle during driving. thres is the acceleration threshold constant.

[0034] Optionally, when the unmanned vehicle passes through a bumpy road, a linear mapping relationship between the longitudinal target acceleration and the vertical bump characteristic signal is established. According to the linear mapping relationship, the longitudinal target acceleration a x Depends on three key parameters: acceleration mapping slope coefficient K a , vertical turbulence characteristic signal And the acceleration threshold constant A thres The vertical turbulence characteristic signal It is determined by the vertical acceleration of the vehicle during driving, reflecting the current bumpiness of the road surface. Through this linear mapping relationship, a quantitative evaluation of the bumpiness and the corresponding speed control strategy are achieved.

[0035] Based on the obtained maximum longitudinal acceleration constraint, the target deceleration curve of the deceleration control is determined. The main purpose of designing the deceleration curve is to ensure that the vehicle can reduce its speed smoothly and avoid violent shaking caused by sudden braking. According to the target deceleration curve, the target vehicle speed after the deceleration control is further determined, and the corresponding control instructions are executed.

[0036] For example, when a 120-ton unmanned mining vehicle is driving on a bumpy road, the onboard sensor measures the RMS value of vertical acceleration within a range of 5 meters. 2.8m / s 2 The acceleration mapping slope coefficient K is known. a is 0.5, the acceleration threshold constant A thres 0.2m / s 2 Substituting these values ​​into the linear mapping formula, the longitudinal target acceleration a is calculated. x =0.5×2.8+0.2=1.6m / s 2 This value is used as the maximum longitudinal acceleration constraint for subsequent deceleration control. If the current vehicle speed is 30km / h, the control system will design a smooth deceleration curve based on this constraint value, and gradually reduce the vehicle speed to 22km / h within 3 seconds, ensuring the smoothness and safety of the deceleration process. During the entire control process, the system will continuously monitor the vertical acceleration data, dynamically update the maximum longitudinal acceleration constraint, and achieve real-time response and adaptive control to bumpy road conditions.

[0037] In an optional embodiment, determining a target vehicle speed after decelerating the unmanned vehicle based on the maximum longitudinal acceleration constraint, and controlling the unmanned vehicle includes:

[0038] (1) determining a target deceleration curve for deceleration control of the unmanned vehicle based on the maximum longitudinal acceleration constraint;

[0039] (2) Determine a target vehicle speed after decelerating the unmanned vehicle according to the target deceleration curve, and control the unmanned vehicle based on the target vehicle speed.

[0040] Optionally, when determining the target deceleration curve based on the maximum longitudinal acceleration constraint, the hyperbolic tangent function is used to design the shape of the deceleration curve. The hyperbolic tangent function has the characteristics of smooth transition, which can avoid sudden changes in the deceleration process and ensure that the deceleration process is smooth and controllable. When designing the deceleration curve, it is first necessary to determine the current vehicle speed and the target deceleration amplitude, which is determined by the maximum longitudinal acceleration constraint. The deceleration curve is adjusted by introducing a time offset constant to make the deceleration process smoother. After determining the deceleration curve, the specific target vehicle speed is obtained by sampling calculation. During the calculation process, time is discretized, and the sampling interval is usually set to 0.1 seconds. For each sampling moment, the corresponding target vehicle speed value is calculated according to the deceleration curve. The calculation process of the target vehicle speed will take into account the actual working conditions of the vehicle, including factors such as the current load state and the road adhesion coefficient, to ensure that the target vehicle speed can meet safety requirements without excessively affecting operational efficiency.

