Information processing method, apparatus, device, storage medium, and program product

Abstract

The present disclosure provides an information processing method, device, equipment, storage medium and program product, and relates to the technical field of traffic control. The method comprises: collecting image data of a target traffic area by a vision sensor carried by an air platform; processing the image data by a target detection model and a semantic processing algorithm to determine road condition semantic information; and sending the road condition semantic information to a target terminal to enable the target terminal to make a driving decision based on the road condition semantic information. The present disclosure effectively solves the technical problem that it is difficult to provide forward-looking decision support under complex road conditions, thereby restricting traffic efficiency and increasing safety hazards, by virtue of the high-altitude perspective advantage of the air platform, collecting images by the vision sensor, and fusing the target detection and semantic processing algorithms to convert the image data into road condition semantic information and issue it to the ground terminal.

Description

Technical Field

[0001] This disclosure relates to the field of traffic control technology, and in particular to an information processing method, apparatus, device, storage medium, and program product. Background Technology

[0002] With the continuous growth of traffic flow, the issues of driving safety and efficiency under complex road conditions are becoming increasingly prominent. However, existing technologies have significant limitations in dealing with such scenarios: on the one hand, mobile phone navigation software can only provide rough time estimates based on experience, resulting in insufficient accuracy of road condition information; on the other hand, traditional in-vehicle driver assistance systems mainly rely on on-vehicle sensors for environmental perception, which, limited by the monitoring range, makes it difficult to provide effective warnings of potential hazards ahead (such as distant sources of congestion, blind spots on sharp bends, or the level of traffic flow before long slopes). Due to the lack of efficient road condition perception and information interaction mechanisms, related technologies cannot provide decision support for vehicles based on actual road conditions, which not only limits the ability of motor vehicles to adjust speed or plan power output in advance, but also leads to low traffic efficiency.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0004] This disclosure provides an information processing method, apparatus, device, storage medium, and program product, which at least to some extent overcomes the technical problem in related technologies that it is difficult to provide forward-looking decision support under complex road conditions, thereby restricting traffic efficiency and increasing safety hazards.

[0005] Other features and advantages of this disclosure will become apparent from the following detailed description, or may be learned in part from practice of this disclosure.

[0006] According to one aspect of this disclosure, an information processing method is provided for use on an aerial platform, comprising: acquiring image data of a target traffic area using an onboard visual sensor; processing the image data using a target detection model and a semantic processing algorithm to determine traffic condition semantic information; and sending the traffic condition semantic information to a target terminal so that the target terminal makes a driving decision based on the traffic condition semantic information.

[0007] In some embodiments, the method further includes: controlling its own hibernation and takeoff states according to the vehicle distribution; when no target terminal is detected, landing and entering hibernation mode; when a target terminal is detected, taking off to perform operations.

[0008] In some embodiments, the method further includes: adjusting its own flight altitude and / or coordinating its own start-stop control based on the real-time traffic conditions of the target traffic area.

[0009] According to another aspect of this disclosure, an information processing method is also provided, applied to a target terminal, comprising: receiving traffic condition semantic information transmitted by an airborne platform deployed in a target traffic area; and adjusting driving speed and / or driving route according to the traffic condition semantic information.

[0010] In some embodiments, adjusting the driving speed and / or driving route according to the road condition semantic information includes: adjusting the driving speed according to a first preset condition when the road condition semantic information indicates that there are no vehicles ahead; and adjusting the driving route according to a second preset condition when the road condition semantic information indicates that there are vehicles ahead.

[0011] According to another aspect of this disclosure, an information processing system is also provided, including an aerial platform and a target terminal; the aerial platform acquires image data of a target traffic area through an onboard visual sensor; the aerial platform processes the image data using a target detection model and a semantic processing algorithm to determine traffic condition semantic information; the aerial platform sends the traffic condition semantic information to the target terminal; the target terminal receives the traffic condition semantic information and adjusts its driving speed and / or driving route according to the traffic condition semantic information.

