Method and device for performing wireless communication

Abstract

In an embodiment, a method performed by a first device is provided. For example, the first device may acquire first information related to a first area related to a first time and second information related to a second area related to a second time after the first time. For example, the first device may generate, on the basis of the first information and the second information, third information related to a blocking object and fourth information related to a filling area related to the blocking object. For example, the first device may detect at least one moving object within a third area related to a third time. For example, the first device may generate an object recognition message related to the detected at least one moving object. For example, the detection of the at least one moving object may be performed within a fourth area in which a blocking area related to the blocking object in the third area related to the third time is replaced with the filling area, on the basis of the third information related to the blocking object and the fourth information related to the filling area.

Description

Method and device for performing wireless communication

[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] Maximum data rate per device: 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 driving, Fully XR, Fully haptic communication

[0005] In one embodiment, a method performed by a first device is provided. For example, the first device may obtain first information related to a first area at a first time and second information related to a second area at a second time after the first time. For example, the first device may generate third information related to a blocking object and fourth information related to a filling area related to the blocking object based on the first information and the second information. For example, the first device may perform detection of at least one moving object within a third area at a third time. For example, the first device may generate an object recognition message related to the at least one detected moving object. For example, the detection of the at least one moving object may be performed within a fourth region in which the blocking region associated with the blocking object among the third regions for the third time is replaced with the filling region, based on the third information associated with the blocking object and the fourth information associated with the filling region.

[0006] FIG. 1 illustrates a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure.

[0007] FIG. 2 illustrates an example of a communication scenario based on a 6G system according to an embodiment of the present disclosure.

[0008] FIG. 3 illustrates an example of a sensing operation according to one embodiment of the present disclosure.

[0009] FIG. 4 is a diagram for comparing and explaining V2X communication based on RAT prior to NR and V2X communication based on NR according to one embodiment of the present disclosure.

[0010] FIG. 5 is a diagram illustrating results before and after performing operations related to object detection / perception or object tracking according to one embodiment of the present disclosure.

[0011] FIG. 6 illustrates a method for performing wireless communication according to one embodiment of the present disclosure.

[0012] FIG. 7 illustrates a method for performing wireless communication according to one embodiment of the present disclosure.

[0013] FIG. 8 illustrates information included in an object perception message according to one embodiment of the present disclosure.

[0014] FIG. 9 illustrates a method for generating an object recognition message according to one embodiment of the present disclosure.

[0015] FIG. 10 illustrates a method for generating an object recognition message according to one embodiment of the present disclosure.

[0016] FIG. 11 illustrates information included in an object perception message according to one embodiment of the present disclosure.

[0017] FIG. 12 illustrates a method performed by a first device according to one embodiment of the present disclosure.

[0018] FIG. 13 illustrates a method performed by a second device according to one embodiment of the present disclosure.

[0019] Fig. 14 illustrates a communication system (1) according to one embodiment of the present disclosure.

[0020] FIG. 15 illustrates a wireless device according to an embodiment of the present disclosure.

[0021] FIG. 16 illustrates a signal processing circuit for a transmission signal according to one embodiment of the present disclosure.

[0022] FIG. 17 illustrates a wireless device according to one embodiment of the present disclosure.

[0023] FIG. 18 illustrates a mobile device according to one embodiment of the present disclosure.

[0024] FIG. 19 illustrates a vehicle or autonomous vehicle according to one embodiment of the present disclosure.

[0025] 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."

[0026] 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."

[0027] 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".

[0028] 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.”

[0029] 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."

[0030] In the following explanation, ‘when, if, in case of’ can be replaced with ‘based on’.

[0031] Technical features individually described in a single drawing in this specification may be implemented individually or simultaneously.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] FIG. 1 illustrates a communication structure that can be provided in a 6G system according to one embodiment of the present disclosure. The embodiment of FIG. 1 can be combined with various embodiments of the present disclosure.

[0037] As core implementation technologies of the 6G system, technologies such as artificial intelligence (AI), THz (terahertz) communication, optical wireless technology, free-space optical transmission (FSO) backhaul networks, massive MIMO (multiple input multiple output) technology, blockchain, 3D networking, quantum communication, unmanned aerial vehicles, cell-free communication, wireless information and energy transfer (WIET), integration of sensing and communication, integration of access backhaul networks, holographic beamforming, big data analysis, and large intelligent surface (LIS) can be adopted.

[0038] - 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. For example, 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. AI can also 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.

[0039] - THz communication (terahertz communication): Data rates can be increased by increasing the bandwidth. This can be achieved by using sub-THz communication with wide bandwidths 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 key part of the THz spectrum 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. Key characteristics of THz communications include (i) the widely available bandwidth to support very high data rates and (ii) the high path loss that occurs at high frequencies (requiring highly directional antennas). The narrow beamwidths generated by highly directional antennas reduce interference. The small wavelength of THz signals allows for a significantly larger number of antenna elements to be integrated into devices and base stations operating in this band. This enables the use of advanced adaptive array technologies to overcome range limitations.

[0040] - Large-scale MIMO technology

[0041] - Hologram beamforming (HBF)

[0042] - Optical wireless technology

[0043] - Free-space optical transmission backhaul network (FSO backhaul network)

[0044] - Quantum communication

[0045] - Cell-free communication

[0046] - Integration of wireless information and power transmission

[0047] - Integration of wireless communication and sensing

[0048] - Integrated access and backhaul network

[0049] - Big data analysis

[0050] - Reconfigurable intelligent surface

[0051] - metaverse

[0052] - Block chain

[0053] Advanced Air Mobility (AAM): AAM can be a broad concept encompassing urban air mobility (UAM), regional air mobility (RAM), and uncrewed aerial systems (UAS). For example, AAM can include UAM, RAM, UAS, and uncrewed aerial vehicles (UAVs).

[0054] - 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) wireless communication and vehicle to infrastructure (V2I) wireless communication.

[0055] Non-terrestrial network (NTN): NTN can refer to a network or network segment that utilizes radio frequency (RF) resources mounted on satellites (or UAS platforms). NTN services may be considered to secure wider coverage or provide wireless communication services in locations where the installation of wireless communication base stations is difficult.

[0056] - Integrated sensing and communication (ISAC): Wireless sensing is a technology that uses radio frequencies to determine the instantaneous linear velocity, angle, distance (range), etc. of an object, thereby obtaining information about the characteristics of the environment and / or objects within the environment.

[0057] - Reconfigurable intelligent surface (RIS): RIS can be used to manipulate and enhance signal propagation in wireless communication environments. For example, a RIS can be composed of many small antennas, or metasurfaces, arranged on a surface, each of which can actively control the phase, amplitude, polarization, etc. of the reflected signal. For example, a RIS can improve signal reception by controlling the path, phase, and / or intensity of the propagating signal. For example, in the case of a RIS, power consumption can be very low because power is consumed only for controlling the phase and amplitude of the small antennas. For example, because a RIS can be reconfigured to suit different environments, it can meet diverse communication requirements and operate effectively in dynamic network environments.

[0058] FIG. 2 illustrates an example of a communication scenario based on a 6G system, according to an embodiment of the present disclosure. The embodiment of FIG. 2 may be combined with various embodiments of the present disclosure.

[0059] Referring to FIG. 2, NTN communication can be performed based on satellite networks, high-altitude platform stations (HAPS) as international mobile telecommunications (IMT) base stations (BS), terminals capable of aerial communication (e.g., AAM), etc. For example, to improve coverage, etc., devices such as satellite networks, HIBS, terminals capable of aerial communication (e.g., AAM), etc. can act as relays. For example, AAMs can communicate with base stations, satellite networks, etc., and / or AAMs can communicate directly with terminals, other AAMs, etc.

[0060] Below, the integrated sensing and communication (ISAC) mentioned above is described in detail.

