Method for calculating vehicle safe obstacle avoidance distance, path planning method, and control system

Abstract

A method for calculating a vehicle safe obstacle avoidance distance, a path planning method, and a control system. The method for calculating a vehicle safe obstacle avoidance distance comprises: on the basis of a steering wheel angle curve and a plurality of simulation end times, acquiring steering wheel angle functions; then inputting set vehicle parameters of a target vehicle and each steering wheel angle function into a preset vehicle model to acquire end point vehicle coordinates corresponding to each simulation end time; and acquiring the shortest safe obstacle avoidance distance on the basis of all the end point vehicle coordinates. The actual dynamic characteristics of the vehicle are considered, so that more authentic assessment of the safety of the vehicle during traveling can be achieved; and the shortest safe obstacle avoidance distances in different situations can be flexibly calculated by means of specific steering wheel angles and vehicle parameters, so that wide applicability is achieved, and the capability of the vehicle of responding to obstacles or emergencies can be effectively improved, thereby enhancing driving safety.

Description

Vehicle safe obstacle avoidance distance calculation method, path planning method and control system Technical Field

[0001] This application relates to the field of driver assistance technology, and in particular to a method for calculating vehicle safe obstacle avoidance distance, a path planning method, and a control system. Background Technology

[0002] Driver assistance systems are various systems and devices installed in vehicles to improve driving safety, comfort, and convenience. These technologies can help drivers make better decisions and reduce the risk of accidents. Some common driver assistance technologies include automatic emergency braking, vehicle departure warning, adaptive cruise control, blind spot monitoring, automatic parking, and driver fatigue warning, among others.

[0003] Memory parking, a type of automated parking, is an autonomous driving function that automatically navigates and parks itself in a parking space according to a user-memorized route, or automatically exits a parking space and navigates back to the starting point of the route. During the navigation phase of memory parking, if an obstacle appears in the vehicle's navigation path, the planning algorithm needs to dynamically plan the path to avoid the obstacle. If the obstacle avoidance timing is incorrect, the vehicle will collide with the obstacle.

[0004] Therefore, a method is needed to calculate the shortest longitudinal distance that the vehicle can safely avoid obstacles in the current state. The planning algorithm uses this distance for path planning so that the vehicle can safely bypass obstacles without collision. Summary of the Invention

[0005] This application provides a method for calculating safe obstacle avoidance distance for vehicles, a path planning method, and a control system to solve the above-mentioned technical problems.

[0006] Specifically, this application provides a method for calculating the safe obstacle avoidance distance of a vehicle, including the following steps: obtaining the corresponding steering wheel angle function based on a predefined steering wheel angle curve and multiple simulation end times; setting the vehicle parameters of the target vehicle, and inputting the vehicle parameters and each steering wheel angle function into a preset vehicle model to obtain the coordinates of the endpoint vehicle corresponding to each simulation end time; and obtaining the shortest safe obstacle avoidance distance based on each endpoint vehicle coordinate.

[0007] The above technical solution simulates the vehicle's movement process, taking into account the vehicle's actual dynamic characteristics, and can more realistically assess the safety of the vehicle while driving; it can also flexibly calculate the shortest safe obstacle avoidance distance under different conditions by using specific steering wheel angles and vehicle parameters, making it widely applicable.

[0008] Furthermore, by accurately calculating the shortest safe obstacle avoidance distance, the vehicle's ability to respond to obstacles or emergencies can be effectively improved, thereby enhancing driving safety.

[0009] Furthermore, before obtaining the steering wheel angle function, the process also includes setting the simulation end time range and step size to obtain multiple simulation end times.

[0010] In the above technical solution, by setting the simulation end time range and step size, a wider range of situations and scenarios can be covered, thereby obtaining the target vehicle's motion trajectory more accurately; and the simulation end time range and step size can be adjusted according to specific needs and actual conditions, allowing for flexible parameter settings to adapt to different simulation requirements and calculation accuracy requirements.

[0011] Furthermore, obtaining the steering wheel angle function includes: setting the simulation start time, and substituting the simulation start time and each simulation end time into the steering wheel angle curve to obtain the steering wheel angle function corresponding to each simulation end time.

[0012] In the above technical solution, by substituting the simulation start time and the end time of each simulation into the steering wheel angle curve, a complete simulation result can be obtained, covering the changes throughout the simulation process and improving the completeness and reliability of the calculation results. By unifying the start time and matching it with the end time of each simulation, the continuity and consistency of the steering wheel angle function throughout the simulation process can be ensured, making the results more interpretable and comparable.

