Turning adaptive control method and device for electric transport vehicle

Abstract

The present application discloses a method and device for adaptive turning control of an electric transport vehicle. The method includes: when the distance between the electric transport vehicle's turning position in its forward direction and itself is less than or equal to a preset threshold, performing environmental perception to obtain multiple types of environmental perception information; when it is determined based on the multiple types of environmental perception information that there is a dynamic target object, determining the motion trend information of the target object; or when it is determined based on the multiple types of environmental perception information that there is a static object, obtaining the contour parameters of the static object; predicting the optimal turning radius based on the motion trend information of the target object; or predicting the optimal turning radius based on the contour parameters of the static object; and adaptively adjusting the current rotation angle and current rotation speed of the electric transport vehicle's wheels based on the size of the optimal turning radius. By adopting the present application, the electric transport vehicle can quickly and accurately adaptively adjust its own operating parameters when facing complex scenes, thereby improving the stability and safety of the electric transport vehicle.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a turning adaptive control method and device for an electric transport vehicle. Background Art

[0002] In logistics and manufacturing, electric transport trucks are widely used in material handling scenarios. For example, in large warehouses, vehicles need to frequently navigate narrow aisles to transport large quantities of goods to designated locations. This application scenario places extremely high demands on the vehicle's turning flexibility and stability.

[0003] In terms of cornering technology, existing heavy-duty electric transporters primarily improve cornering performance by optimizing wheel layout and drive methods. For example, the TMR flatbed heavy-duty AGV utilizes a dual-steering wheel structure, enabling omnidirectional travel and the ability to flexibly turn in confined spaces.

[0004] However, the current system relies on a fixed wheel layout and drive mode, lacks real-time perception of environmental information and intelligent decision-making capabilities, and fails to fully consider dynamic changes in complex environments, such as the sudden appearance of pedestrians and obstacles. This makes it difficult for electric transport trucks to make fast and accurate adaptive adjustments when facing complex scenes, thus affecting the stability and safety of electric transport trucks. Summary of the Invention

[0005] The present embodiments provide a method and apparatus for adaptive cornering control of an electric transport vehicle. To provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is provided below. This summary is not intended to be a comprehensive review, identify key or important elements, or delineate the scope of protection for these embodiments. Its sole purpose is to present some concepts in a simplified form, serving as a prelude to the detailed description that follows.

[0006] In a first aspect, an embodiment of the present application provides a method for adaptive turning control of an electric transport vehicle, wherein the electric transport vehicle is equipped with a laser radar, a camera, and a millimeter-wave radar, and the method includes:

[0007] During the driving process of the electric transport vehicle, when the distance between the electric transport vehicle and the turning position in the forward direction of the electric transport vehicle in the preset driving route is less than or equal to the preset distance threshold, the environment is perceived through the laser radar, camera and millimeter wave radar to obtain multiple types of environmental perception information;

[0008] When a dynamic target object is determined to exist within a preset area in the direction of travel based on the multi-type environmental perception information, determining the motion trend information of the target object; or when a static object is determined to exist within the preset area in the direction of travel based on the multi-type environmental perception information, obtaining the contour parameters of the static object;

[0009] Predicting the optimal turning radius of the electric transport vehicle during a turn based on the motion trend information of the target object and the current operating parameters of the electric transport vehicle; or predicting the optimal turning radius of the electric transport vehicle during a turn based on the contour parameters of the static object;

[0010] According to the size of the optimal turning radius, the current rotation angle and current rotation speed of the wheels of the electric transport vehicle are adaptively adjusted.

[0011] In a second aspect, an embodiment of the present application provides a turning adaptive control device for an electric transport vehicle, the device comprising:

[0012] An environmental perception module is used to sense the environment through lidar, cameras, and millimeter-wave radar to obtain multiple types of environmental perception information when the distance between the electric transport vehicle and the turning position in the forward direction of the electric transport vehicle in the preset driving route is less than or equal to a preset distance threshold.

[0013] a determination module for determining the motion trend information of a target object if a dynamic target object is present within a preset area in the direction of travel determined based on the multi-type environmental perception information; or for obtaining the contour parameters of a static object if a static object is present within the preset area in the direction of travel determined based on the multi-type environmental perception information;

[0014] A prediction module is used to predict the optimal turning radius of the electric transport vehicle during a turning process based on the motion trend information of the target object and the current operating parameters of the electric transport vehicle; or to predict the optimal turning radius of the electric transport vehicle during a turning process based on the contour parameters of the static object;

[0015] The adaptive adjustment module is used to adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric transport vehicle according to the size of the optimal turning radius.

[0016] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0017] In an embodiment of the present application, on the one hand, when a vehicle approaches a turning position in a preset driving route, multiple types of environmental perception information can be obtained through lidar, cameras, and millimeter-wave radar. This information enables the predicted optimal turning radius to fully consider the dynamic changes in complex environments. Based on the optimal turning radius, the electric transport vehicle can quickly and accurately adaptively adjust the wheel angle and speed to adapt to different turning scenarios, thereby improving the stability and safety of the electric transport vehicle. On the other hand, the present application provides a pre-fitted MAP coordinate curve between the potential collision risk value and the turning radius. Combined with the query mechanism of the MAP coordinate curve, the vehicle can quickly select the optimal turning radius based on different potential collision risk levels. The selection of the optimal turning radius ensures that the electric transport vehicle maintains a stable motion state during the turning process, reducing the risk of rollover due to sharp turns or sudden deceleration.

[0018] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0020] Figure 1 This is a flow chart of a method for adaptively controlling a turning motion of an electric transport vehicle provided by an embodiment of the present application;

[0021] Figure 2 This is a schematic diagram of sensor distribution of an electric transport vehicle provided in an embodiment of the present application;

[0022] Figure 3 This is a pre-fitted MAP coordinate curve diagram between the potential collision risk value and the turning radius provided in an embodiment of the present application;

[0023] Figure 4 This is a flow chart of a method for generating a MAP coordinate curve graph provided in an embodiment of the present application;

[0024] Figure 5 1 is a schematic structural diagram of a turning adaptive control device for an electric transport vehicle provided in an embodiment of the present application;

[0025] Figure 6 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following description and the drawings sufficiently illustrate specific embodiments of the application to enable those skilled in the art to practice them.

[0027] It should be clear that the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0028] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Instead, they are merely examples of devices and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0029] In the description of this application, it should be understood that the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in this application can be understood according to specific circumstances. In addition, in the description of this application, unless otherwise specified, "multiple" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship.

[0030] Currently, existing heavy-duty electric transporters primarily improve turning performance by optimizing wheel layout and drive methods. For example, the TMR flatbed heavy-duty AGV utilizes a dual-steering wheel structure, enabling omnidirectional travel and the ability to flexibly turn in confined spaces.

[0031] The inventors realized that the current reliance on fixed wheel layouts and drive modes lacks real-time perception of environmental information and intelligent decision-making capabilities, and fails to fully consider dynamic changes in complex environments, such as the sudden appearance of pedestrians and obstacles. This makes it difficult for electric transport trucks to make fast and accurate adaptive adjustments when facing complex scenarios, thereby affecting the stability and safety of the electric transport trucks.

