A method, device, and storage medium for curve speed limiting based on prediction of the trajectory of the vehicle in front.

Abstract

This invention proposes a method, device, and storage medium for curve speed limiting based on the predicted trajectory of the vehicle ahead. Based on the predicted trajectories of surrounding vehicles, the radius of curvature of the curve ahead is inferred. When the obtained radius of curvature meets the speed limit activation condition, the current vehicle's speed is controlled within the target speed corresponding to the radius of curvature. By combining the trajectory prediction algorithm with the curve speed limit control strategy, the vehicle speed can be reduced in advance when cornering, improving the safety and success rate of curve speed limit control.

Description

Technical Field

[0001] This invention belongs to the field of vehicle engineering technology, and in particular relates to a method, device and storage medium for curve speed limiting based on the prediction of the trajectory of the preceding vehicle. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the increasingly widespread application of autonomous driving technology, the issue of autonomous driving safety has become a key focus in the field, especially when operating autonomous driving functions on highways. The ability to safely navigate curves is crucial for the safety of passengers. When vehicles are traveling at high speeds on highways and navigating curves, the vehicle's inertia can cause it to veer off the curve or even run out of its lane, easily leading to accidents.

[0004] Current technology uses cameras to sense lane lines when approaching a curve or turning, calculates the current lane line curvature in real time, and sets speed limits to prevent accidents while driving on curves. However, it is affected by external factors such as weather and insufficient lighting, and relying solely on lane line information has low confidence and is unstable, which can easily lead to dangerous situations. Summary of the Invention

[0005] To overcome the shortcomings of the prior art, this invention provides a method, device, and storage medium for curve speed limiting based on preceding vehicle trajectory prediction. It combines trajectory prediction algorithms with curve speed limiting control strategies to achieve early speed reduction during curves, thereby improving the safety and success rate of curve speed limiting control. The technical solution is as follows:

[0006] On the one hand, a curve speed limit method based on the prediction of the trajectory of the vehicle in front is provided, the method comprising:

[0007] Based on the current operating status of the surrounding vehicles, the trajectory of the surrounding vehicles is predicted using a vehicle dynamics model.

[0008] Based on the predicted trajectories of the surrounding vehicles, determine the lane curvature radius of the curve ahead.

[0009] If the lane curvature radius meets the speed limit activation condition, the current vehicle speed will be controlled within the target speed corresponding to the lane curvature radius.

[0010] In some embodiments, predicting the trajectory of the surrounding vehicles based on their operating status specifically includes:

[0011] Use intelligent driver models to predict the speeds of vehicles around the current vehicle.

[0012] Based on the speed of the surrounding vehicles and the corresponding target lane, the front wheel steering angle of the surrounding vehicles is predicted using a pure tracking algorithm;

[0013] The predicted trajectories of the surrounding vehicles are calculated iteratively based on their speed and front wheel angle.

[0014] In some embodiments, the determination of the target lane for the surrounding vehicles specifically involves:

[0015] Based on the current state of the surrounding vehicles, predict the state at different times within the surrounding area in the future;

[0016] Based on the predicted state quantities at different times within a future surrounding area, determine whether surrounding vehicles are changing lanes.

[0017] The target lane is determined based on the predicted lane-changing behavior of surrounding vehicles.

[0018] In some embodiments, the step of iteratively calculating the predicted trajectory of the surrounding vehicles based on their speed and front wheel angle specifically involves:

[0019] Based on the speed and front wheel angle of the surrounding vehicles, the vehicle state at the next moment is obtained by substituting them into the vehicle state transition equation; the vehicle state includes the vehicle position, speed and yaw angle.

[0020] Based on the vehicle status of the surrounding vehicles at the next moment, the driving speed and front wheel steering angle of the surrounding vehicles at the next moment are predicted.

[0021] Repeat the above process until the predicted trajectories of surrounding vehicles within the prediction period are obtained.

[0022] In some embodiments, the lane curvature radius of the upcoming curve is determined based on the predicted trajectories of the surrounding vehicles. Specifically, the lane curvature radius of the upcoming curve is determined based on the predicted speeds and lateral accelerations of the surrounding vehicles at the predicted trajectory points.

