KR20210095296A - Driving method of autonomous vehicles and autonomous vehicles - Google Patents

Abstract

Disclosed are a driving method of an autonomous vehicle and an autonomous vehicle. According to the present specification, the driving method of an autonomous vehicle, in consideration of the movement of a target vehicle, comprises the following steps of: receiving state information of the target vehicle from the target vehicle; generating first prediction information for driving of the target vehicle based on the state information; and corresponding to the driving of the target vehicle based on the first prediction information. The state information may include a command value input to the target vehicle through an accelerator pedal or a brake pedal of the target vehicle. According to the present specification, there is an effect of generating prediction information for predicting the movement of the target vehicle as well as information sensing the position and speed of the target vehicle, and corresponding to the driving of the target vehicle based on the generated prediction information.

Description

Driving method of autonomous vehicle and autonomous vehicle

The present specification relates to a driving method of an autonomous vehicle and an autonomous vehicle.

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

The autonomous vehicle may analyze the movement of another vehicle and drive according to the analysis result. Existing autonomous vehicles sense the movement of other vehicles through sensors, and drive according to the sensing results.

In the case of the conventional method, there was a problem in that the motion of another vehicle could not be precisely sensed due to the delay of the sensor itself. This may include a risk of having to perform autonomous driving without accurately reflecting the real-time changing road conditions.

An object of the present specification is to generate prediction information that predicts the motion of the target vehicle as well as information sensed such as the position and speed of the target vehicle, and respond to the driving of the target vehicle based on the generated prediction information .

An object of the present specification is to eliminate inaccuracies caused by a delay of a sensor itself by responding to the driving of a target vehicle based on prediction information.

An object of the present specification is to eliminate the inaccuracy caused by the first delay that occurs between a time when a pedal of a target vehicle is pressed and a time when the target vehicle is actually moved by responding to the driving of the target vehicle based on prediction information. .

The present specification aims to eliminate the inaccuracy caused by the second delay that occurs between the time when the pedal of the target vehicle is pressed and the time when the movement of the target vehicle is actually sensed by responding to the driving of the target vehicle based on the prediction information. do it with

The technical problems to be achieved by the present invention 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 the present invention belongs from the detailed description of the invention below. will be able to be understood

In order to solve the above problems, the present specification provides a driving method of an autonomous vehicle in consideration of the movement of a target vehicle, the method comprising: receiving state information of the target vehicle from the target vehicle; based on the state information, generating first prediction information for driving of a target vehicle, and corresponding to driving of the target vehicle based on the first prediction information, wherein the state information is an accelerator pedal or a brake of the target vehicle It may include a command value input to the target vehicle through the pedal.

In addition, the state information may further include ID information for identifying the target vehicle, location information of the target vehicle, and speed information of the target vehicle.

Also, the state information may include information sensed by a degree of pressing of the accelerator pedal or the brake pedal through at least one sensor installed around the accelerator pedal or the brake pedal.

Also, the command value may include a command for controlling the acceleration of the target vehicle.

In addition, the receiving of the state information may include receiving the state information from the target vehicle through vehicle to vehicle (V2V) communication.

Also, the first prediction information may include at least one of an expected acceleration, an expected speed, and an expected moving distance of the target vehicle after a predetermined time.

In addition, the generating of the first prediction information for the driving of the target vehicle may include receiving a lookup table based on the characteristics of the target vehicle from the target vehicle, and generating the first prediction information based on the lookup table. can

In this case, the lookup table may be a table including the expected acceleration of the target vehicle according to the command value.

Also, the step corresponding to the driving of the target vehicle may include driving to maintain a predetermined distance from the target vehicle.

The method may further include receiving second prediction information generated based on the state information in the target vehicle.

In this case, generating a comparison value of the first prediction information and the second prediction information, and when the comparison value is greater than a preset value, corresponding to the driving of the target vehicle based on the first prediction information may further include.

In addition, generating a comparison value of the first prediction information and the second prediction information, and when the comparison value is less than a preset value, corresponding to the driving of the target vehicle based on the second prediction information may further include.

In addition, in order to solve the above-described problem, the present specification provides an autonomous vehicle driving in consideration of the movement of a target vehicle, including a communication module, a memory, and a processor for controlling the communication module and the memory, and the communication The module receives status information from the target vehicle, and the processor generates first prediction information for driving of the target vehicle based on the status information, and performs driving of the target vehicle based on the first prediction information. Correspondingly, the state information may include a command value input to the target vehicle through an accelerator pedal or a brake pedal of the target vehicle.

In addition, the state information may include ID information for identifying the target vehicle, location information of the target vehicle, and speed information of the target vehicle.

In addition, the state information may include information that senses a degree of pressing of the accelerator pedal or the brake pedal through at least one sensor installed in the vicinity of the accelerator pedal or the brake pedal of the target vehicle.

Also, the command value may include a command for controlling the acceleration of the target vehicle.

Also, the communication module may receive the state information from the target vehicle through vehicle to vehicle (V2V) communication.

Also, the first prediction information may include at least one of an expected acceleration, an expected speed, and an expected moving distance of the target vehicle after a predetermined time.

In addition, the processor may receive a lookup table based on the characteristics of the target vehicle from the target vehicle, and generate the first prediction information based on the lookup table.

In this case, the lookup table may be a table including the expected acceleration of the target vehicle according to the command value.

Also, the processor may correspond to the driving of the target vehicle in order to maintain a predetermined distance from the target vehicle based on the first prediction information.

Also, the communication module may receive second prediction information generated based on the state information in the target vehicle.

In addition, the processor may generate a comparison value between the first prediction information and the second prediction information, and when the comparison value is greater than a preset value, the processor may correspond to the driving of the target vehicle based on the first prediction information. .

Also, the processor may generate a comparison value between the first prediction information and the second prediction information, and when the comparison value is less than a preset value, the processor may correspond to the driving of the target vehicle based on the second prediction information. .

According to the present specification, there is an effect of generating prediction information that predicts the motion of the target vehicle as well as information sensing the position and speed of the target vehicle, and corresponding to the driving of the target vehicle based on the generated prediction information.

The present specification has an effect of eliminating inaccuracies caused by the delay of the sensor itself by responding to the driving of the target vehicle based on the prediction information.