[0032] In order to solve the above problems, the present application provides a turning adaptive control method and device for an electric transport vehicle to solve the problems existing in the above-mentioned related technical problems. In an embodiment of the present application, on the one hand, when the vehicle approaches the turning position in the preset driving route, multiple types of environmental perception information can be obtained through laser radar, camera and millimeter wave radar. This information can make the predicted optimal turning radius fully consider the dynamic changes in complex environments. Based on the optimal turning radius, the electric transport vehicle can quickly and accurately adaptively adjust the wheel angle and speed to adapt to different turning scenarios, thereby improving the stability and safety of the electric transport vehicle. On the other hand, the present application provides a pre-fitted MAP coordinate curve diagram between the potential collision risk value and the turning radius. Combined with the query mechanism of the MAP coordinate curve diagram, the vehicle can quickly select the optimal turning radius according to different potential collision risk levels. The selection of the optimal turning radius ensures that the electric transport vehicle maintains a stable motion state during the turning process, reducing the risk of rollover due to sharp turns or sudden deceleration. The following is a detailed description using an exemplary embodiment.

[0033] The following will be combined with the Figure 1 -Attached Figure 4 This article details the adaptive turning control method for an electric transport truck provided by an embodiment of the present application. This method can be implemented using a computer program and run on a von Neumann-based adaptive turning control device for an electric transport truck. This computer program can be integrated into an application or run as a standalone tool application.

[0034] See Figure 1 , is a flow chart of a turning adaptive control method for an electric transport vehicle provided in an embodiment of the present application, wherein the electric transport vehicle is equipped with a laser radar, a camera and a millimeter wave radar. Figure 1 As shown, the method of the embodiment of the present application may include the following steps:

[0035] S101, during the travel of the electric transport vehicle, when the distance between the electric transport vehicle and a position to be turned in the forward direction of the electric transport vehicle in a preset travel route is less than or equal to a preset distance threshold, performing environmental perception using a laser radar, a camera, and a millimeter-wave radar to obtain multiple types of environmental perception information;

[0036] Among them, electric transport trucks are automated vehicles used for material handling, usually used in logistics, warehousing or manufacturing, which can travel on a preset path and complete the handling task. Figure 2As shown, the electric transport truck is equipped with a lidar, camera, and millimeter-wave radar. The preset driving route is a pre-planned vehicle path generated by a navigation system or path planning algorithm, used to guide the electric transport truck's driving direction and target location. The forward direction is the current forward direction of the electric transport truck. The turning position is a specific location in the vehicle's driving path where it needs to turn. It is a key node on the path, such as an intersection, a curve, or the entrance to a narrow passage. The preset distance threshold is a pre-set distance value used to determine whether the vehicle is approaching the turning position. When the distance between the vehicle and the turning position is less than or equal to this threshold, the environmental perception operation is triggered. The camera is a sensor that captures visual images and provides visual information about the vehicle's surroundings for identifying objects, pedestrians, traffic signs, etc. The lidar is a sensor that uses lasers for distance measurement and environmental scanning. It can generate high-precision three-dimensional point cloud data of the vehicle's surroundings for obstacle detection. The millimeter-wave radar is a radar sensor that uses millimeter-wave frequency bands to detect the speed and distance between the electric transport truck and surrounding objects. Multi-category environmental perception information refers to different types of environmental information obtained through multiple sensors, which is used to comprehensively evaluate the environmental status around the vehicle.

[0037] The LiDAR is mounted on the top of the electric transport truck for 360-degree environmental scanning. A camera is installed at the front of the vehicle to capture visual information. Millimeter-wave radars are installed on the front and sides of the vehicle to detect the speed and distance of dynamic targets. The preset distance threshold can be set to 3 meters, triggering the environmental perception process when the electric transport truck is within 3 meters of a turning point.

[0038] In some embodiments of the present application, an electric transport truck travels along a pre-set route within a logistics warehouse, tasked with transporting goods from one end of the warehouse to the other. The vehicle's route includes multiple turning points, and there may be dynamic objects within the warehouse (such as pedestrians, forklifts, or other transport trucks). As the vehicle follows the pre-set route, a navigation system monitors the vehicle's position in real time. When the vehicle is within 3 meters of the next turning point, the navigation system triggers an environmental perception process. A lidar (LiDAR) sensor begins scanning the vehicle's surroundings to generate a 3D point cloud map of the electric transport truck's surroundings. A camera captures visual images in front of the vehicle, and an image recognition algorithm detects moving objects in front of the vehicle and identifies their direction of movement. A millimeter-wave radar (MMW) sensor detects dynamic objects in front of and to the sides of the vehicle, measuring their speed and distance. The electric transport truck's control system pre-processes the data from the MMW radar, camera, and mmWW radar to generate multiple types of environmental perception information about the vehicle's surroundings, including the position and shape of the target object (LIDAR data), its shape and distance, its direction and speed, its visual image (camera data), and its speed and direction (MMW radar data).

[0039] S102, when it is determined based on the multiple types of environmental perception information that a dynamic target object exists within a preset area in the direction of travel, determining motion trend information of the target object; or when it is determined based on the multiple types of environmental perception information that a static object exists within the preset area in the direction of travel, obtaining contour parameters of the static object; the contour parameters of the static object include a position, size, and shape of the static object;

[0040] The preset area is a turning area that the electric transport vehicle is about to enter, and the area is used to limit the range of the target object to be detected. The motion trend information is information describing the motion state of the target object, and the motion trend information of the target object includes a first direction and a first acceleration.

[0041] In some embodiments of the present application, the vehicle's control system fuses data from a lidar, camera, and millimeter-wave radar to determine the presence of a dynamic target within a preset area in the vehicle's forward direction. Using the millimeter-wave radar and camera data, the system determines that the target is moving to the left. Combining the target's position at different times, the target's acceleration can be determined. Alternatively, the system determines the presence of a static object within a preset area in the vehicle's forward direction and, using the millimeter-wave radar and camera data, obtains the static object's position, size, and shape.

[0042] S103, predicting the optimal turning radius of the electric transport vehicle during a turning process based on the motion trend information of the target object and the current operating parameters of the electric transport vehicle; or predicting the optimal turning radius of the electric transport vehicle during a turning process based on the contour parameters of the static object;

[0043] The current operating parameters of the electric transport truck refer to the real-time parameters of the electric transport truck during driving, including the current speed, driving direction, and steering angle. The optimal turning radius is the predicted radius that is most suitable for the electric transport truck to turn.

[0044] In an embodiment of the present application, a specific process of predicting the optimal turning radius of the electric transport vehicle during a turning process based on the motion trend information of the target object and the current operating parameters of the electric transport vehicle includes: establishing a vehicle coordinate system with the current position of the electric transport vehicle as the origin, the forward direction of the vehicle in the vehicle coordinate system is the positive direction of the x-axis, and the left side of the vehicle is the positive direction of the y-axis; judging whether the target object meets the electric transport vehicle within a preset area of ​​the position to be turned based on a first direction and a first acceleration; when the target object meets the electric transport vehicle within the preset area of ​​the position to be turned, calculating a first relative position and a first relative speed of the target object and the electric transport vehicle within the preset area based on the first direction and the first acceleration and the current operating parameters; determining a potential collision risk value corresponding to the target object and the electric transport vehicle in the vehicle coordinate system based on the first relative position and the first relative speed; and querying the optimal turning radius of the electric transport vehicle during a turning process from a pre-fitted MAP coordinate curve diagram between the potential collision risk value and the turning radius based on the potential collision risk value; wherein the MAP coordinate curve diagram is used to characterize the optimal turning radius taken by the electric transport vehicle under different potential collision risks.