[0012] According to another aspect of this disclosure, an information processing apparatus is also provided for use on an aerial platform, comprising: an image data acquisition module for acquiring image data of a target traffic area through an onboard visual sensor; a road condition semantic information determination module for processing the image data using a target detection model and a semantic processing algorithm to determine road condition semantic information; and a road condition semantic information transmission module for transmitting the road condition semantic information to a target terminal so that the target terminal makes a driving decision based on the road condition semantic information.

[0013] According to another aspect of this disclosure, an information processing apparatus is also provided, applied to a target terminal, comprising: an information receiving module for receiving traffic condition semantic information transmitted by an aerial platform deployed in a target traffic area; and a driving adjustment module for adjusting driving speed and / or driving route according to the traffic condition semantic information.

[0014] According to another aspect of this disclosure, an electronic device is also provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the information processing method described in any of the preceding claims by executing the executable instructions.

[0015] According to another aspect of this disclosure, a computer-readable storage medium is also provided, on which a computer program is stored, which, when executed by a processor, implements the information processing method described in any of the preceding claims.

[0016] According to another aspect of this disclosure, a computer program product is also provided, including a computer program that, when executed by a processor, implements the information processing method described in any of the above.

[0017] The information processing method provided in the embodiments of this disclosure is applied to an aerial platform. It acquires image data of a target traffic area using an onboard visual sensor; processes the image data using a target detection model and semantic processing algorithms to determine road condition semantic information; and sends the road condition semantic information to a target terminal so that the target terminal can make driving decisions based on the road condition semantic information. This disclosure leverages the high-altitude perspective advantage of the aerial platform, acquiring images through a visual sensor and fusing target detection and semantic processing algorithms to convert image data into road condition semantic information, which is then sent to a ground terminal. This not only overcomes the line-of-sight limitations of ground sensors and effectively eliminates blind spot risks to improve driving safety, but also significantly reduces communication bandwidth pressure and latency. This assists the target terminal in making more accurate and efficient driving decisions, effectively solving the technical problem of difficulty in providing forward-looking decision support under complex road conditions, thus restricting traffic efficiency and increasing safety hazards.

[0018] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0020] Figure 1 This diagram illustrates an information processing system structure according to an embodiment of the present disclosure; Figure 2 This diagram illustrates a flowchart of an information processing method applied to an airborne platform according to an embodiment of the present disclosure. Figure 3 This is a flowchart illustrating a specific example of an information processing method applied to an airborne platform according to an embodiment of the present disclosure; Figure 4 A flowchart illustrating another specific example of an information processing method applied to an airborne platform according to an embodiment of this disclosure is shown. Figure 5A flowchart illustrating another specific example of an information processing method applied to a target terminal according to an embodiment of the present disclosure; Figure 6 This diagram illustrates an information processing apparatus according to an embodiment of the present disclosure. Figure 7 This diagram illustrates an information processing apparatus according to an embodiment of the present disclosure. Figure 8 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0021] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0022] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0023] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0024] Figure 1 A schematic diagram of an exemplary application system architecture to which the information processing methods of the embodiments of this disclosure can be applied is shown. For example... Figure 1 As shown, the system architecture may include a target terminal 101, a network 102, and an air platform 103.

[0025] Network 102 is a medium used to provide a communication link between target terminal 101 and air platform 103, and may be a wireless network.

[0026] Optionally, the aforementioned wireless or wired networks use standard communication technologies and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to any combination of Local Area Networks (LANs), Metropolitan Area Networks (MANs), Wide Area Networks (WANs), mobile wireless networks, private networks, or Virtual Private Networks. In some embodiments, technologies and / or formats, including Hyper Text Markup Language (HTML), Extensible Markup Language (XML), etc., are used to represent data exchanged over the network. Furthermore, conventional encryption technologies such as Secure Socket Layer (SSL), Transport Layer Security (TLS), Virtual Private Networks (VPNs), and Internet Protocol Security (IPSec) can be used to encrypt all or some links. In other embodiments, custom and / or dedicated data communication technologies can be used to replace or supplement the aforementioned data communication technologies.

[0027] The target terminal 101 can be various electronic devices, including but not limited to in-vehicle smart terminals, in-vehicle central control screens / vehicle systems, head-up displays, in-vehicle navigation systems, personal mobile devices, mobile phones, wearable devices, augmented reality devices, virtual reality devices, etc.