[0013] Furthermore, the vehicle parameters include at least steering wheel speed, maximum steering wheel angle, vehicle speed, steering ratio, and wheelbase.

[0014] In the above technical solution, the vehicle parameters cover key information about the vehicle steering system, power system, and vehicle structure. Taking these parameters into account comprehensively reflects the vehicle's motion state and driving characteristics, thereby improving the comprehensiveness and reliability of the calculation results.

[0015] Furthermore, before obtaining the coordinates of the destination vehicle, the process also includes: initializing the initial heading angle and initial vehicle coordinates of the target vehicle.

[0016] In the above technical solution, accurate initial heading angle and initial vehicle coordinates can reduce initial errors, thereby reducing the accumulation of errors throughout the simulation process and improving the accuracy of subsequent simulations.

[0017] Furthermore, obtaining the coordinates of the destination vehicle includes: obtaining the wheel rotation angles, inputting the initial heading angle, vehicle speed, wheelbase and each wheel rotation angle into a preset vehicle model, and combining the initial vehicle coordinates to obtain the coordinates of the destination vehicle corresponding to each simulation end time.

[0018] In the above technical solution, by initializing the initial heading angle and initial vehicle coordinates of the target vehicle, and by combining the information of various parameters for simulation calculation, more accurate and reliable endpoint vehicle coordinates can be obtained, thus improving the accuracy of the simulation results. By inputting the initial heading angle, vehicle speed, wheelbase and the rotation angle of each wheel into the preset vehicle model, the motion state and driving process of the actual vehicle can be better simulated, making the simulation results more realistic and credible.

[0019] Furthermore, obtaining the wheel angle includes: substituting the steering wheel speed and the maximum steering wheel angle into the steering wheel angle function as the target angle function; and obtaining the wheel angle corresponding to each simulation end time based on each target angle function and the steering ratio.

[0020] In the above technical solution, considering the correlation between steering wheel speed, maximum steering wheel angle and steering ratio, this process can effectively combine these parameters and reflect them in the wheel angle, making the simulation results more reasonable and accurate.

[0021] Furthermore, the endpoint vehicle coordinates include longitudinal and lateral coordinates; obtaining the shortest safe obstacle avoidance distance includes: obtaining all corresponding simulation end times whose lateral coordinates are greater than a preset obstacle avoidance distance based on the endpoint vehicle coordinates, as a simulation time group; and filtering the simulation end time with the smallest value in the simulation time group as the target simulation time; wherein, the longitudinal coordinate corresponding to the target simulation time is the shortest safe obstacle avoidance distance.

[0022] In the above technical solution, by ensuring that the longitudinal coordinate corresponding to the target simulation time is the shortest safe obstacle avoidance distance, the safety of vehicle obstacle avoidance operation can be effectively improved. This ensures that the vehicle's position at the end of the simulation is within a safe range, avoiding potential collision risks. By filtering out simulation end times with a lateral coordinate greater than the preset obstacle avoidance distance and selecting the smallest time as the target simulation time, the simulation process can be optimized. This strategy enables the vehicle to complete the obstacle avoidance operation in the shortest time, improving efficiency and practicality.

[0023] Based on the same concept, this application provides a safe obstacle avoidance path planning method, including: obtaining the initial vehicle coordinates of the target vehicle, and performing path planning based on the initial vehicle coordinates and the shortest safe obstacle avoidance distance; wherein, the shortest safe obstacle avoidance distance is obtained by the vehicle safe obstacle avoidance distance calculation method.

[0024] In the above technical solution, the path planning process takes into account the shortest safe obstacle avoidance distance, which can optimize the vehicle's driving path and enable the vehicle to move forward in a more efficient manner during obstacle avoidance. This can save time and energy and improve overall efficiency.

[0025] Based on the same concept, this application provides a vehicle control system, including at least an intelligent driving domain controller and a vehicle controller; the intelligent driving domain controller is used to calculate the shortest safe obstacle avoidance distance and determine the path planning of the target vehicle based on the initial vehicle coordinates of the target vehicle and the shortest safe obstacle avoidance distance; the vehicle controller is used to control the target vehicle to drive based on the path planning result of the intelligent driving domain controller.

[0026] In the above technical solution, the system can support vehicle safety obstacle avoidance, enabling vehicles to drive safely. By calculating the shortest safe obstacle avoidance distance, the system can intelligently select the optimal path planning scheme, enabling vehicles to drive safely and efficiently in various complex obstacle environments. At the same time, the system design has a certain degree of versatility and scalability, and can be applied to different types of vehicles and scenarios.

