KR20190106844A - Autonomous driving control method ACCORDING TO INPUT SIGNAL PRIORiTY and autonomous driving system using the same - Google Patents

Abstract

Disclosed are a method of controlling autonomous driving in accordance with input signal priority and an autonomous driving system using the same. The method of controlling autonomous driving in accordance with input signal priority includes the following steps of: determining processing priority and the total number of emergency processing sensors based on information about an autonomous driving situation when residual time to a collision with a neighboring object is no more than a first threshold value; transmitting sensor data of the emergency processing sensors through a high-speed bus; adjusting a sampling rate for at least one of the emergency processing sensors; determining processing priority and the total number of emergency processing sensors additionally considering traffic accident statistics data; and increasing sampling rates of sensors determined as the emergency processing sensors while lowering sampling rates of the other sensors considering the processing capability of a processor generating information about the autonomous driving situation by processing the sensor data. According to the present invention, at least one among an autonomous vehicle, a user terminal and a server can be connected with an artificial intelligence module, drone (unmanned aerial vehicle: UAV), a robot, an augmented reality (AR) device, a virtual reality (VR) device and devices related with 5G services.

Description

Autonomous driving control method according to input signal priority and autonomous driving system using same {Autonomous driving control method ACCORDING TO INPUT SIGNAL PRIORiTY and autonomous driving system using the same}

The present invention relates to an autonomous driving system and a control method thereof, and more particularly, to an autonomous driving control method corresponding to an emergency situation and an autonomous driving system using the same according to a processing sequence of an input signal.

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

An autonomous vehicle refers to a car that can drive itself without a driver or passenger. An autonomous driving system refers to a system that monitors and controls such an autonomous vehicle to be driven by itself.

Unexpected emergencies are inevitable in the operation of the vehicle, and to ensure a very high level of safety, autonomous vehicles process a large number of sensors to recognize the surrounding situation and the data they output in real time. Adopt a high computing power processor that can.

Depending on the type of sensor, the amount of data to be sent varies, so processing time is different. For example, the camera has the largest amount of data to transmit, so other data may be delayed while the camera processes the data.

In this way, no matter how excellent the computing power of the processor, it is difficult to process a large amount of data sent by several sensors in real time at the same time, the autonomous vehicle needs to determine the processing order of the input signal according to the situation.

However, there is a lack of a method for efficiently processing data input from various sensors to cope with emergency situations safely.

Accordingly, the present invention aims at solving the above-mentioned necessity and / or problems in view of such a situation.

An object of the present invention is to provide a method for determining the processing priority and the number of priorities of sensor input data in an emergency and an autonomous driving system using the same.

The autonomous driving control method according to the input signal priority according to an embodiment of the present invention, the emergency processing based on the information on the autonomous driving situation of the own vehicle when the remaining time until the collision with the surrounding objects is less than the first threshold value Determining a total number of processing sensors and a processing priority; Transmitting sensor data of an emergency sensor through a high speed bus; And adjusting a sampling rate for at least one of the sensors for emergency treatment.

An autonomous driving control system according to an embodiment of the present invention includes a sensing system including two or more sensors for outputting sensor data; A main processor which generates information on an autonomous driving situation of the own vehicle based on the sensor data; A preprocessor that receives sensor data from the sensing system and transmits the sensor data to the main processor and determines the total number and priority of the sensors for emergency treatment based on the information on the autonomous driving situation fed back by the main processor; And a controller that generates a vehicle control signal suitable for an autonomous driving situation and adjusts a sampling rate for at least one of the sensors for emergency treatment.

According to the present invention, by determining the priority of sensor data to be input according to the current driving situation and determining the maximum number of sensor inputs to be processed first, it is possible to safely and effectively cope with an emergency even with limited computing ability.

In addition, by determining the priority and number of sensor data processing by reflecting traffic accident statistics data, it is possible to quickly recognize the cause of the risk and to quickly cope with the recognized cause.

In addition, by changing the sampling rate of the sensor signal in accordance with the priority, it is possible to overcome the limitation of the computing power of the processor.

In addition, by providing a monitoring mechanism for sensors that are deactivated or operate at low sampling rates, they can respond effectively to unexpected emergencies.