[0028] Optionally, the application clients installed on different target terminals 101 may be the same, or clients of the same type of application based on different operating systems. Depending on the terminal platform, the specific form of the application client may also differ; for example, the application client may be a mobile phone client, an in-vehicle client, etc.

[0029] The aerial platform 103 can be any carrier with flight capability and visual perception capability, including but not limited to rotary-wing aircraft, fixed-wing aircraft, tethered drones, and hovering platforms.

[0030] For example, when the aforementioned aerial platform is a multi-rotor drone, it possesses maneuverability and flexibility, enabling it to hover over specific intersections and perform high-precision visual acquisition and semantic analysis of target terminals; when the aforementioned aerial platform is a tethered drone, it is connected to a ground control station via cable, providing a continuous power supply, allowing it to remain over key nodes for extended periods and transmit congestion data in real time; when the aforementioned aerial platform is a fixed-wing drone, it is suitable for long-distance patrols of highways or urban expressways, capable of quickly covering large road sections and transmitting traffic information to target terminals after aggregation.

[0031] In one example, an airborne platform acquires image data of a target traffic area using onboard visual sensors. The platform then processes the image data using a target detection model and semantic processing algorithms to determine traffic condition semantic information. This information is then transmitted to a target terminal. The target terminal receives the traffic condition semantic information and adjusts its speed and / or route accordingly. Sensitive personal information can be anonymized during the image data processing by the airborne platform or the target terminal.

[0032] In a specific example, the system deployment preparation first includes: UAV equipment selection and deployment: Six multi-rotor UAVs were selected, equipped with high-definition visual sensors, edge computing modules, and 5G vehicle-to-everything (V2X) communication modules. Based on the road alignment characteristics, the UAVs were initially deployed at six key monitoring points, one monitoring point every 2.5 kilometers, focusing on covering blind spots such as sharp bend entrances and uphill start points. The flight altitude was set at 80-100 meters to ensure unobstructed aerial views while avoiding interference with vehicle traffic. Vehicle-side equipment configuration: The test vehicles included 10 sedans and 5 trucks, all equipped with V2X receiver terminals. These terminals supported semantic communication protocols with the UAVs and provided road condition information display and driving decision prompts. Algorithm and model parameter settings: The vehicle detection model on the UAVs adopted a lightweight version of YOLOv8, which is compatible with the computing power of the edge computing module. The semantic compression algorithm was set to retain 95% of key information and compress redundant data at an 8:1 ratio to ensure accurate semantic information and efficient transmission.The implementation process then includes: road condition image acquisition, where the drone hovers and monitors along a preset flight path, with its visual sensor acquiring road condition images ahead at a frequency of 30 frames per second. During acquisition, key scenes such as "oncoming vehicles, road congestion, and lane occupancy" are captured, with the acquisition range covering a 500-meter section of road in front of its monitoring point. This coverage is sufficient to meet the time requirements for vehicles to make advance decisions. Semantic information extraction involves the drone's edge computing module running a vehicle detection model to process the acquired image data in real time, identifying key information such as "one truck is approaching from the opposite direction" and "the current lane is clear." Finally, a semantic compression algorithm is used to extract the semantic information. The raw image data is transformed into two concise semantic commands: "Truck approaching from the opposite direction on a sharp bend" and "Road ahead is clear." Redundant information such as background and non-critical objects are removed from the image. For semantic information transmission, the drone uses a 5G vehicle-to-everything (V2X) communication module to transmit the extracted semantic information with millisecond-level latency to the three vehicles closest to the monitoring point. Two cars are about to enter the sharp bend, and one truck is climbing an incline. The vehicle terminals display the semantic prompts in real time. For vehicle driving decision adjustment, the two cars about to enter the sharp bend automatically trigger deceleration prompts upon receiving the "Truck approaching from the opposite direction on a sharp bend" command. The drivers then adjust their driving decisions accordingly. The vehicle speed was reduced from 60km / h to 30km / h, and the vehicle moved to the right lane in advance to prepare for oncoming traffic. Trucks climbing hills, upon receiving the "road conditions clear ahead" instruction, adjusted their throttle output based on the vehicle's power status, maintaining a constant speed of 40km / h to avoid increased energy consumption due to frequent acceleration and deceleration. The drone's status was dynamically adjusted; when there were no vehicles on a 2km stretch of road corresponding to a monitoring point, the system issued a hibernation command after visual detection by the drone. The drone in that area slowly descended to a pre-set resting point beside the road, away from the driving lane. After entering a low-power hibernation mode, energy consumption was reduced. 70%; When a new vehicle enters the road section, the back-end management system detects the approaching vehicle through vehicle network signals and immediately sends a takeoff command. The drone takes off and resumes monitoring within 30 seconds; Drone deployment optimization: The system continuously analyzes vehicle distribution data within 1 hour through back-end algorithms, such as "the vehicle throughput rate on sharp bends is twice that on straight sections," and automatically optimizes the drone deployment location based on the analysis results: two drones originally deployed on straight sections are adjusted to sharp bend monitoring points with higher traffic volume, increasing the monitoring density in the sharp bend area to one drone per 1.25 kilometers, achieving comprehensive coverage of road condition information.

