Vehicle control method and vehicle

Abstract

The invention provides a vehicle control method and a vehicle. The method comprises the steps that environment sensing data and vehicle state data are acquired; based on the environment perception data, target crowd density and scene sensitivity are determined, and the scene sensitivity is used for quantifying the risk level of a scene where the vehicle is located; and performing vehicle control based on the target crowd density, the scene sensitivity and the vehicle state data. In this way, the technical problems that in the prior art, the accuracy of vehicle control is not high, and safety accidents are likely to happen can be solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a vehicle control method and a vehicle. BACKGROUND

[0002] With the rapid popularization of vehicles, a series of challenges such as rear-end collision and collision accidents occur frequently.

[0003] The existing vehicle collision avoidance system mainly relies on distance sensors to monitor the obstacles in front and issue an alarm or automatically brake when the distance to the obstacle is close. However, in practice, it is found that in densely populated areas such as schools and shopping malls, no consideration and protection are given, resulting in low accuracy of vehicle control and easy occurrence of safety accidents. SUMMARY

[0004] Therefore, the embodiments of the present application aim to provide a vehicle control method and a vehicle, which can solve the technical problems of low accuracy of vehicle control and easy occurrence of safety accidents in the prior art.

[0005] In a first aspect, the present application provides a vehicle control method, comprising: obtaining environment perception data and vehicle state data, wherein the environment perception data at least includes radar perception data and visual perception data, and the vehicle state data at least includes a vehicle speed of the vehicle; determining a target crowd density and a scene sensitivity based on the environment perception data, wherein the scene sensitivity is used to quantify a risk level of a scene where the vehicle is located; performing vehicle control based on the target crowd density, the scene sensitivity and the vehicle state data.

[0006] In the embodiments, the target crowd density, the scene sensitivity and the vehicle state data of the scene where the vehicle is located can be used to perform safe and accurate vehicle control, which is beneficial to improve the accuracy and safety of vehicle control. At the same time, the technical problems of not considering densely populated areas, resulting in low accuracy of vehicle control and easy occurrence of safety accidents in the prior art are solved.

[0007] In some embodiments, the performing vehicle control based on the crowd density, the scene sensitivity and the vehicle state data comprises: determining a corresponding driving intention based on the vehicle state data; performing vehicle control based on the target crowd density, the scene sensitivity and the driving intention.

[0008] In this embodiment, the driver's driving intention is first determined based on vehicle status data; then, vehicle control is performed based on the target crowd density, scene sensitivity, and driving intention of the vehicle's location, further improving the precision or accuracy of vehicle control and enhancing the accuracy and safety of vehicle control.

[0009] In some embodiments, the vehicle control based on the target crowd density, the scene sensitivity, and the driving intention includes: The risk level is quantified based on the target population density, the scene sensitivity, the driving intention, and the vehicle speed to obtain a numerical value for the risk level; When the value of the risk level is greater than a preset first threshold, the acceleration of the vehicle is restricted.

[0010] In this embodiment, the target population density, scene sensitivity, driving intention, and vehicle speed are quantified into risk level values; then, vehicle control is performed based on the number of risk levels. For example, if the risk level value is too high, the vehicle's acceleration is limited to ensure the safety of vehicle driving.

[0011] In some embodiments, the acceleration limit on the vehicle includes: When the risk level value is greater than a preset first threshold and less than a preset second threshold, the vehicle is subject to a first-level acceleration restriction; or, When the value of the risk level is greater than or equal to the preset second threshold, the vehicle is subject to a second level of acceleration restriction; The second level is higher than the first level.

[0012] In this embodiment, different levels of acceleration restrictions are applied based on the numerical value of the risk level, which helps to improve the precision and accuracy of vehicle control, enhance vehicle control safety, and improve user experience.

[0013] In some embodiments, the method further includes: In response to the vehicle's restriction release command, a second confirmation is made regarding the release of the acceleration restriction; After the second confirmation is passed, the acceleration restriction is lifted.

[0014] In this embodiment, the vehicle acceleration limit is lifted by secondary confirmation to prevent accidental activation by the driver, thereby further improving the safety and reliability of vehicle control.

[0015] In some embodiments, determining the target population density based on the environmental perception data includes: Based on the visual perception data and radar perception data in the environmental perception data, grid division and confidence processing are performed to obtain the visual confidence and radar confidence of the corresponding target in m networks, where m is a positive integer; The weighted confidence scores of the corresponding targets in the m grids are obtained by weighting the visual confidence scores and radar confidence scores of the targets in the m grids. The target population density is obtained by performing crowd density processing based on the weighted confidence scores of the corresponding targets in the m grids and the grid areas corresponding to each of the m grids.