[0041] Specifically, in the implementation process, take a 120-ton unmanned mining vehicle as an example: when the vehicle is traveling at a speed of 30 km / h, the maximum longitudinal acceleration constraint is 1.4 m / s according to the previous calculation. 2 . Based on this constraint value, a deceleration curve is designed. The design parameters of the deceleration curve include: the deceleration amplitude is set to 8km / h, the time offset constant is set to 1.5 seconds, and the curve smoothing coefficient is 0.3. Through these parameter settings, the deceleration curve is divided into 30 sampling points on the time axis (sampling interval is 0.1 seconds), and a continuous target speed sequence from 30km / h to 22km / h is generated. The target speed at each sampling moment is calculated by the deceleration curve function. For example, the target speed at the 5th sampling point (i.e., 0.5 seconds) is 28.5km / h, the target speed at the 15th sampling point (1.5 seconds) is 25.2km / h, and the target speed at the 25th sampling point (2.5 seconds) is 22.8km / h. The entire deceleration process is completed within 3 seconds, during which the speed always maintains a steady downward trend without drastic fluctuations. The speed reduction control command will be executed step by step according to the target speed sequence, while continuously monitoring the actual status of the vehicle to ensure that the actual speed can closely follow the target speed change. The whole process fully reflects the stability and safety of the control, and effectively reduces the adverse effects of bumpy road conditions on the vehicle.

[0042] In an optional embodiment, determining a target deceleration curve for decelerating the unmanned vehicle based on the maximum longitudinal acceleration constraint includes:

[0043] (1) constructing a deceleration curve with a control period as an independent variable and a target vehicle speed to be decelerated within the corresponding control period as a dependent variable; wherein the independent variable is provided with a deceleration gradient coefficient, and a larger deceleration gradient coefficient indicates a faster vehicle speed reduction;

[0044] (2) determining a target deceleration gradient coefficient according to the maximum longitudinal acceleration constraint, and substituting the coefficient into the deceleration curve to obtain a target deceleration curve;

[0045] (3) determining a target vehicle speed after decelerating the unmanned vehicle according to the target deceleration curve, and controlling the unmanned vehicle based on the target vehicle speed, including:

[0046] (4) calculating a target vehicle speed for each control cycle according to the target speed reduction curve;

[0047] (5) In each control cycle, the actual speed of the unmanned vehicle is adjusted according to the corresponding target speed.

[0048] Optionally, when an unmanned vehicle passes through a bumpy road, the deceleration control process must first construct a suitable deceleration curve. This deceleration curve uses the control period as the independent variable and the target vehicle speed as the dependent variable. The control period refers to the time interval for executing a complete control instruction. In practical applications, it is usually set to 0.1 seconds. This time interval can ensure the timeliness of the control without placing too much burden on the control system. The shape of the deceleration curve is determined by the deceleration gradient coefficient. The larger the deceleration gradient coefficient value, the faster the vehicle speed decreases per unit time. In order to ensure the smoothness of the deceleration process, the deceleration gradient coefficient needs to be determined based on the maximum longitudinal acceleration constraint. When the maximum longitudinal acceleration constraint is large, the corresponding deceleration gradient coefficient is also large, which means that the vehicle needs to reduce its speed faster to cope with severe bumps.

[0049] After determining the target speed reduction gradient coefficient, substitute it into the calculation formula of the speed reduction curve to obtain the complete target speed reduction curve. This curve describes the entire process from the current vehicle speed to the target speed, ensuring the smoothness of the speed reduction process. According to the target speed reduction curve, the target vehicle speed corresponding to each control cycle is calculated to form a continuous target vehicle speed sequence.

[0050] For example, a 120-ton unmanned mining vehicle detects strong bumps during driving through sensors and needs to reduce speed. The current vehicle speed is 30km / h, and the maximum longitudinal acceleration constraint obtained through previous calculations is 1.4m / s 2. According to this constraint value, the speed reduction gradient coefficient is calculated to be 1.26, indicating that the speed reduction needs to be completed in a shorter time. The entire speed reduction process is divided into 30 control cycles (0.1 seconds per cycle), and the corresponding target vehicle speed is calculated in each control cycle. Specifically, in the execution process, the target vehicle speed in the 5th control cycle (i.e., 0.5 seconds) is 28.5km / h, the target vehicle speed in the 15th control cycle (1.5 seconds) is reduced to 25.2km / h, and the target vehicle speed in the 25th control cycle (2.5 seconds) is further reduced to 22.8km / h. In each 0.1 second cycle, the control system adjusts the actual vehicle speed according to the calculated target vehicle speed, and gradually reduces the vehicle speed by adjusting the braking system. The entire speed reduction process is completed smoothly within 3 seconds, which not only ensures the safety of the vehicle and cargo, but also avoids the negative impact of sudden braking. At the same time, the control system will continuously monitor the bumpy state of the vehicle and dynamically adjust the speed reduction process when necessary.