The present specification has an effect of eliminating inaccuracies caused by the first delay occurring between a time when the pedal of the target vehicle is pressed and a time when the target vehicle is actually moved by responding to the driving of the target vehicle based on the prediction information.

The present specification provides an effect of removing the inaccuracy caused by the second delay occurring between the time when the pedal of the target vehicle is pressed and the time when the movement of the target vehicle is actually sensed by responding to the driving of the target vehicle based on the prediction information. there is.

Effects that can be obtained in the present specification are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those of ordinary skill in the art to which the present invention belongs from the following description. .

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are included as a part of the detailed description to aid understanding of the present specification, provide embodiments of the present invention, and together with the detailed description, describe the technical features of the present invention.
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 an example of V2X communication to which this specification can be applied.
6 illustrates a resource allocation method in a sidelink in which V2X is used.
7 is a diagram illustrating a procedure for a broadcast mode of V2X communication using PC5.
8 is a diagram illustrating a vehicle of the present specification.
9 is a control block diagram of a vehicle according to the present specification.
10 is a diagram illustrating a driving method of an autonomous vehicle according to a first embodiment of the present specification.
11 is a diagram illustrating a driving method of the autonomous vehicle 100 according to the first embodiment of the present specification.
12 is a view showing the degree of pressing of the pedal according to the first embodiment of the present specification.
13 is a view showing types of pedals according to the first embodiment of the present specification.
14 and 15 are tables showing a lookup table according to the first embodiment of the present specification.
16 and 17 are diagrams illustrating prediction of a motion of a target vehicle according to the first embodiment of the present specification.
18 is a diagram illustrating an autonomous vehicle according to a second embodiment of the present specification.
19 is a diagram illustrating a system including an autonomous vehicle and a target vehicle according to a second embodiment of the present specification.
20 is a diagram illustrating a system including an autonomous vehicle and a target vehicle according to a second embodiment of the present specification.
21 to 24 are views illustrating an autonomous driving vehicle and a target vehicle according to a second embodiment of the present specification.
25 is a diagram illustrating a driving path predicted by an autonomous vehicle sensing a target vehicle according to a second embodiment 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 this specification, if it is determined that detailed descriptions of related known technologies may obscure the gist of the embodiments disclosed in this 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 invention , 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 said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that no other element is present 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.

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.

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

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

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. 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 111 ports in terms of spatial Rx parameters. When the UE receives signals from a plurality of DL antenna 111 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 is (1) a relatively low traffic size, (2) a relatively low arrival rate (low arrival rate), (3) extremely low latency requirements (eg, 0.5, 1ms), (4) a relatively short transmission duration (eg, 2 OFDM symbols), and (5) transmission for an urgent service/message. In the case of UL, transmission for a specific type of traffic (eg, URLLC) is multiplexed with other previously scheduled transmission (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 and from the 5G network, the autonomous vehicle performs an initial access procedure with the 5G network before step S1 of FIG. 3 . and a random access procedure.

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

Next, the method proposed in the present specification, 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.

V2X (Vehicle-to-Everything)

5 is an example of V2X communication to which this specification can be applied.

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

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

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

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

In addition, the UE performing V2X communication, as well as a general handheld UE (handheld UE), vehicle UE (V-UE (Vehicle UE)), pedestrian UE (pedestrian UE), BS type (eNB type) RSU, or UE It may mean an RSU of a UE type, a robot equipped with a communication module, and the like.

V2X communication may be performed directly between UEs, or may be performed through the network entity(s). A V2X operation mode may be divided according to a method of performing such V2X communication.

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

Terms frequently used in V2X communication are defined as follows.

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

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

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

- V2X service: A 3GPP communication service type involving a vehicle transmitting or receiving device.

-V2X enabled (enabled) UE: UE supporting the V2X service.

- V2V service: A type of V2X service, where both sides of the communication are vehicles.

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

V2X applications, called Vehicle-to-Everything (V2X), are (1) vehicle-to-vehicle (V2V), (2) vehicle-to-infrastructure (V2I), (3) vehicle-to-network (V2N), (4) vehicle There are 4 types of pedestrians (V2P).

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

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

NR V2X

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

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

(1) Vehicle Platooning allows vehicles to dynamically form platoons that move together. All vehicles in the Platoon get information from the lead vehicle to manage this Platoon. This information allows vehicles to drive more harmoniously than normal, go in the same direction and drive together.

(2) extended sensors are collected through a local sensor or a live video image in a vehicle, a road site unit, a pedestrian device, and a V2X application server raw (raw) ) or to exchange processed data. Vehicles can increase their environmental awareness beyond what their sensors can detect, and provide a broader and holistic picture of local conditions. A high data rate is one of the main characteristics.

(3) Advanced driving enables semi-automatic or fully-automatic driving. Each vehicle and/or RSU shares self-awareness data obtained from local sensors with nearby vehicles, allowing the vehicle to synchronize and coordinate its trajectory or maneuver. Each vehicle shares a driving intent with the proximity-driving vehicle.

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

Identifier for V2X communication through PC5

Each terminal has a Layer-2 identifier for V2 communication through one or more PC5. This includes a source Layer-2 ID and a destination Layer-2 ID.

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

Source and destination Layer-2 ID selection of the terminal is based on the communication mode of V2X communication of PC5 of the layer-2 link. The source Layer-2 ID may be different between different communication modes.

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

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

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

Broadcast mode

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

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

2. The V2X application layer of the transmitting terminal may provide a data unit, and may provide V2X application requirements (Application Requirements).

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

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

Driving

(1) Vehicle exterior

8 is a diagram illustrating a vehicle according to an embodiment of the present invention.

Referring to FIG. 8 , a vehicle 10 according to an exemplary embodiment of the present invention 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

9 is a control block diagram of a vehicle according to an embodiment of the present invention.

