Blind spot detection method and system, electronic device, intelligent carriage, and vehicle

Abstract

Disclosed in the present invention are a blind spot detection method and system, an electronic device, an intelligent carriage, and a vehicle. The method comprises: acquiring turning information of a vehicle, and preprocessing the turning information of the vehicle; on the basis of the current traveling state of the ego vehicle, estimating a curve radius, establishing a curve scene mathematical model, and calculating a blind spot position, so as to obtain a current curve blind spot; and detecting whether an obstacle is present in the current curve blind spot, and if yes, issuing an early warning. In the present invention, the current curve blind spot is obtained by estimating the curve radius, establishing the curve scene mathematical model and calculating the blind spot position, and whether an obstacle is present in the current curve blind spot is detected, thereby improving the precision of the blind spot detection system in a curve scene, improving the accuracy of blind spot system detection, and guaranteeing the safety of a driver.

Description

Blind spot detection method, system, electronic equipment, intelligent cockpit and vehicle thereof Technical Field

[0001] The present invention relates to a detection method, system, electronic equipment, smart cockpit and vehicle thereof, and in particular to a blind spot detection method, system, electronic equipment, smart cockpit and vehicle thereof. Background Art

[0002] A blind spot detection system, based on short-range microwave radar technology, monitors moving objects (such as cars, motorcycles, bicycles, and pedestrians). When approaching a car, it emits sound, light, and other signals based on the degree of danger and urgency, assisting the driver in avoiding objects in the blind spot and achieving safe lane changes. Currently, blind spot detection systems require inspection within a range of 0-70 meters behind the vehicle. While generally effective for detecting obstacles in the rear blind spot when driving on straight roads, they also require detection in curves. However, these systems often suffer from false detections and missed detections, failing to meet user expectations and urgently in need of improvement.

[0003] Summary of the Invention

[0004] The purpose of the present invention is to provide a blind spot detection method, system, electronic equipment, intelligent cockpit and vehicle thereof to solve the shortcomings of the existing technology.

[0005] The present invention provides the following solutions:

[0006] A blind spot detection method in a turning scenario, comprising:

[0007] Obtain vehicle turning information and pre-process the vehicle turning information;

[0008] The curve radius is estimated based on the current driving state of the vehicle, a mathematical model of the curve scene is established, the blind spot position is calculated, and the current curve blind spot is obtained;

[0009] Detect whether there are obstacles in the blind spot of the current curve, and issue an early warning if there are obstacles.

[0010] Furthermore, before obtaining the vehicle turning information, it is detected whether the current vehicle is in a turning state;

[0011] The pre-processing of the vehicle turning information further includes: receiving a signal from an on-board sensor, and processing the signal from the on-board sensor via a domain controller;

[0012] Preprocessing the vehicle turning information to obtain preprocessed information, wherein the preprocessed information includes lane line information, obstacle position information, vehicle speed, and vehicle yaw angle;

[0013] The vehicle-mounted sensors further include: a front-view camera and a millimeter-wave radar.

[0014] Furthermore, the estimating of the curve radius according to the current driving state of the vehicle further includes:

[0015] The calculation of the curve radius behind the vehicle satisfies the following formula:

[0016] Where speed is the vehicle and yawrate is the vehicle yaw angle.

[0017] Furthermore, a first-order low-pass filtering algorithm is used to process the vehicle yaw angle. The algorithm is as follows: f = A*X+(1-X)*Y

[0018] Where: X is the vehicle yaw angle at the current moment, Y is the vehicle yaw angle at the previous moment, and A is the filter coefficient;

[0019] The calculated curve radius behind the vehicle is verified and calculated with the curve radius in front of the vehicle to obtain the verified curve radius behind the vehicle.

[0020] Furthermore, the establishment of a mathematical model for a curve scene further includes:

[0021] A lane line function model is established using a cubic polynomial, and a function is fitted based on the scattered points of the detection results. The cubic polynomial includes the unknown coefficients of the lane line model.