[0023] In some embodiments, based on the radius of curvature corresponding to each predicted trajectory point, it is determined whether the radius of curvature meets the curve speed limit activation condition. If it does not meet the condition, the current vehicle maintains its current driving speed. If it does meet the condition, the target vehicle speed corresponding to the radius of curvature is obtained by looking up a table, and the current vehicle speed is controlled within the target vehicle speed.

[0024] On the other hand, a curve speed limiting device based on the prediction of the trajectory of the preceding vehicle is provided, the device comprising:

[0025] The trajectory prediction module is used to predict the trajectory of the surrounding vehicles based on the vehicle dynamics model, according to the operating status of the surrounding vehicles of the current vehicle.

[0026] The determination module is used to determine the lane curvature radius of the curve ahead based on the predicted running trajectories of the surrounding vehicles.

[0027] The control module is used to control the current vehicle speed within the target speed corresponding to the lane curvature radius if the lane curvature radius meets the speed limit activation condition.

[0028] In some embodiments, the trajectory prediction module includes:

[0029] The first prediction submodule is used to predict the speed of vehicles around the current vehicle using an intelligent driver model.

[0030] The second prediction submodule is used to predict the front wheel steering angle of the surrounding vehicles based on their driving speed and the corresponding target lane using a pure tracking algorithm.

[0031] The third prediction submodule is used to iteratively calculate the predicted trajectory of the surrounding vehicles based on their speed and front wheel angle.

[0032] On the other hand, a vehicle is provided, the vehicle including a memory and a processor, the memory for storing a computer program, and the processor for executing the computer program stored in the memory to implement the steps of the curve speed limit method based on the prediction of the trajectory of the preceding vehicle described above.

[0033] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when the computer program is executed by a processor, it implements the steps of the curve speed limit method based on the prediction of the trajectory of the preceding vehicle described above.

[0034] On the other hand, a computer program product containing instructions is provided that, when run on a computer, causes the computer to perform the steps of the curve speed limit method based on the prediction of the trajectory of the preceding vehicle described above.

[0035] The above one or more technical solutions have the following beneficial effects:

[0036] This invention is based on the predicted driving trajectories of surrounding vehicles. It infers the radius of curvature of the curve ahead based on the predicted driving trajectories of surrounding vehicles. When the obtained radius of curvature meets the speed limit activation condition, the current vehicle speed is controlled within the target speed corresponding to the radius of curvature. By combining the trajectory prediction algorithm and the curve speed limit control strategy, the vehicle speed can be reduced in advance when cornering, thereby improving the safety and success rate of curve speed limit control.

[0037] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0038] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0039] Figure 1 This is a schematic diagram of the curve following speed limit control in the embodiments of this application;

[0040] Figure 2 This is a schematic diagram illustrating the principle of front vehicle trajectory prediction in the embodiments of this application;

[0041] Figure 3 This is a flowchart of the curve speed limit control in the embodiments of this application;

[0042] Figure 4 This is a schematic diagram of a curve speed limiting device based on the trajectory prediction of the preceding vehicle in an embodiment of this application;

[0043] Figure 5 This is a schematic diagram of the vehicle structure in an embodiment of this application. Detailed Implementation

[0044] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0045] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0046] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0047] First, the application scenarios involved in the embodiments of this application will be introduced.

[0048] Current technology uses cameras to sense lane lines when approaching a curve or turning, calculates the current lane line curvature in real time, and sets speed limits to prevent accidents while driving on curves. However, it is affected by external factors such as weather and insufficient lighting, and relying solely on lane line information has low confidence and is unstable, which can easily lead to dangerous situations.

[0049] Based on this application scenario, this application provides a vehicle speed control method that can improve the success rate and safety of driving speed limit control.

[0050] Next, the system architecture involved in the embodiments of this application will be described.

[0051] This application provides a vehicle speed control system architecture, in which a sensing system and a speed control device are installed on the vehicle. The sensing system acquires the state variables of surrounding vehicles and predicts their trajectories based on these variables. Based on the predicted trajectories, it determines the lane curvature radius of the upcoming curve and checks whether the determined lane curvature radius meets the speed limit activation condition. If the condition is met, the system sends the target speed corresponding to the determined lane curvature radius to the speed control device. The speed control device detects the current vehicle speed and, if the current speed exceeds the target speed, controls the vehicle to reduce its speed.