[0045] The potential collision risk value is a quantified value of collision risk calculated based on the relative position and velocity of the target object. A higher risk value indicates a greater likelihood of collision. The MAP coordinate curve is a pre-fitted graph that represents the relationship between the potential collision risk value and the optimal turning radius. By querying this graph, you can determine the optimal turning radius for the corresponding risk value.

[0046] In this embodiment, a MAP coordinate curve graphically represents the relationship between potential collision risk and turning radius. Using this MAP curve, the electric transport truck can dynamically adjust its turning radius based on the perceived potential collision risk. This dynamic adjustment capability ensures the vehicle consistently selects the optimal turning strategy in complex environments, improving driving efficiency and safety.

[0047] For example, the current position of the electric transport truck is the origin, the vehicle's forward direction is the positive direction of the x-axis, and the left side of the vehicle is the positive direction of the y-axis. A pedestrian is detected in front of the vehicle, whose movement direction (first direction) is to the left and whose acceleration (first acceleration) is 0.2 m / s². Based on the target object's movement trend information, it is determined whether the pedestrian will meet the electric transport truck in the preset area of ​​the turning position. Assume that the pedestrian is moving to the left and is about to enter the turning area of ​​the vehicle. The relative position of the pedestrian and the vehicle is =(3 m,1 m). The relative speed between pedestrians and vehicles is =(-1 m / s, 0 m / s), indicating that the relative speed of the pedestrian and vehicle in the x-direction is 1 m / s. Based on the relative position and speed, the risk assessment model calculates a potential collision risk value, for example, 0.95 (on a scale of 0 to 1, with 1 indicating the highest risk). Based on a potential collision risk value of 0.65, the MAP coordinate curve is queried and the optimal turning radius is 2 meters. When the risk is zero, the system can turn using the default turning radius of 0.5 meters.

[0048] Specifically, the specific process of determining whether the target object meets the electric transport vehicle within the preset area of ​​the position to be turned includes: obtaining the current speed and current position of the electric transport vehicle; calculating the first time required for the electric transport vehicle to enter the preset range of the position to be turned based on the current speed and current position; predicting the first position of the target object after moving for the first time based on the first direction and the first acceleration; when the first position is within the preset range, determining that the target object meets the electric transport vehicle within the preset area of ​​the position to be turned; or, when the first position is not within the preset range, determining that the target object and the electric transport vehicle do not meet within the preset area of ​​the position to be turned.

[0049] For example, suppose the current speed of the electric truck is =2m / s. The current position of the electric transport vehicle is =(0,0). The distance between the turning position and the current position of the electric transport vehicle is =10 m. Calculate the required time based on the vehicle speed:

[0050]

[0051] For example, the initial position of the target object (pedestrian) is =(8,2). The target object's movement direction (first direction) is to the left, and its speed is =1m / s. Based on the speed and direction of the target object, predict its = Position after 5s:

[0052]

[0053]

[0054] Therefore, the first position of the target object is (3,2).

[0055] The preset range is a circular area with a radius of R = 2m centered at the turning position. The turning position is = (10,0)

[0056] Calculate the distance between the first position of the target object and the position to be turned:

[0057]

[0058] because A radius greater than the center of the position to be turned is R=2m, indicating that the first position of the target object is not within the preset range. That is, the target object and the electric transport vehicle do not meet within the preset area of ​​the position to be turned.

[0059] The current operating parameters include the current speed, the current acceleration and the current position.

[0060] Specifically, the specific process of calculating the first relative position and the first relative speed of the target object and the electric transport vehicle in the preset area according to the first direction, the first acceleration and the current operating parameters is: obtaining the current position of the target object; in the vehicle coordinate system, according to the current position, the first direction and the first acceleration of the target object, calculating the first absolute position and the first absolute speed of the target object at the moment of encounter; the first absolute position includes the first position of the target object in the horizontal direction and the second position of the target object in the vertical direction in the vehicle coordinate system; the first absolute speed includes the first speed of the target object in the horizontal direction and the second speed of the target object in the vertical direction in the vehicle coordinate system; in the vehicle coordinate system, according to the current position, current speed, current acceleration and direction of the electric transport vehicle, calculating the second absolute position and the second absolute speed of the electric transport vehicle at the moment of encounter; the second absolute position includes the third position of the electric transport vehicle in the horizontal direction and the fourth position of the electric transport vehicle in the vertical direction in the vehicle coordinate system; An absolute speed includes a third speed of the electric transport vehicle in the horizontal direction and a fourth speed in the vertical direction in the vehicle coordinate system; calculating the position difference between the first position and the third position to obtain a first position difference of the target object relative to the electric transport vehicle in the horizontal direction in the vehicle coordinate system; calculating the position difference between the second position and the fourth position to obtain a second position difference of the target object relative to the electric transport vehicle in the vertical direction in the vehicle coordinate system; combining the first position difference with the second position difference to obtain a first relative position of the target object and the electric transport vehicle in a preset area; calculating the speed difference between the first speed and the third speed to obtain a first speed difference of the target object relative to the electric transport vehicle in the horizontal direction in the vehicle coordinate system; calculating the speed difference between the second speed and the fourth speed to obtain a second speed difference of the target object relative to the electric transport vehicle in the vertical direction in the vehicle coordinate system; combining the first speed difference with the second speed difference to obtain a first relative speed of the target object and the electric transport vehicle in the preset area.

[0061] For example, calculate the first absolute position and first absolute velocity of the target object at the moment of encounter. For example, the initial position of the target object (pedestrian) is =(8,2). In the vehicle coordinate system, assume that the target object's motion direction (first direction) is to the left, and its speed is =1m / s, the acceleration of the target object (first acceleration) is =0.2m / After t=5s, the position of the target object is:

[0062] ;

[0063]

[0064] The velocity of the target object is:

[0065]

[0066] Calculate the second absolute position and second absolute speed of the electric transport vehicle at the moment of encounter: For example, the current position of the electric transport vehicle is =(0,0), the current speed of the electric transport vehicle is =2m / s, the vehicle's acceleration is =0.1m / , after t=5s, the position of the electric transport vehicle is:

[0067] ;

[0068]

[0069] The speed of the electric transport truck is:

[0070]

[0071] Calculate relative position and relative velocity:

[0072] Relative position:

[0073]

[0074]

[0075] Relative speed:

[0076] ;

[0077] =0-0=0m / s.

[0078] Therefore, the first relative position of the target object and the electric transport vehicle in the preset area is (-7, 2). The first relative speed of the target object and the electric transport vehicle in the preset area is (-2.5, 0).

[0079] Among them, the first relative position includes a first position difference in the horizontal direction and a second position difference in the vertical direction of the target object relative to the electric transport vehicle in the vehicle coordinate system; the first relative speed includes a first speed difference in the horizontal direction and a second speed difference in the vertical direction of the target object relative to the electric transport vehicle in the vehicle coordinate system.

[0080] In some embodiments of the present application, the specific process of determining the potential collision risk value corresponding to the target object and the electric transport vehicle based on the first relative position and the first relative speed includes: taking the root of the sum of the squares of the first position difference and the second position difference to obtain the actual distance between the target object and the electric transport vehicle; taking the root of the sum of the squares of the first speed difference and the second speed difference to obtain the relative movement speed between the target object and the electric transport vehicle; calculating the ratio between the actual distance and the absolute value of the relative movement speed to obtain the collision time between the target object and the electric transport vehicle; comparing the collision time with a preset interval threshold to determine the target interval corresponding to the collision time; and obtaining the target collision risk value corresponding to the target interval from the pre-established mapping relationship between the interval and the collision risk value as the potential collision risk value corresponding to the target object and the electric transport vehicle.