[0033] Those skilled in the art will know that Figure 1 The number of target terminals, networks, and air platforms shown is merely illustrative; any number of target terminals, networks, and air platforms can be used as needed. This disclosure does not limit this.

[0034] Figure 2This invention discloses a flowchart of an information processing method applied to an airborne platform, as shown in an embodiment of the present disclosure. Figure 2 As shown in the embodiments of this disclosure, the information processing method applied to an airborne platform includes the following steps: S202 collects image data of the target traffic area using its onboard visual sensors.

[0035] It should be noted that the aforementioned visual sensors can be sensing devices based on optical imaging principles. For example, these visual sensors may include, but are not limited to, devices with image acquisition capabilities such as cameras, stereo cameras, depth cameras, infrared cameras, or event cameras. Specifically, cameras are used to capture optical image information of the target traffic area; stereo cameras acquire parallax information of images through binocular or multi-view imaging principles, thereby calculating the depth data of the scene; depth cameras can directly output point cloud data or depth maps containing distance information; and infrared cameras provide thermal imaging or near-infrared images at night or in low-light environments to enhance the robustness of environmental perception. The aforementioned target traffic area can be an area dynamically determined according to a preset strategy or real-time needs. For example, the aforementioned target traffic area may include, but is not limited to, mountain roads, continuous tunnel groups, multi-level overpasses, or complex road sections with limited visibility. Within the target traffic area, at least one or more of the following traffic elements are included: lane lines, traffic lights, traffic signs, passing vehicles, pedestrians, non-motorized vehicles, or other road obstacles. This embodiment of the disclosure uses visual sensors to identify and track these elements within the area, thereby determining road condition semantic information (such as traffic light status, traffic density, average vehicle speed, etc.).

[0036] S204 uses a target detection model and semantic processing algorithm to process image data and determine road condition semantic information.

[0037] It should be noted that the aforementioned object detection model can refer to a computer vision model built on a deep learning architecture, used to identify specific traffic entities and their position coordinates in the image from input image data. For example, the object detection model can employ a single-stage detector (such as the YOLO detection model, SSD detection model, etc.) or a two-stage detector (such as the Faster R-CNN detection model, Mask R-CNN detection model, etc.). In this embodiment, the model is trained to identify at least one of the following traffic objects: vehicles: including cars, trucks, buses, motorcycles, etc.; pedestrians and non-motorized vehicles: including pedestrians, bicycles, electric vehicles, etc.; traffic facilities: including traffic lights, traffic signs, guardrails, zebra crossings, etc. The output of the model typically includes the category label of each detected object and the corresponding bounding box or mask, thereby achieving the localization and classification of key elements in the traffic scene. The aforementioned semantic processing algorithm can be a set of algorithms used to further analyze the scene meaning, relationships between objects, and dynamic states in the image data. The semantic processing algorithm, based on the preliminary detection results output by the object detection model, combines the global contextual information of the image to perform inference to determine traffic semantic information with business value. For example, the aforementioned semantic processing algorithms may include, but are not limited to: instance segmentation algorithms: accurately distinguishing different individuals of the same category (e.g., distinguishing two different vehicles driving side by side); lane line detection algorithms: identifying the geometry, direction, and type of lanes (e.g., solid lines, dashed lines, directional arrows); behavior recognition algorithms: determining whether vehicles illegally change lanes, stop, or drive against traffic, or whether pedestrians run red lights, by analyzing the motion trajectories of objects in consecutive frames; and density estimation algorithms: counting the number of vehicles or pedestrians in a specific area to calculate the congestion index. Through this algorithm, high-order traffic semantic information (e.g., red light on, congestion ahead, pedestrians crossing at intersections, etc.) can be extracted from raw pixel data.

