Method and device for adjusting resolution related to occupancy prediction

Abstract

Provided are a method by which a first device performs wireless communication and a device supporting same. The method may comprise: a step for performing a first occupancy prediction on at least one object; a step in which a first device receives a message including information related to a state; a step for changing a first resolution related to the first occupancy prediction to a second resolution on the basis of the information related to the state; and a step for performing a second occupancy prediction on the at least one object on the basis of the second resolution.

Description

Method and device for adjusting resolution related to occupancy prediction

[0001] The present disclosure relates to a wireless communication system.

[0002] 5G NR, the successor to LTE (long-term evolution), is a new clean-slate mobile communications system characterized by high performance, low latency, and high availability. 5G NR can utilize all available spectrum resources, from low-frequency bands below 1 GHz, mid-frequency bands between 1 GHz and 10 GHz, and high-frequency (millimeter wave) bands above 24 GHz.

[0003] The 6G (wireless communication) system aims to achieve (i) very high data rates per device, (ii) a very large number of connected devices, (iii) global connectivity, (iv) very low latency, (v) low energy consumption for battery-free Internet of Things (IoT) devices, (vi) ultra-reliable connectivity, and (vii) connected intelligence with machine learning capabilities. The vision of the 6G system can be divided into four aspects: intelligent connectivity, deep connectivity, holographic connectivity, and ubiquitous connectivity, and the 6G system can satisfy the requirements as shown in Table 1 below. For example, Table 1 can represent an example of the requirements of a 6G system.

[0004] Per device peak data rate 1 Tbps E2E latency 1 ms Maximum spectral efficiency 100 bps / Hz Mobility support Up to 1000 km / hr Satellite integration Fully AI Fully autonomous vehicle Fully XR Fully haptic communication Fully

[0005] In one embodiment, a method for performing wireless communication by a first device is provided. The method may include: performing a first occupancy prediction for at least one object; receiving, by the first device, a message including information related to a state; changing a first resolution related to the first occupancy prediction to a second resolution based on the information related to the state; and performing a second occupancy prediction for the at least one object based on the second resolution.

[0006] In one embodiment, a first device configured to perform wireless communication is provided. The first device may include at least one transceiver; at least one processor; and at least one memory coupled to the at least one processor and storing instructions. For example, the instructions, when executed by the at least one processor, may cause the first device to: perform a first occupancy prediction for at least one object; receive a message including information related to a state; change a first resolution related to the first occupancy prediction to a second resolution based on the information related to the state; and perform a second occupancy prediction for the at least one object based on the second resolution.

[0007] In one embodiment, a processing device configured to control a first device is provided. The processing device comprises at least one processor; and at least one memory coupled to the at least one processor and storing instructions, wherein the instructions, when executed by the at least one processor, cause the first device to: perform a first occupancy prediction for at least one object; cause the first device to receive a message including information related to a state; change a first resolution related to the first occupancy prediction to a second resolution based on the information related to the state; and perform a second occupancy prediction for the at least one object based on the second resolution.

[0008] In one embodiment, a non-transitory computer-readable storage medium having instructions recorded thereon is provided. The instructions, when executed, cause a first device to: perform a first occupancy prediction for at least one object; cause the first device to receive a message including information related to a state; change a first resolution associated with the first occupancy prediction to a second resolution based on the information related to the state; and perform a second occupancy prediction for the at least one object based on the second resolution.

[0009] FIG. 1 illustrates a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure.

[0010] FIG. 2 illustrates an electromagnetic spectrum according to one embodiment of the present disclosure.

[0011] FIG. 3 illustrates an example of a voxel-based resolution according to one embodiment of the present disclosure.

[0012] FIG. 4 illustrates an occupancy prediction method of an autonomous driving system according to one embodiment of the present disclosure.

[0013] FIG. 5 illustrates geometric structure-based object detection according to one embodiment of the present disclosure.

[0014] FIG. 6 illustrates a method for performing object occupancy prediction based on object information according to precision, according to one embodiment of the present disclosure.

[0015] FIG. 7 illustrates a method for a first device to perform wireless communication according to one embodiment of the present disclosure.

[0016] FIG. 8 illustrates a method for a second device to perform wireless communication according to one embodiment of the present disclosure.

[0017] FIG. 9 illustrates a communication system (1) according to one embodiment of the present disclosure.

[0018] FIG. 10 illustrates a wireless device according to an embodiment of the present disclosure.

[0019] FIG. 11 illustrates a signal processing circuit for a transmission signal according to one embodiment of the present disclosure.

[0020] FIG. 12 illustrates a wireless device according to one embodiment of the present disclosure.

[0021] FIG. 13 illustrates a mobile device according to one embodiment of the present disclosure.

[0022] FIG. 14 illustrates a vehicle or autonomous vehicle according to one embodiment of the present disclosure.

[0023] As used herein, "A or B" can mean "only A," "only B," or "both A and B." In other words, as used herein, "A or B" can be interpreted as "A and / or B." For example, as used herein, "A, B or C" can mean "only A," "only B," "only C," or "any combination of A, B and C."

[0024] As used herein, a slash ( / ) or a comma can mean "and / or." For example, "A / B" can mean "A and / or B." Accordingly, "A / B" can mean "only A," "only B," or "both A and B." For example, "A, B, C" can mean "A, B, or C."

[0025] In this specification, "at least one of A and B" may mean "only A", "only B" or "both A and B". Additionally, in this specification, the expressions "at least one of A or B" or "at least one of A and / or B" may be interpreted identically to "at least one of A and B".

[0026] Additionally, in this specification, “at least one of A, B and C” can mean “only A,” “only B,” “only C,” or “any combination of A, B and C.” Additionally, “at least one of A, B or C” or “at least one of A, B and / or C” can mean “at least one of A, B and C.”

[0027] Additionally, parentheses used herein may mean "for example." Specifically, when indicated as "control information (PDCCH)", "PDCCH" may be proposed as an example of "control information." In other words, "control information" in this specification is not limited to "PDCCH," and "PDCCH" may be proposed as an example of "control information." Furthermore, even when indicated as "control information (i.e., PDCCH)", "PDCCH" may be proposed as an example of "control information."

[0028] In the following explanation, ‘when, if, in case of’ can be replaced with ‘based on’.

[0029] Technical features individually described in a single drawing in this specification may be implemented individually or simultaneously.

[0030] In this specification, higher layer parameters may be parameters that are set for the terminal, preset, or predefined. For example, a base station or network may transmit higher layer parameters to the terminal. For example, higher layer parameters may be transmitted via radio resource control (RRC) signaling or medium access control (MAC) signaling.

[0031] In this specification, "configured or defined" may be interpreted as being configured or preset to a device through predefined signaling (e.g., SIB, MAC, RRC) from a base station or network. In this specification, "configured or defined" may be interpreted as being preset to a device.

[0032] The technology proposed in this specification can be used in various wireless communication systems such as CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), and SC-FDMA (single carrier frequency division multiple access). CDMA can be implemented with wireless technologies such as UTRA (universal terrestrial radio access) or CDMA2000. TDMA can be implemented with wireless technologies such as GSM (global system for mobile communications) / GPRS (general packet radio service) / EDGE (enhanced data rates for GSM evolution). OFDMA can be implemented with wireless technologies such as IEEE (Institute of Electrical and Electronics Engineers) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX), IEEE 802-20, E-UTRA (evolved UTRA), LTE (long term evolution), and 5G NR.

[0033] The technology proposed in this specification can be implemented with 6G wireless technology and applied to various 6G systems. For example, 6G systems can have key factors such as enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), artificial intelligence (AI) integrated communication, tactile internet, high throughput, high network capacity, high energy efficiency, low backhaul and access network congestion, and enhanced data security.

[0034] Figure 1 illustrates a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure. The embodiment of Figure 1 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0035] New network characteristics in 6G may include:

[0036] - Satellite integrated network

[0037] - Connected Intelligence: Unlike previous generations of wireless communication systems, 6G is revolutionary, upgrading the wireless evolution from "connected objects" to "connected intelligence." AI can be applied at every stage of the communication process (or at every signal processing step, as described below).

[0038] - Seamless integration of wireless information and energy transfer

[0039] - Ubiquitous super 3D connectivity: Access to networks and core network functions of drones and very low Earth orbit satellites will create super 3D connectivity in 6G ubiquitous.

[0040] Some general requirements for the new network characteristics of 6G, such as the above, may be as follows:

[0041] - small cell networks

[0042] - Ultra-dense heterogeneous network

[0043] - High-capacity backhaul

[0044] - Radar technology integrated with mobile technology: High-precision localization (or location-based services) through communications is a key feature of 6G wireless communication systems. Therefore, radar systems will be integrated with 6G networks.

[0045] - Softwarization and virtualization

[0046] Below, the core implementation technologies of the 6G system are described.