[0081] In some embodiments of the present application, a specific process of generating a pre-fitted MAP coordinate curve graph between potential collision risk values ​​and turning radius includes: simulating the operating state of an electric transport vehicle at a preset turning position point under different preset turning radii using simulation software, where the different preset turning radii include at least one moving object within a preset area; recording multiple simulated operating parameters of the electric transport vehicle and multiple simulated motion trends of the at least one moving object under each preset turning radius to obtain multiple simulated operating parameters and multiple simulated motion trends corresponding to each preset turning radius; permuting and combining the multiple simulated operating parameters and multiple simulated motion trends corresponding to each preset turning radius to obtain multiple groups of simulation data corresponding to each preset turning radius; determining a potential collision risk value corresponding to each group of simulation data based on the second direction and second acceleration included in each simulated motion trend and each simulation operating parameter in each group of simulation data corresponding to each preset turning radius, as multiple potential collision risk values ​​corresponding to each preset turning radius; using each potential collision risk value corresponding to each preset turning radius as an input variable and each preset turning radius as an output variable, and fitting a relationship between the input variable and the output variable; and drawing a curve graph between each preset turning radius and each potential collision risk value based on the fitted relationship to serve as the pre-fitted MAP coordinate curve graph between the potential collision risk value and the turning radius.

[0082] Among them, the specific process of determining the potential collision risk value corresponding to each set of simulation data according to the second direction and the second acceleration contained in each simulated motion trend in each set of simulation data corresponding to each preset turning radius and each simulation operating parameter includes: calculating the second relative position and the second relative speed of the moving object and the electric transport vehicle in the preset area according to the second direction and the second acceleration contained in each simulated motion trend in each set of simulation data corresponding to each preset turning radius and each simulation operating parameter; determining the potential collision risk value corresponding to the moving object and the electric transport vehicle according to the second relative position and the second relative speed; and using the potential collision risk value corresponding to the moving object and the electric transport vehicle as the potential collision risk value corresponding to each set of simulation data.

[0083] It should be noted that the logic for determining the potential collision risk value between the moving object and the electric transport vehicle based on the second relative position and the second relative speed is the same as the logic for determining the potential collision risk value between the target object and the electric transport vehicle based on the first relative position and the first relative speed, and will not be repeated here.

[0084] It should be noted that the MAP coordinate curve between the pre-fitted potential collision risk value and the turning radius is as follows: Figure 3 As shown in the figure, when the potential collision risk is high, the electric transport truck needs to select a larger turning radius to avoid instability or collision risk caused by sharp turns. When the potential collision risk is low, the vehicle can select a smaller turning radius to complete the turn more tightly. A larger turning radius can reduce the vehicle's lateral acceleration during the turn, lowering the risk of loss of control or collision.

[0085] In other embodiments of the present application, the specific process of predicting the optimal turning radius of the electric transport vehicle during a turning process based on the contour parameters of the static object includes: determining the minimum safety distance between the electric transport vehicle and the static object based on the size and shape of the static object, and the minimum safety distance is used to indicate that the electric transport vehicle will not collide with the static object when turning; and calculating the optimal turning radius of the electric transport vehicle during a turning process based on the position of the static object and the minimum safety distance.

[0086] Among them, the calculation formula for the optimal turning radius of the electric transport vehicle during the turning process is:

[0087]

[0088] in, is the horizontal coordinate position of the static object, is the ordinate position of the static object, is the horizontal coordinate position of the electric transport vehicle, is the vertical coordinate position of the electric transport vehicle.

[0089] For example, the static object position is (5 meters, 2 meters), the coordinates of the electric transport truck in the vehicle coordinate system established with the electric transport truck as the origin are (0, 0), the minimum safety distance is 0.5 meters, and the vehicle width is 1 meter.

[0090] at this time,

[0091]

[0092] Specifically, when determining the minimum safe distance between the electric transport vehicle and the static object based on the size and shape of the static object, a preset safety margin set for the electric transport vehicle is obtained. The preset safety margin is to ensure that the vehicle can still safely avoid the static object when there are errors in perception and control; the width of the static object is quantified based on the size and shape of the static object; and the minimum safe distance between the electric transport vehicle and the static object is calculated based on the width of the static object, the width of the electric transport vehicle and the preset safety margin.

[0093] Minimum safety distance = ;

[0094] In an embodiment of the present application, the electric transport vehicle can predict the optimal turning radius based on the contour parameters of the static object and adjust the driving strategy to avoid static obstacles, thereby ensuring safety and flexibility in complex environments.

[0095] S104 , adaptively adjusting the current rotation angle and current rotation speed of the wheels of the electric transport vehicle according to the size of the optimal turning radius.

[0096] In some embodiments of the present application, the specific process of adaptively adjusting the current rotation angle and current rotation speed of the wheels of the electric transport vehicle according to the size of the optimal turning radius includes: obtaining the current speed of the wheels of the electric transport vehicle and the vehicle wheelbase; calculating the target steering angle of the electric transport vehicle according to the vehicle wheelbase and the size of the optimal turning radius; calculating the target linear speed of the wheels of the electric transport vehicle according to the current speed, the vehicle wheelbase and the size of the optimal turning radius; and adaptively adjusting the current rotation angle and current rotation speed of the wheels of the electric transport vehicle according to the target steering angle and the target linear speed.

[0097] Among them, the calculation formula of the target steering angle is:

[0098]

[0099] in, is the target steering angle, is the vehicle wheelbase, is the optimal turning radius, is the inverse tangent function;

[0100] The calculation formula of the target linear velocity is:

[0101]

[0102] in, and are the target linear speeds of the left and right wheels of the electric transport truck, is the current speed;

[0103] Specifically, the specific process of adaptively adjusting the current rotation angle and current rotation speed of the wheels of the electric transport vehicle according to the target steering angle and the target linear speed includes: controlling the current steering angle of the electric transport vehicle to smoothly transition to the target steering angle through the PID controller of the electric transport vehicle, so as to adaptively adjust the current rotation angle of the wheels of the electric transport vehicle; controlling the current speed of the electric transport vehicle to smoothly transition to the target linear speed through the speed controller of the electric transport vehicle, so as to adaptively adjust the current rotation speed of the wheels of the electric transport vehicle.

[0104] In an embodiment of the present application, on the one hand, when a vehicle approaches a turning position in a preset driving route, multiple types of environmental perception information can be obtained through lidar, cameras, and millimeter-wave radar. This information enables the predicted optimal turning radius to fully consider the dynamic changes in complex environments. Based on the optimal turning radius, the electric transport vehicle can quickly and accurately adaptively adjust the wheel angle and speed to adapt to different turning scenarios, thereby improving the stability and safety of the electric transport vehicle. On the other hand, the present application provides a pre-fitted MAP coordinate curve between the potential collision risk value and the turning radius. Combined with the query mechanism of the MAP coordinate curve, the vehicle can quickly select the optimal turning radius based on different potential collision risk levels. The selection of the optimal turning radius ensures that the electric transport vehicle maintains a stable motion state during the turning process, reducing the risk of rollover due to sharp turns or sudden deceleration.