[0052] The perception system includes one millimeter-wave radar sensor, one vision sensor, several ultrasonic radar sensors, and two single-line lidar sensors. The single-line lidar sensor has a scanning range of 180 degrees and can be installed at any horizontal position on either of the two corresponding sides of the vehicle. The longitudinal installation position should be determined according to the lidar installation requirements. Precise measurements are required for the installation positions of the vision sensor and millimeter-wave radar. Generally, they are installed at the center of the front of the vehicle to ensure that the angles on both sides of the front of the vehicle are the same, facilitating fusion calculations. If the vision sensor or millimeter-wave radar cannot be installed at the front of the vehicle, the offset position of the sensor installation is used in the fusion calculation process. The ultrasonic radars are evenly distributed horizontally at the front of the vehicle, with the standard being that the scanning range completely covers the front of the vehicle. An information fusion system is installed on the vehicle, which includes a processor. The millimeter-wave radar sensor, vision sensor, ultrasonic radar sensor, and single-line lidar sensor are connected to the information fusion system.

[0053] Those skilled in the art should understand that the above system architecture is merely an example, and other existing or future devices or modules that are applicable to this application should also be included within the scope of protection of this application, and are hereby incorporated by reference.

[0054] The vehicle speed control method provided in the embodiments of this application will now be explained in detail with reference to the accompanying drawings.

[0055] Figure 2 This is a flowchart illustrating a curve speed limit method based on the prediction of the trajectory of the vehicle in front, provided in an embodiment of this application. This method is applied to vehicles. Please refer to... Figure 2 The method includes the following steps.

[0056] Step 1: Based on the operating status of surrounding vehicles, predict the operating trajectories of surrounding vehicles using a vehicle dynamics model.

[0057] Step 2: Determine the lane curvature radius of the curve ahead based on the predicted trajectory of surrounding vehicles.

[0058] Step 3: If the lane curvature radius meets the speed limit activation condition, then control the current vehicle speed within the target speed corresponding to the lane curvature radius.

[0059] This application predicts the curvature radius of the curve ahead based on the predicted trajectories of surrounding vehicles. When the obtained curvature radius meets the speed limit activation condition, the current vehicle speed is controlled within the target speed corresponding to the curvature radius. By combining the trajectory prediction algorithm with the curve speed limit control strategy, the vehicle speed can be reduced in advance when cornering, thereby improving the safety and success rate of curve speed limit control.

[0060] In some embodiments, the trajectory of surrounding vehicles is predicted based on a vehicle dynamics model according to the operating status of the current vehicle, specifically including:

[0061] Use intelligent driver models to predict the speeds of vehicles around the current vehicle.

[0062] Based on the speed of surrounding vehicles and the corresponding target lane, the front wheel steering angle of surrounding vehicles is predicted using a pure tracking algorithm;

[0063] The predicted trajectories of surrounding vehicles are calculated iteratively based on the speed of surrounding vehicles and the front wheel angle.

[0064] In some embodiments, the determination of the target lane for surrounding vehicles specifically involves:

[0065] Based on the current state of surrounding vehicles, predict the state at different times within the next surrounding area.

[0066] Based on the predicted state quantities at different times within a future surrounding area, determine whether surrounding vehicles are changing lanes.

[0067] The target lane is determined based on the predicted lane-changing behavior of surrounding vehicles.

[0068] In some embodiments, the predicted trajectories of the surrounding vehicles are iteratively calculated based on the speed of the surrounding vehicles and the front wheel steering angle, specifically as follows:

[0069] Based on the speed of surrounding vehicles and the steering angle of the front wheels, substitute them into the vehicle state transition equation to obtain the vehicle state of the surrounding vehicles at the next moment; the vehicle state includes the vehicle position, speed and yaw angle.

[0070] Based on the vehicle status of surrounding vehicles at the next moment, the driving speed and front wheel angle of surrounding vehicles at the next moment can be predicted.

[0071] Repeat the above process until the predicted trajectories of surrounding vehicles within the prediction period are obtained.

[0072] In some embodiments, the lane curvature radius of the upcoming curve is determined based on the predicted trajectories of surrounding vehicles. Specifically, the lane curvature radius of the upcoming curve is determined based on the predicted driving speeds and lateral accelerations of surrounding vehicles at the predicted trajectory points.

