KR20210089469A - Method for controlling a vehicle by prediciting the behavior of an object in automated vehicle and highway systems and apparatus therefor - Google Patents

Abstract

Disclosed are a method for controlling a vehicle by predicting the behavior of an object in automated vehicle and highway systems and an apparatus therefor. According to an embodiment of the present invention, the method performed by a control apparatus is able to receive the behavior sequence list including behavior information for predicting the behavior of objects in a specific situation from a server apparatus, receive external environment information through a sensor apparatus of the vehicle, predict the behavior of the object based on the behavior information and the external environment information, and control the vehicle. Through this, the present invention is able to precisely predict the behavior of an object around a vehicle in automated vehicle and highway systems, control the vehicle accordingly, and remarkably reduce the risk of accidents. The automated vehicle can be connected to an artificial intelligence module, uncrewed aerial vehicle (UAV), robot, augmented reality (AR) apparatus, virtual reality (VR) apparatus, apparatus related to 5G service, etc.

Description

A method for controlling a vehicle by predicting the behavior of an object in an autonomous driving system, and an apparatus for the same

The present specification relates to a method for controlling a vehicle and an apparatus therefor, and more particularly, to a method and an apparatus for predicting an object's behavior based on a behavior sequence list and controlling the vehicle.

A vehicle may be classified into an internal combustion engine vehicle, an external combustion engine vehicle, a gas turbine vehicle, an electric vehicle, or the like, according to a type of a prime mover used.

Autonomous vehicle refers to a vehicle that can operate on its own 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

On the other hand, the existing autonomous driving vehicle predicts the behavior of the other party through behavior prediction after classification of an object, and controls the vehicle accordingly.

However, this method has a problem in that the behavior of the object may be different from the prediction in a situation where the manual driving vehicle is mixed, and may lead to a serious accident.

The present specification proposes a method and an apparatus for predicting an object's behavior based on a behavior sequence list including behaviors according to time, not the predicted behavior, and controlling a vehicle.

In addition, the present specification proposes a method and apparatus for improving prediction reliability by periodically checking whether an object behaves according to a predicted behavior through an in-vehicle sensor device.

The technical problems to be achieved by this specification are not limited to the technical problems mentioned above, and other technical problems not mentioned are clear to those of ordinary skill in the art to which this specification belongs from the detailed description of the invention below. will be able to be understood

In the method for controlling a vehicle by predicting the behavior of an object in an autonomous driving system (Automated Vehicle & Highway Systems) according to an embodiment of the present specification, the method performed by the control device includes: Receiving from a server device a behavior sequence list including behavior information for predicting the behavior of the people, receiving external environment information through a sensor device of the vehicle, and based on the behavior information and the external environment information Predicting the behavior of the object at one time and controlling the vehicle, wherein the behavior information includes information about the behavior of the object over time.

In addition, in the method of the present specification, predicting the behavior of the object and controlling the vehicle may include detecting the object, classifying the type of the object, and the classified object in the specific situation. Checking the behavior information of the object with the highest probability among the behavior sequence list based on the type of , predicting the behavior of the object at the first time based on the behavior information and controlling the vehicle may include.

In addition, in the method of the present specification, the preset condition may be a condition for at least one of a distance between the object and the vehicle, a distance between the object and a road installation device, and/or time.

In addition, in the method of the present specification, when a preset condition is satisfied, the method may further include determining whether the behavior of the object in the external environment information matches the behavior information at a second time.

In addition, in the method of the present specification, when the behavior of the object in the external environment information and the behavior information at the second time coincide, the behavior of the object is predicted at the third time based on the behavior information, The method may further include controlling the vehicle.

In addition, in the method of the present specification, when the behavior of the object of the external environment information and the behavior information do not match at the second time, the method may further include the step of identifying matching behavior information in the behavior sequence list. can

Also, in the method of the present specification, when the coincident behavior information is confirmed, the method may further include predicting the behavior of the object at a third time based on the coincident behavior information and controlling the vehicle.

In addition, in the method of the present specification, when matching behavior information is not confirmed in the behavior sequence list, the method may further include controlling the vehicle by determining the behavior of the object as an unpredicted behavior.

In addition, the control device for controlling the vehicle by predicting the behavior of an object in an autonomous driving system (Automated Vehicle & Highway Systems) according to another embodiment of the present specification includes a transceiver for transmitting and receiving a wireless signal; a processor functionally connected to the transceiver, wherein the processor receives, from a server device, a behavior sequence list including behavior information for predicting behavior of objects in a specific situation, and externally through a sensor device of the vehicle Receive environmental information, predict the behavior of the object at a first time based on the behavior information and the external environment information, and control the vehicle, wherein the behavior information includes information on the behavior of the object over time .

A method for transmitting control information according to an embodiment of the present specification and an effect of an apparatus therefor will be described as follows.

According to the present specification, it is possible to accurately predict the behavior of the object and control the vehicle based on the behavior sequence list including behaviors according to time, not the predicted behavior.

Also, according to the present specification, it is possible to increase the reliability of the behavior prediction and significantly reduce the risk of an accident by periodically checking whether the object behaves according to the predicted behavior through the in-vehicle sensor device.

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 to which this specification belongs from the description below. .

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as part of the detailed description to help the understanding of the present specification, provide embodiments of the present specification, and together with the detailed description, explain the technical features of the present specification.
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 basic operations 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 diagram illustrating a vehicle according to an embodiment of the present specification.
6 is a control block diagram of a vehicle according to an 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 view illustrating the interior of a vehicle according to an embodiment of the present specification.
10 is a block diagram referenced to describe a vehicle cabin system according to an embodiment of the present specification.
11 is a diagram referenced to describe a user's usage scenario according to an embodiment of the present specification.
12 is a diagram for explaining a method for controlling a vehicle by predicting the behavior of an object according to an embodiment of the present specification.
13 is a flowchart illustrating a method for controlling a vehicle by predicting the behavior of an object according to another embodiment of the present specification.
14 is a diagram illustrating an example for explaining a method for controlling a vehicle by predicting the behavior of an object.
15 is a diagram illustrating another example for explaining a method for controlling a vehicle by predicting the behavior of an object.
16 is a diagram illustrating another example for explaining a method for controlling a vehicle by predicting the behavior of an object.
17 is a diagram illustrating another example for explaining a method for controlling a vehicle by predicting the behavior of an object.
18 is a diagram illustrating another example for explaining a method for controlling a vehicle by predicting the behavior of an object.
19 is a flowchart illustrating a method of operating a control device according to an embodiment of the present specification.
20 is a diagram illustrating a block diagram of a control device according to an embodiment of the present specification.
BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as part of the detailed description to help the understanding of the present specification, provide embodiments of the present specification, and together with the detailed description, explain the technical features of the present specification.

Hereinafter, the embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but the same or similar components are assigned the same reference numbers regardless of reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "part" for the components used in the following description are given or mixed in consideration of only the ease of writing the specification, and do not have a meaning or role distinct from each other by themselves. In addition, in describing the embodiments disclosed in the present specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in the present specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easy understanding of the embodiments disclosed in the present specification, and the technical spirit disclosed herein is not limited by the accompanying drawings, and all changes included in the spirit and scope of the present specification , should be understood to include equivalents or substitutes.

Terms including an ordinal number, such as first, second, etc., 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.

When a component is referred to as being “connected” or “connected” to another component, it is understood that the other component may be directly connected or connected to the other component, but other components may exist in between. it should be On the other hand, when it is mentioned that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle.

The singular expression includes the plural expression unless the context clearly dictates otherwise.

In the present application, terms such as "comprises" or "have" are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Hereinafter, 5G communication (5th generation mobile communication) required by a device requiring autonomous driving processed information and/or an autonomous driving processor will be described through paragraphs A to G.

A. Example UE and 5G network block diagram

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 (autonomous driving device) including an autonomous driving module may be 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 another vehicle 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 represented as the first communication device and the autonomous driving device may be represented as the second communication device.

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

For example, a terminal or user equipment (UE) includes a vehicle, a mobile phone, a smart phone, a laptop computer, a digital broadcasting terminal, personal digital assistants (PDA), and a portable multimedia player (PMP). , navigation, slate PC, tablet PC, ultrabook, wearable device, for example, watch-type terminal (smartwatch), glass-type terminal (smart glass), HMD ( head mounted display) and the like. For example, the HMD may be a display device worn on the head. For example, an HMD may 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 911,921, a memory 914,924, and one or more Tx/Rx RF modules (radio frequency module, 915,925). , including Tx processors 912 and 922 , Rx processors 913 and 923 , and antennas 916 and 926 . Tx/Rx modules are also called transceivers. Each Tx/Rx module 915 transmits a signal via a respective antenna 926 . The processor implements the functions, processes and/or methods salpinned above. The processor 921 may be associated with a memory 924 that stores program code and data. Memory may be referred to as a computer-readable medium. More specifically, in DL (communication from a first communication device to a second communication device), the transmit (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 (second communication device to 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 via 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. 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 signal transmission/reception method in a wireless communication system.