[0027] Furthermore, the intelligent driving domain controller includes at least a memory and a processor; the memory is used to store computer instructions for multiple functional layers, each functional layer including at least a computing functional layer and a planning functional layer, and each functional layer including one or more functional modules; the processor communicates with the memory via a bus to execute the computer instructions for each of the multiple functional layers stored in the memory.

[0028] In the above technical solution, different functions are divided into a computing function layer and a planning function layer. Each function layer contains multiple function modules. This hierarchical and modular design allows the system to flexibly combine and configure function modules according to different application scenarios and needs.

[0029] Furthermore, the calculation function layer includes at least an acquisition function module and a calculation function module; the acquisition function module includes computer instructions for obtaining the corresponding steering wheel angle function based on a predefined steering wheel angle curve and multiple simulation end times, and for obtaining the coordinates of the endpoint vehicle corresponding to each simulation end time based on vehicle parameters and each steering wheel angle function using a preset vehicle model; wherein, the acquisition function module provides input data to the calculation function module; the calculation function module includes computer instructions for obtaining the shortest safe obstacle avoidance distance based on each endpoint vehicle coordinate.

[0030] In the above technical solution, through modular design, the acquisition function module can flexibly adjust parameters according to the needs of specific scenarios and calculate the coordinates of the destination vehicle suitable for that scenario; while the calculation function module can calculate the corresponding shortest safe obstacle avoidance distance based on the coordinates of the destination vehicle under these different scenarios, so that the whole system can better adapt to various complex and ever-changing intelligent driving scenarios.

[0031] Furthermore, the planning function layer includes at least a planning function module; the calculation function module provides input data to the planning function module; the planning function module includes computer instructions for determining the path planning of the target vehicle based on the initial vehicle coordinates of the target vehicle and the shortest safe obstacle avoidance distance.

[0032] In the above technical solution, the shortest safe obstacle avoidance distance provided by the calculation function module is precisely calculated. The planning function module incorporates it into the path planning considerations, which can ensure that the target vehicle maintains a safe distance from obstacles during driving and effectively avoid collision accidents.

[0033] Furthermore, the intelligent driving domain controller is connected to the cloud server via an in-vehicle communication module; the cloud server is used to set the vehicle parameters of the target vehicle and train a preset vehicle model, and sends the vehicle parameters and the preset vehicle model as input data to the acquisition function module through the in-vehicle communication module.

[0034] In the above technical solution, the cloud server has powerful computing resources and storage capabilities, which can handle large-scale data and complex computing tasks, and can customize personalized vehicle parameters according to the specific situation of each target vehicle.

[0035] Furthermore, the vehicle control system also includes a sensor used to acquire the initial vehicle coordinates of the target vehicle and send the initial vehicle coordinates as input data to the planning function module.

[0036] In the above technical solution, the initial vehicle coordinates are the basis for path planning. The sensor can accurately obtain the position of the target vehicle in the real world. The planning function module performs path planning based on this accurate starting point information, which can avoid planning errors caused by inaccurate starting point positioning, thereby planning a driving route for the vehicle that is more in line with the actual situation.

[0037] Compared with the prior art, the beneficial effects of this application are as follows:

[0038] This application first obtains the steering wheel angle function based on the steering wheel angle curve and multiple simulation end times. Then, it inputs the vehicle parameters of the target vehicle and the steering wheel angle functions into a preset vehicle model to obtain the endpoint vehicle coordinates corresponding to each simulation end time. Based on these endpoint vehicle coordinates, the shortest safe obstacle avoidance distance is then obtained. This application considers the actual dynamic characteristics of the vehicle, enabling a more realistic assessment of vehicle safety during driving. Furthermore, it can flexibly calculate the shortest safe obstacle avoidance distance under different conditions using specific steering wheel angles and vehicle parameters, making it widely applicable and effectively improving the vehicle's ability to respond to obstacles or emergencies, thereby enhancing driving safety. Attached Figure Description

[0039] Figure 1 is a flowchart of the vehicle safe obstacle avoidance distance calculation method described in Embodiment 1.

[0040] Figure 2 is a schematic diagram of the change of steering wheel angle over time calculated in Example 1.

[0041] Figure 3 is a schematic diagram of the vehicle driving path calculated in Example 1.

[0042] Figure 4 is a flowchart of the safe obstacle avoidance path planning method described in Embodiment 2.

[0043] Figure 5 is a framework diagram of the vehicle control system described in Embodiment 3.