Referring to FIG. 9 , 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 a vehicle and a user. The user interface device 200 may receive a user input and provide information generated in the vehicle 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 an object, location data of the object, distance information between the vehicle 10 and the object, and relative speed information between the vehicle 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 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 data of an object, distance information with respect to the object, or relative speed information with respect to 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 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 as 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 by using the 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 can detect objects around the vehicle. 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. The vehicle may include a plurality of non-driven lidars. 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. 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 220 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, the communication device 220 is based on the IEEE 802.11p PHY / MAC layer technology and the IEEE 1609 Network / Transport layer technology based on DSRC (Dedicated Short Range Communications) technology or WAVE (Wireless Access in Vehicular Environment) standard based on external It can exchange signals with devices. 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 in-vehicle 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 220 of the present invention may exchange a signal with an external device by using only one of the C-V2X technology or the DSRC technology. Alternatively, the communication device 220 of the present invention may exchange a signal with an external device by hybridizing the C-V2X technology and the 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 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.

6) drive control device

The drive control device 250 is a device that electrically controls various vehicle drive devices in the vehicle. 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 pitch 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 change the mode of the vehicle from the autonomous driving mode to the manual driving mode or to switch the vehicle mode from the manual driving mode to the autonomous driving mode based on a signal received from the user interface device 200 . can

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

How to drive an autonomous vehicle

Hereinafter, the driving method of the autonomous vehicle according to the first preferred embodiment of the present specification will be described in detail based on the above contents.

However, the driving method of the autonomous vehicle according to the first embodiment of the present specification may be executed by the subject of the second embodiment to be described later. For reference, in the first embodiment of the present specification, content identical to or overlapping with the second embodiment to be described later may be omitted.

10 is a diagram illustrating a driving method of an autonomous vehicle according to a first embodiment of the present specification.

The driving method of the autonomous vehicle according to the first embodiment of the present specification may be a driving method of the autonomous vehicle in consideration of the movement of the target vehicle.

Referring to FIG. 10 , in the driving method of the autonomous vehicle 100 according to the first embodiment of the present specification, receiving state information of the target vehicle 110 from the target vehicle 110 ( S110 ), It may include a step of generating first prediction information for driving of the target vehicle 110 based on (S120), and a step of corresponding to driving of the target vehicle 110 based on the first prediction information (S130). .

In this case, the state information may include a command value input to the target vehicle 110 through an accelerator pedal or a brake pedal of the target vehicle 110 . The command value may include a command for controlling the acceleration of the target vehicle 110 .

In addition, the state information may include ID information for identifying the target vehicle 110 , location information of the target vehicle 110 , and speed information of the target vehicle 110 . The ID information may be a unique identification number of the target vehicle 110 . The ID information may include information on the type of vehicle, the year of production, the size of the vehicle, and the like.

In addition, the state information may include information sensed by a degree of pressing of the accelerator pedal or the brake pedal of the target vehicle 110 through at least one sensor installed in the vicinity of the accelerator pedal or the brake pedal of the target vehicle 110 . .

Receiving the state information of the target vehicle 110 ( S110 ) may be a step of receiving the state information of the target vehicle 110 from the target vehicle 110 through vehicle to vehicle (V2V) communication.

The first prediction information may include at least one of an expected acceleration, an expected speed, and an expected moving distance of the target vehicle 110 after a predetermined time.

The generating of the first prediction information ( S120 ) may include receiving a lookup table based on the characteristics of the target vehicle 110 from the target vehicle 110 and generating the first prediction information based on the lookup table. .

The lookup table may be a table including an expected acceleration of the target vehicle 110 according to a command value input to the target vehicle 110 through an accelerator pedal or a brake pedal of the target vehicle 110 .

The predicted acceleration may include an expected value for acceleration or deceleration of the target vehicle 110 . That is, the predicted acceleration may include an expected value for the speed change amount of the target vehicle 110 , and the predicted acceleration may be predicted according to an acceleration or braking force according to a lookup table.

The command value may be a command for acceleration or deceleration input to the target vehicle 110 through the accelerator pedal and the brake pedal. In this case, the command value may vary depending on the degree to which the accelerator pedal and the brake pedal are pressed.

Also, when the target vehicle 110 performs autonomous driving, it may be a command for acceleration and deceleration transmitted from a processor provided in the target vehicle 110 rather than the accelerator pedal and brake pedal.

The step S130 corresponding to the driving of the target vehicle 110 may be a step of driving the autonomous driving vehicle 100 so that the distance between the autonomous driving vehicle 100 and the target vehicle 110 is maintained at a predetermined distance. .

11 is a diagram illustrating a driving method of an autonomous vehicle according to a first embodiment of the present specification.

Referring to FIG. 11 , in the driving method of the autonomous vehicle 100 according to the first embodiment of the present specification, the step of selecting the target vehicle 110 ( S210 ), the presence of a prediction module in the selected target vehicle 110 . A step of checking whether or not (S220), if the prediction module exists in the selected target vehicle 110, the step of receiving the state information and the second prediction information from the target vehicle 110 (S230), the received state information Based on the step of generating first prediction information for the driving of the target vehicle 110 ( S240 ), generating a comparison value between the generated first prediction information and the received second prediction information ( S250 ), and the generated comparison When the value is greater than the preset value, the method may include a step ( S260 ) corresponding to the driving of the target vehicle 110 based on the first prediction information. In addition, when the generated comparison value is smaller than the preset value, the method may include a step S262 corresponding to the driving of the target vehicle 110 based on the second prediction information.

As such, the generation of the comparison value is to generate a criterion for determining whether to drive based on the prediction information generated by the target vehicle 110 or to drive based on the prediction information generated by the autonomous vehicle 100 . can When the comparison value is greater than the preset value, it may be interpreted that the prediction information generated by the target vehicle 110 includes an error or that a failure has occurred in the prediction module or sensor of the target vehicle 110 .

Also, generating the comparison value may be generating a difference value between numerical information of the same category among the prediction information generated by the target vehicle 110 and the prediction information generated by the autonomous driving vehicle 100 . The ratio of the difference value and the numerical information included in the prediction information generated by the autonomous driving vehicle 100 may be calculated, and it may be determined whether it is greater or less than a preset value based on the ratio.