[0047] - Artificial Intelligence: Incorporating AI into communications can streamline and improve real-time data transmission. AI can use numerous analytics to determine how complex target tasks should be performed. This means AI can increase efficiency and reduce processing delays. Time-consuming tasks such as handovers, network selection, and resource scheduling can be performed instantly using AI. AI can also play a crucial role in machine-to-machine (M2M), machine-to-human, and human-to-machine communications. Furthermore, AI can facilitate rapid communication in brain-computer interfaces (BCIs). AI-based communication systems can be supported by metamaterials, intelligent structures, intelligent networks, intelligent devices, intelligent cognitive radios, self-sustaining wireless networks, and machine learning.

[0048] - THz communication (terahertz communication): Data rates can be increased by increasing the bandwidth. This can be achieved by utilizing sub-THz communication with a wide bandwidth and applying advanced massive MIMO technology. THz waves, also known as sub-millimeter waves, typically refer to the frequency range between 0.1 THz and 10 THz with corresponding wavelengths ranging from 0.03 mm to 3 mm. The 100 GHz to 300 GHz band (Sub-THz band) is considered a major portion of the THz band for cellular communications. Adding the Sub-THz band to the mmWave band will increase the capacity of 6G cellular communications. Among the defined THz bands, 300 GHz to 3 THz lies in the far infrared (IR) frequency band. While part of the optical band, the 300 GHz to 3 THz band lies at the boundary of the optical band, immediately following the RF band. Therefore, this 300 GHz to 3 THz band exhibits similarities to RF. Figure 2 illustrates the electromagnetic spectrum according to one embodiment of the present disclosure. The embodiment of FIG. 2 can be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted. Key characteristics of THz communications include (i) widely available bandwidth to support very high data rates, and (ii) high path loss at high frequencies (highly directional antennas are essential). The narrow beam width generated by the highly directional antenna reduces interference. The small wavelength of THz signals allows for a much larger number of antenna elements to be integrated into devices and BSs operating in this band. This enables the use of advanced adaptive array techniques to overcome range limitations.

[0049] - Large-scale MIMO technology

[0050] - Hologram beamforming (HBF)

[0051] - Optical wireless technology

[0052] - Free-space optical transmission backhaul network (FSO backhaul network)

[0053] - Quantum communication

[0054] - Cell-free communication

[0055] - Integration of wireless information and power transmission

[0056] - Integration of wireless communication and sensing

[0057] - Integrated access and backhaul network

[0058] - Big data analysis

[0059] - Reconfigurable intelligent surface

[0060] - metaverse

[0061] - Block chain

[0062] Unmanned aerial vehicles (UAVs): UAVs, or drones, will be a key element in 6G wireless communications. In most cases, high-speed data wireless connectivity can be provided using UAV technology. Base stations (BSs) can be installed on UAVs to provide cellular connectivity. UAVs may offer specific capabilities not found in fixed BS infrastructure, such as easy deployment, robust line-of-sight links, and controlled mobility. During emergencies such as natural disasters, deploying terrestrial communications infrastructure is not economically feasible and sometimes cannot provide services in volatile environments. UAVs can easily handle these situations. UAVs will become a new paradigm in wireless communications. This technology facilitates three fundamental requirements for wireless networks: enhanced mobile broadband (eMBB), URLLC, and mMTC. UAVs can also support various purposes, such as enhancing network connectivity, fire detection, disaster emergency services, security and surveillance, pollution monitoring, parking monitoring, and accident monitoring. Therefore, UAV technology is recognized as one of the most important technologies for 6G communications.

[0063] - Advanced air mobility (AAM): AAM is a higher concept than urban air mobility (UAM), which is an air transportation method available in urban areas, and can refer to a means of transportation that includes movement between regional hubs as well as urban areas.

[0064] - Autonomous driving (self-driving): V2X (vehicle to everything), a key element in building autonomous driving infrastructure, can be a technology that allows cars to communicate and share with various elements on the road for autonomous driving, such as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless communication. Fast transmission speeds and low-latency technologies are essential to maximize autonomous driving performance and ensure high safety. Furthermore, in the future, autonomous driving will go beyond simply providing warnings or guidance messages to drivers and may require active intervention in vehicle operation and direct control of the vehicle in dangerous situations. To this end, the amount of information that needs to be transmitted and received may become enormous, so 6G is expected to maximize autonomous driving with faster transmission speeds and lower latency than 5G.

[0065] Meanwhile, in relation to V2X communication, in radio access technology (RAT) prior to NR, methods for providing safety services based on V2X messages such as Basic Safety Message (BSM), Cooperative Awareness Message (CAM), and Decentralized Environmental Notification Message (DENM) were mainly discussed. For example, V2X messages may include location information, dynamic information, attribute information, etc. For example, a terminal may transmit a CAM of a periodic message type and / or a DENM of an event triggered message type to another terminal.

[0066] For example, a CAM may include basic vehicle information such as dynamic vehicle status information such as direction and speed, static vehicle data such as dimensions, external lighting conditions, or route history. For example, a terminal may broadcast a CAM, and the latency of the CAM may be less than 100 ms. For example, in the event of an emergency such as a vehicle breakdown or accident, a terminal may generate a DENM and transmit it to other terminals. For example, all vehicles within the transmission range of the terminal may receive the CAM and / or DENM. In this case, the DENM may have a higher priority than the CAM.

[0067] Additionally, with regard to V2X communications, various V2X scenarios are being proposed in NR. For example, various V2X scenarios may include vehicle platooning, advanced driving, extended sensors, and remote driving.

[0068] For example, based on vehicle platooning, vehicles can dynamically form groups and move together. For example, to perform platoon operations based on vehicle platooning, vehicles in the group can receive periodic data from the lead vehicle. For example, vehicles in the group can use this periodic data to narrow or widen the gap between vehicles.

[0069] For example, based on improved driving, vehicles can become semi-autonomous or fully automated. For example, each vehicle can adjust its trajectories or maneuvers based on data acquired from local sensors of nearby vehicles and / or nearby logical entities. Furthermore, for example, each vehicle can share driving intentions with nearby vehicles.

[0070] For example, based on extended sensors, raw data, processed data, or live video data acquired through local sensors can be exchanged between vehicles, logical entities, pedestrian terminals, and / or V2X application servers. Thus, for example, a vehicle can perceive its environment better than it can perceive using its own sensors.

[0071] For example, based on remote driving, a remote driver or V2X application can operate or control the remote vehicle for people who cannot drive or for remote vehicles located in hazardous environments. For example, in cases where the route is predictable, such as public transportation, cloud computing-based driving can be utilized to operate or control the remote vehicle. Additionally, access to a cloud-based back-end service platform, for example, can be considered for remote driving.

[0072] Meanwhile, a method to specify service requirements for various V2X scenarios, such as vehicle platooning, enhanced driving, expanded sensors, and remote driving, is being discussed in NR-based V2X communication.

[0073] Meanwhile, numerous autonomous driving technology companies, including Tesla, Waymo, NVIDIA, and 42dot, are currently developing their technology through various experiments and trial and error. In particular, Tesla, a leading technology company, has proposed an occupancy network. Instead of distinguishing between dynamic and static obstacles, as is the case with conventional methods, the network divides the space into grids or voxels from a bird's-eye view (e.g., BEV (bird's-eye view)) and implements autonomous driving technology based on occupancy values ​​and occupancy flows (or motion flow vectors). For example, this network cannot completely resolve the problem of incorrectly recognizing objects such as people in costume, vehicle luggage, or bicycles. Furthermore, even if a fixed object on the road, such as a fence, is mistakenly recognized as an obstacle, it still needs to be avoided if it flies in. Therefore, recognizing all objects as obstacles and avoiding them may be more reasonable in terms of autonomous driving performance and processing speed than identifying object classes.

[0074] To address autonomous driving performance, occlusion, and distance-dependent resolution issues, it may be reasonable to divide space into grids (e.g., 2D) or voxels (e.g., 3D) and generate occupancy maps for each area. However, while larger grids or voxels increase spatial and resource efficiency, their resolution decreases, potentially reducing spatial positioning accuracy. Furthermore, decreasing the grid or voxel size to improve positioning accuracy increases computational complexity and reduces processing speed. Therefore, it is necessary to set the grid or voxel size appropriately small for locations or times where collisions are expected. Furthermore, even with optimal grid or voxel sizes, the need for collision avoidance may increase depending on the situation, space, vehicle speed, or the movement of VRUs such as people and bicycles. Therefore, rather than uniformly dividing grids or voxels into units of a single size, there is a need to standardize data units into variable sizes based on precise data to ensure traffic safety and efficiency.