[0016] In this embodiment, the crowd density per unit area is calculated by combining the aforementioned visual perception data and radar perception data, thereby obtaining the target crowd density. This helps to improve the accuracy of crowd density calculation and enhance the accuracy and safety of subsequent vehicle control.

[0017] In some embodiments, the process of processing the crowd density based on the weighted confidence scores of the corresponding targets in the m grids and the grid areas corresponding to each of the m grids to obtain the crowd density includes: Based on the weighted confidence scores of the corresponding targets in the m grids and the grid areas corresponding to each of the m grids, the crowd density is processed to obtain the crowd density of each of the m grids. The target population density is obtained by weighted averaging the population densities of the m grids.

[0018] In this embodiment, a weighted average method is used to calculate the target population density in a differentiated manner, which helps to improve the accuracy or precision of the target population density calculation.

[0019] In some embodiments, determining scene sensitivity based on the environmental perception data includes: Scene recognition is performed based on the visual perception data in the environmental perception data to obtain the corresponding scene type; The sensitivity of the scene is determined based on the scene type.

[0020] In this embodiment, scene sensitivity is determined by scene recognition, which helps to improve the accuracy and efficiency of scene sensitivity determination.

[0021] In some embodiments, determining the scene sensitivity based on the scene type includes: Based on the scenario type, determine the scenario score of the scenario in which the vehicle is located; Sensitivity processing is performed based on the scene score to obtain the scene sensitivity.

[0022] In this embodiment, the corresponding scene sensitivity is calculated by the scene score corresponding to the above-mentioned scene type, which helps to improve the accuracy or precision of scene sensitivity calculation and improve the accuracy and safety of subsequent vehicle control.

[0023] In some embodiments, the method further includes: Display the risk level and / or the scenario type.

[0024] This application can display the aforementioned risk levels and / or scenario types on, for example, a dashboard or interactive interface, improving the user experience. It also facilitates the driver's proactive adjustment and control of the vehicle's safe driving, enhancing overall vehicle safety.

[0025] Secondly, this application provides a vehicle control device, comprising: An acquisition module is used to acquire environmental perception data and vehicle status data. The environmental perception data includes at least radar perception data and visual perception data, and the vehicle status data includes at least the vehicle speed. The processing module is used to determine the target crowd density and scene sensitivity based on the environmental perception data, wherein the scene sensitivity is used to quantify the risk level of the scene in which the vehicle is located; The processing module is used to control the vehicle based on the target crowd density, the scene sensitivity, and the vehicle status data.

[0026] For any content not introduced or described in the embodiments of this application, please refer to the relevant descriptions in the foregoing method embodiments; they will not be repeated here.

[0027] Thirdly, this application provides a vehicle, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement the steps of the above-described vehicle control method.

[0028] Fourthly, this application provides a computer-readable storage medium having computer program instructions stored thereon, which, when executed by a processor, implement the steps of the above-described vehicle control method.

[0029] The technical solution provided in this application embodiment can include the following beneficial effects: This application acquires environmental perception data and vehicle status data, wherein the environmental perception data includes at least radar perception data and visual perception data, and the vehicle status data includes at least the vehicle speed; based on the environmental perception data, it determines the target crowd density and scene sensitivity, wherein the scene sensitivity is used to quantify the risk level of the scene in which the vehicle is located; and it performs vehicle control based on the target crowd density, scene sensitivity, and vehicle status data. In this way, safe and accurate vehicle control can be performed based on the target crowd density, scene sensitivity, and vehicle status data of the scene in which the vehicle is located, thereby improving the accuracy and safety of vehicle control. It also solves the technical problems in the prior art, such as the lack of consideration for protection in densely populated areas, leading to low vehicle control accuracy and a high risk of safety accidents.

[0030] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0031] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0032] Figure 1 This is a schematic flowchart of a vehicle control method provided in an embodiment of this application.

[0033] Figure 2 This is a schematic diagram illustrating a process for determining the density of a target population, as provided in an embodiment of this application.

[0034] Figure 3 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of this application.

[0035] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0036] Figure 5 This is a structural schematic diagram of a vehicle provided in an embodiment of this application. Detailed Implementation

[0037] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0038] Unless otherwise defined, the technical or scientific terms used in the embodiments of this specification shall have the ordinary meaning understood by one of ordinary skill in the art to which this specification pertains. The terms "first," "second," and similar terms used in the embodiments of this specification do not indicate any order, quantity, or importance, but are merely used to avoid confusion of constituent elements.