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

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, included as part of the detailed description in order to provide a thorough understanding of the present invention, provide embodiments of the present invention and together with the description, describe the technical features of the present invention.
1 illustrates a block diagram of a wireless communication system to which the methods proposed herein may be applied.
2 illustrates an example of a signal transmission / reception method in a wireless communication system.
3 illustrates an example of basic operations of a user terminal and a 5G network in a 5G communication system.
4 illustrates an example of a basic operation between a vehicle and a vehicle using 5G communication.
5 is a view showing a vehicle according to an embodiment of the present invention.
6 is a control block diagram of a vehicle according to an embodiment of the present invention.
7 is a control block diagram of an autonomous vehicle according to an embodiment of the present invention.
8 is a signal flowchart of an autonomous vehicle according to an embodiment of the present invention.
9 is a diagram illustrating an example of a V2X application.
10 is a diagram illustrating a resource allocation method in V2X sidelink.
11 is a diagram illustrating a connection between an autonomous driving device and a sensing system according to an exemplary embodiment of the present invention.
12 illustrates an example of traffic accident statistics data.
13 is a flowchart illustrating a method of determining the number and priority of sensors requiring emergency transmission according to an embodiment of the present invention.
14 is a flowchart illustrating a method of setting a sampling rate of a sensor according to an embodiment of the present invention.
15 is a flowchart illustrating a method of processing an event when a sensor is deactivated according to an embodiment of the present invention.
16A and 16B illustrate a scenario in which an autonomous vehicle according to an embodiment of the present invention copes with a situation in which a collision with a pedestrian in front is imminent.
FIG. 17 is a diagram illustrating a scenario in which an autonomous vehicle according to an embodiment of the present invention copes with a lane change.

DETAILED DESCRIPTION Hereinafter, exemplary embodiments disclosed herein will be described in detail with reference to the accompanying drawings, and the same or similar components will be given the same reference numerals regardless of the reference numerals, and redundant description thereof will be omitted. The suffixes "module" and "unit" for components used in the following description are given or mixed in consideration of ease of specification, and do not have distinct meanings or roles from each other. In addition, in describing the embodiments disclosed herein, when it is determined that the detailed description of the related known technology may obscure the gist of the embodiments disclosed herein, the detailed description thereof will be omitted. In addition, the accompanying drawings are only for easily understanding the embodiments disclosed herein, the technical spirit disclosed in the specification by the accompanying drawings are not limited, and all changes included in the spirit and scope of the present invention. It should be understood to include equivalents and substitutes.

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

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

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

In this application, the terms "comprises" or "having" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, and one or more other features. It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof.

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

A. UE  And 5G network block diagram example

1 illustrates a block diagram of a wireless communication system to which the methods proposed herein may 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 of FIG. 1), and the processor 911 may perform an autonomous driving detailed operation.

A 5G network including another vehicle communicating with the autonomous driving device is defined as the second communication device (920 of FIG. 1), and the processor 921 may perform the autonomous driving detailed operation.

The 5G network may be represented as the first communication device and the autonomous driving device 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, the terminal or user equipment (UE) may be a vehicle, a mobile phone, a smart phone, a laptop computer, a digital broadcasting terminal, personal digital assistants, a portable multimedia player (PMP). , Navigation, slate PC, tablet PC, ultrabook, wearable device (e.g., smartwatch, smart glass, HMD ( head mounted display)). For example, the HMD may be a display device worn on the head. For example, the HMD can be used to implement VR, AR or MR. Referring to FIG. 1, the first communication device 910 and the second communication device 920 may include a processor (911, 921), a memory (914,924), and one or more Tx / Rx RF modules (radio frequency module, 915,925). , Tx processors 912 and 922, Rx processors 913 and 923, and antennas 916 and 926. Tx / Rx modules are also known as transceivers. Each Tx / Rx module 915 transmits a signal through each antenna 926. The processor implements the salping functions, processes and / or methods above. The processor 921 may be associated with a memory 924 that stores program code and data. The memory may be referred to as a computer readable medium. More specifically, in the DL (communication from the first communication device to the second communication device), the transmit (TX) processor 912 implements various signal processing functions for the L1 layer (ie, the physical layer). The receive (RX) processor implements various signal processing functions of L1 (ie, the physical layer).

The UL (communication from the second communication device to the first communication device) is processed at the first communication device 910 in a manner similar to that described with respect to the receiver function at 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. The memory may be referred to as a computer readable medium.

B. wireless  Signal transmission / reception method in communication system

2 illustrates an example of a signal transmission / reception method in a wireless communication system.