In a wireless communication system, a terminal receives information through a downlink (DL) from a base station, and the terminal transmits information through an uplink (UL) to the base station. Information transmitted and received between the base station and the terminal includes data and various control information, and various physical channels exist according to the type/use of the information they transmit and receive.

The terminal performs an initial cell search operation such as synchronizing with the base station when the power is turned on or a new cell is entered (S201). To this end, the terminal receives a primary synchronization signal (PSS) and a secondary synchronization signal (SSS) from the base station, synchronizes with the base station, and obtains information such as a cell ID. Thereafter, the terminal may receive a physical broadcast channel (PBCH) from the base station to obtain intra-cell broadcast information. On the other hand, the terminal may receive a downlink reference signal (DL RS) in the initial cell search step to check the downlink channel state.

After completing the initial cell search, the UE acquires more specific system information by receiving a Physical Downlink Control Channel (PDCCH) and a Physical Downlink Control Channel (PDSCH) according to information carried on the PDCCH. It can be done (S202).

On the other hand, when first accessing the base station or when there is no radio resource for signal transmission, the terminal may perform a random access procedure (RACH) for the base station (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 response message to the preamble through the PDCCH and the corresponding PDSCH ((Random Access (RAR)) Response) message) In the case of contention-based RACH, a contention resolution procedure may be additionally performed (S206).

After performing the process as described above, the UE receives PDCCH/PDSCH (S207) and a 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. Here, the DCI includes control information such as resource allocation information for the terminal, and different formats may be applied according to the purpose of use.

On the other hand, the control information that the terminal transmits to the base station through the uplink or the terminal receives from the base station includes a downlink/uplink ACK/NACK signal, a channel quality indicator (CQI), a precoding matrix index (PMI), and a rank indicator (RI). ) and the like. The UE may transmit the above-described control information such as CQI/PMI/RI through PUSCH and/or PUCCH.

The UE monitors a set of PDCCH candidates in monitoring opportunities set in one or more control element sets (CORESETs) on a serving cell according to corresponding search space configurations. The set of PDCCH candidates to be monitored by the UE is defined in terms of search space sets, which 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 may configure the UE to have multiple CORESETs. The UE monitors PDCCH candidates in one or more search space sets. Here, monitoring means trying to decode PDCCH candidate(s) in the search space. If the UE succeeds in decoding one of the PDCCH candidates in the search space, the UE determines that the PDCCH is detected in the corresponding PDCCH candidate, and performs PDSCH reception or PUSCH transmission based on the DCI in the detected PDCCH. The PDCCH may be used to schedule DL transmissions on PDSCH and UL transmissions on PUSCH. Here, the DCI on the PDCCH is a downlink assignment (i.e., downlink grant; DL grant) including at least modulation and coding format and resource allocation information related to the downlink shared channel, or uplink It includes an uplink grant (UL grant) including a modulation and coding format and resource allocation information related to a shared channel.

Referring 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, DL measurement, and the like based on the SSB. The SSB is mixed with an SS/PBCH (Synchronization Signal/Physical Broadcast channel) block.

SSB consists of PSS, SSS and PBCH. The SSB is configured in four consecutive OFDM symbols, and PSS, PBCH, SSS/PBCH or PBCH are transmitted for each OFDM symbol. PSS and SSS consist of 1 OFDM symbol and 127 subcarriers, respectively, and PBCH consists 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 ID (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 there are 3 cell IDs 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

The SSB is transmitted periodically according to the SSB period (periodicity). The SSB basic period assumed by the UE during initial cell discovery 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, the acquisition of system information (SI) will be described.

The 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 Remaining Minimum System Information (RMSI). The MIB includes information/parameters for monitoring the PDCCH scheduling the PDSCH carrying the 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, where x is an integer of 2 or more). SIBx is included in the SI message and transmitted through the PDSCH. Each SI message is transmitted within a periodically occurring time window (ie, an 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 a variety of purposes. For example, the random access procedure may be used for network initial access, handover, and UE-triggered UL data transmission. The UE may acquire UL synchronization and UL transmission resources through a random access procedure. 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 through the PRACH as Msg1 of the random access procedure in the UL. Random access preamble sequences having two different lengths are supported. The long sequence length 839 applies for subcarrier spacings of 1.25 and 5 kHz, and the short sequence length 139 applies for subcarrier spacings of 15, 30, 60 and 120 kHz.

When the BS receives the random access preamble from the UE, the BS sends a random access response (RAR) message (Msg2) to the UE. The PDCCH scheduling the PDSCH carrying the RAR is CRC-masked and transmitted with a random access (RA) radio network temporary identifier (RNTI) (RA-RNTI). The UE detecting the PDCCH masked by the RA-RNTI may receive the RAR from the PDSCH scheduled by the DCI carried by the PDCCH. The UE checks whether the random access response information for the preamble it has transmitted, that is, Msg1, is in the RAR. Whether or not random access information for Msg1 transmitted by itself exists may be determined by whether a random access preamble ID for the preamble transmitted by the UE exists. 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 transmit power for the retransmission of the preamble based on the most recent path loss and power ramping counter.

The UE may transmit UL transmission on the uplink shared channel as Msg3 of the random access procedure based on the random access response information. Msg3 may include the RRC connection request and UE identifier. As a response to Msg3, the network may send Msg4, which may be treated as a contention resolution message on 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 a Tx beam and Rx beam sweeping to determine an Rx beam.

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

A configuration for a beam report using the SSB is performed during channel state information (CSI)/beam configuration in RRC_CONNECTED.

- The UE receives from the BS a CSI-ResourceConfig IE including a CSI-SSB-ResourceSetList for SSB resources used for BM. The RRC parameter csi-SSB-ResourceSetList indicates 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.

- 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 the corresponding RSRP to the BS.

If the CSI-RS resource is configured in the same OFDM symbol(s) as the SSB, and 'QCL-TypeD' is applicable, the UE has the CSI-RS and the SSB similarly located in the 'QCL-TypeD' point of view ( quasi co-located, QCL). Here, QCL-TypeD may mean QCL between antenna ports in terms of spatial Rx parameters. When the UE receives signals of 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 the CSI-RS and the Tx beam sweeping process of the BS will be described in turn. In the UE Rx beam determination process, the repetition parameter is set to 'ON', and in the BS 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 the CSI report when the multi-RRC parameter 'repetition' is set to 'ON'.

Next, the Tx beam determination process 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 filter) 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 to the BS.

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

- The UE receives the RRC signaling (eg, SRS-Config IE) including the (RRC parameter) usage parameter set to 'beam management' 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 that used in SSB, CSI-RS, or SRS for each SRS resource.

- If SRS-SpatialRelationInfo is configured in the SRS resource, the same beamforming as that used in SSB, CSI-RS, or SRS is applied and transmitted. However, if SRS-SpatialRelationInfo is not configured in the SRS resource, the UE arbitrarily 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 can be supported when the UE knows new candidate beam(s). For beam failure detection, the BS sets beam failure detection reference signals to the UE, and the UE determines that the number of beam failure indications from the physical layer of the UE is within a period set by the RRC signaling of the BS. When a threshold set by RRC signaling is reached (reach), a beam failure is declared (declare). after 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 provides dedicated random access resources for certain beams, these are prioritized by the UE). Upon completion of the random access procedure, it is considered that beam failure recovery has been completed.

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

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

For NR, dynamic resource sharing between eMBB and URLLC is supported. eMBB and URLLC services may be scheduled on non-overlapping time/frequency resources, and URLLC transmission may occur on resources scheduled for ongoing eMBB traffic. The eMBB UE may not 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, NR provides a preemption indication. The preemption indication may be referred to as an interrupted transmission indication.

With respect to the preemption indication, the UE receives the DownlinkPreemption IE through RRC signaling from the BS. When the UE is provided with the DownlinkPreemption IE, for monitoring the PDCCH carrying DCI format 2_1, the UE is configured with the INT-RNTI provided by the parameter int-RNTI in the DownlinkPreemption IE. The UE is additionally configured with a set of serving cells by INT-ConfigurationPerServing Cell including a set of serving cell indices provided by servingCellID and a corresponding set of positions for fields in DCI format 2_1 by positionInDCI, dci-PayloadSize It is established with the information payload size for DCI format 2_1 by , and is set with the indicated 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 configured set of serving cells, the UE determines that the DCI format of the set of PRBs and the set of symbols of the monitoring period immediately preceding the monitoring period to which the DCI format 2_1 belongs. It can be assumed that there is no transmission to the UE in the PRBs and symbols indicated by 2_1. For example, the UE sees that the signal in the time-frequency resource indicated by the preemption is not the scheduled DL transmission for itself and decodes data based on the signals received in the remaining resource region.

