KR20210041213A - Method and apparatus of tracking objects using map information in autonomous driving system - Google Patents

Abstract

The present invention includes the following steps of: mapping sensing information obtained through a sensor of a vehicle to map information in automated vehicle & highway systems; extracting road information and information of an area where there is a risk factor regarding the driving of the vehicle, by using the map information; and setting a region of interest for tracking an object, based on the road information and the information of the area where there is a risk factor regarding the driving of the vehicle. The region of interest can include a geographical radius to be monitored by the vehicle for driving. Therethrough, the vehicle can use the region of interest to improve an object tracking algorithm. At least one of an autonomous vehicle, a user terminal and a server of the present invention can be interconnected with an artificial intelligence module, a drone (unmanned aerial vehicle: UAV) robot, an augmented reality (AR) device, a virtual reality (VR) device, a 5G service-related device and the like.

Description

A method for tracking objects using map information in an autonomous driving system and a device therefor {METHOD AND APPARATUS OF TRACKING OBJECTS USING MAP INFORMATION IN AUTONOMOUS DRIVING SYSTEM}

The present specification relates to an autonomous driving system, and is a method and apparatus for improving an object tracking algorithm using HD map information.

Vehicles can be classified into internal combustion engine vehicles, external combustion engine vehicles, gas turbine vehicles, or electric vehicles, depending on the type of prime mover used.

Autonomous Vehicle refers to a vehicle that can operate by itself without driver or passenger manipulation, and Automated Vehicle & Highway Systems is a system that monitors and controls such autonomous vehicles so that they can operate on their own. Say.

An object of the present specification is to propose a method of improving an object tracking algorithm using map information in an autonomous driving system.

In addition, an object of the present specification is to propose a method for efficient object tracking by limiting the space required for sensing by using HD map information in an autonomous driving system.

The technical problems to be achieved by the present specification are not limited to the technical problems mentioned above, and other technical problems that are not mentioned are obvious to those of ordinary skill in the technical field to which the present specification belongs from the detailed description of the invention below. It will be understandable.

An aspect of the present specification includes the steps of mapping sensing information acquired through a sensor of a vehicle to map information in an autonomous driving system; Extracting road information and information on a region where there is a risk factor for driving of the vehicle using the map information; And setting a region of interest for object tracking based on the road information and information on a region where there is a risk factor for driving of the vehicle. And the region of interest may include a geographic range that the vehicle must monitor for driving.

In addition, the area where there is a risk factor in the driving of the vehicle may include a child protection area, a crosswalk, a construction area, an intersection or an area in which the entrance and exit of the parking lot are located.

In addition, when the vehicle enters a predetermined radius of an area where the crosswalk is located, receiving a pedestrian signal of a traffic light for the crosswalk from a road side unit (RSU); And resetting the region of interest based on the pedestrian signal of the traffic light for the crosswalk. It may further include.

In addition, the step of resetting may be to change the geographic range of the region of interest based on the remaining time of the pedestrian signal of the traffic light for the crosswalk.

In addition, when the vehicle entering the intersection receives signal information of a traffic light for the intersection, using the signal information of the traffic light and the road information for the intersection, the vehicle is in a simultaneous signal relationship with the driving lane of the vehicle. Obtaining lane information; And resetting a region of interest for a lane having a simultaneous signal relationship with the driving lane of the vehicle. It may further include.

In addition, the resetting may be for setting a region of interest having a geographic range including lanes in a simultaneous signal relationship with the driving lane of the vehicle.

In addition, resetting a region of interest for a lane that does not have a simultaneous signal relationship with the driving lane of the vehicle; It may further include.

In addition, for a lane that does not have a simultaneous signal relationship with the driving lane of the vehicle, the step of resetting the region of interest may be for excluding a lane that does not have a simultaneous signal relationship with the driving lane of the vehicle from the geographic range of the region of interest. .

In addition, when the signal information of the traffic light for the intersection is not received, receiving a V2X (Vehicle-To-Everything) message from another vehicle; And resetting the ROI based on the V2X message. It may further include.

In addition, the step of setting the region of interest may be based on a current position, a driving direction, or a driving route of the vehicle.

Further, based on the region of interest, object tracking (Object Tracking); It further includes, and the sensing information may be generated through LIDAR.

In addition, acquiring driving lane information of the vehicle; And resetting the ROI based on the driving lane of the vehicle. It may further include.

In addition, the resetting may be to include an area of the opposite lane in the geographic range of the region of interest when the driving lane of the vehicle is adjacent to the center line.

In addition, the resetting may be for excluding an area of an opposite lane from a geographic range of the region of interest when the driving lane of the vehicle is not adjacent to the center line.

Another aspect of the present invention is a vehicle for performing object tracking in an autonomous driving system, comprising: a sensor; A transceiver; Memory; And a processor that controls the sensor, the transceiver, and the memory. Including, the processor maps the sensing information acquired through the sensor to the map information (mapping), and by using the map information, extracting (extracting) road information and information on the area where there is a risk factor in the driving of the vehicle ), and based on the road information and information on the area where there is a risk factor in the driving of the vehicle, a region of interest for object tracking is set, and the region of interest must be monitored for the vehicle to drive. It may include a geographic range.

According to an embodiment of the present specification, an object tracking algorithm may be improved by using map information in an autonomous driving system.

In addition, according to an embodiment of the present specification, by using HD map information in an autonomous driving system, a space required for sensing may be limited to perform efficient object tracking.

The effects obtainable in the present specification are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by those of ordinary skill in the art from the following description. .

1 illustrates a block diagram of a wireless communication system to which the methods proposed in the present specification can be applied.
2 shows an example of a signal transmission/reception method in a wireless communication system.
3 shows an example of a basic operation of an autonomous vehicle and a 5G network in a 5G communication system.
4 shows an example of a vehicle-to-vehicle basic operation using 5G communication.
5 is a view showing a vehicle according to an embodiment of the present specification.
6 is a control block diagram of a vehicle according to an exemplary embodiment of the present specification.
7 is a control block diagram of an autonomous driving apparatus according to an embodiment of the present specification.
8 is a signal flow diagram of an autonomous vehicle according to an embodiment of the present specification.
9 is a diagram referenced to explain a usage scenario of a user according to an embodiment of the present specification.
10 is an example of V2X communication to which the present specification can be applied.
11 illustrates a resource allocation method in a sidelink in which V2X is used.
12 is a diagram illustrating a procedure for a broadcast mode of V2X communication using PC5.
13 is a block diagram of an AI device according to an embodiment of the present specification.
14 is an embodiment to which the present specification may be applied.
15 and 16 are an embodiment of a method of setting an ROI to which the present specification can be applied.
17 is an embodiment of an object tracking algorithm to which the present specification can be applied.
18 is an example of resetting an ROI based on a traffic light signal to which the present specification can be applied.
19 is a flowchart of resetting an ROI based on a traffic light signal to which the present specification can be applied.
20 is an example of resetting an ROI based on a lane in which a vehicle travels to which the present specification can be applied.
21 is a flowchart of resetting an ROI based on a lane on which a vehicle travels to which the present specification can be applied.
22 is an example of resetting an ROI at an intersection to which the present specification can be applied.
23 is an example of reconfiguration of an ROI for reducing a geographic range to which the present specification can be applied.
24 is a flowchart of resetting an ROI at an intersection to which the present specification can be applied.
25 is an example of a general device to which the present specification can be applied.
The accompanying drawings, which are included as part of the detailed description to aid in understanding of the present specification, provide embodiments of the present specification, and describe technical features of the present specification together with the detailed description.

Hereinafter, exemplary embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but identical or similar elements are denoted by the same reference numerals regardless of reference numerals, and redundant descriptions thereof will be omitted. The suffixes "module" and "unit" for constituent elements used in the following description are given or used interchangeably in consideration of only the ease of preparation of the specification, and do not have meanings or roles that are distinguished from each other by themselves. In addition, in describing the embodiments disclosed in the present specification, when it is determined that a detailed description of related known technologies may obscure the subject matter of the embodiments disclosed in the present specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are for easy understanding of the embodiments disclosed in the present specification, and the technical idea disclosed in the present specification is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present specification It should be understood to include equivalents or substitutes.

Terms including ordinal numbers such as first and second may be used to describe various elements, but the elements are not limited by the terms. The above terms are used only for the purpose of distinguishing one component from another component.

When a component is referred to as being "connected" or "connected" to another component, it is understood that it may be directly connected or connected to the other component, but other components may exist in the middle. It should be. On the other hand, when a component is referred to as being "directly connected" or "directly connected" to another component, it should be understood that there is no other component in the middle.

Singular expressions include plural expressions unless the context clearly indicates otherwise.

In this application, terms such as "comprises" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof does not preclude in advance.

A. UE and 5G network block diagram example

1 illustrates a block diagram of a wireless communication system to which the methods proposed in the present specification can be applied.

Referring to FIG. 1, a device including an autonomous driving module (autonomous driving device) is defined as a first communication device (910 in FIG. 1 ), and a processor 911 may perform a detailed autonomous driving operation.

A 5G network including other vehicles communicating with the autonomous driving device may be defined as a second communication device (920 in FIG. 1), and the processor 921 may perform a detailed autonomous driving operation.

The 5G network may be referred to as a first communication device and an autonomous driving device may be referred to as a second communication device.

For example, the first communication device or the second communication device may be a base station, a network node, a transmission terminal, a reception terminal, a wireless device, a wireless communication device, an autonomous driving device, and the like.

For example, a terminal or user equipment (UE) is a vehicle, mobile phone, smart phone, laptop computer, digital broadcasting terminal, personal digital assistants (PDA), portable multimedia player (PMP). , Navigation, slate PC, tablet PC, ultrabook, wearable device, for example, smartwatch, smart glass, HMD ( head mounted display)). For example, the HMD may be a display device worn on the head. For example, HMD can be used to implement VR, AR or MR. Referring to FIG. 1, a first communication device 910 and a second communication device 920 include a processor (processor, 911,921), a memory (memory, 914,924), one or more Tx/Rx RF modules (radio frequency modules, 915,925). , Tx processors 912 and 922, Rx processors 913 and 923, and antennas 916 and 926. The Tx/Rx module is also called a transceiver. Each Tx/Rx module 915 transmits a signal through a respective antenna 926. The processor implements the previously salpin functions, processes and/or methods. The processor 921 may be associated with a memory 924 that stores program code and data. The memory may be referred to as a computer-readable medium. More specifically, in the DL (communication from the first communication device to the second communication device), the transmission (TX) processor 912 implements various signal processing functions for the L1 layer (ie, the physical layer). The receive (RX) processor implements the various signal processing functions of L1 (ie, the physical layer).

The UL (communication from the second communication device to the first communication device) is handled in the first communication device 910 in a manner similar to that described with respect to the receiver function in the second communication device 920. Each Tx/Rx module 925 receives a signal through a respective antenna 926. Each Tx/Rx module provides an RF carrier and information to the RX processor 923. The processor 921 may be associated with a memory 924 that stores program code and data. The memory may be referred to as a computer-readable medium.

B. Signal transmission/reception method in wireless communication system

2 is a diagram illustrating an example of a method of transmitting/receiving a signal in a wireless communication system.

Referring to FIG. 2, when the UE is powered on or newly enters a cell, the UE performs an initial cell search operation such as synchronizing with the BS (S201). To this end, the UE receives a primary synchronization channel (P-SCH) and a secondary synchronization channel (S-SCH) from the BS, synchronizes with the BS, and obtains information such as cell ID. can do. In the LTE system and the NR system, the P-SCH and S-SCH are referred to as a primary synchronization signal (PSS) and a secondary synchronization signal (SSS), respectively. After initial cell discovery, the UE may obtain intra-cell broadcast information by receiving a physical broadcast channel (PBCH) from the BS. Meanwhile, the UE may check a downlink channel state by receiving a downlink reference signal (DL RS) in the initial cell search step. Upon completion of the initial cell search, the UE acquires more detailed system information by receiving a physical downlink control channel (PDCCH) and a physical downlink shared channel (PDSCH) according to the information carried on the PDCCH. It can be done (S202).

Meanwhile, when accessing the BS for the first time or when there is no radio resource for signal transmission, the UE may perform a random access procedure (RACH) for the BS (steps S203 to S206). To this end, the UE transmits a specific sequence as a preamble through a physical random access channel (PRACH) (S203 and S205), and a random access response to the preamble through a PDCCH and a corresponding PDSCH. RAR) message can be received (S204 and S206). In the case of contention-based RACH, a contention resolution procedure may be additionally performed.