[0051] In an optional embodiment, the speed reduction curve is characterized by the following formula:

[0052]

[0053] Among them, v x (t) is the target speed, t is the time, v dropMax is the maximum speed drop constraint, Q is the speed drop gradient coefficient, T shift is the offset constant, M is the constraint coefficient, and N is the vehicle speed control coefficient, which is related to the current vehicle speed.

[0054] In an optional embodiment, determining a target deceleration gradient coefficient according to the maximum longitudinal acceleration constraint includes:

[0055] The calculation formula of the deceleration curve is derived to obtain the acceleration curve, wherein the expression of the acceleration curve is:

[0056]

[0057] The maximum longitudinal acceleration constraint max(a x ) is substituted into the following inequality to calculate and obtain the maximum value max(Q) of the deceleration gradient coefficient Q, and the maximum value max(Q) is used as the target deceleration gradient coefficient:

[0058]

[0059] Optionally, determine the expression of the target deceleration curve. The deceleration curve uses time as a variable and outputs the target vehicle speed. Specifically, the target vehicle speed is determined by the following key parameters: the maximum speed reduction constraint value (indicating the maximum vehicle speed value that needs to be reduced), the deceleration gradient coefficient (controlling the speed of the vehicle speed reduction), the time offset constant (used to adjust the deceleration start time), the constraint coefficient (used to adjust the curve shape) and the vehicle speed control coefficient (compensation value related to the current vehicle speed).

[0060] For the deceleration curve solution process, the acceleration curve expression needs to be calculated first. The acceleration curve reflects the instantaneous acceleration change during the deceleration process, and its value is determined by parameters such as the maximum speed drop constraint value, the deceleration gradient coefficient, and the time offset constant. Based on the calculated acceleration curve, the maximum longitudinal acceleration constraint is substituted into the inequality for calculation to obtain the maximum value of the deceleration gradient coefficient. This maximum value is used as the final target deceleration gradient coefficient for subsequent deceleration control.

[0061] Take a fully loaded 120-ton unmanned mining vehicle as an example: when the vehicle is traveling at a speed of 30 km / h, the maximum longitudinal acceleration constraint obtained through preliminary calculation is 1.4 m / s 2 , the maximum speed reduction constraint value is set to 5km / h. The time offset constant is set to 1.5 seconds, and the constraint coefficient is taken as 0.8. Substituting these parameters into the calculation process, the acceleration curve expression is first obtained, and then the maximum value of the speed reduction gradient coefficient is obtained by solving the inequality, which is 0.6. This speed reduction gradient coefficient represents a relatively gentle speed reduction process, allowing the vehicle to smoothly reduce its speed from 30km / h to 25km / h within 3 seconds. The entire speed reduction process is divided into 30 control cycles (0.1 seconds per cycle), and the corresponding target vehicle speed is calculated according to the speed reduction curve in each cycle. For example, in the 5th control cycle (0.5 seconds), the target vehicle speed is 29km / h; in the 15th control cycle (1.5 seconds), the target vehicle speed is reduced to 27km / h; in the 25th control cycle (2.5 seconds), the target vehicle speed is further reduced to 25.5km / h. This gradual speed adjustment not only ensures the safety of vehicles and cargo, but also avoids the impact caused by sudden speed reduction.

[0062] In an optional embodiment, the deceleration curve is a deceleration trajectory characterized by a deformation of a hyperbolic tangent function.

[0063] The above describes the control method of the unmanned vehicle on a bumpy road in the embodiment of the present application. The following describes the control device of the unmanned vehicle on a bumpy road in the embodiment of the present application. Figure 2 In one embodiment of the present application, an unmanned vehicle control device for a bumpy road comprises:

[0064] The acquisition module 201 is used to determine the driving control strategy of the unmanned vehicle according to the bumpiness of the current road surface;

[0065] A collision zone analysis module 202 is used to obtain a vertical bump feature signal set representing the bump degree within a preset distance range when the driving control strategy indicates that the unmanned vehicle needs to be controlled to slow down;

[0066] The position relationship determination module 203 is used to calculate the longitudinal acceleration constraint corresponding to each vertical bump characteristic signal in the vertical bump characteristic signal set and select the maximum longitudinal acceleration constraint based on the linear mapping relationship between the longitudinal acceleration and the vertical bump characteristic signal;

[0067] The decision module 204 is used to determine the target vehicle speed after the unmanned vehicle is decelerated based on the maximum longitudinal acceleration constraint, and control the unmanned vehicle.