[0073] In some embodiments, based on the radius of curvature corresponding to each predicted trajectory point, it is determined whether the radius of curvature meets the curve speed limit activation condition. If it does not meet the condition, the current vehicle maintains its current driving speed. If it does meet the condition, the target speed corresponding to the radius of curvature is obtained by looking up a table, and the current vehicle's driving speed is controlled within the target speed range.

[0074] All of the above-mentioned optional technical solutions can be combined in any way to form optional embodiments of this application, and the embodiments of this application will not be described in detail one by one.

[0075] This embodiment provides a curve speed limit method based on the prediction of the trajectory of the preceding vehicle. The method includes the following steps:

[0076] Step 101: Use the intelligent driver model to predict the speed of vehicles around the current vehicle.

[0077] This application uses an Intelligent Driver Model (IDM) to predict the speed of surrounding vehicles.

[0078] The parameters of the intelligent driver model all have clear physical meanings, which can intuitively show the changes in driving behavior, and the model can also be applied to vehicle speed prediction.

[0079] Step 102: Based on the speed of surrounding vehicles and the corresponding target lane, use a pure tracking algorithm to predict the front wheel turning angle of surrounding vehicles.

[0080] As an example, based on the current vehicle's speed, surrounding vehicles within a certain distance are marked. Based on the camera's perception results, the domain controller calculates the current state variables x, y, and v of the important surrounding vehicles. x v y a x a y .

[0081] Within a prediction period, the acceleration of surrounding vehicles remains constant. Based on the current state of the surrounding vehicles, the state quantities at different times in the next period are inferred:

[0082] X = (x, y, v) x v y a x a y ) T

[0083] Where x and y are the vertical and horizontal positions, respectively, and v x and v y These are the longitudinal velocity and the lateral velocity, respectively, a x and a y These are longitudinal acceleration and lateral acceleration, respectively.

[0084] The lateral position is the x-coordinate of surrounding important vehicles within the current vehicle's own coordinate system. Optionally, the lateral position can also indicate the lateral distance of surrounding vehicles from the x-axis of the current vehicle's own coordinate system.

[0085] The longitudinal position refers to the longitudinal coordinates of surrounding important vehicles within the current vehicle's vehicle coordinate system. Optionally, the longitudinal position can also indicate the longitudinal distance from surrounding vehicles to the y-axis of the current vehicle's vehicle coordinate system.

[0086] Optionally, in the current vehicle's corresponding vehicle coordinate system Oyx, the y-axis represents the lateral distance of important surrounding vehicles from the current vehicle, and the x-axis represents the longitudinal distance of the vehicles to be filtered from the current vehicle. Different vehicles have their own corresponding coordinates (y, x) based on the current vehicle's corresponding vehicle coordinate system. (y, x) can reflect the position of different important surrounding vehicles relative to the current vehicle. y is the x-coordinate of each important surrounding vehicle in the current vehicle's corresponding vehicle coordinate system; x is the y-coordinate of each important surrounding vehicle in the current vehicle's corresponding vehicle coordinate system.

[0087] Specifically, based on the lateral displacement y of surrounding important vehicles within a predicted period, it is possible to predict whether surrounding vehicles will change lanes.

[0088]

[0089] Among them, RLC, LK and LLC represent three behaviors: changing lanes to the right, staying in the original lane and changing lanes to the left, respectively; For the predicted T p Lateral position of the vehicle after time; y sl The lateral position of the center line of the lane where important surrounding vehicles are located; w l Lane width; α y This is the threshold coefficient.

[0090] After predicting the driving behavior of important surrounding vehicles, the driving trajectory of important surrounding vehicles is predicted by considering prior information about their behavioral intentions; and their target lanes are determined based on the driving behavior of important surrounding vehicles, and a pure tracking algorithm is used to predict the front wheel turning angle of important surrounding vehicles.

[0091] The basic idea of ​​pure tracking algorithms is to refer to human driving behavior, calculate the curvature from the vehicle's current position to the target point, and make the vehicle travel along the arc passing through the target point to achieve trajectory tracking. Based on the predicted driving intentions of important surrounding vehicles, the target lane of the vehicle can be inferred from three different behavioral intentions: changing lanes to the right, keeping the current lane, and changing lanes to the left.