[0022] Establish a vehicle body coordinate system with the center of the vehicle's front bumper as the origin, where the front of the vehicle is the positive direction of the X axis and the left side of the X axis is the positive direction of the Y axis;

[0023] Use the cubic lane boundary model function of scientific computing software to describe the cubic polynomial and solve the undetermined coefficients in the polynomial lane line model;

[0024] The undetermined coefficients obtained by the solution are substituted into the cubic polynomial, and the fitting results are verified to obtain the verified cubic polynomial.

[0025] A blind spot detection system in a turning scenario, comprising:

[0026] A vehicle turning information preprocessing module obtains vehicle turning information and preprocesses the vehicle turning information;

[0027] The module for obtaining the current curve information estimates the curve radius based on the current driving state of the vehicle, establishes a mathematical model for the curve scene, calculates the blind spot position, and obtains the current curve blind spot;

[0028] The obstacle detection and warning module detects whether there are obstacles in the current blind spot of the curve, and issues a warning if there are obstacles.

[0029] A smart cockpit is provided with the blind spot detection system in a turning scenario and executes the blind spot detection method in a turning scenario.

[0030] An electronic device comprises: a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method.

[0031] A computer-readable storage medium stores a computer program executable by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method.

[0032] A vehicle, comprising:

[0033] An electronic device for implementing the method described;

[0034] a processor that runs a program, and when the program runs, performs the steps of the method on data output from the electronic device;

[0035] The storage medium is used to store a program, and when the program is run, the program executes the steps of the method for data output from the electronic device.

[0036] Compared with the existing technology, the present invention has the following advantages: the present invention estimates the curve radius, establishes a mathematical model of the curve scene and calculates the blind spot position to obtain the current curve blind spot and detect whether there is an obstacle in the current curve blind spot, thereby improving the accuracy of the blind spot detection system in the curve scene, improving the accuracy of the blind spot system detection, and ensuring the safety of the driver. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] FIG1 is a flow chart of a blind spot detection method in a turning scenario.

[0039] Figure 2 is an architectural diagram of the blind spot detection system in a turning scenario.

[0040] FIG3 is a schematic diagram of the blind spot detection alarm range.

[0041] FIG4 is a flow chart of a method according to an embodiment of the present invention in a specific application scenario.

[0042] Figure 5 is the vehicle coordinate diagram.

[0043] FIG6 is a coordinate diagram of the mathematical modeling of the curve behind the left side of the vehicle.

[0044] FIG7 is a schematic structural diagram of an electronic device. DETAILED DESCRIPTION

[0045] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0046] The blind spot detection method in a turning scenario as shown in FIG1 includes:

[0047] Step S1, obtain vehicle turning information and pre-process the vehicle turning information; in this step, vehicle refers to the current vehicle and the state of the vehicle.

[0048] Step S2: Estimate the curve radius based on the current driving state of the vehicle, establish a mathematical model of the curve scene, calculate the blind spot position, and obtain the current curve blind spot;

[0049] Step S3: Detect whether there is an obstacle in the blind spot of the current curve, and issue an early warning if there is an obstacle.

[0050] Preferably, in step S1, before obtaining the vehicle turning information, it is detected whether the current vehicle is in a turning state;

[0051] The pre-processing of the vehicle turning information further includes: receiving a signal from an on-board sensor, and processing the signal from the on-board sensor via a domain controller;

[0052] Preprocessing the vehicle turning information to obtain preprocessed information, wherein the preprocessed information includes lane line information, obstacle position information, vehicle speed, and vehicle yaw angle;

[0053] Lane markings refer to the markings or lines on the road that guide vehicles and distinguish lanes. Lane markings help drivers accurately identify driving directions, speed limits, and rules prohibiting overtaking. For example, on a highway, a solid white line indicates that lane changes are prohibited, while a dashed yellow line indicates that lane changes are permitted.

