Vehicle path tracking transverse control method, device and equipment and storage medium

Abstract

The invention discloses a vehicle path tracking transverse control method, apparatus and device, and a storage medium. The method comprises the steps of obtaining current motion information and a target expected path of a target vehicle; determining a current preview distance of the target vehicle based on the current motion information and a target mapping relation between the motion deviation and preview time; determining a target steering wheel angle of the target vehicle based on the current motion information, the target expected path and the current preview distance; and generating a target control signal based on the target steering wheel angle, and performing lateral control on the target vehicle based on the target control signal, so that the target vehicle travels along the target expected path. Through the technical scheme of the embodiment of the invention, automatic transverse control of vehicle path tracking is realized, a complex vehicle driving environment can be adapted, the precision and stability of transverse control of vehicle path tracking are improved, and the safety of intelligent driving of the vehicle is ensured.

Description

Technical Field

[0001] The present invention relates to the field of intelligent driving technology, and in particular to a vehicle path tracking lateral control method, device, equipment and storage medium. Background Art

[0002] With the development of intelligent driving technology, vehicle tracking lateral control, as a key technology of intelligent driving, directly affects the stability and safety of vehicle intelligent driving.

[0003] At present, the traditional vehicle tracking lateral control method usually requires the establishment of an accurate vehicle dynamics model and the use of a linear control method to achieve vehicle tracking lateral control. However, the traditional vehicle tracking lateral control method often relies on an accurate vehicle dynamics model, which is difficult to adapt to complex vehicle driving environments. The accuracy and stability of vehicle tracking lateral control are poor, and it cannot meet the needs of ensuring intelligent and safe vehicle driving. Summary of the invention

[0004] The present invention provides a vehicle path tracking lateral control method, device, equipment and storage medium to realize automatic lateral control of vehicle path tracking, which can adapt to complex vehicle driving environments, improve the accuracy and stability of vehicle path tracking lateral control, and ensure the safety of vehicle intelligent driving.

[0005] In a first aspect, an embodiment of the present invention provides a vehicle path tracking lateral control method, comprising:

[0006] Obtain the current motion information and target expected path of the unmanned target vehicle;

[0007] Determine the current preview distance of the target vehicle based on the current motion information and the target mapping relationship between the motion deviation and the preview time, wherein the target mapping relationship is obtained by fuzzy control based on a preset motion deviation range and a preview time range of the target vehicle;

[0008] Determining a target steering wheel angle of the target vehicle based on the current motion information, the target expected path, and the current preview distance;

[0009] A target control signal is generated based on the target steering wheel angle, and the target vehicle is laterally controlled based on the target control signal so that the target vehicle travels along the target desired path.

[0010] In a second aspect, an embodiment of the present invention further provides a vehicle path tracking lateral control device, comprising:

[0011] A motion information acquisition module is used to obtain the current motion information and target expected path of the unmanned target vehicle;

[0012] A preview distance determination module, used to determine the current preview distance of the target vehicle based on the current motion information and a target mapping relationship between motion deviation and preview time, wherein the target mapping relationship is obtained by fuzzy control based on a preset motion deviation range and preview time range of the target vehicle;

[0013] A target angle determination module, used to determine a target steering wheel angle of the target vehicle based on the current motion information, the target expected path and the current preview distance;

[0014] The target signal generating module is used to generate a target control signal based on the target steering wheel angle, and to perform lateral control on the target vehicle based on the target control signal so that the target vehicle travels along the target desired path.

[0015] In a third aspect, an embodiment of the present invention further provides an electronic device, characterized in that the electronic device comprises: at least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle path tracking lateral control method provided by any embodiment of the present invention.

[0018] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the vehicle path tracking lateral control method provided by any embodiment of the present invention when executing.

[0019] The technical solution of the embodiment of the present invention obtains the current motion information and target expected path of the unmanned target vehicle, and determines the current preview distance of the target vehicle based on the current motion information and the target mapping relationship between the motion deviation and the preview time, thereby realizing dynamic adjustment of the preview distance and improving the flexibility and adaptability of vehicle control, wherein the target mapping relationship is obtained by fuzzy control based on the preset motion deviation range and the preview time range of the target vehicle. Based on the current motion information, the target expected path and the current preview distance, the target steering wheel angle of the target vehicle is determined, which can ensure that the vehicle can accurately track the target expected path. Based on the target steering wheel angle, a target control signal is generated, and based on the target control signal, the target vehicle is laterally controlled so that the target vehicle travels along the target expected path. By dynamically adjusting the preview distance, the dynamic adjustment of the target steering wheel angle is further realized, thereby realizing automatic lateral control of vehicle path tracking, which can adapt to complex vehicle driving environments, improve the accuracy and stability of vehicle path tracking lateral control, and ensure that the vehicle can travel smoothly and safely along the target expected path.