[0039] Unless the context otherwise requires, throughout this specification, "a plurality of" means "at least two," and "including" is interpreted as open-ended or encompassing, that is, "including, but not limited to." In the description of this specification, terms such as "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples" are intended to indicate that a particular feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of this specification. The illustrative representations of the above terms do not necessarily refer to the same embodiment or example.

[0040] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0041] Please see Figure 1 This is a schematic flowchart of a vehicle control method provided in an embodiment of this application. Figure 1 The method shown can be applied to electronic devices or vehicles, and the method may include the following implementation steps: S101. Acquire environmental perception data and vehicle status data, wherein the environmental perception data includes at least radar perception data and visual perception data, and the vehicle status data includes at least the vehicle speed.

[0042] The environmental perception data mentioned in this application can refer to environmental data of the vehicle's location perceived by, for example, corresponding sensors. This may include, but is not limited to, radar perception data obtained through radar sensors, visual perception data obtained through visual sensors such as cameras, or environmental data obtained through other sensors. The vehicle state data mentioned above can refer to data related to the vehicle's state during driving. This may include, but is not limited to, vehicle speed, acceleration, steering wheel angle, throttle opening (also known as throttle depth), or other vehicle-related data. This application does not limit the type or installation location of the radar sensor and the visual sensor mentioned above. For example, the radar sensor may include millimeter-wave radar, and the visual sensor may include wide-angle or infrared cameras. They can all be installed in the front bumper or hood of the vehicle, covering a range of 150 meters in front and a horizontal field of view of 120°. This is merely an example and does not constitute a limitation. They can be set according to the actual needs of the system, and this application does not impose further limitations on this.

[0043] This application does not limit the implementation methods for acquiring the aforementioned environmental perception data and vehicle status data. For example, they can be directly collected by corresponding sensors, or they can be obtained from other devices (such as terminals, servers, etc.) through a network. This application will not impose too many limitations or details on these methods.

[0044] S102. Based on the environmental perception data, determine the target population density and scene sensitivity, wherein the scene sensitivity is used to quantify the risk level of the scene in which the vehicle is located.

[0045] The target population density mentioned above in this application can refer to the population density per unit area in the scene where the vehicle is located. The scene sensitivity mentioned above is related to the scene type where the vehicle is located, and it can be used to quantify the risk level of the scene where the vehicle is located. Generally, the scene sensitivity mentioned above is positively correlated with the risk level mentioned above. Specifically, for example, the higher the scene sensitivity mentioned above, the higher the corresponding risk level; conversely, the lower the scene sensitivity mentioned above, the lower the corresponding risk level.

[0046] S103. Perform vehicle control based on the target crowd density, the scene sensitivity, and the vehicle status data.

[0047] This application may impose restrictions such as vehicle speed and throttle opening based on the aforementioned target population density, scenario sensitivity, and vehicle status data, specifically such as vehicle acceleration restrictions mentioned below, but this application does not impose further limitations on these.

[0048] By implementing the embodiments of this application, this application acquires environmental perception data and vehicle status data. The environmental perception data includes at least radar perception data and visual perception data, and the vehicle status data includes at least the vehicle's speed. Based on the environmental perception data, the target crowd density and scene sensitivity are determined, with the scene sensitivity used to quantify the risk level of the scene in which the vehicle is located. Vehicle control is then performed based on the target crowd density, scene sensitivity, and vehicle status data. This allows for safe and accurate vehicle control based on the target crowd density, scene sensitivity, and vehicle status data of the vehicle's location, thereby improving the accuracy and safety of vehicle control. It also solves the technical problems in existing technologies, such as the lack of consideration for protection in densely populated areas, leading to low vehicle control accuracy and a higher risk of safety accidents.

[0049] The following describes some specific and optional embodiments related to this application.

[0050] In step S102, this application does not limit the implementation method for determining the target crowd density. For example, this application can use preset rules / formulas to process the environmental perception data to obtain the target crowd density. See also... Figure 2 This is a schematic diagram illustrating a process for determining the density of a target population, as provided in an embodiment of this application. Figure 2 The process shown may include the following implementation steps: S201. Based on the visual perception data and radar perception data in the environmental perception data, perform grid division and confidence processing to obtain the visual confidence and radar confidence of the corresponding target in m networks, where m is a positive integer.