[0061] Integrated Sensing and Communications (ISAC) is a technology that uses radio frequencies to determine the instantaneous linear velocity, angle, distance (range), etc. of an object, thereby obtaining information about the environment and / or the characteristics of objects within the environment. Because radio frequency sensing does not require a device to connect to the object through a network, it can provide services for object positioning without a device. The ability to obtain range, velocity, and angle information from radio frequency signals can enable a wide range of new capabilities, such as various object detection, object recognition (e.g., vehicles, humans, animals, UAVs), and high-precision localization, tracking, and activity recognition. Wireless sensing services can provide information to a variety of industries (e.g., unmanned aerial vehicles, smart homes, V2X, factories, railways, public safety, etc.), enabling applications such as intruder detection, assisted vehicle steering and navigation, trajectory tracking, collision avoidance, traffic management, and health and traffic management. In some cases, wireless sensing can utilize non-3GPP type sensors (e.g., radar, cameras) to further support 3GPP-based sensing. For example, the operation of a wireless sensing service, e.g., a sensing operation, may depend on the transmission, reflection, and scattering of wireless sensing signals. Therefore, wireless sensing may provide an opportunity to enhance existing communication systems from a communication network to a wireless communication and sensing network. FIG. 3 illustrates an example of a sensing operation according to an embodiment of the present disclosure. The embodiment of FIG. 3 may be combined with various embodiments of the present disclosure. Specifically, FIG. 3 (a) illustrates an example of sensing using a sensing receiver and a sensing transmitter located at the same location (e.g., monostatic sensing), and FIG. 3 (b) illustrates an example of sensing using a separated sensing receiver and a sensing transmitter (e.g., bistatic sensing).

[0062] FIG. 4 is a diagram for explaining and comparing V2X communication based on RAT prior to NR and V2X communication based on NR according to one embodiment of the present disclosure. The embodiment of FIG. 4 can be combined with various embodiments of the present disclosure.

[0063] In relation to V2X communication, in RATs 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. 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.

[0064] 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, and 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.

[0065] Since then, various V2X scenarios have been proposed in NR in relation to V2X communications. For example, various V2X scenarios may include vehicle platooning, advanced driving, extended sensors, and remote driving.

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] According to one embodiment of the present disclosure, for example, an object detection / perception system and / or tracking system for general vision information and / or an artificial intelligence system for object recognition / tracking may have a problem in that the performance and accuracy of object recognition or tracking deteriorates when an obstacle such as a fixed structure forming a road marking such as a crosswalk or lane or a traffic light or a sign overlaps with a vehicle or pedestrian.

[0072] FIG. 5 is a diagram illustrating results before and after performing operations related to object detection / perception or object tracking according to one embodiment of the present disclosure. The embodiment of FIG. 5 can be combined with various embodiments of the present disclosure.

[0073] According to one embodiment of the present disclosure, referring to FIG. 5, for example, when a pedestrian suddenly runs on a crosswalk, motion blur may occur due to the orange crosswalk marking and / or the high-speed movement of the pedestrian or moving object, and / or there may be cases where object recognition and tracking performance is reduced.

[0074] For example, to solve the problems of these cases, high-definition or high-performance cameras can be installed, or other sensor devices such as LiDAR (light detection and ranging) and radar can be additionally installed, and there may be a method to perform sensor fusion, but these may entail high costs and inconvenience.

[0075] According to one embodiment of the present disclosure, an object of a relatively large scale (e.g., 32 x 32 pixels or more than 1 / 10 of the total width of the object) in an image or video to be detected and tracked may be a general object that does not move at a high speed with a large width (e.g., moves at a width not exceeding the size of the object bounding box per second) and may be a target of an object recognition and / or tracking system. For example, research may be conducted to track small objects or FMOs (Fast Moving Objects) (TSFMO, Tracking Small and Fast Moving Object), and research may be conducted for the purpose of detecting and tracking balls or shuttlecocks in sports scenarios such as table tennis, badminton, golf, basketball, and polo. For example, it can be named as TbD (Tracking by Deblatting), and Debatting can be proposed as an abbreviation of deblurring and matting, and TbD-NC (Non-Casual Tracking by Deblatting) can be improved upon by using dynamic programming algorithm techniques to predict the trajectory of a fast moving object (FMO).

[0076] According to one embodiment of the present disclosure, in a sports game use case, studies can be conducted on patterns in which a ball or shuttlecock suddenly changes its direction of movement or moves in parabolic curves due to gravity by bouncing off structures such as the floor or walls or the bodies of players. This can be significantly different from the movement patterns of vulnerable road users (VRUs) moving along the ground, and thus can be significantly different from existing studies. Since the present disclosure mainly targets the use of closed-circuit television (CCTV) installed on a fixed structure of a road side unit (RSU), a technology for removing the background (background subtraction / removal) by comparing a background model without a moving object with a current image frame can be utilized, and it may be appropriate to utilize such a technology. And, for example, tracking a foreground object such as a small, brightly colored ball on a stadium with a monotonous colored background may require different techniques than detecting and tracking a Vulnerable Road User (VRU) moving closer or farther away from a CCTV on a road side unit (RSU), and road marking patterns such as crosswalks may need to be removed first to apply a deblurring technique.

[0077] According to one embodiment of the present disclosure, for example, the technology(s) for detecting a fast moving object (FMO) may mainly apply a semantic segmentation technique, or may be implemented with a pipeline structure of trajectory tracking after blur removal, but the system may be overly complex and the detection speed may be slow when applied to a situation where a fast moving object is detected in real time on a road and a collision is detected. In addition, for example, in the case of a road side unit (RSU) CCTV, since it is installed at a relatively long distance, in order to detect a vulnerable road user (VRU) crossing or passing by on the road, there may be a combination of a detector and a tracker that can detect and track a small and fast moving object.

[0078] According to one embodiment of the present disclosure, in order to implement a simpler service, a technology may be performed to estimate and / or notify a collision risk between road users by using the object detection result or POI (point of interest) information(s) in the RSU (road side unit) or server. For example, the RSU (road side unit) may be installed in fixed areas such as crosswalks and school zones, and the object detection result or POI information(s) may be initially set and stored in the RSU (road side unit) or server, and / or may be used for V2X (vehicle-to-everything) services, etc. In this case, for example, the collision probability for all combinations of pedestrians and all vehicles within the detection area, or at least for all combinations of pedestrians and vehicles that satisfy a specific condition (e.g., when the expected collision time or TTC (Time To Collision) (this threshold (e.g., braking time) is less than or equal to) may be estimated in the RSU (road side unit) or server.

[0079] In the present disclosure, a method and device may be proposed that can remove motion blur and / or road marking patterns in at least one region where a vulnerable road user (VRU) is expected to exist or move in a region of interest (ROI) of a road side unit (RSU) that needs to improve object recognition and tracking performance, and / or learn or infer the same.

[0080] According to one embodiment of the present disclosure, in addition to performing general tracking for detecting variable distance or fast moving objects that are getting closer or farther away, special tracking utilizing a detector and tracker learned by a method and device that can be configured with a convolutional neural network that performs convolution operations suitable for small and fast moving objects can be performed, and / or a method and device that complements the recognition and / or tracking performance that can switch between general tracking and / or special tracking can be proposed.

[0081] According to one embodiment of the present disclosure, a collision risk can be estimated from detection and / or tracking information results from a detector and / or tracker (e.g., performing general tracking or special tracking). For example, the collision risk estimation (message / information) can be performed in a roadside unit (RSU) or a server and transmitted to a neighboring roadside unit (RSU) or another server.

[0082] According to one embodiment of the present disclosure, when a risk is detected as a result of the estimation (e.g., when the risk of collision is greater than or equal to a threshold, when the (representative value of) the probability of collision risk is greater than or equal to a threshold), a message (e.g., a decentralized environmental notification message (DENM), a roadside safety message (RSM), etc.) that transmits cognitive data to a relevant vulnerable road user (VRU) or vehicle can be transmitted, thereby warning of the risk of collision and reducing the occurrence of an accident.

[0083] According to one embodiment of the present disclosure, it may be proposed that at least one of the operation(s) proposed above be transmitted as included in a service message (e.g., a collective perception message (CPM) or a sensor data sharing message (SDSM)) that shares information related to the detected object.

[0084] In the present disclosure, for example, a method and device for improving the performance of detecting and tracking high-speed moving objects to prevent collisions between high-speed moving vulnerable road users (VRUs) and vehicles, and a method and device for including collision risk (prevention) information in a V2X (vehicle-to-everything) message and providing it to the relevant road users may be proposed. For example, high-performance sensor equipment may be installed, or by utilizing an inference engine and / or a tracker composed of compositely learned detectors without the need for additional sensor equipment, the problems and performance of an object recognition and tracking system may be improved, thereby improving traffic safety and efficiency.

[0085] FIG. 6 illustrates a method for performing wireless communication according to an embodiment of the present disclosure. The embodiment of FIG. 6 may be combined with various embodiments of the present disclosure.

