KR20210043039A - Method and apparatus of vehicle motion prediction using high definition map in autonomous driving system - Google Patents

Abstract

The present invention relates to a method for predicting a movement of a vehicle using an high-definition (HD) map in an automated vehicle and highway system, and an apparatus for the same, which can extract a link information for directing a drivable path from the HD map of the automated vehicle and highway system, use the link information for acquiring a group of candidate links of an external vehicle including one or more links of the external vehicle, use the results of the prediction of the movement of the external vehicle to select the link of the external vehicle which directs a path expected to be driven by the external vehicle from the group of candidate links, and increase the accuracy of the results of prediction of movement. According to the present invention, one or more of the autonomous driving vehicle, a user terminal, and a server can be connected to an artificial intelligence module, an unmanned aerial vehicle (UAV) robot, an augmented reality (AR) apparatus, a virtual reality (VR) apparatus, an apparatus related to the 5G service, etc.

Description

A vehicle motion prediction method using HD MAP in an autonomous driving system and a device therefor {METHOD AND APPARATUS OF VEHICLE MOTION PREDICTION USING HIGH DEFINITION MAP IN AUTONOMOUS DRIVING SYSTEM}

The present specification relates to an autonomous driving system, and relates to a method and apparatus for predicting a movement of a vehicle using a high-precision map.

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 for predicting vehicle movement using HD MAP in an autonomous driving system.

In addition, an object of the present specification is to propose a method of controlling driving based on a predicted vehicle movement using HD MAP 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 extracting link information indicating a drivable route from a high-definition (HD) MAP in an autonomous driving system; Obtaining a group of candidate links of the external vehicle including at least one link of the external vehicle using the link information; And selecting a link of an external vehicle indicating a path in which the external vehicle is expected to travel from the group of the candidate links, using a result of the motion prediction of the external vehicle. Including, it is possible to obtain a result of the motion prediction of the external vehicle through the sensing information.

In addition, using the link information, acquiring a link associated with the planned driving route of the vehicle; It further includes, and may be driven by using a link related to the route of the vehicle to be driven.

In addition, obtaining the sensing information related to the movement of the external vehicle; Generating history data of the external vehicle by using the time value at which the sensing information is acquired; And obtaining a result of predicting a motion of the external vehicle by using the history data. It may further include.

Further, based on the link of the external vehicle, performing a control operation for preventing a collision with the external vehicle; It may further include.

In addition, the vehicle selects a link of the external vehicle when there is a risk of collision with the vehicle when the external vehicle travels on a link in the group of the candidate link based on a link associated with the vehicle's scheduled travel route. I can.

In addition, the link of the external vehicle may be selected based on a curve of a two-dimensional coordinate system related to the movement of the external vehicle and a distance value of the candidate link generated through a result of the motion prediction of the external vehicle.

Further, the control operation may be for stopping or decelerating the vehicle based on the width of the vehicle and the width of the external vehicle.

In addition, the control operation is based on a priority value of a lane in which the vehicle is traveling or a priority value of a lane in which the external vehicle is traveling, and the priority value may be related to a traffic rule.

In addition, when there is a risk of collision between the vehicle and the external vehicle, broadcasting a V2X message to inform the driving route of the vehicle; It may further include.

In addition, the control operation may be for changing a driving lane.

Another aspect of the present invention is a vehicle that performs motion prediction using a high-definition (HD) MAP in an autonomous driving system, comprising: a sensor; A transceiver; Memory; And a processor that controls the sensor, the transceiver, and the memory. Including, wherein the processor extracts link information indicating a drivable route from the HD MAP, and using the link information, a group of candidate links of the external vehicle including one or more links of the external vehicle And selecting a link of an external vehicle indicating a path in which the external vehicle is expected to travel from the group of the candidate links, using the result of the motion prediction of the external vehicle, and obtained using the sensor. Through the sensing information, a result of the motion prediction of the external vehicle may be obtained.

According to an embodiment of the present specification, a vehicle motion may be predicted using HD MAP in an autonomous driving system.

In addition, according to an embodiment of the present specification, driving may be controlled based on the predicted vehicle movement using HD MAP in the autonomous driving system.