[0020] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 is a flow chart of a vehicle path tracking lateral control method provided according to Embodiment 1 of the present invention;

[0023] Figure 2 is a flow chart of a vehicle path tracking lateral control method provided according to Embodiment 2 of the present invention;

[0024] Figure 3 is a schematic structural diagram of a vehicle path tracking lateral control device provided according to Embodiment 3 of the present invention;

[0025] Figure 4 It is a schematic diagram of the structure of an electronic device for implementing the vehicle path tracking lateral control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "target", "current", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Embodiment 1

[0029] Figure 1 A flow chart of a vehicle path tracking lateral control method is provided for the first embodiment of the present invention. This embodiment is applicable to the case where a vehicle is lateral controlled while traveling along a desired path. Figure 1 As shown, the method can be executed by a vehicle path tracking lateral control device, which can be implemented in the form of hardware and / or software, and can be configured in an electronic device. Figure 1 As shown, the method specifically comprises the following steps:

[0030] S110: Acquire current motion information and target expected path of the unmanned target vehicle.

[0031] Among them, the target vehicle may refer to a vehicle that is tracking the target desired path. Current motion information may refer to the motion state and related parameters exhibited by the vehicle at the current moment. This information is usually acquired in real time through a variety of sensors carried on the vehicle, including all the information required for the target vehicle to track the lateral control of the target desired path. The target desired path may refer to the path that the target vehicle is expected or instructed to follow. For example, the target desired path may be a path calculated by a navigation system or a path planning algorithm based on multiple factors such as the vehicle's destination, traffic rules, and road conditions.

[0032] Specifically, after the target vehicle has passed the self-check, the signal information on the vehicle's CAN bus is read in real time through the sensors on the vehicle to obtain the vehicle's current position, driving speed, acceleration, heading angle and other current motion information and the target expected path, so as to grasp the vehicle status in real time and provide basic data for subsequent control decisions.

[0033] S120, determining a current preview distance of the target vehicle based on current motion information and a target mapping relationship between motion deviation and preview time, wherein the target mapping relationship is obtained by fuzzy control based on a preset motion deviation range and preview time range of the target vehicle.

[0034] Among them, the current motion information includes motion deviation, and the motion deviation includes: lateral deviation and heading deviation. The current motion deviation may refer to the deviation between the driving route of the target vehicle at the current moment and the target expected route. The lateral deviation may refer to the lateral distance between the current position of the target vehicle and the target expected path. The heading deviation may refer to the angle between the current heading of the target vehicle and the tangent direction of the target expected path. The preview time may refer to the length of time the target vehicle looks forward at the current moment, which represents the foresight of the vehicle control system for future road conditions. The longer the preview time, the longer the corresponding preview distance. The target mapping relationship may refer to the corresponding relationship between a specific motion deviation and a specific preview time. The current preview distance may refer to a distance that the target vehicle detects and pays attention to in order to perceive and respond to the road conditions ahead in advance at the current moment. The preset motion deviation range may refer to a preset range of motion deviation values. The preset lateral deviation range may refer to a preset range of lateral deviation values. The preset heading deviation range may refer to a preset range of heading deviation values.

[0035] Specifically, through the fuzzy control algorithm, according to the preset motion deviation range (such as the degree of deviation of the vehicle from the target path) and the preview time range (i.e., the distance the vehicle looks forward), a target mapping relationship between motion deviation and preview time is established, and this relationship allows the preview distance to be dynamically adjusted according to the current motion deviation. According to the motion deviation in the current motion information, the current preview distance that best suits the current motion information is calculated using the target mapping relationship between motion deviation and preview time, thereby realizing dynamic adjustment of the preview distance, improving the flexibility and adaptability of vehicle control, and reducing the control lag or overreaction problems caused by the fixed preview distance.

[0036] Exemplarily, fuzzy control is performed based on a preset motion deviation range and a preview time range of the target vehicle to obtain a target mapping relationship, including: performing fuzzy control on a preset lateral deviation range, a preset heading deviation range, and a preview time range of the target vehicle to obtain at least one lateral deviation sub-range corresponding to the preset lateral deviation range, at least one heading deviation sub-range corresponding to the preset heading deviation range, at least one preview time sub-range corresponding to the preview time range, and a target fuzzy control strategy; based on the lateral deviation sub-range, the heading deviation sub-range, and the preview time sub-range, determining a first membership function corresponding to the lateral deviation sub-range, a second membership function corresponding to the heading deviation sub-range, and a third membership function corresponding to the preview time sub-range; performing fuzzy control on the first membership function, the second membership function, and the third membership function based on the target fuzzy control strategy to obtain a target mapping relationship.