[0075] In summary, most leading autonomous driving companies are utilizing occupancy prediction technology, but they lack consideration or preparation for occupancy prediction resolution from a communication perspective, such as V2X. Furthermore, in leading technologies such as digital twin, on-device AI, and federated learning, each RU (road user) and server (e.g., Edge / Cloud / RSU, etc.) may have different occupancy prediction resolutions, and technical considerations may be needed to dynamically adjust or synthesize different occupancy prediction resolutions during federated learning or data aggregation. Furthermore, the reasons for having or dynamically adjusting different occupancy prediction resolutions for each entity (e.g., RU / Edge / Cloud / RSU, etc.) may be as follows. For example, each entity may have different available system resources, and there may be situations where a trade-off between accuracy and performance may be necessary depending on the communication network conditions, such as network bandwidth. Alternatively, there may be situations where the resolution (e.g., precision) of the occupancy map needs to be increased or performance prioritized based on various circumstances and policies, such as traffic congestion, speed limits, or accident warning points. By dynamically adjusting the precision of the occupancy map, traffic safety can be ensured with optimal or optimal precision based on available system resources or communication network conditions.

[0076] The present disclosure proposes a method and a device supporting the method for dynamically adjusting the resolution or granularity of object classification for areas or viewpoints requiring relatively high precision in an autonomous driving system or device utilizing object recognition information or occupancy maps. Furthermore, the present disclosure proposes a V2X complementary technology for transmitting recognized object classification or occupancy status values.

[0077] For example, the resolution or grid unit size of object classification can typically be grid (e.g., 2D) or voxel (e.g., 3D) units, and can take the form of upscaling to high resolution (or splitting into smaller sizes) or downscaling to low resolution (or merging into larger sizes).

[0078] FIG. 3 illustrates an example of voxel-based resolution according to an embodiment of the present disclosure. The embodiment of FIG. 3 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0079] Referring to Fig. 3, Fig. 3 (a) shows a low-resolution voxel, and Fig. 3 (b) shows a high-resolution voxel.

[0080] For example, assuming that an object detection system utilizing artificial intelligence recognition technology, a pixel-level object classification system (e.g., a system applying segmentation), an occupancy map (e.g., a system using an occupancy value, an occupancy flow, or a motion vector), or a signal transmission and reception system indicating an occupancy status is used, the occupancy value here may simply be a value indicating whether an object occupies a corresponding location, or may be a value indicating an object classification value or an attribute value, and the occupancy flow or motion vector may mean a vector value expressing the direction and speed in which an object at the corresponding location moves.

[0081] For example, V2X cognitive augmentation technology can be applied to the current ETSI (European Telecommunications Standards Institute) ITS (Intelligent Transport System) or SAE (Society of Automotive Engineers) standard CPM (Collective Perception Message) / SDSM (Sensor Data Sharing Message), and can appropriately upscale / downscale information into the form of polygonal or polyhedral reference points or occupancy areas based on a rectangular bounding box in the existing object recognition system and transmit it. For example, even in the case of a geometry structure such as a polygon or an occupancy map composed of a set of only a few points, it is possible to derive an accurate region of interest (e.g., ROI (Region Of Interest) or AOI (Area Of Interest)) rather than a simple rectangular shape. Therefore, for example, if the region of interest information can be appropriately included in the data container of CPM / SDSM, the existing V2X cognitive information can be improved.

[0082] For example, to ensure compatibility with existing systems for improved segmentation, a technology may be required to convert maps in the form of polygons or occupancy maps into rectangular 2D bounding boxes. This conversion can be accomplished, for example, through 2D-to-3D transformation or projection techniques. For example, the conversion into a rectangular shape could be based on the median, average, or maximum / minimum values ​​of the points forming the polygon's outline.

[0083] For example, in accordance with traffic safety policies, for small but fast-moving objects that are difficult to detect and track, a method of precisely converting the grid or voxel into a small size, including the surrounding area, can be applied. Or, for example, in the case of pedestrians with large direction changes, trajectory prediction is difficult, so even if they move slowly, occupancy or trajectory prediction can be performed by precisely subdividing the grid or voxel size. For example, since these safety policies are important for traffic safety, it is necessary to minimize delays in the prediction or message transmission process. Therefore, in addition to object class map information at the pixel, voxel, or vector level, information on the precision or resolution required for each unit can be added to the existing V2X message and transmitted.

[0084] For example, in object recognition systems such as object detection or segmentation, traffic congestion can be predicted simply by the number of detected objects, and the grid or voxel precision can be adjusted based on the number of objects. Alternatively, for example, the precision can be adjusted based on the size of the object detection area.

[0085] For example, if values ​​representing occupancy, etc. are used, traffic congestion can be assumed to be high through occupancy density, and if occupancy flow is used, a grid or voxel with higher accuracy or resolution can be applied if collisions are expected through relative velocity and orientation. For example, predicted occupancy values, occupancy flow, or information important to traffic safety such as traffic congestion, occupancy density, and collision risk can be transmitted through CAM (Cooperative Awareness Message), DENM (Decentralized Environmental Notification Message), CPM (Collective Perception Message), SPaT (Signal Phase and Timing) / MAP, MAPEM (MAP Extended Message) of the ETSI ITS standard, or BSM (Basic Safety Message), PSM (Pedestrian Safety Message), SDSM (Sensor Data Sharing Message), MAPEM (MAP Extended Message) of the SAE standard.

[0086] For example, in the case of autonomous driving systems or devices that do not use data such as occupancy values ​​and occupancy flows, different sized grids or voxels can be applied depending on the region / time point by predicting areas of roads or intersections where collisions should be prepared for or traffic congestion based on road lane topology or V2X messages. For example, traffic congestion or collision risk situations can be predicted by referring to the traffic condition information of the DENM situation container of the ETSI ITS standard.

[0087] FIG. 4 illustrates a method for predicting occupancy in an autonomous driving system, according to one embodiment of the present disclosure. The embodiment of FIG. 4 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0088] Referring to FIG. 4, the method proposed in the present disclosure can be applied based on occupancy prediction technology utilized in a general autonomous driving system.

[0089] For example, in step S410, data may be input from sensors such as camera vision, radar, lidar, and ultrasonic. For example, in step S420, the input sensor data may be converted into a bird's-eye view (e.g., BEV (bird's-eye view)) or may undergo a preprocessing process for sensor fusion. For example, sensor fusion, which integrates data from various types of sensors or multiple sensors of the same type, may be required, and it may be common to perform a perspective transform process to a BEV perspective to facilitate such integration. For example, in step S430, a 3D reconstruction technology that reconstructs a 3D image from multiple 2D still images or an AI technology such as NeRF (Neural Radiance Field) may be utilized. For example, Tesla vehicles are currently implementing autonomous driving technology by integrating 360-degree forward data into the BEV through eight 1.2-megapixel cameras.

[0090] For example, in step S450, the sensor input data converted to the BEV viewpoint may be generated as an Occupancy Grid Map (OGM) that expresses occupancy, etc. based on a grid or voxel of grid units, or occupancy values ​​and occupancy flow data may be obtained through a 3D occupancy prediction process. For example, generating an occupancy grid map can be commonly utilized in robotics autonomous driving technology, and vehicle autonomous driving can also be implemented using a high-definition map (HD map). However, since, for example, a high-definition map requires continuous updating and must be secured in advance, real-time autonomous driving can be generally implemented through occupancy prediction.

[0091] Meanwhile, the method for providing dynamic occupancy prediction information proposed in the present disclosure is as follows. For example, in step S440, geographical information can be acquired in advance through V2X, or traffic condition information or communication bandwidth condition information can be received. And, based on this, segmentation of specific time points and points can be implemented in step 1. Alternatively, for example, the occupancy prediction value calculated in step 1 can be segmented in step 2. Alternatively, for example, in step S460, upscaling and downscaling can be variably performed by further dividing grid units according to the situation, or integrating them through methods such as normalization. For example, as an example of information that can be acquired in advance through V2X, traffic congestion or collision risk situations can be predicted by referring to the Road Lane Topology (RLT) service of the ETSI ITS standard or the traffic condition information of the DENM situation container.

[0092] For example, V2X prior information can be traffic congestion situations, situations / times when precision is required, or communication channel / bandwidth situations (e.g., CBR used in Decentralized Congestion Control (DCC) defined in the ETSI ITS standard, or CBR values ​​used in 3GPP-based C-V2X (e.g., LTE / NR V2X) congestion control), or a combination of these. For example, in a situation where high precision is required and high utilization of the communication channel / bandwidth is detected at the same time, occupancy prediction can be performed in a way that sets the highest level of optimal precision within the available communication resources by defining situation-specific measurement elements and setting weights for each element. Specifically, for example, when the congestion level of a wireless communication resource is above a (pre-set) certain threshold, the amount of data generated can be prevented from increasing excessively by limiting the minimum grid / voxel size that can be set / adjusted. For example, in steps S440 and S450, when the congestion level is higher than a certain threshold (set in advance), the minimum grid / voxel size that can be set / adjusted can be set larger (than when the congestion level is lower than the threshold), and in this case, the terminal / RSU, etc. can make a final decision on the grid / voxel size by considering other criteria (e.g., traffic congestion situation or speed of transmitting / receiving terminals, etc.) within the minimum grid / voxel size allowed according to the congestion level.

