Vehicle door control method, device, equipment and medium

Abstract

The embodiment of the invention discloses a vehicle door control method, device and equipment and a medium, and the method comprises the steps that in response to a received vehicle door opening command of a vehicle, environment point cloud data of the surrounding environment of the vehicle at multiple moments are obtained, and the environment point cloud data comprise candidate obstacles; determining a predicted motion track of the candidate obstacle according to the environment point cloud data at multiple moments; determining a target obstacle according to the predicted motion trajectory of each candidate obstacle; and determining an alarm grade according to the target point cloud data of the target obstacle, and controlling a vehicle door according to the alarm grade. According to the technical scheme provided by the invention, the determination accuracy of the target obstacle and the safety of the vehicle can be improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle alarms, and more particularly to a vehicle door control method, device, equipment, and medium. Background Technology

[0002] In densely parked urban environments, car doors often fail to open in time due to blind spots caused by the A-pillar, B-pillar, and rearward visibility, making it difficult for drivers and passengers to notice nearby obstacles such as walls, fire hydrants, pedestrians, or cyclists. This leads to frequent scrapes, collisions, and even personal injury accidents.

[0003] The current solution mainly involves installing cameras on the vehicle body and then observing the scene through a screen inside the car to determine if there are any safety hazards. However, this requires users to check the screen before opening the door, which affects the efficiency of getting out of the car. In addition, the accuracy of manually observing obstacles is relatively low. Summary of the Invention

[0004] This invention provides a vehicle door control method, device, equipment, and medium. Through the technical solution of this invention, target obstacles and alarm levels can be accurately determined, thereby improving vehicle safety.

[0005] In a first aspect, embodiments of the present invention provide a vehicle door control method, comprising:

[0006] In response to receiving a vehicle door opening command, the system acquires multi-moment environmental point cloud data of the vehicle's surrounding environment, wherein the environmental point cloud data includes candidate obstacles.

[0007] The predicted motion trajectory of the candidate obstacle is determined based on environmental point cloud data at multiple time points;

[0008] The target obstacle is determined based on the predicted motion trajectory of each of the candidate obstacles;

[0009] The alarm level is determined based on the target point cloud data of the target obstacle, and the vehicle door is controlled according to the alarm level.

[0010] Secondly, embodiments of the present invention provide a vehicle door control device, comprising:

[0011] The acquisition module, in response to receiving a vehicle door opening command, acquires multi-moment environmental point cloud data of the vehicle's surrounding environment, wherein the environmental point cloud data includes candidate obstacles;

[0012] The prediction module is used to determine the predicted motion trajectory of the candidate obstacle based on environmental point cloud data at multiple time points;

[0013] The target obstacle determination module is used to determine the target obstacle based on the motion trajectory of each of the candidate obstacles;

[0014] An alarm module is used to determine the alarm level based on the target point cloud data of the target obstacle, and control the vehicle door according to the alarm level.

[0015] Thirdly, embodiments of the present invention provide an electronic device, the electronic device comprising:

[0016] At least one processor; and,

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle door control method as described in any one of the embodiments of the present invention.

[0019] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the vehicle door control method described in any one of the embodiments of the present invention.

[0020] This invention provides a vehicle door control method, apparatus, device, and medium. The method includes: in response to receiving a vehicle door opening command, acquiring multi-moment environmental point cloud data of the vehicle's surrounding environment, wherein the environmental point cloud data includes candidate obstacles; determining the predicted motion trajectory of the candidate obstacles based on the multi-moment environmental point cloud data; determining a target obstacle based on the predicted motion trajectory of each candidate obstacle; determining an alarm level based on the target obstacle's target point cloud data; and controlling the vehicle door based on the alarm level. Specifically, by determining the alarm level through the target obstacle's target point cloud data, and then controlling the vehicle door based on the alarm level, vehicle safety is improved, and accidents are avoided. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a vehicle door control method provided in Embodiment 1 of the present invention;

[0023] Figure 2 A flowchart of a vehicle door control method provided in Embodiment 2 of the present invention.

[0024] Figure 3 A schematic diagram of a vehicle door control system provided in an embodiment of the present invention;

[0025] Figure 4 A schematic diagram of a vehicle alarm is provided for an embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of a vehicle door control device provided in Embodiment 3 of the present invention;

[0027] Figure 6 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] It should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0031] Example 1

[0032] Figure 1 This is a flowchart illustrating a vehicle door control method according to Embodiment 1 of the present invention. This method is specifically applicable to situations requiring a hazard warning during vehicle door opening. The method can be executed by a vehicle door control device, which can consist of software and / or hardware and is configured within the vehicle control system. Figure 1 As shown, it includes:

[0033] Step 110: In response to receiving a vehicle door opening command, acquire multi-moment environmental point cloud data of the vehicle's surrounding environment, wherein the environmental point cloud data includes candidate obstacles.