[0037] Among them, the target fuzzy control strategy can be a fuzzy controller, which is a rule for fuzzy control of the motion deviation and preview time of the target vehicle. The first membership function can refer to the membership function corresponding to the preset lateral deviation range, which describes the degree of membership of the actual lateral deviation value of the vehicle to each sub-range within the preset lateral deviation range. The second membership function can refer to the membership function corresponding to the preset heading deviation range, which describes the degree of membership of the actual heading deviation value of the vehicle to each sub-range within the preset heading deviation range. The third membership function can refer to the membership function corresponding to the preview time range, which describes the degree of membership of the actual preview time of the vehicle to each sub-range within the preview time range.

[0038] Specifically, according to the preset lateral deviation range, preset heading deviation range and preview time range of the target vehicle, they are divided into several sub-ranges (such as small, medium, large, etc.), and at least one lateral deviation sub-range corresponding to the preset lateral deviation range, at least one heading deviation sub-range corresponding to the preset heading deviation range, and at least one preview time sub-range corresponding to the preview time range are obtained, and each sub-range corresponds to a fuzzy set. A first membership function is defined for each lateral deviation sub-range, which describes the degree to which the actual lateral deviation of the target vehicle belongs to the sub-range; a second membership function is defined for each heading deviation sub-range, which describes the degree of belonging to the actual heading deviation of the vehicle; a third membership function is defined for the preview time sub-range, which represents the degree of membership under different preview times. A set of target fuzzy control strategies can be designed based on expert experience or experimental data, which defines the corresponding preview time under different lateral deviations and heading deviations. The input (lateral deviation and heading deviation) of the target fuzzy control strategy is fuzzified according to the first membership function and the second membership function, and the output of the target fuzzy control strategy is defuzzified according to the third membership function to obtain a mapping relationship between the input and output (preview time) of the target fuzzy control strategy, namely, a target mapping relationship. This can handle various uncertainties and interference factors in the vehicle driving process without relying on an accurate mathematical model, thereby improving the robustness of the vehicle control system.

[0039] Exemplarily, fuzzy control is performed on the first membership function, the second membership function and the third membership function based on the target fuzzy control strategy to obtain a target mapping relationship, including: determining at least one pair of input data pairs based on the first membership function and the second membership function; and fuzzy control is performed on the input data pairs and the third membership function based on the target fuzzy control strategy to determine the target mapping relationship.

[0040] The input data pair may refer to a combination of an actual lateral deviation and a heading deviation.

[0041] Specifically, according to the first membership function and the second membership function, the actual lateral deviation and heading deviation values ​​are converted into the membership of the lateral deviation sub-range and the heading deviation sub-range, respectively, which usually involves comparing the input value with the boundary of each sub-range and calculating the corresponding membership. According to the target fuzzy control strategy, the input data pair is fuzzy inferred to obtain the membership of the output variable, and the output membership obtained by the fuzzy inference is converted into a specific preview time using the third membership function, and the target mapping relationship between the input and the output is obtained, so as to facilitate the real-time adjustment of the preview time according to the current driving state, thereby improving the accuracy and stability of the path tracking.

[0042] Exemplarily, fuzzy control is performed on input data pairs and a third membership function based on a target fuzzy control strategy to determine a target mapping relationship, including: performing fuzzy control on the input data pairs based on the target fuzzy control strategy to obtain a preview time fuzzy representation corresponding to each pair of input data pairs; defuzzifying the preview time fuzzy representation based on the third membership function to obtain a preview time corresponding to each pair of input data pairs, and generating a target mapping relationship.

[0043] The fuzzy representation of the preview time may refer to a fuzzy expression of the preview time obtained for each pair of input data (ie, lateral deviation and heading deviation) after matching the fuzzy control rules.

[0044] Specifically, each pair of input data (lateral deviation and heading deviation) is fuzzified by the first membership function and the second membership function respectively. This means that specific values ​​(such as the number of meters of lateral deviation and the angle of heading deviation) are converted into the membership of fuzzy sets, such as the membership of fuzzy concepts such as "small", "medium" and "large". Then, according to the fuzzy control rules defined in the target fuzzy control strategy, the fuzzified input data pairs are matched with the rules in the target fuzzy control strategy. Through fuzzy rule matching, one or more possible preview time fuzzy representations are determined for each pair of input data pairs. These representations may be the membership distribution of fuzzy sets, indicating the possibility or degree at different preview times. The pre-selected defuzzification method is applied to the preview time fuzzy representation corresponding to each pair of input data pairs to calculate the specific preview time value. Each pair of input data pairs and their corresponding preview time values ​​are combined to generate a target mapping relationship, so that the preview time can be adjusted in real time according to the current driving state, thereby achieving precise control of the vehicle.