[0093] For example, as mentioned in the proposal of the present disclosure, partial upscaling can be performed for small or fast objects that are difficult to detect and track, and more accurate occupancy prediction information can be provided through upscaling for pedestrians or bicycles that are difficult to predict paths and vulnerable to traffic safety. For example, in step S470, information according to the detection and / or tracking technique proposed in the present disclosure can be provided by utilizing a new protocol / message and / or an existing standardized message (e.g., DENM, RSM, CPM, or SDSM, etc.) and / or a combination of existing standard messages, a combination of new messages, or a combination of existing standard messages and new messages.

[0094] For example, complementary technologies for V2X perception can be applied to the current ETSI ITS or SAE CPM / SDSM standards. For example, in the CPM standard, the Perceived Object Container and Free Space Addendum Container each have a size of 128 bits, which may be insufficient for transmitting complex information such as pixel-level segments. In this case, reference point or free space area information can be transmitted, for example, through downscaling of grid or voxel information.

[0095] FIG. 5 illustrates geometric structure-based object detection according to an embodiment of the present disclosure. The embodiment of FIG. 5 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0096] Referring to Fig. 5, Fig. 5(a) shows an example of detecting an object based on the geometric structure of a rectangular bounding box. Fig. 5(b) shows an example of detecting an object based on the geometric structure of a set of four points. Fig. 5(c) shows an example of detecting an object based on the geometric structure of a set of 12 points.

[0097] For example, rather than simply expressing a recognized object as a rectangular bounding box (see (a) of Fig. 5), even if it is expressed as a polygon consisting of only a few points (see (b) and (c) of Fig. 5), the area or reference point where the recognized object is located can be expressed much more accurately. Therefore, even if downscaling is performed, reference point or area information in the shape of a polygon or polyhedron may be more meaningful in terms of ensuring traffic safety than a general rectangular bounding box.

[0098] For example, the occupancy prediction process is typically performed by updating according to sensor input, but as in the embodiment of FIG. 4 described above, multiple V2X prior information receptions and upscaling / downscaling of occupancy predictions may be performed for sensor inputs. For example, an example where upscaling to high resolution is required is when there are many occupied values ​​or when the occupancy density is high. Conversely, when the network performance is expected to deteriorate due to an event or sports game being expected to be crowded, or when there are too many objects expected to deteriorate recognition performance, downscaling to a low resolution can be applied.

[0099] The new or additional messages / data elements / data frames proposed in the present disclosure can be defined as examples in Table 2 below. For example, with the operations and methods proposed in the present disclosure, additional information regarding occupancy prediction (e.g., VRU prediction, traffic congestion, small objects, fast objects, bandwidth limitations, high occupancy density, dynamic occupancy prediction application, distance from road, etc.) can be included in the message.

[0100] Descriptive Name occupancyPredictionSchemeType ASN.1 representation OccupancyPredictionSchemeType : := INTEGER {undefined(0),VeryVRU(1),VRU(2),NotVRU(3),HighCongestion(11),NormalCongestion(12),LowCongestion(13),SmallObject(21),FastObject(22),NormalObject(23),HighBandwidth(31),LimitedBandwidth(33),HighOccupancyDensity(41),NormalOccupancyDensity(42),LowOccupancyDensity(43),CloseToRoad(51),FarFromRoad(53),DynamicPredictionRequired(60)}(0..63) Definition Describes the type of dynamic occupancy prediction scheme. It can be set to one of the above values, or it can be set to a higher value to set a type with different characteristics. If the value is set to 7, the scheme type value can be set to variable depending on the situation. Unit-

[0101] For example, if values ​​representing occupancy, etc. are used, the degree of occupancy density can be used to assume high traffic congestion, and if occupancy flow is used, if collisions are expected through position, orientation, and velocity information, a grid or voxel, which is an object occupancy unit with higher accuracy or resolution, can be dynamically applied. Or, for example, it can be data that applies a geometric structure of a set consisting of a larger number of points.

[0102] For example, situational information important to traffic safety, such as predicted bounding boxes or geometric structure data, occupancy values, occupancy flows, traffic congestion, insufficient communication bandwidth, occupancy density, and collision risk prediction in the above-described embodiments, may be transmitted and utilized in various ways through CAM, DENM, CPM, SPaT / MAP, MAPEM, etc. of the ETSI ITS standard, or BSM, PSM, SDSM, MAPEM, etc. of the SAE standard, or a combination thereof.

[0103] FIG. 6 illustrates a method for performing object occupancy prediction based on object information according to precision, according to an embodiment of the present disclosure. The embodiment of FIG. 6 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0104] Referring to Fig. 6, when V2N2X (vehicle-to-network-to-everything) unicast communication is possible, when a receiving vehicle recognizes a traffic or communication situation and requests the required precision, a server or RSU can implement a service that transmits information with the precision that meets the request. For example, the part indicated in parentheses in Fig. 6 may be omitted, and the flow of Fig. 6 may be changed depending on the entity that provides the service or produces the information, such as the server, RSU, vehicle, or terminal. For example, for this service, the server or RSU may notify what precision of service is possible, or may inquire what precision of service is provided by the vehicle or terminal.

[0105] As an example of the present disclosure, an intersection section in a city center may be a frequent accident site due to the presence of a mixture of vehicles and pedestrians, and the frequent entry of personal mobility devices such as kickboards at high speeds from unpredictable directions. In this case, for example, a vehicle (e.g., an autonomous vehicle) or road infrastructure (e.g., an RSU) may dynamically adjust the resolution based on the characteristics or situational information of a detected object to improve the accuracy of occupancy prediction in the area. For example, the vehicle or road infrastructure may perform occupancy prediction for surrounding objects based on a default resolution. In this case, for example, the vehicle or road infrastructure may exchange information with each other or receive messages (e.g., V2X messages) from a server to obtain information related to the status of the object, the road conditions, or the status of the communication channel. Specifically, for example, if the detected object is a small kickboard, information may be obtained that the object is small in size, fast in speed, and has significant directional changes. Alternatively, information may be obtained that the intersection where the detected object is located is classified as an accident-prone area. Alternatively, for example, information may be obtained that there is a high object density situation due to a dense cluster of objects around the detected object. Alternatively, information may be obtained that there is a high volume of traffic at the intersection where the detected object is located, resulting in high traffic congestion and high communication channel congestion. Alternatively, information may be obtained that a service provided by a vehicle or infrastructure requires high precision. In this case, for example, the vehicle or infrastructure may comprehensively consider the obtained information and adjust the default resolution to the optimal resolution for occupancy prediction. Specifically, for example, if it is determined that the object is small in size, has a high speed, has a large change in direction, or has a high probability of collision, the resolution unit size may be reduced.Alternatively, for example, if an object is classified as a child or elderly person, or is determined to be located in an accident-prone area, a very high resolution may be required for policy reasons, so the resolution unit size may be reduced. Alternatively, if channel congestion is determined to be high, the likelihood of delays or packet loss increases, so the resolution unit size may be increased to reduce the amount of data transmitted to facilitate efficient communication. For example, occupancy results predicted with a more precise resolution can be transmitted to an autonomous driving control system or traffic control server for use in immediate speed control, emergency braking, and warnings.

[0106] FIG. 7 illustrates a method for a first device to perform wireless communication according to an embodiment of the present disclosure. The embodiment of FIG. 7 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0107] Referring to FIG. 7, in step S710, the first device may perform a first occupancy prediction for at least one object. In step S720, the first device may receive a message including information related to a state. In step S730, the first device may change the first resolution related to the first occupancy prediction to a second resolution based on the information related to the state. In step S740, the first device may perform a second occupancy prediction for the at least one object based on the second resolution.

[0108] For example, based on the fact that information related to traffic congestion is included in the information related to the state, the higher the traffic congestion, the smaller the unit size of the second resolution may be than the unit size of the first resolution.

[0109] For example, based on the fact that information related to channel congestion is included in the information related to the state, the higher the channel congestion, the larger the unit size of the second resolution may be than the unit size of the first resolution. For example, based on the fact that the channel congestion is greater than or equal to a threshold value, the unit size of the second resolution may be larger than or equal to a minimum unit size set based on the channel congestion.