[0044] Figure 6 is a framework diagram of the intelligent driving domain controller described in Embodiment 3.

[0045] Figure 7 is a memory framework diagram of Embodiment 3.

[0046] Figure 8 is a schematic diagram of the connection between the cloud server and the intelligent driving domain controller as described in Embodiment 3. Detailed Implementation

[0047] The following describes in further detail, with reference to specific embodiments and accompanying drawings, a method for calculating vehicle safe obstacle avoidance distance, a path planning method, and a control system according to this application.

[0048] Example 1:

[0049] Please refer to Figure 1. This application provides a method for calculating the safe obstacle avoidance distance of a vehicle, including the following steps S100-S300.

[0050] S100: Obtain the corresponding steering wheel angle function based on a predefined steering wheel angle curve and multiple simulation end times.

[0051] In this embodiment, the steering wheel angle curve is set as follows:

[0052]

[0053] in For simulation time, Steering wheel speed, This is the maximum steering wheel angle. , This is the simulation end time. , , .

[0054] Furthermore, before obtaining the steering wheel angle function, the process includes: setting the simulation end time range and step size to obtain multiple simulation end times.

[0055] In this embodiment, a simulation end time range is set. ∈ [1, 100], unit is s, set step size is 0.1s; This yields... Sequence ( ), and its length is defined as n.

[0056] It should be noted that, in practical applications, the simulation end time range and step size can be adaptively set by those skilled in the art, and are not limited to these.

[0057] For example, in scenarios requiring high precision, a smaller step size can be selected to increase the accuracy of the simulation; while in scenarios requiring fast calculation or where precision requirements are not high, a larger step size can be selected to improve computational efficiency.

[0058] In the above technical solution, by setting the simulation end time range and step size, a wider range of situations and scenarios can be covered, thereby obtaining the target vehicle's motion trajectory more accurately; and the simulation end time range and step size can be adjusted according to specific needs and actual conditions, allowing for flexible parameter settings to adapt to different simulation requirements and calculation accuracy requirements.

[0059] Furthermore, obtaining the steering wheel angle function includes: setting the simulation start time, and substituting the simulation start time and each simulation end time into the steering wheel angle curve to obtain the steering wheel angle function corresponding to each simulation end time.

[0060] In this embodiment, the simulation start time is set to 0, and the current steering wheel speed is assumed to be... i° / s is the maximum steering wheel angle. It is J degree.

[0061] For example, the steering wheel rotation speed is 400° / s, and the maximum steering wheel angle is 450°.

[0062] It should be noted that the steering wheel speed and maximum steering wheel angle values ​​here are just examples. They may vary depending on the vehicle model, and those skilled in the art can adjust them according to the actual application.

[0063] It should be noted that the simulation start time can be adaptively set by those skilled in the art, and is not limited to this.

[0064] Based on the current simulation end time Taking 3 as an example, we can obtain the following: , , , Substituting the above values ​​into the steering wheel angle curve, we can obtain... The steering wheel angle function corresponding to =3 is:

[0065]

[0066] By analogy, we can obtain Sequence ( The steering wheel angle function corresponding to n simulation end times () ).

[0067] In the above technical solution, substituting the simulation start time and the end time of each simulation into the steering wheel angle curve can yield complete simulation results, covering the changes throughout the entire simulation process and improving the completeness and reliability of the calculation results. By unifying the start time and coordinating it with the end times of each simulation, the continuity and consistency of the steering wheel angle function throughout the simulation process can be ensured, which helps to eliminate discontinuities in timing, ensures the coherence and logic between data, and makes the results more interpretable and comparable. At the same time, this also helps to reduce potential errors and data inconsistencies, and improve the accuracy and reliability of the results.

[0068] After obtaining the steering wheel angle function, step S200 can be executed.

[0069] S200: Set the vehicle parameters of the target vehicle, and input the vehicle parameters and each steering wheel angle function into the preset vehicle model to obtain the coordinates of the endpoint vehicle corresponding to each simulation end time.

[0070] Furthermore, the vehicle parameters include at least steering wheel speed, maximum steering wheel angle, vehicle speed, steering ratio, and wheelbase.

[0071] In this embodiment, the vehicle speed, steering ratio, and wheelbase are set to v, r, and L, respectively; assuming a vehicle speed of 1 m / s, a steering ratio of 14, and a wheelbase of 2.778 m.

[0072] It should be noted that the values ​​for vehicle speed, steering ratio, and wheelbase here are just examples and may vary depending on the vehicle model. Those skilled in the art can adjust them according to the actual application.