After performing the above-described process, the UE receives PDCCH/PDSCH (S207) and physical uplink shared channel (PUSCH)/physical uplink control channel as a general uplink/downlink signal transmission process. Uplink control channel, PUCCH) transmission (S208) may be performed. In particular, the UE receives downlink control information (DCI) through the PDCCH. The UE monitors the set of PDCCH candidates from monitoring opportunities set in one or more control element sets (CORESET) on the serving cell according to the corresponding search space configurations. The set of PDCCH candidates to be monitored by the UE is defined in terms of search space sets, and the search space set may be a common search space set or a UE-specific search space set. CORESET consists of a set of (physical) resource blocks with a time duration of 1 to 3 OFDM symbols. The network can configure the UE to have multiple CORESETs. The UE monitors PDCCH candidates in one or more search space sets. Here, monitoring means attempting to decode PDCCH candidate(s) in the search space. If the UE succeeds in decoding one of the PDCCH candidates in the discovery space, the UE determines that the PDCCH is detected in the corresponding PDCCH candidate, and performs PDSCH reception or PUSCH transmission based on the detected DCI in the PDCCH. The PDCCH can be used to schedule DL transmissions on the PDSCH and UL transmissions on the PUSCH. Here, the DCI on the PDCCH is a downlink assignment (ie, downlink grant; DL grant) including at least information on modulation and coding format and resource allocation related to a downlink shared channel, or uplink It includes an uplink grant (UL grant) including modulation and coding format and resource allocation information related to the shared channel.

With reference to FIG. 2, an initial access (IA) procedure in a 5G communication system will be additionally described.

The UE may perform cell search, system information acquisition, beam alignment for initial access, and DL measurement based on the SSB. SSB is used interchangeably with a Synchronization Signal/Physical Broadcast Channel (SS/PBCH) block.

SSB consists of PSS, SSS and PBCH. The SSB is composed of four consecutive OFDM symbols, and PSS, PBCH, SSS/PBCH or PBCH are transmitted for each OFDM symbol. The PSS and SSS are each composed of 1 OFDM symbol and 127 subcarriers, and the PBCH is composed of 3 OFDM symbols and 576 subcarriers.

Cell discovery refers to a process in which the UE acquires time/frequency synchronization of a cell and detects a cell identifier (eg, Physical layer Cell ID, PCI) of the cell. PSS is used to detect a cell ID within a cell ID group, and SSS is used to detect a cell ID group. PBCH is used for SSB (time) index detection and half-frame detection.

There are 336 cell ID groups, and 3 cell IDs exist for each cell ID group. There are a total of 1008 cell IDs. Information on the cell ID group to which the cell ID of the cell belongs is provided/obtained through the SSS of the cell, and information on the cell ID among 336 cells in the cell ID is provided/obtained through the PSS.

SSB is transmitted periodically according to the SSB period. The SSB basic period assumed by the UE during initial cell search is defined as 20 ms. After cell access, the SSB period may be set to one of {5ms, 10ms, 20ms, 40ms, 80ms, 160ms} by the network (eg, BS).

Next, it looks at obtaining system information (SI).

SI is divided into a master information block (MIB) and a plurality of system information blocks (SIB). SI other than MIB may be referred to as RMSI (Remaining Minimum System Information). The MIB includes information/parameters for monitoring the PDCCH that schedules the PDSCH carrying System Information Block1 (SIB1), and is transmitted by the BS through the PBCH of the SSB. SIB1 includes information related to availability and scheduling (eg, transmission period, SI-window size) of the remaining SIBs (hereinafter, SIBx, x is an integer greater than or equal to 2). SIBx is included in the SI message and is transmitted through the PDSCH. Each SI message is transmitted within a periodic time window (ie, SI-window).

Referring to FIG. 2, a random access (RA) process in a 5G communication system will be additionally described.

The random access process is used for various purposes. For example, the random access procedure may be used for initial network access, handover, and UE-triggered UL data transmission. The UE may acquire UL synchronization and UL transmission resources through a random access process. The random access process is divided into a contention-based random access process and a contention free random access process. The detailed procedure for the contention-based random access process is as follows.

The UE may transmit the random access preamble as Msg1 of the random access procedure in the UL through the PRACH. Random access preamble sequences having two different lengths are supported. The long sequence length 839 is applied for subcarrier spacing of 1.25 and 5 kHz, and the short sequence length 139 is applied for subcarrier spacing of 15, 30, 60 and 120 kHz.

When the BS receives the random access preamble from the UE, the BS transmits a random access response (RAR) message (Msg2) to the UE. The PDCCH for scheduling the PDSCH carrying RAR is transmitted after being CRC masked with a random access (RA) radio network temporary identifier (RNTI) (RA-RNTI). A UE that detects a PDCCH masked with RA-RNTI may receive an RAR from a PDSCH scheduled by a DCI carried by the PDCCH. The UE checks whether the preamble transmitted by the UE, that is, random access response information for Msg1, is in the RAR. Whether there is random access information for Msg1 transmitted by the UE may be determined based on whether there is a random access preamble ID for the preamble transmitted by the UE. If there is no response to Msg1, the UE may retransmit the RACH preamble within a predetermined number of times while performing power ramping. The UE calculates the PRACH transmission power for retransmission of the preamble based on the most recent path loss and power ramping counter.

The UE may transmit UL transmission as Msg3 in a random access procedure on an uplink shared channel based on random access response information. Msg3 may include an RRC connection request and a UE identifier. In response to Msg3, the network may send Msg4, which may be treated as a contention resolution message on the DL. By receiving Msg4, the UE can enter the RRC connected state.

C. Beam Management (BM) procedure of 5G communication system

The BM process may be divided into (1) a DL BM process using SSB or CSI-RS and (2) a UL BM process using a sounding reference signal (SRS). In addition, each BM process may include Tx beam sweeping to determine the Tx beam and Rx beam sweeping to determine the Rx beam.

Let's look at the DL BM process using SSB.

Configuration for beam report using SSB is performed when channel state information (CSI)/beam is configured in RRC_CONNECTED.

-The UE receives a CSI-ResourceConfig IE including CSI-SSB-ResourceSetList for SSB resources used for BM from BS. The RRC parameter csi-SSB-ResourceSetList represents a list of SSB resources used for beam management and reporting in one resource set. Here, the SSB resource set may be set to {SSBx1, SSBx2, SSBx3, SSBx4, ??}. The SSB index may be defined from 0 to 63.

-The UE receives signals on SSB resources from the BS based on the CSI-SSB-ResourceSetList.

-When the CSI-RS reportConfig related to reporting on SSBRI and reference signal received power (RSRP) is configured, the UE reports the best SSBRI and RSRP corresponding thereto to the BS. For example, when the reportQuantity of the CSI-RS reportConfig IE is set to'ssb-Index-RSRP', the UE reports the best SSBRI and corresponding RSRP to the BS.

When the UE is configured with CSI-RS resources in the same OFDM symbol(s) as the SSB, and'QCL-TypeD' is applicable, the UE is similarly co-located in terms of'QCL-TypeD' where the CSI-RS and SSB are ( quasi co-located, QCL). Here, QCL-TypeD may mean that QCL is performed between antenna ports in terms of a spatial Rx parameter. When the UE receives signals from a plurality of DL antenna ports in a QCL-TypeD relationship, the same reception beam may be applied.

Next, a DL BM process using CSI-RS will be described.

The Rx beam determination (or refinement) process of the UE using CSI-RS and the Tx beam sweeping process of the BS are sequentially described. In the UE's Rx beam determination process, the repetition parameter is set to'ON', and in the BS's Tx beam sweeping process, the repetition parameter is set to'OFF'.

First, a process of determining the Rx beam of the UE will be described.

-The UE receives the NZP CSI-RS resource set IE including the RRC parameter for'repetition' from the BS through RRC signaling. Here, the RRC parameter'repetition' is set to'ON'.

-The UE repeats signals on the resource(s) in the CSI-RS resource set in which the RRC parameter'repetition' is set to'ON' in different OFDM symbols through the same Tx beam (or DL spatial domain transmission filter) of the BS Receive.

-The UE determines its own Rx beam.

-The UE omits CSI reporting. That is, the UE may omit CSI reporting when the shopping price RRC parameter'repetition' is set to'ON'.

Next, a process of determining the Tx beam of the BS will be described.

-The UE receives the NZP CSI-RS resource set IE including the RRC parameter for'repetition' from the BS through RRC signaling. Here, the RRC parameter'repetition' is set to'OFF', and is related to the Tx beam sweeping process of the BS.

-The UE receives signals on resources in the CSI-RS resource set in which the RRC parameter'repetition' is set to'OFF' through different Tx beams (DL spatial domain transmission filters) of the BS.

-The UE selects (or determines) the best beam.

-The UE reports the ID (eg, CRI) and related quality information (eg, RSRP) for the selected beam to the BS. That is, when the CSI-RS is transmitted for the BM, the UE reports the CRI and the RSRP for it to the BS.

Next, a UL BM process using SRS will be described.

-The UE receives RRC signaling (eg, SRS-Config IE) including a usage parameter set to'beam management' (RRC parameter) from the BS. SRS-Config IE is used for SRS transmission configuration. The SRS-Config IE includes a list of SRS-Resources and a list of SRS-ResourceSets. Each SRS resource set means a set of SRS-resources.

-The UE determines Tx beamforming for the SRS resource to be transmitted based on the SRS-SpatialRelation Info included in the SRS-Config IE. Here, the SRS-SpatialRelation Info is set for each SRS resource, and indicates whether to apply the same beamforming as the beamforming used in SSB, CSI-RS or SRS for each SRS resource.

-If SRS-SpatialRelationInfo is set in the SRS resource, the same beamforming as the beamforming used in SSB, CSI-RS or SRS is applied and transmitted. However, if SRS-SpatialRelationInfo is not set in the SRS resource, the UE randomly determines Tx beamforming and transmits the SRS through the determined Tx beamforming.

Next, a beam failure recovery (BFR) process will be described.

In a beamformed system, Radio Link Failure (RLF) may frequently occur due to rotation, movement, or beamforming blockage of the UE. Therefore, BFR is supported in NR to prevent frequent RLF from occurring. BFR is similar to the radio link failure recovery process, and may be supported when the UE knows the new candidate beam(s). For beam failure detection, the BS sets beam failure detection reference signals to the UE, and the UE sets the number of beam failure indications from the physical layer of the UE within a period set by RRC signaling of the BS. When a threshold set by RRC signaling is reached, a beam failure is declared. After the beam failure is detected, the UE triggers beam failure recovery by initiating a random access procedure on the PCell; Beam failure recovery is performed by selecting a suitable beam (if the BS has provided dedicated random access resources for certain beams, these are prioritized by the UE). Upon completion of the random access procedure, it is considered that the beam failure recovery is complete.

D. URLLC (Ultra-Reliable and Low Latency Communication)

URLLC transmission as defined by NR is (1) relatively low traffic size, (2) relatively low arrival rate, (3) extremely low latency requirement (e.g. 0.5, 1ms), (4) It may mean a relatively short transmission duration (eg, 2 OFDM symbols), and (5) transmission of an urgent service/message. In the case of UL, transmission for a specific type of traffic (e.g., URLLC) must be multiplexed with other transmissions (e.g., eMBB) previously scheduled in order to satisfy a more stringent latency requirement. Needs to be. In this regard, as one method, information that a specific resource will be preempted is given to the previously scheduled UE, and the URLLC UE uses the corresponding resource for UL transmission.

In the case of NR, dynamic resource sharing between eMBB and URLLC is supported. eMBB and URLLC services can be scheduled on non-overlapping time/frequency resources, and URLLC transmission can occur on resources scheduled for ongoing eMBB traffic. The eMBB UE may not be able to know whether the PDSCH transmission of the corresponding UE is partially punctured, and the UE may not be able to decode the PDSCH due to corrupted coded bits. In consideration of this point, the NR provides a preemption indication. The preemption indication may be referred to as an interrupted transmission indication.

Regarding the preemption indication, the UE receives the DownlinkPreemption IE through RRC signaling from the BS. When the UE is provided with the DownlinkPreemption IE, the UE is configured with the INT-RNTI provided by the parameter int-RNTI in the DownlinkPreemption IE for monitoring of the PDCCH carrying DCI format 2_1. The UE is additionally configured with a set of serving cells by INT-ConfigurationPerServing Cell including a set of serving cell indexes provided by servingCellID and a corresponding set of positions for fields in DCI format 2_1 by positionInDCI, and dci-PayloadSize It is set with the information payload size for DCI format 2_1 by and is set with the indication granularity of time-frequency resources by timeFrequencySect.