[0110] For example, information related to the at least one object may be included in the message. For example, the smaller the size of the at least one object, the smaller the unit size of the second resolution may be than the unit size of the first resolution. For example, the faster the speed of the at least one object, the smaller the unit size of the second resolution may be than the unit size of the first resolution. For example, the larger the change in direction of the at least one object, the smaller the unit size of the second resolution may be than the unit size of the first resolution. For example, the higher the collision probability of the at least one object, the smaller the unit size of the second resolution may be than the unit size of the first resolution. For example, the higher the vulnerability level set based on the type of the at least one object, the smaller the unit size of the second resolution may be than the unit size of the first resolution. For example, as the density of a plurality of objects including at least one object increases, the unit size of the second resolution may become smaller than the unit size of the first resolution.

[0111] For example, the message may include information regarding the unit of resolution required in the service related to the first occupancy prediction or the second occupancy prediction.

[0112] For example, the unit of the first resolution associated with the first occupancy prediction and the unit of the second resolution associated with the second occupancy prediction may be based on at least one of a grid or a voxel.

[0113] The proposed method can be applied to devices according to various embodiments of the present disclosure. First, the processor (102) of the first device (100) can perform a first occupancy prediction for at least one object. Then, the processor (102) of the first device (100) can receive a message including information related to a state. Then, the processor (102) of the first device (100) can change the first resolution related to the first occupancy prediction to a second resolution based on the information related to the state. Then, the processor (102) of the first device (100) can perform a second occupancy prediction for the at least one object based on the second resolution.

[0114] According to one embodiment of the present disclosure, a first device configured to perform wireless communication may be provided. For example, the first device may include at least one transceiver; at least one processor; and at least one memory coupled to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the first device to: perform a first occupancy prediction for at least one object; receive a message including information related to a state; change a first resolution associated with the first occupancy prediction to a second resolution based on the information related to the state; and perform a second occupancy prediction for the at least one object based on the second resolution.

[0115] According to one embodiment of the present disclosure, a processing device configured to control a first device may be provided. For example, the processing device may include at least one processor; and at least one memory coupled to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the first device to: perform a first occupancy prediction for at least one object; cause the first device to receive a message including information related to a state; change a first resolution associated with the first occupancy prediction to a second resolution based on the information related to the state; and perform a second occupancy prediction for the at least one object based on the second resolution.

[0116] According to one embodiment of the present disclosure, a non-transitory computer-readable storage medium having instructions recorded thereon may be provided. For example, the instructions, when executed, may cause a first device to: perform a first occupancy prediction for at least one object; cause the first device to receive a message including information related to a state; change a first resolution associated with the first occupancy prediction to a second resolution based on the information related to the state; and perform a second occupancy prediction for the at least one object based on the second resolution.

[0117] FIG. 8 illustrates a method for a second device to perform wireless communication according to an embodiment of the present disclosure. The embodiment of FIG. 8 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0118] Referring to FIG. 8, in step S810, the second device may receive information requesting information for performing occupancy prediction on at least one object from the first device. In step S820, the second device may transmit a message to the first device, including at least one of information related to the at least one object or information related to a state. For example, the unit of resolution related to the occupancy prediction may be changed based on at least one of information related to the at least one object or information related to a state.

[0119] The proposed method can be applied to devices according to various embodiments of the present disclosure. First, the processor (202) of the second device (200) can control the transceiver (206) to receive, from the first device, information requesting information for performing occupancy prediction for at least one object. Then, the processor (202) of the second device (200) can control the transceiver (206) to transmit, to the first device, a message including at least one of information related to the at least one object or information related to a state. For example, the unit of resolution related to the occupancy prediction can be changed based on at least one of the information related to the at least one object or information related to a state.

[0120] According to one embodiment of the present disclosure, a second device configured to perform wireless communication may be provided. For example, the second device may include at least one transceiver; at least one processor; and at least one memory connected to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the second device to: receive, from a first device, information requesting information for performing occupancy prediction for at least one object; and transmit, to the first device, a message including at least one of information related to the at least one object or information related to a state. For example, a unit of resolution related to the occupancy prediction may be changed based on at least one of the information related to the at least one object or the information related to the state.

[0121] According to one embodiment of the present disclosure, a processing device configured to control a second device may be provided. For example, the processing device may include at least one processor; and at least one memory coupled to the at least one processor and storing instructions. For example, the instructions, based on execution by the at least one processor, may cause the second device to: receive, from a first device, information requesting information for performing occupancy prediction on at least one object; and transmit, to the first device, a message including at least one of information related to the at least one object or information related to a state. For example, a unit of resolution related to the occupancy prediction may be changed based on at least one of the information related to the at least one object or the information related to the state.

[0122] According to one embodiment of the present disclosure, a non-transitory computer-readable storage medium having instructions recorded thereon may be provided. For example, the instructions, when executed, may cause a second device to: receive, from a first device, information requesting information for performing occupancy prediction on at least one object; and transmit, to the first device, a message including at least one of information related to the at least one object or information related to a state. For example, a unit of resolution related to the occupancy prediction may be changed based on at least one of the information related to the at least one object or the information related to the state.

[0123] According to various embodiments of the present disclosure, the precision of the object classification unit required for autonomous driving can be adjusted, or the size or resolution of the grid or voxel, which is the grid unit of the occupancy map, can be variably applied. For example, in cases where precise prediction is required, such as when the object is small or moves quickly, or when the number of pixels occupied is small or the occupancy density is high, or when the area is expected to be a collision area or has frequent accidents, or when traffic congestion is severe, the size or resolution of the grid or voxel of the map can be refined to induce more precise prediction. In addition, for example, traffic safety can be promoted through precise occupancy or path prediction, and the efficiency, availability, and performance of existing traffic safety systems can be improved. Alternatively, for example, the dynamic prediction method proposed in the present disclosure can operate similarly to the method of varying the coding rate according to the available bandwidth in the adaptive streaming technology of the Internet DASH (Dynamic Adaptive Streaming over HTTP), YouTube, or Netflix. Specifically, for example, in cases of heavy traffic congestion or areas where accidents are likely to occur, such as roads or intersections, occupancy prediction is performed with high density or precision, and in areas with little traffic or a low accident rate, occupancy prediction is performed with low density or precision, thereby allowing the occupancy prediction precision or resolution to be variably changed depending on the traffic situation or time.

[0124] Or, for example, conventional resolution adjustment techniques such as image upscaling / downscaling adjust the resolution of an existing or produced image according to the network environment or available resource status, but in the case of the occupancy prediction proposed in the present disclosure, the accuracy of the obstacle map or path prediction, which is the second information (e.g., current / future time information), can be upwardly adjusted through the first information (e.g., past information), thereby preventing accidents, etc. Or, for example, by downwardly adjusting the accuracy of the obstacle map or path prediction at a less dangerous point, the available resources of the digital twin, server, or each RU device can be dynamically secured. Or, for example, according to the proposal of the present disclosure, the optimal resolution can be predicted in real time. For example, in a situation where the number of VRUs is large or traffic increases, it is necessary to increase the resolution, but it cannot be increased infinitely, so the optimal resolution can be dynamically or automatically selected within the range of the available environment and resources. Alternatively, for example, for critical points such as small and fast VRUs, very vulnerable VRUs such as children and the elderly, fast VRUs such as kickboards, fatal accident occurrence points, and accident-prone points, high-resolution occupancy prediction can be implemented as a policy to effectively prepare for fatal accidents.

[0125] The various embodiments of the present disclosure may be combined with each other.

[0126] Below, a description is given of devices to which various embodiments of the present disclosure can be applied.

[0127] Although not limited thereto, the various descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed in this document may be applied to various fields requiring wireless communication / connectivity (e.g., 5G) between devices.

[0128] Hereinafter, more specific examples will be provided with reference to the drawings. In the drawings / descriptions below, the same drawing reference numerals may represent identical or corresponding hardware blocks, software blocks, or functional blocks, unless otherwise described.

[0129] FIG. 9 illustrates a communication system (1) according to one embodiment of the present disclosure. The embodiment of FIG. 9 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0130] Referring to FIG. 9, a communication system (1) to which various embodiments of the present disclosure are applied includes a wireless device, a base station, and a network. Here, the wireless device refers to a device that performs communication using a wireless access technology (e.g., 5G NR (New RAT), LTE (Long Term Evolution)) and may be referred to as a communication / wireless / 5G device. Although not limited thereto, the wireless device may include a robot (100a), a vehicle (100b-1, 100b-2), an XR (eXtended Reality) device (100c), a hand-held device (100d), a home appliance (100e), an IoT (Internet of Things) device (100f), and an AI device / server (400). For example, the vehicle may include a vehicle equipped with a wireless communication function, an autonomous vehicle, a vehicle capable of performing vehicle-to-vehicle communication, etc. Here, the vehicle may include an Unmanned Aerial Vehicle (UAV) (e.g., a drone) and / or an Aerial Vehicle (AV) (e.g., an Advanced Air Mobility (AAM)). The XR device may include an Augmented Reality (AR) / Virtual Reality (VR) / Mixed Reality (MR) device, and may be implemented in the form of a Head-Mounted Device (HMD), a Head-Up Display (HUD) equipped in a vehicle, a television, a smartphone, a computer, a wearable device, a home appliance, a digital signage, a vehicle, a robot, etc. The portable device may include a smartphone, a smart pad, a wearable device (e.g., a smart watch, smart glasses), a computer (e.g., a laptop, etc.), etc. The home appliance may include a TV, a refrigerator, a washing machine, etc. The IoT device may include a sensor, a smart meter, etc. For example, a base station and a network may also be implemented as a wireless device, and a specific wireless device (200a) may operate as a base station / network node to other wireless devices.