[0086] In order to explain a detailed application method of the present disclosure, for example, according to <Fig. 6>, a case of a pedestrian (VRU (vulnerable road user)) waiting in front of a crosswalk and then suddenly running away can be explained as an application example in a CCTV installed in an RSU (road side unit). For example, the waiting pedestrian (e.g., VRU) was detectable and / or trackable, but if the pedestrian suddenly runs away, the performance of both detection and / or tracking may deteriorate, so that detection and / or tracking may be possible only intermittently, and / or it has been experimentally confirmed that object detection or tracking is performed (by linking with vision AI equipment) through one embodiment of the present disclosure.

[0087] Referring to FIG. 6, according to one embodiment of the present disclosure, for example, the target (object) selection and / or correction module (100) can remove motion blur and / or road marking patterns in an area where a vulnerable road user (VRU) is expected to exist or move within a region of interest (ROI) of a road side unit (RSU) that needs to improve object recognition and tracking performance. For example, in order to precisely detect / track a fast moving object (FMO) (e.g., a pedestrian), not only can the motion blur around the object (e.g., a pedestrian) be removed, but also a fixed area of ​​the background (e.g., a crosswalk marking pattern) can be removed, so that better detection and tracking performance can be expected.

[0088] According to one embodiment of the present disclosure, for example, in the preliminary step (111) of designating a region of interest (ROI), if designating a region of interest is not necessary, it may be omitted, and for example, the region of interest may be designated as the entire image. For example, (in the case of CCTV) background model detection (112) without a front object may be possible, and for example, the current image frame may be cropped for the region of interest and extracted (113), and for example, the forward mask may be extracted (114) in a format in which the extracted image and the background model are differentiated and compared with a threshold value. For example, by extracting in this format, road marking patterns such as crosswalks may be removed (115), and for example, if the marking pattern has a certain color and shape, only the marking pattern may be excluded. For example, since it can target CCTV installed on a fixed structure of a road side unit (RSU), a technique for removing the background by comparing the current image frame with a background model without moving objects can be used to separate the background and the foreground, and / or if a foreground mask is extracted, additional work for removing road marking patterns may not be required. For example, in addition to an embodiment for removing road marking patterns, an embodiment(s) for backgrounds that interfere with detection may also be included. For example, deblurring of surrounding fixed environments such as street trees, facilities, and parked vehicles may be proposed.

[0089] According to one embodiment of the present disclosure, for example, if the CCTV camera itself shakes, motion blur (e.g., background blur) may occur in the entire image, and thus the blur of the entire image may need to be removed. However, for example, since the CCTV of a road side unit (RSU) structure may be installed fixed to the floor, only the motion blur (e.g., foreground blur) of a running pedestrian may need to be removed (116). Therefore, for example, the blur may be removed through a deblatting (deblurring and matting) technique that distinguishes the background and the VRU. However, for example, instead of blurring the entire screen, deblurring may be performed only in a region of interest (ROI) such as a crosswalk or a point where accidents frequently occur, thereby improving the performance of detection and / or tracking.

[0090] FIG. 7 illustrates a method for performing 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.

[0091] Referring to FIG. 7, according to one embodiment of the present disclosure, for example, in the case of a high-speed moving object, since the frequency of motion blur may be high, if a step of removing it is performed, object detection and / or tracking performance may be improved, and for example, in the case of a small or distant object, since the number of pixels that distinguish features constituting the object may be small, the number of feature pixels may be increased, or a super resolution or upscaling step may be applied to improve precision, and if this step is performed, object detection and / or tracking performance may be improved. For example, the application of super resolution or upscaling to improve performance may be performed in units of bounding boxes or segmentation maps.

[0092] According to one embodiment of the present disclosure, referring to the flowchart of FIG. 7 to explain in various embodiments(s) the order of operations (e.g., steps 100 and 200) that complement the performance of detecting and / or tracking high-speed moving or distant objects, for example, the speed of a moving object can be estimated or measured, for example, through a difference operation of consecutive frames, and the speed can also be measured, for example, using a motion sensor or other methods. For example, equipment such as a depth camera or radar, lidar can be utilized to estimate or measure the distance of a moving object, and for example, this process can be omitted if there is no sensor for additional depth measurement. For example, by comparing the speed, size, distance, etc. of the moving object with a threshold, a high-speed or long-distance object can be detected, and for example, blurring can be removed in the case of a high-speed moving object, or for example, object detection and / or tracking performance can be supplemented by applying super-resolution or upscaling in the case of a long-distance object. For example, if there is an additional sensor capable of additional sensor fusion, it may be possible to supplement the detection and / or tracking performance of a high-speed or long-distance object by utilizing it.

[0093] According to one embodiment of the present disclosure, (e.g., in the second step of the proposed disclosure), the general / special recognition switching precision detection / tracking module (200) can perform special tracking consisting of a convolutional neural network that performs convolution operations suitable for small and fast moving objects in addition to performing general tracking for detecting fast moving objects (FMO) (vulnerable road users (VRU)) that are getting closer or farther away. For example, (e.g., in the first step), a video image from which motion blur and / or road marking patterns of an area of ​​interest have been removed can be input (211) to the recognition unit of the precision detection / tracking module (200).

[0094] According to one embodiment of the present disclosure, if a running pedestrian (in the above case) moves away from the CCTV and / or if the general detection and / or tracking deteriorates (e.g., if the bounding box size is less than or equal to a threshold or the depth is less than or equal to a threshold depth), the performance of the detection and / or tracking may be improved by switching to special detection and / or tracking (212). Conversely, for example, if a running pedestrian moves closer to the CCTV and / or if the bounding box size is greater than or equal to a threshold or the depth is greater than or equal to a threshold depth, the performance of the detection and / or tracking may be improved by switching to general detection and / or tracking (212).

[0095] According to one embodiment of the present disclosure, general or special detection and / or tracking may differ in the size of the convolution kernel of the artificial intelligence neural network, or in the size and annotation size of the object in the image used in the learning stage. Or, for example, the artificial intelligence neural network may be the same, but there may be differences in the detection and / or tracking operation algorithm. For example, due to the difference between general and special detection and / or tracking, if the appropriate detector and / or tracker is switched (handover) at an appropriate time depending on the size of the fast-moving object, the object that is rapidly approaching or moving away can be quickly recognized and tracked.

[0096] According to one embodiment of the present disclosure, (in the third step of the proposed disclosure) the collision risk determination unit (300) can estimate (301) the collision risk from the detection and / or tracking information results of the detector and / or tracker (in the second step), and, for example, if the estimation result indicates a risk, the collision risk warning transmission unit (400) (in the fourth step) can warn (401) of the collision risk by transmitting a V2X (vehicle-to-everything) service event trigger message (e.g., a decentralized environmental notification message (DENM), a roadside safety message (RSM), etc.) that transmits collision warning data to a related vulnerable road user (VRU) or vehicle.

[0097] According to one embodiment of the present disclosure, information detected by applying the method proposed above may be included in a V2X (vehicle-to-everything) service periodic message (e.g., CPM (collective perception message), SDSM (sensor data sharing message)) that periodically transmits object information detected by a sensor (e.g., after performing steps 1 and 2 of disclosure), and may be transmitted (501).

[0098] FIG. 8 illustrates information included in an object perception message according to an embodiment of the present disclosure. The embodiment of FIG. 8 may be combined with various embodiments of the present disclosure.

[0099]

[0100] Referring to FIG. 8, for example, in the case where high-speed moving object detection and / or tracking information is missing in an object detection and / or tracking technology according to an embodiment of the present disclosure, the information may not be loaded into the Perception Data Container of the CPM (collective perception message) / SDSM (sensor data sharing message). For example, in the case of the CPM (collective perception message), if the proposed method is utilized, a perception data message may be transmitted to a vulnerable road user (VRU) or a vehicle, and, for example, the information may be contained in a Perceived Object in the Perceived Object Container of the Perception Data Container of FIG. 8, and the message receiving terminal may be able to implement standard features such as a collision warning or threat assessment.

[0101] According to one embodiment of the present disclosure, the proposed technology can improve detection accuracy and confidence by combining general and specialized detection techniques. As detection accuracy increases, traceability, accuracy, and confidence, which are directly affected by detection performance, can also increase. Furthermore, for example, additional message fields may be generated depending on the detection and / or tracking technique, which can affect, for example, the baseline accuracy / confidence level or the threshold for redundancy mitigation.

[0102] FIG. 9 illustrates a method for generating an object recognition message according to an embodiment of the present disclosure. The embodiment of FIG. 9 may be combined with various embodiments of the present disclosure. FIG. 10 illustrates a method for generating an object recognition message according to an embodiment of the present disclosure. The embodiment of FIG. 10 may be combined with various embodiments of the present disclosure.