[0092] The predicted trajectories of the surrounding vehicles are calculated iteratively based on their speed and front wheel angle.

[0093] Step 103: Based on the speed of surrounding vehicles and the front wheel angle, iteratively calculate the predicted trajectory of surrounding vehicles.

[0094] As an example, by combining the predicted longitudinal velocities and front wheel steering angles of the surrounding important vehicles mentioned above, a vehicle kinematics model is used to iteratively generate the trajectories of the surrounding important vehicles within the prediction period:

[0095]

[0096] in, v is the predicted acceleration of the vehicle at time k; a and b are the maximum acceleration and comfortable deceleration of the vehicle, respectively; k Let k be the speed of the vehicle at time k. Let Δs be the speed of the vehicle in front at time k; k v represents the relative distance between the vehicle and the vehicle in front at time k; l The speed limit is δ, the acceleration exponent factor is s0, and the minimum distance between vehicles when stationary is T. h For the desired headway; T s For discrete time intervals; This is the predicted vehicle speed at time k+1.

[0097] The predicted speeds and front wheel angles of surrounding important vehicles are substituted into the vehicle kinematics model formula to calculate the vehicle's trajectory point at the next moment. Based on the predicted acceleration and front wheel angle at this trajectory point, the trajectories of surrounding vehicles in the entire prediction time domain are iteratively calculated.

[0098]

[0099] In the formula, It is the horizontal swing angle; and These are the predicted speed and predicted front wheel steering angle obtained from this calculation, respectively.

[0100] Therefore, the discretized vehicle state transition equation obtained by the forward Euler method can be simplified as follows:

[0101]

[0102] In the formula,

[0103] Substituting the predicted driving speed and front wheel steering angle at time k into the state transition equation, the vehicle position, speed, yaw angle, and other states of surrounding important vehicles at time k+1 are calculated. Then, based on the vehicle states at time k+1, the vehicle driving speed and front wheel steering angle are predicted, and this process is repeated until the vehicle trajectory for the entire prediction time domain is predicted.

[0104] Step 301: If the lane curvature radius meets the speed limit activation condition, then control the current vehicle speed within the target speed corresponding to the lane curvature radius.

[0105] As an example, such as Figure 3 As shown, based on the predicted acceleration, speed, and turning angle of the vehicle ahead, the radius of curvature of the curve ahead is calculated using kinematic equations:

[0106]

[0107] In the formula, v is the vehicle's speed, and a y R is the lateral acceleration of the vehicle, and R is the radius of curvature of the curve.

[0108] Step 501: If the lane curvature radius meets the speed limit activation condition, then control the current vehicle speed within the target speed corresponding to the lane curvature radius.

[0109] Based on the calculated radius of curvature of the curve ahead, there is a prescribed table. Different radii of curvature correspond to different speed limits. The different speed limits are the target speeds, which can be used to control the vehicle to decelerate in advance.

[0110] Based on a mathematical model, this application can predict whether important surrounding vehicles are turning. Based on the predicted lateral acceleration and speed of important surrounding vehicles, the lane curvature of the curve ahead can be calculated. The radius of curvature is used to determine whether the activation conditions for the curve speed limit are met. If not, the vehicle continues to maintain its original cruising speed. If the conditions are met, the vehicle uses the curve curvature radius to look up the corresponding required speed limit value (i.e., the target speed) in a table. By controlling the vehicle to decelerate to the target speed, the success rate and safety of the curve speed limit control function are improved.

[0111] This application calculates the predicted trajectory of a vehicle over a period of time based on a vehicle dynamics model, and infers the radius of curvature at each path point, enabling advance vehicle control and comfortable deceleration, thereby improving the performance of smooth cornering in cornering assisted driving scenarios.

[0112] After explaining the vehicle speed control method provided in the embodiments of this application, the vehicle speed control device provided in the embodiments of this application will be introduced next.

[0113] Figure 5 This is a schematic diagram of a vehicle speed control device provided in an embodiment of this application. The vehicle speed control device can be implemented as part or all of the vehicle's speed control capabilities through software, hardware, or a combination of both. Please refer to... Figure 4 The device includes: a trajectory prediction module, a determination module, and a control module.