For example, among the prediction information generated by the target vehicle 110 , the speed may be 90 km/h. If the speed of the target vehicle 110 among the prediction information generated by the autonomous vehicle 100 is 95 km/h, the two values may have a difference value of 5 km/h. At this time, when the ratio of 5 km/h and 95 km/h, which is the speed of the target vehicle 110 , among the prediction information generated by the autonomous driving vehicle 100 is calculated, 5/95*100 is calculated to be about 5.26%. If the preset value is 5%, it may be determined that the speed among the prediction information generated by the target vehicle 110 is erroneously measured or includes an error. Accordingly, in this case, the autonomous vehicle 100 may respond to the driving of the target vehicle 110 based on the prediction information generated by the autonomous vehicle 100 rather than the prediction information generated by the target vehicle 110 .

In addition, according to FIG. 11 , in the driving method of the autonomous vehicle 100 according to the first embodiment of the present specification, the step of selecting the target vehicle 110 ( S210 ), the prediction module for the selected target vehicle 110 . Step (S220) of confirming the existence of a, if the prediction module does not exist in the selected target vehicle 110, the step of receiving status information from the target vehicle 110 (S231), based on the received status information It may include generating first prediction information for driving of the target vehicle 110 ( S241 ), and corresponding to driving of the target vehicle 110 based on the generated first prediction information ( S251 ). .

In this way, the first prediction information and the second prediction information may be compared, and one of the prediction information may be selected and used as a basis for driving. In general, in the case of the autonomous vehicle 100 , it may include a function for self-checking whether a configuration such as a sensor is faulty. However, since it is not known whether the target vehicle 110 includes a function to check whether a component of a sensor or the like is faulty by itself, if the difference between the two prediction information is greater than a specific value, the autonomy to check whether a fault exists It may be more preferable to drive using the prediction information of the driving vehicle 100 .

12 is a view showing the degree of pressing of the pedal according to the first embodiment of the present specification, and FIG. 13 is a view showing the type of the pedal according to the first embodiment of the present specification.

도를 이룰 수 있다. 또한, 도 12(b)에 따르면, 운전자가 발로 페달을 최대치까지 누른 경우, 페달의 축으로부터 지표면으로 수직한 직선과 페달이 이루는 각도가

도를 이룰 수 있다. According to FIG. 12( a ), when the driver does not press the pedal with his foot, the angle between the pedal and a straight line perpendicular to the ground from the axis of the pedal is

can achieve Also, according to FIG. 12( b ), when the driver presses the pedal to the maximum value with his foot, the angle between the straight line perpendicular to the ground from the axis of the pedal and the pedal is

can achieve

Referring to FIG. 13 , a pedal used in a vehicle may be largely divided into a suspended pedal, an organ-type pedal, and an organ-style pedal. Figure 13 (a) is a suspended pedal, the pedal can move by using the upper end of the pedal as a fixed shaft. 13( b ) is an organ-type pedal in which the pedal can move by using the lower end of the pedal as a fixed shaft. 13(c) is an organ-style pedal, which may have a form in which the characteristics of the suspended pedal and the characteristics of the organ-type pedal are combined.

As described above, FIG. 12 illustrates a suspended pedal in which the pedal moves by using the upper part as a fixed shaft as an example. However, this is only an example and does not limit the scope of the present specification. All of the suspended pedals, organ-type pedals, and organ-style pedals can measure the change in angle formed by a straight line perpendicular to the ground from the fixed axis of the pedal and each pedal. can

14 and 15 are tables showing a lookup table according to the first embodiment of the present specification. In this case, the lookup table may refer to a table indicating acceleration or braking force generated in the target vehicle based on the degree to which the pedal is pressed according to FIG. 12 .

를 n으로 나눈 각도를 단위로 판단될 수 있다. Referring to FIG. 14 , the lookup table according to the first embodiment of the present specification may indicate the acceleration force of the target vehicle 110 based on the angle at which the accelerator pedal is pressed. The acceleration force of the target vehicle 110 is

An angle divided by n may be determined as a unit.

에서 1까지

단위로 증가할 수 있다. As an example, when n is 10, the acceleration force is

from 1 to

unit can be increased.

일 수 있다. 이때,

를 n으로 나누어 가속 페달이 눌려지는 각도를 구간별로 센싱하고, 센싱된 구간별 각도를 기준으로 룩업 테이블은 타겟 차량(110)의 가속력을 나타낼 수 있다. 즉, 가속 페달이 눌려진 각도와 가속력은 선형성을 가질 수 있고, 비례관계를 가질 수 있다. The total angle the accelerator pedal can move is

can be At this time,

By dividing by n, the angle at which the accelerator pedal is pressed is sensed for each section, and the lookup table may indicate the acceleration force of the target vehicle 110 based on the sensed angle for each section. That is, the angle at which the accelerator pedal is pressed and the acceleration force may have linearity and a proportional relationship.

를 n으로 나눈 각도를 단위로 판단될 수 있다. Referring to FIG. 15 , the lookup table according to the first embodiment of the present specification may indicate the acceleration force of the target vehicle 110 based on the angle at which the brake pedal is pressed. The braking force of the target vehicle 110 is

An angle divided by n may be determined as a unit.

에서 1까지

단위로 증가할 수 있다. For example, when n is 10, the braking force is

from 1 to

unit can be increased.

일 수 있다. 이때,

를 n으로 나누어 브레이크 페달이 눌려지는 각도를 구간별로 센싱하고, 센싱된 구간별 각도를 기준으로 룩업 테이블은 타겟 차량(110)의 제동력을 나타낼 수 있다. 즉, 브레이크 페달이 눌려진 각도와 제동력은 선형성을 가질 수 있고, 비례관계를 가질 수 있다. The total angle the brake pedal can move is

can be At this time,

By dividing by n, the angle at which the brake pedal is pressed is sensed for each section, and the lookup table may indicate the braking force of the target vehicle 110 based on the sensed angle for each section. That is, the angle at which the brake pedal is pressed and the braking force may have linearity and a proportional relationship.

However, FIGS. 14 and 15 are examples of the lookup table of the present specification, and the lookup table may be generated in a different way. Accordingly, the scope of the present specification may not be limited to the lookup tables of FIGS. 14 and 15 .

16 and 17 are diagrams illustrating prediction of a motion of a target vehicle according to the first embodiment of the present specification.

16 and 17 , the movement of the target vehicle 110 according to the first embodiment of the present specification may appear based on the acceleration, speed, and movement distance of the target vehicle 110 .