[0103] Referring to FIG. 9 or FIG. 10 , information of a detection and / or tracking technique according to an embodiment of the present disclosure may be provided by utilizing a new protocol / message and / or message (e.g., a decentralized environmental notification message (DENM), a roadside safety message (RSM), a collective perception message (CPM), a sensor data sharing message (SDSM), etc.), and / or a combination of message(s) (e.g., a decentralized environmental notification message (DENM), a roadside safety message (RSM), a collective perception message (CPM), a sensor data sharing message (SDSM), etc.), a combination of new messages, or a combination of messages (e.g., a decentralized environmental notification message (DENM), a roadside safety message (RSM), a collective perception message (CPM), a sensor data sharing message (SDSM), etc.) and a new message.

[0104] For example, a CPM message can be generated according to the method of <Fig. 9> or <Fig. 10>. For example, <Fig. 9> can be a message generation flowchart for a single channel, and <Fig. 10> can be a message generation flowchart for multiple channels.

[0105] According to one embodiment of the present disclosure, the proposed new or additional message / data element / data frame may be defined as an example in . For example, it may be proposed that additional information about an object detected by the operation and method proposed in the present disclosure (e.g., general detection, general tracking, special detection, special tracking, mixed mode, etc.) be included in the message.

[0106] Descriptive NameperceptionSchemeTypeASN.1 repersentationPerceptionSchemeType : := INTEGER {undefined(0),generalDetectionOnly(1),generalTrackingOnly(2),featuredTSFMOwithDeblurringDetectionOnly(3),featuredTSFMOwithDeblurringTrackingOnly(4),generalDetectionGerenalTracking(5),generalDetectionFeaturedTSFMOwithDeblurringTracking(6),featuredTSFMOwithDeblurringDetectionGeneralTracking(7),featuredTSFMOwithDeblurringDetectionFeaturedTSFMOwithDeblurringTracking(8)} (0..255)DefinitionDescribes the type of the perception scheme or convolution neural network. It can be set to one of the above, or higher value to set any different featured type. The general term means conventional scheme, one the other hand, the feature term is used for the DSFMO or TSFMO with deblurring scheme. DSFMO stands for Detecting Small and Fast Objects, and TSFMO stands for Tracking Small and Fast Objects.Unit-

[0107] FIG. 11 illustrates information included in an object perception message according to an embodiment of the present disclosure. The embodiment of FIG. 11 may be combined with various embodiments of the present disclosure.

[0108] According to one embodiment of the present disclosure, if a new field is added to a collective perception message (CPM), a Perception Scheme field may be added in front of the Perceived Objects field in the CPM structure (see <Fig. 11>) as a field for distinguishing between general and special detection and / or tracking techniques. The Perception Scheme field may distinguish between general and special techniques, and may additionally be defined to distinguish between both detection and tracking, if necessary. For example, if the value of a field is 0 (e.g., as in ), it can be designated as the default value that is not specified, 1 for using only general detection, 2 for using only general tracking, 3 for using only special detection (DSFMO (detecting small and fast object) with deblurring), 4 for using only special tracking (TSFMO (tracking small and fast object) with deblurring), etc.), and various combinations of techniques can be distinguished. For example, by providing such information, it can be helpful for service users to trust and utilize the information they receive.

[0109] Referring to FIG. 11, according to one embodiment of the present disclosure, for example, when information on a detected object, for example, a pedestrian, is transmitted, basically, information on the detection time, location, speed, direction, etc. may be transmitted, and in the case of a stopped pedestrian, information on speed, direction, etc. may not be displayed, may be expressed as 0, or (in the case of direction) an arbitrary value may be expressed, or a previous value may be expressed.

[0110] According to one embodiment of the present disclosure, if a stopped pedestrian is no longer tracked, it may mean that he or she has physically or image-wise (in terms of detection) disappeared.

[0111] According to one embodiment of the present disclosure, being visually lost may mean being out of the range of the image or may mean being undetectable within the image.

[0112] According to one embodiment of the present disclosure, if it is determined that the pedestrian has gone out of the range of the image through tracking of the pedestrian and estimation of the expected path, the occurrence of a handover between cameras (or sensors) may be notified to a surrounding road side unit (RSU) / server, etc., and the RSU (road side unit) may no longer perform tracking and management.

[0113] According to one embodiment of the present disclosure, in the case where an object exists within the range of an image but is not detected, the time of final detection and the speed and direction information at that time may be the most up-to-date information, and the risk of collision, etc. may be estimated using this information.

[0114] According to one embodiment of the present disclosure, for example, when a specific (stationary or moving) pedestrian is between several set areas or crosswalks, it can be determined whether the pedestrian is currently in an area associated with the RSU (road side unit) (i.e., a target for estimating collision risk, etc.) or moving toward a crosswalk or moving toward an unrelated area or crosswalk, and / or estimated information about the pedestrian can be generated and / or transmitted.

[0115] According to one embodiment of the present disclosure, for example, in the case of a stopped pedestrian, it may be estimated which area or crosswalk to use based on direction information before stopping, and if the direction information does not exist or is invalid (for example, if the pedestrian continues to spin in place), the area or crosswalk at the point that is the shortest distance from the pedestrian may be selected.

[0116] According to one embodiment of the present disclosure, the RSU (road side unit) or server may transmit information about the final location and time of discovery or the expected location and related time of the pedestrian expected to be moving in the associated area or crosswalk.

[0117] According to one embodiment of the present disclosure, based on the information on the set area and the detected / tracked pedestrian, the risk of collision, etc. may be simply estimated by the RSU (road side unit) or server. For example, or in order to more accurately determine the possibility of collision, etc., and / or to reduce the computational load on the RSU (road side unit) / server, the risk of collision may be directly estimated for a vehicle approaching the specific area or crosswalk based on the information on the set area and the detected / tracked pedestrian received (or already known) as described above.

[0118] According to one embodiment of the present disclosure, for example, the speed of a pedestrian who is thought to be moving from a previous detection point due to the non-detection may be the last detected speed or may be determined by some other representative value (e.g., the average walking speed or running speed of the pedestrian).

[0119] According to one embodiment of the present disclosure, a first image (a first frame, a first time) and / or a second image (a second frame after the first frame, a first time after the first time) can be acquired. For example, features (points) / regions of interest related to an object / a non-object background can be extracted from the first image and / or the second image. For example, a bounding box for an object can be acquired based on the extracted features (points) / regions of interest.

[0120] For example, the first image and / or the second image may be a data structure expressed in 2D or 3D. For example, in the case of 2D / 3D, the first image and / or the second image may be expressed as at least one of a point cloud, a mesh, a pixel / voxel, an implicit surface, a light field, and a volumetric field.

[0121] For example, the image (frame) may be preprocessed prior to the acquisition / extraction / recognition. For example, the image (frame) may be converted to grayscale / R / G / B, etc. For example, the image (frame) may be subjected to noise removal through an erosion operation to remove noise in dark areas, a dilation operation to remove noise in bright areas, a dilation operation after an erosion operation, an erosion operation after a dilation operation, etc.

[0122] For example, by comparing the features (points) or bounding boxes (boundary points) of moving / static objects / backgrounds with the features (points) or bounding boxes (boundary points) and the (self-)trained model through (self-)supervised learning / unsupervised learning (e.g., if the difference between the two is less than a threshold), it can be recognized what object the feature (point) or bounding box (boundary point) is.

[0123] For example, based on the difference between an image containing a static object / background and the current image (the object / background within the region of interest within it), it can be extracted whether the object is a moving object or a static object / static background. For example, a region where the result of the difference (e.g., brightness difference in grayscale) is greater than a threshold value can be distinguished / recognized as a moving object. For example, a region where the result of the difference (e.g., brightness difference in grayscale) is less than a threshold value can be distinguished / recognized as a static object / static background.

[0124] For example, points with similar differences between an image containing static objects / backgrounds and the current image (objects / background within a region of interest) can be grouped / clustered. For example, among points with similar differences between an image containing static objects / backgrounds and the current image (objects / background within a region of interest), areas with differences greater than a threshold can be grouped / clustered into their respective moving objects. For example, among points with similar differences between an image containing static objects / backgrounds and the current image (objects / background within a region of interest), areas with differences less than a threshold can be grouped / clustered into their respective static objects / static backgrounds. For example, clustering can be classified based on the (mean) velocity / displacement of the points. For example, high-velocity / high-displacement point clusters can be clustered into their respective moving objects, and no-velocity / no-displacement clusters can be clustered into their respective static background / objects.