[0034] The door opening command is an electronic signal or message triggered from inside or outside the vehicle, transmitted via the vehicle bus, and instructs the door lock actuator to enter the unlocked or ready-to-open state. As a startup event of the process, it enables the system to begin collecting and processing subsequent environmental point cloud data.

[0035] Specifically, when the vehicle receives the door opening command, it immediately activates the sensors to collect three-dimensional point cloud data of the vehicle's surrounding environment at multiple discrete moments; the resulting data packets contain candidate obstacles that need to be further identified.

[0036] The vehicle's surrounding environment is a three-dimensional perceptible area extending outward from the vehicle's outline within a preset spatial range (e.g., 0–10m), used to provide the physical spatial boundaries of candidate obstacles to be detected. Environmental point cloud data consists of raw or pre-processed perceptual data collected by lidar, millimeter-wave radar, binocular / structured light cameras, or other depth sensors within the vehicle's surrounding environment, represented as a set of three-dimensional coordinate points.

[0037] Candidate obstacles are a set of objects with physical characteristics that may interfere with the movement of the vehicle door, initially identified in single or multi-frame environmental point cloud data through clustering, segmentation, or object detection algorithms. The specific method for determining candidate obstacles is not limited here.

[0038] Step 120: Determine the predicted motion trajectory of the candidate obstacle based on the environmental point cloud data at multiple time points.

[0039] The predicted trajectory is based on multi-time environmental point cloud data, generated through Kalman filtering, particle filtering, neural networks, or other trajectory estimation algorithms. It describes the spatiotemporal path data describing the changes in the position and velocity of candidate obstacles within a future time window. This data is used to determine whether candidate obstacles will pose a real threat, thereby filtering out target obstacles.

[0040] Specifically, a motion estimation algorithm can be executed for each candidate obstacle to generate its predicted motion trajectory within a future time window.

[0041] Step 130: Determine the target obstacle based on the predicted motion trajectory of each candidate obstacle.

[0042] The target obstacle is an obstacle that is ultimately identified from the candidate obstacles as posing a risk to the door opening action according to preset screening rules. This includes thresholds for the probability of the obstacle's trajectory overlapping with the door area, or preset screening rules including a threshold for the probability of spatiotemporal conflict between the trajectory and the door opening area.

[0043] Specifically, based on preset collision risk rules, the predicted trajectories of all candidate obstacles are evaluated, and target obstacles that pose a real threat to the opening of the car door are selected.

[0044] Step 140: Determine the alarm level based on the target point cloud data of the target obstacle, and control the vehicle door according to the alarm level.

[0045] The risk severity level, represented by discrete or continuous numerical values, can be calculated based on the distance, speed, trajectory overlap probability of the target obstacle, and the door opening angle. This information is used to determine the door control strategy (allow opening, delay opening, limit opening, or prohibit opening) and the warning methods for occupants (audio, visual, and tactile feedback).

[0046] Specifically, for a target obstacle, the alarm level is calculated based on its target point cloud data; then, according to the alarm level, a corresponding control command is sent to the door actuator to enable, delay, limit, or prohibit the opening of the door.

[0047] This invention provides a vehicle door control method, which includes: in response to receiving a vehicle door opening command, acquiring multi-moment environmental point cloud data of the vehicle's surrounding environment, wherein the environmental point cloud data includes candidate obstacles; determining the predicted motion trajectory of the candidate obstacles based on the multi-moment environmental point cloud data; determining a target obstacle based on the predicted motion trajectory of each candidate obstacle; determining an alarm level based on the target obstacle's target point cloud data; and controlling the vehicle door based on the alarm level. Specifically, by determining the alarm level through the target obstacle's target point cloud data, and then controlling the vehicle door based on the alarm level, vehicle safety is improved, and accidents are avoided.

[0048] Example 2

[0049] Figure 2 This is a flowchart of a vehicle door control method provided in Embodiment 2 of the present invention, which further defines the steps of the above embodiments.

[0050] like Figure 2 As shown, it includes:

[0051] Step 201: In response to receiving a vehicle door opening command, acquire multi-moment environmental point cloud data of the vehicle's surrounding environment, wherein the environmental point cloud data includes candidate obstacles.