[0045] Exemplarily, "determining the current preview distance of the target vehicle based on the current motion information and the target mapping relationship between the motion deviation and the preview time" in S120 may include: determining the current preview time based on the current motion deviation information in the current motion information and the target mapping relationship between the motion deviation and the preview time; multiplying the current vehicle speed in the current motion information and the current preview time to obtain the current preview distance of the target vehicle.

[0046] The current motion deviation information may refer to the lateral deviation and heading deviation of the target vehicle at the current moment.

[0047] Specifically, according to the target mapping relationship between the motion deviation and the preview time established in advance, the current motion deviation information is used as input, and the preview time to be currently adopted, i.e., the current preview time, can be determined by table lookup, interpolation or other mathematical methods. The current vehicle speed is multiplied by the current preview time, i.e., the current preview distance = current vehicle speed * current preview time. In this way, the current preview distance based on the current motion state and deviation information is obtained, so that the vehicle can select the appropriate preview distance according to different motion deviations and vehicle speed conditions, thereby more accurately tracking the desired path and reducing the tracking error.

[0048] It should be noted that the current preview distance can also be equal to = current vehicle speed * current preview time + preset distance, where the preset distance can be regarded as a safety margin or static offset to ensure that the vehicle maintains a sufficient safety distance from static obstacles in front (such as parked vehicles, road shoulders, guardrails, etc.). This helps prevent the vehicle from colliding with these obstacles in an emergency.

[0049] S130: Determine a target steering wheel angle of the target vehicle based on current motion information, target expected path, and current preview distance.

[0050] The target steering wheel angle may refer to the angle at which the steering wheel needs to be controlled to deflect when the target vehicle is traveling along the target desired path at the current moment.

[0051] Specifically, algorithms such as pure tracking algorithms and model predictive control can be used, combined with the target expected path within the current preview distance, the vehicle's current motion information and the current preview distance, to calculate the target steering wheel angle required for the target vehicle to travel along the target expected path, thereby accurately calculating the steering wheel angle and ensuring that the vehicle can accurately track the expected path.

[0052] Exemplarily, S130 may include: determining a first position coordinate of a point on the target expected path that is closest to the target vehicle based on current motion information and the target expected path; determining a second position coordinate of a current preview point on the target expected path whose distance from the first position coordinate is the current preview distance based on the first position coordinate and the current preview distance; and generating a target steering wheel angle of the target vehicle based on the current motion information and the second position coordinate.

[0053] The first position coordinates may refer to the coordinates of the point on the target desired path closest to the current position of the target vehicle. The second position coordinates may refer to the coordinates of the point reached after moving forward the current preview distance along the target desired path from the first position coordinates.

[0054] Specifically, the current motion information (such as current position, speed, heading, etc.) of the target vehicle and the target desired path are obtained, and then, using this information, the point closest to the current position of the target vehicle on the target desired path is found, that is, the first position coordinate, which can be achieved through geometric calculation or path search algorithm. According to the current preview distance, starting from the first position coordinate, the current preview distance is moved forward along the target desired path to determine the second position coordinate of the current preview point. Based on the current motion information (such as position, speed, acceleration, etc.) of the vehicle and the position coordinates of the current preview point, the required target steering wheel angle is calculated, so as to accurately track the target desired path and reduce the tracking error.

[0055] S140: Generate a target control signal based on the target steering wheel angle, and perform lateral control on the target vehicle based on the target control signal, so that the target vehicle travels along the target desired path.

[0056] The target control information may refer to a signal for controlling the steering wheel to rotate a target steering wheel angle.

[0057] Specifically, the calculated target steering wheel angle is converted into a target control signal (such as a motor drive signal, a servo motor control signal, etc.) that can be understood by the vehicle control system. These control signals are received and executed by the vehicle control system (such as an EPS electronic power steering system) to achieve lateral control of the vehicle steering wheel, so that the target vehicle can travel along the target desired path, thereby achieving high-precision lateral control of the target vehicle and improving driving safety and stability.