[0051] This application can perform grid division and confidence calculation on the visual perception data and radar perception data in the aforementioned environmental perception data, thereby obtaining the visual confidence and radar confidence of the corresponding targets in m grids, where m is a positive integer pre-defined by the system according to actual conditions. Specifically, this application can use target detection models such as YOLO to divide the aforementioned visual perception data (such as image data collected by a camera) into grids, for example, m grids; and perform target detection and confidence calculation on each grid, for example, for pedestrians, children, etc., thereby obtaining the visual confidence of the corresponding targets in the aforementioned m grids. During the confidence calculation process, if occlusion is found on the target (such as a pedestrian being partially occluded by a vehicle), this application can adjust and determine the corresponding visual confidence by considering the integrity of the detection box containing the target. For example, if the YOLO model originally outputs a visual confidence of 0.9, but a pedestrian target is found to be partially occluded, the confidence can be reduced from 0.9 to 0.7, etc. For example, in a set scenario (such as low light, fog, or other severe weather scenarios), this application can reduce the aforementioned visual confidence level by setting a first ratio (such as 0.8), and this application will not impose further limitations or details on this. The aforementioned set scenario and the aforementioned first ratio are both pre-defined by the system based on actual conditions. For example, the aforementioned set scenario may include, but is not limited to, scenarios such as low light, fog, or other severe weather.

[0052] Meanwhile, this application can calculate the radar confidence level based on the radar sensing data corresponding to each of the above grids, thereby obtaining the radar confidence level of each of the above m grids. Specifically, for example, this application can aggregate the radar sensing data (usually presented in the form of point clouds) into corresponding target clusters using clustering algorithms such as DBSCAN, and calculate the reflection intensity R of the target cluster. Then, the above reflection intensity is mapped / processed into the corresponding radar confidence level, and its specific calculation is shown in the following formula (1): Formula (1) Among them, C radar Indicates radar confidence level. R represents reflection intensity. min R represents the minimum reflection intensity among all reflection intensities. max This represents the maximum reflection intensity among all reflection intensities.

[0053] Optionally, this application can also calculate information such as the velocity of the target corresponding to the aforementioned target cluster using the Doppler effect. Furthermore, when this application detects a drastic change in the velocity of the aforementioned target, such as a sudden acceleration or deceleration, it can perform speed stabilization compensation on the aforementioned radar confidence level. Specifically, for example, the radar confidence level can be reduced according to a set second ratio (e.g., 0.7), etc., which this application does not limit or elaborate on further. Both the aforementioned first ratio and the aforementioned second ratio are pre-defined ratios set by the system based on actual conditions. They can be empirical values ​​set based on user experience, or statistical values ​​calculated based on a series of experimental data, etc., which this application does not limit further.

[0054] S202. Based on the visual confidence and radar confidence of the corresponding targets in the m grids, a weighted confidence of the corresponding targets in the m grids is obtained.

[0055] For each grid cell, this application can perform a weighted calculation based on the visual confidence and radar confidence of the corresponding target in that grid cell to obtain the weighted confidence of the corresponding target in that grid cell. The specific calculation can be shown in the following formula (2): Formula (2) Among them, w j C represents the weighted confidence score of the j-th target in the grid. j-visual C represents the visual confidence score of the j-th target in the grid. j-radar This represents the radar confidence level of the j-th target in the grid. The weight value represents the system's pre-defined configuration based on actual conditions, typically 0.7. Optionally, in specific scenarios (such as low light or rainy weather), this application can automatically adjust the aforementioned weight value. For example, in a rainy scene, the weight value could be 0.5, and in a low light or nighttime scene, it could be 0.4. This application does not impose further limitations on this. The visual confidence, radar confidence, and weighted confidence values ​​involved in this application can each be between 0 and 1, and this application does not impose further limitations or details on this.

[0056] S203. Based on the weighted confidence scores of the targets in the m grids and the grid areas corresponding to each of the m grids, perform crowd density processing to obtain the target crowd density.

[0057] This application can calculate the population density based on the weighted confidence of the corresponding targets in the above m grids and the grid area of ​​each of the above m grids, so as to obtain the population density of each of the above m grids. The specific calculation is shown in the following formula (3): Formula (3) Among them, Di N represents the population density of the i-th grid, where i is a positive integer less than or equal to m. i This represents the number of targets contained in the i-th network. Area i This represents the area of ​​the i-th grid.

[0058] Furthermore, this application can calculate the target population density by weighted averaging the population densities of the above m grids, as shown in the following formula (4): Formula (4) Where D represents the density of the target population. W i This represents the weighted weight value of the i-th grid. It is usually pre-defined by the system based on the actual situation. For example, the larger the grid area, the larger the corresponding weighted weight value; the more targets or the denser the crowd in the grid, the larger the corresponding weighted weight value, etc. This application does not impose any restrictions on this.