[0052] Step 202: For each candidate obstacle, determine the actual trajectory and speed of the candidate obstacle at each time point based on the point cloud data of the candidate obstacle at each time point.

[0053] The actual motion trajectory is the observed trajectory calculated based on multi-time point cloud data, which is used to provide a historical benchmark for trajectory prediction. The motion velocity is a three-dimensional linear velocity vector derived from the actual motion trajectory, which is used to extrapolate the future position.

[0054] Specifically, the point cloud coordinates of candidate obstacles at multiple times can be connected to determine their actual movement trajectory. The movement speed of a candidate obstacle at any given time can be determined by the coordinate difference between the point cloud coordinates of candidate obstacles at adjacent times.

[0055] Step 203: Based on the actual motion trajectory and speed at each moment, determine the predicted motion trajectory of the candidate obstacle at future moments.

[0056] Optionally, the current road information where the candidate obstacle is located can be determined based on map information;

[0057] Based on a pre-trained trajectory prediction model, the predicted trajectory of the candidate obstacle is determined according to the actual motion trajectory, motion speed, and current road information.

[0058] Specifically, the map information consists of static, high-precision map data including lane lines, road boundaries, traffic signs, and drivable areas, used to provide current road information for the candidate obstacle. The current road information includes the geometry, direction of travel, and traffic rules of the lane containing the candidate obstacle, extracted from the map information. The pre-trained trajectory prediction model is a neural network or hybrid model trained using a triplet of historical trajectory - road information - true future trajectory, used to output the predicted trajectory of the candidate obstacle.

[0059] Step 204: Obtain the current location of the vehicle, the preset door opening action, and the door opening time.

[0060] The current location, defined as the vehicle's three-dimensional coordinates and attitude angles when it receives the door opening command, serves as a spatial reference for the door's coverage area. The preset door opening action is a set of angle-displacement parameters that change over time during the door opening process, used to determine the spatial position covered during the door opening. The door opening time is the time interval required from the start of door opening to the maximum opening angle, used as a time window to determine spatial conflicts. The vehicle's current location is determined by the onboard GPS, while the preset door opening action and opening time are stored in the vehicle's internal memory and are considered vehicle attributes.

[0061] Step 205: Determine the door opening coverage area based on the current location and the preset door opening action.

[0062] The door opening coverage area is the three-dimensional spatial volume swept by the door during the opening time.

[0063] Step 206: If, during the door opening time, the predicted motion trajectory of the candidate obstacle has a spatial conflict with the door opening coverage area, then the candidate obstacle is determined as the target obstacle.

[0064] Specifically, spatial conflict refers to the state where the predicted motion trajectory and the door opening coverage area overlap, which is used to identify candidate obstacles as target obstacles.

[0065] Specifically, if there is a spatial conflict between the predicted trajectory of a candidate obstacle and the door opening coverage area, it means that the candidate obstacle will collide with the door during the door opening process. Therefore, the candidate obstacle can be identified as the target obstacle.

[0066] Step 207: Determine the current distance between the target obstacle and the car door based on the latest target point cloud data of the target obstacle.

[0067] Step 208: Determine the shape and volume of the target obstacle based on the target point cloud data of the target obstacle.

[0068] Here, "current distance" is the shortest Euclidean distance between the nearest point of the target obstacle and the door boundary. "shape" refers to the geometric features of the target obstacle's outer contour. "volume" is the three-dimensional spatial volume occupied by the target obstacle.

[0069] Specifically, the current distance directly reflects "how long / how far away it will be before the collision," indicating the time of impact; volume: positively correlated with collision kinetic energy, the larger the volume, the greater the potential impact force, and the more severe the damage consequences; shape: determines the actual contact surface and collision method, for example, a sharp, protruding shape may still cause severe scratches or personal injury even with a small volume, and the shape can also be used to determine the type of target obstacle, thereby determining different alarm strategies based on different types.

[0070] Step 209: Determine the alarm level based on the shape, volume, and current distance of the target obstacle.

[0071] Specifically, the type of the target obstacle is determined based on its shape; preset type weights, preset volume weights, and preset distance weights are obtained that are related to the type, volume, and current distance of the target obstacle; and the alarm level is determined based on the type, volume, and current distance of the target obstacle, as well as the type weights, volume weights, and distance weights.