According to FIG. 16( a ), in the acceleration of the target vehicle 110 according to the first embodiment of the present specification, a time delay may occur from when the accelerator pedal of the target vehicle 110 is pressed until reaching the desired acceleration. , this may be referred to as a first delay.

Also, according to FIG. 16B , the speed of the target vehicle 110 according to the first embodiment of the present specification may be a first graph in which the magnitude of the acceleration of the target vehicle 110 is the slope. The speed of the target vehicle 110 may occur after a first delay has elapsed from the time when the accelerator pedal is pressed.

Also, according to FIG. 16C , the moving distance of the target vehicle 110 according to the first embodiment of the present specification may be sensed by a sensor of the autonomous vehicle 100 . When the moving distance of the target vehicle 110 is sensed by the resolution of the sensor of the autonomous vehicle 100 and the first delay, the moving distance of the target vehicle 110 is a second delay from the time when the accelerator pedal is pressed. It may occur after a certain amount of time has elapsed.

Referring to FIG. 17A , the acceleration of the target vehicle 110 after a predetermined time from the time when the accelerator pedal is pressed may be predicted in advance. That is, it is possible to know the acceleration value of the target vehicle after TBD seconds from the time when the accelerator pedal of the target vehicle is pressed. In this way, unlike FIG. 16( a ), the first delay can be eliminated.

Also, according to FIG. 17B , the speed of the target vehicle 110 after a predetermined time from the time the accelerator pedal is pressed may be predicted in advance based on the predicted acceleration. That is, it is possible to know the speed value of the target vehicle after TBD seconds from the time when the accelerator pedal of the target vehicle is pressed.

Also, according to FIG. 17(c) , the moving distance of the target vehicle 110 after a predetermined time from the time when the accelerator pedal is pressed may be predicted in advance based on the predicted speed. That is, the movement distance value of the target vehicle after TBD seconds from the time when the accelerator pedal of the target vehicle is pressed may be known.

Autonomous vehicle and system including same

Hereinafter, the self-driving vehicle according to the second preferred embodiment of the present specification will be described in detail based on the above contents.

However, the autonomous vehicle according to the second embodiment of the present specification may be a subject executing the above-described first embodiment. For reference, in the second embodiment of the present specification, content identical to or overlapping with the above-described first embodiment may be omitted.

18 is a diagram illustrating an autonomous vehicle according to a second embodiment of the present specification.

Referring to FIG. 18 , the autonomous vehicle 100 according to the second embodiment of the present specification may include a processor 101 , a memory 102 , and a communication module 103 .

The processor 101 is a component capable of performing calculations and controlling other devices. Mainly, it may mean a central processing unit (CPU), an application processor (AP), a graphics processing unit (GPU), or the like. In addition, the CPU, AP, or GPU may include one or more cores therein, and the CPU, AP, or GPU may operate using an operating voltage and a clock signal. However, a CPU or AP may consist of a few cores optimized for serial processing, whereas a GPU may consist of thousands of smaller and more efficient cores designed for parallel processing.

The memory 102 may include, but is not limited to, a hard disk drive (HDD), a solid state disk (SSD), a silicon disk drive (SDD), a ROM, a RAM, and the like.

In the case of the communication module 103 , information is transmitted and received with a base station or a vehicle including a communication function through the antenna 104 . The communication module 103 may include a modulator, a demodulator, a signal processor, and the like.

In addition, the communication module 103 may use V2V communication among the above-described V2X communication.

The wireless communication of the communication module 103 may refer to communication using a communication facility installed by communication companies and a wireless communication network using the frequency. At this time, the communication module 103 is CDMA (code division multiple access), FDMA (frequency division multiple access), TDMA (time division multiple access), OFDMA (orthogonal frequency division multiple access), SC-FDMA (single carrier frequency division multiple) access) may be used in various wireless communication systems, and the like, as well as the communication module 103 may be used in 3rd generation partnership project (3GPP) long term evolution (LTE) and the like. In addition, not only 5G communication, which is currently commercialized, but also 6G, which is scheduled to be commercialized in the future, may be used. However, in the present specification, a pre-installed communication network may be utilized without being limited by such a wireless communication method.

The communication module 103 may receive status information from the target vehicle 110 , and the processor 101 generates and generates first prediction information for driving of the target vehicle 110 based on the received status information. It may correspond to the driving of the target vehicle 110 based on the first prediction information. Also, the communication module 103 may receive state information from the target vehicle 110 through vehicle to vehicle (V2V) communication.

The state information may include a command value input to the target vehicle 110 through an accelerator pedal or a brake pedal of the target vehicle 110 . In this case, the command value may include a command for controlling the acceleration of the target vehicle 110 .

The state information may include ID information for identifying the target vehicle 110 , location information of the target vehicle 110 , and speed information of the target vehicle 110 . Also, the state information may include information that senses a degree of pressing of the accelerator pedal or the brake pedal through at least one sensor installed around the accelerator pedal or the brake pedal of the target vehicle 110 .

19 is a diagram illustrating a system including an autonomous vehicle and a target vehicle according to a second embodiment of the present specification.

According to FIG. 19 , the autonomous vehicle 100 may select the target vehicle 110 ( S301 ), and transmit a confirmation request for the existence of the predictive model to the selected target vehicle 110 ( S302 ), When the vehicle 110 does not have a predictive model, the target vehicle 110 may transmit a message for confirming the absence of the predictive model to the autonomous vehicle 100 ( S303 ).

The autonomous vehicle 100 transmits a message requesting state information and a lookup table to the target vehicle 110 (S304), and the target vehicle 110 transmits the state information by pressing the accelerator pedal or the brake pedal. is sensed ( S305 ), and the target vehicle 110 may transmit state information including the sensed accelerator pedal or brake pedal pressure and a lookup table of the target vehicle 110 to the autonomous vehicle 100 ( S306 ). ).

The autonomous vehicle 100 may generate first prediction information based on the received state information and the lookup table ( S307 ), and may correspond to the driving of the target vehicle 110 based on the generated first prediction information ( S307 ). S308).

20 is a diagram illustrating a system including an autonomous vehicle and a target vehicle according to a second embodiment of the present specification.