E. mMTC (massive MTC)

mMTC (massive machine type communication) is one of the scenarios of 5G to support hyper-connectivity service that communicates simultaneously with a large number of UEs. In this environment, the UE communicates intermittently with a very low transmission rate and mobility. Therefore, mMTC is primarily aimed at how long the UE can run at a low cost. In relation to mMTC technology, 3GPP deals with MTC and NB (NarrowBand)-IoT.

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

That is, a PUSCH (or PUCCH (particularly, long PUCCH) or PRACH) including specific information and a PDSCH (or PDCCH) including a response to specific information are repeatedly transmitted. Repeated transmission is performed through frequency hopping, and for repeated 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 RB (resource block) or 1 RB).

F. Basic operation between autonomous vehicles using 5G communication

3 shows an example of basic operations 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. Then, the 5G network may determine whether to remotely control the vehicle (S2). Here, the 5G network may include a server or module for performing 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 the autonomous vehicle using 5G communication will be described in more detail with reference to FIGS. 1 and 2 and the above salpin wireless communication technology (BM procedure, URLLC, Mmtc, etc.).

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

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

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

In addition, the autonomous vehicle performs a random access procedure with the 5G network for UL synchronization acquisition and/or UL transmission. In addition, the 5G network may transmit a UL grant for scheduling transmission of specific information to the autonomous vehicle. 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, the method proposed in the present specification, which will be described later, and the basic procedure of the application operation to which the URLLC technology of 5G communication is applied 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. Then, the autonomous vehicle receives DCI format 2_1 including a pre-emption indication from the 5G network based on the DownlinkPreemption IE. In addition, 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, which will 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, the parts that are changed by the application of the mMTC technology will be mainly described.

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 the 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. In addition, repeated transmission of specific information may be performed through frequency hopping, transmission of the first specific information may be transmitted in a first frequency resource, and transmission of the 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. Autonomous vehicle-to-vehicle 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 is directly (sidelink communication transmission mode 3) or indirectly (sidelink communication transmission mode 4) involved in the resource allocation of the specific information and the response to the specific information, the vehicle-to-vehicle application operation Configuration may vary.

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

First, how the 5G network is directly involved in resource allocation of vehicle-to-vehicle signal transmission/reception will be described.

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

Next, how the 5G network is indirectly involved in resource allocation of signal transmission/reception will be examined.

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 of transmission of specific information 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.

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.

Driving

(1) Vehicle exterior

5 is a diagram illustrating a vehicle according to an embodiment of the present specification.

Referring to FIG. 5 , a 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 all of an internal combustion engine vehicle having an engine as a power source, a hybrid vehicle having an engine and an electric motor as a power source, and an electric vehicle having an electric motor as a power source. 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) Components of the vehicle

6 is a control block diagram of a vehicle according to an 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 manipulation device 230 , a main ECU 240 , and a driving control device 250 . ), an autonomous driving device 260 , a sensing unit 270 , and a location data generating device 280 . The object detecting device 210 , the communication device 220 , the driving manipulation device 230 , the main ECU 240 , the driving control device 250 , the autonomous driving device 260 , the sensing unit 270 , and the location data generating device 280 may be implemented as electronic devices that each generate electrical signals and exchange electrical signals with each other.

1) User interface device

The user interface device 200 is a device for communication between 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 apparatus 210 may generate information about an object outside the vehicle 10 . The information about the object may include at least one of information on the existence 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 detecting apparatus 210 may detect an object outside the vehicle 10 . The object detecting apparatus 210 may include at least one sensor capable of detecting an object outside the vehicle 10 . The object detecting apparatus 210 may include at least one of a camera, a radar, a lidar, an ultrasonic sensor, and an infrared sensor. The object detecting apparatus 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 about an object outside the vehicle 10 by using the 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 to process a received signal, and generate data about the 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 obtain position information of the object, distance information from the object, or relative speed information with the object by using various image processing algorithms. For example, the camera may acquire distance information and relative velocity information from an object based on a change in the size of the object over time from the acquired image. For example, the camera may acquire distance information and relative speed information with respect to an object through a pinhole model, road surface profiling, or the like. For example, the camera may acquire distance information and relative velocity information from an object based on disparity information in a stereo image obtained from the stereo camera.

The camera may be mounted at a position where a field of view (FOV) can be secured in the vehicle in order to photograph the outside of the vehicle. The camera may be disposed adjacent to the front windshield in the interior of the vehicle to acquire an image of the front of the vehicle. The camera may be placed around the front bumper or radiator grill. The camera may be disposed adjacent to the rear glass in the interior of the vehicle to acquire an image of the rear of the vehicle. The camera may be placed around the rear bumper, trunk or tailgate. The camera may be disposed adjacent 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 generate information about an object outside the vehicle 10 using radio waves. The radar may include an electromagnetic wave transmitter, an electromagnetic wave receiver, and at least one processor that is electrically connected to 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 in terms of a radio wave emission principle. 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 based on an electromagnetic wave, 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. can The radar may be placed at a suitable location outside of the vehicle to detect objects located in front, rear or side of the vehicle.

2.3) Lidar

The lidar may generate information about an object outside the vehicle 10 by using laser light. The lidar may include at least one processor that is electrically connected to the light transmitter, the light receiver, and the light transmitter and the light receiver, processes the received signal, and generates data about the object based on the processed signal. . The lidar may be implemented in a time of flight (TOF) method or a phase-shift method. Lidar can be implemented as driven or non-driven. When implemented as a driving type, the lidar is rotated by a motor and may detect an object around the vehicle 10 . When implemented as a non-driven type, the lidar may detect an object located within a predetermined range with respect to the vehicle by light steering. Vehicle 100 may include a plurality of non-driven lidar. LiDAR detects an object based on a time of flight (TOF) method or a phase-shift method with a laser light medium, and calculates 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 a suitable location outside of the vehicle to detect an object located in front, rear or side of the vehicle.

3) communication device

The communication apparatus 220 may exchange signals with a device 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 transmit antenna, a receive antenna, a radio frequency (RF) circuit capable of implementing various communication protocols, and an RF element to perform communication.

For example, the communication apparatus may exchange a signal with an external device based on C-V2X (Cellular V2X) technology. For example, the 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, communication devices communicate with external devices based on IEEE 802.11p PHY/MAC layer technology and IEEE 1609 Network/Transport layer technology-based Dedicated Short Range Communications (DSRC) technology or WAVE (Wireless Access in Vehicular Environment) standard. can be exchanged for DSRC (or WAVE standard) technology is a communication standard prepared to provide ITS (Intelligent Transport System) service through short-distance dedicated communication between in-vehicle devices or between roadside devices and vehicle-mounted devices. The DSRC technology may use a frequency of 5.9 GHz 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 a signal with an external device using either one of the C-V2X technology or the DSRC technology. Alternatively, the communication apparatus of the present specification may exchange signals with an external device by hybridizing C-V2X technology and DSRC technology.

4) Driving control device

The driving operation device 230 is a device that receives a user input for driving. In the manual mode, the vehicle 10 may be driven based on a signal provided by the driving manipulation device 230 . The driving manipulation 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 included 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 drive control device may include a safety belt drive control device for seat belt control.

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

The driving 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 obtained data. The autonomous driving device 260 may generate a driving plan for driving along the generated path. 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 apparatus 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), Lane Keeping Assist (LKA), ), Lane Change Assist (LCA), Target Following Assist (TFA), Blind Spot Detection (BSD), Adaptive High Beam Control (HBA) , Auto Parking System (APS), Pedestrian Collision Warning System (PD Collision Warning System), Traffic Sign Recognition (TSR), Trafffic Sign Assist (TSA), Night Vision System At least one of a Night Vision (NV), a Driver Status Monitoring (DSM), and a Traffic Jam Assist (TJA) may be implemented.

The autonomous driving device 260 may perform a switching operation from the autonomous driving mode to the manual driving mode or a switching operation from the manual driving mode to the 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 from the manual driving mode to the autonomous driving mode based on a signal received from the user interface device 200 . can be converted to

8) Sensing unit

The sensing unit 270 may sense the state of the vehicle. The sensing unit 270 may include an inertial measurement unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight 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, an inertial measurement unit (IMU) sensor may include at least one 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 may include 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 interior temperature data, vehicle interior humidity data, steering wheel rotation angle data, vehicle exterior illumination Data, pressure data applied to the accelerator pedal, pressure data applied to the brake pedal, and the like may be generated.

9) Location data generating 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 from 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 Inertial Measurement Unit (IMU) of the sensing unit 270 and a camera of the object detecting apparatus 210 . The location data generating device 280 may be referred to as a Global Navigation Satellite System (GNSS).

The vehicle 10 may include an internal communication system 50 . A plurality of electronic devices included in the vehicle 10 may exchange signals via 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 an autonomous driving device

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 the unit, control data for operation control of the unit, and input/output data. The memory 140 may store data processed by the processor 170 . The memory 140 may be configured as at least one of ROM, RAM, EPROM, flash drive, and hard drive in terms of hardware. 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 . According to an embodiment, the memory 140 may be classified into a sub-configuration of the processor 170 .