[0073] The steering ratio refers to the ratio of the actual rotation angle of the wheels to the steering wheel's rotation angle when the vehicle is turning.

[0074] The wheelbase refers to the distance between the center lines of the front and rear axles of a vehicle, which is the distance between the front and rear wheel axles. The length of the wheelbase affects the vehicle's stability, driving comfort, and cornering performance.

[0075] In the above technical solution, the vehicle parameters cover key information about the vehicle steering system, power system, and vehicle structure. Taking these parameters into account comprehensively reflects the vehicle's motion state and driving characteristics, thereby improving the comprehensiveness and reliability of the calculation results.

[0076] Meanwhile, the flexibility of vehicle parameters makes the technical solution applicable to different types and specifications of vehicles, and can be adjusted and optimized according to actual application conditions to improve its applicability and versatility.

[0077] Furthermore, obtaining the coordinates of the destination vehicle includes: initializing the initial heading angle and initial vehicle coordinates of the target vehicle.

[0078] In this embodiment, the initial heading angle θ and the initial vehicle coordinates (x,y) are initialized to 0 and (0,0), respectively.

[0079] It should be noted that the initial heading angle and initial vehicle coordinates can be adaptively set by those skilled in the art, and are not limited to these settings.

[0080] The process of obtaining the coordinates of the destination vehicle further includes: obtaining the wheel rotation angles, inputting the initial heading angle, vehicle speed, wheelbase and each wheel rotation angle into a preset vehicle model, and combining the initial vehicle coordinates to obtain the coordinates of the destination vehicle corresponding to each simulation end time.

[0081] In the above technical solution, by initializing the initial heading angle and initial vehicle coordinates of the target vehicle, and by combining the information of various parameters for simulation calculation, more accurate and reliable endpoint vehicle coordinates can be obtained, thus improving the accuracy of the simulation results. By inputting the initial heading angle, vehicle speed, wheelbase and the rotation angle of each wheel into the preset vehicle model, the motion state and driving process of the actual vehicle can be better simulated, making the simulation results more realistic and credible.

[0082] Furthermore, obtaining the wheel turning angle includes: substituting the steering wheel rotation speed and the maximum steering wheel turning angle into the steering wheel turning angle function as the target turning angle function.

[0083] In this embodiment, the current simulation end time is used. Taking 3 as an example, we can obtain the following: The target rotation function corresponding to =3 is:

[0084]

[0085] Based on the example data above, the steering wheel angle that changes over time can be calculated as shown in Figure 2. The horizontal axis in Figure 2 is in seconds, and the vertical axis is in degrees.

[0086] The method of obtaining the wheel angle further includes: obtaining the wheel angle corresponding to each simulation end time based on each of the target angle functions and the steering ratio.

[0087] In this embodiment, =3 corresponds to the wheel rotation angle .

[0088] In the above technical solution, considering the correlation between steering wheel speed, maximum steering wheel angle and steering ratio, this process can effectively combine these parameters and reflect them in the wheel angle, making the simulation results more reasonable and accurate.

[0089] After obtaining the wheel angles, the initial heading angle θ, vehicle speed v, wheelbase L, and each wheel angle are... The values ​​are input into the preset vehicle model, and the final vehicle coordinates corresponding to each simulation end time are obtained by combining the initial vehicle coordinates. Specifically, the preset vehicle model is as follows:

[0090]

[0091] Based on the above preset vehicle model, the coordinates of the destination vehicle can be obtained. .

[0092] The vehicle trajectory shown in Figure 3 can be obtained, where the horizontal and vertical axes of Figure 3 represent the longitudinal and lateral travel distances of the current vehicle from the starting position, respectively, both in meters.

[0093] After obtaining the coordinates of the destination vehicle, step S300 can be executed.

[0094] S300: Obtain the shortest safe obstacle avoidance distance based on the coordinates of each destination vehicle.

[0095] Furthermore, the endpoint vehicle coordinates include longitudinal and lateral coordinates; obtaining the shortest safe obstacle avoidance distance includes: obtaining all corresponding simulation end times whose lateral coordinates are greater than a preset obstacle avoidance distance based on the endpoint vehicle coordinates, and using them as simulation time groups.

[0096] In this embodiment, This indicates the lateral distance traveled by the vehicle. That is, the horizontal coordinate, filtering out the coordinates of n destination vehicles. The end time of all simulations exceeding the preset obstacle avoidance distance d.