[0059] This application does not limit the implementation method for determining the above-mentioned scene sensitivity. Specifically, this application can perform scene recognition based on the visual perception data in the above-mentioned environmental perception data. For example, it can use deep learning models such as convolutional neural networks (CNN) to classify and recognize the scene of the above-mentioned visual perception data to obtain the scene type of the scene where the vehicle is located, such as school, shopping mall, highway, etc. Then, the corresponding scene sensitivity is determined based on the scene type. For example, this application can pre-calibrate and set a corresponding scene sensitivity table, and find the scene sensitivity corresponding to the above-mentioned scene type by looking up the table. For another example, this application can determine the scene score of the scene where the vehicle is located based on the above-mentioned scene type. For example, the scene score corresponding to the above-mentioned scene type can be obtained by looking up the table or by calculating the scene score of the above-mentioned scene type, etc. This application does not limit this much. Then, the sensitivity is calculated based on the above-mentioned scene score to obtain the above-mentioned scene sensitivity. The specific calculation is shown in the following formula (5): Formula (5) Wherein, S represents the scene sensitivity mentioned above. E represents the scene score mentioned above. Eth represents the scene threshold corresponding to the scene type mentioned above, which is a threshold score pre-defined by the system according to the scene type. For example, the scene threshold for a school can be 1, and the scene threshold for a highway can be 0, etc. This application does not impose many restrictions on this. k represents an adjustable parameter, which is used to control the slope or steepness of the scene sensitivity curve. It can be a parameter pre-defined by the system according to the actual situation. The larger the k value, the steeper the scene sensitivity curve; the smaller the k value, the flatter the scene sensitivity curve. This application does not impose many restrictions or details on this. Optionally, in the set scene (such as rainy days, foggy days, and other severe weather scenes), this application can reduce the k value to make the scene sensitivity curve flatter and avoid misjudgment caused by visual blur. The value of the scene sensitivity mentioned above can also be a value between 0 and 1. This application does not impose many restrictions on this.

[0060] In step S103, this application does not limit the specific implementation of the above-mentioned vehicle control. For example, this application can perform vehicle control based on the target crowd density, the scene sensitivity, and the vehicle speed. Alternatively, this application can determine the driver's driving intention based on the vehicle state data. Specifically, this application can calculate the rate of change of throttle opening based on the throttle opening data in the vehicle state data to represent the driving intention. For example, when the rate of change of throttle opening is greater than a preset threshold, the driving intention can be used to instruct the driver to accelerate, etc. This application does not impose further limitations or details on this. Furthermore, this application can perform vehicle control based on the target crowd density, the scene sensitivity, and the driving intention. This application does not limit its specific implementation. For example, in one implementation, this application can calculate the target crowd density, the scene sensitivity, and the driving intention according to preset rules / formulas, and perform vehicle control based on the calculated value (such as a new vehicle speed), etc. This application does not impose further limitations or details on this.

[0061] In another embodiment, this application can calculate / quantify the risk level based on the above-mentioned target population density, the above-mentioned scene sensitivity, the above-mentioned driving intention and the above-mentioned vehicle speed, so as to obtain the numerical value of the risk level. The specific calculation is shown in the following formula (6): Formula (6) Where R represents the risk level numerical value. D represents the target population density. S represents the scenario sensitivity. V represents the vehicle speed. A represents the driving intention. k1, k2, and k3 are all adjustable coefficients, which can be pre-defined by the system according to the actual situation, and this application does not impose any restrictions on them.

[0062] Therefore, this application can control the vehicle based on the aforementioned risk level values. For example, when the risk level value is greater than a preset first threshold, this application can limit the vehicle's acceleration. Conversely, when the risk level value is less than or equal to the preset first threshold, the current vehicle movement can be maintained without intervention or control, and the process can be terminated. The aforementioned preset first threshold is a value pre-defined by the system based on actual conditions. It can be an empirical value set based on user experience, or a statistical value calculated based on a series of experimental data, etc. This application does not impose further limitations on this.

[0063] This application does not limit the specific implementation of the aforementioned acceleration restriction. For example, when the value of the aforementioned risk level is greater than a preset first threshold and less than a preset second threshold, this application can impose a first-level acceleration restriction on the vehicle, such as limiting the maximum throttle opening, and optionally issuing an audible or visual warning to restrict vehicle acceleration. Alternatively, when the value of the aforementioned risk level is greater than or equal to the aforementioned preset second threshold, this application can impose a second-level acceleration restriction on the vehicle, such as cutting off the throttle and forcibly limiting the vehicle to a preset safe speed, such as 20 km / h. The aforementioned second level is higher than the aforementioned first level; for example, the aforementioned second level can be a severe level, and the aforementioned first level can be a mild level. The aforementioned preset first threshold and the aforementioned preset second threshold are both thresholds pre-defined by the system according to actual conditions. The aforementioned preset second threshold is less than the aforementioned preset first threshold, and this application does not impose further limitations on this.