The UE receives DCI format 2_1 from the BS based on the DownlinkPreemption IE.

When the UE detects the DCI format 2_1 for the serving cell in the set set of serving cells, the UE is the DCI format among the set of PRBs and symbols of the monitoring period immediately preceding the monitoring period to which the DCI format 2_1 belongs. It may be assumed that there is no transmission to the UE in the PRBs and symbols indicated by 2_1. For example, the UE considers that the signal in the time-frequency resource indicated by the preemption is not a DL transmission scheduled to it, and decodes data based on the signals received in the remaining resource regions.

E. mMTC (massive MTC)

Massive Machine Type Communication (mMTC) is one of 5G scenarios to support hyper-connection services that communicate with a large number of UEs at the same time. In this environment, the UE communicates intermittently with a very low transmission rate and mobility. Therefore, mMTC aims at how long the UE can be driven at a low cost for a long time. Regarding mMTC technology, 3GPP deals with MTC and NB (NarrowBand)-IoT.

The mMTC technology has features such as repetitive transmission of PDCCH, PUCCH, physical downlink shared channel (PDSCH), PUSCH, and the like, frequency hopping, retuning, and guard period.

That is, a PUSCH (or PUCCH (especially, long PUCCH) or PRACH) including specific information and a PDSCH (or PDCCH) including a response to specific information are repeatedly transmitted. Repetitive transmission is performed through frequency hopping, and for repetitive transmission, (RF) retuning is performed in a guard period from a first frequency resource to a second frequency resource, and specific information And a response to specific information may be transmitted/received through a narrowband (ex. 6 resource block (RB) or 1 RB).

F. Basic operation between autonomous vehicles using 5G communication

3 shows an example of a basic operation of an autonomous vehicle and a 5G network in a 5G communication system.

The autonomous vehicle transmits specific information transmission to the 5G network (S1). The specific information may include autonomous driving related information. In addition, the 5G network may determine whether to remotely control the vehicle (S2). Here, the 5G network may include a server or module that performs remote control related to autonomous driving. In addition, the 5G network may transmit information (or signals) related to remote control to the autonomous vehicle (S3).

G. Application operation between autonomous vehicle and 5G network in 5G communication system

Hereinafter, the operation of an autonomous vehicle using 5G communication will be described in more detail with reference to Salpin wireless communication technology (BM procedure, URLLC, Mmtc, etc.) prior to FIGS. 1 and 2.

First, a basic procedure of an application operation to which the eMBB technology of 5G communication is applied and the method proposed in the present specification to be described later will be described.

As in steps S1 and S3 of FIG. 3, in order for the autonomous vehicle to transmit/receive the 5G network, signals, information, etc., the autonomous vehicle is an initial access procedure with the 5G network prior to step S1 of FIG. 3. And a random access procedure.

More specifically, the autonomous vehicle performs an initial access procedure with the 5G network based on the SSB in order to obtain DL synchronization and system information. In the initial access procedure, a beam management (BM) process and a beam failure recovery process may be added. In the process of receiving a signal from the 5G network by an autonomous vehicle, a quasi-co location (QCL) ) Relationships can be added.

In addition, the autonomous vehicle performs a random access procedure with a 5G network to obtain UL synchronization and/or transmit UL. And, the 5G network may transmit a UL grant for scheduling transmission of specific information to the autonomous vehicle. have. Accordingly, the autonomous vehicle transmits specific information to the 5G network based on the UL grant. In addition, the 5G network transmits a DL grant for scheduling transmission of a 5G processing result for the specific information to the autonomous vehicle. Accordingly, the 5G network may transmit information (or signals) related to remote control to the autonomous vehicle based on the DL grant.

Next, a basic procedure of an application operation to which the URLLC technology of 5G communication is applied and the method proposed in the present specification to be described later will be described.

As described above, after the autonomous vehicle performs an initial access procedure and/or a random access procedure with the 5G network, the autonomous vehicle may receive a DownlinkPreemption IE from the 5G network. In addition, the autonomous vehicle receives DCI format 2_1 including a pre-emption indication from the 5G network based on the DownlinkPreemption IE. And, the autonomous vehicle does not perform (or expect or assume) the reception of eMBB data in the resource (PRB and/or OFDM symbol) indicated by the pre-emption indication. Thereafter, the autonomous vehicle may receive a UL grant from the 5G network when it is necessary to transmit specific information.

Next, the method proposed in the present specification to be described later and the basic procedure of the application operation to which the mMTC technology of 5G communication is applied will be described.

Among the steps of FIG. 3, a description will be made focusing on the parts that are changed by the application of the mMTC technology.

In step S1 of FIG. 3, the autonomous vehicle receives a UL grant from the 5G network to transmit specific information to the 5G network. Here, the UL grant includes information on the number of repetitions for transmission of the specific information, and the specific information may be repeatedly transmitted based on the information on the number of repetitions. That is, the autonomous vehicle transmits specific information to the 5G network based on the UL grant. Further, repetitive transmission of specific information may be performed through frequency hopping, transmission of first specific information may be transmitted in a first frequency resource, and transmission of second specific information may be transmitted in a second frequency resource. The specific information may be transmitted through a narrowband of 6RB (Resource Block) or 1RB (Resource Block).

H. Vehicle-to-vehicle autonomous driving operation using 5G communication

4 illustrates an example of a vehicle-to-vehicle basic operation using 5G communication.

The first vehicle transmits specific information to the second vehicle (S61). The second vehicle transmits a response to the specific information to the first vehicle (S62).

On the other hand, depending on whether the 5G network directly (side link communication transmission mode 3) or indirectly (sidelink communication transmission mode 4) is involved in the resource allocation of the specific information and the response to the specific information, the vehicle-to-vehicle application operation is The composition may vary.

Next, a vehicle-to-vehicle application operation using 5G communication will be described.

First, a method in which a 5G network is directly involved in resource allocation for vehicle-to-vehicle signal transmission/reception will be described.

The 5G network may transmit DCI format 5A to the first vehicle for scheduling of mode 3 transmission (PSCCH and/or PSSCH transmission). Here, a physical sidelink control channel (PSCCH) is a 5G physical channel for scheduling specific information transmission, and a physical sidelink shared channel (PSSCH) is a 5G physical channel for transmitting specific information. In addition, the first vehicle transmits SCI format 1 for scheduling specific information transmission to the second vehicle on the PSCCH. Then, the first vehicle transmits specific information to the second vehicle on the PSSCH.

Next, we will look at how the 5G network is indirectly involved in resource allocation for signal transmission/reception.

The first vehicle senses a resource for mode 4 transmission in the first window. Then, the first vehicle selects a resource for mode 4 transmission in the second window based on the sensing result. Here, the first window means a sensing window, and the second window means a selection window. The first vehicle transmits SCI format 1 for scheduling specific information transmission to the second vehicle on the PSCCH based on the selected resource. Then, the first vehicle transmits specific information to the second vehicle on the PSSCH.

Driving

(1) Vehicle appearance

5 is a view showing a vehicle according to an embodiment of the present specification.

Referring to FIG. 5, the vehicle 10 according to the embodiment of the present specification is defined as a transportation means traveling on a road or track. The vehicle 10 is a concept including a car, a train, and a motorcycle. The vehicle 10 may be a concept including both an internal combustion engine vehicle including an engine as a power source, a hybrid vehicle including an engine and an electric motor as a power source, an electric vehicle including an electric motor as a power source, and the like. The vehicle 10 may be a vehicle owned by an individual. The vehicle 10 may be a shared vehicle. The vehicle 10 may be an autonomous vehicle.

(2) vehicle components

6 is a control block diagram of a vehicle according to an exemplary embodiment of the present specification.

Referring to FIG. 6, the vehicle 10 includes a user interface device 200, an object detection device 210, a communication device 220, a driving operation device 230, a main ECU 240, and a drive control device 250. ), an autonomous driving device 260, a sensing unit 270, and a location data generating device 280. Object detection device 210, communication device 220, driving operation device 230, main ECU 240, drive control device 250, autonomous driving device 260, sensing unit 270, and position data generating device Each of 280 may be implemented as an electronic device that generates an electrical signal and exchanges electrical signals with each other.

1) User interface device

The user interface device 200 is a device for communicating with the vehicle 10 and a user. The user interface device 200 may receive a user input and provide information generated in the vehicle 10 to the user. The vehicle 10 may implement a user interface (UI) or a user experience (UX) through the user interface device 200. The user interface device 200 may include an input device, an output device, and a user monitoring device.

2) Object detection device

The object detection device 210 may generate information on an object outside the vehicle 10. The information on the object may include at least one of information on the presence or absence of the object, location information of the object, distance information between the vehicle 10 and the object, and relative speed information between the vehicle 10 and the object. . The object detection device 210 may detect an object outside the vehicle 10. The object detection apparatus 210 may include at least one sensor capable of detecting an object outside the vehicle 10. The object detection device 210 may include at least one of a camera, a radar, a lidar, an ultrasonic sensor, and an infrared sensor. The object detection device 210 may provide data on an object generated based on a sensing signal generated by a sensor to at least one electronic device included in the vehicle.

2.1) Camera

The camera may generate information on an object outside the vehicle 10 by using an image. The camera may include at least one lens, at least one image sensor, and at least one processor that is electrically connected to the image sensor and processes a received signal, and generates data on an object based on the processed signal.

The camera may be at least one of a mono camera, a stereo camera, and an AVM (Around View Monitoring) camera. The camera may use various image processing algorithms to obtain position information of an object, distance information to an object, or information on a relative speed to an object. For example, from the acquired image, the camera may acquire distance information and relative speed information from the object based on a change in the size of the object over time. For example, the camera may obtain distance information and relative speed information with an object through a pin hole model, road surface profiling, or the like. For example, the camera may obtain distance information and relative speed information from an object based on disparity information from a stereo image obtained from a stereo camera.

The camera may be mounted in a position where field of view (FOV) can be secured in the vehicle to photograph the outside of the vehicle. The camera may be placed in the interior of the vehicle, close to the front windshield, to acquire an image of the front of the vehicle. The camera can be placed around the front bumper or radiator grille. The camera may be placed close to the rear glass, in the interior of the vehicle, in order to acquire an image of the rear of the vehicle. The camera can be placed around the rear bumper, trunk or tailgate. The camera may be disposed in proximity to at least one of the side windows in the interior of the vehicle in order to acquire an image of the side of the vehicle. Alternatively, the camera may be disposed around a side mirror, a fender, or a door.

2.2) radar

The radar may use radio waves to generate information on objects outside the vehicle 10. The radar may include at least one processor that is electrically connected to the electromagnetic wave transmitter, the electromagnetic wave receiver, and the electromagnetic wave transmitter and the electromagnetic wave receiver, processes a received signal, and generates data for an object based on the processed signal. The radar may be implemented in a pulse radar method or a continuous wave radar method according to the principle of radio wave emission. The radar may be implemented in a frequency modulated continuous wave (FMCW) method or a frequency shift keyong (FSK) method according to a signal waveform among continuous wave radar methods. The radar detects an object by means of an electromagnetic wave, based on a Time of Flight (TOF) method or a phase-shift method, and detects the position of the detected object, the distance to the detected object, and the relative speed. I can. The radar may be placed at a suitable location outside the vehicle to detect objects located in front, rear or side of the vehicle.

2.3) Lida

The lidar may generate information on an object outside the vehicle 10 by using laser light. The radar may include at least one processor that is electrically connected to the optical transmitter, the optical receiver, and the optical transmitter and the optical receiver, processes a received signal, and generates data for an object based on the processed signal. . The rider may be implemented in a Time of Flight (TOF) method or a phase-shift method. The lidar can be implemented either driven or non-driven. When implemented as a drive type, the lidar is rotated by a motor, and objects around the vehicle 10 can be detected. When implemented in a non-driven manner, the lidar can detect an object located within a predetermined range with respect to the vehicle by optical steering. The vehicle 100 may include a plurality of non-driven lidars. The radar detects an object based on a time of flight (TOF) method or a phase-shift method by means of a laser light, and determines the position of the detected object, the distance to the detected object, and the relative speed. Can be detected. The lidar may be placed at an appropriate location outside the vehicle to detect objects located in front, rear or side of the vehicle.

3) Communication device

The communication device 220 may exchange signals with devices located outside the vehicle 10. The communication device 220 may exchange signals with at least one of an infrastructure (eg, a server, a broadcasting station), another vehicle, and a terminal. The communication device 220 may include at least one of a transmission antenna, a reception antenna, a radio frequency (RF) circuit capable of implementing various communication protocols, and an RF element to perform communication.