[0131] Here, the wireless communication technology implemented in the wireless devices (100a to 100f) of the present specification may include not only LTE, NR, and 6G, but also Narrowband Internet of Things for low-power communication. At this time, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology, and may be implemented with standards such as LTE Cat NB1 and / or LTE Cat NB2, and is not limited to the above-described names. Additionally or alternatively, the wireless communication technology implemented in the wireless devices (100a to 100f) of the present specification may perform communication based on LTE-M technology. At this time, for example, LTE-M technology may be an example of LPWAN technology, and may be called by various names such as eMTC (enhanced Machine Type Communication). For example, LTE-M technology can be implemented by at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the above-described names. Additionally or alternatively, the wireless communication technology implemented in the wireless devices (100a to 100f) of the present specification can include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) considering low-power communication, and is not limited to the above-described names. For example, ZigBee technology can create personal area networks (PAN) related to small / low-power digital communication based on various standards such as IEEE 802.15.4, and can be called by various names.

[0132] Wireless devices (100a to 100f) can be connected to a network (300) via a base station (200). Artificial Intelligence (AI) technology can be applied to the wireless devices (100a to 100f), and the wireless devices (100a to 100f) can be connected to an AI server (400) via the network (300). The network (300) can be configured using a 3G network, a 4G (e.g., LTE) network, a 5G (e.g., NR) network, etc. The wireless devices (100a to 100f) can communicate with each other via the base station (200) / network (300), but can also communicate directly (e.g., sidelink communication) without going through the base station / network. For example, vehicles (100b-1, 100b-2) can communicate directly (e.g., V2V (Vehicle to Vehicle) / V2X (Vehicle to Everything) communication). In addition, IoT devices (e.g., sensors) can communicate directly with other IoT devices (e.g., sensors) or other wireless devices (100a to 100f).

[0133] Wireless communication / connection (150a, 150b, 150c) can be established between wireless devices (100a~100f) / base stations (200), and base stations (200) / base stations (200). Here, wireless communication / connection can be achieved through various wireless access technologies (e.g., 5G NR) such as uplink / downlink communication (150a), sidelink communication (150b) (or, D2D communication), and communication between base stations (150c) (e.g., relay, IAB (Integrated Access Backhaul). Through wireless communication / connection (150a, 150b, 150c), wireless devices and base stations / wireless devices, and base stations and base stations can transmit / receive wireless signals to each other. For example, wireless communication / connection (150a, 150b, 150c) can transmit / receive signals through various physical channels. To this end, at least some of various configuration information setting processes for transmitting / receiving wireless signals, various signal processing processes (e.g., channel encoding / decoding, modulation / demodulation, resource mapping / demapping, etc.), and resource allocation processes can be performed based on various proposals of the present disclosure.

[0134] FIG. 10 illustrates a wireless device according to an embodiment of the present disclosure. The embodiment of FIG. 10 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0135] Referring to FIG. 10, the first wireless device (100) and the second wireless device (200) can transmit and receive wireless signals via various wireless access technologies (e.g., LTE, NR). Here, {the first wireless device (100), the second wireless device (200)} can correspond to {the wireless device (100x), the base station (200)} and / or {the wireless device (100x), the wireless device (100x)} of FIG. 9.

[0136] A first wireless device (100) includes one or more processors (102) and one or more memories (104), and may further include one or more transceivers (106) and / or one or more antennas (108). The processor (102) controls the memories (104) and / or the transceivers (106), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (102) may process information in the memory (104) to generate first information / signal, and then transmit a wireless signal including the first information / signal via the transceiver (106). Furthermore, the processor (102) may receive a wireless signal including second information / signal via the transceiver (106), and then store information obtained from signal processing of the second information / signal in the memory (104). The memory (104) may be connected to the processor (102) and may store various information related to the operation of the processor (102). For example, the memory (104) may perform some or all of the processes controlled by the processor (102), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (102) and the memory (104) may be part of a communication modem / circuit / chip designed to implement a wireless communication technology (e.g., LTE, NR). The transceiver (106) may be connected to the processor (102) and may transmit and / or receive wireless signals via one or more antennas (108). The transceiver (106) may include a transmitter and / or a receiver. The transceiver (106) may be used interchangeably with an RF (Radio Frequency) unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0137] A second wireless device (200) includes one or more processors (202), one or more memories (204), and may further include one or more transceivers (206) and / or one or more antennas (208). The processor (202) controls the memories (204) and / or the transceivers (206), and may be configured to implement the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document. For example, the processor (202) may process information in the memory (204) to generate third information / signals, and then transmit a wireless signal including the third information / signals via the transceivers (206). In addition, the processor (202) may receive a wireless signal including fourth information / signals via the transceivers (206), and then store information obtained from signal processing of the fourth information / signals in the memory (204). The memory (204) may be connected to the processor (202) and may store various information related to the operation of the processor (202). For example, the memory (204) may perform some or all of the processes controlled by the processor (202), or may store software code including commands for performing the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. Here, the processor (202) and the memory (204) may be part of a communication modem / circuit / chip designed to implement wireless communication technology (e.g., LTE, NR). The transceiver (206) may be connected to the processor (202) and may transmit and / or receive wireless signals via one or more antennas (208). The transceiver (206) may include a transmitter and / or a receiver. The transceiver (206) may be used interchangeably with an RF unit. In the present disclosure, a wireless device may also mean a communication modem / circuit / chip.

[0138] Hereinafter, the hardware elements of the wireless device (100, 200) will be described in more detail. Although not limited thereto, one or more protocol layers may be implemented by one or more processors (102, 202). For example, one or more processors (102, 202) may implement one or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP, RRC, SDAP). One or more processors (102, 202) may generate one or more Protocol Data Units (PDUs) and / or one or more Service Data Units (SDUs) according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) may generate messages, control information, data, or information according to the descriptions, functions, procedures, proposals, methods, and / or operation flowcharts disclosed in this document. One or more processors (102, 202) can generate signals (e.g., baseband signals) including PDUs, SDUs, messages, control information, data or information according to the functions, procedures, proposals and / or methods disclosed herein, and provide the signals to one or more transceivers (106, 206). One or more processors (102, 202) can receive signals (e.g., baseband signals) from one or more transceivers (106, 206) and obtain PDUs, SDUs, messages, control information, data or information according to the descriptions, functions, procedures, proposals, methods and / or operational flowcharts disclosed herein.

[0139] One or more processors (102, 202) may be referred to as a controller, a microcontroller, a microprocessor, or a microcomputer. One or more processors (102, 202) may be implemented by hardware, firmware, software, or a combination thereof. For example, one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Digital Signal Processing Devices (DSPDs), one or more Programmable Logic Devices (PLDs), or one or more Field Programmable Gate Arrays (FPGAs) may be included in one or more processors (102, 202). The descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed in this document may be implemented using firmware or software, and the firmware or software may be implemented to include modules, procedures, functions, etc. The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software configured to perform one or more processors (102, 202) or stored in one or more memories (104, 204) and driven by one or more processors (102, 202). The descriptions, functions, procedures, suggestions, methods and / or operation flowcharts disclosed in this document may be implemented using firmware or software in the form of codes, instructions and / or sets of instructions.

[0140] One or more memories (104, 204) may be coupled to one or more processors (102, 202) and may store various forms of data, signals, messages, information, programs, codes, instructions, and / or commands. The one or more memories (104, 204) may be configured as ROM, RAM, EPROM, flash memory, hard drives, registers, cache memory, computer-readable storage media, and / or combinations thereof. The one or more memories (104, 204) may be located internally and / or externally to the one or more processors (102, 202). Additionally, the one or more memories (104, 204) may be coupled to the one or more processors (102, 202) via various technologies, such as wired or wireless connections.