[0058] The technical solution of the embodiment of the present invention obtains the current motion information and target expected path of the unmanned target vehicle, and determines the current preview distance of the target vehicle based on the current motion information and the target mapping relationship between the motion deviation and the preview time, thereby realizing dynamic adjustment of the preview distance and improving the flexibility and adaptability of vehicle control, wherein the target mapping relationship is obtained by fuzzy control based on the preset motion deviation range and the preview time range of the target vehicle. Based on the current motion information, the target expected path and the current preview distance, the target steering wheel angle of the target vehicle is determined, which can ensure that the vehicle can accurately track the target expected path. Based on the target steering wheel angle, a target control signal is generated, and the target vehicle is laterally controlled based on the target control signal so that the target vehicle travels along the target expected path. By dynamically adjusting the preview distance, the dynamic adjustment of the target steering wheel angle is further realized, thereby realizing automatic lateral control of vehicle path tracking, which can adapt to complex vehicle driving environments, improve the accuracy and stability of vehicle path tracking lateral control, and ensure that the vehicle can smoothly and safely travel along the target expected path.

[0059] Exemplarily, the target steering wheel angle The calculation formula is as follows:

[0060]

[0061] in, is the heading angle of the target vehicle, L is the wheelbase between the front and rear axles of the target vehicle, l d is to simulate the current preview distance of the driver, i is the transmission ratio of the steering wheel angle to the wheel angle, (E x , E y ) is the coordinate of the current position of the target vehicle, (P x , P y ) are the coordinates of the preview point on the target expected path.

[0062] From the formula for calculating the target steering wheel angle, it can be seen that the control effect of the lateral control of the target vehicle path tracking depends on the preview distance l d The longer the preview distance, the smaller the expected steering wheel target angle change will be, and the smoother the control effect will be. The shorter the preview distance, the more precise the control effect will be, but it will also bring some shocks. Since the faster the vehicle speed, the longer the vehicle moves per unit time, the longer the preview distance we choose should be. Therefore, the selection of the preview distance should be proportional to the vehicle speed; therefore, the current preview distance l can be d With vehicle speed v x The relationship selection is expressed as follows:

[0063] l d =v x *T pre +l0 (2)

[0064] In the formula, v x is the longitudinal speed of the vehicle, T pre To simulate the driver's current preview time, l0 is the preset distance.

[0065] Exemplarily, the principle for formulating the above-mentioned target fuzzy control strategy is that the larger the lateral deviation and heading deviation, the longer the preview distance needs to be, and the longer the preview time; conversely, the smaller the lateral deviation and heading deviation, the closer the preview distance needs to be, and the shorter the preview time.

[0066] Embodiment 2

[0067] Figure 2 This is a flow chart of a vehicle path tracking lateral control method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment adds a new step after the step of "generating a target control signal based on the target steering wheel angle". The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here.

[0068] See also Figure 2 Another vehicle path tracking lateral control method provided in this embodiment specifically includes the following steps:

[0069] S210: Obtain current motion information and target expected path of the unmanned target vehicle.

[0070] S220, determining a current preview distance of the target vehicle based on current motion information and a target mapping relationship between motion deviation and preview time, wherein the target mapping relationship is obtained by fuzzy control based on a preset motion deviation range and preview time range of the target vehicle.

[0071] S230: Determine a target steering wheel angle of the target vehicle based on current motion information, target expected path, and current preview distance.

[0072] S240: Generate a target control signal based on the target steering wheel angle, and perform lateral control on the target vehicle based on the target control signal so that the target vehicle travels along the target desired path.

[0073] S250, obtaining an actual motion path of the target vehicle under the control of the target control signal, and determining a maximum tracking error between the actual motion path and the target expected path.

[0074] The actual motion path may refer to the actual driving trajectory of the target vehicle under the control of the target control signal (such as the steering wheel angle signal). The maximum tracking error may refer to the maximum deviation between the actual motion path and the target expected path during the process of the vehicle tracking the target expected path, and the deviation may be obtained by calculating the distance difference between the two at a specific time or spatial position.

[0075] Specifically, the actual motion path of the target vehicle under the execution of the target control signal (such as the steering wheel angle signal) is obtained in real time, the actual motion path is compared with the target expected path, and the maximum tracking error between the two is calculated, so that deviations in the vehicle's driving process can be discovered and corrected in time, thereby improving the tracking accuracy. It should be noted that the maximum tracking error is a key indicator for measuring vehicle tracking accuracy, reflecting the maximum degree of deviation between the vehicle's actual driving trajectory and the target path.

[0076] S260: If the maximum tracking error does not meet the preset tracking requirement, the future driving path of the target vehicle is predicted based on the actual motion path, and the target control signal is adjusted according to the prediction result.

[0077] The preset tracking requirement may refer to a pre-set requirement for determining whether the target vehicle's tracking of the target expected path is qualified.