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 and 15 are examples of motion prediction algorithms to which the present specification can be applied.
16 to 18 are examples of motion prediction algorithms using HD MAP to which the present specification can be applied.
19 is an example of a method for determining whether a vehicle has a collision in the present specification.
20 is an example of a case where the difference between the minimum lateral error value and the error weight value of a candidate link is out of a predetermined range in the present specification.
21 to 24 are examples when a vehicle enters an intersection in the present specification.
25 and 26 are examples when a vehicle enters a confluence section in the present specification.
27 and 28 are examples when a vehicle enters a roundabout in the present specification.
29 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), memory (914,924), one or more Tx/Rx RF modules (radio frequency module, 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' of the CSI-RS and SSB ( 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 an 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 simultaneously. 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), and PUSCH, 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 this 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 is 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 indirectly participates 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, in order 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 illuminance 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 apparatus 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 generating apparatus 280 may correct the 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 ROM, RAM, EPROM, flash drive, and 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 center of the road. 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 by 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 refer to 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 the BS (referred to as BS-type RSU) or UE (referred to as 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 own 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 the route 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 PC5 on 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 send and receive 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 may be located in different layers and 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 data classification/recognition 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.

When an autonomous vehicle predicts the movement of another vehicle using only the history data about the movement of another vehicle acquired through a sensor, in an environment where various movements of the vehicle being driven, such as a lane or an intersection, may occur. There is a concern that the accuracy of motion prediction is degraded.

In order to improve the accuracy of such vehicle motion prediction, the present specification obtains a result of predicting the motion of another vehicle by using history data about the motion of another vehicle, and this is the road center line in HD MAP data. We propose a method that can more accurately determine the movement of another vehicle through comparison with the included link information.

Motion prediction algorithm

14 and 15 are examples of motion prediction algorithms to which the present specification can be applied. In the present specification, the processor 170 may predict the motion of another object by using sensing information obtained by sensing surrounding objects. An example of a motion prediction algorithm will be described with reference to FIG. 14 as follows.

The processor 170 acquires sensing information of surrounding objects (S1410). In more detail, the processor 170 may acquire sensing information of surrounding objects through the sensing unit 270 of the vehicle 10. Such sensing information includes information related to movement of objects.

The processor 170 generates and stores history data for each object (S1420). In more detail, the processor 170 may generate history data by using the sensing information. The history data refers to data in which sensing information of a specific object is accumulated in chronological order, and includes a time value at which the sensing information is obtained and a coordinate value at which the specific object is located at a specific time.

The processor 170 predicts the motion of the object based on the history data (S1430).

An example of a motion prediction algorithm to which the present specification can be applied will be described in more detail with reference to FIG. 15 as follows.

The processor 170 senses other vehicles and acquires sensing information (S1510).

The processor 170 stores past movements of other vehicles as history data (S1520). For example, when the vehicle 10 enters a specific area, the processor 170 may generate history data and store it in the memory 140. A specific area is an area that is required to predict the movement of other vehicles because there is a risk that the vehicle 10 being driven may collide with other vehicles, and other vehicles enter the driving route of the vehicle 10 or the route to be driven. It refers to the area that is likely to be done (for example, an intersection, a confluence of lanes, a multi-lane road where lane changes are possible).

The processor 170 performs curve fitting on the history data (S1530). Cuff fitting is a method of calculating and calculating an appropriate curve for coordinate values on a plane. The processor 170 may calculate the motion of another vehicle as a curve using coordinate values included in the history data through curve fitting.

The processor 170 calculates the average speed and the average rotational angular speed of other vehicles from the history data (S1540). In more detail, the processor 170 may calculate the average speed and the average rotational angular speed by using the amount of change in the coordinate value of the other vehicle over time.

The processor 170 predicts the movement of another vehicle (S1550). In more detail, the processor 170 may predict coordinate values, which are located on an extension line of a curve about an expected path of another vehicle obtained through curve fitting. These coordinate values may include coordinate values matched to an extension line of the curve corresponding to the average speed and the average rotational angular speed. The processor 170 may perform motion prediction of another vehicle at a predetermined period, and a result of the motion prediction of another vehicle may be updated. Through this, the processor 170 may predict the movement of another vehicle until a specific time (for example, the time it is expected to take the vehicle 10 or another vehicle to pass through a specific area).

In addition to the examples of the present specification, other algorithms for predicting motion of other vehicles for similar purposes may also be applied to the present specification. For example, the processor 170 may predict the movement of another vehicle using the learned model through the AI processor 21.