[0097] The step of obtaining the shortest safe obstacle avoidance distance further includes: selecting the smallest simulation end time in the simulation time group as the target simulation time; wherein the vertical coordinate corresponding to the target simulation time is the shortest safe obstacle avoidance distance.

[0098] In this embodiment, the simulation end time with the smallest value among all simulation end times corresponds to... The coordinate value (representing the vehicle's longitudinal travel distance, i.e., the longitudinal coordinate) is the shortest safe obstacle avoidance distance, or the shortest longitudinal distance for obstacle avoidance.

[0099] In the above technical solution, by ensuring that the longitudinal coordinate corresponding to the target simulation time is the shortest safe obstacle avoidance distance, the safety of vehicle obstacle avoidance operation can be effectively improved. This ensures that the vehicle's position at the end of the simulation is within a safe range, avoiding potential collision risks. By filtering out simulation end times with a lateral coordinate greater than the preset obstacle avoidance distance and selecting the smallest time as the target simulation time, the simulation process can be optimized. This strategy enables the vehicle to complete the obstacle avoidance operation in the shortest time, improving efficiency and practicality.

[0100] Example 2:

[0101] Please refer to Figure 4. This application provides a safe obstacle avoidance path planning method, including:

[0102] The initial vehicle coordinates of the target vehicle are obtained, and path planning is performed based on the initial vehicle coordinates and the shortest safe obstacle avoidance distance; wherein, the shortest safe obstacle avoidance distance is obtained by the vehicle safe obstacle avoidance distance calculation method described in Example 1.

[0103] In this embodiment, the system first obtains the current coordinates of the target vehicle (i.e., the initial vehicle coordinates), and then substitutes the initial vehicle coordinates into the vehicle safety obstacle avoidance calculation method described in Embodiment 1 to calculate the coordinates of each endpoint vehicle in the simulation based on the initial vehicle coordinates. Then, based on the coordinates of each endpoint vehicle, the system obtains all the corresponding simulation end times whose lateral coordinates are greater than the preset obstacle avoidance distance, and then filters the longitudinal coordinates corresponding to the smallest simulation end time among all simulation end times. At this time, the coordinate value of the longitudinal coordinate is the shortest safe obstacle avoidance distance, which can also be regarded as the shortest longitudinal distance for obstacle avoidance.

[0104] In the above technical solutions, by considering the shortest safe obstacle avoidance distance, the path planning process can ensure that the vehicle can safely avoid obstacles with the shortest distance when it encounters them, thereby minimizing the potential collision risk and improving driving safety. Considering the shortest safe obstacle avoidance distance can optimize the vehicle's driving path, allowing the vehicle to choose a more suitable route to bypass obstacles, rather than simply bypassing the outermost edge of the obstacle. This avoids unnecessary detours and saves time and energy. By optimizing the driving path, the vehicle can move forward more efficiently, reducing unnecessary turns and stops, thereby improving overall driving efficiency. This is especially important for autonomous vehicles or vehicles that need to travel for long periods of time.

[0105] Furthermore, this route planning method can be flexibly adjusted according to actual conditions to adapt to different road and environmental conditions.

[0106] Example 3:

[0107] Please refer to Figure 5. This application provides a vehicle control system, which includes at least an intelligent driving domain controller 10 and a vehicle controller 20.

[0108] The intelligent driving domain controller 10 is used to calculate the shortest safe obstacle avoidance distance and determine the path planning of the target vehicle based on the initial vehicle coordinates of the target vehicle and the shortest safe obstacle avoidance distance.

[0109] Referring to Figure 6, the intelligent driving domain controller 10 includes at least a memory 101 and a processor 102. The memory 101 is used to store computer instructions for multiple functional layers, and the functional layers include at least a computing functional layer 104 and a planning functional layer 105 (as shown in Figure 7). Each functional layer includes one or more functional modules. The processor 102 communicates with the memory 101 through a bus 103 and is used to execute the computer instructions for each of the multiple functional layers stored in the memory 101.

[0110] The memory 101 includes, but is not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), and CD-ROM (Compact Disc Read-Only Memory); the processor 102 includes a central processing unit 102 or a device or module with processing capabilities.