[0114] The trajectory prediction module is used to predict the trajectory of surrounding vehicles based on the vehicle dynamics model, according to the operating status of the surrounding vehicles of the current vehicle.

[0115] The determination module is used to determine the lane curvature radius of the curve ahead based on the predicted trajectory of surrounding vehicles.

[0116] The control module is used to control the current vehicle speed within the target speed corresponding to the lane curvature radius if the lane curvature radius meets the speed limit activation condition.

[0117] In some embodiments, the trajectory prediction module includes:

[0118] The first prediction submodule is used to predict the speed of vehicles around the current vehicle using an intelligent driver model.

[0119] The second prediction submodule is used to predict the front wheel steering angle of surrounding vehicles based on their speed and the corresponding target lane using a pure tracking algorithm.

[0120] The third prediction submodule is used to iteratively calculate the predicted trajectory of the surrounding vehicles based on their speed and front wheel angle.

[0121] In some embodiments, the second prediction submodule includes:

[0122] State prediction submodule: Used to predict the state of surrounding vehicles at different times in the future based on the current state of surrounding vehicles.

[0123] Lane change prediction submodule: Used to determine whether surrounding vehicles will change lanes based on the predicted state quantities at different times within a future surrounding area;

[0124] Target Lane Determination Submodule: Used to determine the target lane based on the predicted lane-changing behavior of surrounding vehicles.

[0125] In some embodiments, the third prediction submodule includes:

[0126] The first calculation submodule is used to substitute the speed of the surrounding vehicles and the front wheel steering angle into the vehicle state transition equation to obtain the vehicle state of the surrounding vehicles at the next moment; the vehicle state includes the vehicle position, speed and yaw angle.

[0127] The second calculation submodule is used to predict the driving speed and front wheel angle of the surrounding vehicles in the next moment based on the vehicle state of the surrounding vehicles in the next moment.

[0128] The third calculation submodule is used to repeat the above process until the predicted trajectory of surrounding vehicles within the prediction period is obtained.

[0129] In some embodiments, the control module is used to: determine whether the radius of curvature corresponding to each predicted trajectory point meets the curve speed limit activation condition based on the radius of curvature; if not, the current vehicle maintains its current driving speed; if it does, the target vehicle speed corresponding to the radius of curvature is obtained by looking up a table, and the current vehicle speed is controlled within the target vehicle speed.

[0130] like Figure 5 This is a structural block diagram of a vehicle provided in an embodiment of this application. Typically, a vehicle includes a processor and a memory.

[0131] The processor may include one or more processing cores, such as a quad-core processor or an octa-core processor. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor may also include a main processor and coprocessors. The main processor, also known as a CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state. In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. In some embodiments, the processor may also include an AI (Artificial Intelligence) processor, which handles computational operations related to machine learning.

[0132] The memory may include one or more computer-readable storage media, which may be non-transitory. The memory may also include high-speed random access memory and non-volatile memory, such as one or more disk storage devices or flash memory devices. In some embodiments, the non-transitory computer-readable storage media in the memory are used to store at least one instruction, which is executed by a processor to implement a curve speed limit method based on the prediction of the trajectory of the preceding vehicle provided in the method embodiments of this application.

[0133] In some embodiments, the vehicle may also optionally include: a peripheral device interface and at least one peripheral device. The processor, memory, and peripheral device interface can be connected via a bus or signal line. Each peripheral device can be connected to the peripheral device interface via a bus, signal line, or circuit board. Specifically, the peripheral device includes at least one of: radio frequency circuitry, a touchscreen display, a camera, audio circuitry, a positioning component, and a power supply.

[0134] Peripheral device interfaces can be used to connect at least one I / O (Input / Output) related peripheral device to the processor and memory. In some embodiments, the processor, memory, and peripheral device interface are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor, memory, and peripheral device interface can be implemented on separate chips or circuit boards, which is not limited in this embodiment.