[0125] For example, based on optical flow, it can be distinguished / converted whether the object is a moving object or a static object / static background, or the area where the moving object overlaps the static object / static background can be extracted. For example, the path along which at least one of a feature (point) or a bounding box (a boundary point) moves over time (frame) can be tracked. For example, it can be distinguished / converted whether the object is a moving object or not based on the position (change) / velocity (change) / acceleration (change) / angular velocity (change) of at least one of a feature (point) or a bounding box (a boundary point). For example, the change over time of a feature (point) or a bounding box (a boundary point) can be expressed as a motion vector. For example, by estimating / tracking the motion vector, it can be detected / recognized whether the object is a moving object or not, and if so, which moving object it is. For example, a point (pixel) without the motion vector can be detected / recognized as a static background.

[0126] However, for example, if the movement path of the tracked moving object in the image is very short, or if the speed of the tracked moving object in the image is too fast, or if the object in the image is a moving object but the feature points / bounding box (points within) are obscured by a stationary object / background, or if the size of the object in the image is too small (e.g., a distant object), the performance of object detection / recognition may be reduced due to blurring or feature (point) loss.

[0127] Therefore, according to one embodiment of the present disclosure, a method or a data augmentation method can be proposed for distinguishing whether a stationary object / background is a stationary object / background that obscures a moving object, and selectively and conditionally removing only the distinguished occluding object / background.

[0128] For example, in a case where a moving object (clustered point cloud, representative motion vector, boundary point box, etc.) having a motion vector overlaps with a static object / background (clustered point cloud, representative motion vector, boundary point box, etc.) without a motion vector (e.g., a ratio of moving objects (clustered point cloud, representative motion vector, boundary point box, etc.) having a motion vector among static objects / background (clustered point cloud, representative motion vector, boundary point box, etc.) without a motion vector is greater than a threshold value), a removal operation for the overlapping static object / background (mask) and / or a restoration / filling operation for the dynamic object (mask) can be performed. For example, if there is no change (pixel, voxel) over time in an area where a static object / background without motion vectors (clustered point cloud, representative motion vector, boundary point box, etc.) and a moving object with motion vectors overlap (e.g., when the change value is below a threshold), the moving object may be determined to be occluded by the static object / background, or a removal operation may be performed on the overlapping static object / background and / or a restoration / filling operation may be performed on the dynamic object.

[0129] For example, a restoration / filling operation for the overlapping static / background into a dynamic object can be performed based on images (frames) of the previous time in which the dynamic object / background moved. For example, after the restoration / filling operation for the overlapping static / background into a dynamic object, a pixel (voxel) removal operation for the static object / background overlapping on the restored / filled dynamic object can be performed.

[0130] For example, the operation(s) according to an embodiment of the present disclosure (e.g., a motion blur removal operation, a removal operation for the overlapping static object / background, and / or a restoration / peeling operation for the dynamic object) may be performed when a quality parameter for detection of the dynamic object (e.g., precision, recall, F1 score, confidence score, IoU (intersection over union) between the image frame (bounding box) of the object predicted by the (learning) model and the image frame (bounding box) of the actual object, etc.) is below a threshold value (which may vary for each parameter). Through this, traffic accidents caused by slow collision risk judgment due to overload and computational processing caused by unconditionally performing the removal / restoration operation for the static object / background overlapping on the movement line of the dynamic object can be reduced.

[0131] For example, the operation(s) according to an embodiment of the present disclosure (e.g., a removal operation for the overlapping static object / background and / or a restoration / peeling operation for the dynamic object) may be performed when a value for each color constituting the overlapping static object / background (or a part thereof) is greater than or equal to a threshold value (e.g., when at least one value among grayscale (brightness) / R / G / B among the colors of the (feature points) of the crosswalk and the asphalt road overlapping the pedestrian is greater than or equal to a threshold value), and / or when a quality parameter for detection of the dynamic object (e.g., precision, recall, F1 score, confidence score, intersection over union (IoU) between the image frame (bounding box) of the object predicted by the (learning) model and the image frame (bounding box) of the actual object, etc.)) is less than or equal to a threshold value (which may vary for each parameter). Through this, traffic accidents caused by slow collision risk judgment due to overload and computational processing caused by unconditional removal / restoration operations performed on static objects / backgrounds overlapping the movement line of dynamic objects can be reduced.

[0132] For example, the operation(s) according to one embodiment of the present disclosure (e.g., a motion blur removal operation, a removal operation for the overlapping static object / background, and / or a restoration / peeling operation for the dynamic object) may be performed when a value (brightness in the case of grayscale, saturation of each color in the case of colors such as RGB) for each point (pixel, voxel) constituting the detected dynamic object is greater than or equal to a threshold value (which may vary by parameter). Through this, traffic accidents caused by slow collision risk judgment due to overload and computational processing caused by unconditionally performing a removal / restoration operation for static objects / backgrounds overlapping on the movement line of the dynamic object can be reduced.

[0133] For example, the operation(s) according to one embodiment of the present disclosure (e.g., a motion blur removal operation, a removal operation for the overlapping static object / background, and / or a restoration / filling operation for the dynamic object) may be performed based on a ratio of moving objects (clustered point clouds, representative motion vectors, boundary point boxes, etc.) with motion vectors among static objects / backgrounds (clustered point clouds, representative motion vectors, boundary point boxes, etc.) with no or few motion vectors being greater than a threshold value. For example, if there is no change (pixel, voxel) over time in an area where a static object / background without a motion vector (clustered point cloud, representative motion vector, boundary point box, etc.) and a moving object with a motion vector overlap (e.g., when the change value is below a threshold value), a removal operation for the overlapping static object / background (mask) and / or a restoration / filling operation for the dynamic object (mask) can be performed. Through this, traffic accidents caused by slow collision risk judgment due to overload and computational processing caused by unconditionally performing removal / restoration operations on static objects / backgrounds overlapping on the movement line of dynamic objects can be reduced.

[0134] For example, the operation(s) according to one embodiment of the present disclosure (e.g., motion blur removal operation, removal operation for the overlapping static object / background, and / or restoration / peeling operation for the dynamic object) may be performed within a specific time region. For example, the specific time region may be obtained based on traffic information (e.g., operation / blinking time of red / yellow / green traffic lights). For example, the specific time region may be obtained based on a value obtained by adding or subtracting a correction value to the travel time of a moving object calculated through traffic information. Through this, traffic accidents caused by slow collision risk judgment due to overload and computational processing caused by unconditionally performing removal / restoration operations for static objects / backgrounds overlapping on the movement line of a dynamic object can be reduced.

[0135] For example, the specific time region can be obtained based on the (predicted / measured / defined) speed information (e.g., average speed, maximum speed, etc.) of the corresponding dynamic object and the length information of the (occluding) static object / background (e.g., the length of the horizontal line / vertical line / diagonal line / center line (connecting line from the center to the vertex) of the bounding box, and which length among the horizontal line / vertical line / diagonal line / center line length can be used can be determined based on how much the direction of the movement path of the dynamic object and the direction of the horizontal line / vertical line / diagonal / center line match each other (e.g., whether the difference between the directions (angles) is less than or equal to a threshold value)). For example, the length of the specific time region can be a value obtained by dividing the length obtained through the length information of the corresponding static object by the speed obtained through the speed information of the corresponding dynamic object. For example, the start time of the specific time region can be the time when the moving speed of the corresponding dynamic object becomes less than or equal to a threshold value. For example, the start time of the specific time region can be the time when the position of the corresponding dynamic object is within a specific position range. Through this, traffic accidents caused by slow collision risk judgment due to overload and computational processing caused by unconditional removal / restoration operations performed on static objects / backgrounds overlapping the movement line of dynamic objects can be reduced.

[0136] According to one embodiment of the present disclosure, the problem of deteriorating object detection quality due to a fixed background region overlapping within the moving area of ​​a mobile object can be prevented. For example, by conditionally removing a fixed background region overlapping within the moving area of ​​a mobile object, both the performance of object detection and the speed of object detection can be harmonized.

[0137] FIG. 12 illustrates a method performed by a first device according to one embodiment of the present disclosure. The embodiment of FIG. 12 may be combined with various embodiments of the present disclosure.

