Intelligent driving method and device of vehicle, vehicle and computer readable storage medium

Abstract

The invention provides an intelligent driving method and device of a vehicle, the vehicle, a computer readable storage medium and a computer program product. The method comprises the following steps: performing target detection processing on the surrounding environment of a vehicle through a radar and a camera of the vehicle; when the visual target is detected, judging whether the visual target meets a credible condition or not; wherein the visual target is a target which is detected through a camera and is not detected through a radar; when the visual target meets the credible condition, obstacle avoidance processing is carried out on the visual target; and when the visual target does not meet the credible condition, obstacle avoidance processing is not carried out on the visual target. According to the invention, missing detection can be reduced, and the vehicle driving safety can be improved.

Description

Technical Field

[0001] The present application relates to vehicle technology, and in particular to an intelligent driving method, device, vehicle, computer-readable storage medium, and computer program product for a vehicle. Background Art

[0002] Intelligent driving technology refers to the technology that realizes a series of automated driving functions by sensing and processing the vehicle's surrounding environment through various sensors, and analyzing and making decisions through computer algorithms. At present, many models use a combination of radar + camera, which can realize multi-source information complementation and improve intelligent driving effects.

[0003] For the radar + camera combination solution, the fused target detected by the radar and camera is usually used as the final detection result and intelligent driving is achieved based on it. However, due to the performance limitations of a single sensor, the radar and camera's perception results of the same target may differ greatly and cannot be fused. This missed detection will further lead to collision risks. Summary of the invention

[0004] The present application provides a vehicle intelligent driving method, device, vehicle, computer-readable storage medium and computer program product, which can reduce missed detections and improve vehicle driving safety.

[0005] The technical solution of this application is implemented as follows:

[0006] The present application provides an intelligent driving method for a vehicle, comprising:

[0007] Performing target detection processing on the surrounding environment of the self-vehicle through the radar and camera of the self-vehicle;

[0008] When a visual target is detected, determining whether the visual target satisfies a trustworthy condition; wherein the visual target refers to a target detected by the camera and not detected by the radar;

[0009] When the visual target meets the trust condition, performing obstacle avoidance processing on the visual target;

[0010] When the visual target does not meet the trust condition, no obstacle avoidance processing is performed on the visual target.

[0011] The present application provides an intelligent driving device for a vehicle, comprising:

[0012] The target detection module is used to perform target detection processing on the surrounding environment of the vehicle through the radar and camera of the vehicle;

[0013] A judgment module, used for judging whether the visual target satisfies a trustworthy condition when a visual target is detected; wherein the visual target refers to a target detected by the camera and not detected by the radar;

[0014] A visual obstacle avoidance module, used for performing obstacle avoidance processing on the visual target when the visual target meets the trust condition;

[0015] The visual obstacle avoidance module is further configured to not perform obstacle avoidance processing on the visual target when the visual target does not meet the trust condition.

[0016] The present application provides a vehicle, comprising:

[0017] A memory for storing executable instructions;

[0018] The processor is used to implement the intelligent driving method of the vehicle provided in the present application when executing the executable instructions stored in the memory.

[0019] The present application provides a computer-readable storage medium storing executable instructions for causing a processor to execute instructions to implement the intelligent driving method for a vehicle provided in the present application.

[0020] The present application provides a computer program product, which includes executable instructions for causing a processor to execute and implement the intelligent driving method for a vehicle provided by the present application.

[0021] This application has the following beneficial effects:

[0022] This application uses the vehicle's radar and camera to perform target detection and processing on the vehicle's surroundings; when a visual target is detected, it is determined whether the visual target meets the trust condition; wherein the visual target refers to a target detected by the camera and not detected by the radar; when the visual target meets the trust condition, obstacle avoidance processing is performed on the visual target; when the visual target does not meet the trust condition, obstacle avoidance processing is not performed on the visual target. This application first determines whether the visual target detected only by the camera is trustworthy, and then performs obstacle avoidance processing on the basis of its trustworthiness, thereby reducing missed detections caused by reliance on fusion targets and improving vehicle safety during driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 is a first structural schematic diagram of a vehicle provided in an embodiment of the present application;

[0025] Figure 2 is a second structural schematic diagram of a vehicle provided in an embodiment of the present application;

[0026] Figure 3A It is a first flow chart of the intelligent driving method for a vehicle provided in an embodiment of the present application;

[0027] Figure 3B is a second flow chart of the intelligent driving method for a vehicle provided in an embodiment of the present application;

[0028] Figure 4 This is a third flow chart of the intelligent driving method for a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0030] In the following description, reference is made to "some embodiments", which describes a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. In the following description, reference is made to the term "multiple" to mean at least two.