For example, the communication device may exchange signals with external devices based on C-V2X (Cellular V2X) technology. For example, C-V2X technology may include LTE-based sidelink communication and/or NR-based sidelink communication. Contents related to C-V2X will be described later.

For example, a communication device can communicate with external devices based on the IEEE 802.11p PHY/MAC layer technology and the Dedicated Short Range Communications (DSRC) technology based on the IEEE 1609 Network/Transport layer technology, or the Wireless Access in Vehicular Environment (WAVE) standard. Can be exchanged. DSRC (or WAVE standard) technology is a communication standard designed to provide Intelligent Transport System (ITS) services through short-distance dedicated communication between vehicle-mounted devices or between roadside devices and vehicle-mounted devices. The DSRC technology may use a frequency of 5.9 GHz band, and may be a communication method having a data transmission rate of 3 Mbps to 27 Mbps. IEEE 802.11p technology can be combined with IEEE 1609 technology to support DSRC technology (or WAVE standard).

The communication apparatus of the present specification may exchange signals with an external device using only either C-V2X technology or DSRC technology. Alternatively, the communication device of the present specification may exchange signals with an external device by hybridizing C-V2X technology and DSRC technology.

4) Driving operation device

The driving operation device 230 is a device that receives a user input for driving. In the case of the manual mode, the vehicle 10 may be driven based on a signal provided by the driving operation device 230. The driving operation device 230 may include a steering input device (eg, a steering wheel), an acceleration input device (eg, an accelerator pedal), and a brake input device (eg, a brake pedal).

5) Main ECU

The main ECU 240 may control the overall operation of at least one electronic device provided in the vehicle 10.

6) Drive control device

The drive control device 250 is a device that electrically controls various vehicle drive devices in the vehicle 10. The drive control device 250 may include a power train drive control device, a chassis drive control device, a door/window drive control device, a safety device drive control device, a lamp drive control device, and an air conditioning drive control device. The power train drive control device may include a power source drive control device and a transmission drive control device. The chassis drive control device may include a steering drive control device, a brake drive control device, and a suspension drive control device. Meanwhile, the safety device driving control device may include a safety belt driving control device for controlling the safety belt.

The drive control device 250 includes at least one electronic control device (eg, a control Electronic Control Unit (ECU)).

The vehicle type control device 250 may control the vehicle driving device based on a signal received from the autonomous driving device 260. For example, the control device 250 may control a power train, a steering device, and a brake device based on a signal received from the autonomous driving device 260.

7) Autonomous driving device

The autonomous driving device 260 may generate a path for autonomous driving based on the acquired data. The autonomous driving device 260 may generate a driving plan for driving along the generated route. The autonomous driving device 260 may generate a signal for controlling the movement of the vehicle according to the driving plan. The autonomous driving device 260 may provide the generated signal to the driving control device 250.

The autonomous driving device 260 may implement at least one Advanced Driver Assistance System (ADAS) function. ADAS includes Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), Forward Collision Warning (FCW), and Lane Keeping Assist (LKA). ), Lane Change Assist (LCA), Target Following Assist (TFA), Blind Spot Detection (BSD), Adaptive High Beam Control System (HBA: High Beam Assist) , APS (Auto Parking System), Pedestrian Collision Warning System (PD collision warning system), Traffic Sign Recognition (TSR), Traffic Sign Assist (TSA), Night Vision System At least one of (NV: Night Vision), Driver Status Monitoring (DSM), and Traffic Jam Assist (TJA) may be implemented.

The autonomous driving apparatus 260 may perform a switching operation from an autonomous driving mode to a manual driving mode or a switching operation from a manual driving mode to an autonomous driving mode. For example, the autonomous driving device 260 may switch the mode of the vehicle 10 from the autonomous driving mode to the manual driving mode or the autonomous driving mode from the manual driving mode based on a signal received from the user interface device 200. Can be switched to.

8) Sensing part

The sensing unit 270 may sense the state of the vehicle. The sensing unit 270 includes an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, a tilt sensor, a weight detection sensor, a heading sensor, a position module, and a vehicle. It may include at least one of a forward/reverse sensor, a battery sensor, a fuel sensor, a tire sensor, a steering sensor, a temperature sensor, a humidity sensor, an ultrasonic sensor, an illumination sensor, and a pedal position sensor. Meanwhile, the inertial measurement unit (IMU) sensor may include one or more of an acceleration sensor, a gyro sensor, and a magnetic sensor.

The sensing unit 270 may generate state data of the vehicle based on a signal generated by at least one sensor. The vehicle state data may be information generated based on data sensed by various sensors provided inside the vehicle. The sensing unit 270 includes vehicle attitude data, vehicle motion data, vehicle yaw data, vehicle roll data, vehicle pitch data, vehicle collision data, vehicle direction data, vehicle angle data, and vehicle speed. Data, vehicle acceleration data, vehicle inclination data, vehicle forward/reverse data, vehicle weight data, battery data, fuel data, tire pressure data, vehicle internal temperature data, vehicle internal humidity data, steering wheel rotation angle data, vehicle exterior illuminance Data, pressure data applied to the accelerator pedal, and pressure data applied to the brake pedal can be generated.

9) Location data generation device

The location data generating device 280 may generate location data of the vehicle 10. The location data generating apparatus 280 may include at least one of a Global Positioning System (GPS) and a Differential Global Positioning System (DGPS). The location data generating device 280 may generate location data of the vehicle 10 based on a signal generated by at least one of GPS and DGPS. According to an embodiment, the location data generation apparatus 280 may correct location data based on at least one of an IMU (Inertial Measurement Unit) of the sensing unit 270 and a camera of the object detection apparatus 210. The location data generating device 280 may be referred to as a Global Navigation Satellite System (GNSS).

Vehicle 10 may include an internal communication system 50. A plurality of electronic devices included in the vehicle 10 may exchange signals through the internal communication system 50. Signals may contain data. The internal communication system 50 may use at least one communication protocol (eg, CAN, LIN, FlexRay, MOST, Ethernet).

(3) Components of autonomous driving devices

7 is a control block diagram of an autonomous driving apparatus according to an embodiment of the present specification.

Referring to FIG. 7, the autonomous driving device 260 may include a memory 140, a processor 170, an interface unit 180, and a power supply unit 190.

The memory 140 is electrically connected to the processor 170. The memory 140 may store basic data for a unit, control data for controlling the operation of the unit, and input/output data. The memory 140 may store data processed by the processor 170. In terms of hardware, the memory 140 may be configured with at least one of a ROM, a RAM, an EPROM, a flash drive, and a hard drive. The memory 140 may store various data for the overall operation of the autonomous driving device 260, such as a program for processing or controlling the processor 170. The memory 140 may be implemented integrally with the processor 170. Depending on the embodiment, the memory 140 may be classified as a sub-element of the processor 170.

The interface unit 180 may exchange signals with at least one electronic device provided in the vehicle 10 by wire or wirelessly. The interface unit 280 includes an object detection device 210, a communication device 220, a driving operation device 230, a main ECU 240, a drive control device 250, a sensing unit 270, and a position data generating device. A signal may be exchanged with at least one of 280 by wire or wirelessly. The interface unit 280 may be configured with at least one of a communication module, a terminal, a pin, a cable, a port, a circuit, an element, and a device.

The power supply unit 190 may supply power to the autonomous driving device 260. The power supply unit 190 may receive power from a power source (eg, a battery) included in the vehicle 10 and supply power to each unit of the autonomous driving device 260. The power supply unit 190 may be operated according to a control signal provided from the main ECU 240. The power supply unit 190 may include a switched-mode power supply (SMPS).

The processor 170 may be electrically connected to the memory 140, the interface unit 280, and the power supply unit 190 to exchange signals. The processor 170 includes application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, and controllers. It may be implemented using at least one of (controllers), micro-controllers, microprocessors, and electrical units for performing other functions.

The processor 170 may be driven by power provided from the power supply unit 190. The processor 170 may receive data, process data, generate a signal, and provide a signal while power is supplied by the power supply unit 190.

The processor 170 may receive information from another electronic device in the vehicle 10 through the interface unit 180. The processor 170 may provide a control signal to another electronic device in the vehicle 10 through the interface unit 180.

The autonomous driving device 260 may include at least one printed circuit board (PCB). The memory 140, the interface unit 180, the power supply unit 190, and the processor 170 may be electrically connected to a printed circuit board.

(4) operation of autonomous driving devices

8 is a signal flow diagram of an autonomous vehicle according to an embodiment of the present specification.

1) Receiving operation

Referring to FIG. 8, the processor 170 may perform a reception operation. The processor 170 may receive data from at least one of the object detection device 210, the communication device 220, the sensing unit 270, and the location data generation device 280 through the interface unit 180. I can. The processor 170 may receive object data from the object detection apparatus 210. The processor 170 may receive HD MAP data from the communication device 220. The processor 170 may receive vehicle state data from the sensing unit 270. The processor 170 may receive location data from the location data generating device 280.

2) Processing/judgment operation

The processor 170 may perform a processing/determining operation. The processor 170 may perform a processing/determining operation based on the driving situation information. The processor 170 may perform a processing/determining operation based on at least one of object data, HD MAP data, vehicle state data, and location data.

2.1) Driving plan data generation operation

The processor 170 may generate driving plan data. For example, the processor 1700 may generate electronic horizon data. The electronic horizon data is understood as driving plan data within a range from the point where the vehicle 10 is located to the horizon. Horizon may be understood as a point in front of a preset distance from a point where the vehicle 10 is located based on a preset driving route. It may mean a point at which the vehicle 10 can reach after a predetermined time from the point.

The electronic horizon data may include horizon map data and horizon pass data.

2.1.1) Horizon Map Data

The horizon map data may include at least one of topology data, road data, HD MAP data, and dynamic data. According to an embodiment, the horizon map data may include a plurality of layers. For example, the horizon map data may include one layer matching topology data, a second layer matching road data, a third layer matching HD MAP data, and a fourth layer matching dynamic data. The horizon map data may further include static object data.

Topology data can be described as a map created by connecting the centers of the roads. The topology data is suitable for roughly indicating the location of the vehicle, and may be in the form of data mainly used in a navigation for a driver. The topology data may be understood as data about road information excluding information about a lane. The topology data may be generated based on data received from an external server through the communication device 220. The topology data may be based on data stored in at least one memory provided in the vehicle 10.

The road data may include at least one of slope data of a road, curvature data of a road, and speed limit data of a road. The road data may further include overtaking prohibited section data. Road data may be based on data received from an external server through the communication device 220. The road data may be based on data generated by the object detection apparatus 210.

The HD MAP data includes detailed lane-level topology information of the road, connection information of each lane, and feature information for localization of the vehicle (e.g., traffic signs, lane marking/attributes, road furniture, etc.). I can. The HD MAP data may be based on data received from an external server through the communication device 220.

The dynamic data may include various dynamic information that may be generated on the road. For example, the dynamic data may include construction information, variable speed lane information, road surface condition information, traffic information, moving object information, and the like. The dynamic data may be based on data received from an external server through the communication device 220. The dynamic data may be based on data generated by the object detection apparatus 210.

The processor 170 may provide map data within a range from the point where the vehicle 10 is located to the horizon.

2.1.2) Horizon Pass Data

The horizon pass data may be described as a trajectory that the vehicle 10 can take within a range from the point where the vehicle 10 is located to the horizon. The horizon pass data may include data representing a relative probability of selecting any one road at a decision point (eg, a fork, a fork, an intersection, etc.). The relative probability can be calculated based on the time it takes to reach the final destination. For example, at the decision point, if the first road is selected and the time it takes to reach the final destination is less than the second road is selected, the probability of selecting the first road is less than the probability of selecting the second road. It can be calculated higher.

Horizon pass data may include a main pass and a sub pass. The main path can be understood as a trajectory connecting roads with a high relative probability to be selected. The sub-path may be branched at at least one decision point on the main path. The sub-path may be understood as a trajectory connecting at least one road having a low relative probability to be selected from at least one decision point on the main path.

3) Control signal generation operation

The processor 170 may perform a control signal generation operation. The processor 170 may generate a control signal based on electronic horizon data. For example, the processor 170 may generate at least one of a powertrain control signal, a brake device control signal, and a steering device control signal based on the electronic horizon data.

The processor 170 may transmit the generated control signal to the driving control device 250 through the interface unit 180. The drive control device 250 may transmit a control signal to at least one of the power train 251, the brake device 252, and the steering device 253.