Motion prediction algorithm using HD MAP

16 to 18 are examples of motion prediction algorithms using HD MAP to which the present specification can be applied.

The processor 170 extracts link information from the HD MAP data (S1610). HD MAP includes traffic lights, signs, curbs, road marks, and various structures as 3D data, as well as information of each lane, such as road center line and boundary line. The link information is route information that can be driven for each lane and includes a road center line. For example, when the vehicle 10 enters a specific area where there is a risk of collision with other vehicles, the processor 170 extracts link information on a specific area from HD MAP data including the specific area. can do.

The processor 170 acquires, from the link information, a first link associated with a driving route or a scheduled driving route of the vehicle (S1620). In more detail, the processor 170 may drive the vehicle 10 according to a link of HD MAP data, and for this purpose, according to the driving route or the scheduled driving route of the vehicle 10 in the link information, A first link indicating a path may be obtained.

The processor 170 acquires a group of candidate links of other vehicles (S1630). The group of candidate links means a set of links through which other vehicles can run. The processor 170 may obtain a group of candidate links of other vehicles from the link information of the HD MAP by using the lane and the driving direction in which the other vehicle is located. The processor 170 may select a target vehicle capable of performing motion prediction using HD MAP from among a plurality of other vehicles by using a group of candidate links of other vehicles. For example, the processor 170 sets the other vehicles as target vehicles, except for other vehicles that do not include a link that has a risk of collision with the first link among the links in the group of the candidate links of other vehicles. In limited terms, motion prediction using HD MAP can be performed.

The processor 170 selects a second link from a group of candidate links of another vehicle based on the result of motion prediction of another vehicle (S1640). The second link refers to a link that is closest in terms of distance to a curve representing the motion of another vehicle predicted by the processor 170 through the motion prediction algorithm described above among candidate links. The processor 170 may select a link having the smallest error range in terms of distance as the second link.

The processor 170 performs a vehicle control operation based on the movement of the second vehicle (S1650). An example of the control operation of the vehicle 10 performed by the processor 170 will be described later.

A motion prediction algorithm using HD MAP to which the present specification can be applied will be described in more detail with reference to FIG. 17 as follows.

The processor 170 acquires a group of candidate links of other vehicles (S1710).

The processor 170 calculates a sum of a coordinate value included in a result value of motion prediction of another vehicle and a lateral error value between the candidate links (S1720). The lateral error value refers to the shortest distance value of the candidate link represented by the curve and the coordinate value included in the result value of the motion prediction of another vehicle.

The processor 170 calculates an error weight value of the candidate link by using the sum of the lateral error values (S1730). In more detail, the error weight value (Error_Weight(j)) of the candidate link j can be calculated using the sum of the lateral error values of the candidate link j (Error_Sum(j)), which can be expressed by Equation 1 below. have.

Equation 1: Error_Weight(j) = Average of Error_Sum(j) / Error_Sum(j)

The processor 170 determines whether the difference between the minimum lateral error value and the error weight value of the candidate link is within a predetermined range (S1740). The certain range (Error_Threshold) may be set to a unique value for each vehicle 10, or may have a specific value for each specific area according to the degree of danger of a specific area to which the vehicle 10 enters. Through this, according to the specifications of the vehicle 10 or the road environment of a specific area, the required accuracy of the motion prediction of other vehicles may be differently set and used for safe driving of the vehicle 10. Determination of whether the difference between the minimum lateral error value and the error weight value of the candidate link is within a predetermined range may be calculated by Equation 2 below.

Equation 2: |Min(Lateral_error)-Error_Weight(j)| <Error_Threshold

When the difference between the minimum lateral error value and the error weight value of the candidate link is within a predetermined range, the processor 170 selects the candidate link as the second link (S1750). Also, the processor 170 may update the second link. For example, even if there is a candidate link selected as the second link, if the difference between the minimum lateral error value and the error weight value of the other candidate link is within a certain range, the minimum lateral error value and the error weight value of the other candidate link are If the difference is less than the difference between the minimum lateral error value and the error weight value of the previous candidate link, the second link may be updated with another candidate link.

When the difference between the minimum lateral error value and the error weight value of the candidate link is out of a predetermined range, the processor 170 does not select the candidate link as the second link (S1760).

Referring to FIG. 18, the processor 170 may acquire sensing information related to the movement of another vehicle 1810 through the sensing unit 270. The processor 170 may generate history data by using the sensing information and store it in the memory 140. The processor 170 may obtain a result value 1830 of motion prediction of another vehicle by using the history data.