[0031] In the following description, the terms "first\second\third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0033] The embodiments of the present application provide a vehicle intelligent driving method, device, vehicle, computer-readable storage medium and computer program product, which can reduce missed detections and improve vehicle driving safety.

[0034] See also Figure 1 , Figure 1It is a schematic diagram of the architecture of the vehicle 100 provided in an embodiment of the present application, showing the radar 100-1, camera 100-2 and intelligent driving system 100-3 in the vehicle 100. It is worth noting that the radar 100-1 and camera 100-2 can be regarded as part of the intelligent driving system 100-3, or can be regarded as independent of the intelligent driving system 100-3. In some embodiments, the intelligent driving system 100-3 may include a control unit and an actuator, the control unit is used to receive data from the sensor and perform relevant calculations to obtain control instructions, and the actuator is used to control the vehicle to perform corresponding actions according to the control instructions, such as acceleration, deceleration, steering, etc.

[0035] In some embodiments, the intelligent driving system 100-3 performs target detection processing on the surrounding environment of the vehicle (i.e., vehicle 100) through the radar 100-1 and camera 100-2 of the vehicle; when a visual target is detected, it is determined whether the visual target meets the trust condition; wherein the visual target refers to a target detected by the camera 100-2 and not detected by the radar 100-1; when the visual target meets the trust condition, obstacle avoidance processing is performed on the visual target; when the visual target does not meet the trust condition, obstacle avoidance processing is not performed on the visual target.

[0036] In some embodiments, the vehicle 100 can implement the intelligent driving method of the vehicle provided in the embodiments of the present application by running a computer program. For example, the computer program can be a native program or software module in the operating system; it can be a native application (APP, Application), that is, a program that needs to be installed in the operating system to run; it can also be a small program, that is, a program that can be run only by downloading it to a browser environment; it can also be a small program that can be embedded in any APP, and the small program can be controlled to run or closed by the user. In short, the above-mentioned computer program can be an application, module or plug-in in any form.

[0037] See also Figure 2 , Figure 2 is a schematic diagram of the structure of a vehicle 100 provided in an embodiment of the present application, Figure 2 The vehicle 100 shown includes: at least one processor 110, a memory 150, at least one network interface 120 and a user interface 130. The various components in the vehicle 100 are coupled together via a bus system 140. It is understood that the bus system 140 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 140 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, the bus system 140 is not described in detail. Figure 2 Various buses are labeled as bus system 140 .

[0038] The processor 110 can be an integrated circuit chip with signal processing capabilities, such as a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc., where the general-purpose processor can be a microprocessor or any conventional processor, etc.

[0039] The user interface 130 includes one or more output devices 131 that enable presentation of media content, including one or more speakers and / or one or more visual display screens. The user interface 130 also includes one or more input devices 132, including user interface components that facilitate user input, such as microphones, touch screen displays, other input buttons and controls.

[0040] The memory 150 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid-state memory, hard disk drives, etc. The memory 150 may optionally include one or more storage devices that are physically remote from the processor 110.

[0041] The memory 150 includes a volatile memory or a non-volatile memory, and may also include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), and the volatile memory may be a random access memory (RAM). The memory 150 described in the embodiments of the present application is intended to include any suitable type of memory.

[0042] In some embodiments, the memory 150 can store data to support various operations, examples of which include programs, modules, and data structures, or a subset or superset thereof, as exemplarily described below.