[0141] One or more transceivers (106, 206) can transmit user data, control information, wireless signals / channels, etc., as mentioned in the methods and / or flowcharts of this document, to one or more other devices. One or more transceivers (106, 206) can receive user data, control information, wireless signals / channels, etc., as mentioned in the descriptions, functions, procedures, proposals, methods and / or flowcharts of this document, from one or more other devices. For example, one or more transceivers (106, 206) can be connected to one or more processors (102, 202) and can transmit and receive wireless signals. For example, one or more processors (102, 202) can control one or more transceivers (106, 206) to transmit user data, control information, or wireless signals to one or more other devices. Additionally, one or more processors (102, 202) may control one or more transceivers (106, 206) to receive user data, control information, or wireless signals from one or more other devices. Additionally, one or more transceivers (106, 206) may be coupled to one or more antennas (108, 208), and one or more transceivers (106, 206) may be configured to transmit and receive user data, control information, wireless signals / channels, or the like, as referred to in the descriptions, functions, procedures, proposals, methods, and / or operational flowcharts disclosed herein, via one or more antennas (108, 208). In this document, one or more antennas may be multiple physical antennas or multiple logical antennas (e.g., antenna ports). One or more transceivers (106, 206) can convert received user data, control information, wireless signals / channels, etc. from RF band signals to baseband signals in order to process the received user data, control information, wireless signals / channels, etc. using one or more processors (102, 202).One or more transceivers (106, 206) may convert user data, control information, wireless signals / channels, etc. processed by one or more processors (102, 202) from baseband signals to RF band signals. For this purpose, one or more transceivers (106, 206) may include an (analog) oscillator and / or filter.

[0142] Fig. 11 illustrates a signal processing circuit for a transmission signal according to an embodiment of the present disclosure. The embodiment of Fig. 11 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0143] Referring to FIG. 11, the signal processing circuit (1000) may include a scrambler (1010), a modulator (1020), a layer mapper (1030), a precoder (1040), a resource mapper (1050), and a signal generator (1060). Although not limited thereto, the operations / functions of FIG. 11 may be performed in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 10. The hardware elements of FIG. 11 may be implemented in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 10. For example, blocks 1010 to 1060 may be implemented in the processor (102, 202) of FIG. 10. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 10, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 10.

[0144] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 11. Here, the codeword is an encoded bit sequence of an information block. The information block may include a transport block (e.g., an UL-SCH transport block, a DL-SCH transport block). The wireless signal may be transmitted through various physical channels (e.g., a PUSCH or a PDSCH).

[0145] Specifically, the codeword can be converted into a bit sequence scrambled by a scrambler (1010). The scramble sequence used for scrambling is generated based on an initialization value, and the initialization value may include ID information of the wireless device, etc. The scrambled bit sequence can be modulated into a modulation symbol sequence by a modulator (1020). The modulation method may include pi / 2-BPSK (pi / 2-Binary Phase Shift Keying), m-PSK (m-Phase Shift Keying), m-QAM (m-Quadrature Amplitude Modulation), etc. The complex modulation symbol sequence can be mapped to one or more transmission layers by a layer mapper (1030). The modulation symbols of each transmission layer can be mapped to the corresponding antenna port(s) by a precoder (1040) (precoding). The output z of the precoder (1040) can be obtained by multiplying the output y of the layer mapper (1030) by a precoding matrix W of N*M. Here, N is the number of antenna ports, and M is the number of transmission layers. Here, the precoder (1040) can perform precoding after performing transform precoding (e.g., DFT transform) on complex modulation symbols. In addition, the precoder (1040) can perform precoding without performing transform precoding.

[0146] The resource mapper (1050) can map modulation symbols of each antenna port to time-frequency resources. The time-frequency resources can include multiple symbols (e.g., CP-OFDMA symbols, DFT-s-OFDMA symbols) in the time domain and multiple subcarriers in the frequency domain. The signal generator (1060) generates a wireless signal from the mapped modulation symbols, and the generated wireless signal can be transmitted to another device through each antenna. To this end, the signal generator (1060) can include an Inverse Fast Fourier Transform (IFFT) module, a Cyclic Prefix (CP) inserter, a Digital-to-Analog Converter (DAC), a frequency uplink converter, etc.

[0147] The signal processing process for receiving signals in a wireless device can be configured in reverse order of the signal processing process (1010 to 1060) of FIG. 11. For example, a wireless device (e.g., 100, 200 of FIG. 10) can receive wireless signals from the outside through an antenna port / transceiver. The received wireless signals can be converted into baseband signals through a signal restorer. For this purpose, the signal restorer can include a frequency downlink converter, an analog-to-digital converter (ADC), a CP remover, and a fast Fourier transform (FFT) module. Thereafter, the baseband signal can be restored to a codeword through a resource demapper process, a postcoding process, a demodulation process, and a descrambling process. The codewords can be restored to the original information blocks through decoding. Accordingly, a signal processing circuit (not shown) for a received signal may include a signal restorer, a resource de-mapper, a postcoder, a demodulator, a de-scrambler, and a decoder.

[0148] Figure 12 illustrates a wireless device according to an embodiment of the present disclosure. The wireless device may be implemented in various forms depending on the use case / service (see Figure 9). The embodiment of Figure 12 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0149] Referring to FIG. 12, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 10 and may be composed of various elements, components, units / units, and / or modules. For example, the wireless device (100, 200) may include a communication unit (110), a control unit (120), a memory unit (130), and additional elements (140). The communication unit may include a communication circuit (112) and a transceiver(s) (114). For example, the communication circuit (112) may include one or more processors (102, 202) and / or one or more memories (104, 204) of FIG. 10. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 10. The control unit (120) is electrically connected to the communication unit (110), the memory unit (130), and the additional elements (140) and controls the overall operation of the wireless device. For example, the control unit (120) may control the electrical / mechanical operation of the wireless device based on the program / code / command / information stored in the memory unit (130). In addition, the control unit (120) may transmit information stored in the memory unit (130) to an external device (e.g., another communication device) via a wireless / wired interface through the communication unit (110), or store information received from an external device (e.g., another communication device) via a wireless / wired interface in the memory unit (130).

[0150] The additional element (140) may be configured in various ways depending on the type of the wireless device. For example, the additional element (140) may include at least one of a power unit / battery, an input / output unit (I / O unit), a driving unit, and a computing unit. Although not limited thereto, the wireless device may be implemented in the form of a robot (Fig. 9, 100a), a vehicle (Fig. 9, 100b-1, 100b-2), an XR device (Fig. 9, 100c), a portable device (Fig. 9, 100d), a home appliance (Fig. 9, 100e), an IoT device (Fig. 9, 100f), a digital broadcasting terminal, a hologram device, a public safety device, an MTC device, a medical device, a fintech device (or a financial device), a security device, a climate / environmental device, an AI server / device (Fig. 9, 400), a base station (Fig. 9, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0151] In FIG. 12, various elements, components, units / parts, and / or modules within the wireless device (100, 200) may be interconnected entirely via a wired interface, or at least some may be wirelessly connected via a communication unit (110). For example, within the wireless device (100, 200), the control unit (120) and the communication unit (110) may be wired, and the control unit (120) and the first unit (e.g., 130, 140) may be wirelessly connected via the communication unit (110). In addition, each element, component, unit / part, and / or module within the wireless device (100, 200) may further include one or more elements. For example, the control unit (120) may be composed of one or more processor sets. For example, the control unit (120) may be composed of a set of a communication control processor, an application processor, an electronic control unit (ECU), a graphics processing processor, a memory control processor, etc. As another example, the memory unit (130) may be composed of a random access memory (RAM), a dynamic RAM (DRAM), a read only memory (ROM), a flash memory, a volatile memory, a non-volatile memory, and / or a combination thereof.

[0152] Below, the implementation example of Fig. 12 is described in more detail with reference to the drawings.

[0153] FIG. 13 illustrates a mobile device according to an embodiment of the present disclosure. The mobile device may include a smartphone, a smart pad, a wearable device (e.g., a smartwatch, smartglasses), or a portable computer (e.g., a laptop, etc.). The mobile device may be referred to as a Mobile Station (MS), a User Terminal (UT), a Mobile Subscriber Station (MSS), a Subscriber Station (SS), an Advanced Mobile Station (AMS), or a Wireless Terminal (WT). The embodiment of FIG. 13 may be combined with various embodiments of the present disclosure, and some descriptions, functions, procedures, proposals, methods, and / or operations of the embodiments may be omitted.

[0154] Referring to FIG. 13, the portable device (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a memory unit (130), a power supply unit (140a), an interface unit (140b), and an input / output unit (140c). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 to 130 / 140a to 140c correspond to blocks 110 to 130 / 140 of FIG. 12, respectively.

[0155] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with other wireless devices and base stations. The control unit (120) can control components of the mobile device (100) to perform various operations. The control unit (120) can include an AP (Application Processor). The memory unit (130) can store data / parameters / programs / codes / commands required for operating the mobile device (100). In addition, the memory unit (130) can store input / output data / information, etc. The power supply unit (140a) supplies power to the mobile device (100) and can include a wired / wireless charging circuit, a battery, etc. The interface unit (140b) can support connection between the mobile device (100) and other external devices. The interface unit (140b) can include various ports (e.g., audio input / output ports, video input / output ports) for connection with external devices. The input / output unit (140c) can input or output video information / signals, audio information / signals, data, and / or information input from a user. The input / output unit (140c) may include a camera, a microphone, a user input unit, a display unit (140d), a speaker, and / or a haptic module.