The interface unit 180 may exchange signals with at least one electronic device provided in the vehicle 10 in a wired or wireless manner. The interface unit 280 includes an object detecting device 210 , a communication device 220 , a driving manipulation device 230 , a main ECU 240 , a driving control device 250 , a sensing unit 270 , and a location data generating device. A signal may be exchanged with at least one of 280 by wire or wirelessly. The interface unit 280 may be composed of 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 apparatus 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 is, ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors (processors), controller It may be implemented using at least one of controllers, micro-controllers, microprocessors, and other electrical units for performing functions.

The processor 170 may be driven by power provided from the power supply 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 the printed circuit board.

(4) Operation of autonomous driving device

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

1) Receive 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 detecting device 210 , the communication device 220 , the sensing unit 270 , and the location data generating device 280 through the interface unit 180 . 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 action

The processor 170 may perform a processing/determination operation. The processor 170 may perform a processing/determination operation based on the driving situation information. The processor 170 may perform a processing/determination 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, which is understood as driving plan data within a range from a point where the vehicle 10 is located to a 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 The horizon is a point where the vehicle 10 is located along a preset driving route. It may mean a point to 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 a first 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 road centers. The topology data is suitable for roughly indicating the location of the vehicle, and may be in the form of data mainly used in navigation for drivers. The topology data may be understood as data on road information excluding information on lanes. 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 the road, curvature data of the road, and speed limit data of the road. The road data may further include data on an overtaking prohibited section. The 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 detecting apparatus 210 .

HD map data includes detailed lane-by-lane topology information of the road, connection information of each lane, and characteristic information for vehicle localization (eg, traffic signs, Lane Marking/attributes, Road furniture, etc.). 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 a 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 junction, an intersection, etc.). The relative probability may be calculated based on the time it takes to arrive at the final destination. For example, at the decision point, if the time taken to arrive at the final destination is shorter when selecting the first road than when selecting the second road, the probability of selecting the first road is higher than the probability of selecting the second road. can be calculated higher.

The horizon pass data may include a main path and a sub path. The main path may be understood as a track connecting roads with a high relative probability of being selected. The sub-path may diverge 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 of being selected from at least one decision point on the main path.

3) Control signal generation operation

The processor 170 may perform a control signal generating operation. The processor 170 may generate a control signal based on the 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 .

cabin

9 is a view illustrating the interior of a vehicle according to an embodiment of the present specification. 10 is a block diagram referenced to describe a vehicle cabin system according to an embodiment of the present specification.

(1) Components of the cabin

9 to 10 , the vehicle cabin system 300 (hereinafter, the cabin system) may be defined as a convenience system for a user using the vehicle 10 . The cabin system 300 may be described as a top-level system including a display system 350 , a cargo system 355 , a seat system 360 , and a payment system 365 . The cabin system 300 includes a main controller 370 , a memory 340 , an interface unit 380 , a power supply unit 390 , an input device 310 , an imaging device 320 , a communication device 330 , and a display system. 350 , a cargo system 355 , a seat system 360 , and a payment system 365 . Depending on the embodiment, the cabin system 300 may further include other components in addition to the components described herein, or may not include some of the components described herein.

1) Main controller

The main controller 370 is electrically connected to the input device 310 , the communication device 330 , the display system 350 , the cargo system 355 , the seat system 360 , and the payment system 365 to exchange signals. can do. The main controller 370 may control the input device 310 , the communication device 330 , the display system 350 , the cargo system 355 , the seat system 360 , and the payment system 365 . The main controller 370, ASICs (application specific integrated circuits), DSPs (digital signal processors), DSPDs (digital signal processing devices), PLDs (programmable logic devices), FPGAs (field programmable gate arrays), processors (processors), It may be implemented using at least one of controllers, micro-controllers, microprocessors, and other electrical units for performing functions.

The main controller 370 may include at least one sub-controller. According to an embodiment, the main controller 370 may include a plurality of sub-controllers. Each of the plurality of sub-controllers may individually control devices and systems included in the grouped cabin system 300 . Devices and systems included in the cabin system 300 may be grouped by function or grouped based on a seatable seat.

The main controller 370 may include at least one processor 371 . Although the main controller 370 is illustrated as including one processor 371 in FIG. 6 , the main controller 371 may include a plurality of processors. The processor 371 may be classified as any one of the sub-controllers described above.

The processor 371 may receive a signal, information, or data from the user terminal through the communication device 330 . The user terminal may transmit a signal, information or data to the cabin system 300 .

The processor 371 may specify a user based on image data received from at least one of an internal camera and an external camera included in the imaging device. The processor 371 may specify a user by applying an image processing algorithm to image data. For example, the processor 371 may specify the user by comparing the information received from the user terminal with the image data. For example, the information may include at least one of route information, body information, passenger information, luggage information, location information, preferred content information, preferred food information, disability information, and use history information of the user. .

The main controller 370 may include an artificial intelligence agent 372 . The artificial intelligence agent 372 may perform machine learning based on data obtained through the input device 310 . The artificial intelligence agent 372 may control at least one of the display system 350 , the cargo system 355 , the seat system 360 , and the payment system 365 based on the machine learning result.

2) Essential components

The memory 340 is electrically connected to the main controller 370 . The memory 340 may store basic data for the unit, control data for operation control of the unit, and input/output data. The memory 340 may store data processed by the main controller 370 . The memory 340 may be configured as at least one of ROM, RAM, EPROM, flash drive, and hard drive in terms of hardware. The memory 340 may store various data for the overall operation of the cabin system 300 , such as a program for processing or controlling the main controller 370 . The memory 340 may be implemented integrally with the main controller 370 .

The interface unit 380 may exchange signals with at least one electronic device provided in the vehicle 10 in a wired or wireless manner. The interface unit 380 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 390 may supply power to the cabin system 300 . The power supply unit 390 may receive power from a power source (eg, a battery) included in the vehicle 10 to supply power to each unit of the cabin system 300 . The power supply 390 may be operated according to a control signal provided from the main controller 370 . For example, the power supply unit 390 may be implemented as a switched-mode power supply (SMPS).

The cabin system 300 may include at least one printed circuit board (PCB). The main controller 370 , the memory 340 , the interface unit 380 , and the power supply unit 390 may be mounted on at least one printed circuit board.

3) input device

The input device 310 may receive a user input. The input device 310 may convert a user input into an electrical signal. The electrical signal converted by the input device 310 may be converted into a control signal and provided to at least one of the display system 350 , the cargo system 355 , the seat system 360 , and the payment system 365 . At least one processor included in the main controller 370 or the cabin system 300 may generate a control signal based on an electrical signal received from the input device 310 .

The input device 310 may include at least one of a touch input unit, a gesture input unit, a mechanical input unit, and a voice input unit. The touch input unit may convert the user's touch input into an electrical signal. The touch input unit may include at least one touch sensor to detect a user's touch input. According to an embodiment, the touch input unit may be formed integrally with at least one display included in the display system 350 to implement a touch screen. Such a touch screen may provide both an input interface and an output interface between the cabin system 300 and a user. The gesture input unit may convert the user's gesture input into an electrical signal. The gesture input unit may include at least one of an infrared sensor and an image sensor for detecting a user's gesture input. According to an embodiment, the gesture input unit may detect a user's 3D gesture input. To this end, the gesture input unit may include a light output unit that outputs a plurality of infrared rays or a plurality of image sensors. The gesture input unit may sense the user's 3D gesture input through a time of flight (TOF) method, a structured light method, or a disparity method. The mechanical input unit may convert a user's physical input (eg, pressing or rotating) through the mechanical device into an electrical signal. The mechanical input unit may include at least one of a button, a dome switch, a jog wheel, and a jog switch. Meanwhile, the gesture input unit and the mechanical input unit may be integrally formed. For example, the input device 310 includes a gesture sensor and may include a jog dial device that is formed so as to be able to deposit and withdraw from a part of a surrounding structure (eg, at least one of a seat, an armrest, and a door). . When the jog dial device is in a flat state with the surrounding structures, the jog dial device may function as a gesture input unit. When the jog dial device protrudes relative to the surrounding structure, the jog dial device may function as a mechanical input unit. The voice input unit may convert the user's voice input into an electrical signal. The voice input unit may include at least one microphone. The voice input unit may include a beam foaming MIC.