[0043] Operating system 151, including system programs for processing various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and processing hardware-based tasks;

[0044] A network communication module 152, used to reach other electronic devices via one or more (wired or wireless) network interfaces 120, exemplary network interfaces 120 include: Bluetooth, Wireless Compatibility Authentication (WiFi), and Universal Serial Bus (USB), etc.;

[0045] a presentation module 153 for enabling presentation of information via one or more output devices 131 (e.g., display screen, speaker, etc.) associated with the user interface 130 (e.g., a user interface for operating peripherals and displaying content and information);

[0046] The input processing module 154 is configured to detect one or more user inputs or interactions from one of the one or more input devices 132 and to translate the detected inputs or interactions.

[0047] In some embodiments, the intelligent driving device for a vehicle provided in the embodiments of the present application may be implemented in a software manner. Figure 2 The intelligent driving device 155 of the vehicle stored in the memory 150 is shown, which can be software in the form of programs and plug-ins, including the following software modules: target detection module 1551, judgment module 1552 and visual obstacle avoidance module 1553. These modules are logical, so they can be arbitrarily combined or further split according to the functions implemented. The functions of each module will be explained below.

[0048] The intelligent driving method of the vehicle provided in the embodiment of the present application will be explained in combination with the exemplary application and implementation of the vehicle provided in the embodiment of the present application.

[0049] See also Figure 3A , Figure 3A is a flow chart of the intelligent driving method of a vehicle provided in an embodiment of the present application, which will be combined with Figure 3A The steps shown are explained.

[0050] In step 101, target detection processing is performed on the surrounding environment of the vehicle through the radar and camera of the vehicle.

[0051] Here, when the vehicle is in the intelligent driving mode, the vehicle's radar and camera are used to perform target detection processing on the vehicle's surrounding environment, and target detection results corresponding to the radar and camera are obtained. There is no limitation on the installation location and type of the radar and camera.

[0052] It is worth noting that the targets involved in the embodiments of the present application refer to various types of traffic entities participating in road traffic activities, such as other vehicles, pedestrians, etc.

[0053] In some embodiments, the above-mentioned target detection and processing of the surrounding environment of the vehicle through the radar and camera of the vehicle can be achieved in the following way: the target detection and processing of the environment in front of the vehicle is performed through the forward radar and the forward camera of the vehicle. Considering that most collision risks are caused by the vehicle in front, or in some special intelligent driving modes (such as cruise assisted driving mode), only the environment in front of the vehicle needs to be considered, therefore, the target detection and processing of the environment in front of the vehicle can be performed through the forward radar and the forward camera of the vehicle, thereby saving costs and ensuring safety to a certain extent. In this case, the target to be detected can be another vehicle located in front of the vehicle and in the lane where the vehicle is located.

[0054] In some embodiments, after performing target detection processing on the vehicle's surrounding environment through the vehicle's radar and camera, the vehicle's intelligent driving method also includes: when a radar target is detected, no obstacle avoidance processing is performed on the radar target; wherein, the radar target refers to a target detected by the radar but not by the camera.

[0055] Here, radar has certain performance limitations. Taking millimeter-wave radar as an example, there are the following performance limitations: poor perception of stationary targets, inability to perceive lateral movement of targets, inability to distinguish target types, and inability to perceive lane lines (i.e., inability to determine whether the target is in the lane through lane lines). Therefore, based on the target detection results of the radar, unnecessary obstacle avoidance processing may occur in the following scenarios: ground iron plates, tunnel entrances, underground garage entrances, viaducts, and vehicles in adjacent lanes on curves.

[0056] Considering that the probability of a radar target being a real target is low, when a radar target is detected, no obstacle avoidance processing is performed on the radar target, that is, the radar target is ignored. A radar target refers to a target detected by the radar but not by the camera. In this way, the user's driving experience is minimized without increasing the risk of collision due to unnecessary obstacle avoidance processing.

[0057] In some embodiments, when a radar target is detected, the intelligent driving method of the vehicle also includes: outputting a warning prompt.

[0058] Here, when a radar target is detected, although obstacle avoidance is not performed on the radar target, a warning prompt can be output to prompt the user to pay attention to the surrounding environment of the vehicle, so that the user can take over the vehicle when necessary, further improving the safety of the vehicle during driving. Among them, there is no restriction on the content and output method of the warning prompt, such as "There is a suspected vehicle around, please pay attention to it", and the output method may be displayed on the vehicle display screen or output through voice.