[0111] The calculation function layer 104 includes at least an acquisition function module 1041 and a calculation function module 1042. The acquisition function module 1041 includes computer instructions for obtaining the corresponding steering wheel angle function based on a predefined steering wheel angle curve and multiple simulation end times, and for obtaining the coordinates of the endpoint vehicle corresponding to each simulation end time based on vehicle parameters and each steering wheel angle function using a preset vehicle model. The acquisition function module 1041 provides input data to the calculation function module 1042. The calculation function module 1042 includes computer instructions for obtaining the shortest safe obstacle avoidance distance based on each endpoint vehicle coordinate. The planning function layer 105 includes at least a planning function module 1051. The calculation function module 1042 provides input data to the planning function module 1051. The planning function module 1051 includes computer instructions for determining the path planning of the target vehicle based on the initial vehicle coordinates of the target vehicle and the shortest safe obstacle avoidance distance.

[0112] Furthermore, referring to Figure 8, the intelligent driving domain controller 10 is connected to the cloud server 40 via the vehicle communication module 30. The cloud server 40 is used to set the vehicle parameters of the target vehicle and train a preset vehicle model, and sends the vehicle parameters and the preset vehicle model as input data to the acquisition function module 1041 through the vehicle communication module 30. The vehicle communication module 30 can be a remote information processor 102, such as a TBOX.

[0113] Furthermore, the vehicle control system also includes a sensor 50, which is used to acquire the initial vehicle coordinates of the target vehicle and send the initial vehicle coordinates as input data to the planning function module 1051.

[0114] In some embodiments, the cloud server 40 pre-sets vehicle parameters based on the information of the target vehicle. The vehicle parameters include at least steering wheel speed, maximum steering wheel angle, vehicle speed, steering ratio, and wheelbase. At the same time, the cloud server 40 uses a large amount of driving data to train a preset vehicle model to simulate the motion state and driving process of the actual vehicle.

[0115] The intelligent driving domain controller 10 calculates the shortest safe obstacle avoidance distance using the specific implementation method described in Embodiment 1; the sensor 50, such as a global satellite navigation system sensor 50 or an inertial measurement unit, is used to obtain the initial vehicle coordinates of the target vehicle; and then path planning for the target vehicle is further implemented based on the memory 101 and the processor 102.

[0116] Furthermore, the vehicle controller 20 is used to control the target vehicle to drive based on the path planning results of the intelligent driving domain controller 10.

[0117] The vehicle controller 20 is mainly based on controllers such as engine controller, brake controller, and steering controller. These controllers receive control commands from the intelligent driving domain controller 10 to perform operations such as vehicle acceleration, deceleration, and steering.

[0118] In summary, this application provides a method for calculating safe obstacle avoidance distance, a path planning method, and a control system for vehicles. First, it obtains the steering wheel angle function based on the steering wheel angle curve and multiple simulation end times. Then, it inputs the vehicle parameters of the target vehicle and the steering wheel angle functions into a preset vehicle model to obtain the coordinates of the endpoint vehicle corresponding to each simulation end time. Finally, it obtains the shortest safe obstacle avoidance distance based on these coordinates. By accurately calculating the shortest safe obstacle avoidance distance, this application can effectively improve the vehicle's ability to respond to obstacles or emergencies, thereby enhancing driving safety.

[0119] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of this application. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of this application. All such changes and modifications are intended to be included within the scope of this application as claimed in the appended claims.

[0120] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0121] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0122] The various component embodiments of this application can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules according to the embodiments of this application. This application can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such an implementation of this application can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0123] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0124] Although the description of this application has been made in conjunction with the specific embodiments described above, it will be apparent to those skilled in the art that many substitutions, modifications, and variations can be made based on the foregoing. Therefore, all such substitutions, modifications, and variations are included within the spirit and scope of the appended claims.

Claims

1. A method of calculating a safe obstacle avoidance distance for a vehicle, wherein, The method comprises the following steps: obtaining a corresponding steering wheel rotation angle function based on a predefined steering wheel angle curve and a plurality of simulation end times (S100); setting vehicle parameters of a target vehicle, inputting the vehicle parameters and each steering wheel rotation angle function into a preset vehicle model respectively to obtain end vehicle coordinates corresponding to each simulation end time (S200); and obtaining a shortest safe obstacle avoidance distance based on each end vehicle coordinate (S300).

2. The vehicle safety obstacle avoidance distance calculation method of claim 1, wherein, Before obtaining the steering wheel rotation angle function, the method further comprises setting a simulation end time range and a step to obtain a plurality of simulation end times.

3. The vehicle safety obstacle distance calculation method of claim 2, wherein, The obtaining of the steering wheel rotation angle function comprises setting a simulation start time and substituting the simulation start time and each simulation end time into the steering wheel angle curve to obtain a steering wheel rotation angle function corresponding to each simulation end time.