[0138] Referring to FIG. 12, according to an embodiment of the present disclosure, in step S1210, for example, the first device may obtain first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time. In step S1220, for example, the first device may generate third information related to a blocking object and fourth information related to a filling area regarding the blocking object based on the first information and the second information. In step S1230, for example, the first device may perform detection of at least one moving object within a third area regarding a third time. In step S1240, for example, the first device may generate an object recognition message related to the at least one detected moving object. For example, the detection of the at least one moving object may be performed within a fourth region in which the blocking region associated with the blocking object among the third regions for the third time is replaced with the filling region, based on the third information associated with the blocking object and the fourth information associated with the filling region.

[0139] Additionally or alternatively, the object recognition message may include a collective perception message (CPM).

[0140] Additionally or alternatively, the object recognition message may include perception scheme type information related to a detection method for the at least one moving object.

[0141] Additionally or alternatively, the recognition scheme type information may be generated based on at least one of a speed of the at least one detected moving object, or a size of the at least one detected moving object.

[0142] Additionally or alternatively, the third information and the fourth information may be generated based on at least one of a difference between the first region and the second region, a difference between the second region and a reference region, or a difference between the reference region and the first region.

[0143] Additionally or alternatively, the third information and the fourth information may be generated based on a difference between a first value processed in grayscale for the first area and a second value processed in grayscale for the second area, or a difference between a reference value processed in grayscale for the reference frame and the first value, or a difference between a reference value processed in grayscale for the reference frame and the second value.

[0144] Additionally or alternatively, the third information related to the blocking object may be generated based on the ratio of the area of ​​the first static object included in the first area and the second area among the areas of the first moving object included in the first area and the second area being greater than or equal to a threshold value.

[0145] Additionally or alternatively, the fourth information related to the filling area regarding the blocking object may be generated based on a movement area of ​​a second moving object included within the first area and the second area within the fourth area regarding a fourth time prior to the first time or prior to the second time.

[0146] Additionally or alternatively, (i) based on a parameter value relating to a quality of detection of said at least one moving object being less than or equal to a threshold value, and (ii) based on said third information relating to said blocking object and said fourth information relating to said filling area, said detection of said at least one moving object may be performed within a fourth area in which a blocking area relating to said blocking object among said third areas relating to said third time is replaced with the filling area.

[0147] Additionally or alternatively, (i) based on a scale value of color or brightness constituting the blocking object being greater than or equal to a threshold value, and (ii) based on the third information related to the blocking object and the fourth information related to the filling area, the detection of the at least one moving object may be performed within a fourth area in which the blocking area related to the blocking object among the third areas related to the third time is replaced with the filling area.

[0148] Additionally or alternatively, (i) based on the fact that a ratio of an area of ​​a third static object included in the third area among areas of a third moving object included in the third area is greater than or equal to a threshold value, and (ii) based on the third information related to the blocking object and the fourth information related to the filling area, the detection of the at least one moving object may be performed within a fourth area in which a blocking area related to the blocking object among the third areas with respect to the third time is replaced with the filling area.

[0149] Additionally or alternatively, (i) within a time window determined based on said third area, and (ii) based on said third information related to said blocking object and said fourth information related to said filling area, said detection of said at least one moving object may be performed within a fourth area in which a blocking area related to said blocking object among said third areas with respect to said third time is replaced by said filling area.

[0150] The proposed method can be applied to devices according to various embodiments of the present disclosure. First, the memory (104) of the first device (100) may have instructions recorded thereon that cause the first device (e.g., the processor (102), the transceiver (106)) to perform operations based on being executed by the processor (102). For example, the operations may include: the step of: the first device (e.g., the processor (102), the transceiver (106)) acquiring first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time; the step of generating third information related to a blocking object and fourth information related to a filling area regarding the blocking object based on the first information and the second information; the step of performing detection of at least one moving object within the third area regarding a third time; and / or generating an object recognition message related to the at least one detected moving object; wherein the detection of the at least one moving object may be performed within a fourth area in which the blocking area related to the blocking object among the third areas with respect to the third time is replaced with the filling area, based on the third information related to the blocking object and the fourth information related to the filling area.

[0151] In one embodiment, a method performed by a first device is provided. For example, the first device may obtain first information related to a first area at a first time and second information related to a second area at a second time after the first time. For example, the first device may generate third information related to a blocking object and fourth information related to a filling area related to the blocking object based on the first information and the second information. For example, the first device may perform detection of at least one moving object within a third area at a third time. For example, the first device may generate an object recognition message related to the at least one detected moving object. For example, the detection of the at least one moving object may be performed within a fourth region in which the blocking region associated with the blocking object among the third regions for the third time is replaced with the filling region, based on the third information associated with the blocking object and the fourth information associated with the filling region.

[0152] In one embodiment, a first device for performing wireless communication is provided. The first device may include at least one transceiver; at least one processor; and at least one memory executably connected to the at least one processor and storing instructions that cause the first device to perform operations based on being executed by the at least one processor. For example, the operations may include: obtaining first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time; generating third information related to a blocking object and fourth information related to a filling area regarding the blocking object based on the first information and the second information; performing detection of at least one moving object within a third area regarding a third time; and / or generating an object recognition message related to the at least one detected moving object; , wherein the detection of the at least one moving object may be performed within a fourth region in which the blocking region related to the blocking object among the third regions with respect to the third time is replaced with the filling region, based on the third information related to the blocking object and the fourth information related to the filling region.

[0153] In one embodiment, a processing device adapted to control a first device is provided. The processing device may include at least one processor; and at least one memory executable to the at least one processor and having instructions recorded thereon, the instructions being executed by the at least one processor to cause the first device to perform operations. For example, the operations may include: obtaining first information related to a first area at a first time and second information related to a second area at a second time after the first time; generating third information related to a blocking object and fourth information related to a filling area related to the blocking object based on the first information and the second information; performing detection of at least one moving object within a third area at a third time; and / or generating an object recognition message related to the detected at least one moving object; , wherein the detection of the at least one moving object may be performed within a fourth region in which the blocking region related to the blocking object among the third regions with respect to the third time is replaced with the filling region, based on the third information related to the blocking object and the fourth information related to the filling region.

[0154] In one embodiment, a non-transitory computer-readable storage medium having instructions recorded thereon is provided. The instructions, when executed, may cause a first device to perform operations. For example, the operations may include: obtaining first information related to a first area at a first time and second information related to a second area at a second time after the first time; generating third information related to a blocking object and fourth information related to a filling area related to the blocking object based on the first information and the second information; performing detection of at least one moving object within a third area at a third time; and / or generating an object recognition message related to the detected at least one moving object; , wherein the detection of the at least one moving object may be performed within a fourth region in which the blocking region related to the blocking object among the third regions with respect to the third time is replaced with the filling region, based on the third information related to the blocking object and the fourth information related to the filling region.

[0155] FIG. 13 is a diagram illustrating a method performed by a second device according to an embodiment of the present disclosure. The embodiment of FIG. 13 may be combined with various embodiments of the present disclosure.

[0156] Referring to FIG. 13, in step S1310, for example, the second device may receive an object recognition message related to at least one detected moving object. In step S1320, for example, the second device may generate information regarding an estimation of a collision risk related to the at least one detected moving object based on the object recognition message. For example, first information related to a first area related to a first time and second information related to a second area related to a second time after the first time may be acquired. For example, based on the first information and the second information, third information related to a blocking object and fourth information related to a filling area related to the blocking object may be generated. For example, detection of at least one moving object may be performed within a third area related to a third time. For example, the detection of the at least one moving object may be performed within a fourth region in which the blocking region associated with the blocking object among the third regions for the third time is replaced with the filling region, based on the third information associated with the blocking object and the fourth information associated with the filling region.

[0157] Additionally or alternatively, the object recognition message may include a collective perception message (CPM).

[0158] Additionally or alternatively, the object recognition message may include perception scheme type information related to a detection method for the at least one moving object.

[0159] Additionally or alternatively, the recognition scheme type information may be generated based on at least one of a speed of the at least one detected moving object, or a size of the at least one detected moving object.

[0160] Additionally or alternatively, the third information and the fourth information may be generated based on at least one of a difference between the first region and the second region, a difference between the second region and a reference region, or a difference between the reference region and the first region.

[0161] Additionally or alternatively, the third information and the fourth information may be generated based on a difference between a first value processed in grayscale for the first area and a second value processed in grayscale for the second area, or a difference between a reference value processed in grayscale for the reference frame and the first value, or a difference between a reference value processed in grayscale for the reference frame and the second value.