[0072] The target obstacle type is a semantic category derived from shape feature mapping, used to distinguish between pedestrians, bicycles, cars, or stationary objects. The preset type weight is a pre-defined risk coefficient for each type, used to amplify or reduce the dangerous impact of that type in alarm level calculations. The preset volume weight is a preset coefficient corresponding to a volume range, used to reflect the weighted impact of different volumes on collision consequences. The preset distance weight is a preset coefficient corresponding to a distance range, used to reflect the weighted effect of distance on the degree of urgency.

[0073] Specifically, the type of the target obstacle is first identified by its shape. Then, three preset weights bound to the type, current volume, and current distance are retrieved. The type, volume, and distance are weighted and fused with their corresponding weights to output a comprehensive alarm level, which can characterize the severity of the collision.

[0074] For example, regarding type weighting: pedestrians, bicycles, and other vulnerable road users are assigned high weights, while stationary poles or vehicles are assigned low weights—the higher the weight, the higher the alarm level; this is because pedestrians are more likely to be injured under the same collision energy. Volume weighting: the larger the volume, the higher the weight, and the alarm level rises accordingly; because a larger volume means greater kinetic energy and a potentially larger area of ​​damage. Distance weighting: the closer the distance, the higher the weight, and the alarm level rises accordingly; because the reaction time is shortened, the probability of collision and its severity increase simultaneously.

[0075] Step 210: Activate the alarm signal corresponding to the alarm level.

[0076] Among them, the alarm signal is a sound, light or tactile prompt issued according to the alarm level, which is used to warn the occupants or the environment.

[0077] Step 211: If the alarm level is greater than the preset level, the door opening command is rejected; otherwise, the door opening command is executed.

[0078] For example, low level (door openable, alert only)

[0079] The smart surface of the car door projects a soft welcome pattern onto the ground, with the edges of the pattern accompanied by a fade-in and fade-out animation of "Door opening, please be careful" to alert pedestrians; the ambient lighting inside the car remains constantly lit or breathes slowly, and the audio-visual entertainment system plays "Please be careful when opening the door" once in a gentle female voice, without any additional flashing or warning colors.

[0080] Medium level (delayed opening, warning)

[0081] The ground projection switches to a bright warning frame with a dynamic border, and the animation loops twice; the front and rear smart lights outside the vehicle simultaneously project red warning light strips to form a "light wall" to warn vehicles approaching from behind; the ambient lights inside the vehicle change from white to orange and flash rapidly, and the audio-visual entertainment system continuously warns twice, "There is a risk of collision when opening the door, please open the door with caution," with sound and light enhancements in sync.

[0082] High-level (Do not open, mandatory warning)

[0083] The ground projection is changed to a large area of ​​flashing red "STOP" with a high-frequency pulse border; the exterior smart lights repeatedly sweep in a high-brightness red flashing mode, creating a strong visual impact; the interior ambient lights switch to red high-frequency flashing, and the audio-visual entertainment system continuously loops the alarm sound "Door opening collision risk, do not open the door" until the obstacle is away or the user cancels the intention to open the door.

[0084] By associating alarm levels with alarm methods in the above manner, the user experience is improved and potential risks are avoided.

[0085] For example, Figure 3 This is a schematic diagram of the vehicle door control system provided in an embodiment of the present invention. Specifically, the detection unit (camera + millimeter-wave radar) continuously scans the front, rear, and sides of the vehicle to acquire multi-moment point cloud and image data, completing the capture of candidate obstacles and real-time perception of the door lock opening and closing status; the information processing unit receives raw data, performs target recognition, trajectory prediction, and risk assessment, and outputs key information such as the target obstacle and its type, volume, and distance; the intelligent control unit calculates the alarm level based on the above information and generates corresponding control commands: on the one hand, it directly executes the "allow / delay / prohibit" door opening action through the door lock, and on the other hand, it sends display and warning schemes to various human-machine interaction subsystems; the human-machine interaction part plays prompt sounds through entertainment audio-visual control, intelligent lighting control drives the front and rear lights to project door opening animations or warning lights, and intelligent surface control projects text, icons, or ambient light flashing on the door exterior panel and interior, forming a hierarchical alarm closed loop that is visible to pedestrians outside the vehicle and perceptible to occupants inside the vehicle; vehicle speed information provides dynamic threshold correction for the entire process, and the power supply unit supplies power to all modules to ensure reliable operation of the system under any working conditions. Figure 4This invention provides a vehicle alarm schematic diagram. Specifically, after the vehicle is powered on, it first performs a system self-check. If a fault is found, it enters the fault repair process; otherwise, it enters standby mode. When the door unlock signal arrives, the detection unit (camera + millimeter-wave radar) immediately collects real-time environmental data from the front and rear and hands it over to the information processing unit to complete obstacle recognition, trajectory prediction, and risk calculation. The intelligent control unit determines the alarm level based on the weighted result of "type-volume-distance" and simultaneously sends instructions to the human-machine interaction system: at low risk, only a gentle "Door opening, please be careful" animation is played on the smart surface of the door exterior and the ground projection, the ambient light inside the vehicle remains on, and a single warning sound is given; at medium risk, red warning light strips are projected by the front and rear smart lights, the ground animation becomes bright and flashing, the ambient light inside the vehicle flashes orange, and the message "Door opening collision risk, please open the door with caution" is repeatedly given; at high risk, the lights, projection, and ambient light flash red at a high frequency, the entertainment system continuously broadcasts "Do not open the door," and the intelligent control unit directly locks the door lock, refusing to execute the door opening action. Once the obstacle is removed or the risk is eliminated, the system will resume allowing the door to open, completing one closed-loop alarm process.