4. The vehicle safe following distance calculation method of claim 1, wherein, The vehicle parameters at least include a steering wheel rotation speed, a maximum steering wheel rotation angle, a vehicle speed, a steering ratio and a wheelbase.

5. The method for calculating the safe obstacle avoidance distance of a vehicle according to claim 4, wherein, Before obtaining the end vehicle coordinates, the method further comprises initializing an initial heading angle and an initial vehicle coordinate of the target vehicle.

6. The vehicle safety obstacle distance calculation method of claim 5, wherein, The obtaining of the end vehicle coordinates comprises obtaining wheel rotation angles, inputting the initial heading angle, the vehicle speed, the wheelbase and each wheel rotation angle into the preset vehicle model respectively, and obtaining end vehicle coordinates corresponding to each simulation end time in combination with the initial vehicle coordinate.

7. The vehicle safety obstacle avoidance distance calculation method according to claim 3 and 6, wherein, The obtaining of the wheel rotation angles comprises: substituting the steering wheel rotation speed and the maximum steering wheel rotation angle into the steering wheel rotation angle function as a target rotation angle function, and obtaining wheel rotation angles corresponding to each simulation end time based on each target rotation angle function and the steering ratio.

8. The vehicle safe following distance calculation method of claim 6, wherein, The end vehicle coordinates include longitudinal coordinates and lateral coordinates; the obtaining of the shortest safe obstacle avoidance distance comprises obtaining all corresponding simulation end times with lateral coordinates greater than a preset obstacle avoidance distance as a simulation time group based on each end vehicle coordinate, and screening a smallest simulation end time in the simulation time group as a target simulation time; wherein a longitudinal coordinate corresponding to the target simulation time is the shortest safe obstacle avoidance distance.

9. A safe obstacle avoidance path planning method, wherein, The method comprises: obtaining an initial vehicle coordinate of a target vehicle, and performing path planning based on the initial vehicle coordinate and a shortest safe obstacle avoidance distance; wherein the shortest safe obstacle avoidance distance is obtained by the vehicle safe obstacle avoidance distance calculation method according to claim 1.

10. A vehicle control system, wherein, At least comprising an intelligent driving domain controller (10) and a vehicle controller (20); The intelligent driving domain controller (10) is configured to calculate a shortest safe obstacle avoidance distance, and determine path planning of a target vehicle according to an initial vehicle coordinate of the target vehicle and the shortest safe obstacle avoidance distance; The vehicle controller (20) is configured to control the target vehicle to travel based on a path planning result of the intelligent driving domain controller (10).

11. The vehicle control system according to claim 10, wherein The intelligent driving domain controller (10) at least comprises a memory (101) and a processor (102); The memory (101) is configured to store computer instructions of a plurality of functional layers, the functional layers at least comprising a calculation functional layer (104) and a planning functional layer (105), each functional layer comprising one or more functional modules; The processor (102) communicates with the memory (101) through a bus (103) and is configured to execute computer instructions of each of a plurality of function layers stored in the memory (101).

12. The vehicle control system according to claim 11, wherein The computing function layer (104) comprises an acquisition function module (1041) and a computing function module (1042). The acquisition function module (1041) comprises computer instructions for obtaining corresponding steering wheel rotation angle functions based on a predefined steering wheel angle curve and a plurality of simulation end times, and obtaining end point vehicle coordinates corresponding to each simulation end time based on a preset vehicle model and vehicle parameters and each steering wheel rotation angle function; wherein the acquisition function module (1041) provides input data to the computing function module (1042). The computing function module (1042) comprises computer instructions for obtaining the shortest safe obstacle avoidance distance based on each end point vehicle coordinate.

13. The vehicle control system of claim 11, wherein, The planning function layer (105) comprises a planning function module (1051); the computing function module (1042) provides input data to the planning function module (1051). The planning function module (1051) comprises computer instructions for determining path planning of the target vehicle based on initial vehicle coordinates of the target vehicle and the shortest safe obstacle avoidance distance.

14. The vehicle control system of claim 12, wherein, The intelligent driving domain controller (10) is connected with a cloud server (40) through a vehicle-mounted communication module (30). The cloud server (40) is configured to set vehicle parameters of the target vehicle and train a preset vehicle model, and deliver the vehicle parameters and the preset vehicle model as input data to the acquisition function module (1041) through the vehicle-mounted communication module (30).

15. The vehicle control system of claim 13, wherein, The vehicle control system further comprises a sensor (50) configured to obtain initial vehicle coordinates of the target vehicle and deliver the initial vehicle coordinates as input data to the planning function module (1051).

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