4) video device

The imaging device 320 may include at least one camera. The imaging device 320 may include at least one of an internal camera and an external camera. The internal camera may capture an image in the cabin. The external camera may capture an image outside the vehicle. The internal camera may acquire an image in the cabin. The imaging device 320 may include at least one internal camera. It is preferable that the imaging device 320 includes the number of cameras corresponding to the number of people who can board. The imaging device 320 may provide an image acquired by an internal camera. At least one processor included in the main controller 370 or the cabin system 300 detects the user's motion based on the image acquired by the internal camera, and generates a signal based on the detected motion, the display system 350 , it may be provided to at least one of the cargo system 355 , the seat system 360 , and the payment system 365 . The external camera may acquire an image outside the vehicle. The imaging device 320 may include at least one external camera. The imaging device 320 preferably includes the number of cameras corresponding to the boarding door. The imaging device 320 may provide an image acquired by an external camera. At least one processor included in the main controller 370 or the cabin system 300 may acquire user information based on an image acquired by an external camera. At least one processor included in the main controller 370 or the cabin system 300, based on user information, authenticates the user, the user's body information (eg, height information, weight information, etc.), the user's Passenger information, luggage information of the user, and the like may be acquired.

5) communication device

The communication device 330 may wirelessly exchange signals with an external device. The communication apparatus 330 may exchange a signal with an external device through a network or may directly exchange a signal with an external device. The external device may include at least one of a server, a mobile terminal, and another vehicle. The communication device 330 may exchange signals with at least one user terminal. The communication device 330 may include at least one of an antenna, a radio frequency (RF) circuit capable of implementing at least one communication protocol, and an RF element to perform communication. According to an embodiment, the communication device 330 may use a plurality of communication protocols. The communication device 330 may switch a communication protocol according to a distance from the mobile terminal.

For example, the communication apparatus may exchange a signal with an external device based on C-V2X (Cellular V2X) technology. For example, the 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, communication devices communicate with external devices based on IEEE 802.11p PHY/MAC layer technology and IEEE 1609 Network/Transport layer technology-based Dedicated Short Range Communications (DSRC) technology or WAVE (Wireless Access in Vehicular Environment) standard. can be exchanged for DSRC (or WAVE standard) technology is a communication standard prepared to provide ITS (Intelligent Transport System) service through short-distance dedicated communication between in-vehicle devices or between roadside devices and vehicle-mounted devices. The DSRC technology may use a frequency of 5.9 GHz 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 a signal with an external device using either one of the C-V2X technology or the DSRC technology. Alternatively, the communication apparatus of the present specification may exchange signals with an external device by hybridizing C-V2X technology and DSRC technology.

6) Display system

The display system 350 may display a graphic object. The display system 350 may include at least one display device. For example, the display system 350 may include a publicly available first display device 410 and an individually available second display device 420 .

6.1) Common Display Devices

The first display device 410 may include at least one display 411 for outputting visual content. The display 411 included in the first display device 410 is a flat panel display. It may be implemented as at least one of a curved display, a rollable display, and a flexible display. For example, the first display device 410 may include a first display 411 positioned at the rear of the seat and formed to be able to be put in and out of the cabin and a first mechanism for moving the first display 411 . The first display 411 may be disposed in a slot formed in the seat main frame so as to be retractable. According to an embodiment, the first display device 410 may further include a flexible area control mechanism. The first display may be formed to be flexible, and the flexible area of the first display may be adjusted according to the location of the user. For example, the first display device 410 may include a second display positioned on the ceiling of the cabin and formed to be rollable and a second mechanism for winding or unwinding the second display. The second display may be formed to enable screen output on both sides. For example, the first display device 410 may include a third display positioned on the ceiling in the cabin and formed to be flexible and a third mechanism for bending or unfolding the third display. According to an embodiment, the display system 350 may further include at least one processor that provides a control signal to at least one of the first display device 410 and the second display device 420 . The processor included in the display system 350 may generate a control signal based on a signal received from at least one of the main controller 370 , the input device 310 , the imaging device 320 , and the communication device 330 . can

The display area of the display included in the first display device 410 may be divided into a first area 411a and a second area 411b. The first area 411a may be defined as a content display area. For example, the first area 411 may display at least one of entertainment contents (eg, movies, sports, shopping, music, etc.), video conferences, food menus, and graphic objects corresponding to the augmented reality screen. can The first area 411a may display a graphic object corresponding to driving situation information of the vehicle 10 . The driving situation information may include at least one of object information outside the vehicle, navigation information, and vehicle state information. The object information outside the vehicle may include information on the existence of an object, location information of the object, distance information between the vehicle 300 and the object, and relative speed information between the vehicle 300 and the object. The navigation information may include at least one of map information, set destination information, route information according to the destination setting, information on various objects on a route, lane information, and current location information of the vehicle. The vehicle state information may include vehicle attitude information, vehicle speed information, vehicle inclination information, vehicle weight information, vehicle direction information, vehicle battery information, vehicle fuel information, vehicle tire pressure information, and vehicle steering information. , vehicle interior temperature information, vehicle interior humidity information, pedal position information, vehicle engine temperature information, and the like. The second area 411b may be defined as a user interface area. For example, the second area 411b may output an artificial intelligence agent screen. According to an exemplary embodiment, the second area 411b may be located in an area divided by the seat frame. In this case, the user may look at the content displayed in the second area 411b between the plurality of sheets. According to an embodiment, the first display device 410 may provide holographic content. For example, the first display device 410 may provide holographic content for a plurality of users so that only the user who requested the content can view the corresponding content.

6.2) Personal Display Devices

The second display device 420 may include at least one display 421 . The second display device 420 may provide the display 421 at a position where only individual passengers can check the display contents. For example, the display 421 may be disposed on an arm rest of the seat. The second display device 420 may display a graphic object corresponding to the user's personal information. The second display device 420 may include the number of displays 421 corresponding to the number of passengers available to board. The second display device 420 may implement a touch screen by forming a layer structure with each other or integrally formed with the touch sensor. The second display device 420 may display a graphic object for receiving a user input of seat adjustment or room temperature adjustment.

7) Cargo system

The cargo system 355 may provide a product to the user according to the user's request. The cargo system 355 may be operated based on an electrical signal generated by the input device 310 or the communication device 330 . The cargo system 355 may include a cargo box. The cargo box may be hidden in a portion of the bottom of the seat in a state in which the goods are loaded. When an electrical signal based on a user input is received, the cargo box may be exposed as a cabin. A user may select a necessary product from among the items loaded in the exposed cargo box. The cargo system 355 may include a sliding moving mechanism and a product pop-up mechanism for exposure of the cargo box according to a user input. The cargo system 355 may include a plurality of cargo boxes to provide various types of goods. In the cargo box, a weight sensor for determining whether to provide each product may be built-in.

8) Seat system

The seat system 360 may provide the user with a seat tailored to the user. The seat system 360 may be operated based on an electrical signal generated by the input device 310 or the communication device 330 . The seat system 360 may adjust at least one element of the seat based on the obtained user body data. The seat system 360 may include a user detection sensor (eg, a pressure sensor) for determining whether the user is seated. The seat system 360 may include a plurality of seats on which a plurality of users can each be seated. Any one of the plurality of sheets may be disposed to face at least another one. At least two users inside the cabin can sit facing each other.

9) Payment system

The payment system 365 may provide a payment service to the user. The payment system 365 may operate based on an electrical signal generated by the input device 310 or the communication device 330 . The payment system 365 may calculate a price for at least one service used by the user and request that the calculated price be paid.

(2) Scenarios for using autonomous vehicles

11 is a diagram referenced to describe a user's usage scenario 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 vacancy information in the cabin through the 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 about 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 may 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 a visible light band or an infrared band.

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

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 the boarding of the user 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 the seat matching the user based on the obtained body information.

5) Scenarios for providing personal content

The fifth scenario S115 is a personal content provision 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 the user's personal data.

6) Product offering scenario

A 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's preference data and user's destination data. Cargo system 355, based on the user data, may provide a product.

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 the user's vehicle usage price based on the received data. The payment system 365 may request payment of a fee from the user (eg, the 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 the 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, based on the electrical signal converted by the plurality of user individual user inputs, at least one of the display system 350 , the cargo system 355 , the seat system 360 , and the payment system 365 . can control

10) Multimedia content provision scenario for multiple users

The tenth scenario S120 is a multimedia content provision scenario targeting a plurality of users. The display system 350 may provide content that all users can view 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 sound through a speaker provided for each sheet.

11) Scenarios for ensuring user safety

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

12) Loss Prevention Scenario

A twelfth scenario ( S122 ) is a scenario for preventing loss of belongings of the user. The main controller 370 may obtain data about the user's belongings through the input device 310 . The main controller 370 may acquire the user's movement data through the input device 310 . The main controller 370 may determine whether the user leaves the belongings and alights based on the movement data and the data on the belongings. The main controller 370 may control an alarm related to belongings to be output through the display system 350 .

13) Drop-off report scenario

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

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.

12 is a diagram for explaining a method for controlling a vehicle by predicting the behavior of an object according to an embodiment of the present specification.

Referring to FIG. 12 , first, the control device of a vehicle may request a list of action sequences of objects from the server device (S1201).

Next, the control device may receive the action sequence list from the server device (S1202).

The control device may receive external environment information from the external environment through the sensor device of the vehicle ( S1203 ).