[0059] In some embodiments, after performing target detection processing on the vehicle's surrounding environment through the vehicle's radar and camera, the vehicle's intelligent driving method also includes: when a fusion target is detected, performing obstacle avoidance processing on the fusion target; wherein the fusion target refers to a target detected by the camera and by the radar.

[0060] Here, the fused target refers to the target detected by the camera and the radar, that is, the target detected by sensor fusion of the radar and the camera. When the fused target is detected, since the fused target has high accuracy and is usually a real target, obstacle avoidance processing is performed on the fused target, thereby improving the safety of the vehicle during driving.

[0061] It is worth mentioning that obstacle avoidance processing for the fusion target refers to controlling the ego vehicle for the purpose of preventing the ego vehicle from colliding with the fusion target; in the cruise assisted driving mode, obstacle avoidance processing for the fusion target may also refer to controlling the ego vehicle for the purpose of maintaining a safe distance between the ego vehicle and the fusion target.

[0062] In step 102, when a visual target is detected, it is determined whether the visual target satisfies a trustworthy condition; wherein the visual target refers to a target detected by a camera and not detected by a radar.

[0063] Here, the camera senses the movement of the target through vision, and the camera has a lower probability of misjudgment than the radar. Although the visual target has a higher probability (compared to the radar target) of being a real target, in order to avoid false obstacle avoidance, when the visual target is detected, it is further determined whether the visual target meets the trust condition, where the visual target refers to the target detected by the camera but not detected by the radar.

[0064] In step 103, when the visual target meets the trust condition, obstacle avoidance processing is performed on the visual target.

[0065] When the visual target meets the credible conditions, it is proved that the visual target is a real target, so the visual target is processed to avoid obstacles, thereby eliminating the risk of collision. Among them, the credible conditions are such as the duration of the visual target reaching the duration threshold. The principle is that if the visual target is not a real target, its duration will not be too long; or the credible conditions are such as the speed change rate (acceleration / deceleration) of the visual target is less than or equal to the change rate threshold. The principle is that the real target usually does not have a large speed change.

[0066] In some embodiments, the above-mentioned obstacle avoidance processing for the visual target can be achieved in the following manner: braking the vehicle according to the deceleration; wherein the deceleration is calculated based on the motion parameters of the visual target; the motion parameters of the visual target include at least one of the following: the longitudinal distance between the visual target and the vehicle, the longitudinal speed of the visual target, the lateral distance between the visual target and the vehicle, and the lateral speed of the visual target.

[0067] Here, while detecting the visual target, the motion parameters of the visual target can also be detected at the same time, wherein the motion parameters of the visual target include at least one of the following: the longitudinal distance between the visual target and the vehicle, the longitudinal speed of the visual target, the lateral distance between the visual target and the vehicle, and the lateral speed of the visual target. In the process of implementing obstacle avoidance, the deceleration required for the vehicle to avoid collision with the visual target or to maintain a safe distance between the vehicle and the visual target is calculated based on the motion parameters of the visual target, and the vehicle is braked based on the deceleration, wherein the decision strategy for calculating the deceleration is not limited. In the above manner, the avoidance of the visual target is achieved through braking, which can achieve effective obstacle avoidance.

[0068] It is worth noting that the method of performing obstacle avoidance processing on visual targets can also be applied to performing obstacle avoidance processing on fusion targets.

[0069] In some embodiments, after the vehicle's radar and camera perform target detection on the vehicle's surrounding environment, the vehicle's intelligent driving method further includes: when the fusion target and the visual target are detected at the same time, obstacle avoidance processing is performed on the fusion target. Here, when the fusion target and the visual target are detected at the same time, since the fusion target is obtained through sensor fusion, the accuracy is high, and the probability that the fusion target is a real target is also greater (compared to the visual target), only the fusion target can be subjected to obstacle avoidance processing, that is, the visual target is ignored.

[0070] In step 104, when the visual target does not meet the trust condition, no obstacle avoidance processing is performed on the visual target.