According to FIG. 20 , the autonomous vehicle 100 may select the target vehicle 110 ( S401 ) and transmit a confirmation request for the existence of the predictive model to the selected target vehicle 110 ( S402 ), When the predictive model exists in the vehicle 110 , the target vehicle 110 may transmit a message confirming the existence of the predictive model to the autonomous vehicle 100 ( S403 ).

The target vehicle 110 may sense the degree of pressing of the accelerator pedal or the brake pedal in order to transmit the state information (S404), and may generate second prediction information based on the sensed degree of pressing (S405).

The autonomous vehicle 100 transmits a message requesting the state information, the lookup table, and the generated second prediction information to the target vehicle 110 ( S406 ), and the target vehicle 110 receives the state information and the lookup information according to the request. The table and the generated second prediction information may be transmitted to the autonomous vehicle 100 (S407).

The autonomous vehicle 100 may generate first prediction information based on the received state information and the lookup table ( S408 ), and generate a comparison value between the generated first prediction information and the received second prediction information. (S409). At this time, according to the generated comparison value, the autonomous driving vehicle 100 may select which information to use among the first prediction information and the second prediction information to drive (S410), and the autonomous vehicle 100 may select the selected prediction information. Based on the driving of the target vehicle 110 may be driven (S411).

21 to 24 are views illustrating an autonomous driving vehicle and a target vehicle according to a second embodiment of the present specification.

Referring to FIG. 21 , the target vehicle 110 may include a position sensor 111 , a driving sensor 112 , a pedal sensor 113 , and a communication module 115 . Hereinafter, the communication module 115 of the target vehicle 110 may have the same or similar configuration as the communication module 103 of the autonomous vehicle.

The position sensor 111 may include at least one of a Global Positioning System (GPS) and a Differential Global Positioning System (DGPS).

The driving sensor 112 is at least one of a direction sensor sensing a driving direction of the target vehicle 110 , a line sensor sensing a rotation amount of a wheel of the target vehicle 110 , and a gyro sensor sensing an acceleration of the target vehicle 110 . may contain one.

The pedal sensor 113 may be a sensor that senses a degree to which a pedal inside the target vehicle 110 is pressed. Since the method of the pedal sensor 113 sensing the degree to which the pedal is pressed is the same as or overlaps with the method described in FIG. 12 and the like, a detailed description thereof will be omitted.

Referring to FIG. 21 , the target vehicle 110 may transmit sensed information to the autonomous vehicle 100 through the communication module 115 . In this case, the communication used for information transmission may be V2V communication.

According to FIG. 21 , the target vehicle 110 may not include the first module 114a or the second module 114b unlike FIGS. 22 to 24 . In this case, the target vehicle 110 may not analyze information sensed by the sensors. In this case, the autonomous vehicle 100 may analyze the driving of the target vehicle 110 based on the sensed information, and may drive according to the driving of the target vehicle 110 according to the analysis result.

Also, according to FIG. 21 , the autonomous vehicle 100 may further include a sensor 104 as well as a processor 101 , a memory 102 , and a communication module 103 . The sensing unit 104 may be configured to sense the target vehicle 110 . The sensing unit 104 may include a camera sensing the target vehicle 110 through an image or an image, a microphone sensing through a sound, and the like.

The processor 101 may include a first prediction module 101a. The first prediction module 101a may be a module for analyzing information sensed from the location sensor 111 and the driving sensor 112 of the target vehicle 110 . In this case, information sensed by the position sensor 111 and the driving sensor 112 may be transmitted to the first prediction module 101a through the communication module 103 of the autonomous vehicle 100 . The first prediction module 101a may generate prediction information on the driving of the target vehicle 110 based on the received information.

For example, the first prediction module 101a may receive real-time location information of the target vehicle 110 from the location sensor 111 of the target vehicle 110 . The first prediction module 101a may indicate the location of the target vehicle 110 as a feature point based on real-time location information of the target vehicle 110 . The first prediction module 101a may predict a change in the position of the target vehicle 110 indicated by the feature point. That is, the first prediction module 101a may predict a direction in which the target vehicle 110 will move forward by connecting at least two feature points with a line and extending the connected line.

For example, the first prediction module 101a may receive real-time driving information of the target vehicle 110 from the driving sensor 112 of the target vehicle 110 . The real-time driving information of the target vehicle 110 may include a driving direction of the target vehicle 110 and a wheel rotation amount of the target vehicle 110 . The first prediction module 101a may represent the real-time driving direction of the target vehicle 110 as a feature line. The first prediction module 101a may predict a change in the driving direction of the target vehicle 110 indicated by the characteristic line. That is, the first prediction module 101a may measure the angular change of at least two feature lines, and may predict the direction in which the target vehicle 110 will travel in the future according to the measured angular change amount.

For example, the first prediction module 101a may receive real-time driving information of the target vehicle 110 from the driving sensor 112 of the target vehicle 110 . The real-time driving information of the target vehicle 110 may include a driving direction of the target vehicle 110 and a wheel rotation amount of the target vehicle 110 . The first prediction module 101a may represent the wheel rotation amount of the target vehicle 110 as a numerical value in real time. The first prediction module 101a may measure a change amount of a wheel rotation amount of the target vehicle 110 . The first prediction module 101a may predict changes in the speed and acceleration of the target vehicle 110 based on the measured changes in the wheel rotation amount.

In addition, the first prediction module 101a of the autonomous vehicle 100 receives information sensed by the speed, acceleration, driving direction, etc. of the target vehicle 110 through the sensing unit 104, and based on the received information. can predict the movement of the target vehicle 110 .

For example, the first prediction module 101a may receive information sensed by the speed and acceleration of the target vehicle 110 from the sensing unit 104 . The first prediction module 101a may numerically represent the speed and acceleration of the target vehicle 110 in real time. The first prediction module 101a may measure an amount of change in speed and an amount of change in acceleration of the target vehicle 110 . The first prediction module 101a may predict the speed of the target vehicle 110 based on the measured speed change amount and the acceleration change amount. The first prediction module 101a may represent the speed and acceleration of the target vehicle 110 as a linear graph. The first prediction module 101a may predict the speed and acceleration of the target vehicle 110 in the future based on the linearity of the speed and acceleration of the target vehicle 110 . The first prediction module 101a may extend the linear graph along the time axis, and may predict the speed and acceleration of the target vehicle 110 based on the extended linear graph.