[0064] In some optional embodiments, this application may display the above-mentioned risk level and / or the above-mentioned scenario type, for example, by displaying the above-mentioned risk level and the above-mentioned scenario type on the corresponding dashboard or interactive interface in the vehicle, so that the driver can view it in real time and manually adjust or control the safe driving of the vehicle, etc. This application does not impose too many limitations on this.

[0065] In some alternative embodiments, this application also provides a "restriction removal" function. Specifically, for example, in response to the vehicle's restriction removal command, this application can perform a secondary confirmation to remove the acceleration restriction. If the secondary confirmation is successful, the acceleration restriction can be automatically removed; otherwise, if the secondary confirmation fails, the status quo can be maintained, and the process can end. This application does not limit the specific implementation of the restriction removal command; for example, the driver can input the restriction removal command via voice, button, key, or other means. This application also does not limit the specific implementation of the secondary confirmation; for example, secondary confirmation can be performed through an interactive interface, such as displaying a message like "Please confirm restriction removal" for the driver to confirm. This application does not impose further limitations on this aspect.

[0066] As can be seen, this application's solution enhances the reliability of environmental perception data by complementing visual and radar sensors. It avoids collisions or impacts with crowds by accelerating and limiting movement, preventing aggressive driving, strengthening active protection in densely populated areas, and improving the safety and reliability of vehicle control. In specific implementation, this application acquires environmental perception data and vehicle status data. The environmental perception data includes at least radar and visual perception data, and the vehicle status data includes at least the vehicle's speed. Based on the environmental perception data, it determines the target crowd density and scene sensitivity, where scene sensitivity quantifies the risk level of the scene in which the vehicle is located. Vehicle control is then performed based on the target crowd density, scene sensitivity, and vehicle status data. This allows for safe and accurate vehicle control based on the target crowd density, scene sensitivity, and vehicle status data of the scene in which the vehicle is located, thereby improving the accuracy and safety of vehicle control. It also solves the technical problems of existing technologies that do not consider protection in densely populated areas, leading to low vehicle control accuracy and a higher risk of accidents.

[0067] Based on the foregoing embodiments, please refer to Figure 3 This is a schematic diagram of the structure of a vehicle control device provided in an embodiment of this application. Figure 3 The illustrated device can be applied to electronic devices or vehicles. The device may include an acquisition module 301 and a processing module 302, wherein: The acquisition module 301 is used to acquire environmental perception data and vehicle status data. The environmental perception data includes at least radar perception data and visual perception data, and the vehicle status data includes at least the vehicle speed. The processing module 302 is used to determine the target crowd density and scene sensitivity based on the environmental perception data, wherein the scene sensitivity is used to quantify the risk level of the scene in which the vehicle is located; The processing module 302 is also used to perform vehicle control based on the target crowd density, the scene sensitivity, and the vehicle status data.

[0068] In some embodiments, the processing module 302 is specifically used for: Based on the vehicle status data, the corresponding driving intention is determined; Vehicle control is performed based on the target population density, the scene sensitivity, and the driving intention.

[0069] In some embodiments, the processing module 302 is specifically used for: The risk level is quantified based on the target population density, the scene sensitivity, the driving intention, and the vehicle speed to obtain a numerical value for the risk level; When the value of the risk level is greater than a preset first threshold, the acceleration of the vehicle is restricted.

[0070] In some embodiments, the processing module 302 is specifically used for: When the risk level value is greater than a preset first threshold and less than a preset second threshold, the vehicle is subject to a first-level acceleration restriction; or, When the value of the risk level is greater than or equal to the preset second threshold, the vehicle is subject to a second level of acceleration restriction; The second level is higher than the first level.

[0071] In some embodiments, the processing module 302 is further configured to: In response to the vehicle's restriction release command, a second confirmation is made regarding the release of the acceleration restriction; After the second confirmation is passed, the acceleration restriction is lifted.

[0072] In some embodiments, the processing module 302 is specifically used for: Based on the visual perception data and radar perception data in the environmental perception data, grid division and confidence processing are performed to obtain the visual confidence and radar confidence of the corresponding target in m networks, where m is a positive integer; The weighted confidence scores of the corresponding targets in the m grids are obtained by weighting the visual confidence scores and radar confidence scores of the targets in the m grids. The target population density is obtained by performing crowd density processing based on the weighted confidence scores of the corresponding targets in the m grids and the grid areas corresponding to each of the m grids.