Self-driving vehicle use scenario

9 is a diagram referenced to explain a usage scenario of a user according to an embodiment of the present specification.

1) Destination prediction scenario

The first scenario S111 is a user's destination prediction scenario. The user terminal may install an application capable of interworking with the cabin system 300. The user terminal may predict the destination of the user based on the user's contextual information through the application. The user terminal may provide information on empty seats in the cabin through an application.

2) Cabin interior layout preparation scenario

The second scenario S112 is a cabin interior layout preparation scenario. The cabin system 300 may further include a scanning device for acquiring data on a user located outside the vehicle 300. The scanning device may scan the user to obtain body data and baggage data of the user. The user's body data and baggage data can be used to set the layout. The user's body data may be used for user authentication. The scanning device may include at least one image sensor. The image sensor may acquire a user image using light in the visible or infrared band.

The seat system 360 may set a layout in the cabin based on at least one of a user's body data and baggage data. For example, the seat system 360 may provide a luggage storage space or a car seat installation space.

3) User welcome scenario

The third scenario S113 is a user welcome scenario. The cabin system 300 may further include at least one guide light. The guide light may be disposed on the floor in the cabin. When a user's boarding is detected, the cabin system 300 may output a guide light to allow the user to sit on a preset seat among a plurality of seats. For example, the main controller 370 may implement a moving light by sequentially lighting a plurality of light sources according to time from an opened door to a preset user seat.

4) Seat adjustment service scenario

The fourth scenario S114 is a seat adjustment service scenario. The seat system 360 may adjust at least one element of a seat matching the user based on the acquired body information.

5) Personal content provision scenario

The fifth scenario S115 is a personal content providing scenario. The display system 350 may receive user personal data through the input device 310 or the communication device 330. The display system 350 may provide content corresponding to user personal data.

6) Product provision scenario

The sixth scenario S116 is a product provision scenario. The cargo system 355 may receive user data through the input device 310 or the communication device 330. The user data may include user preference data and user destination data. The cargo system 355 may provide a product based on user data.

7) Payment scenario

The seventh scenario S117 is a payment scenario. The payment system 365 may receive data for price calculation from at least one of the input device 310, the communication device 330, and the cargo system 355. The payment system 365 may calculate a vehicle usage price of the user based on the received data. The payment system 365 may request payment of a fee from a user (eg, a user's mobile terminal) at the calculated price.

8) User's display system control scenario

The eighth scenario S118 is a user's display system control scenario. The input device 310 may receive a user input in at least one form and convert it into an electrical signal. The display system 350 may control displayed content based on an electrical signal.

9) AI agent scenario

The ninth scenario S119 is a multi-channel artificial intelligence (AI) agent scenario for a plurality of users. The artificial intelligence agent 372 may classify a user input for each of a plurality of users. The artificial intelligence agent 372 is at least one of the display system 350, the cargo system 355, the seat system 360, and the payment system 365 based on the electrical signals converted from a plurality of user individual user inputs. Can be controlled.

10) Scenario for providing multimedia contents for multiple users

The tenth scenario S120 is a scenario for providing multimedia contents targeting a plurality of users. The display system 350 may provide content that all users can watch together. In this case, the display system 350 may individually provide the same sound to a plurality of users through speakers provided for each sheet. The display system 350 may provide content that can be individually viewed by a plurality of users. In this case, the display system 350 may provide individual sounds through speakers provided for each sheet.

11) User safety security scenario

The eleventh scenario S121 is a user safety securing scenario. When obtaining information on objects around the vehicle that threatens the user, the main controller 370 may control to output an alarm for objects around the vehicle through the display system 350.

12) Loss of belongings prevention scenario

The twelfth scenario S122 is a scenario for preventing the loss of belongings by the user. The main controller 370 may acquire data on the user's belongings through the input device 310. The main controller 370 may acquire motion data of a user through the input device 310. The main controller 370 may determine whether the user leaves the belongings and gets off the vehicle based on the data and movement data on the belongings. The main controller 370 may control an alarm for belongings to be output through the display system 350.

13) Alight Report Scenario

The thirteenth scenario S123 is a getting off report scenario. The main controller 370 may receive a user's getting off data through the input device 310. After getting off the user, the main controller 370 may provide report data according to the getting off to the user's mobile terminal through the communication device 330. The report data may include data on the total usage fee of the vehicle 10.

V2X (Vehicle-to-Everything)

10 is an example of V2X communication to which the present specification can be applied.

V2X communication is V2V (Vehicle-to-Vehicle), which refers to communication between vehicles, V2I (Vehicle to Infrastructure), which refers to communication between a vehicle and an eNB or RSU (Road Side Unit), and vehicle and individual. It includes communication between the vehicle and all entities such as V2P (Vehicle-to-Pedestrian) and V2N (vehicle-to-network), which refer to communication between UEs possessed by (pedestrian, cyclist, vehicle driver, or passenger).

V2X communication may represent the same meaning as V2X sidelink or NR V2X, or may represent a broader meaning including V2X sidelink or NR V2X.

V2X communication includes, for example, forward collision warning, automatic parking system, cooperative adaptive cruise control (CACC), control loss warning, traffic matrix warning, traffic vulnerable safety warning, emergency vehicle warning, and driving on curved roads. It can be applied to various services such as speed warning and traffic flow control.

V2X communication may be provided through a PC5 interface and/or a Uu interface. In this case, in a wireless communication system supporting V2X communication, specific network entities for supporting communication between the vehicle and all entities may exist. For example, the network entity may be a BS (eNB), a road side unit (RSU), a UE, or an application server (eg, a traffic safety server).

In addition, the UE performing V2X communication is not only a general portable UE (handheld UE), but also a vehicle UE (V-UE (Vehicle UE)), a pedestrian UE (pedestrian UE), a BS type (eNB type) RSU, or a UE It may mean a type (UE type) RSU, a robot equipped with a communication module, or the like.

V2X communication may be performed directly between UEs or may be performed through the network entity(s). V2X operation modes can be classified according to the V2X communication method.

V2X communication is required to support the pseudonymity and privacy of the UE when using the V2X application so that an operator or a third party cannot track the UE identifier in the region where V2X is supported. do.

The terms frequently used in V2X communication are defined as follows.

-RSU (Road Side Unit): RSU is a V2X service capable device that can transmit/receive with a mobile vehicle using V2I service. In addition, RSU is a fixed infrastructure entity that supports V2X applications, and can exchange messages with other entities that support V2X applications. RSU is a term frequently used in the existing ITS specification, and the reason for introducing this term in the 3GPP specification is to make the document easier to read in the ITS industry. The RSU is a logical entity that combines the V2X application logic with the function of a BS (referred to as a BS-type RSU) or a UE (referred to as a UE-type RSU).

-V2I service: A type of V2X service, an entity belonging to one side of the vehicle and the other side of the infrastructure.

-V2P service: A type of V2X service, with one side being a vehicle and the other side being a personal device (eg, a portable UE device carried by a pedestrian, cyclist, driver, or passenger).

-V2X service: 3GPP communication service type in which a transmitting or receiving device is related to a vehicle.

-V2X enabled (enabled) UE: UE that supports V2X service.

-V2V service: This is a type of V2X service, both of which are vehicles.

-V2V communication range: Direct communication range between two vehicles participating in V2V service.

V2X applications, called Vehicle-to-Everything (V2X), look like you're looking at: (1) Vehicle-to-Vehicle (V2V), (2) Vehicle-to-Infrastructure (V2I), (3) Vehicle-to-Network (V2N), (4) Vehicle There are four types of pedestrians (V2P).

11 illustrates a resource allocation method in a sidelink in which V2X is used.

In the sidelink, different sidelink control channels (physical sidelink control channels, PSCCHs) may be allocated spaced apart from each other in the frequency domain, and different sidelink shared channels (physical sidelink shared channels, PSSCHs) may be allocated spaced apart from each other. Alternatively, different PSCCHs may be consecutively allocated in the frequency domain, and PSSCHs may be consecutively allocated in the frequency domain.

NR V2X

During 3GPP Releases 14 and 15, to extend the 3GPP platform to the automotive industry, support for V2V and V2X services in LTE was introduced.

The requirements for support for the enhanced V2X use case are largely organized into four use case groups.

(1) Vehicle Platooning enables vehicles to dynamically form a platoon that moves together. All of Platoon's vehicles get information from the leading vehicle to manage this Platoon. This information allows vehicles to drive more harmoniously than normal, go in the same direction and travel together.

(2) Extended sensors are raw data collected from vehicles, road site units, pedestrian devices, and V2X application servers via local sensors or live video images. ) Or exchange of processed data. Vehicles can increase their awareness of the environment beyond what their sensors can detect, and can grasp the local situation more broadly and holistically. A high data transfer rate is one of its main features.

(3) Advanced driving enables semi-automatic or fully-automatic driving. Each vehicle and/or RSU shares its own recognition data from local sensors with nearby vehicles, allowing the vehicle to synchronize and adjust trajectory or manoeuvre. Each vehicle shares a driving intention with a nearby driving vehicle.

(4) Remote driving allows remote drivers or V2X applications to drive remote vehicles for passengers who cannot drive themselves or with remote vehicles in hazardous environments. When fluctuations are limited and routes can be predicted, such as in public transport, driving based on cloud computing can be used. High reliability and low latency are the main requirements.

Identifier for V2X communication through PC5

Each terminal has a Layer-2 identifier for V2 communication through one or more PC5s. This includes the source Layer-2 ID and the destination Layer-2 ID.

The source and destination Layer-2 IDs are included in the Layer-2 frame, and the Layer-2 frame is transmitted through a layer-2 link of PC5 that identifies the source and destination of Layer-2 on the frame.

The UE's source and destination Layer-2 ID selection is based on the communication mode of the V2X communication of the PC5 of the layer-2 link. The source Layer-2 ID can be different between different communication modes.

When IP-based V2X communication is allowed, the terminal configures the link-local IPv6 address to be used as the source IP address. The UE can use this IP address for V2X communication of PC5 without sending a Neighbor Solicitation and Neighbor Advertisement message for redundant address discovery.

If one terminal has an active V2X application that requires personal information protection supported in the current geographic area, the source terminal (eg, vehicle) is tracked or identified from other terminals only for a specific time, so that the source layer- 2 IDs change over time and can be randomized. In the case of IP-based V2X communication, the source IP address must also change over time and must be randomized.

Changes of the identifiers of the source terminal must be synchronized in the layer used for PC5. That is, if the application layer identifier is changed, the source Layer-2 ID and source IP address are also required to be changed.

Broadcast mode

12 is a diagram illustrating a procedure for a broadcast mode of V2X communication using PC5.

One. The receiving terminal determines a destination Layer-2 ID for broadcast reception. The destination Layer-2 ID is transmitted to the AS layer of the receiving terminal for reception.

2. The V2X application layer of the transmitting terminal may provide a data unit and may provide V2X application requirements.

3. The transmitting terminal determines a destination Layer-2 ID for broadcast. The transmitting terminal allocates itself with a source Layer-2 ID.

4. One broadcast message transmitted by the transmitting terminal transmits V2X service data using the source Layer-2 ID and the destination Layer-2 ID.

Basic Safety Message (BSM)

About encryption of messages sent and received in a vehicle communication environment The representative form of the standard is BSM (Basic Safety Message) defined in'SAE J2735'. BSM is a broadcasting message that is periodically received from a vehicle and is designed to increase safety. Vehicles transmit a message every 100msec, and the received vehicle determines the safety of the vehicle through it. BSM is divided into transmitted information and additional information, which are defined as Part 1 and Part 2. The content of such information may include vehicle location, moving direction, current time, and vehicle status information.

The message ID of the vehicle may be designated as msgID, msgCnt, id, and secMark, and 8 bytes may be allocated. The vehicle location value can be designated as lat, long, elev, or accuriacy, and 14 bytes can be allocated. For detailed values of the field values, refer to'SAE J235'.

Table 1 is an example of BSM that can be applied in the present specification.

In the present specification, the BSM may be substituted with a V2X message or a V2X safety message performing a similar operation.

13 is a block diagram of an AI device according to an embodiment of the present specification.

The AI device 20 may include an electronic device including an AI module capable of performing AI processing or a server including the AI module. In addition, the AI device 20 may be included as a component of at least a part of the vehicle 10 shown in FIG. 1 and may be provided to perform at least a part of AI processing together.