[0038] S206, traffic condition semantic information is sent to the target terminal so that the target terminal can make driving decisions based on the traffic condition semantic information.

[0039] It should be noted that the aforementioned driving decision may refer to the target terminal generating corresponding control commands based on the received road condition semantic information through a preset method to control the target terminal's driving speed and / or driving route.

[0040] This disclosure leverages the high-altitude perspective of an aerial platform to acquire images via visual sensors and integrates target detection and semantic processing algorithms. The image data is then transformed into road condition semantic information and transmitted to ground terminals. This not only overcomes the line-of-sight limitations of ground sensors and effectively eliminates blind spot risks to improve driving safety, but also significantly reduces communication bandwidth pressure and latency. This assists target terminals in making more accurate and efficient driving decisions, effectively solving the technical problem of providing forward-looking decision support under complex road conditions, which in turn restricts traffic efficiency and increases safety hazards.

[0041] In one embodiment of this disclosure, such as Figure 3 As shown, the information processing method for an airborne platform provided in this embodiment of the disclosure further includes the following steps: S302 controls its hibernation and takeoff states based on the vehicle distribution. S3021, when no target terminal is detected, it falls and enters sleep mode; S3022, when a target terminal is detected, takes off to carry out operations.

[0042] This disclosure achieves an on-demand operation and maintenance mode by dynamically controlling the hibernation and takeoff states of the aerial platform based on vehicle distribution: when no target terminal is detected, it automatically descends into low-power hibernation, significantly reducing energy consumption caused by ineffective hovering and extending the equipment's battery life and mechanical lifespan; when a target terminal is detected, it quickly takes off to work, ensuring the real-time and accuracy of road condition information collection while effectively reducing the occupation of airspace resources and potential interference during non-working hours, thus achieving efficient and sustainable traffic monitoring.

[0043] In one embodiment of this disclosure, such as Figure 4 As shown, the information processing method for an airborne platform provided in this embodiment of the disclosure further includes the following steps: S402 adjusts its flight altitude and / or coordinates its start and stop with the real-time traffic conditions of the target traffic area.

[0044] For example, in response to the complex terrain and obstruction characteristics of mountain roads, multiple aerial platforms form an aerial platform cluster and adopt a distributed / centralized collaborative control method to schedule the aerial platform cluster to fill visual blind spots with the best attitude and position, ensuring full coverage capture of image data for the entire long-distance, highly undulating road section.

[0045] In one instance, a centralized scheduling system is employed. The aerial platforms report the perceived vehicle distribution to a cloud server. The cloud server then runs a multi-aerial platform deployment optimization algorithm and sends hibernation or wake-up commands to the aerial platforms to adjust the number of platforms deployed in the area. Specifically, when an aerial platform determines it can hibernate based on pre-detected real-time traffic conditions (e.g., extremely low vehicle density on the current road segment) or detects a coverage blind spot, it sends its status information (e.g., remaining battery power, current location) and environmental assessment information to the backend management device. Upon receiving reports from multiple aerial platforms, the backend management device runs a global optimization algorithm (e.g., differential evolution algorithm, virtual force algorithm) to calculate the optimal number and location distribution of aerial platforms in the current area. The backend then sends the corresponding hibernation or wake-up command to the corresponding aerial platform. Upon receiving the command, the aerial platform executes a landing hibernation or takeoff maneuver.