[0071] When the visual target does not meet the credibility condition, it is proved that the visual target is not a real target. Therefore, obstacle avoidance processing is not performed on the visual target, that is, the visual target is ignored. This avoids affecting the user's riding experience due to unnecessary obstacle avoidance processing without increasing the risk of collision.

[0072] In some embodiments, when the visual target does not meet the trustworthy condition, the intelligent driving method of the vehicle further includes: outputting a warning prompt.

[0073] Here, when a visual target is detected, although obstacle avoidance is not performed on the visual target, a warning prompt can be output to prompt the user to pay attention to the environment around the vehicle, so that the user can take over the vehicle when necessary, further improving the safety of the vehicle during driving.

[0074] like Figure 3AAs shown, the embodiment of the present application performs target detection processing on the surrounding environment of the vehicle through the radar and camera of the vehicle; when a visual target is detected, it is determined whether the visual target meets the trust condition; wherein the visual target refers to a target detected by the camera and not detected by the radar; when the visual target meets the trust condition, obstacle avoidance processing is performed on the visual target; when the visual target does not meet the trust condition, obstacle avoidance processing is not performed on the visual target. The embodiment of the present application first determines whether the visual target detected only by the camera is trustworthy, and then performs obstacle avoidance processing on the basis of its trustworthiness, thereby reducing the missed detection caused by relying on the fusion target and improving the safety of the vehicle during driving.

[0075] In some embodiments, see Figure 3B , Figure 3B is a flow chart of the intelligent driving method of a vehicle provided in an embodiment of the present application, Figure 3A The illustrated step 103 can be implemented through steps 201 to 203 , which will be described in conjunction with each step.

[0076] In step 201, when the visual target meets the trust condition, the visual target is determined as a trustworthy visual target.

[0077] Here, when the visual target meets the trust condition, the visual target is determined as a trustworthy visual target, wherein the trustworthy visual target can be understood as a real target.

[0078] In step 202, it is determined whether the credible visual target meets the risk condition.

[0079] Here, judging whether the credible visual target satisfies the risk condition is essentially judging whether there is a collision risk between the ego vehicle and the credible visual target. The risk condition may be a condition constraining the motion parameters of the credible visual target.

[0080] In some embodiments, the trust condition is that the duration of the visual target's continuous existence reaches a duration threshold; the risk condition includes at least one of the following: the longitudinal distance between the trustworthy visual target and the vehicle is less than or equal to a distance threshold; the deceleration required for obstacle avoidance processing of the trustworthy visual target is greater than or equal to a deceleration threshold. The calculation method of the deceleration can refer to the above.

[0081] In some embodiments, before determining whether the visual target satisfies the trustworthy condition, the intelligent driving method of the vehicle also includes: performing environmental recognition processing on the environment around the vehicle to obtain the current environment type; determining the duration threshold corresponding to the current environment type as the duration threshold in the trustworthy condition; wherein the current environment type is one of a plurality of environment types, and each environment type corresponds to a duration threshold.

[0082] Here, the duration threshold in the trustworthy condition may be preset or determined according to the current environment type of the vehicle's surroundings. The latter case will be described below.

[0083] For example, when the vehicle's surrounding environment is subjected to target detection processing through the vehicle's radar and camera, the vehicle's surrounding environment is also subjected to environment recognition processing to obtain the current environment type, wherein the environment recognition processing can be implemented through at least one of the radar and the camera.

[0084] It is worth noting that the current environment type is one of multiple environment types, and multiple environment types can be pre-set, such as sunny days, rainy days, dense fog, etc. Different environment types have different effects on the performance of the camera. For example, compared with sunny days, the performance of the camera will be worse on rainy days. Therefore, the corresponding duration threshold can be set according to the degree of interference of each environment type on the performance of the camera. Among them, the duration threshold is positively correlated with the degree of performance interference, that is, the stronger the degree of performance interference, the greater the probability that the visual target is not a real target, so a larger duration threshold needs to be set to enhance the constraint capability.

[0085] Since the current environment type of the vehicle's surroundings has been identified, the duration threshold corresponding to the current environment type is used as the duration threshold in the trusted condition, that is, when the duration of the visual target's continuous existence reaches the duration threshold corresponding to the current environment type, the visual target is determined to be a trusted visual target.