[0135] Radio frequency (RF) circuits are used to receive and transmit RF signals, also known as electromagnetic signals. RF circuits communicate with communication networks and other communication devices via electromagnetic signals. RF circuits convert electrical signals into electromagnetic signals for transmission, or convert received electromagnetic signals back into electrical signals. Optionally, RF circuits include: antenna systems, RF transceivers, one or more amplifiers, tuners, oscillators, digital signal processors, codec chipsets, user identity module cards, etc. RF circuits can communicate with other terminals through at least one wireless communication protocol. These wireless communication protocols include, but are not limited to: the World Wide Web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and / or WiFi (Wireless Fidelity) networks. In some embodiments, the RF circuit may also include circuitry related to NFC (Near Field Communication), which is not limited in this application.

[0136] The display screen is used to display the UI (User Interface). This UI can include graphics, text, icons, videos, and any combination thereof. When the display screen is a touch screen, it also has the ability to collect touch signals on or above the surface of the display. These touch signals can be input as control signals to a processor for processing. In this case, the display screen can also be used to provide virtual buttons and / or a virtual keyboard, also known as soft buttons and / or a soft keyboard. In some embodiments, there may be one display screen, located on the front panel of the vehicle; in other embodiments, there may be at least two display screens, respectively located on different surfaces of the vehicle or in a folded design; in still other embodiments, the display screen may be a flexible display screen, located on a curved or folded surface of the vehicle. Furthermore, the display screen can even be configured as a non-rectangular, irregular shape, i.e., a non-rectangular screen. The display screen can be made of materials such as LCD (Liquid Crystal Display) and OLED (Organic Light-Emitting Diode).

[0137] A camera assembly is used to acquire images or videos. Optionally, the camera assembly includes any one of a main camera, a depth-sensing camera, a wide-angle camera, and a telephoto camera to achieve background blurring by fusion of the main camera and the depth-sensing camera, panoramic shooting by fusion of the main camera and the wide-angle camera, VR (Virtual Reality) shooting, or other fusion shooting functions. In some embodiments, the camera assembly may also include a flash. The flash can be a single-color temperature flash or a dual-color temperature flash. A dual-color temperature flash refers to a combination of a warm-light flash and a cool-light flash, which can be used for light compensation at different color temperatures.

[0138] The audio circuitry may include a microphone and a speaker. The microphone is used to collect sound waves from the user and the environment, converting them into electrical signals that are input to a processor for processing, or to radio frequency circuitry for voice communication. For stereo sound acquisition or noise reduction purposes, multiple microphones may be used, positioned in different parts of the vehicle. The microphone may also be an array microphone or an omnidirectional microphone. The speaker is used to convert electrical signals from the processor or radio frequency circuitry into sound waves. The speaker may be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can convert electrical signals not only into audible sound waves but also into inaudible sound waves for purposes such as distance measurement. In some embodiments, the audio circuitry may also include a headphone jack.

[0139] The positioning component is used to determine the vehicle's current geographical location for navigation or LBS (Location Based Service). The positioning component can be based on the US GPS (Global Positioning System), China's BeiDou system, or Russia's Galileo system.

[0140] The power source is used to supply power to various components in the vehicle. The power source can be alternating current (AC), direct current (DC), a disposable battery, or a rechargeable battery. When the power source includes a rechargeable battery, it can be a wired or wirelessly rechargeable battery. A wired rechargeable battery is charged via a wired connection, while a wirelessly rechargeable battery is charged via a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0141] In some embodiments, the vehicle also includes one or more sensors.

[0142] Those skilled in the art will understand that the structure shown above does not constitute a limitation on the vehicle and may include more or fewer components than illustrated, or combine certain components, or employ different component arrangements.

[0143] In some embodiments, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements the steps of a curve speed limit method based on the prediction of the trajectory of a preceding vehicle in the above embodiments. For example, the computer-readable storage medium may be a ROM, RAM, CD-ROM, magnetic tape, floppy disk, or optical data storage device, etc.

[0144] It is worth noting that the computer-readable storage medium mentioned in this application can be a non-volatile storage medium, in other words, it can be a non-transient storage medium.

[0145] It should be understood that all or part of the steps of the above embodiments can be implemented by software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented wholly or partially in the form of a computer program product. The computer program product includes one or more computer instructions. The computer instructions can be stored in the above-described computer-readable storage medium.

[0146] That is, in some embodiments, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to perform the steps of the curve speed limit method based on the prediction of the trajectory of the preceding vehicle described above.