[0046] In another example, a distributed cluster collaboration is employed. Airborne platforms establish ad hoc network communication with other airborne platforms in the same area. Based on swarm intelligence algorithms, they negotiate with neighboring airborne platforms to determine task allocation strategies within the area and autonomously execute hibernation or takeoff maneuvers according to these strategies to adjust the deployment number of airborne platforms in the region. Specifically, based on distributed collaboration combining inter-aircraft communication with swarm intelligence, the airborne platform cluster possesses a high degree of autonomy, without relying on a central backend node. Airborne platforms interact with neighboring airborne platforms through onboard communication modules, sharing their detected road condition information and their own battery status. Based on reinforcement learning or consensus algorithms, the airborne platform cluster autonomously allocates tasks without a central node. For example, when multiple airborne platforms detect a road segment without vehicles for an extended period, the cluster, through a negotiation mechanism, decides to retain one airborne platform to continue monitoring, while the remaining platforms proactively land and hibernate. When a new vehicle enters, the hibernating airborne platform is awakened, or the cluster automatically dispatches nearby airborne platforms with sufficient battery power to provide support, achieving dynamic adaptive deployment.

[0047] In another instance, the edge computing nodes on the aerial platform combine historical traffic data to predict changes in traffic flow in the area over a future period. If it predicts that there will be no vehicles passing through the mountain road section in the next 10 minutes, the aerial platform requests to go into hibernation from the cluster or backend in advance; if it predicts that congestion is about to occur, it wakes up other aerial platforms in advance.

[0048] This disclosure achieves adaptive optimization of the spatial distribution of the aerial platform by dynamically adjusting the flight altitude or start / stop (take-off or grounding) status according to real-time traffic conditions, thereby significantly improving the integrity, accuracy and real-time performance of traffic monitoring data in adverse terrain.

[0049] Figure 5This illustration shows a flowchart of an information processing method applied to a target terminal according to an embodiment of the present disclosure, such as... Figure 5 As shown, the information processing method applied to the target terminal provided in this embodiment includes the following steps: S502 receives traffic semantic information sent by an airborne platform deployed in the target traffic area; S504, adjusts driving speed and / or driving route based on road condition semantic information.

[0050] This disclosure enables target terminals to overcome the limitations of their own sensor line of sight by receiving road condition semantic information from an airborne platform, and to perceive blind spot risks in complex terrains such as sharp bends and slopes in advance. This assists drivers or autonomous driving systems in making more accurate driving decisions such as deceleration and lane changing, improving the safety and efficiency of vehicles on complex road sections and realizing traffic management under air-ground collaboration.

[0051] In a specific example of this disclosure, adjusting the driving speed and / or driving route based on traffic semantic information includes: adjusting the driving speed according to a first preset condition when the traffic semantic information indicates that there are no vehicles ahead; and adjusting the driving route according to a second preset condition when the traffic semantic information indicates that there are vehicles ahead.

[0052] This disclosure achieves refined driving control and scene adaptability by processing road condition semantic information in a differentiated manner: when there are no vehicles ahead, the traffic efficiency is maximized based on the first preset conditions (such as road speed limit, road geometric features, etc.); when there are vehicles ahead, the driving route is adjusted based on the second preset conditions (such as relative speed, safe distance, lane occupancy, etc.) to avoid potential conflicts or optimize the following trajectory.

[0053] Based on the same inventive concept, this disclosure also provides two information processing devices, as described in the following embodiments. Since the principle by which these devices solve the problem is similar to that of the method embodiments described above, the implementation of these devices can refer to the implementation of the method embodiments described above, and repeated details will not be repeated.

[0054] Figure 6 This illustration shows a schematic diagram of an information processing device applied to an airborne platform according to an embodiment of the present disclosure, such as... Figure 6 As shown, the device includes: an image data acquisition module 61, a road condition semantic information determination module 62, and a road condition semantic information transmission module 63.

[0055] The image data acquisition module 61 is used to acquire image data of the target traffic area through the onboard visual sensor.

[0056] The road condition semantic information determination module 62 is used to process image data through a target detection model and a semantic processing algorithm to determine road condition semantic information.