[0068] The decision module 204 includes:

[0069] An analysis unit, configured to determine a target deceleration curve for decelerating the unmanned vehicle based on the maximum longitudinal acceleration constraint;

[0070] A control unit is used to determine a target vehicle speed after the unmanned vehicle is decelerated according to the target deceleration curve, and to control the unmanned vehicle based on the target vehicle speed.

[0071] The analysis unit comprises:

[0072] A construction subunit is used to construct a deceleration curve with the control period as an independent variable and the target vehicle speed to be decelerated within the corresponding control period as a dependent variable; wherein the independent variable is provided with a deceleration gradient coefficient, and the larger the deceleration gradient coefficient is, the faster the vehicle speed decreases;

[0073] A substitution subunit is used to determine a target deceleration gradient coefficient according to the maximum longitudinal acceleration constraint, and substitute it into the deceleration curve to obtain a target deceleration curve;

[0074] The control subunit is used to determine the target vehicle speed after the unmanned vehicle is decelerated according to the target deceleration curve, and control the unmanned vehicle based on the target vehicle speed, including: calculating the target vehicle speed of each control cycle according to the target deceleration curve; and adjusting the actual vehicle speed of the unmanned vehicle according to the corresponding target vehicle speed in each control cycle.

[0075] The substitution subunit is specifically used to: derive the calculation formula of the deceleration curve to obtain the acceleration curve, wherein the expression of the acceleration curve is:

[0076]

[0077] The maximum longitudinal acceleration constraint max(a x ) is substituted into the following inequality to calculate and obtain the maximum value max(Q) of the deceleration gradient coefficient Q, and the maximum value max(Q) is used as the target deceleration gradient coefficient:

[0078]

[0079] Through the coordinated cooperation of the above-mentioned components, firstly, by acquiring the vertical bump characteristic signal set within the preset distance range, accurate perception of the road condition is achieved. The data collection method based on the preset distance range not only ensures the real-time nature of the data, but also avoids the misjudgment that may be caused by single-point data, making the road condition assessment more comprehensive and reliable; secondly, the longitudinal acceleration constraint is calculated using the linear mapping relationship between the longitudinal acceleration and the vertical bump characteristic signal, and the maximum longitudinal acceleration constraint is screened. This mapping method converts the abstract bump degree into a specific acceleration constraint, providing a clear quantitative basis for subsequent speed control. At the same time, by screening the maximum constraint value, it ensures that the control strategy can effectively cope with the most unfavorable road conditions; finally, the target vehicle speed is determined and controlled based on the maximum longitudinal acceleration constraint, realizing closed-loop control from road condition perception to speed regulation. The entire process is fully automated and does not require human intervention, greatly improving the intelligence level and operation efficiency of the unmanned vehicle. In addition, the control process of this method is smooth and reliable, avoiding the potential safety hazards caused by sudden braking, protecting the vehicle from damage caused by severe bumps, and ensuring the safety of transported goods. At the same time, due to the use of an adaptive control strategy based on actual road conditions, it is more flexible and efficient than the traditional unified speed limit method, and can maximize transportation efficiency while ensuring safety. Through this intelligent road condition perception and speed control solution, the adaptability and reliability of unmanned vehicles in complex road conditions have been significantly improved.

[0080] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the method for controlling an unmanned vehicle on a bumpy road.

[0081] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling an unmanned vehicle on a bumpy road, characterized in that: include: Determine the driving control strategy of the unmanned vehicle based on the current road bumpiness; When the driving control strategy indicates that the unmanned vehicle needs to be controlled to slow down, a vertical bump characteristic signal set representing the bump degree within a preset distance range is obtained; Based on the linear mapping relationship between the longitudinal acceleration and the vertical bump characteristic signal, the longitudinal acceleration constraint corresponding to each vertical bump characteristic signal in the vertical bump characteristic signal set is calculated and the maximum longitudinal acceleration constraint is screened; Based on the maximum longitudinal acceleration constraint, a target vehicle speed after decelerating the unmanned vehicle is determined, and the unmanned vehicle is controlled.