The AI processing may include all operations related to driving of the vehicle 10 shown in FIG. 5. For example, an autonomous vehicle may perform AI processing on sensing data or driver data to perform processing/decision and control signal generation operations. In addition, for example, the autonomous driving vehicle may perform autonomous driving control by AI processing data acquired through interactions with other electronic devices provided in the vehicle.

The AI device 20 may include an AI processor 21, a memory 25 and/or a communication unit 27.

The AI device 20 is a computing device capable of learning a neural network, and may be implemented as various electronic devices such as a server, a desktop PC, a notebook PC, and a tablet PC.

The AI processor 21 may learn a neural network using a program stored in the memory 25. In particular, the AI processor 21 may learn a neural network for recognizing vehicle-related data. Here, the neural network for recognizing vehicle-related data may be designed to simulate a human brain structure on a computer, and may include a plurality of network nodes having weights that simulate neurons of the human neural network. The plurality of network modes can exchange data according to their respective connection relationships so as to simulate the synaptic activity of neurons that send and receive signals through synapses. Here, the neural network may include a deep learning model developed from a neural network model. In a deep learning model, a plurality of network nodes are located in different layers and can exchange data according to a convolutional connection relationship. Examples of neural network models include deep neural networks (DNN), convolutional deep neural networks (CNN), Recurrent Boltzmann Machine (RNN), Restricted Boltzmann Machine (RBM), and deep trust. It includes various deep learning techniques such as deep belief networks (DBN) and deep Q-network, and can be applied to fields such as computer vision, speech recognition, natural language processing, and speech/signal processing.

Meanwhile, a processor that performs the functions as described above may be a general-purpose processor (eg, a CPU), but may be an AI-only processor (eg, a GPU) for artificial intelligence learning.

The memory 25 may store various programs and data required for the operation of the AI device 20. The memory 25 may be implemented as a non-volatile memory, a volatile memory, a flash memory, a hard disk drive (HDD), or a solid state drive (SDD). The memory 25 is accessed by the AI processor 21, and data read/write/edit/delete/update by the AI processor 21 may be performed. In addition, the memory 25 may store a neural network model (eg, a deep learning model 26) generated through a learning algorithm for classifying/recognizing data according to an embodiment of the present specification.

Meanwhile, the AI processor 21 may include a data learning unit 22 that learns a neural network for data classification/recognition. The data learning unit 22 may learn a criterion for how to classify and recognize data using which training data to use in order to determine data classification/recognition. The data learning unit 22 may learn the deep learning model by acquiring training data to be used for training and applying the acquired training data to the deep learning model.

The data learning unit 22 may be manufactured in the form of at least one hardware chip and mounted on the AI device 20. For example, the data learning unit 22 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or may be manufactured as a part of a general-purpose processor (CPU) or a dedicated graphics processor (GPU) to the AI device 20. It can also be mounted. In addition, the data learning unit 22 may be implemented as a software module. When implemented as a software module (or a program module including an instruction), the software module may be stored in a computer-readable non-transitory computer readable media. In this case, at least one software module may be provided by an operating system (OS) or an application.

The data learning unit 22 may include a learning data acquisition unit 23 and a model learning unit 24.

The training data acquisition unit 23 may acquire training data necessary for a neural network model for classifying and recognizing data. For example, the training data acquisition unit 23 may acquire vehicle data and/or sample data for input into a neural network model as training data.

The model learning unit 24 may learn to have a criterion for determining how the neural network model classifies predetermined data by using the acquired training data. In this case, the model learning unit 24 may train the neural network model through supervised learning using at least a portion of the training data as a criterion for determination. Alternatively, the model learning unit 24 may train the neural network model through unsupervised learning to discover a criterion by self-learning using the training data without guidance. In addition, the model learning unit 24 may train the neural network model through reinforcement learning by using feedback on whether the result of the situation determination according to the learning is correct. In addition, the model learning unit 24 may train the neural network model by using a learning algorithm including an error back-propagation method or a gradient decent method.

When the neural network model is trained, the model learning unit 24 may store the learned neural network model in a memory. The model learning unit 24 may store the learned neural network model in a memory of a server connected to the AI device 20 via a wired or wireless network.

The data learning unit 22 further includes a training data preprocessor (not shown) and a training data selection unit (not shown) to improve the analysis result of the recognition model or to save resources or time required for generating the recognition model. You may.

The learning data preprocessor may preprocess the acquired data so that the acquired data can be used for learning to determine a situation. For example, the training data preprocessor may process the acquired data into a preset format so that the model training unit 24 can use the training data acquired for learning for image recognition.

In addition, the learning data selection unit may select data necessary for learning from the learning data obtained by the learning data acquisition unit 23 or the learning data preprocessed by the preprocessor. The selected training data may be provided to the model learning unit 24. For example, the learning data selection unit may select only data on an object included in the specific region as the learning data by detecting a specific region among images acquired through a camera of the vehicle.

In addition, the data learning unit 22 may further include a model evaluation unit (not shown) to improve the analysis result of the neural network model.

The model evaluation unit may input evaluation data to the neural network model, and when an analysis result output from the evaluation data does not satisfy a predetermined criterion, the model learning unit 22 may retrain. In this case, the evaluation data may be predefined data for evaluating the recognition model. As an example, the model evaluation unit may evaluate as not satisfying a predetermined criterion if the number or ratio of evaluation data whose analysis result is not accurate among the analysis results of the learned recognition model for evaluation data exceeds a threshold value. have.

The communication unit 27 may transmit the AI processing result by the AI processor 21 to an external electronic device.

Here, the external electronic device may be defined as an autonomous vehicle. In addition, the AI device 20 may be defined as another vehicle or a 5G network that communicates with the autonomous driving module vehicle. Meanwhile, the AI device 20 may be functionally embedded and implemented in an autonomous driving module provided in a vehicle. In addition, the 5G network may include a server or module that performs autonomous driving-related control.

Meanwhile, the AI device 20 shown in FIG. 12 has been functionally divided into an AI processor 21, a memory 25, and a communication unit 27, but the above-described components are integrated into a single module to provide an AI module. It should be noted that it may also be called as.

The above salpin 5G communication technology may be applied in combination with the methods proposed in the present specification to be described later, or may be supplemented to specify or clarify the technical characteristics of the methods proposed in the present specification.

Hereinafter, various embodiments of the present specification will be described in detail with reference to the accompanying drawings.

In general, in the autonomous driving system, using map information and using Lidar, the object detection method is to determine the road recognized by the vehicle's camera and the area where the object exists. ).

However, there may be a problem in that the road and the object are not accurately recognized on a driving path where there is no curb and the boundary of the road is ambiguous, and thus, the vehicle has difficulty in detecting an obstacle or detecting a drivable area. Accordingly, a method of finding a driving area is also used with a technique such as some image-based ground classification, but there is a problem in that the use of the technique is limited due to the characteristics of a camera sensor that depends on an external light source. In addition, even if a road and an object are accurately recognized through a camera, an error may occur in setting the region of interest because the camera and the lidar are not calibrated correctly and there is a difference in the sampling time. In addition, dangerous areas such as children's protection zones, crosswalks, construction zones, intersections, and parking lot entrances and exits, even if there are no objects to be detected, are areas with a high probability of a dangerous situation, so they should be set as the area of interest, and the geographic range of the area of interest to be set at this time. Should be able to be magnified.

This specification discloses a method for solving this problem.

In the present specification, the map information may include road information (eg, Lane Number, Lane Vector, Traffic Signal, Crosswalk). Such map information may be HD Map data, may be provided through a separate server connected to the vehicle 10 or may be held by the first vehicle 10 and updated. In addition, in order to limit the region of interest to be monitored by the processor 170 through the lidar, the point of the lidar may be limited on the road of the map information. To this end, the processor 170 may divide a road using road vector information.

In addition, the processor 170 may expand and set a geographic range to an area of interest in areas with a high probability of dangerous situations such as child protection zones, crosswalks, construction zones, intersections, parking lot entrances, etc., and through a V2X message, By receiving signal information from a vehicle or a traffic light, the region of interest can be reset. To this end, the map information may include information on the danger zone.

When limiting the range of the region of interest set by the processor 170, since the number of points of the lidar can be reduced, the processor 170 can reduce the amount of calculation and can reduce the number of objects to be monitored, The performance of object tracking can be improved. In addition, when the processor 170 uses road vector information of the map information, it is possible to improve the road segmentation performance of the road segmentation algorithm.

In addition, when the processor 170 expands and sets a geographic range to an area of interest with a high probability of a dangerous situation, the processor 170 monitors the area of interest having the expanded geographic range, so that a person suddenly jumps on the road. You can anticipate risk factors such as lifting and prevent accidents.

How to set the region of interest

The processor 170 may set a space on a road as an ROI using the map information, and sense the set ROI using a lidar.

In the operation of the lidar for object detection, there is a problem in that an excessive amount of calculation is required for the lidar point requiring calculation of the processor 170.

If the ROI is limited to roads, the number of generated lidar points may decrease by about 70%. For example, in an experiment using Velodyne 64CH LiDAR, before limiting the region of interest to roads, 133,632 lidar points were generated, but after the region of interest was limited to roads, 39,190 lidar points were generated.

In addition, when the ROI is limited to a road, the processor 170 may not perform an operation for recognizing an object such as a tree or an obstacle located on the sidewalk, so the performance of object detection for autonomous driving may be improved. .

If the region of interest is not limited to a road, when the vehicle 10 is traveling near a sidewalk, the processor 170 can cluster objects such as trees, barriers, and streetlights located on the sidewalk, and thus calculate this. Overloading may occur.

The processor 170 may recognize, through a camera, a person, a bicycle, etc. located in an area off the road set as the region of interest, and display the data generated by tracking the LiDAR by combining the generated data. Although the radar can accurately calculate the distance to the object, it is difficult to accurately determine the type of the object, and the camera is easy to recognize the type of the object, but it is inappropriate to calculate the distance to the object, so the processor 170 Can be used by combining the data generated through the camera and the data generated through the lidar.

In addition, the processor 170 may divide a road by using road vector information of the map information. For example, algorithms used for road segmentation may include Planar Segmentation, Ray Ground Filter, and DNN.

-Planar Segmentation: Remove the road using Plane RANSAC (RANdom SAmple Consensus). Although the point loss of the object is small, it is vulnerable to changes in the slope of the road.

-Ray Ground Filter: According to the amount of change in the vertical direction of the point, the section with less change than the standard is divided into roads. Depending on the parameter, there is a trade-off relationship between the road removal rate and the point loss amount of the object.

When adjusting the parameters of the Ray Ground Filter using the load vector, it is possible to divide the road while minimizing the point loss of the object. In more detail, when the road is divided through the Ray Ground Filter using the vertical vector component of the road vector, efficient road division is possible.

In addition, the processor 170 may expand a geographic range of areas with many risk factors, such as near a crosswalk, a construction area, a child protection area, and an entrance/exit of a parking lot, to be set as a region of interest.

The map information includes location information of an area with many risk factors, and using this, if the area with many risk factors is set as an area of interest with an expanded geographic range, the processor 170 can recognize the object of the area of interest. And, since it can efficiently cope with unexpected situations, the stability of autonomous driving can be improved. For example, the processor 170 may set the region of interest and control the vehicle 10 as follows.

-Set by expanding the geographic range of the area of interest to the sidewalk connected to the crosswalk.

-In order to prepare for the case of workers in the construction area entering the roadway, the geographical range of the area of interest near the construction area is expanded and set.

-Children's protection zones are set by expanding the geographic area of the area of interest to the sidewalk, and the speed of vehicles driving through the children's protection zone is slowly controlled in order to be prepared for children suddenly entering the roadway.

-In order to prepare for a sudden appearance of a vehicle at the entrance to the parking lot, the geographic range of the area of interest is expanded and set.

In addition, the processor 170 may actively prepare for a dangerous situation by using a conservative parameter for the ghost phenomenon removal of the object tracking algorithm in the vicinity of the dangerous area.

14 is an embodiment to which the present specification may be applied.

The processor 170 acquires map information, and maps sensing information acquired through a sensor of the vehicle 10 to the acquired map information (S1410). In more detail, such map information may be HD MAP data, and the processor 170 may obtain a 3D space by mapping the sensing information acquired through the lidar, and determine the location on the map of the vehicle 10. I can.

The processor 170 extracts road information and dangerous area information from the map information (S1420). Hazardous areas mean areas with many hazards, and may include areas with children's protection zones, crosswalks, construction zones, intersections, or parking lot entrances and exits. The information on the hazardous area includes geographical scope and location information of the hazardous area.

The processor 170 configures a region of interest based on the extracted road information and information on the dangerous region (S1430). In more detail, the processor 170 may set the region of interest based on a road to be driven or to be driven in consideration of the current position and driving direction of the vehicle 10.