[0057] The road condition semantic information sending module 63 is used to send road condition semantic information to the target terminal so that the target terminal can make driving decisions based on the road condition semantic information.

[0058] In one example of this disclosure, the aforementioned information processing device applied to an airborne platform further includes a status control module for controlling its own hibernation and takeoff states based on vehicle distribution; when no target terminal is detected, it descends and enters hibernation mode; when a target terminal is detected, it takes off to perform operations.

[0059] In one example of this disclosure, the aforementioned state control module is also used to adjust its own flight altitude and / or coordinate with the control of its own start and stop based on the real-time traffic conditions of the target traffic area.

[0060] It should be noted that the image data acquisition module 61, the road condition semantic information determination module 62, and the road condition semantic information sending module 63 mentioned above correspond to S202 to S206 in the method embodiment. The examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in the above method embodiment. It should be noted that the above modules, as part of the device, can be executed in a computer system such as a set of computer-executable instructions.

[0061] Figure 7 This illustration shows a schematic diagram of an information processing device applied to a target terminal according to an embodiment of the present disclosure, such as... Figure 7 As shown, the device includes an information receiving module 71 and a driving adjustment module 72.

[0062] Among them, the information receiving module 71 is used to receive traffic condition semantic information sent by an aerial platform deployed in the target traffic area.

[0063] The driving adjustment module 72 is used to adjust the driving speed and / or driving route based on road condition semantic information.

[0064] In one example of this disclosure, the driving adjustment module is further configured to adjust the driving speed according to a first preset condition when the road condition semantic information indicates that there are no vehicles ahead; and to adjust the driving route according to a second preset condition when the road condition semantic information indicates that there are vehicles ahead.

[0065] It should be noted that the information receiving module 71 and the driving adjustment module 72 mentioned above correspond to S502 to S504 in the method embodiment. The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above method embodiment. It should be noted that the above modules, as part of the device, can be executed in a computer system such as a set of computer-executable instructions.

[0066] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "system."

[0067] The following reference Figure 8 To describe an electronic device 800 according to such an embodiment of the present disclosure. Figure 8 The electronic device 800 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments disclosed herein.

[0068] like Figure 8 As shown, the electronic device 800 is manifested in the form of a general-purpose computing device. The components of the electronic device 800 may include, but are not limited to: at least one processing unit 810, at least one storage unit 820, and a bus 830 connecting different system components (including storage unit 820 and processing unit 810).

[0069] The storage unit stores program code that can be executed by the processing unit 810, causing the processing unit 810 to perform the steps described in the "Exemplary Methods" section above according to various exemplary embodiments of this disclosure.

[0070] For example, the processing unit 810 can perform the following steps in the above method embodiment: acquiring image data of the target traffic area through the onboard visual sensor; processing the image data through a target detection model and a semantic processing algorithm to determine road condition semantic information; and sending the road condition semantic information to the target terminal so that the target terminal can make driving decisions based on the road condition semantic information.

[0071] For example, the processing unit 810 can execute the following steps in the above method embodiment: control its own hibernation and takeoff states according to the vehicle distribution; when no target terminal is detected, it falls and enters hibernation mode; when a target terminal is detected, it takes off to work.

[0072] For example, the processing unit 810 can perform the following steps in the above method embodiment: adjust its own flight altitude and / or coordinate with the control of its own start and stop based on the real-time traffic conditions of the target traffic area.

[0073] For example, the processing unit 810 can perform the following steps in the above method embodiment: receiving traffic condition semantic information sent by an aerial platform deployed in the target traffic area; and adjusting the driving speed and / or driving route according to the traffic condition semantic information.

[0074] For example, the processing unit 810 can execute the following steps of the above method embodiment: when the road condition semantic information indicates that there are no vehicles ahead, adjust the driving speed according to the first preset condition; when the road condition semantic information indicates that there are vehicles ahead, adjust the driving route according to the second preset condition.

[0075] Storage unit 820 may include readable media in the form of volatile storage units, such as random access memory (RAM) 8201 and / or cache 8202, and may further include read-only memory (ROM) 8203.