2. The method according to claim 1, characterized in that The following formula is used to characterize the linear mapping relationship between longitudinal acceleration and vertical turbulence characteristic signal: Among them, a x is the longitudinal target acceleration, K a is the acceleration mapping slope coefficient, is a vertical bump characteristic signal, which is determined based on the vertical acceleration of the unmanned vehicle during driving. thres is the acceleration threshold constant.

3. The method according to claim 1, characterized in that Determining a target vehicle speed after decelerating the unmanned vehicle based on the maximum longitudinal acceleration constraint, and controlling the unmanned vehicle, including: Based on the maximum longitudinal acceleration constraint, a target deceleration curve for deceleration control of the unmanned vehicle is determined: According to the target deceleration curve, a target vehicle speed after the unmanned vehicle is decelerated is determined, and the unmanned vehicle is controlled based on the target vehicle speed.

4. The method according to claim 3, characterized in that Determining a target deceleration curve for decelerating the unmanned vehicle based on the maximum longitudinal acceleration constraint includes: A deceleration curve is constructed with a control period as an independent variable and a target vehicle speed to be decelerated within the corresponding control period as a dependent variable; wherein the independent variable is provided with a deceleration gradient coefficient, and a larger deceleration gradient coefficient indicates a faster vehicle speed reduction; Determine a target deceleration gradient coefficient according to the maximum longitudinal acceleration constraint, and substitute it into the deceleration curve to obtain a target deceleration curve; Determining a target vehicle speed after decelerating the unmanned vehicle according to the target deceleration curve, and controlling the unmanned vehicle based on the target vehicle speed, including: Calculating a target vehicle speed for each control cycle according to the target speed reduction curve; In each control cycle, the actual speed of the unmanned vehicle is adjusted according to the corresponding target speed.

5. The method according to claim 4, characterized in that The speed reduction curve is characterized by the following formula: Among them, v x (t) is the target speed, t is the time, v dropMax is the maximum speed drop constraint, Q is the speed drop gradient coefficient, T shift is the offset constant, M is the constraint coefficient, N is the vehicle speed control coefficient, and N is related to the current vehicle speed.

6. The method according to claim 5, characterized in that Determining a target deceleration gradient coefficient according to the maximum longitudinal acceleration constraint includes: The calculation formula of the deceleration curve is derived to obtain the acceleration curve, wherein the expression of the acceleration curve is: The maximum longitudinal acceleration constraint max(a x ) is substituted into the following inequality to calculate and obtain the maximum value max(Q) of the deceleration gradient coefficient Q, and the maximum value max(Q) is used as the target deceleration gradient coefficient:

7. The method according to any one of claims 3 to 6, characterized in that: The deceleration curve is a deceleration trajectory characterized by the deformation of a hyperbolic tangent function.

8. A control device for an unmanned vehicle on a bumpy road, characterized in that: include: The acquisition module is used to determine the driving control strategy of the unmanned vehicle according to the bumpiness of the current road surface; A collision zone analysis module, configured to obtain a vertical bump feature signal set representing the bump degree within a preset distance range when the driving control strategy indicates that the unmanned vehicle needs to be controlled to slow down; A position relationship determination module, used for calculating the longitudinal acceleration constraint corresponding to each vertical bump characteristic signal in the vertical bump characteristic signal set and screening the maximum longitudinal acceleration constraint based on the linear mapping relationship between the longitudinal acceleration and the vertical bump characteristic signal; A decision module is used to determine a target vehicle speed after decelerating the unmanned vehicle based on the maximum longitudinal acceleration constraint, and to control the unmanned vehicle.

9. An unmanned vehicle, characterized in that: include: A control device for an unmanned vehicle on a bumpy road as claimed in claim 8.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the unmanned vehicle control method on a bumpy road as described in any one of claims 1-7 is implemented.

Images