[0054] Obstacle location information refers to objects or structures on the road that may hinder the smooth flow of traffic. These obstacles include, but are not limited to, buildings, trees, guardrails, bridges, etc. In this embodiment, obstacle location information obtained through on-board sensors can be used to predict and avoid potential dangerous situations.

[0055] The yaw angle of a vehicle is the angle between the front wheels and the longitudinal axis when the vehicle turns. The front wheels rotate in opposite directions to keep the vehicle moving around the curve and maintaining stability through corners.

[0056] The vehicle-mounted sensors further include: a front-view camera and a millimeter-wave radar.

[0057] The text mentions two technical devices: a forward-looking camera and a millimeter-wave radar.

[0058] A forward-facing camera is a camera mounted on the front of a vehicle, capturing real-time images of road conditions. It primarily relies on image processing technology to obtain road information. Using image processing algorithms, it identifies and analyzes traffic signs, pedestrians, vehicles, and other road information, helping the driver make informed decisions. For example, in a self-driving car, a forward-facing camera can detect traffic lights and control whether the vehicle should stop or continue driving.

[0059] Millimeter-wave radar is a radio sensor that uses millimeter waves for ranging and sensing. It primarily uses radio sensors for ranging and sensing. It emits weak electromagnetic waves within a specific frequency range, receives reflected signals, and calculates the time difference between the target object and the radar. For example, when the vehicle of this embodiment is used in specific scenarios such as autonomous driving and intelligent cruise control, the millimeter-wave radar can detect surrounding vehicles or obstacles and adjust the vehicle speed based on their position and speed changes to maintain a safe distance between vehicles.

[0060] Preferably, in step S2, the estimating the curve radius according to the current driving state of the vehicle further includes:

[0061] The calculation of the curve radius behind the vehicle satisfies the following formula:

[0062] Where speed is the vehicle and yawrate is the vehicle yaw angle.

[0063] Preferably, a first-order low-pass filtering algorithm is used to process the vehicle yaw angle, and the algorithm is as follows: f = A*X+(1-X)*Y

[0064] Where: X is the vehicle yaw angle at the current moment, Y is the vehicle yaw angle at the previous moment, and A is the filter coefficient;

[0065] Vehicle yaw angle refers to the side-to-side swaying or roll that occurs when a vehicle is driving or turning. This embodiment uses a first-order low-pass filtering algorithm to remove high-frequency noise from the vehicle yaw angle data while retaining the low-frequency signal components. In actual driving, road conditions, wind, and other factors can cause vehicle instability, necessitating monitoring and control of the vehicle's yaw angle. For example, the first-order low-pass filtering algorithm smoothly transitions sudden increases or decreases in vehicle conditions to a new state, ensuring a smoother and more reliable steering process.

[0066] The calculated curve radius behind the vehicle is verified and calculated with the curve radius in front of the vehicle to obtain the verified curve radius behind the vehicle.

[0067] Preferably, in step S2, establishing a mathematical model for a curve scene further includes:

[0068] A lane line function model is established using a cubic polynomial, and a function is fitted based on the scattered points of the detection results. The cubic polynomial includes the unknown coefficients of the lane line model.

[0069] For example, a curve is created to describe the lane lines on a road. The coordinates of different points along the lane lines are collected, and this data is used to fit a cubic polynomial equation. This equation is then used to predict the position of the lane lines at any given location. By establishing a lane function model, we can better understand and analyze lane-related issues during vehicle operation. By detecting the vehicle's current position and comparing it with the known lane function model, we can determine whether it has deviated from the original path and take appropriate measures to correct it.

[0070] A vehicle body coordinate system is established with the center of the vehicle's front bumper as the origin, where the front of the vehicle is the positive X-axis direction and the left side of the X-axis is the positive Y-axis direction. In this embodiment, the positive X-axis direction is the same as the current vehicle's front direction, and the left side of the X-axis is the main driving direction.