[0086] Through the above method, the credible conditions can be more adapted to the environment around the vehicle, so that it is possible to more accurately judge whether the visual target is a real target.

[0087] In step 203, when the credible visual target meets the risk condition, obstacle avoidance processing is performed on the credible visual target.

[0088] Here, when the credible visual target meets the risk condition, it proves that there is a collision risk between the ego vehicle and the credible visual target, so obstacle avoidance processing is performed on the credible visual target to eliminate the collision risk.

[0089] exist Figure 3B middle, Figure 3A After step 202 shown, in step 204, when the credible visual target does not meet the risk condition, obstacle avoidance processing is not performed on the credible visual target.

[0090] Here, when the credible visual target does not meet the risk condition, it is proved that there is no collision risk between the vehicle and the credible visual target, so no obstacle avoidance processing is performed on the credible visual target, thereby avoiding affecting the user's driving experience due to unnecessary obstacle avoidance processing.

[0091] In some embodiments, the above steps 202 to 204 can also be applied to fusion targets.

[0092] like Figure 3B As shown, the embodiment of the present application further determines whether the visual target meets the risk condition on the basis that the visual target meets the trust condition, and performs obstacle avoidance processing when the risk condition is met, and does not perform obstacle avoidance processing when the risk condition is not met, thereby increasing the necessity of obstacle avoidance processing and avoiding affecting the user's driving experience due to unnecessary obstacle avoidance processing.

[0093] Below, an exemplary application of the embodiment of the present application in an actual application scenario will be described. Take the example of a vehicle equipped with a forward millimeter-wave radar and a forward-looking camera (forward-facing camera), and the vehicle turns on the cruise assisted driving mode. Among them, the cruise assisted driving mode is implemented by a cruise assisted driving system, and the cruise assisted driving system includes the following modules:

[0094] 1) Cruise Assist Driving System Control Module: used to output the cruise assisted driving function operation status and acceleration / deceleration request. If the cruise assisted driving function operation status is active, it means that the cruise assisted driving function is running, that is, the vehicle is currently in the cruise assisted driving mode.

[0095] 2) Cruise Assist Driving System Perception Module: It is used to detect the vehicle in front of the vehicle through the forward millimeter-wave radar and the front-view camera, and output the type of perceived target and the motion parameters of the perceived target. The perceived target refers to the detected other vehicle. Among them, there are three types of perceived targets: ① fusion target, namely Radar+Vision; ② radar target, namely Radaronly; ③ visual target, namely Visiononly. The motion parameters of the perceived target include: the longitudinal distance between the perceived target and the vehicle, the longitudinal speed of the perceived target, the lateral distance between the perceived target and the vehicle, and the lateral speed of the perceived target.

[0096] 3) Power and brake control module: used to execute the acceleration / deceleration request output by the cruise assistance driving system control module.

[0097] On this basis, if Figure 4 As shown, the embodiment of the present application formulates different braking strategies for different types of perceived targets, which will be explained separately below.

[0098] 1) Fusion target. The cruise control module calculates the deceleration based on the motion parameters of the fusion target and outputs a deceleration request. The power and braking control module brakes (decelerates) the vehicle based on the deceleration request.

[0099] 2) Radar target: The cruise control module does not output a deceleration request, that is, it ignores the radar target, thereby preventing false braking.

[0100] 3) Visual target. The braking strategies for visual targets include strategy a and strategy b. In strategy a, determine whether the continuous existence duration of the visual target reaches 100ms (corresponding to the duration threshold above). When the continuous existence duration of the visual target reaches 100ms, the visual target is determined as a credible visual target and strategy b is executed. In strategy b, determine whether the credible visual target satisfies the following conditions ① and ② at the same time. If both conditions are met, the cruise assistance driving system control module does not output a deceleration request; if any condition is not met, the cruise assistance driving system control module outputs a deceleration request, and the power and braking control module brakes the vehicle according to the deceleration request.

[0101] Among them, condition ①: the longitudinal distance between the credible visual target output by the perception module of the cruise assisted driving system and the vehicle is greater than 80m (corresponding to the distance threshold above).