The control device may determine whether to enter a specific situation based on external environment information. Here, the specific situation may mean a surrounding environment of the vehicle determined based on at least one of a specific area, a specific object, a shape of a road, a road installation, an obstacle, and/or a time.

Also, the control device may detect an object located in the vicinity of the vehicle and classify the type based on the external environment information.

When it is determined that a specific situation has been entered, the control device may perform a behavior prediction at the first time with the most probable behavior based on the behavior information of the detected and classified object included in the behavior sequence list (S1204). ).

Also, the control device may control the vehicle based on the behavior prediction at the first time.

The control device may determine whether the behavior of the object included in the external environment information matches the behavior information at the second time. In this case, the match determination may be performed by the control device only when a preset condition is satisfied.

At the second time, when the behavior of the object included in the external environment information and the behavior information do not match, the control device may check the matching behavior information from the behavior sequence list.

When the coincident behavior information is confirmed, the control device may predict the behavior of the object at the third time based on the coincident behavior information and control the vehicle.

When the matching behavior information is not confirmed from the behavior sequence list, the control device may control the vehicle by determining the behavior of the corresponding object as an unpredicted behavior. For example, the control device may control the vehicle to travel at a low speed in preparation for an unexpected movement of the corresponding object or to travel at a distance from the corresponding object. In other words, the control device may control the vehicle to be safely driven.

13 is a flowchart illustrating a method for controlling a vehicle by predicting the behavior of an object according to another embodiment of the present specification.

Referring to FIG. 13 , first, the control device of the vehicle may request and receive an action sequence list from the server device when entering a certain area while driving ( S1301 ).

The control device may check the behavior information with the highest probability in the current situation of the object recognized by the sensor device of the vehicle from the behavior sequence list (S1302).

The control device may predict the behavior (primary behavior) of the object at the first time T1 based on the identified behavior information and control the vehicle (primary control) ( S1303 ).

When a preset condition is satisfied, the control device may determine whether the behavior information matches the behavior of the object at a second time Tn ( S1304 ).

When it is determined that the behavior information matches the behavior of the object, the control device may predict the behavior of the object based on the behavior information and control the vehicle (S1305).

When it is determined that the behavior information and the behavior of the object do not match, the control device may check the matching behavior information of the corresponding object from the behavior sequence list (S1306).

When the coincident behavior information is confirmed, the control device may predict an object's behavior (additional behavior) based on the coincident behavior information and control the vehicle (additional control) ( S1307 ).

When the matching behavior information is not confirmed, the control device may control the vehicle by determining the behavior of the object as an unpredicted behavior ( S1308 ).

Next, the control device may update the behavior of the objects by transmitting various information, such as external environment information and behavior information on the object, to the server device after passing through a predetermined area (S1309).

14 is a diagram illustrating an example for explaining a method for controlling a vehicle by predicting the behavior of an object.

Referring to FIG. 14 , the vehicle 1401 may identify a first vehicle 1402 preceding and a second vehicle 1403 following a second lane at the intersection. Also, the vehicle 1401 may check the right turn direction indicator of the first vehicle 1402 .

In such a situation (external environment information), the prior art may perform behavior prediction and vehicle control based on behavior information shown in Table 1. For example, the conventional invention may predict a deceleration stop of the second vehicle 1403 based on behavior information of deceleration or stop with high probability and control the vehicle 1401 to change lanes to the second lane.

However, the control method of the present specification may perform behavior prediction and vehicle control based on the behavior information in Table 2 below. For example, the behavior information (action context) of the object of Table 2 below may be received from the server device. In other words, the control device may receive the behavior information of the second vehicle 1403 (object) from the server device in the above-described situation.

First, the control device predicts that the second vehicle 1403 will maintain the speed at the first time T1 based on the behavior information of deceleration or stop with high probability, and the vehicle 1401 changes the lane to the second lane. can be controlled to prepare

Next, when the behavior of the second vehicle 1403 based on the external environment information and the behavior of the second vehicle 1403 based on the behavior information do not match at the second time T2, the control device 1403 ) to maintain the speed and stop changing lanes in preparation for acceleration. In other words, when the control device confirms the acceleration of the second vehicle 1403 based on the external environment information, the behavior information of the deceleration or stop coincides with the behavior at the second time T2 (the behavior of deceleration or strong deceleration) It is confirmed that the vehicle does not do so, and control can be performed to stop the lane change in preparation for maintaining and accelerating the speed of the second vehicle 1403 .

In the above example, the control device predicts a behavior and performs vehicle control at a first time T1 based on the determined behavior information, and at a second time T1 , the behavior of an object based on external environment information and the behavior information Although it is described as an operation to check whether the behavior of the .

Through this, the present specification can implement a safe and reliable autonomous driving system through accurate environmental prediction and vehicle control.

15 is a diagram illustrating another example for explaining a method for controlling a vehicle by predicting the behavior of an object.

15 , the vehicle 1501 confirms that the first vehicle 1502 driving in the second lane on a straight road proceeds to the left of the lane rather than the center of the lane, and there is no direction indicator (external environment information is checked). can Also, the second vehicle 1503 driving in the first lane may be checked.

In such a situation (external environment information), the conventional invention may perform behavior prediction and vehicle control based on behavior information shown in Table 3. For example, in the prior art, it is possible to predict that the first vehicle 1502 will not invade the third lane based on the behavior information of going straight and to control the speed of the vehicle 1501 to be maintained.

In other words, the conventional invention predicts that the first vehicle 1502 will not invade the third lane and go straight because the first vehicle 1502 maintains the speed without a turn indicator at the first time T1, and controls to maintain the speed of the vehicle 1501 can do.

However, in the present specification, it is possible to predict the behavior of an object and control the vehicle based on the behavior information shown in Table 4 below.

For example, in such a situation (external environment information), the control device of the present specification may receive behavior information (action context) of the object of Table 4 (the first vehicle 1502 ) from the server device.

Next, the control device may predict that the second vehicle 1502 will maintain the speed at the first time T1 based on the high-probability straight-line behavior information, and may control the vehicle to prepare to maintain the speed or accelerate.

Next, the control device may confirm that the first vehicle 1502 moves from the left side of the lane to the center of the lane at the second time T2 , and may perform a behavior prediction based on the behavior information of a right lane change. That is, at the second time T2 , the control device confirms that the behavior of the first vehicle 1502 based on the external environment information and the behavior of the second vehicle 1502 based on the straight-line behavior information do not match, and the second It is determined to predict the behavior of the object based on the behavior information of the right lane change having a high probability of the first vehicle 1502 in the behavior sequence list based on the external environment information at the time T2, and accordingly, the third time At ( T3 ), the behavior prediction of the first vehicle 1502 and the behavior control of the vehicle 1501 may be performed. The vehicle 1503 may predict that the second vehicle 1502 will change to the third lane at the third time T3 and may control the vehicle to decelerate or maintain a safe distance.

16 is a diagram illustrating another example for explaining a method for controlling a vehicle by predicting the behavior of an object.

Referring to FIG. 16 , the vehicle 1601 may identify a bus 1602 and a bus stop 1603 on a straight road.

In such a situation (external environment information), the conventional invention can perform behavior prediction and vehicle control based on behavior information shown in Table 5.

For example, the conventional invention may predict that the bus 1602 will stop at the bus stop 1603 based on the stop behavior information in Table 5, and control the vehicle 1601 at the first time T1. . Alternatively, the conventional invention may control the vehicle by checking the lane change of the bus 1602 at the first time T1 and predicting the stop at the bus stop 1603 thereafter.

In such a situation (external environment information), the control device of the present specification may predict the behavior of the object and control the vehicle based on the behavior information of the object in Tables 6 and 7 below.

For example, the control device of the present specification may receive behavior information (action context) of the object of Table 6 from the server device.

Next, the control device may predict that the bus 1602 will change a lane at the first time T1 based on the behavior information of the stop with high probability, and may control the vehicle 1601 to accelerate or prepare for acceleration.

Next, the control device may confirm that the bus 1602 does not decelerate at the second time T2, and may check the corresponding behavior information from the behavior information of Table 6 .

Next, if the control device does not check the matching behavior information in Table 6, it may receive additional behavior information as shown in Table 7 of the bus 1602 based on the current external environment information.

Next, the control device predicts that the bus 1602 changes to the second lane at the third time T3 based on the behavior information of the change to the second lane with the highest probability among the additional behavior information, and the vehicle 1601 decelerates Or it can be controlled to change lanes to one lane. In other words, when the behavior of the object based on the behavior information with the highest probability among the received behavior information does not match the behavior by the external environment information, the control device of the present specification selects the matching behavior information from among the received behavior information. check, and if there is no matching behavior information, new behavior information is received (or updated) based on the current external environment information, and based on the most probable behavior information among the new behavior information, the object behavior prediction and vehicle control can be performed.

17 is a diagram illustrating another example for explaining a method for controlling a vehicle by predicting the behavior of an object.

Referring to FIG. 17 , the vehicle 1701 may identify the merging vehicle 1702 merging on the path exiting the lane junction after the lane merging point.