The processor 170 may acquire a group 1820 of candidate links of other vehicles by using the link information extracted from the HD MAP. The processor 170 determines that the difference between the minimum lateral error value and the error weight value of the candidate link having the smallest lateral error value and the result value 1830 of motion prediction of another vehicle among the obtained group of candidate links is within a certain range. In this case, the candidate link may be selected as the second link 1840.

The processor 170 may perform a control operation of the vehicle 10 based on the second link 1840. Hereinafter, examples of the control operation of the vehicle 10 that the processor 170 can perform will be described.

19 is an example of a method for determining whether a vehicle has a collision in the present specification.

A time enters the vehicle 10 is the intersection in Fig. 19 T _0, when defining the amount of time that passes through the intersection reaches the next lane to T _1, the second link in the T ₀ of the other vehicle to T ₁ If there is a possibility of a collision in the relationship with the planned driving route from T ₀ to T _{1 of the vehicle 10, the vehicle 10 may stop without entering the intersection.} For example, in the case of FIG. 19(b), since the second link of another vehicle overlaps the intended driving route of the vehicle 10, the vehicle 10 can stop without entering the intersection. In Fig. 19(a), the second link of the other vehicle does not overlap with the expected driving path of the vehicle 10, but considering the width of the vehicle 10 and the width of the other vehicle, if there is a possibility of a collision, the vehicle 10 Can stop without entering an intersection. The width of the other vehicle may be obtained through sensing information.

20 is an example of a case where the difference between the minimum lateral error value and the error weight value of a candidate link is out of a predetermined range in the present specification.

As described above, the processor 170 may select the second link from the group of candidate links of other vehicles. However, if the difference between the minimum lateral error value and the error weight value of the candidate link is out of a certain range, the second link of another vehicle may not be selected, and the vehicle 10 cannot predict the movement of the other vehicle. This can happen.

If the second link of another vehicle is not selected, the processor 170 may stop the vehicle 10 without entering the intersection for safe driving of the vehicle 10. In addition, when attempting to change lanes of the vehicle 10 similarly, if the second link of another vehicle driving the lane is not selected, the vehicle 10 ) May not attempt to change lanes.

21 to 24 are examples when a vehicle enters an intersection in the present specification.

Referring to FIG. 21, after the vehicle 10 enters an intersection, the processor 170 acquires a first link, which is a path that makes a right turn. In addition, the processor 170 obtains a group of candidate links of other vehicles through which other vehicles can go straight, turn right, and turn left.

21(a) and 21(c), when the processor 170 determines that there is no risk of collision between the second link of another vehicle and the first link of the vehicle 10, the vehicle 10 Using the first link, it is possible to travel.

Referring to FIG. 21(b), when the processor 170 determines that there is a risk of collision between the second link of another vehicle and the first link of the vehicle 10, the processor 170 stops the vehicle 10. Or, it is possible to perform a control operation for decelerating. The processor 170 may consider the priority of a lane for a control operation of the vehicle 10. The priority of such lanes may be included in road information in HD MAP data, and may be set according to traffic rules. For example, the processor 170 may set a second link of another vehicle that is expected to go straight ahead of the first link of the vehicle 10 to turn right at an intersection with a higher priority. Even when the vehicle 10 first enters the intersection, the processor 170 may perform a control operation for stopping or decelerating the vehicle 10, taking into account that the priority of the second link is higher than that of the first link. .

Referring to FIG. 22, after the vehicle 10 enters an intersection, the processor 170 acquires a first link, which is a straight path. In addition, the processor 170 obtains a group of candidate links of other vehicles through which other vehicles can go straight, turn right, and turn left.

22(a) and (b), when the processor 170 determines that there is a risk of collision between the second link of another vehicle and the first link of the vehicle 10, the processor 170 10) can perform a control operation to stop or decelerate. In addition, the processor 170 may broadcast an alarm message to inform the driving route of the vehicle 10 through the V2X message.

Referring to FIG. 22(c), when the processor 170 determines that there is no risk of collision between the second link of another vehicle and the first link of the vehicle 10, the vehicle 10 uses the first link. , You can drive.