[0102] Condition ②: The deceleration calculated by the cruise control module is less than 1m / s 2 (Corresponding to the deceleration threshold above).

[0103] Through the embodiments of the present application, at least the following technical effects can be achieved:

[0104] 1) It can reduce missed detections caused by relying solely on fusion targets and brake the vehicle in time when there is a risk of collision, thereby improving vehicle safety.

[0105] 2) It can reduce the possibility of accidental braking against the target ahead without increasing the risk of collision.

[0106] The following is a description of an exemplary structure of the vehicle intelligent driving device 155 provided in the embodiment of the present application implemented as a software module. In some embodiments, such as Figure 2 As shown, the software modules in the intelligent driving device 155 of the vehicle stored in the memory 150 may include: a target detection module 1551, which is used to perform target detection processing on the surrounding environment of the vehicle through the radar and camera of the vehicle; a judgment module 1552, which is used to judge whether the visual target meets the trustworthy condition when the visual target is detected; wherein the visual target refers to the target detected by the camera and not detected by the radar; a visual obstacle avoidance module 1553, which is used to perform obstacle avoidance processing on the visual target when the visual target meets the trustworthy condition; and a visual obstacle avoidance module 1554, which is also used to not perform obstacle avoidance processing on the visual target when the visual target does not meet the trustworthy condition.

[0107] In some embodiments, the visual obstacle avoidance module 1553 is also used to: determine the visual target as a trusted visual target when the visual target meets the trust condition; determine whether the trusted visual target meets the risk condition; when the trusted visual target meets the risk condition, perform obstacle avoidance processing on the trusted visual target; when the trusted visual target does not meet the risk condition, do not perform obstacle avoidance processing on the trusted visual target.

[0108] In some embodiments, the credible condition is that the duration of continuous existence of the visual target reaches a duration threshold; the risk condition includes at least one of the following: the longitudinal distance between the credible visual target and the vehicle is less than or equal to a distance threshold; the deceleration required for obstacle avoidance processing of the credible visual target is greater than or equal to a deceleration threshold.

[0109] In some embodiments, the visual obstacle avoidance module 1553 is also used to: perform environmental recognition processing on the environment around the vehicle to obtain the current environment type; determine the duration threshold corresponding to the current environment type as the duration threshold in the trusted condition; wherein the current environment type is one of multiple environment types, and each environment type corresponds to a duration threshold.

[0110] In some embodiments, the vehicle's intelligent driving device 155 also includes a radar obstacle avoidance module, which is used to: when a radar target is detected, not perform obstacle avoidance processing on the radar target; wherein the radar target refers to a target detected by radar but not detected by a camera.

[0111] In some embodiments, the vehicle's intelligent driving device 155 also includes a warning module for outputting a warning prompt when the visual target does not meet the trustworthy condition or when a radar target is detected.

[0112] In some embodiments, the vehicle's intelligent driving device 155 also includes a fusion obstacle avoidance module, which is used to: when a fusion target is detected, perform obstacle avoidance processing on the fusion target; wherein the fusion target refers to a target detected by a camera and by a radar.

[0113] In some embodiments, the visual obstacle avoidance module 1553 is also used to: brake the vehicle according to the deceleration; wherein the deceleration is calculated based on the motion parameters of the visual target; the motion parameters of the visual target include at least one of the following: the longitudinal distance between the visual target and the vehicle, the longitudinal speed of the visual target, the lateral distance between the visual target and the vehicle, and the lateral speed of the visual target.

[0114] The embodiment of the present application provides a computer program product or a computer program, which includes executable instructions stored in a computer-readable storage medium. The processor of the vehicle reads the executable instructions from the computer-readable storage medium, and the processor executes the executable instructions, so that the vehicle implements the intelligent driving method of the vehicle described in the embodiment of the present application.

[0115] An embodiment of the present application provides a computer-readable storage medium storing executable instructions, wherein executable instructions are stored. When the executable instructions are executed by a processor, the processor will implement the intelligent driving method for a vehicle provided in the embodiment of the present application.