In such a situation (external environment information), the conventional invention may perform behavior prediction and vehicle control based on behavior information shown in Table 8.

For example, the conventional invention predicts that the merging vehicle 1702 moves to the left within the lane at the first time T1 based on the behavior information in Table 8 to change the lane and controls the vehicle 1701. have.

In such a situation (external environment information), the control device of the present specification may predict the behavior of the object and control the vehicle based on the behavior information of the object in Table 9 below.

First, the control device may receive behavior information (action context) of the object of Table 9 below from the server device.

Next, the control device may predict that the merging vehicle 1702 will change a lane at the first time T1 and control the vehicle 1701 based on the lane change behavior information to the left lane with the highest probability.

Next, the control device confirms that the merging vehicle 1702 moves to the center of the existing lane without changing the lane at the second time T3 based on the external environment information, and confirms other behavioral information in Table 9 and , when it is not possible to check the behavior information including the matching behavior, it is possible to request the server device to receive new behavior information about the object based on the current external context information.

Next, when it is confirmed that there is no new behavior information that can be received, the control device determines that the merging vehicle 1702 behaves according to the unpredicted behavior, and controls the deceleration of the vehicle 1701 and increases the inter-vehicle distance. can

18 is a diagram illustrating another example for explaining a method for controlling a vehicle by predicting the behavior of an object.

Referring to FIG. 18 , the vehicle 1801 may identify a pedestrian 1802 and a red walking signal at a crosswalk with a signal building.

In such a situation (external environment information), the conventional invention may perform behavior prediction and vehicle control based on behavior information shown in Table 10.

For example, the conventional invention may predict the behavior of the pedestrian 1802 and control the vehicle 1801 based on the most probable behavior information among the behavior information of Table 10 at the first time T1 .

Alternatively, the conventional invention may predict a stop based on the behavior (external environment information) of not seeing the vehicles of the pedestrian 1802 at the first time T1 and control the vehicle 1801 .

In such a situation (external environment information), the control device of the present specification may predict the behavior of the object and control the vehicle based on the behavior information of the object shown in Table 11 below.

For example, first, the control device may receive behavior information (action context) of the object of Table 11 below from the server device.

Next, the control device may predict the behavior of not seeing the vehicle of the pedestrian 1802 at the first time T1 based on the behavior information of the probability of stopping on the pedestrian path (or crosswalk).

Next, the control device confirms that the pedestrian 1802 moves to the crosswalk at the second time T2, checks the matching behavior information among the behavior information in Table 11, and if there is no matching behavior information, the current New behavior information is requested from the server device based on external environment information, and when it is confirmed that there is no new behavior information, the behavior of the pedestrian 1802 is checked as an unpredicted behavior and the vehicle 1801 is controlled to decelerate or stop can do.

19 is a flowchart illustrating a method of operating a control device according to an embodiment of the present specification.

Referring to FIG. 19 , the control device receives, from the server device, a behavior sequence list including behavior information for predicting behavior of objects in a specific situation ( S1901 ), and external environment information through the sensor device of the vehicle. It may include receiving (S1902), predicting the behavior of the object at a first time based on the behavior information and the external environment information, and controlling the vehicle (S1903). The external environment information may include information for determining that it is a specific situation. When it is determined that the control device is a specific situation, the control device may request and receive the action sequence list from the server device.

The behavior sequence list may include behavior information of multiple types of objects. The behavior sequence list may include a plurality of behavior information for each of the objects in a specific situation. And/or, the action sequence list or action information may be a temporal arrangement of actions (acceleration/deceleration, lane change) learned in the server device in a specific situation. And/or, the action sequence list or action information may be information including all possible actions, not just the most likely action. The behavior information may be a set (or sequence) of behaviors over time for a specific object in a specific situation. Alternatively, the behavior information may be a list in which movement and/or behavior pattern data for a specific object in a specific situation are collected in time (or region) order.

The action sequence list and/or action information may be classified or classified according to a situation or a type of an object. For example, the situation may mean a surrounding environment in which the vehicle is located, such as a shape of a road, a road installation, a situation in which an obstacle exists, a specific area, or a specific time. As an additional example, whether an object moves, whether a vehicle direction indicator can be checked, etc. may be included in the context.

An object may mean any object that needs to predict movement or behavior to control a vehicle, such as a vehicle, a bus, a city bus, a pedestrian, or a child. The type of object may mean a group or a group of objects showing a similar behavior pattern in a specific situation.

The behavior information may include information on the behavior of the object over time. The behavior information may include information on behavior, probability, and behavior sequence. The action may be information for distinguishing a predicted action context. Probability may mean a probability that the corresponding action is possible in a specific situation. The probability may be updated in real time according to whether the corresponding behavior is matched. The action sequence may mean a change in action over time for a corresponding action, a basis for determining the action, a list of movements, a sequence, or a group. For example, in the case of the behavior of changing the left lane, the information on the behavior sequence includes 30% entry to the left of the lane at the first time and a distance of 200% or more from the vehicle (control vehicle) compared to the speed, at the second time, 50% of entering the outside of the lane, 100% or more respectively with respect to the vehicle for blinking and speed, 40% entering the left lane at the third time, and 50% or more from the vehicle for blinking and speed.

As another example, when entering an intersection, information on the behavior of gradually entering the left lane of the straight roadrenser lane with the merging lane is information on the behavioral power of entering the left lane at the first time, changing to the left lane at the first time, rapid acceleration, the left lane at the second time It may include entering into a junction, slow deceleration, moving to the right lane again at the 3rd time (centering, sleepy behavior, inexperienced driving), and accelerating rapidly at the 4th time. In this case, the control device may control the vehicle to enter a left turn lane or change lanes while avoiding a merging lane.

External environment information refers to all information about things to be predicted in order to control the vehicle, such as information about objects around the vehicle, information about obstacles, information about roads, information about pedestrians, information about surrounding buildings, etc. can do.

The control device may periodically or in real time check the behavior (eg, acceleration, deceleration, steering wheel adjustment) of objects around the vehicle through the sensor device in the vehicle.

Predicting the behavior of the object and controlling the vehicle (S1903) includes the steps of detecting the object, classifying the type of the object, and the behavior based on the type of the classified object in the specific situation. The method may include: confirming the behavior information of the object with a highest probability among a sequence list; predicting the behavior of the object at the first time based on the behavior information and controlling the vehicle.

The preset condition may be a condition for at least one of a distance between the object and the vehicle, a distance between the object and the road installation device, and/or a time.

The control device may receive the action sequence list or action information for each object from the server device.

For example, the control device determines the behavior of an object by external environmental information at a specific time and

If the behavior based on the behavior information does not match more than a preset criterion, behavior information having the highest probability for the corresponding object from the behavior sequence list may be checked. In addition, when the object performs an action that is not in the action information in the predicted range, the number of actions predicted for the object may be increased. And/or, the control device may calculate the probability of an accident according to the new prediction behavior and perform control of the own vehicle again in a direction to reduce the possibility of an accident. And/or, the method performed by the control device may further include determining whether the behavior of the object in the external environment information matches the behavior information at a second time when a preset condition is satisfied have.

The control device may update the behavior information or the behavior sequence list by transmitting information about the new predicted behavior of the object and control of the vehicle to the server device. And/or, when the vehicle is controlled, it may be controlled according to the behavior information to clearly transmit or predict the movement or behavior to another vehicle (or object).

and/or the method performed by the control device may include, when the behavior of the object in the external environment information at the second time and the behavior information match, the behavior of the object at the third time based on the behavior information The method may further include predicting and controlling the vehicle.

The control device may determine the probability and/or degree of whether the behavior of the object of the external environment information matches the behavior of the behavior information.

For example, if the control device is equal to or greater than the first threshold point A-1, the coincidence probability between the behavior of the behavior information and the actual behavior of the object (or the behavior of the object determined by the external environment information) is the first threshold point A-1. If it is more than that, it continues to check whether it matches. If the matching probability between the behavior of the behavior information and the actual behavior of the object (or the behavior of the object determined by the external environment information) is less than or equal to the second threshold (A-2), the highest probability behavior information or behavior sequence list decide If there is no behavior information or behavior sequence lister in which the coincidence probability between the behavior of the behavior information and the actual behavior of the object (or the behavior of the object determined by the external environment information) is equal to or greater than the second threshold (A-1), the object You can control the vehicle by setting the behavior to the unpredicted behavior. The control device may increase the number of predictions of the behavior by increasing the priority of sensing power and V2X filtering for the behavior of the object set as the unpredicted behavior.

And/or, the method performed by the control device, when the behavior of the object of the external environment information and the behavior information do not match at the second time, checking the matching behavior information in the behavior sequence list may include more.

and/or the method performed by the control device may further include, when the coincident behavior information is confirmed, predicting the behavior of the object at a third time based on the coincident behavior information and controlling the vehicle. can

And/or, when matching behavior information is not confirmed in the behavior sequence list, the method may further include controlling the vehicle by determining the behavior of the object as an unpredicted behavior.