[0078] Specifically, if the maximum tracking error does not meet the preset tracking requirements, the target vehicle's driving path in the future will be predicted based on the actual motion path to obtain the future driving path. The target control signal is adjusted based on the future driving path to correct the target vehicle's driving trajectory to make it closer to the target expected path. By obtaining the target vehicle's actual motion path in real time and calculating the tracking error, deviations in the target vehicle's driving process can be discovered and corrected in a timely manner, thereby improving tracking accuracy.

[0079] The technical solution of the embodiment of the present invention obtains the actual motion path of the target vehicle under the control of the target control signal and determines the maximum tracking error between the actual motion path and the target desired path, thereby detecting the tracking accuracy of the target desired path in real time. If the maximum tracking error does not meet the preset tracking requirements, the future driving path of the target vehicle is predicted based on the actual motion path, and the target control signal is adjusted according to the prediction result, thereby improving the tracking accuracy of the vehicle on the target desired path. By real-time monitoring and evaluating the tracking error and adjusting it when necessary, the tracking accuracy of the vehicle on the target desired path can be significantly improved, ensuring the stability and safety of the vehicle's driving.

[0080] Embodiment 3

[0081] Figure 3 This is a schematic diagram of the structure of a vehicle path tracking lateral control device provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a motion information acquisition module 310, a preview distance determination module 320, a target angle determination module 330 and a target signal generation module 340.

[0082] The motion information acquisition module 310 is used to acquire the current motion information and target expected path of the unmanned target vehicle;

[0083] A preview distance determination module 320 is used to determine the current preview distance of the target vehicle based on the current motion information and a target mapping relationship between the motion deviation and the preview time, wherein the target mapping relationship is obtained by fuzzy control based on a preset motion deviation range and a preview time range of the target vehicle;

[0084] A target steering angle determination module 330 is used to determine a target steering wheel angle of the target vehicle based on the current motion information, the target expected path and the current preview distance;

[0085] The target signal generating module 340 is used to generate a target control signal based on the target steering wheel angle, and to perform lateral control on the target vehicle based on the target control signal so that the target vehicle travels along the target desired path.

[0086] The technical solution of this embodiment obtains the current motion information and target expected path of the unmanned target vehicle, and determines the current preview distance of the target vehicle based on the current motion information and the target mapping relationship between the motion deviation and the preview time, thereby realizing dynamic adjustment of the preview distance and improving the flexibility and adaptability of vehicle control, wherein the target mapping relationship is obtained by fuzzy control based on the preset motion deviation range and preview time range of the target vehicle. Based on the current motion information, the target expected path and the current preview distance, the target steering wheel angle of the target vehicle is determined, which can ensure that the vehicle can accurately track the target expected path. Based on the target steering wheel angle, a target control signal is generated, and the target vehicle is laterally controlled based on the target control signal so that the target vehicle travels along the target expected path. By dynamically adjusting the preview distance, the dynamic adjustment of the target steering wheel angle is further realized, thereby realizing automatic lateral control of vehicle path tracking, which can adapt to complex vehicle driving environments, improve the accuracy and stability of vehicle path tracking lateral control, and ensure that the vehicle can smoothly and safely travel along the target expected path.

[0087] Optionally, the preview distance determination module 320 is specifically used to: determine the current preview time based on the current motion deviation information in the current motion information and the target mapping relationship between the motion deviation and the preview time; multiply the current vehicle speed in the current motion information and the current preview time to obtain the current preview distance of the target vehicle.

[0088] Optionally, the target turning angle determination module 330 is specifically used to: determine the first position coordinates of the point on the target expected path that is closest to the target vehicle based on the current motion information and the target expected path; determine the second position coordinates of the current preview point on the target expected path whose distance from the first position coordinates is the current preview distance based on the first position coordinates and the current preview distance; and generate the target steering wheel angle of the target vehicle based on the current motion information and the second position coordinates.

[0089] Optionally, the above device further comprises: a signal adjustment module, wherein:

[0090] The signal adjustment module is specifically used to: after generating a target control signal based on the target steering wheel angle, it also includes: obtaining the actual motion path of the target vehicle under the control of the target control signal, and determining the maximum tracking error between the actual motion path and the target expected path; if the maximum tracking error does not meet the preset tracking requirements, predicting the future driving path of the target vehicle based on the actual motion path, and adjusting the target control signal according to the prediction result.