[0162] Additionally or alternatively, the third information related to the blocking object may be generated based on the ratio of the area of ​​the first static object included in the first area and the second area among the areas of the first moving object included in the first area and the second area being greater than or equal to a threshold value.

[0163] Additionally or alternatively, the fourth information related to the filling area regarding the blocking object may be generated based on a movement area of ​​a second moving object included within the first area and the second area within the fourth area regarding a fourth time prior to the first time or prior to the second time.

[0164] Additionally or alternatively, (i) based on a parameter value relating to a quality of detection of said at least one moving object being less than or equal to a threshold value, and (ii) based on said third information relating to said blocking object and said fourth information relating to said filling area, said detection of said at least one moving object may be performed within a fourth area in which a blocking area relating to said blocking object among said third areas relating to said third time is replaced with the filling area.

[0165] Additionally or alternatively, (i) based on a scale value of color or brightness constituting the blocking object being greater than or equal to a threshold value, and (ii) based on the third information related to the blocking object and the fourth information related to the filling area, the detection of the at least one moving object may be performed within a fourth area in which the blocking area related to the blocking object among the third areas related to the third time is replaced with the filling area.

[0166] Additionally or alternatively, (i) based on the fact that a ratio of an area of ​​a third static object included in the third area among areas of a third moving object included in the third area is greater than or equal to a threshold value, and (ii) based on the third information related to the blocking object and the fourth information related to the filling area, the detection of the at least one moving object may be performed within a fourth area in which a blocking area related to the blocking object among the third areas with respect to the third time is replaced with the filling area.

[0167] Additionally or alternatively, (i) within a time window determined based on said third area, and (ii) based on said third information related to said blocking object and said fourth information related to said filling area, said detection of said at least one moving object may be performed within a fourth area in which a blocking area related to said blocking object among said third areas with respect to said third time is replaced by said filling area.

[0168] The proposed method can be applied to devices according to various embodiments of the present disclosure. First, the memory (204) of the second device (200) may have instructions recorded thereon that cause the second device (e.g., the processor (202), the transceiver (206)) to perform operations based on being executed by the processor (202). For example, the operations may include: receiving an object recognition message related to at least one detected moving object; and / or generating information about an estimation of a collision risk related to at least one detected moving object based on the object recognition message; Including, for example, first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time can be obtained, and for example, based on the first information and the second information, third information related to a blocking object and fourth information related to a filling area regarding the blocking object can be generated, and for example, detection of at least one moving object within the third area regarding a third time can be performed, and for example, the detection of the at least one moving object can be performed within a fourth area in which a blocking area related to the blocking object among the third area regarding the third time is replaced with the filling area, based on the third information related to the blocking object and the fourth information related to the filling area.

[0169] In one embodiment, a method performed by a second device is provided. For example, the second device may receive an object recognition message related to at least one detected moving object. For example, the second device may generate information regarding an estimation of a collision risk related to the at least one detected moving object based on the object recognition message. For example, first information related to a first area at a first time and second information related to a second area at a second time after the first time may be obtained. For example, based on the first information and the second information, third information related to a blocking object and fourth information related to a filling area related to the blocking object may be generated. For example, detection of at least one moving object may be performed within a third area at a third time. For example, the detection of the at least one moving object may be performed within a fourth region in which the blocking region associated with the blocking object among the third regions for the third time is replaced with the filling region, based on the third information associated with the blocking object and the fourth information associated with the filling region.

[0170] In one embodiment, a second device is provided. The second device may include at least one transceiver; at least one processor; and at least one memory executable connected to the at least one processor and having instructions recorded thereon that cause the second device to perform operations based on execution by the at least one processor. For example, the operations may include: receiving an object recognition message associated with at least one detected moving object; and / or generating information regarding an estimation of a collision risk associated with the at least one detected moving object based on the object recognition message; Including, for example, first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time can be obtained, and for example, based on the first information and the second information, third information related to a blocking object and fourth information related to a filling area regarding the blocking object can be generated, and for example, detection of at least one moving object within the third area regarding a third time can be performed, and for example, the detection of the at least one moving object can be performed within a fourth area in which a blocking area related to the blocking object among the third area regarding the third time is replaced with the filling area, based on the third information related to the blocking object and the fourth information related to the filling area.

[0171] In one embodiment, a processing apparatus configured to control a second device is provided. The apparatus may include at least one processor; and at least one memory executable to the at least one processor, and having instructions recorded thereon, the instructions causing the second device to perform operations based on the instructions being executed by the at least one processor. For example, the operations may include: receiving an object recognition message associated with at least one detected moving object; and / or generating information regarding an estimation of a collision risk associated with the at least one detected moving object based on the object recognition message; Including, for example, first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time can be obtained, and for example, based on the first information and the second information, third information related to a blocking object and fourth information related to a filling area regarding the blocking object can be generated, and for example, detection of at least one moving object within the third area regarding a third time can be performed, and for example, the detection of the at least one moving object can be performed within a fourth area in which a blocking area related to the blocking object among the third area regarding the third time is replaced with the filling area, based on the third information related to the blocking object and the fourth information related to the filling area.

[0172] In one embodiment, a non-transitory computer-readable storage medium having instructions recorded thereon is proposed. The instructions, when executed by at least one processor, may cause a second device to perform operations. For example, the operations may include: receiving an object recognition message associated with at least one detected moving object; and / or generating information regarding an estimation of a collision risk associated with the at least one detected moving object based on the object recognition message; Including, for example, first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time can be obtained, and for example, based on the first information and the second information, third information related to a blocking object and fourth information related to a filling area regarding the blocking object can be generated, and for example, detection of at least one moving object within the third area regarding a third time can be performed, and for example, the detection of the at least one moving object can be performed within a fourth area in which a blocking area related to the blocking object among the third area regarding the third time is replaced with the filling area, based on the third information related to the blocking object and the fourth information related to the filling area.

[0173] The various embodiments of the present disclosure may be combined with each other.

[0174] Below, a description is given of devices to which various embodiments of the present disclosure can be applied.

[0175] 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.

[0176] 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.

[0177] Fig. 14 illustrates a communication system (1) according to one embodiment of the present disclosure. The embodiment of Fig. 14 can be combined with various embodiments of the present disclosure.

[0178] Referring to FIG. 14, 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] FIG. 15 illustrates a wireless device according to an embodiment of the present disclosure. The embodiment of FIG. 15 may be combined with various embodiments of the present disclosure.

[0183] Referring to FIG. 15, 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. 14.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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 executed 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.

[0188] 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.

[0189] 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.

[0190] FIG. 16 illustrates a signal processing circuit for a transmission signal according to an embodiment of the present disclosure. The embodiment of FIG. 16 may be combined with various embodiments of the present disclosure.

[0191] Referring to FIG. 16, 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. 16 may be performed in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 15. The hardware elements of FIG. 16 may be implemented in the processor (102, 202) and / or the transceiver (106, 206) of FIG. 15. For example, blocks 1010 to 1060 may be implemented in the processor (102, 202) of FIG. 15. Additionally, blocks 1010 to 1050 may be implemented in the processor (102, 202) of FIG. 15, and block 1060 may be implemented in the transceiver (106, 206) of FIG. 15.

[0192] The codeword can be converted into a wireless signal through the signal processing circuit (1000) of FIG. 16. Here, the codeword is an encoded bit sequence of an information block. The information block can include a transport block (e.g., an UL-SCH transport block, a DL-SCH transport block). The wireless signal can be transmitted through various physical channels (e.g., a PUSCH or a PDSCH).

[0193] 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.

[0194] 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.

[0195] 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. 16. For example, a wireless device (e.g., 100, 200 of FIG. 15) 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.

[0196] Figure 17 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 14). The embodiment of Figure 17 may be combined with various embodiments of the present disclosure.

[0197] Referring to FIG. 17, the wireless device (100, 200) corresponds to the wireless device (100, 200) of FIG. 15 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. 15. For example, the transceiver(s) (114) may include one or more transceivers (106, 206) and / or one or more antennas (108, 208) of FIG. 15. 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).

[0198] 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. 14, 100a), a vehicle (Fig. 14, 100b-1, 100b-2), an XR device (Fig. 14, 100c), a portable device (Fig. 14, 100d), a home appliance (Fig. 14, 100e), an IoT device (Fig. 14, 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. 14, 400), a base station (Fig. 14, 200), a network node, etc. Wireless devices may be mobile or stationary depending on the use / service.

[0199] In FIG. 17, 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.

[0200] Below, the implementation example of Fig. 17 is described in more detail with reference to the drawings.