20 is a diagram illustrating a block diagram of a control device according to an embodiment of the present specification.

Referring to FIG. 20 , the control device 2000 includes a transceiver 2010 for transmitting and receiving a wireless signal, and a processor 2020 functionally connected to the transceiver 2010, and the processor 2020 ) receives, from a server device, a behavior sequence list including behavior information for predicting behavior of objects in a specific situation, receives external environment information through a sensor device of the vehicle, and receives behavior information and the external environment information Based on the prediction of the behavior of the object at a first time and controlling the vehicle, the behavior information includes information on the behavior of the object over time.

Since the operation of the control device described with reference to FIG. 20 is the same as the operation of the control device described with reference to FIGS. 1 to 19 , a detailed description other than that is omitted.

Embodiment 1: In a method for controlling a vehicle by predicting the behavior of an object in an autonomous driving system (Automated Vehicle & Highway Systems), the method performed by the control device predicts the behavior of the objects in a specific situation Receiving an action sequence list including action information to be performed from a server device; receiving external environment information through a sensor device of the vehicle; and predicting the behavior of the object at a first time and controlling the vehicle based on the behavior information and the external environment information, wherein the behavior information includes information on the behavior of the object over time.

Embodiment 2: The method according to Embodiment 1, wherein predicting the behavior of the object and controlling the vehicle comprises: detecting the object; classifying the type of the object; checking the behavior information of the object having the highest probability in the behavior sequence list based on the type of the classified object in the specific situation; and predicting the behavior of the object at the first time based on the behavior information and controlling the vehicle.

Embodiment 3: The preset condition may be a condition for at least one of a distance between the object and the vehicle, a distance between the object and a road installation device, and/or time.

Embodiment 4: The method according to Embodiment 1, further comprising determining whether the behavior of the object in the external environment information matches the behavior information at a second time when a preset condition is satisfied.

Embodiment 5: The behavior of the object according to Embodiment 4, when the behavior of the object in the external environment information at the second time and the behavior information match, predict the behavior of the object at the third time based on the behavior information, The method may further include controlling the vehicle.

Embodiment 6: The method according to Embodiment 4, further comprising: when the behavior of the object in the external environment information and the behavior information do not match at the second time, checking matching behavior information in the behavior sequence list can

Embodiment 7: The method according to Embodiment 6, further comprising, when the coincident behavior information is confirmed, predicting the behavior of the object at a third time based on the coincident behavior information and controlling the vehicle.

Embodiment 8: The method according to Embodiment 6, further comprising controlling the vehicle by determining the behavior of the object as an unpredicted behavior when matching behavior information is not identified in the behavior sequence list.

Example 9: A control device for controlling a vehicle by predicting the behavior of an object in an autonomous driving system (Automated Vehicle & Highway Systems) is a transceiver for transmitting and receiving a wireless signal, and is functionally connected to the transceiver A processor comprising: a processor, configured to receive a behavior sequence list including behavior information for predicting behavior of objects in a specific situation from a server device, receive external environment information through a sensor device of the vehicle, and Predict the behavior of the object at a first time based on the information and the external environment information and control the vehicle, wherein the behavior information includes information on the behavior of the object over time.

Embodiment 10: The method according to embodiment 9, wherein the processor detects the object, classifies the type of the object, and in the specific situation, based on the type of the classified object, has a highest probability of the action sequence list. It is possible to check the behavior information of the object, predict the behavior of the object at the first time based on the behavior information, and control the vehicle.

Embodiment 11: The preset condition may be a condition for at least one of a distance between the object and the vehicle, a distance between the object and a road installation device, and/or time.

Embodiment 12: The processor according to Embodiment 9, if a preset condition is satisfied, at a second time, determine whether the behavior of the object in the external environment information and the behavior information match.

Embodiment 13: The processor according to Embodiment 12, wherein, if the behavior of the object in the external environment information at the second time and the behavior information match, the processor is configured to select the object at the third time based on the behavior information. It can predict behavior and control the vehicle.

Embodiment 14: The processor according to Embodiment 12, wherein when the behavior of the object in the external environment information and the behavior information do not match at the second time, the processor may check the matching behavior information from the behavior sequence list .

Embodiment 15: The method according to embodiment 14, wherein when the coincident behavior information is confirmed, the processor may predict the behavior of the object at a third time based on the coincident behavior information and control the vehicle.

Embodiment 16: The method according to embodiment 14, wherein when matching behavior information is not identified in the behavior sequence list, the processor may control the vehicle by determining the behavior of the object as an unpredicted behavior.

The above-described 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 in which data readable by a computer system is stored. Examples of computer-readable media include Hard Disk Drive (HDD), Solid State Disk (SSD), Silicon Disk Drive (SDD), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is also a carrier wave (eg, transmission over the Internet) that is implemented in the form of. Accordingly, the above detailed description should not be construed as restrictive in all respects but as exemplary. The scope of this specification should be determined by a reasonable interpretation of the appended claims, and all modifications 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 merely examples and do not limit the present specification, and those of ordinary skill in the art to which this specification pertains are exemplified above in a range that does not depart from the essential characteristics of the present embodiment. It can be seen that various modifications and applications that have not been made are possible. For example, each component specifically shown in the embodiments may be implemented by modification. And differences related to such modifications and applications should be construed as being included in the scope of the present specification defined in the appended claims.

Claims (16)

In the method for controlling a vehicle by predicting the behavior of an object in an autonomous driving system (Automated Vehicle & Highway Systems), the method performed by a control device comprises:
Receiving a behavior sequence list including behavior information for predicting behavior of objects in a specific situation from a server device;
receiving external environment information through a sensor device of the vehicle; and
Predicting the behavior of the object at a first time based on the behavior information and the external environment information and controlling the vehicle,
The behavior information includes information about the behavior of the object over time.
The method of claim 1,
Predicting the behavior of the object and controlling the vehicle comprises:
detecting the object;
classifying the type of the object;
checking the behavior information of the object having the highest probability in the behavior sequence list based on the type of the classified object in the specific situation; and
predicting the behavior of the object at the first time based on the behavior information and controlling the vehicle.
The method of claim 1,
The preset condition is a condition for at least one of a distance between the object and the vehicle, a distance between the object and a road installation device, and/or a time.
The method of claim 1,
The method further comprising the step of determining whether the behavior of the object in the external environment information matches the behavior information at a second time when a preset condition is satisfied.
5. The method of claim 4,
When the behavior of the object in the external environment information at the second time and the behavior information match, predicting the behavior of the object at a third time based on the behavior information and controlling the vehicle Way.
5. The method of claim 4,
and when the behavior of the object in the external environment information and the behavior information do not match at the second time, checking matching behavior information from the behavior sequence list.
7. The method of claim 6,
When the coincident behavior information is confirmed, predicting the behavior of the object at a third time based on the coincident behavior information and controlling the vehicle.
7. The method of claim 6,
and controlling the vehicle by judging the behavior of the object as an unpredicted behavior when matching behavior information is not confirmed in the behavior sequence list.
In the control device for controlling a vehicle by predicting the behavior of an object in an autonomous driving system (Automated Vehicle & Highway Systems),
A transceiver for transmitting and receiving a radio signal;
It includes a processor that is functionally connected to the transceiver,
The processor is
Receiving a behavior sequence list including behavior information for predicting behavior of objects in a specific situation from the server device,
Receive external environment information through the sensor device of the vehicle,
Predicting the behavior of the object at a first time based on the behavior information and the external environment information and controlling the vehicle,
The behavior information is a control device including information on the behavior of the object over time.
10. The method of claim 9,
The processor is
detect the object,
Classify the type of the object,
Check the behavior information of the object with the highest probability among the behavior sequence list based on the type of the classified object in the specific situation,
A control device for predicting the behavior of the object at the first time based on the behavior information and controlling the vehicle.
10. The method of claim 9,
The preset condition is a condition for at least one of a distance between the object and the vehicle, a distance between the object and the road installation device, and/or time.
10. The method of claim 9,
The processor is
A control device for determining whether the behavior of the object in the external environment information matches the behavior information at a second time when a preset condition is satisfied.
13. The method of claim 12,
The processor is
When the behavior of the object in the external environment information at the second time coincides with the behavior information, the control device predicts the behavior of the object at a third time based on the behavior information and controls the vehicle.
13. The method of claim 12,
The processor is
In the second time, when the behavior of the object in the external environment information and the behavior information do not match, the control device checks matching behavior information from the behavior sequence list.
15. The method of claim 14,
The processor is
When the coincident behavior information is confirmed, the control device predicts the behavior of the object at a third time based on the coincident behavior information and controls the vehicle.
15. The method of claim 14,
The processor is
When the matching behavior information is not confirmed in the behavior sequence list, the control device determines the behavior of the object as an unpredicted behavior and controls the vehicle.

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