For example, the first prediction module 101a may receive information sensing the driving direction of the target vehicle 110 from the sensing unit 104 . The first prediction module 101a may indicate the driving direction of the target vehicle 110 in real time. The first prediction module 101a may measure the measured change in the driving direction of the target vehicle 110 . The first prediction module 101a may assign a feature point to the center of the target vehicle 110 in the image or image of the target vehicle 110 captured by the sensing unit 104 . The first prediction module 101a assigns feature points to each frame of the image, and connects the assigned feature points to know the driving direction of the target vehicle 110 . The first prediction module 101a may know the driving direction of the target vehicle 110 based on the inclination of the straight line to which the feature points are connected. The first prediction module 101a may measure the amount of change in the slope of the straight line to which the feature points are connected. The first prediction module 101a may predict the driving direction of the target vehicle 110 based on this.

The processor 101 may include a second prediction module 101b. The second prediction module 101b may be a module for analyzing information sensed by the pedal sensor 113 of the target vehicle 110 . In this case, information sensed by the pedal sensor 113 may be transmitted to the second prediction module 101b through the communication module 103 of the autonomous vehicle 100 .

The second prediction module 101b may use the lookup table received from the target vehicle 110 . The second prediction module 101b may predict the acceleration and braking force of the target vehicle 110 according to the degree of pressing the pedal, based on the lookup table received from the target vehicle 110 . Also, a function performed by the second prediction module 101b may be performed by the first prediction module 101a.

22 , the target vehicle 110 may further include a processor 114 . The processor 114 may include a first module 114a. The first module 114a may be a module for analyzing information sensed from the location sensor 111 and the driving sensor 112 of the target vehicle 110 .

The first module 114a may be a module that performs dead reckoning. Dead reckoning may refer to a location tracking technology that tracks the current location by calculating the travel distance and direction from a known location to the current location. That is, the current location of the target vehicle 110 may be tracked using an odometer and a gyro sensor included in the target vehicle 110 . That is, when the position sensor 111 included in the target vehicle 110 does not operate, the first module 114a may perform dead reckoning to track the current position.

Also, the first module 114a may include a Kalman filter. The Kalman filter may refer to a cyclic filter applicable to all linear systems including time-varying, nonstationary, and multi-channel systems. Since the movement of the target vehicle 110 follows a linear system, it can be predicted by applying the Kalman filter.

As such, the first module 114a may predict the location, speed, acceleration, etc. of the target vehicle 110 based on information obtained from the location sensor 111 or the driving sensor 112 of the target vehicle 110 . For the corresponding prediction, the first module 114a may perform dead reckoning or include a Kalman filter.

The position, speed, acceleration, etc. of the target vehicle 110 predicted by the first module 114a may be transmitted to the autonomous vehicle 100 through the communication module 115 . The transmitted predicted position, velocity, acceleration, etc. may be transmitted to the processor 101 of the autonomous vehicle 100 .

The processor 101 of the autonomous vehicle 100 determines the position, speed, and acceleration predicted by the first module 114a of the target vehicle 110 , etc. of the target vehicle 110 predicted by the first prediction module 101a. It is possible to generate a comparison value that compares movement and the like. When the comparison value generated by the processor 101 is greater than the specific value, the processor 101 of the autonomous driving vehicle 100 may be configured to use the first module 114a of the target vehicle 110 or the position sensor of the target vehicle 110 . (111), it is possible to derive a result that the travel sensor 112 is broken.

At this time, the method for the processor 101 to generate the comparison value is a numerical value for the position, speed, acceleration, etc. predicted by the first module 114a, and the position sensed or predicted by the first prediction module 101a, It may be a method of calculating a difference value by comparing numerical values for speed, acceleration, etc. with each other. That is, a difference value between numerical values of the same category may be calculated.

23 , the processor 114 of the target vehicle 110 may include a second module 114b. The second module 114b may be a module for analyzing information sensed from the pedal sensor 113 of the target vehicle 110 . As such, in order for the second module 114b to analyze information sensed from the pedal sensor 113 of the target vehicle 110 , the second module 114b may include a lookup table.

The lookup table included in the second module 114b may be the lookup table according to FIGS. 14 and 15 described above. The second module 114b receives information sensing the angle at which the pedal is pressed from the pedal sensor 113 , and substitutes the received information into lookup tables to generate second prediction information about the driving of the target vehicle 110 . can do.

Also, the second module 114b may continuously update the lookup table included in the second module 114b. That is, when the target vehicle 110 is continuously used by the driver, the braking force applied to the target vehicle 110 may be reduced even if the brake pedal is pressed to the same extent as the brake is worn. This may also be the case for the accelerator pedal.

Accordingly, the second module 114b may continuously sense the acceleration or braking force generated in the target vehicle 110 according to the degree to which the accelerator pedal or the brake pedal is pressed. That is, the acceleration or braking force generated in the target vehicle 110 may be continuously sensed according to the degree to which the accelerator pedal or the brake pedal is pressed, and this may be reflected in the lookup table. The lookup table may be continuously updated by the second module 114b. Through continuous updating, the lookup table may accurately reflect the degree of acceleration and the degree of braking of each target vehicle 110 .

The acceleration or braking force measured by the second module 114b may be transmitted to the autonomous vehicle 100 through the communication module 115 . The transmitted acceleration or braking force may be transmitted to the processor 101 of the autonomous vehicle 100 .

The processor 101 of the autonomous vehicle 100 is configured such as the acceleration or braking force measured by the second module 114b of the target vehicle 110 and the movement of the target vehicle 110 predicted by the second prediction module 101b. You can create a comparison value that compares . When the comparison value generated by the processor 101 is greater than the specific value, the processor 101 of the autonomous driving vehicle 100 may be configured to include the second module 114b of the target vehicle 110 or the pedal sensor of the target vehicle 110 . (113) can be deduced as a result of failure.

24 , the processor 114 of the target vehicle 110 may include a first module 114a and a second module 114b. Since the first module 114a and the second module 114b are the same as the first module 114a and the second module 114b according to FIGS. 22 and 23 described above, a detailed description thereof will be omitted.