[0105] See Figure 4 , provides a flow chart of a method for generating a MAP coordinate curve diagram according to an embodiment of the present application. Figure 4 As shown, the method of the embodiment of the present application may include the following steps:

[0106] S201, simulating the operation of an electric transport vehicle at a preset turning position with different preset turning radii using simulation software, where the different preset turning radii include at least one moving object within a preset area;

[0107] S202, recording multiple simulated operating parameters of the electric transport vehicle and multiple simulated motion trends of at least one moving object at each preset turning radius, to obtain multiple simulated operating parameters and multiple simulated motion trends corresponding to each preset turning radius;

[0108] S203, arranging and combining multiple simulation operation parameters and multiple simulation motion trends corresponding to each preset turning radius to obtain multiple sets of simulation data corresponding to each preset turning radius;

[0109] S204: determining a potential collision risk value corresponding to each set of simulation data based on the second direction and the second acceleration included in each simulated motion trend in each set of simulation data corresponding to each preset turning radius and each simulation operation parameter, as a plurality of potential collision risk values ​​corresponding to each preset turning radius;

[0110] S205, using each potential collision risk value corresponding to each preset turning radius as an input variable and each preset turning radius as an output variable, fitting the relationship between the input variable and the output variable;

[0111] S206 , based on the fitted relationship, a curve graph is drawn between each preset turning radius and each potential collision risk value, as a pre-fitted MAP coordinate curve graph between the potential collision risk value and the turning radius.

[0112] In some embodiments of the present application, simulation software is used to simulate the operating state of an electric transport truck at preset turning locations at different preset turning radii. The preset turning radii are 3 meters, 4 meters, 5 meters, and 6 meters. For each preset turning radius, multiple simulated operating parameters of the electric transport truck (e.g., speed, acceleration, steering angle) and multiple simulated motion trends (e.g., speed, direction, acceleration) of at least one moving object (e.g., pedestrian) are recorded. For example, for a 3-meter turning radius, the vehicle speed is recorded as 2 m / s, the pedestrian speed is recorded as 1 m / s, and the pedestrian's direction of movement is leftward. The multiple simulated operating parameters and multiple simulated motion trends corresponding to each preset turning radius are permuted and combined to obtain multiple sets of simulation data corresponding to each preset turning radius. For example, for a 3-meter turning radius, the following sets of simulation data are obtained: Data Set 1: Vehicle speed 2 m / s, pedestrian speed 1 m / s, pedestrian direction leftward. Data Set 2: Vehicle speed 2 m / s, pedestrian speed 1.5 m / s, pedestrian direction rightward. Based on the second direction and second acceleration included in each simulated motion trend and each simulation operating parameter in each set of simulation data corresponding to each preset turning radius, the potential collision risk value corresponding to each set of simulation data is determined. For example, for data set 1 (3-meter turning radius, vehicle speed of 2 m / s, pedestrian speed of 1 m / s, and pedestrian turning left), the collision risk assessment model calculates a potential collision risk value of 0.6. Using each potential collision risk value corresponding to each preset turning radius as an input variable and each preset turning radius as an output variable, a relationship between the input and output variables is fitted. Using regression analysis, a curve is fitted to the relationship between the potential collision risk value and the turning radius. For example, a graph is plotted showing the potential collision risk values ​​(0.6, 0.4, 0.2, and 0.1) corresponding to turning radii of 3, 4, 5, and 6 meters.

[0113] In an embodiment of the present application, the present application provides a pre-fitted MAP coordinate curve diagram between the potential collision risk value and the turning radius. Combined with the query mechanism of the MAP coordinate curve diagram, the vehicle can quickly select the optimal turning radius according to different potential collision risk levels. The selection of the optimal turning radius ensures that the electric transport vehicle maintains a stable movement state during the turning process, reducing the risk of rollover caused by sharp turns or sudden deceleration.

[0114] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0115] See Figure 5, which shows a schematic diagram of the structure of an adaptive turning control device for an electric transport vehicle according to an exemplary embodiment of the present application. The adaptive turning control device for an electric transport vehicle can be implemented as all or part of an electronic device through software, hardware, or a combination of both. The device 1 includes an environment perception module 10, a determination module 20, a prediction module 30, and an adaptive adjustment module 40.

[0116] The environmental perception module 10 is used to sense the environment through a laser radar, a camera, and a millimeter-wave radar to obtain multiple types of environmental perception information when the distance between the electric transport vehicle and the turning position in the forward direction of the electric transport vehicle in the preset driving route is less than or equal to a preset distance threshold.

[0117] Determination module 20, configured to determine the motion trend information of a target object if a dynamic target object is present within a preset area in the direction of travel determined based on the multi-type environmental perception information; or to obtain the contour parameters of a static object if a static object is present within the preset area in the direction of travel determined based on the multi-type environmental perception information;

[0118] The prediction module 30 is used to predict the optimal turning radius of the electric transport vehicle during a turning process based on the motion trend information of the target object and the current operating parameters of the electric transport vehicle; or to predict the optimal turning radius of the electric transport vehicle during a turning process based on the contour parameters of the static object;

[0119] The adaptive adjustment module 40 is used to adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric transport vehicle according to the size of the optimal turning radius.

[0120] It should be noted that the aforementioned embodiments of the adaptive turning control device for an electric transport vehicle, when executing the adaptive turning control method for an electric transport vehicle, only illustrate the division of the aforementioned functional modules. In actual applications, the aforementioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the adaptive turning control device for an electric transport vehicle provided in the aforementioned embodiments and the adaptive turning control method for an electric transport vehicle are based on the same concept. The implementation process is detailed in the method embodiments and will not be repeated here.

[0121] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0122] In an embodiment of the present application, on the one hand, when a vehicle approaches a turning position in a preset driving route, multiple types of environmental perception information can be obtained through lidar, cameras, and millimeter-wave radar. This information enables the predicted optimal turning radius to fully consider the dynamic changes in complex environments. Based on the optimal turning radius, the electric transport vehicle can quickly and accurately adaptively adjust the wheel angle and speed to adapt to different turning scenarios, thereby improving the stability and safety of the electric transport vehicle. On the other hand, the present application provides a pre-fitted MAP coordinate curve between the potential collision risk value and the turning radius. Combined with the query mechanism of the MAP coordinate curve, the vehicle can quickly select the optimal turning radius based on different potential collision risk levels. The selection of the optimal turning radius ensures that the electric transport vehicle maintains a stable motion state during the turning process, reducing the risk of rollover due to sharp turns or sudden deceleration.

[0123] The present application also provides a computer-readable medium having program instructions stored thereon, which, when executed by a processor, implements the turning adaptive control method for the electric transport vehicle provided by the above-mentioned various method embodiments.

[0124] The present application also provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the turning adaptive control method for an electric transport vehicle according to each of the above method embodiments.

[0125] See Figure 6 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 6 As shown, the electronic device 1000 may include: at least one processor 1001 , at least one network interface 1004 , a user interface 1003 , a memory 1005 , and at least one communication bus 1002 .

[0126] The communication bus 1002 is used to implement the connection and communication between these components.

[0127] The user interface 1003 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 1003 may also include a standard wired interface and a wireless interface.

[0128] The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0129] The processor 1001 may include one or more processing cores. The processor 1001 utilizes various interfaces and circuits to connect various components within the electronic device 1000. It executes instructions, programs, code sets, or instruction sets stored in the memory 1005, and accesses data stored in the memory 1005 to perform various functions and process data within the electronic device 1000. Optionally, the processor 1001 may be implemented in hardware using at least one of a digital signal processing (DSP), a field-programmable gate array (FPGA), and a programmable logic array (PLA). The processor 1001 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display; and the modem handles wireless communications. It is understood that the modem may also be implemented independently of the processor 1001 and implemented on a separate chip.