[0147] The above descriptions are embodiments provided in this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for limiting speed on curves based on prediction of the trajectory of the vehicle ahead, characterized in that, The method includes: Based on the current operating status of the surrounding vehicles, the trajectory of the surrounding vehicles is predicted using a vehicle dynamics model. Based on the predicted trajectories of the surrounding vehicles, determine the lane curvature radius of the curve ahead. If the lane curvature radius meets the speed limit activation condition, then the current vehicle speed will be controlled within the target speed corresponding to the lane curvature radius. Specifically, based on the operating status of surrounding vehicles, the trajectory of those surrounding vehicles is predicted using a vehicle dynamics model, including: Use intelligent driver models to predict the speeds of vehicles around the current vehicle. Based on the speed of the surrounding vehicles and the corresponding target lane, the front wheel steering angle of the surrounding vehicles is predicted using a pure tracking algorithm; The predicted trajectories of the surrounding vehicles are calculated iteratively based on their speed and front wheel angle. Specifically, the lane curvature radius of the upcoming curve is determined based on the predicted trajectories of the surrounding vehicles. This is achieved by determining the lane curvature radius of the upcoming curve based on the predicted speeds and lateral accelerations of the surrounding vehicles at the predicted trajectory points.

2. The curve speed limit method based on the prediction of the trajectory of the preceding vehicle as described in claim 1, characterized in that, The determination of the target lane for the surrounding vehicles is specifically as follows: Based on the current state of the surrounding vehicles, predict the state at different times within the surrounding area in the future; Based on the predicted state quantities at different times within a future surrounding area, determine whether surrounding vehicles are changing lanes. The target lane is determined based on the predicted lane-changing behavior of surrounding vehicles.

3. The curve speed limit method based on the prediction of the trajectory of the preceding vehicle as described in claim 1, characterized in that, The predicted trajectory of the surrounding vehicles is calculated iteratively based on their speed and front wheel angle, specifically as follows: Based on the speed and front wheel angle of the surrounding vehicles, the vehicle state at the next moment is obtained by substituting them into the vehicle state transition equation; the vehicle state includes the vehicle position, speed and yaw angle. Based on the vehicle status of the surrounding vehicles at the next moment, the driving speed and front wheel steering angle of the surrounding vehicles at the next moment are predicted. Repeat the above process until the predicted trajectories of surrounding vehicles within the prediction period are obtained.

4. The curve speed limit method based on the prediction of the trajectory of the preceding vehicle as described in claim 1, characterized in that, Based on the radius of curvature corresponding to each predicted trajectory point, determine whether the radius of curvature meets the curve speed limit activation condition. If not, the current vehicle maintains its current speed. If the conditions are met, the target vehicle speed corresponding to the radius of curvature is obtained by looking up a table, and the current vehicle speed is controlled within the target vehicle speed range.

5. A curve speed limiting device based on the prediction of the trajectory of the preceding vehicle, characterized in that, The device includes: The trajectory prediction module is used to predict the trajectories of surrounding vehicles based on the current vehicle's operating status and a vehicle dynamics model; specifically, it includes: The first prediction submodule is used to predict the speed of vehicles around the current vehicle using an intelligent driver model. The second prediction submodule is used to predict the front wheel steering angle of the surrounding vehicles based on their driving speed and the corresponding target lane using a pure tracking algorithm. The third prediction submodule is used to iteratively calculate the predicted trajectory of the surrounding vehicles based on their speed and front wheel angle. The determination module is used to determine the lane curvature radius of the curve ahead based on the predicted trajectory of the surrounding vehicles; specifically, it determines the lane curvature radius of the curve ahead based on the predicted driving speed and lateral acceleration of the surrounding vehicles at the predicted trajectory points. The control module is used to control the current vehicle speed within the target speed corresponding to the lane curvature radius if the lane curvature radius meets the speed limit activation condition.

6. A vehicle comprising a memory and a processor, the memory for storing a computer program, the processor for executing the computer program stored in the memory to implement the steps of a curve speed limit method based on the prediction of the trajectory of a preceding vehicle as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the curve speed limit method based on the prediction of the trajectory of the preceding vehicle as described in any one of claims 1-4.

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