[0073] In some embodiments, the processing module 302 is specifically used for: Based on the weighted confidence scores of the corresponding targets in the m grids and the grid areas corresponding to each of the m grids, the crowd density is processed to obtain the crowd density of each of the m grids. The target population density is obtained by weighted averaging the population densities of the m grids.

[0074] In some embodiments, the processing module 302 is specifically used for: Scene recognition is performed based on the visual perception data in the environmental perception data to obtain the corresponding scene type; The sensitivity of the scene is determined based on the scene type.

[0075] In some embodiments, the processing module 302 is specifically used for: Based on the scenario type, determine the scenario score of the scenario in which the vehicle is located; Sensitivity processing is performed based on the scene score to obtain the scene sensitivity.

[0076] In some embodiments, the processing module 302 is further configured to: Display the risk level and / or the scenario type.

[0077] By implementing the embodiments of this application, the aforementioned device can acquire environmental perception data and vehicle status data. The environmental perception data includes at least radar perception data and visual perception data, and the vehicle status data includes at least the vehicle's speed. Based on the environmental perception data, the target crowd density and scene sensitivity are determined, with the scene sensitivity used to quantify the risk level of the scene in which the vehicle is located. Vehicle control is then performed based on the target crowd density, scene sensitivity, and vehicle status data. This allows for safe and accurate vehicle control based on the target crowd density, scene sensitivity, and vehicle status data of the scene in which the vehicle is located, thereby improving the accuracy and safety of vehicle control. It also solves the technical problems in existing technologies, such as the lack of consideration for protection in densely populated areas, leading to low vehicle control accuracy and a higher risk of safety accidents.

[0078] Please see Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 4 The electronic device shown can be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc. This electronic device can be used in various types of vehicles, etc.

[0079] Reference Figure 4 The electronic device 400 may include one or more of the following components: processing component 402, memory 404, power supply component 406, multimedia component 408, audio component 410, input / output interface 412, sensor component 414, and communication component 416.

[0080] Processing component 402 typically controls the overall operation of electronic device 400, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 402 may include one or more processors 420 to execute instructions to complete all or part of the steps of the vehicle control method described above. Furthermore, processing component 402 may include one or more modules to facilitate interaction between processing component 402 and other components. For example, processing component 402 may include a multimedia module to facilitate interaction between multimedia component 408 and processing component 402.

[0081] Memory 404 is configured to store various types of data to support the operation of electronic device 400. Examples of such data include instructions for any application or method operating on electronic device 400, contact data, phonebook data, messages, pictures, videos, etc. Memory 404 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0082] Power supply component 406 provides power to various components of electronic device 400. Power supply component 406 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 400.

[0083] Multimedia component 408 includes a screen that provides an output interface between the electronic device 400 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 408 includes a front-facing camera and / or a rear-facing camera. When the electronic device 400 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0084] Audio component 410 is configured to output and / or input audio signals. For example, audio component 410 includes a microphone (MIC) configured to receive external audio signals when electronic device 400 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 404 or transmitted via communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.

[0085] Input / output interface 412 provides an interface between processing component 402 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, start buttons, and lock buttons.

[0086] Sensor assembly 414 includes one or more sensors for providing state assessments of various aspects of electronic device 400. For example, sensor assembly 414 may detect the on / off state of electronic device 400, the relative positioning of components such as the display and keypad of electronic device 400, changes in position of electronic device 400 or a component of electronic device 400, the presence or absence of user contact with electronic device 400, orientation or acceleration / deceleration of electronic device 400, and temperature changes of electronic device 400. Sensor assembly 414 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 414 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0087] Communication component 416 is configured to facilitate wired or wireless communication between electronic device 400 and other devices. Electronic device 400 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 416 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 416 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0088] In an exemplary embodiment, the electronic device 400 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the vehicle control method described above.

[0089] Understandably, the processor 420 in this application embodiment can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor described above can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0090] Understandably, the memory 404 in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (SLDRAM), and direct rambus RAM (DR RAM). It should be noted that the memory used in the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0091] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 404 including instructions, which can be executed by a processor 420 of an electronic device 400 to complete the aforementioned upper-level vehicle control method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0092] The aforementioned device can be a standalone electronic device or a part of a standalone electronic device. For example, in one embodiment, the device can be an integrated circuit (IC) or a chip, wherein the integrated circuit can be a single IC or a collection of multiple ICs. The chip can include, but is not limited to, the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), and SoC (System on Chip). The aforementioned integrated circuit or chip can be used to execute executable instructions (or code) to implement the aforementioned vehicle control method. The executable instructions can be stored in the integrated circuit or chip or obtained from other devices or equipment. For example, the integrated circuit or chip includes a processor, memory, and an interface for communicating with other devices. The executable instructions can be stored in the memory, and when the executable instructions are executed by the processor, the above-mentioned vehicle control method is implemented; or, the integrated circuit or chip can receive the executable instructions through the interface and transmit them to the processor for execution to implement the above-mentioned vehicle control method.