[0071] The cubic lane boundary model function cubicLaneBoudary in scientific computing software is used to describe the cubic polynomial and solve the unknown coefficients in the polynomial lane line model.

[0072] The undetermined coefficients obtained by the solution are substituted into the cubic polynomial, and the fitting results are verified to obtain the verified cubic polynomial.

[0073] In this embodiment, a cubic polynomial is used as the lane line function model, which has the following advantages: strong adaptability: the cubic polynomial has a higher order and is more flexible in describing complex curves; good smoothness: due to its continuous and differentiable properties, it can maintain a smooth transition when connecting various coordinate points.

[0074] For the method steps disclosed in the above embodiments, for the purpose of simple description, the method steps are expressed as a series of action combinations. However, those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0075] The blind spot detection system in a turning scenario as shown in Figure 2 includes:

[0076] A vehicle turning information preprocessing module obtains vehicle turning information and preprocesses the vehicle turning information;

[0077] The module for obtaining the current curve information estimates the curve radius based on the current driving state of the vehicle, establishes a mathematical model for the curve scene, calculates the blind spot position, and obtains the current curve blind spot;

[0078] The obstacle detection and warning module detects whether there are obstacles in the current blind spot of the curve, and issues a warning if there are obstacles.

[0079] The above-described system implementations are merely illustrative. For example, the various functional modules, units, or subsystems in the system may or may not be physically separate, or may or may not be physical units. They may be located in the same location or distributed across multiple different systems and their subsystems or modules. Those skilled in the art may select some or all of the functional modules, units, or subsystems to achieve the objectives of the embodiments of the present invention based on actual needs. In these cases, those of ordinary skill in the art can understand and implement them without inventive effort.

[0080] As shown in FIG3 , in order to improve the accuracy of the blind spot detection system and ensure the safety of the driver and passengers when the vehicle is traveling on a curve, this embodiment proposes a blind spot detection method for vehicles traveling on a curve.

[0081] As shown in Figure 4, the vehicle's sensor configuration in this implementation includes a forward-looking camera, millimeter-wave radar, and a domain controller. The forward-looking camera primarily senses obstacles and lane markings in front of the vehicle. The millimeter-wave radar primarily detects movable obstacles around the vehicle. The domain controller serves as the platform for the blind spot detection system software, with information communication primarily conducted via in-vehicle Ethernet.

[0082] As shown in Figure 5, this embodiment constructs a vehicle body coordinate system with the center of the vehicle's front bumper as the origin, with the front of the vehicle as the positive direction of the X axis and the left side as the positive direction of the Y axis. The functional modules and method steps of this embodiment are as follows:

[0083] 1. Input information preprocessing: This module mainly receives the signals of various sensors required by the system.

[0084] Front camera: Lane information (equation): y = C0 + C1x + C2x 2 +C3x 3

[0085] Millimeter-wave radar: obstacle location information (x, y).

[0086] Vehicle chassis information: vehicle speed, vehicle yaw angle yawrate.

[0087] Curve radius estimation: The lane line information in front of the vehicle is identified by a camera, but there is no relevant camera for identification behind the vehicle. At this time, we need to estimate the required curve radius behind the vehicle based on the status of our own vehicle.

[0088] Ackerman Turning Geometry is a method used to solve the problem of different centers of the inner and outer steering wheels when turning a vehicle. The present invention uses this method to estimate the radius of the curve behind the vehicle.

[0089] Ackermann steering geometry is a design method for steering systems used in automobiles and other vehicles. Turning is achieved by adjusting the steering angle of the front wheels. In Ackermann steering geometry, a fixed relationship exists between the front and rear wheels, ensuring that the inner and outer front wheels maintain a suitable angle difference during cornering, ensuring a stable and smooth turn.