[0076] The storage unit 820 may also include a program / utility 8204 having a set (at least one) of program modules 8205, such program modules 8205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0077] Bus 830 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0078] Electronic device 800 can also communicate with one or more external devices 840 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 800, and / or with any device that enables electronic device 800 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 850. Furthermore, electronic device 800 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 860. As shown, network adapter 860 communicates with other modules of electronic device 800 via bus 830. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 800, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0079] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0080] In particular, according to embodiments of this disclosure, the process described above with reference to the flowchart can be implemented as a computer program product, which includes a computer program that, when executed by a processor, implements the above-described information processing method.

[0081] In exemplary embodiments of this disclosure, a computer-readable storage medium is also provided, which may be a readable signal medium or a readable storage medium. A program product capable of implementing the methods described above is stored thereon. In some possible implementations, various aspects of this disclosure may also be implemented as a program product including program code, which, when run on a terminal device, causes the terminal device to perform the steps described in the "Exemplary Methods" section of this specification according to various exemplary embodiments of this disclosure.

[0082] More specific examples of computer-readable storage media in this disclosure may include, but are not limited to: electrical connections having 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 fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0083] In this disclosure, a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting a program for use by or in connection with an instruction execution system, apparatus, or device.

[0084] Optionally, the program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0085] In practical implementation, program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0086] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to embodiments of this disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0087] Furthermore, although the steps of the method in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0088] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this disclosure.

[0089] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. An information processing method, characterized in that, Applied to aerial platforms, including: Image data of the target traffic area is acquired through the onboard visual sensor; The image data is processed using a target detection model and a semantic processing algorithm to determine road condition semantic information; The traffic condition semantic information is sent to the target terminal so that the target terminal can make driving decisions based on the traffic condition semantic information.

2. The information processing method according to claim 1, characterized in that, The method further includes: Control its own hibernation and takeoff states based on the vehicle distribution; When no target terminal is detected, it will fall and enter sleep mode; When the target terminal is detected, take off to begin operations.

3. The information processing method according to claim 2, characterized in that, The method further includes: Based on the real-time traffic conditions of the target traffic area, adjust its own flight altitude and / or coordinate with the control of its own start and stop.

4. An information processing method, characterized in that, Applied to target terminals, including: Receive traffic semantic information sent by an aerial platform deployed in the target traffic area; Adjust driving speed and / or driving route based on the road condition semantic information.

5. The information processing method according to claim 4, characterized in that, The step of adjusting the driving speed and / or driving route based on the road condition semantic information includes: When the road condition semantic information indicates that there are no vehicles ahead, the driving speed is adjusted according to the first preset condition; When the road condition semantic information indicates that there is a vehicle ahead, the driving route is adjusted according to the second preset condition.

6. An information processing system, characterized in that, Including aerial platforms and target terminals; The aerial platform acquires image data of the target traffic area using onboard visual sensors; The aerial platform processes the image data using a target detection model and semantic processing algorithm to determine road condition semantic information; The aerial platform sends the traffic condition semantic information to the target terminal; The target terminal receives the traffic condition semantic information and adjusts its driving speed and / or driving route based on the traffic condition semantic information.

7. An information processing device, characterized in that, Applied to aerial platforms, including: The image data acquisition module is used to acquire image data of the target traffic area through the onboard visual sensor; The road condition semantic information determination module is used to process the image data through a target detection model and a semantic processing algorithm to determine the road condition semantic information; The traffic condition semantic information sending module is used to send the traffic condition semantic information to the target terminal so that the target terminal can make driving decisions based on the traffic condition semantic information.

8. An information processing device, characterized in that, Applied to target terminals, including: The information receiving module is used to receive traffic condition semantic information sent by an aerial platform deployed in the target traffic area; The driving adjustment module is used to adjust the driving speed and / or driving route based on the road condition semantic information.

9. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the information processing method of any one of claims 1 to 5 by executing the executable instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the information processing method according to any one of claims 1 to 5.

11. A computer program product, comprising: A computer program or instruction, characterized in that, when executed by a processor, the computer program or instruction implements the information processing method according to any one of claims 1 to 5.

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