Referring to FIG. 23, after the vehicle 10 enters an intersection, the processor 170 acquires a first link, which is a path through which the vehicle 10 turns left. In addition, the processor 170 obtains a group of candidate links of other vehicles through which other vehicles can go straight, turn right, and turn left.

22(a) and (b), when the processor 170 determines that there is a risk of collision between the second link of another vehicle and the first link of the vehicle 10, the processor 170 10) can perform a control operation to stop or decelerate. In addition, the processor 170 may broadcast an alarm message to inform the driving route of the vehicle 10 through the V2X message.

Referring to FIG. 22(c), when the processor 170 determines that there is no risk of collision between the second link of another vehicle and the first link of the vehicle 10, the vehicle 10 uses the first link. , You can drive.

Referring to FIG. 24, the processor 170 acquires a first link, which is a path through which the vehicle 10 enters a second lane while passing through an intersection through a left turn. In addition, the processor 170 obtains a group of candidate links of other vehicles entering the first lane and the second lane while the other vehicle passes through the intersection through a left turn.

When the processor 170 selects a path through which another vehicle enters the first lane as the second link of the other vehicle, the processor 170 may control the vehicle 10 to travel along the first link.

If the second link of the other vehicle selected by the processor 170 is changed to a path through which another vehicle enters the second lane, the processor 170 is the second link of the other vehicle and the first link of the vehicle 10. It can be judged that there is a risk of collision between them. In this case, the processor 170 may perform a control operation to stop or decelerate the vehicle 10. In addition, the processor 170 may broadcast an alarm message to inform the driving route of the vehicle 10 through the V2X message.

25 and 26 are examples when a vehicle enters a confluence section in the present specification.

Referring to FIG. 25, the processor 170 may determine that there is a risk of collision between the second link of the other vehicle 2510 and the first link of the vehicle 10. In this case, the processor 170 may perform a control operation to stop or decelerate the vehicle 10. Alternatively, when it is determined that the lane change of the vehicle 10 is possible, the processor 170 may perform a control operation for changing the lane of the vehicle 10. For example, the processor 170 detects another vehicle 2520 traveling in a lane to be changed by the vehicle 10 and obtains a second link of the other vehicle 2520. Using the second link of the other vehicle 2520, the movement of the other vehicle 2520 is predicted, and the processor 170 determines that the movement of the other vehicle 2520 does not interfere with the lane change of the vehicle 10. If so, a lane change can be performed. If there is no other vehicle 2520 traveling in a lane to be changed by the vehicle 10, the processor 170 may immediately perform a lane change of the vehicle 10.

Referring to FIG. 26, the processor 170 may determine that there is a risk of collision between the second link of another vehicle and the first link of the vehicle 10. In this case, the processor 170 may perform a control operation to stop or decelerate the vehicle 10. Alternatively, when it is determined that the lane change of the vehicle 10 is possible, the processor 170 may perform a control operation for changing the lane of the vehicle 10. For example, the processor 170 may sense a lane to be changed by the vehicle 10. However, in this case, the processor 170 may know information that the lane to be changed is a lane that merges into two lanes, and in this case, the processor 170 may perform a lane change of the vehicle 10 again. As it can be determined that the vehicle 10 should be returned, lane change may not be performed.

27 and 28 are examples when a vehicle enters a roundabout in the present specification.

Referring to FIG. 27, the processor 170 may determine that there is a risk of collision between the second link of another vehicle and the first link of the vehicle 10. The processor 170 may perform a control operation for stopping or decelerating the vehicle 10. The processor 170 may consider the priority of a lane for a control operation of the vehicle 10. For example, the processor 170 may set a second link of another vehicle, which is expected to perform rotation, to a higher priority than the first link of the vehicle 10 to turn right at the roundabout. The processor 170 may perform a control operation for stopping or decelerating the vehicle 10, considering that the priority of the second link is higher than that of the first link even when the vehicle 10 first enters the roundabout. have.

Referring to FIG. 28, the processor 170 may determine that there is a risk of collision between the second link of another vehicle and the first link of the vehicle 10. However, unlike FIG. 27, when the vehicle 10 in the roundabout stops or decelerates, there is a risk of collision with a rear vehicle or other vehicle, and thus, through the V2X message, the driving path of the vehicle 10 To notify, you can broadcast an alarm message.