25 is a diagram illustrating a driving path predicted by an autonomous vehicle sensing a target vehicle according to a second embodiment of the present specification.

Referring to FIG. 25 , a driving path in which the sensing unit 104 of the autonomous vehicle 100 senses a target vehicle and predicts its movement is shown. Also, according to FIG. 25 , a driving path in which the driving path predicted by the target vehicle 110 is transmitted to the autonomous vehicle 100 through V2V communication is shown.

According to FIG. 25 , it is confirmed that a lot of noise is formed in the travel route received from the target vehicle 110 . As such, when noise of a predetermined size and a predetermined frequency or more exists in the driving path received from the target vehicle 110 , the autonomous vehicle 100 may include a position sensor, a driving sensor, and a pedal sensor included in the target vehicle 110 . It can be determined that at least one of them is faulty or that an error has occurred.

It will be apparent to those skilled in the art that the above contents described in detail according to the second embodiment of the present specification can be applied to the first embodiment of the present specification as well.

Hereinafter, additional examples of the autonomous vehicle applied in the present specification are as follows.

The autonomous vehicle according to the second embodiment of the present specification may include a communication module, a memory, and a processor for controlling the communication module and the memory.

Example 1: The processor is Based on the first prediction information, it may correspond to the driving of the target vehicle in order to maintain a predetermined distance from the target vehicle.

Second example: The communication module may receive second prediction information generated based on the state information in the target vehicle.

Third Example: In the second example, the processor generates a comparison value of the first prediction information and the second prediction information, and when the comparison value is greater than a preset value, based on the first prediction information It may correspond to the driving of the target vehicle.

Fourth Example: In the second example, the processor generates a comparison value of the first prediction information and the second prediction information, and when the comparison value is smaller than a preset value, based on the second prediction information It may correspond to the driving of the target vehicle.

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 equivalent scope of this specification are included in the scope of this specification.

In addition, although the embodiments have been mainly described above, these are merely examples and are not intended to limit the present specification, and those of ordinary skill in the art to which this specification belongs 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 can 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.

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

100: autonomous vehicle 101: processor
101a: first prediction module 101b: second prediction module
102: memory 103: communication module
104: sensing unit 110: target vehicle
111: position sensor 112: travel sensor
113: pedal sensor 114: processor
114a: first module 114b: second module
115: communication module

Claims (20)

In the driving method of an autonomous vehicle in consideration of the movement of the target vehicle,
receiving state information of the target vehicle from the target vehicle;
generating first prediction information for driving of the target vehicle based on the state information; and
Including; based on the first prediction information, corresponding to the driving of the target vehicle;
The status information is
and a command value input to the target vehicle through an accelerator pedal or a brake pedal of the target vehicle.
According to claim 1,
The status information is
The method of claim 1, further comprising ID information for identifying the target vehicle, location information of the target vehicle, and speed information of the target vehicle.
According to claim 1,
The status information is
The driving method of an autonomous vehicle comprising information sensed by a degree of pressing of the accelerator pedal or the brake pedal through at least one sensor installed around the accelerator pedal or the brake pedal.
According to claim 1,
The command value is
and a command for controlling the acceleration of the target vehicle.
According to claim 1,
Receiving the status information comprises:
The driving method of the autonomous vehicle, which receives the state information from the target vehicle through V2V (Vehicle to Vehicle) communication.
According to claim 1,
The first prediction information is
The driving method of the autonomous vehicle, which includes at least one of an expected acceleration, an expected speed, and an expected moving distance of the target vehicle after a predetermined time.
According to claim 1,
The step of generating the first prediction information for the driving of the target vehicle,
receiving a lookup table based on the characteristics of the target vehicle from the target vehicle, and generating the first prediction information based on the lookup table.
8. The method of claim 7,
The lookup table is
The driving method of the autonomous vehicle, which is a table including the expected acceleration of the target vehicle according to the command value.
According to claim 1,
The step corresponding to the driving of the target vehicle,
The driving method of the autonomous vehicle, which is driven to maintain a predetermined distance from the target vehicle.
According to claim 1,
The method of claim 1, further comprising receiving second prediction information generated based on the state information in the target vehicle.
11. The method of claim 10,
generating a comparison value between the first prediction information and the second prediction information; and
When the comparison value is greater than a preset value, corresponding to the driving of the target vehicle based on the first prediction information; the driving method of the autonomous vehicle further comprising a.
11. The method of claim 10,
generating a comparison value between the first prediction information and the second prediction information; and
When the comparison value is smaller than a preset value, corresponding to the driving of the target vehicle based on the second prediction information; the driving method of the autonomous vehicle further comprising a.
In an autonomous vehicle that drives in consideration of the movement of a target vehicle,
communication module;
Memory; and
Including; a processor for controlling the communication module and the memory;
The communication module is
receiving status information from the target vehicle;
The processor is
generate first prediction information for driving of the target vehicle based on the state information, and respond to driving of the target vehicle based on the first prediction information;
The status information is
and a command value input to the target vehicle through an accelerator pedal or a brake pedal of the target vehicle.
14. The method of claim 13,
The status information is
ID information for identifying the target vehicle, location information of the target vehicle, and speed information of the target vehicle, the autonomous driving vehicle.
14. The method of claim 13,
The status information is
The self-driving vehicle comprising information sensed by a degree of pressing of the accelerator pedal or the brake pedal through at least one sensor installed around the accelerator pedal or the brake pedal of the target vehicle.
14. The method of claim 13,
The command value is
and a command for controlling the acceleration of the target vehicle.
14. The method of claim 13,
The communication module is
The autonomous vehicle that receives the state information from the target vehicle through V2V (Vehicle to Vehicle) communication.
14. The method of claim 13,
The first prediction information is
The self-driving vehicle, which includes at least one of an expected acceleration, an expected speed, and an expected moving distance of the target vehicle after a predetermined time.
14. The method of claim 13,
The processor is
receiving a lookup table based on the characteristics of the target vehicle from the target vehicle, and generating the first prediction information based on the lookup table.
20. The method of claim 19,
The lookup table is
and a table including the expected acceleration of the target vehicle according to the command value.

Images