[0086] This invention provides a vehicle door control method. The method can accurately identify target obstacles and determine alarm levels based on the attributes of the target obstacles, thereby triggering different alarm signals at different alarm levels, thus improving safety and alarm accuracy.

[0087] Example 3

[0088] Figure 5 This is a schematic diagram of a vehicle door control device provided in Embodiment 3 of the present invention.

[0089] like Figure 5 As shown, the device includes:

[0090] The acquisition module 510, in response to receiving a vehicle door opening command, acquires multi-moment environmental point cloud data of the vehicle's surrounding environment, wherein the environmental point cloud data includes candidate obstacles;

[0091] Prediction module 520 is used to determine the predicted motion trajectory of the candidate obstacle based on environmental point cloud data at multiple time points;

[0092] The target obstacle determination module 530 is used to determine the target obstacle based on the motion trajectory of each of the candidate obstacles;

[0093] The alarm module 540 is used to determine the alarm level based on the target point cloud data of the target obstacle, and control the vehicle door according to the alarm level.

[0094] This invention provides a vehicle door control device that, in response to receiving a vehicle door opening command, acquires multi-moment environmental point cloud data of the vehicle's surrounding environment, wherein the environmental point cloud data includes candidate obstacles; determines the predicted motion trajectory of the candidate obstacles based on the multi-moment environmental point cloud data; determines a target obstacle based on the predicted motion trajectory of each candidate obstacle; determines an alarm level based on the target obstacle's target point cloud data; and controls the vehicle door based on the alarm level. Specifically, by determining the alarm level through the target obstacle's target point cloud data, and then controlling the vehicle door based on the alarm level, vehicle safety is improved, and accidents are prevented.

[0095] Optionally, the prediction module 520 includes:

[0096] The determining unit is used to determine the actual motion trajectory and motion speed of each candidate obstacle at each time step based on the point cloud data of the candidate obstacle at each time step.

[0097] The prediction unit is used to determine the predicted motion trajectory of the candidate obstacle at future times based on the actual motion trajectory and motion speed at each time.

[0098] The prediction unit includes:

[0099] The road information determination subunit is used to determine the current road information of the candidate obstacle based on the map information;

[0100] The prediction subunit is used to determine the predicted trajectory of the candidate obstacle based on the pre-trained trajectory prediction model, the actual motion trajectory, the motion speed, and the current road information.

[0101] Optionally, the target obstacle determination module 530 includes:

[0102] The acquisition module is used to acquire the vehicle's current location, preset door opening action, and door opening time;

[0103] The door opening coverage area determination module is used to determine the door opening coverage area based on the current location and preset door opening actions;

[0104] The judgment module is used to determine the candidate obstacle as the target obstacle if there is a spatial conflict between the predicted motion trajectory of the candidate obstacle and the door opening coverage area during the door opening time.

[0105] Optionally, the alarm module 540 includes:

[0106] The current distance determination unit is used to determine the current distance between the target obstacle and the car door based on the latest target point cloud data of the target obstacle;

[0107] A shape and volume determination unit is used to determine the shape and volume of the target obstacle based on the target point cloud data of the target obstacle;

[0108] The alarm level determination unit is used to determine the alarm level based on the shape, volume, and current distance of the target obstacle.

[0109] Optionally, the alarm level determination unit includes:

[0110] The type determination subunit is used to determine the type of the target obstacle based on its shape.