General devices to which this specification can be applied

Referring to FIG. 29, 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/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 detailed configuration 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 scope of equivalents 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 the present 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 have not been made 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)

Extracting link information indicating a drivable route from a high-definition (HD) MAP;
Obtaining a group of candidate links of the external vehicle including at least one link of the external vehicle using the link information; And
Selecting a link of an external vehicle indicating a path in which the external vehicle is expected to travel from the group of the candidate links, using a result of the motion prediction of the external vehicle;
Including,
A method of predicting a motion of a vehicle, which is obtained through sensing information as a result of predicting the motion of the external vehicle.
The method of claim 1,
Acquiring a link associated with the planned driving route of the vehicle by using the link information;
It further includes,
A method for predicting a motion of a vehicle traveling by using a link associated with the vehicle's scheduled travel route.
The method of claim 1,
Acquiring the sensing information related to the movement of the external vehicle;
Generating history data of the external vehicle by using the time value at which the sensing information is acquired; And
Obtaining a result of the motion prediction of the external vehicle by using the history data;
Vehicle motion prediction method further comprising a.
The method of claim 1,
Performing a control operation for preventing a collision with the external vehicle based on the link of the external vehicle;
Vehicle motion prediction method further comprising a.
The method of claim 2,
The vehicle is
When the external vehicle drives a link in the group of the candidate link, based on the link associated with the vehicle's scheduled travel path, if there is a risk of collision with the vehicle, the vehicle motion prediction for selecting the link of the external vehicle Way.
The method of claim 1,
The link of the external vehicle is
A method for predicting a motion of a vehicle, which is selected based on a distance value of the candidate link and a curve of a two-dimensional coordinate system related to the motion of the external vehicle, generated through a result of the motion prediction of the external vehicle.
The method of claim 4,
The control operation is
A vehicle motion prediction method for stopping or decelerating the vehicle based on the width of the vehicle and the width of the external vehicle.
The method of claim 4,
The control operation is
A method for predicting a vehicle motion related to a traffic rule based on a priority value of a lane in which the vehicle is traveling or a priority value of a lane in which the external vehicle is traveling.
The method of claim 4,
Broadcasting a V2X message for informing the driving route of the vehicle when there is a risk of collision between the vehicle and the external vehicle;
Vehicle motion prediction method further comprising a.
The method of claim 4,
The control operation is
A vehicle motion prediction method for changing a driving lane.
In a vehicle that performs motion prediction using HD (High-Definition) MAP 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
Extracting link information indicating a drivable route from the HD MAP,
Using the link information, obtaining a group of candidate links of the external vehicle including one or more links of the external vehicle,
Selecting a link of an external vehicle indicating a path in which the external vehicle is expected to travel from the group of the candidate links, using the result of the motion prediction of the external vehicle,
A vehicle capable of obtaining a result of the motion prediction of the external vehicle through sensing information obtained using the sensor.
The method of claim 11,
The processor is
A vehicle that acquires a link associated with the planned driving route of the vehicle, using the link information, and drives using a link associated with the planned driving route of the vehicle.
The method of claim 11,
The processor is
Obtaining the sensing information related to the movement of the external vehicle through the sensor,
Using the time value at which the sensing information was acquired, history data of the external vehicle is generated and stored in the memory,
A vehicle that obtains a result of the motion prediction of the external vehicle by using the history data.
The method of claim 11,
The processor is
A vehicle that performs a control operation for preventing a collision with the external vehicle based on the link of the external vehicle.
The method of claim 12,
The processor is
A vehicle for selecting a link of the external vehicle when there is a risk of collision with the vehicle when the external vehicle travels on a link in the group of the candidate link based on a link associated with the vehicle's scheduled travel path.
The method of claim 11,
The link of the external vehicle is
A vehicle selected based on a distance value of the candidate link and a curve of a two-dimensional coordinate system related to the movement of the external vehicle, which is generated through a result of the motion prediction of the external vehicle.
The method of claim 14,
The control operation is
A vehicle for stopping or decelerating the vehicle based on the width of the vehicle and the width of the external vehicle.
The method of claim 14,
The control operation is
The vehicle is based on a priority value of a lane in which the vehicle is traveling or a priority value of a lane in which the external vehicle is traveling, and the priority value is related to a traffic rule.
The method of claim 14,
The processor is
When there is a risk of collision between the vehicle and the external vehicle, a vehicle that broadcasts a V2X message to inform the driving route of the vehicle through the transceiver.
The method of claim 14,
The control operation is
A vehicle for changing the driving lane.

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