[0116] In some embodiments, the computer-readable storage medium may be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface storage, optical disk, or CD-ROM; or it may be various devices including one or any combination of the above memories.

[0117] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0118] As an example, executable instructions may, but need not, correspond to a file in a file system, may be stored as part of a file that stores other programs or data, such as in one or more scripts in a HyperText Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions).

[0119] The above are only embodiments of the present application and are not intended to limit the protection scope of the present application. Any modifications, equivalent substitutions and improvements made within the spirit and scope of the present application are included in the protection scope of the present application.

Claims

1. A vehicle intelligent driving method, characterized in that: include: Performing target detection processing on the surrounding environment of the self-vehicle through the radar and camera of the self-vehicle; When a visual target is detected, determining whether the visual target satisfies a trustworthy condition; wherein the visual target refers to a target detected by the camera and not detected by the radar; When the visual target meets the trust condition, performing obstacle avoidance processing on the visual target; When the visual target does not meet the trust condition, no obstacle avoidance processing is performed on the visual target.

2. The method according to claim 1, characterized in that When the visual target meets the trust condition, performing obstacle avoidance processing on the visual target includes: When the visual target meets the trust condition, determining the visual target as a trustworthy visual target; Determining whether the credible visual target meets the risk condition; When the credible visual target meets the risk condition, performing obstacle avoidance processing on the credible visual target; The method further comprises: When the credible visual target does not satisfy the risk condition, no obstacle avoidance processing is performed on the credible visual target.

3. The method according to claim 2, characterized in that The credible condition is that the duration of the continuous existence of the visual target reaches a duration threshold; The risk condition includes at least one of the following: the longitudinal distance between the credible visual target and the vehicle is less than or equal to a distance threshold; the deceleration required for obstacle avoidance processing for the credible visual target is greater than or equal to a deceleration threshold.

4. The method according to claim 3, characterized in that The method further comprises: Performing environmental recognition processing on the surrounding environment of the vehicle to obtain the current environment type; Determine the duration threshold corresponding to the current environment type as the duration threshold in the trusted condition; The current environment type is one of multiple environment types, and each environment type corresponds to a duration threshold.

5. The method according to claim 1, characterized in that After performing target detection processing on the surrounding environment of the vehicle by using the radar and camera of the vehicle, the method further includes: When a radar target is detected, no obstacle avoidance processing is performed on the radar target; The radar target refers to a target detected by the radar but not detected by the camera.

6. The method according to claim 5, characterized in that The method further comprises: When the visual target does not meet the credible condition, or when the radar target is detected, an early warning prompt is output.

7. The method according to claim 1, characterized in that After performing target detection processing on the surrounding environment of the vehicle by using the radar and camera of the vehicle, the method further includes: When a fusion target is detected, obstacle avoidance processing is performed on the fusion target; The fusion target refers to a target detected by the camera and the radar.

8. The method according to claim 1, characterized in that The performing obstacle avoidance processing on the visual target includes: performing braking processing on the vehicle according to the deceleration; Wherein, the deceleration is calculated based on the motion parameters of the visual target; the motion parameters of the visual target include at least one of the following: the longitudinal distance between the visual target and the vehicle, the longitudinal speed of the visual target, the lateral distance between the visual target and the vehicle, and the lateral speed of the visual target.

9. An intelligent driving device for a vehicle, characterized in that: include: The target detection module is used to perform target detection processing on the surrounding environment of the vehicle through the radar and camera of the vehicle; A judgment module, used for judging whether the visual target satisfies a trustworthy condition when a visual target is detected; wherein the visual target refers to a target detected by the camera and not detected by the radar; A visual obstacle avoidance module, used for performing obstacle avoidance processing on the visual target when the visual target meets the trust condition; The visual obstacle avoidance module is further configured to not perform obstacle avoidance processing on the visual target when the visual target does not meet the trust condition.

10. A vehicle, characterized in that: include: A memory for storing executable instructions; A processor, configured to implement the method according to any one of claims 1 to 8 when executing the executable instructions stored in the memory.

11. A computer-readable storage medium, characterized in that: Executable instructions are stored, and when executed by a processor, the method described in any one of claims 1 to 8 is implemented.

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