[0130] Among them, the memory 1005 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable storage medium. The memory 1005 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 1005 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 1005 may also be optionally at least one storage system located away from the aforementioned processor 1001. As Figure 6 As shown, the memory 1005 as a computer storage medium may include an operating system, a network communication module, a user interface module, and a turning adaptive control application for an electric transport vehicle.

[0131] exist Figure 6In the electronic device 1000 shown, the user interface 1003 is mainly used to provide an input interface for the user and obtain user input data; and the processor 1001 can be used to call the electric transport vehicle turning adaptive control application stored in the memory 1005 and specifically perform the following operations:

[0132] During the driving process of the electric transport vehicle, when the distance between the electric transport vehicle and the turning position in the forward direction of the electric transport vehicle in the preset driving route is less than or equal to the preset distance threshold, the environment is perceived through the laser radar, camera and millimeter wave radar to obtain multiple types of environmental perception information;

[0133] When a dynamic target object is determined to exist within a preset area in the direction of travel based on the multi-type environmental perception information, determining the motion trend information of the target object; or when a static object is determined to exist within the preset area in the direction of travel based on the multi-type environmental perception information, obtaining the contour parameters of the static object;

[0134] Predicting the optimal turning radius of the electric transport vehicle during a turn based on the motion trend information of the target object and the current operating parameters of the electric transport vehicle; or predicting the optimal turning radius of the electric transport vehicle during a turn based on the contour parameters of the static object;

[0135] According to the size of the optimal turning radius, the current rotation angle and current rotation speed of the wheels of the electric transport vehicle are adaptively adjusted.

[0136] In one embodiment, when the processor 1001 predicts the optimal turning radius of the electric transport vehicle during a turning process based on the motion trend information of the target object and the current operating parameters of the electric transport vehicle, the processor 1001 specifically performs the following operations:

[0137] A vehicle coordinate system is established with the current position of the electric transport truck as the origin. In the vehicle coordinate system, the forward direction of the vehicle is the positive direction of the x-axis, and the left side of the vehicle is the positive direction of the y-axis.

[0138] Determining, based on the first direction and the first acceleration, whether the target object and the electric transport vehicle meet within a preset area of ​​the position to be turned;

[0139] When the target object and the electric transport vehicle meet in a preset area of ​​the position to be turned, calculating a first relative position and a first relative speed between the target object and the electric transport vehicle in the preset area according to the first direction and the first acceleration and the current operating parameters;

[0140] In the vehicle coordinate system, determining a potential collision risk value corresponding to the target object and the electric transport vehicle according to the first relative position and the first relative speed;

[0141] According to the potential collision risk value, the optimal turning radius of the electric transport vehicle during the turning process is queried from the pre-fitted MAP coordinate curve diagram between the potential collision risk value and the turning radius; wherein the MAP coordinate curve diagram is used to characterize the optimal turning radius adopted by the electric transport vehicle under different potential collision risks.

[0142] In one embodiment, when generating a pre-fitted MAP coordinate curve graph between the potential collision risk value and the turning radius, the processor 1001 specifically performs the following operations:

[0143] Simulating the operation of the electric transport vehicle at different preset turning radiuses at a preset turning position using simulation software, wherein the different preset turning radiuses include at least one moving object within a preset area;

[0144] Recording multiple simulated operating parameters of the electric transport vehicle and multiple simulated motion trends of at least one moving object at each preset turning radius, and obtaining multiple simulated operating parameters and multiple simulated motion trends corresponding to each preset turning radius;

[0145] Arrange and combine multiple simulation operation parameters and multiple simulation motion trends corresponding to each preset turning radius to obtain multiple sets of simulation data corresponding to each preset turning radius;

[0146] Determining, based on the second direction and the second acceleration included in each simulated motion trend in each set of simulation data corresponding to each preset turning radius and each simulation operating parameter, a potential collision risk value corresponding to each set of simulation data as multiple potential collision risk values ​​corresponding to each preset turning radius;

[0147] Each potential collision risk value corresponding to each preset turning radius is used as an input variable, each preset turning radius is used as an output variable, and the relationship between the input variable and the output variable is fitted;

[0148] According to the fitted relationship, a curve graph between each preset turning radius and each potential collision risk value is drawn as a MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius.

[0149] In one embodiment, when determining the potential collision risk value corresponding to each set of simulation data based on the second direction and second acceleration included in each simulated motion trend and each simulation operation parameter in each set of simulation data corresponding to each preset turning radius, the processor 1001 specifically performs the following operations:

[0150] Calculating a second relative position and a second relative speed between the moving object and the electric transport vehicle within the preset area based on the second direction and the second acceleration included in each simulated motion trend in each set of simulation data corresponding to each preset turning radius and each simulation operation parameter;

[0151] determining a potential collision risk value corresponding to the moving object and the electric transport according to the second relative position and the second relative speed;

[0152] The potential collision risk value corresponding to the moving object and the electric transport is used as the potential collision risk value corresponding to each set of simulation data.

[0153] In one embodiment, when the processor 1001 determines whether the target object meets the electric transport vehicle within a preset area of ​​the turning position, the processor 1001 specifically performs the following operations:

[0154] Get the current speed and current position of the electric transport vehicle;

[0155] Calculating a first time required for the electric transport vehicle to enter a preset range of a position to be turned based on the current speed and the current position;

[0156] Predicting a first position of the target object after it moves for a first period of time based on the first direction and the first acceleration;

[0157] When the first position is within the preset range, it is determined that the target object and the electric transport vehicle meet within the preset area of ​​the position to be turned; or

[0158] When the first position is not within the preset range, it is determined that the target object and the electric transport vehicle do not meet within the preset area of ​​the position to be turned.

[0159] In one embodiment, when the processor 1001 calculates the first relative position and the first relative speed of the target object and the electric transport vehicle in a preset area according to the first direction, the first acceleration, and the current operating parameters, the processor 1001 specifically performs the following operations:

[0160] Get the current position of the target object;

[0161] In the vehicle coordinate system, a first absolute position and a first absolute velocity of the target object at the moment of encounter are calculated based on the current position, the first direction, and the first acceleration of the target object; the first absolute position includes a first position of the target object in the horizontal direction and a second position of the target object in the vertical direction in the vehicle coordinate system; the first absolute velocity includes a first velocity of the target object in the horizontal direction and a second velocity of the target object in the vertical direction in the vehicle coordinate system;

[0162] In the vehicle coordinate system, a second absolute position and a second absolute velocity of the electric transport vehicle at the moment of encounter are calculated based on the current position, current velocity, current acceleration, and direction of the electric transport vehicle; the second absolute position includes a third position in the horizontal direction and a fourth position in the vertical direction of the electric transport vehicle in the vehicle coordinate system; and the first absolute velocity includes the third velocity in the horizontal direction and the fourth velocity in the vertical direction of the electric transport vehicle in the vehicle coordinate system;

[0163] Calculating a position difference between the first position and the third position to obtain a first position difference of the target object relative to the electric transport vehicle in the horizontal direction in the vehicle coordinate system;

[0164] Calculating a position difference between the second position and the fourth position to obtain a second position difference of the target object relative to the electric transport vehicle in a vertical direction in the vehicle coordinate system;

[0165] Combining the first position difference with the second position difference to obtain a first relative position of the target object and the electric transport vehicle within a preset area;

[0166] Calculating a speed difference between the first speed and the third speed to obtain a first speed difference of the target object relative to the electric transport vehicle in a horizontal direction in the vehicle coordinate system;

[0167] Calculating a speed difference between the second speed and the fourth speed to obtain a second speed difference of the target object in a vertical direction relative to the electric transport vehicle in the vehicle coordinate system;

[0168] The first speed difference and the second speed difference are combined to obtain a first relative speed between the target object and the electric transport vehicle in a preset area.