[0201] FIG. 18 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. 18 may be combined with various embodiments of the present disclosure.

[0202] Referring to FIG. 18, 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. 17, respectively.

[0203] 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.

[0204] 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).

[0205] FIG. 19 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. 19 may be combined with various embodiments of the present disclosure.

[0206] Referring to FIG. 19, 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. 17, respectively.

[0207] 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.

[0208] 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.

[0209] 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 step of obtaining first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time; A step of generating third information related to a blocking object and fourth information related to a filling area for the blocking object based on the first information and the second information; and A step of performing detection of at least one moving object within a third area with respect to a third time; and A step of generating an object recognition message related to at least one detected moving object; comprising: A method wherein the detection of the at least one moving object is performed within a fourth region in which the blocking region associated with the blocking object among the third regions for the third time is replaced with the filling region, based on the third information associated with the blocking object and the fourth information associated with the filling region.

2. In paragraph 1, A method wherein the above object recognition message includes a collective perception message (CPM).

3. In paragraph 1, A method wherein the object recognition message includes perception scheme type information related to a detection method for the at least one moving object.

4. In paragraph 3, A method in which the recognition scheme type information is generated based on at least one of the speed of the at least one detected moving object or the size of the at least one detected moving object.

5. In paragraph 1, A method wherein the third information and the fourth information are generated based on at least one of a difference between the first region and the second region, a difference between the second region and a reference region, or a difference between the reference region and the first region.

6. In paragraph 5, A method in which the third information and the fourth information are generated based on a difference between a first value processed in grayscale for the first area and a second value processed in grayscale for the second area, or a difference between a reference value processed in grayscale for the reference frame and the first value, or a difference between a reference value processed in grayscale for the reference frame and the second value.

7. In paragraph 1, A method in which the third information related to the blocking object is generated based on the fact that, among the areas of the first moving object included in the first area and the second area, the area occupied by the first static object included in the first area and the second area is greater than or equal to a threshold value.

8. In paragraph 7, A method in which the fourth information related to the filling area regarding the blocking object is generated based on a movement area of ​​a second moving object included in the first area and the second area within the fourth area regarding a fourth time before the first time or before the second time.

9. In paragraph 1, A method wherein (i) based on a parameter value relating to a quality of detection of at least one moving object being less than or equal to a threshold value, and (ii) based on the third information relating to the blocking object and the fourth information relating to the filling area, the detection of the at least one moving object is performed within a fourth area in which the blocking area relating to the blocking object among the third areas relating to the third time is replaced with the filling area.

10. In paragraph 1, (i) based on a scale value of color or brightness constituting the blocking object being greater than or equal to a threshold value, and (ii) based on the third information related to the blocking object and the fourth information related to the filling area, the detection of the at least one moving object is performed within a fourth area in which the blocking area related to the blocking object among the third areas related to the third time is replaced with the filling area.

11. In paragraph 1, (i) based on the ratio of the area of ​​the third static object included in the third area among the areas of the third moving object included in the third area being greater than or equal to a threshold value, and (ii) based on the third information related to the blocking object and the fourth information related to the filling area, the detection of the at least one moving object is performed in a fourth area in which the blocking area related to the blocking object among the third areas with respect to the third time is replaced with the filling area.

12. In paragraph 1, (i) within a time window determined based on the third area, and (ii) based on the third information related to the blocking object and the fourth information related to the filling area, the detection of the at least one moving object is performed within a fourth area in which the blocking area related to the blocking object among the third areas with respect to the third time is replaced with the filling area.

13. In the first device, At least one transmitter / receiver; at least one processor; and At least one memory executable connected to said at least one processor and having instructions recorded thereon that cause said first device to perform operations based on being executed by said at least one processor, said operations comprising: A step of obtaining first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time; A step of generating third information related to a blocking object and fourth information related to a filling area for the blocking object based on the first information and the second information; and A step of performing detection of at least one moving object within a third area with respect to a third time; and A step of generating an object recognition message related to at least one detected moving object; comprising: A first device, wherein the detection of the at least one moving object is performed within a fourth area in which the blocking area associated with the blocking object among the third areas for the third time is replaced with the filling area, based on the third information associated with the blocking object and the fourth information associated with the filling area.

14. In a processing device adapted to control a first device, The above processing device, at least one processor; and At least one memory executable connected to said at least one processor and having instructions recorded thereon that cause said first device to perform operations based on being executed by said at least one processor, said operations comprising: A step of obtaining first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time; A step of generating third information related to a blocking object and fourth information related to a filling area for the blocking object based on the first information and the second information; and A step of performing detection of at least one moving object within a third area with respect to a third time; and A step of generating an object recognition message related to at least one detected moving object; comprising: A processing device wherein the detection of the at least one moving object is performed within a fourth region in which the blocking region associated with the blocking object among the third regions for the third time is replaced with the filling region, based on the third information associated with the blocking object and the fourth information associated with the filling region.

15. A non-transitory computer-readable storage medium that records commands, The above instructions, when executed, cause the first device to perform actions, wherein the actions are: A step of obtaining first information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time; A step of generating third information related to a blocking object and fourth information related to a filling area for the blocking object based on the first information and the second information; and A step of performing detection of at least one moving object within a third area with respect to a third time; and A step of generating an object recognition message related to at least one detected moving object; comprising: A non-transitory computer-readable storage medium, wherein the detection of at least one moving object is performed within a fourth area in which the blocking area associated with the blocking object among the third areas for the third time is replaced with the filling area, based on the third information associated with the blocking object and the fourth information associated with the filling area.

16. In a method performed by a second device, receiving an object recognition message associated with at least one detected moving object; and A step of generating information regarding an estimation of a collision risk associated with at least one detected moving object based on the object recognition message; including: First information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time are obtained, Based on the first information and the second information, third information related to a blocking object and fourth information related to a filling area for the blocking object are generated, Detection of at least one moving object within a third area with respect to a third time is performed, and A method wherein the detection of the at least one moving object is performed within a fourth region in which the blocking region associated with the blocking object among the third regions for the third time is replaced with the filling region, based on the third information associated with the blocking object and the fourth information associated with the filling region.

17. In the second device, At least one transmitter / receiver; at least one processor; and At least one memory executable connected to said at least one processor and having instructions recorded thereon that cause said second device to perform operations based on being executed by said at least one processor, said operations comprising: receiving an object recognition message associated with at least one detected moving object; and A step of generating information regarding an estimation of a collision risk associated with at least one detected moving object based on the object recognition message; including: First information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time are obtained, Based on the first information and the second information, third information related to a blocking object and fourth information related to a filling area for the blocking object are generated, Detection of at least one moving object within a third area with respect to a third time is performed, and A second device, wherein the detection of the at least one moving object is performed within a fourth area in which the blocking area associated with the blocking object among the third areas for the third time is replaced with the filling area, based on the third information associated with the blocking object and the fourth information associated with the filling area.

18. In a processing apparatus adapted to control a second device, the processing apparatus comprises: at least one processor; and At least one memory executable connected to said at least one processor and having instructions recorded thereon that cause said second device to perform operations based on being executed by said at least one processor, said operations comprising: receiving an object recognition message associated with at least one detected moving object; and A step of generating information regarding an estimation of a collision risk associated with at least one detected moving object based on the object recognition message; including: First information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time are obtained, Based on the first information and the second information, third information related to a blocking object and fourth information related to a filling area for the blocking object are generated, Detection of at least one moving object within a third area with respect to a third time is performed, and A processing device wherein the detection of the at least one moving object is performed within a fourth region in which the blocking region associated with the blocking object among the third regions for the third time is replaced with the filling region, based on the third information associated with the blocking object and the fourth information associated with the filling region.

19. A non-transitory computer-readable storage medium that records commands, The above instructions, when executed, cause the second device to perform actions, wherein the actions are: receiving an object recognition message associated with at least one detected moving object; and A step of generating information regarding an estimation of a collision risk associated with at least one detected moving object based on the object recognition message; including: First information related to a first area regarding a first time and second information related to a second area regarding a second time after the first time are obtained, Based on the first information and the second information, third information related to a blocking object and fourth information related to a filling area for the blocking object are generated, Detection of at least one moving object within a third area with respect to a third time is performed, and A non-transitory computer-readable storage medium, wherein the detection of at least one moving object is performed within a fourth area in which the blocking area associated with the blocking object among the third areas for the third time is replaced with the filling area, based on the third information associated with the blocking object and the fourth information associated with the filling area.

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