[0156] For example, in the case of data communication, the input / output unit (140c) obtains information / signals (e.g., touch, text, voice, image, video) input by the user, and the obtained information / signals can be stored in the memory unit (130). The communication unit (110) converts the information / signals stored in the memory into wireless signals, and can directly transmit the converted wireless signals to other wireless devices or to a base station. In addition, the communication unit (110) can receive wireless signals from other wireless devices or base stations, and then restore the received wireless signals to the original information / signals. The restored information / signals can be stored in the memory unit (130) and then output in various forms (e.g., text, voice, image, video, haptic) through the input / output unit (140c).

[0157] FIG. 14 illustrates a vehicle or autonomous vehicle according to one embodiment of the present disclosure. The vehicle or autonomous vehicle may be implemented as a mobile robot, a car, a train, a manned or unmanned aerial vehicle (AV), a ship, or the like. The embodiment of FIG. 14 may be combined with various embodiments of the present disclosure.

[0158] Referring to FIG. 14, a vehicle or autonomous vehicle (100) may include an antenna unit (108), a communication unit (110), a control unit (120), a driving unit (140a), a power supply unit (140b), a sensor unit (140c), and an autonomous driving unit (140d). The antenna unit (108) may be configured as a part of the communication unit (110). Blocks 110 / 130 / 140a to 140d correspond to blocks 110 / 130 / 140 of FIG. 12, respectively.

[0159] The communication unit (110) can transmit and receive signals (e.g., data, control signals, etc.) with external devices such as other vehicles, base stations (e.g., base stations, road side units, etc.), and servers. The control unit (120) can control elements of the vehicle or autonomous vehicle (100) to perform various operations. The control unit (120) can include an ECU (Electronic Control Unit). The drive unit (140a) can drive the vehicle or autonomous vehicle (100) on the ground. The drive unit (140a) can include an engine, a motor, a power train, wheels, brakes, a steering device, etc. The power supply unit (140b) supplies power to the vehicle or autonomous vehicle (100) and can include a wired / wireless charging circuit, a battery, etc. The sensor unit (140c) can obtain vehicle status, surrounding environment information, user information, etc. The sensor unit (140c) may include an IMU (inertial measurement unit) sensor, a collision sensor, a wheel sensor, a speed sensor, an incline sensor, a weight detection sensor, a heading sensor, a position module, a vehicle forward / backward sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illuminance sensor, a pedal position sensor, etc. The autonomous driving unit (140d) may implement a technology for maintaining a driving lane, a technology for automatically controlling speed such as adaptive cruise control, a technology for automatically driving along a set path, a technology for automatically setting a path and driving when a destination is set, etc.

[0160] For example, the communication unit (110) can receive map data, traffic information data, etc. from an external server. The autonomous driving unit (140d) can generate an autonomous driving route and driving plan based on the acquired data. The control unit (120) can control the drive unit (140a) so that the vehicle or autonomous vehicle (100) moves along the autonomous driving route according to the driving plan (e.g., speed / direction control). During autonomous driving, the communication unit (110) can irregularly / periodically acquire the latest traffic information data from an external server and can acquire surrounding traffic information data from surrounding vehicles. In addition, during autonomous driving, the sensor unit (140c) can acquire vehicle status and surrounding environment information. The autonomous driving unit (140d) can update the autonomous driving route and driving plan based on newly acquired data / information. The communication unit (110) can transmit information regarding the vehicle location, autonomous driving route, driving plan, etc. to the external server. External servers can predict traffic information data in advance using AI technology or other technologies based on information collected from vehicles or autonomous vehicles, and provide the predicted traffic information data to the vehicles or autonomous vehicles.

[0161] The claims set forth in this specification may be combined in various ways. For example, the technical features of the method claims of this specification may be combined and implemented as a device, and the technical features of the device claims of this specification may be combined and implemented as a method. Furthermore, the technical features of the method claims and the technical features of the device claims of this specification may be combined and implemented as a device, and the technical features of the method claims and the technical features of the device claims of this specification may be combined and implemented as a method.

Claims

1. In the method, A first device performs a first occupancy prediction for at least one object; A step in which the first device receives a message including information related to a status; A step of changing the first resolution related to the first occupancy prediction to a second resolution based on information related to the above state; and A method comprising: performing a second occupancy prediction for the at least one object based on the second resolution; 2. In paragraph 1, A method wherein, based on information related to traffic congestion being included in the information related to the state, the higher the traffic congestion, the smaller the unit size of the second resolution becomes than the unit size of the first resolution.

3. In paragraph 1, A method wherein, based on information related to channel congestion being included in the information related to the state, the higher the channel congestion, the larger the unit size of the second resolution becomes than the unit size of the first resolution.

4. In paragraph 3, A method wherein the unit size of the second resolution is greater than or equal to a minimum unit size set based on the channel congestion, based on the channel congestion being greater than or equal to a threshold value.

5. In paragraph 1, A method wherein information related to at least one object is included in the message.

6. In paragraph 5, A method wherein the smaller the size of the at least one object, the smaller the unit size of the second resolution becomes than the unit size of the first resolution.

7. In paragraph 5, A method wherein the faster the speed of the at least one object, the smaller the unit size of the second resolution becomes than the unit size of the first resolution.

8. In paragraph 5, A method wherein the larger the change in direction of the at least one object, the smaller the unit size of the second resolution becomes than the unit size of the first resolution.

9. In paragraph 5, A method wherein the unit size of the second resolution becomes smaller than the unit size of the first resolution as the collision probability of the at least one object increases.

10. In paragraph 5, A method wherein the higher the vulnerability level set based on the type of at least one object, the smaller the unit size of the second resolution is than the unit size of the first resolution.

11. In paragraph 5, A method wherein the higher the density of the plurality of objects including at least one object, the smaller the unit size of the second resolution becomes than the unit size of the first resolution.

12. In paragraph 1, A method wherein the message includes information related to a unit of resolution required in a service related to the first occupancy prediction or the second occupancy prediction.

13. In paragraph 1, A method wherein the unit of the first resolution associated with the first occupancy prediction and the unit of the second resolution associated with the second occupancy prediction are based on at least one of a grid or a voxel.

14. In the first device, At least one transmitter / receiver; at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said first device causes: Perform a first occupancy prediction for at least one object; The first device receives a message containing information related to the status; Based on the information related to the above state, changing the first resolution related to the first occupancy prediction to a second resolution; and A first device that performs a second occupancy prediction for at least one object based on the second resolution.

15. In a processing device set to control the first device, at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said first device causes: Perform a first occupancy prediction for at least one object; The first device receives a message containing information related to the status; Based on the information related to the above state, changing the first resolution related to the first occupancy prediction to a second resolution; and A processing device that performs a second occupancy prediction for the at least one object based on the second resolution.

16. A non-transitory computer-readable storage medium that records commands, The above commands, when executed, cause the first device to: Perform a first occupancy prediction for at least one object; The first device receives a message containing information related to the status; Based on the information related to the above state, changing the first resolution related to the first occupancy prediction to a second resolution; and A non-transitory computer-readable storage medium that performs a second occupancy prediction for at least one object based on the second resolution.

17. In the method, A step in which a second device receives information requesting information for performing occupancy prediction for at least one object from a first device; and A step of transmitting a message including at least one of information related to the at least one object or information related to a state of the at least one object to the first device; A method in which a unit of resolution related to the occupancy prediction is changed based on at least one of information related to the at least one object or information related to the state.

18. In the second device, At least one transmitter / receiver; at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said second device causes: Receive information from a first device requesting information for performing occupancy prediction for at least one object; and To transmit a message including at least one of information related to the at least one object or information related to a state of the at least one object, A second device, wherein the unit of resolution related to the occupancy prediction is changed based on at least one of information related to the at least one object or information related to the state.

19. In a processing device set to control a second device, at least one processor; and At least one memory connected to said at least one processor and storing instructions, said instructions being executed by said at least one processor, wherein said second device causes: Receive information from a first device requesting information for performing occupancy prediction for at least one object; and To transmit a message including at least one of information related to the at least one object or information related to a state of the at least one object, A processing device in which a unit of resolution related to the occupancy prediction is changed based on at least one of information related to the at least one object or information related to the state.

20. A non-transitory computer-readable storage medium that records commands, The above commands, when executed, cause the second device to: Receive information from a first device requesting information for performing occupancy prediction for at least one object; and To transmit a message including at least one of information related to the at least one object or information related to a state of the at least one object, A non-transitory computer-readable storage medium in which a unit of resolution related to the occupancy prediction is changed based on at least one of information related to the at least one object or information related to the state.

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