[0090] For example, when driving straight ahead, both front wheels are in the same direction and perpendicular to the vehicle body. When turning left, the right front wheel deviates slightly from the vertical direction compared to the left front wheel to compensate for the smaller inner path radius than the outer path radius. This ensures even wear on all four tires and improves handling and stability, allowing the vehicle or other vehicle to more flexibly and smoothly navigate and turn in a variety of road conditions.

[0091] The vehicle's yaw angle is often affected by environmental factors during driving. The present invention uses a first-order low-pass filter to process the vehicle's yaw angle. The algorithm equation is as follows: f = A*X + (1-X)*Y

[0092] X: The current vehicle yaw angle; Y: The previous vehicle yaw angle; A: The filter coefficient

[0093] Based on the above, the system will calculate the curve radius behind the vehicle. In order to avoid occasional false detection by the system, the system will record the curve radius of the vehicle in front during the vehicle's driving process. After verifying the two, the curve radius R behind the vehicle will be further determined.

[0094] As shown in Figure 6, after calculating the curve radius R behind the vehicle, r1 and r2 are calculated: r1 = R - width1, (width1 = width + 0.5) r2 = R - width2, (width2 = width + 3.5)

[0095] width is half the width of the vehicle, and then according to point O, deduce:

[0096] Calculate the equation for y1 based on the detection range behind the vehicle (0-70m). Calculate the arc corresponding to the 70m length within the circle of r1 and r2: theta1=70 / r1 theta2=70 / r2

[0097] Calculate the coordinates of the corresponding point on the arc

[0098] Then the two points determine a straight line and calculate y1=k1*x+b1. At this point, the calculation of the left curve range is completed.

[0099] Then calculate the blind spot position according to the coordinates of the obstacle (x v y v ) is within the following range, if so, calculate If ttc also meets the conditions, an alarm will be issued:

[0100] Blind spot detection system alarm: If the system detects an obstacle in the area behind the vehicle, the system will sound an alarm to warn the driver.

[0101] As shown in FIG7 , the present invention not only discloses a blind spot detection method and system, but also discloses corresponding electronic equipment, storage medium, smart cockpit, and vehicle thereof:

[0102] A smart cockpit is provided with a blind spot detection system in a turning scenario, which executes the blind spot detection method in a turning scenario.

[0103] An electronic device includes: a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of a blind spot detection method in a turning scenario.

[0104] A computer-readable storage medium stores a computer program executable by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of a blind spot detection method in a turning scenario.

[0105] A vehicle, comprising:

[0106] Electronic equipment for implementing a blind spot detection method in a turning scenario;

[0107] a processor, the processor running a program, and when the program is running, executing steps of a blind spot detection method in a turning scenario on data output from the electronic device;

[0108] The storage medium is used to store a program, which, when running, executes the steps of the blind spot detection method in a turning scenario for data output from an electronic device.

[0109] As shown in Figure 7, device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory 602 (ROM) or a computer program loaded from a storage unit 608 into a random access memory 603 (RAM). In the RAM, various programs and data required for the operation of device 600 can also be stored. Computing unit 601, ROM and RAM are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to bus 604.

[0110] Various components in device 600 are connected to I / O interface 605, including an input unit 606, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, optical disk, etc.; and a communication unit 609, such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0111] The computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the blind spot detection method for turning scenarios. For example, in some embodiments, the blind spot detection method for turning scenarios can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the blind spot detection method for turning scenarios described above can be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the blind spot detection method in a turning scenario in any other appropriate manner (for example, by means of firmware).

[0112] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0113] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other blind spot detection device for cornering scenarios, such that, when executed by the processor or controller, the program code implements the functions / operations specified in the flowcharts and / or block diagrams. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0114] In the context of the present invention, machine-readable medium can be a tangible medium that can contain or store a program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0115] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0116] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0117] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0118] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in the present invention can be achieved. This is not limited herein.

[0119] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features that are included in other embodiments but not other features, combinations of features from different embodiments are intended to be within the scope of the present invention and to form different embodiments. For example, any one of the embodiments claimed in the claims may be used in any combination in the embodiments of the present invention.