[0169] In one embodiment, the processor 1001, in the process of determining the potential collision risk value corresponding to the target object and the electric transport vehicle according to the first relative position and the first relative speed, specifically performs the following operations:

[0170] The actual distance between the target object and the electric transport vehicle is obtained by summing the squares of the first position difference and the second position difference and taking the root thereof;

[0171] The square of the first speed difference and the second speed difference are summed and the root value is taken to obtain the relative motion speed between the target object and the electric transport vehicle;

[0172] Calculate the ratio between the actual distance and the absolute value of the relative motion speed to obtain the collision time between the target object and the electric transport vehicle;

[0173] Comparing the collision time with a preset interval threshold to determine a target interval corresponding to the collision time;

[0174] From the pre-established mapping relationship between the interval and the collision risk value, the target collision risk value corresponding to the target interval is obtained as the potential collision risk value corresponding to the target object and the electric transport vehicle.

[0175] In one embodiment, when the processor 1001 adaptively adjusts the current rotation angle and current rotation speed of the wheels of the electric transport vehicle according to the size of the optimal turning radius, the processor 1001 specifically performs the following operations:

[0176] Get the current speed of the wheels and the wheelbase of the electric transport vehicle;

[0177] Calculate the target steering angle of the electric transport truck based on the vehicle wheelbase and the optimal turning radius;

[0178] Calculate the target linear speed of the electric truck's wheels based on the current speed, vehicle wheelbase, and optimal turning radius;

[0179] According to the target steering angle and the target linear speed, the current rotation angle and the current rotation speed of the wheels of the electric transport vehicle are adaptively adjusted.

[0180] In one embodiment, when the processor 1001 adaptively adjusts the current rotation angle and current rotation speed of the wheels of the electric transport vehicle according to the target steering angle and the target linear speed, the processor 1001 specifically performs the following operations:

[0181] The PID controller of the electric transport vehicle is used to control the current steering angle of the electric transport vehicle to smoothly transition to the target steering angle, so as to adaptively adjust the current rotation angle of the wheels of the electric transport vehicle;

[0182] The speed controller of the electric transport vehicle is used to control the current speed of the electric transport vehicle to smoothly transition to the target linear speed, so as to adaptively adjust the current rotation speed of the wheels of the electric transport vehicle.

[0183] In one embodiment, when the processor 1001 predicts the optimal turning radius of the electric transport vehicle during a turning process based on the contour parameters of the static object, the processor 1001 specifically performs the following operations:

[0184] determining a minimum safety distance between the electric transport vehicle and the static object according to the size and shape of the static object, wherein the minimum safety distance is used to indicate that the electric transport vehicle will not collide with the static object when turning;

[0185] An optimal turning radius of the electric transport vehicle during a turning process is calculated according to the position of the static object and the minimum safety distance.

[0186] In an embodiment of the present application, on the one hand, when a vehicle approaches a turning position in a preset driving route, multiple types of environmental perception information can be obtained through lidar, cameras, and millimeter-wave radar. This information enables the predicted optimal turning radius to fully consider the dynamic changes in complex environments. Based on the optimal turning radius, the electric transport vehicle can quickly and accurately adaptively adjust the wheel angle and speed to adapt to different turning scenarios, thereby improving the stability and safety of the electric transport vehicle. On the other hand, the present application provides a pre-fitted MAP coordinate curve between the potential collision risk value and the turning radius. Combined with the query mechanism of the MAP coordinate curve, the vehicle can quickly select the optimal turning radius based on different potential collision risk levels. The selection of the optimal turning radius ensures that the electric transport vehicle maintains a stable motion state during the turning process, reducing the risk of rollover due to sharp turns or sudden deceleration.

[0187] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program for adaptive turning control of an electric transport truck can be stored in a computer-readable storage medium. When executed, the program can include the processes of the above-described method embodiments. The storage medium for the program for adaptive turning control of an electric transport truck can be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0188] The above disclosure is only a preferred embodiment of the present application, and certainly cannot be used to limit the scope of rights of the present application. Therefore, equivalent changes made according to the claims of the present application are still within the scope covered by the present application.

Claims

1. A turning adaptive control method for an electric transport vehicle, characterized in that: The electric transport vehicle is equipped with a laser radar, a camera, and a millimeter-wave radar, and the method includes: During the driving process of the electric transport vehicle, when the distance between the electric transport vehicle and the turning position in the forward direction of the electric transport vehicle in the preset driving route is less than or equal to a preset distance threshold, the laser radar, camera and millimeter wave radar are used to sense the environment and obtain multiple types of environmental perception information; If it is determined based on the multiple types of environmental perception information that a dynamic target object exists within the preset area in the forward direction, determining the motion trend information of the target object; or if it is determined based on the multiple types of environmental perception information that a static object exists within the preset area in the forward direction, obtaining the contour parameters of the static object; According to the motion trend information of the target object and the current operating parameters of the electric transport vehicle, the optimal turning radius of the electric transport vehicle during the turning process is predicted; or according to the contour parameters of the static object, the optimal turning radius of the electric transport vehicle during the turning process is predicted; wherein, The motion trend information of the target object includes a first direction and a first acceleration; The step of predicting the optimal turning radius of the electric transport vehicle during a turning process based on the movement trend information of the target object and the current operating parameters of the electric transport vehicle includes: A vehicle coordinate system is established with the current position of the electric transport vehicle as the origin, wherein the forward direction of the vehicle is the positive direction of the x-axis and the left side of the vehicle is the positive direction of the y-axis; determining, based on the first direction and the first acceleration, whether the target object meets the electric transport vehicle within a preset area of ​​the position to be turned; When the target object and the electric transport vehicle meet within the preset area of ​​the position to be turned, calculating a first relative position and a first relative speed between the target object and the electric transport vehicle within the preset area according to the first direction and the first acceleration and the current operating parameters; In the vehicle coordinate system, determining a potential collision risk value corresponding to the target object and the electric transport vehicle according to the first relative position and the first relative speed; According to the potential collision risk value, querying the optimal turning radius of the electric transport vehicle during the turning process from a pre-fitted MAP coordinate curve diagram between the potential collision risk value and the turning radius; wherein the MAP coordinate curve diagram is used to represent the optimal turning radius adopted by the electric transport vehicle under different potential collision risks; According to the size of the optimal turning radius, the current rotation angle and the current rotation speed of the wheels of the electric transport vehicle are adaptively adjusted.

2. The method according to claim 1, characterized in that The following steps are performed to generate a pre-fitted MAP coordinate curve graph between the potential collision risk value and the turning radius, including: Simulating the operating state of the electric transport vehicle at different preset turning radiuses at a preset turning position point by simulation software, wherein the different preset turning radiuses include at least one moving object within a preset area; Recording a plurality of simulated operating parameters of the electric transport vehicle and a plurality of simulated motion trends of the at least one moving object at each preset turning radius, and obtaining a plurality of simulated operating parameters and a plurality of simulated motion trends corresponding to each preset turning radius; Arrange and combine the multiple simulation operation parameters and the multiple simulation motion trends corresponding to each preset turning radius to obtain multiple sets of simulation data corresponding to each preset turning radius; Determining, based on the second direction and the second acceleration included in each simulated motion trend in each set of simulation data corresponding to each preset turning radius and each simulation operation parameter, a potential collision risk value corresponding to each set of simulation data as a plurality of potential collision risk values ​​corresponding to each preset turning radius; Using each potential collision risk value corresponding to each preset turning radius as an input variable and each preset turning radius as an output variable, fitting the relationship between the input variable and the output variable; According to the fitted relationship, a curve graph between each preset turning radius and each potential collision risk value is drawn as a MAP coordinate curve graph between the pre-fitted potential collision risk value and the turning radius.