[0111] The weight acquisition subunit is used to acquire preset type weights, preset volume weights, and preset distance weights that are respectively related to the type, volume, and current distance of the target obstacle.

[0112] The alarm level determination subunit is used to determine the alarm level based on the type, volume, and current distance of the target obstacle, as well as the type weight, volume weight, and distance weight.

[0113] Optionally, the alarm module 540 includes:

[0114] An activation unit is used to activate an alarm signal corresponding to the alarm level.

[0115] The execution unit is configured to refuse to execute the door opening command if the alarm level is greater than a preset level; otherwise, it executes the door opening command.

[0116] The vehicle door control device provided in the embodiments of the present invention can execute the vehicle door control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0117] Example 4

[0118] Figure 6 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0119] like Figure 6As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0120] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0121] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as vehicle door control methods.

[0122] In some embodiments, the vehicle door control method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded into and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle door control method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle door control method by any other suitable means (e.g., by means of firmware).

[0123] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0124] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0125] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0126] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0127] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0128] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0129] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0130] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A vehicle door control method, characterized in that, include: In response to receiving a vehicle door opening command, the system acquires multi-moment environmental point cloud data of the vehicle's surrounding environment, wherein the environmental point cloud data includes candidate obstacles. The predicted motion trajectory of the candidate obstacle is determined based on environmental point cloud data at multiple time points; The target obstacle is determined based on the predicted motion trajectory of each of the candidate obstacles; The alarm level is determined based on the target point cloud data of the target obstacle, and the vehicle door is controlled according to the alarm level.

2. The method according to claim 1, characterized in that, Determining the predicted motion trajectory of the candidate obstacle based on multi-time environmental point cloud data includes: For each candidate obstacle, the actual trajectory and speed of the candidate obstacle at each time point are determined based on the point cloud data of the candidate obstacle at each time point. Based on the actual motion trajectory and speed at each moment, the predicted motion trajectory of the candidate obstacle at future moments is determined.

3. The method according to claim 2, characterized in that, The step of determining the predicted motion trajectory of the candidate obstacle at future times based on the actual motion trajectory and motion speed at each time moment includes: Determine the current road information where the candidate obstacle is located based on map information; Based on a pre-trained trajectory prediction model, the predicted trajectory of the candidate obstacle is determined according to the actual motion trajectory, motion speed, and current road information.

4. The method according to claim 1, characterized in that, The step of determining the target obstacle based on the predicted motion trajectory of each of the candidate obstacles includes: Obtain the vehicle's current location, preset door opening action, and door opening time; Determine the door opening coverage area based on the current location and the preset door opening action; If, during the door opening time, the predicted trajectory of the candidate obstacle conflicts spatially with the door opening coverage area, then the candidate obstacle is identified as the target obstacle.

5. The method according to claim 1, characterized in that, The step of determining the alarm level based on the target point cloud data of the target obstacle includes: Based on the latest target point cloud data of the target obstacle, determine the current distance between the target obstacle and the car door; The shape and volume of the target obstacle are determined based on the target point cloud data of the target obstacle; The alarm level is determined based on the shape, volume, and current distance of the target obstacle.

6. The method according to claim 5, characterized in that, The process of determining the alarm level based on the shape, volume, and current distance of the target obstacle includes: The type of target obstacle is determined based on its shape; Obtain preset type weights, preset volume weights, and preset distance weights that are respectively related to the type, volume, and current distance of the target obstacle; The alarm level is determined based on the type, volume, and current distance of the target obstacle, as well as the type weight, volume weight, and distance weight.

7. The method according to claim 1, characterized in that, The step of controlling the vehicle door according to the alarm level includes: Activate the alarm signal corresponding to the alarm level; If the alarm level is greater than the preset level, the door opening command will be refused; otherwise, the door opening command will be executed.

8. A vehicle door control device, characterized in that, include: The acquisition module, in response to receiving a vehicle door opening command, acquires multi-moment environmental point cloud data of the vehicle's surrounding environment, wherein the environmental point cloud data includes candidate obstacles; The prediction module is used to determine the predicted motion trajectory of the candidate obstacle based on environmental point cloud data at multiple time points; The target obstacle determination module is used to determine the target obstacle based on the motion trajectory of each of the candidate obstacles; An alarm module is used to determine the alarm level based on the target point cloud data of the target obstacle, and control the vehicle door according to the alarm level.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle door control method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the vehicle door control method according to any one of claims 1-7.

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