15 and 16 are an embodiment of a method of setting an ROI to which the present specification can be applied.

The processor 170 divides the road area from the received map information and sets the road area as an ROI (S1510). Referring to FIG. 16A, the processor 170 may receive HD MAP data and map the POINT CLOUD to the received HD MAP data. POINT CLOUD is a 3D shape or a set of points representing a shape. Each point contains a unique set of X, Y, and Z coordinates. The processor 170 may generate a POINT CLOUD through a LiDAR sensor. The processor 170 may map the previously collected, POINT CLOUD to HD MAP data. The processor 170 may divide a road area from HD MAP data to which POINT CLOUD is mapped. To segment the road area, the processor 170 may use Planar Segmentation, Ray Ground Filter, and DNN. In addition, the vertical vector component of the road vector included in the HD MAP can be used.

The processor 170 sets an area around the intersection as an ROI (S1520). The processor 170 may determine the location of the intersection by using road information included in the map information, and set a certain area including the intersection in the road area as the region of interest.

The processor 170 sets the dangerous area as an ROI (S1530). The map information includes location information on the hazardous area. Accordingly, the processor 170 may acquire an accurate location of the dangerous area through the mapping of the lidar point cloud and HD MAP data. The processor 170 may set the dangerous area as the area of interest, while expanding and setting the geographic range of the area of interest indicating the dangerous area. Accordingly, the geographic range of the region of interest set for each danger region and the predetermined region including the aforementioned intersection may be different.

Referring to FIG. 16(b), the processor 170 may set a geographic range based on a lidar point in a dangerous area. Alternatively, it is possible to consider the driving status of a vehicle traveling in a dangerous area, traffic light signal information, and the like. For example, the distance value of the points farthest from the lidar point indicating the point where the crosswalk meets the sidewalk or the lidar point indicating the edge of the construction site is R, and When the remaining time of the pedestrian crossing signal obtained through the speed or the V2X message is set as the weight value, the radius of the region of interest may be determined through Equation 1 below.

[Equation 1]

Radius = R * Weight_velocity * Weight_remain_time

The processor 170 resets the region of interest in consideration of the degree of density of pedestrians obtained through the sensing information (S1540). In more detail, the processor 170 may determine the degree of concentration of pedestrians in the region of interest through the sensing information. For example, a pedestrian waiting to walk on Hwangdan Crossing may be detected by expanding and setting a geographic range of a region of interest for crosswalks on the sidewalk connected to the crosswalk by the processor 170. The detected density of pedestrians may be calculated using the number of objects detected by the processor 170 based on a certain geographic range of a region of interest including a sidewalk connected to a crosswalk.

Referring to FIG. 16B, the processor 170 may enlarge and set the geographic range of the ROI on the sidewalk connected to the crosswalk 1601 (1611, 1612). In addition, the processor 170 may reset the region of interest set on the sidewalk connected to the pedestrian crossing 1601 in consideration of the acquired density of pedestrians through the sensing information. For example, when the degree of pedestrian concentration of the second region of interest 1612 is greater than the degree of concentration of pedestrians of the first region of interest 1611, the processor 170 determines the geographic range of the second region of interest 1612 as a first. The second ROI 1612 may be reset to be larger than the geographic range of the ROI 1611.

The processor 170 resets the ROI in consideration of the driving route of the vehicle (S1550). The processor 170 may determine the current location by using the GPS of the vehicle 10 or the like, and receive or calculate destination information and a driving route of the vehicle 10. Accordingly, the processor 170 may release a region not located on the driving path of the vehicle or a region of interest set in a lane. Through this, the processor 170 may limit the region of interest, thereby reducing the region to be detected by the vehicle 10.

17 is an embodiment of an object tracking algorithm to which the present specification can be applied.

Object tracking is an operation that receives information from sensors such as Lidar and Camera to obtain the location, speed, and type of an object (for example, an opponent vehicle, a pedestrian, or an obstacle). Data processing of object tracking can be classified into sensing data filtering and tracking. Tracking includes clustering, data association, object motion prediction, selective track list update, and object tracking operations. Here, the object tracking operation includes merge, classification of the tracked object, and start of object tracking.

The processor 170 receives raw data from the sensor and filters the sensing data (S1710). Sensing data filtering is a process of processing raw sensor data before tracking is executed. Sensing data filtering refers to an operation of setting an ROI, reducing the number of sensing points, and classifying only object points necessary for tracking through ground removal.

The processor 170 clusters the filtered sensing data and performs an association operation (S1720). For example, using a lidar, multiple points can be created on a single object. The processor 170 may generate a single point by clustering several points generated in one object through clustering. In addition, in order for the processor 170 to track an object, an operation of associating data of points created through clustering and points being tracked is required. For example, the processor 170 traverses the previously tracked objects, and selects a point of the clustered object having the closest distance to the point of the previously tracked objects among the points of the clustered object through the current sensor, Both data can be linked. In order to increase the accuracy of the object tracking algorithm in this data linkage operation, the processor 170 removes the clustered object when the movement of the clustered object is uncertain or cannot be predicted, even when the distance is selected as the point of the clustered object closest to the distance. I can. For this, the learned AI processor 21 may be used.

The processor 170 predicts the motion of the object (S1730). In more detail, the processor 170 may predict the positions of the objects to be tracked through values related to the measured motion of the object through steps S1610 and S1620. For this, the learned AI processor 21 may be used. If there is no value related to the motion of the measured object, the predicted motion of the object may be a value related to the motion of the measured object. In contrast, if there is a value related to the measured motion of the object, the value related to the motion of the object may be updated through the step of predicting the motion of the object.

The processor 170 selectively updates the track list and performs an object tracking operation (S1740). When there is a value related to the motion of the measured object as described above, the processor 170 may update a track list managed for each object. For this, a Kalman filter may be used. In addition, in order to perform an object tracking operation, the processor 170 performs a process of merging objects to be tracked and objects moving at a similar speed at a certain distance into one object and points of sensing data matching the tracked object. If it is less than the critical point, object tracking may be started after performing a tracking object classification task to stop tracking. However, even in this case, in order to determine whether the pointer of the sensing data is a ghost, object tracking may be started after the pointer of the sensing data that is not linked to the data is verified.

In the present specification, the object tracking algorithm is not limited to the above operation, and an object tracking algorithm for a similar purpose may be included in the present specification.

18 is an example of resetting an ROI based on a traffic light signal to which the present specification can be applied.

The vehicle 10 may receive a signal of a traffic light through an RSU or the like. 18(a) illustrates an ROI when a walking signal remains 5 seconds, and FIG. 18(b) illustrates an ROI when a walking signal remains 1 second.

When a large number of pedestrian signals remain, since a large number of objects to be detected in the area around the crosswalk is required, the area of interest may have a wider geographic range. Accordingly, the processor 170 may reset the ROI according to a signal from a traffic light periodically received through an RSU or the like.

19 is a flowchart of resetting an ROI based on a traffic light signal to which the present specification can be applied.

When the vehicle 10 enters a certain radius of the crosswalk, the processor 170 receives a pedestrian signal such as a traffic light from the RSU (S1910). The schedule radius of the crosswalk may be set differently depending on the size of the road on which the crosswalk is located or the degree of risk of traffic accidents of the crosswalk. For example, the larger the size of the road or the higher the risk of a traffic accident, the wider the schedule radius may be set.

The processor 170 resets the region of interest based on the received pedestrian signal of the traffic light (S1920). For example, when the state of the traffic light is a pedestrian signal and the pedestrian signal remains for 10 seconds, the processor 170 may set the geographic range of the region of interest to the maximum. If the pedestrian signal remains for 5 seconds, the processor 170 may reduce the geographic range of the region of interest, and when the pedestrian signal ends, the processor 170 defaults to the geographic range of the region of interest of the crosswalk. You can zoom out and reset.

20 is an example of resetting an ROI based on a lane in which a vehicle travels to which the present specification can be applied.

The processor 170 may reset the region of interest according to the lane information on which the vehicle 10 is traveling. FIG. 20(a) is an example of a region of interest when the vehicle 10 travels in a lane located away from the center line, and FIG. 20(b) is a region of interest when the vehicle 10 travels in a lane located close to the center line Is an example of. When the vehicle 10 is located close to the center line, the vehicle 10 is also required to track objects in the opposite lane for safe driving, so the processor 170 may enlarge and reset the geographic range of the ROI to the opposite lane.

21 is a flowchart of resetting an ROI based on a lane on which a vehicle travels to which the present specification can be applied.

The processor 170 acquires driving lane information of the vehicle (S2110). The driving lane information may be periodically obtained by using road information included in the map information through the GPS of the vehicle 10.

When the driving lane of the vehicle 10 is adjacent to the center line, the processor 170 resets the ROI (S2120). The processor 170 may expand the geographic range of the ROI to the area of the opposite lane.

When the driving lane is not adjacent to the center line, the processor 170 resets the ROI (S2130). The processor 170 may reduce the geographic range of the region of interest that has been expanded to the area of the opposite lane.

22 is an example of resetting an ROI at an intersection to which the present specification can be applied.

The vehicle 10 may receive a V2X message from another vehicle. These V2X messages may include BSM information. The processor 170 may know driving information of other vehicles through the V2X message. Accordingly, the processor 170 may reset the region of interest based on the driving route of another vehicle using the V2X message. In addition, the processor 170 may reset the region of interest in a lane having a simultaneous signal relationship with the driving lane of the vehicle 10 based on signal information of a traffic light.

23 is an example of reconfiguration of an ROI for reducing a geographic range to which the present specification can be applied.

When the vehicle 10 enters the intersection, the processor 170 may receive traffic light signal information from the RSU, and based on the traffic light signal information, a geographic range including lanes irrelevant to the driving route of the vehicle 10 To reduce, you can reset the region of interest. Through this, the processor 170 can focus on object detection for an ROI with a high collision risk.

24 is a flowchart of resetting an ROI at an intersection to which the present specification can be applied.

The processor 170 determines whether the vehicle 10 entering the intersection has received the traffic light signal information of the intersection from the RSU (S2410). The traffic light signal information includes traffic light signal information of all lanes entering and leaving the intersection.

When receiving the traffic light signal information, the processor 170 uses the traffic light signal information and the road information to obtain information on a lane in a simultaneous signal relationship with the driving lane of the vehicle 10 (S2420).

The processor 170 resets a region of interest for a lane having a simultaneous signal relationship with the driving lane of the vehicle 10 (S2430). In more detail, the processor 170 may reset the region of interest to have a geographic range for including lanes in a simultaneous signal relationship. Through this, the processor 170 may prevent a collision risk by detecting an object in the lane in a simultaneous signal relationship.

The processor 170 resets a region of interest for a lane having no simultaneous signal relationship with the driving lane of the vehicle 10 (S2440). In more detail, the processor 170 may reset the ROI to have a geographic range in order to exclude lanes that do not have a simultaneous signal relationship. Through this, the processor 170 can prevent a collision risk by focusing on detecting an object in the lane in a simultaneous signal relationship.

If the processor 170 fails to receive the traffic light signal information from the RSU and receives the V2X message from another vehicle, the processor 170 resets the ROI based on the received V2X message (S2450). The processor 170 is based on the driving information of the other vehicle included in the V2X message broadcast by the other vehicle, and the other vehicle enters the lane where the vehicle 10 enters or the lane adjacent to the lane where the vehicle 10 enters. If it is determined to be, the region of interest may be reset to have a geographic range including a driving lane of another vehicle or a lane scheduled to be driven. Through this, the processor 170 can prevent the risk of collision with other vehicles at the intersection even when the signal information of the traffic light is not received.

In FIG. 24, a case where the vehicle 10 enters the intersection is exemplified, but even when the vehicle 10 enters the danger area, the processor 170 may reset the region of interest through an operation similar to the above.

The embodiments of FIGS. 14 to 24 may be applied simultaneously or individually, and are not limited to the order illustrated in this specification.

As an embodiment to which the present invention can be applied, Embodiment 1 includes the steps of mapping sensing information acquired through a sensor of a vehicle to map information; Extracting road information and information on a region where there is a risk factor for driving of the vehicle using the map information; And setting a region of interest for object tracking based on the road information and information on a region where there is a risk factor for driving of the vehicle. And the region of interest may include a geographic range that the vehicle must monitor for driving.

Example 2: In Example 1, the area where there is a risk factor in the driving of the vehicle may include a child protection area, a crosswalk, a construction area, an intersection or an area where the entrance and exit of a parking lot is located.