[0121] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blind spot detection method in a turning scenario, characterized in that: include: Obtain vehicle turning information and pre-process the vehicle turning information; The curve radius is estimated based on the current driving state of the vehicle, a mathematical model of the curve scene is established, the blind spot position is calculated, and the current curve blind spot is obtained; Detect whether there are obstacles in the blind spot of the current curve, and issue an early warning if there are obstacles.

2. The blind spot detection method in a turning scenario according to claim 1, characterized in that: Before obtaining vehicle turning information, detect whether the current vehicle is in a turning state; The pre-processing of the vehicle turning information further includes: receiving a signal from an on-board sensor, and processing the signal from the on-board sensor via a domain controller; Preprocessing the vehicle turning information to obtain preprocessed information, wherein the preprocessed information includes lane line information, obstacle position information, vehicle speed, and vehicle yaw angle; The vehicle-mounted sensors further include: a front-view camera and a millimeter-wave radar.

3. The blind spot detection method in a turning scenario according to claim 1, characterized in that: The estimating of the curve radius according to the current driving state of the vehicle further includes: The calculation of the curve radius behind the vehicle satisfies the following formula: Where speed is the vehicle and yawrate is the vehicle yaw angle.

4. The blind spot detection method in a turning scenario according to claim 3, characterized in that: The vehicle yaw angle is processed using a first-order low-pass filtering algorithm. The algorithm is as follows: f = A*X+(1-X)*Y Where: X is the vehicle yaw angle at the current moment, Y is the vehicle yaw angle at the previous moment, and A is the filter coefficient; The calculated curve radius behind the vehicle is verified and calculated with the curve radius in front of the vehicle to obtain the verified curve radius behind the vehicle.

5. The blind spot detection method in a turning scenario according to claim 1, characterized in that: The step of establishing a mathematical model for a curve scene further includes: A lane line function model is established using a cubic polynomial, and a function is fitted based on the scattered points of the detection results. The cubic polynomial includes the unknown coefficients of the lane line model. Establish a vehicle body coordinate system with the center of the vehicle's front bumper as the origin, where the front of the vehicle is the positive direction of the X axis and the left side of the X axis is the positive direction of the Y axis; Use the cubic lane boundary model function of scientific computing software to describe the cubic polynomial and solve the undetermined coefficients in the polynomial lane line model; The undetermined coefficients obtained by the solution are substituted into the cubic polynomial, and the fitting results are verified to obtain the verified cubic polynomial.

6. A blind spot detection system in a turning scenario, characterized in that: include: A vehicle turning information preprocessing module obtains vehicle turning information and preprocesses the vehicle turning information; The module for obtaining the current curve information estimates the curve radius based on the current driving state of the vehicle, establishes a mathematical model for the curve scene, calculates the blind spot position, and obtains the current curve blind spot; The obstacle detection and warning module detects whether there are obstacles in the current blind spot of the curve, and issues a warning if there are obstacles.

7. A smart cockpit, characterized in that: The smart cockpit is provided with the blind spot detection system in a turning scenario according to claim 6, and executes the blind spot detection method in a turning scenario according to any one of claims 1 to 5.

8. An electronic device, characterized in that: include: A processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; the memory stores a computer program, and when the computer program is executed by the processor, the processor performs the steps of the method according to any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that It stores a computer program that can be executed by an electronic device. When the computer program runs on the electronic device, the electronic device executes the steps of the method according to any one of claims 1 to 5.

10. A vehicle, characterized in that: Specifically include: An electronic device for implementing the method according to any one of claims 1 to 5; a processor, the processor running a program, wherein when the program is running, the processor performs the steps of the method according to any one of claims 1 to 5 on the data output from the electronic device; A storage medium for storing a program, wherein when the program is run, the program executes the steps of the method according to any one of claims 1 to 5 for data output from an electronic device.

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