[0091] Optionally, the motion deviation includes: lateral deviation and heading deviation; the preset motion deviation range includes a preset lateral deviation range and a preset heading deviation range; the above device also includes: a mapping relationship determination module, wherein:

[0092] The mapping relationship determination module includes:

[0093] A fuzzy control unit is used to perform fuzzy control on a preset lateral deviation range, a preset heading deviation range and a preview time range of the target vehicle, and obtain at least one lateral deviation sub-range corresponding to the preset lateral deviation range, at least one heading deviation sub-range corresponding to the preset heading deviation range, at least one preview time sub-range corresponding to the preview time range, and a target fuzzy control strategy;

[0094] a membership determination unit, configured to determine, based on the lateral deviation subrange, the heading deviation subrange and the preview time subrange, a first membership function corresponding to the lateral deviation subrange, a second membership function corresponding to the heading deviation subrange and a third membership function corresponding to the preview time subrange;

[0095] A mapping relationship determination unit is used to perform fuzzy control on the first membership function, the second membership function and the third membership function based on the target fuzzy control strategy to obtain a target mapping relationship.

[0096] Optionally, the mapping relationship determining unit includes:

[0097] an input data determination subunit, configured to determine at least one pair of input data based on the first membership function and the second membership function;

[0098] The mapping relationship determination subunit is used to perform fuzzy control on the input data pair and the third membership function based on the target fuzzy control strategy to determine the target mapping relationship.

[0099] Optionally, the mapping relationship determination subunit is specifically used to: perform fuzzy control on the input data pairs based on the target fuzzy control strategy to obtain a preview time fuzzy representation corresponding to each pair of input data pairs; defuzzify the preview time fuzzy representation based on the third membership function to obtain the preview time corresponding to each pair of input data pairs, and generate a target mapping relationship.

[0100] The vehicle path tracking lateral control device provided in the embodiment of the present invention can execute the vehicle path tracking lateral control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0101] Figure 4A schematic diagram of the structure of an electronic device 12 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as desktop computers, workstations, servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0102] like Figure 4 As shown, the electronic device 12 is in the form of a general purpose computing device. The components of the electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that connects various system components (including the system memory 28 and the processing unit 16).

[0103] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. By way of example, these architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MAC) bus, an Enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

[0104] The electronic device 12 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the electronic device 12, including volatile and non-volatile media, removable and non-removable media.

[0105] The system memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. The electronic device 12 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the storage system 34 may be used to read and write non-removable, non-volatile magnetic media ( Figure 4 not shown, usually called a "hard drive"). Although Figure 4Not shown in the figure, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk"), and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, a DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to the bus 18 via one or more data medium interfaces. The system memory 28 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present invention.

[0106] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment. Program modules 42 generally perform the functions and / or methods of the embodiments described herein.

[0107] The electronic device 12 may also communicate with one or more external devices 14 (e.g., keyboards, pointing devices, displays 24, etc.), may communicate with one or more devices that enable a user to interact with the electronic device 12, and / or may communicate with any device that enables the electronic device 12 to communicate with one or more other computing devices (e.g., network cards, modems, etc.). Such communication may be performed via an input / output (I / O) interface 22. Furthermore, the electronic device 12 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 20. As shown, the network adapter 20 communicates with other modules of the electronic device 12 via a bus 18. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 12, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0108] The processing unit 16 executes various functional applications and data processing by running the program stored in the system memory 28, for example, implementing a vehicle path tracking lateral control method provided by the embodiment of the present invention, the method comprising:

[0109] Obtain the current motion information and target expected path of the unmanned target vehicle;

[0110] Determine the current preview distance of the target vehicle based on the current motion information and the target mapping relationship between the motion deviation and the preview time, wherein the target mapping relationship is obtained by fuzzy control based on a preset motion deviation range and a preview time range of the target vehicle;

[0111] Determining a target steering wheel angle of the target vehicle based on the current motion information, the target expected path, and the current preview distance;

[0112] A target control signal is generated based on the target steering wheel angle, and the target vehicle is laterally controlled based on the target control signal so that the target vehicle travels along the target desired path.

[0113] Of course, those skilled in the art will appreciate that the processor may also implement the technical solution of the vehicle path tracking lateral control method provided in any embodiment of the present invention.

[0114] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the vehicle path tracking lateral control method steps provided in any embodiment of the present invention are implemented. The method includes:

[0115] Obtain the current motion information and target expected path of the unmanned target vehicle;

[0116] Determine the current preview distance of the target vehicle based on the current motion information and the target mapping relationship between the motion deviation and the preview time, wherein the target mapping relationship is obtained by fuzzy control based on a preset motion deviation range and a preview time range of the target vehicle;

[0117] Determining a target steering wheel angle of the target vehicle based on the current motion information, the target expected path, and the current preview distance;

[0118] A target control signal is generated based on the target steering wheel angle, and the target vehicle is laterally controlled based on the target control signal so that the target vehicle travels along the target desired path.