Embodiment 3: In Embodiment 2, when the vehicle enters a predetermined radius of an area where the crosswalk is located, receiving a pedestrian signal such as a traffic light for the crosswalk from a Road Side Unit (RSU); And resetting the region of interest based on the pedestrian signal of the traffic light for the crosswalk. It may further include.

Embodiment 4: In Embodiment 3, the resetting may be to change the geographic range of the ROI based on the remaining time of the pedestrian signal of the traffic light for the crosswalk.

Example 5: In Example 2, when the vehicle entering the intersection receives signal information of a traffic light for the intersection, using the signal information of the traffic light and the road information for the intersection, Acquiring information on a lane having a simultaneous signal relationship with the driving lane; And resetting a region of interest for a lane having a simultaneous signal relationship with the driving lane of the vehicle. It may further include.

Example 6: In Example 5, the step of resetting a region of interest for a lane having a simultaneous signal relationship with a driving lane of the vehicle includes a geographic range including a lane having a simultaneous signal relationship with the driving lane of the vehicle. It may be for setting a region of interest to have.

Embodiment 7: In Embodiment 5, resetting a region of interest for a lane having no simultaneous signal relationship with the driving lane of the vehicle; It may further include.

Example 8: In Example 7, in the step of resetting the region of interest for a lane that does not have a simultaneous signal relationship with the driving lane of the vehicle, a lane having no simultaneous signal relationship with the driving lane of the vehicle is May be for exclusion from the range.

Embodiment 9: In Embodiment 5, when the signal information of a traffic light for the intersection is not received, receiving a V2X (Vehicle-To-Everything) message from another vehicle; And resetting the ROI based on the V2X message. It may further include.

Example 10: In Example 1,

The step of setting the region of interest may be based on a current position, a driving direction, or a driving route of the vehicle.

Embodiment 11: According to the embodiment 1, object tracking based on the region of interest; It further includes, and the sensing information may be generated through LIDAR.

Embodiment 12: In Embodiment 1, the steps of acquiring driving lane information of the vehicle; And resetting the ROI based on the driving lane of the vehicle. It may further include.

Embodiment 13: In Embodiment 12, in the resetting step, when the driving lane of the vehicle is adjacent to the center line, the area of the opposite lane may be included in the geographic range of the ROI.

Embodiment 14: In Embodiment 12, the resetting step may be to exclude a region of an opposite lane from a geographic range of the region of interest when the driving lane of the vehicle is not adjacent to the center line.

Embodiment 15: A vehicle performing object tracking in an autonomous driving system, comprising: a sensor; A transceiver; Memory; And a processor that controls the sensor, the transceiver, and the memory. Including, the processor maps the sensing information acquired through the sensor to the map information (mapping), and by using the map information, extracting (extracting) road information and information on the area where there is a risk factor in the driving of the vehicle ), and based on the road information and information on the area where there is a risk factor in the driving of the vehicle, a region of interest for object tracking is set, and the region of interest must be monitored for the vehicle to drive. It may include a geographic range.

Embodiment 16: In Embodiment 15, the area where there is a risk factor in the driving of the vehicle may include a child protection area, a crosswalk, a construction area, an intersection or an area where the entrance and exit of a parking lot is located.

Embodiment 17: In Embodiment 16, when the vehicle enters a certain radius of an area where the crosswalk is located, the processor is a pedestrian signal of a traffic light for the crosswalk from the RSU (Road Side Unit) through the transceiver. May be received, and the region of interest may be reset based on a pedestrian signal of a traffic light for the crosswalk.

Embodiment 18: In Embodiment 17, the processor may reset the ROI to change the geographic range of the ROI based on the remaining time of the pedestrian signal of the traffic light for the crosswalk.

Embodiment 19: In Embodiment 16, when the vehicle entering the intersection receives signal information of a traffic light for the intersection through the transceiver, the processor is Using the information, information on a lane having a simultaneous signal relationship with the driving lane of the vehicle may be obtained, and an ROI may be reset for a lane having a simultaneous signal relationship with the driving lane of the vehicle.

Embodiment 20: In Embodiment 19, the processor may reset the ROI to set the ROI having a geographic range including a lane in a simultaneous signal relationship with the driving lane of the vehicle.

Embodiment 21: In Embodiment 19, the processor may reset a region of interest for a lane that does not have a simultaneous signal relationship with the driving lane of the vehicle.

Embodiment 22: In Embodiment 21, the processor may reset the ROI to exclude lanes that do not have a simultaneous signal relationship with the driving lane of the vehicle from the geographic range of the ROI.

Embodiment 23: In Embodiment 19, the processor receives a V2X (Vehicle-To-Everything) message from another vehicle when the signal information of the traffic light for the intersection is not received through the transceiver, and the V2X Based on the message, the ROI can be reset.

Embodiment 24: In Embodiment 15, the processor may set the region of interest based on the current position, driving direction, or driving route of the vehicle.

Embodiment 25: In Embodiment 15, the processor performs object tracking based on the ROI, the sensor includes a LIDAR, and the sensing information may be generated through the LIDAR.

Embodiment 26: In Embodiment 15, the processor may obtain information on a driving lane of the vehicle and reset the ROI based on the driving lane of the vehicle.

Embodiment 27: In Embodiment 26, when the driving lane of the vehicle is adjacent to the center line, the processor may reset the ROI to include an area of the opposite lane in the geographic range of the ROI.

Embodiment 28: In Embodiment 26, when the driving lane of the vehicle is not adjacent to the center line, the processor may reset the ROI to exclude an area of the opposite lane from the geographic range of the ROI. .

General devices to which this specification can be applied

Referring to FIG. 25, the server X200 according to the proposed embodiment may be a MEC server or a cloud server, and may include a communication module X210, a processor X220, and a memory X230. The communication module X210 is also referred to as a radio frequency (RF) unit. The communication module X210 may be configured to transmit various signals, data, and information to an external device, and to receive various signals, data, and information to an external device. The server X200 may be connected to an external device by wire and/or wirelessly. The communication module X210 may be implemented separately as a transmission unit and a reception unit. The processor X220 may control the overall operation of the server X200, and may be configured to perform a function for the server X200 to calculate and process information to be transmitted and received with an external device. In addition, the processor X220 may be configured to perform the server operation proposed in the present specification. The processor X220 may control the communication module X210 to transmit data or a message to the UE, another vehicle, or another server according to the proposal of the present specification. The memory X230 may store operation-processed information and the like for a predetermined period of time, and may be replaced with a component such as a buffer.

In addition, the specific configurations of the terminal device X100 and the server X200 as described above may be implemented so that the above-described various embodiments of the present specification are applied independently or two or more embodiments may be applied simultaneously, and overlapping Contents are omitted for clarity.

The foregoing specification can be implemented as computer-readable code on a medium in which a program is recorded. The computer-readable medium includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include hard disk drives (HDDs), solid state disks (SSDs), silicon disk drives (SDDs), ROMs, RAM, CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, etc. There is also a carrier wave (for example, transmission over the Internet) also includes the implementation of the form. Therefore, the detailed description above should not be construed as restrictive in all respects and should be considered as illustrative. The scope of this specification should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of this specification are included in the scope of this specification.

In addition, although the embodiments have been described above, these are only examples and do not limit the present specification, and those of ordinary skill in the field to which this specification belongs are illustrated above within the scope not departing from the essential characteristics of the present embodiment. It will be seen that various modifications and applications that are not available are possible. For example, each component specifically shown in the embodiments can be modified and implemented. And differences related to these modifications and applications should be construed as being included in the scope of the present specification defined in the appended claims.

In this specification, an example applied to an Automated Vehicle & Highway Systems based on a 5G (5 generation) system has been described, but it can be applied to various wireless communication systems and autonomous driving devices.

Claims (20)

Mapping the sensing information acquired through the vehicle sensor to the map information;
Extracting road information and information on a region where there is a risk factor for driving of the vehicle using the map information; And
Setting a region of interest for object tracking based on the road information and information on a region where there is a risk factor for driving of the vehicle;
Including,
The ROI is a vehicle object tracking method including a geographic range that the vehicle must monitor for driving.
The method of claim 1,
Areas where there is a risk factor in the driving of the vehicle
Vehicle object tracking method including areas where children's protection zones, crosswalks, construction zones, intersections, or parking lot entrances and exits are located.
The method of claim 2,
Receiving a pedestrian signal such as a traffic light for the crosswalk from a road side unit (RSU) when the vehicle enters a predetermined radius of an area where the crosswalk is located; And
Resetting the region of interest based on the pedestrian signal of the traffic light for the crosswalk;
Object tracking method of a vehicle further comprising a.
The method of claim 3,
The resetting step
The vehicle object tracking method for changing the geographic range of the region of interest based on the remaining time of the pedestrian signal of the traffic light for the crosswalk.
The method of claim 2,
When the vehicle entering the intersection receives signal information of a traffic light for the intersection, using the signal information of the traffic light and the road information for the intersection, Obtaining information; And
Resetting a region of interest for a lane having a simultaneous signal relationship with the driving lane of the vehicle;
Object tracking method of a vehicle further comprising a.
The method of claim 5,
Resetting the region of interest for a lane having a simultaneous signal relationship with the driving lane of the vehicle
The vehicle object tracking method for setting a region of interest having a geographic range including a lane in a simultaneous signal relationship with the driving lane of the vehicle.
The method of claim 5,
Resetting a region of interest for a lane that does not have a simultaneous signal relationship with the driving lane of the vehicle;
Object tracking method of a vehicle further comprising a.
The method of claim 7,
Resetting the region of interest for a lane that does not have a simultaneous signal relationship with the driving lane of the vehicle is
A vehicle object tracking method for excluding a lane that does not have a simultaneous signal relationship with the driving lane of the vehicle from the geographic range of the region of interest.
The method of claim 5,
Receiving a V2X (Vehicle-To-Everything) message from another vehicle when the signal information of the traffic light for the intersection is not received; And
Resetting the ROI based on the V2X message;
Object tracking method of a vehicle further comprising a.
The method of claim 1,
The step of setting the region of interest
Object tracking method of a vehicle based on the current position, driving direction, or driving route of the vehicle.
The method of claim 1,
Object tracking based on the region of interest;
It further includes,
The sensing information is a vehicle object tracking method generated through LIDAR.
The method of claim 1,
Acquiring driving lane information of the vehicle; And
Resetting the ROI based on the driving lane of the vehicle;
Object tracking method of a vehicle further comprising a.
The method of claim 12,
The step of resetting the region of interest
When the driving lane of the vehicle is adjacent to the center line, the object tracking method of the vehicle is to include an area of the opposite lane in the geographic range of the region of interest.
The method of claim 12,
The resetting step
When the driving lane of the vehicle is not adjacent to the center line, the object tracking method of the vehicle is to exclude an area of the opposite lane from the geographic range of the region of interest.
In a vehicle that performs object tracking in an autonomous driving system,
sensor;
A transceiver;
Memory; And
A processor that controls the sensor, the transceiver, and the memory; Including,
The processor is
Mapping the sensing information acquired through the sensor to the map information (mapping),
Using the map information, road information and information on areas where there is a risk factor in the driving of the vehicle are extracted,
Based on the road information and information on a region where there is a risk factor in the driving of the vehicle, a region of interest for object tracking is set, and
The region of interest is a vehicle including a geographic range that the vehicle must monitor for driving.
The method of claim 15,
Areas where there is a risk factor in the driving of the vehicle
Vehicles including areas where children's protection zones, crosswalks, construction zones, intersections, or parking lot entrances and exits are located.
The method of claim 16,
The processor is
When the vehicle enters a certain radius of the area where the crosswalk is located, a pedestrian signal such as a traffic light for the crosswalk is received from the RSU (Road Side Unit) through the transceiver,
A vehicle that resets the region of interest based on a pedestrian signal of a traffic light for the crosswalk.
The method of claim 16,
The processor is
When the vehicle entering the intersection receives signal information of a traffic light for the intersection through the transceiver, the signal information of the traffic light for the intersection and the road information are used to provide a simultaneous signal with the driving lane of the vehicle. Obtain information of the lane in the relationship,
A vehicle for resetting a region of interest for a lane having a simultaneous signal relationship with the driving lane of the vehicle.
The method of claim 15,
The processor is
Based on the region of interest, object tracking is performed,
The sensor includes a LIDAR, and the sensing information is generated through the LIDAR.
The method of claim 15,
The processor is
Acquiring driving lane information of the vehicle,
A vehicle that resets the region of interest based on a driving lane of the vehicle.

Images