[0093] Please see Figure 5 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. For example, as shown... Figure 5 As shown, the vehicle 500 includes a memory 501 and a processor 502. The memory 501 stores executable program code 5011, and the processor 502 is used to call and execute the executable program code 5011 to perform a vehicle control method.

[0094] This application embodiment can divide the vehicle into functional modules according to the above method embodiment. For example, each function can be assigned to a separate module, or two or more functions can be integrated into a processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division. In actual implementation, there may be other division methods. When dividing each functional module according to its corresponding function, the vehicle may include a processing module and a communication module, etc.

[0095] It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional description of the corresponding functional module, and will not be repeated here. The vehicle provided in this embodiment is used to execute the above-described vehicle control method, and therefore can achieve the same effect as the above implementation method.

[0096] In another exemplary embodiment, a computer program product is also provided, the computer program product comprising a computer program executable by a programmable device, the computer program having a code portion for performing the above-described vehicle control method when executed by the programmable device.

[0097] It should be noted that the descriptions of the above embodiments of storage media, devices, and equipment are similar to the descriptions of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the embodiments of storage media, devices, and equipment of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0098] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of this application. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed in this application. The specification and embodiments are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0099] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications or equivalent substitutions made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A vehicle control method characterized by, The method comprises: acquiring environment perception data and vehicle state data, the environment perception data at least comprising radar perception data and visual perception data, the vehicle state data at least comprising a vehicle speed of the vehicle; determining a target crowd density and a scene sensitivity based on the environment perception data, the scene sensitivity being used to quantify a risk level of a scene where the vehicle is located; performing vehicle control based on the target crowd density, the scene sensitivity, and the vehicle state data.

2. The method of claim 1, wherein, The vehicle control based on the crowd density, the scene sensitivity, and the vehicle state data comprises: determining a corresponding driving intention based on the vehicle state data; performing vehicle control based on the target crowd density, the scene sensitivity, and the driving intention.

3. The method of claim 2, wherein, The vehicle control based on the target crowd density, the scene sensitivity, and the driving intention comprises: quantifying a risk level based on the target crowd density, the scene sensitivity, the driving intention, and the vehicle speed, to obtain a numerical value of the risk level; when the numerical value of the risk level is greater than a preset first threshold, performing acceleration limitation on the vehicle.

4. The method of claim 3, wherein, The acceleration limitation on the vehicle comprises: when the numerical value of the risk level is greater than the preset first threshold and less than a preset second threshold, performing first-level acceleration limitation on the vehicle; or when the numerical value of the risk level is greater than or equal to the preset second threshold, performing second-level acceleration limitation on the vehicle; wherein the second level is higher than the first level.

5. The method of claim 4, wherein, The method further comprises: in response to a limitation release instruction of the vehicle, performing secondary confirmation on releasing the acceleration limitation; after the secondary confirmation is passed, releasing the acceleration limitation.

6. The method of claim 1, wherein, The determination of the target crowd density based on the environment perception data comprises: based on the visual perception data and the radar perception data in the environment perception data, performing grid division and confidence processing to obtain visual confidence and radar confidence of corresponding targets in m grids, m being a positive integer; based on the visual confidence and the radar confidence of corresponding targets in the m grids, performing weighted processing to obtain weighted confidence of corresponding targets in the m grids; based on the weighted confidence of corresponding targets in the m grids and the grid area corresponding to each of the m grids, performing crowd density processing to obtain the target crowd density.

7. The method of claim 6, wherein, The crowd density processing based on the weighted confidence of corresponding targets in the m grids and the grid area corresponding to each of the m grids comprises: based on the weighted confidence of corresponding targets in the m grids and the grid area corresponding to each of the m grids, performing crowd density processing to obtain the crowd density of each of the m grids; based on the crowd density of each of the m grids, performing weighted average to obtain the target crowd density.

8. The method of claim 1, wherein, The determination of the scene sensitivity based on the environment perception data comprises: based on the visual perception data in the environment perception data, performing scene recognition to obtain a corresponding scene type; based on the scene type, determining the scene sensitivity.

9. The method of claim 8, wherein, The determination of the scene sensitivity based on the scene type comprises: determine a scene score of a scene where the vehicle is located based on the scene type; perform sensitivity processing based on the scene score to obtain a scene sensitivity.

10. A vehicle characterized by comprising: comprise: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement the steps of the method of any one of claims 1-9.

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