[0119] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0120] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0121] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0122] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0123] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0124] Note that the above are only preferred embodiments of the present invention and the technical principles used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in more detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A vehicle path tracking lateral control method, characterized in that: include: Obtain the current motion information and target expected path of the unmanned target vehicle; Determine the current preview distance of the target vehicle based on the current motion information and the target mapping relationship between the motion deviation and the preview time, wherein the target mapping relationship is obtained by fuzzy control based on a preset motion deviation range and a preview time range of the target vehicle; Determining a target steering wheel angle of the target vehicle based on the current motion information, the target expected path, and the current preview distance; A target control signal is generated based on the target steering wheel angle, and the target vehicle is laterally controlled based on the target control signal so that the target vehicle travels along the target desired path.

2. The method according to claim 1, characterized in that The motion deviation includes: lateral deviation and heading deviation; the preset motion deviation range includes a preset lateral deviation range and a preset heading deviation range; The fuzzy control is performed based on the preset motion deviation range and preview time range of the target vehicle to obtain the target mapping relationship, including: Performing fuzzy control on a preset lateral deviation range, a preset heading deviation range, and a preview time range of the target vehicle to obtain at least one lateral deviation sub-range corresponding to the preset lateral deviation range, at least one heading deviation sub-range corresponding to the preset heading deviation range, at least one preview time sub-range corresponding to the preview time range, and a target fuzzy control strategy; Based on the lateral deviation sub-range, the heading deviation sub-range and the preview time sub-range, determine a first membership function corresponding to the lateral deviation sub-range, a second membership function corresponding to the heading deviation sub-range and a third membership function corresponding to the preview time sub-range; Based on the target fuzzy control strategy, fuzzy control is performed on the first membership function, the second membership function and the third membership function to obtain a target mapping relationship.

3. The method according to claim 2, characterized in that The performing fuzzy control on the first membership function, the second membership function and the third membership function based on the target fuzzy control strategy to obtain a target mapping relationship includes: Determining at least one pair of input data based on the first membership function and the second membership function; Based on the target fuzzy control strategy, fuzzy control is performed on the input data pair and the third membership function to determine a target mapping relationship.

4. The method according to claim 3, characterized in that The step of performing fuzzy control on the input data pair and the third membership function based on the target fuzzy control strategy to determine the target mapping relationship includes: Perform fuzzy control on the input data pairs based on the target fuzzy control strategy to obtain a preview time fuzzy representation corresponding to each pair of input data pairs; The preview time fuzzy representation is defuzzified based on the third membership function to obtain the preview time corresponding to each pair of input data, and generate a target mapping relationship.

5. The method according to claim 1, characterized in that The determining of the current preview distance of the target vehicle based on the current motion information and the target mapping relationship between the motion deviation and the preview time includes: Determine the current preview time based on the current motion deviation information in the current motion information and the target mapping relationship between the motion deviation and the preview time; The current vehicle speed in the current motion information and the current preview time are multiplied to obtain the current preview distance of the target vehicle.

6. The method according to claim 1, characterized in that The step of determining a target steering wheel angle of the target vehicle based on the current motion information, the target expected path, and the current preview distance includes: Based on the current motion information and the target desired path, determining a first position coordinate of a point on the target desired path that is closest to the target vehicle; Based on the first position coordinate and the current preview distance, determine a second position coordinate of a current preview point on the target expected path whose distance from the first position coordinate is the current preview distance; A target steering wheel angle of the target vehicle is generated based on the current motion information and the second position coordinates.

7. The method according to claim 1, characterized in that After generating a target control signal based on the target steering wheel angle, the method further includes: Acquire an actual motion path of the target vehicle under the control of the target control signal, and determine a maximum tracking error between the actual motion path and the target expected path; If the maximum tracking error does not meet the preset tracking requirement, the future driving path of the target vehicle is predicted based on the actual motion path, and the target control signal is adjusted according to the prediction result.

8. A vehicle path tracking lateral control device, characterized in that: include: A motion information acquisition module is used to obtain the current motion information and target expected path of the unmanned target vehicle; A preview distance determination module, used to determine the current preview distance of the target vehicle based on the current motion information and a target mapping relationship between motion deviation and preview time, wherein the target mapping relationship is obtained by fuzzy control based on a preset motion deviation range and preview time range of the target vehicle; A target angle determination module, used to determine a target steering wheel angle of the target vehicle based on the current motion information, the target expected path and the current preview distance; The target signal generating module is used to generate a target control signal based on the target steering wheel angle, and to perform lateral control on the target vehicle based on the target control signal so that the target vehicle travels along the target desired path.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the vehicle path tracking lateral control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle path tracking lateral control method according to any one of claims 1 to 7 when executed.

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