3. The method according to claim 2, characterized in that The determining, based on the second direction and the second acceleration included in each simulated motion trend and each simulation operation parameter in each set of simulation data corresponding to each preset turning radius, a potential collision risk value corresponding to each set of simulation data includes: Calculating a second relative position and a second relative speed between the moving object and the electric transport vehicle within a preset area based on the second direction and the second acceleration included in each simulated motion trend in each set of simulation data corresponding to each preset turning radius and each simulation operation parameter; determining a potential collision risk value corresponding to the moving object and the electric transport according to the second relative position and the second relative speed; The potential collision risk value corresponding to the moving object and the electric transport is used as the potential collision risk value corresponding to each set of simulation data.

4. The method according to claim 1, wherein The determining whether the target object meets the electric transport vehicle within a preset area of ​​the position to be turned includes: Obtaining the current speed and current position of the electric transport vehicle; Calculating a first time required for the electric transport vehicle to enter a preset range of the position to be turned according to the current speed and the current position; Predicting, based on the first direction and the first acceleration, a first position of the target object after it moves for the first duration; When the first position is within the preset range, it is determined that the target object and the electric transport vehicle meet within the preset area of ​​the position to be turned; or When the first position is not within the preset range, it is determined that the target object and the electric transport vehicle do not meet within the preset area of ​​the position to be turned.

5. The method according to claim 1, wherein The current operating parameters include current speed, current acceleration and current position; The calculating, based on the first direction and the first acceleration and the current operating parameters, a first relative position and a first relative speed between the target object and the electric transport vehicle within a preset area includes: Get the current position of the target object; calculating, in the vehicle coordinate system, a first absolute position and a first absolute velocity of the target object at the moment of encounter based on the current position, the first direction, and the first acceleration of the target object; the first absolute position comprising a first horizontal position and a second vertical position of the target object in the vehicle coordinate system; and the first absolute velocity comprising a first horizontal velocity and a second vertical velocity of the target object in the vehicle coordinate system; In the vehicle coordinate system, a second absolute position and a second absolute speed of the electric transport vehicle at the moment of encounter are calculated based on the current position, the current speed, the current acceleration, and the direction of the electric transport vehicle; the second absolute position includes a third position in the horizontal direction and a fourth position in the vertical direction of the electric transport vehicle in the vehicle coordinate system; and the first absolute speed includes the third speed in the horizontal direction and the fourth speed in the vertical direction of the electric transport vehicle in the vehicle coordinate system; Calculating a position difference between the first position and the third position to obtain a first position difference of the target object relative to the electric transport vehicle in a horizontal direction in a vehicle coordinate system; Calculating a position difference between the second position and the fourth position to obtain a second position difference of the target object in a vertical direction relative to the electric transport vehicle in the vehicle coordinate system; Combining the first position difference with the second position difference to obtain a first relative position between the target object and the electric transport vehicle within a preset area; Calculating a speed difference between the first speed and the third speed to obtain a first speed difference of the target object relative to the electric transport vehicle in a horizontal direction in a vehicle coordinate system; Calculating a speed difference between the second speed and the fourth speed to obtain a second speed difference of the target object in a vertical direction relative to the electric transport vehicle in the vehicle coordinate system; The first speed difference and the second speed difference are combined to obtain a first relative speed between the target object and the electric transport vehicle in a preset area.

6. The method according to claim 1, characterized in that The first relative position includes a first position difference in the horizontal direction and a second position difference in the vertical direction of the target object relative to the electric transport vehicle in the vehicle coordinate system; the first relative speed includes a first speed difference in the horizontal direction and a second speed difference in the vertical direction of the target object relative to the electric transport vehicle in the vehicle coordinate system; The determining, based on the first relative position and the first relative speed, a potential collision risk value corresponding to the target object and the electric transport vehicle includes: The actual distance between the target object and the electric transport vehicle is obtained by summing the squares of the first position difference and the second position difference and taking the root thereof; The relative motion speed between the target object and the electric transport vehicle is obtained by summing the square of the first speed difference and the second speed difference and taking the root thereof; Calculating a ratio between the actual distance and the absolute value of the relative motion speed to obtain a collision time between the target object and the electric transport vehicle; Comparing the collision time with a preset interval threshold to determine a target interval corresponding to the collision time; A target collision risk value corresponding to the target interval is obtained from a pre-established mapping relationship between intervals and collision risk values ​​as a potential collision risk value corresponding to the target object and the electric transport vehicle.

7. The method according to claim 1, characterized in that Adaptively adjusting the current rotation angle and current rotation speed of the wheels of the electric transport vehicle according to the size of the optimal turning radius includes: Obtaining the current speed of the wheels and the vehicle wheelbase of the electric transport vehicle; Calculating a target steering angle of the electric transport vehicle according to the vehicle wheelbase and the optimal turning radius; Calculating a target linear speed of the wheels of the electric transport vehicle according to the current speed, the vehicle wheelbase, and the optimal turning radius; The PID controller of the electric transport vehicle is used to control the current steering angle of the electric transport vehicle to smoothly transition to the target steering angle, so as to adaptively adjust the current rotation angle of the wheels of the electric transport vehicle; The speed controller of the electric transport vehicle is used to control the current speed of the electric transport vehicle to smoothly transition to the target linear speed, so as to adaptively adjust the current rotation speed of the wheels of the electric transport vehicle.

8. The method according to claim 1, characterized in that The contour parameters of the static object include the static object position, the static object size and the static object shape; The step of predicting the optimal turning radius of the electric transport vehicle during a turning process based on the contour parameters of the static object includes: determining a minimum safety distance between the electric transport vehicle and the static object according to the size and shape of the static object, wherein the minimum safety distance is used to indicate that the electric transport vehicle will not collide with the static object when turning; An optimal turning radius of the electric transport vehicle during a turning process is calculated according to the position of the static object and the minimum safety distance.

9. A turning adaptive control device for an electric transport vehicle implemented using the method according to any one of claims 1 to 8, characterized in that: The device comprises: An environmental perception module is configured to, during the driving process of the electric transport vehicle, perform environmental perception using a laser radar, a camera, and a millimeter-wave radar to obtain multiple types of environmental perception information when the distance between the electric transport vehicle and the turning position in the forward direction of the electric transport vehicle in a preset driving route is less than or equal to a preset distance threshold; a determination module, configured to, if it is determined based on the multiple types of environmental perception information that a dynamic target object exists within the preset area in the forward direction, determine the motion trend information of the target object; or, if it is determined based on the multiple types of environmental perception information that a static object exists within the preset area in the forward direction, obtain the contour parameters of the static object; a prediction module, configured to predict an optimal turning radius of the electric transport vehicle during a turning process based on the motion trend information of the target object and the current operating parameters of the electric transport vehicle; or to predict an optimal turning radius of the electric transport vehicle during a turning process based on the contour parameters of the static object; The adaptive adjustment module is used to adaptively adjust the current rotation angle and current rotation speed of the wheels of the electric transport vehicle according to the size of the optimal turning radius.

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