Referring to FIG. 2, when the UE is powered on or enters a new cell, the UE performs an initial cell search operation such as synchronizing with the BS (S201). To this end, the UE receives a primary synchronization channel (P-SCH) and a secondary synchronization channel (S-SCH) from the BS to synchronize with the BS, and obtains information such as a 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 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 an initial cell search step. After the initial cell discovery, the UE acquires more specific system information by receiving a physical downlink shared channel (PDSCH) according to a physical downlink control channel (PDCCH) and information on the PDCCH. It may be (S202).

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

After performing the above-described process, the UE then transmits a PDCCH / PDSCH (S207) and a physical uplink shared channel (PUSCH) / physical uplink control channel (physical) as a general uplink / downlink signal transmission process. Uplink control channel (PUCCH) transmission may be performed (S208). In particular, the UE receives downlink control information (DCI) through the PDCCH. The UE monitors the set of PDCCH candidates at the monitoring opportunities established in one or more control element sets (CORESETs) on the serving cell according to the corresponding search space configurations. The set of PDCCH candidates to be monitored by the UE is defined in terms of search space sets, 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 set the UE to have a plurality of CORESETs. The UE monitors PDCCH candidates in one or more search space sets. Here, monitoring means attempting to decode the 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 detected DCI in the PDCCH. The PDCCH may be used to schedule DL transmissions on the PDSCH and UL transmissions on the PUSCH. Wherein the DCI on the PDCCH is a downlink assignment (i.e., downlink grant; DL grant), or uplink, which includes at least modulation and coding format and resource allocation information associated with the downlink shared channel. An uplink grant (UL grant) containing modulation and coding format and resource allocation information associated with the shared channel.

Referring to FIG. 2, the initial access (IA) procedure in the 5G communication system will be further described.

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

SSB is composed of PSS, SSS and PBCH. The SSB is composed of four consecutive OFDM symbols, and PSS, PBCH, SSS / PBCH, or PBCH is 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.

The cell discovery refers to a process in which the UE acquires time / frequency synchronization of a cell and detects a cell ID (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 three cell IDs exist for each cell ID group. There are a total of 1008 cell IDs. Information about a cell ID group to which a cell ID of a cell belongs is provided / obtained through the SSS of the cell, and information about the cell ID among the 336 cells in the cell ID is provided / obtained through the PSS.

SSB is transmitted periodically in accordance with SSB period (periodicity). The SSB basic period assumed by the UE at the initial cell search is defined as 20 ms. After the cell connection, 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.

SI is divided into a master information block (MIB) and a plurality of system information blocks (SIB). SI other than the MIB may be referred to as Remaining Minimum System Information (RSI). The MIB includes information / parameters for monitoring the PDCCH scheduling the PDSCH carrying SIB1 (SystemInformationBlock1) and is transmitted by the BS through the PBCH of the SSB. SIB1 includes information related to the availability and scheduling (eg, transmission period, SI-window size) of the remaining SIBs (hereinafter, SIBx, 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, the random access (RA) process in the 5G communication system will be further described.

The random access procedure is used for various purposes. For example, the random access procedure may be used for network initial access, handover, UE-triggered UL data transmission. The UE may acquire UL synchronization and UL transmission resource 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 procedure is as follows.

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

When the BS receives a 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 transmitted by the UE, that is, Msg1, is in the RAR. Whether there is random access information for the Msg1 transmitted by the UE may be determined by whether there is a random access preamble ID for the preamble transmitted by the UE. If there is no response to Msg1, the UE may retransmit the RACH preamble within a predetermined number of times while performing power ramping. The UE calculates the PRACH transmit power for retransmission of the preamble based on the most recent path loss and power ramp counter.

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

C. 5G  Beam Management (BM) Procedures for Communication Systems

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

We will look at the DL BM process using SSB.

The beam report setting using the SSB is performed at the channel state information (CSI) / beam setting in RRC_CONNECTED.

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

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

If the CSI-RS reportConfig related to reporting on the SSBRI and reference signal received power (RSRP) is configured, the UE reports the best SSBRI and the corresponding RSRP to the BS. For example, when 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.

When the CSI-RS resource is configured in the same OFDM symbol (s) as the SSB, and the 'QCL-TypeD' is applicable, the UE is similarly co-located in terms of the 'QCL-TypeD' with the CSI-RS and the SSB ( quasi co-located (QCL). In this case, QCL-TypeD may mean that QCLs are interposed between antenna ports in terms of spatial Rx parameters. The UE may apply the same reception beam when receiving signals of a plurality of DL antenna ports in a QCL-TypeD relationship.

Next, look at the DL BM process using the CSI-RS.

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 order. In the Rx beam determination process of the UE, the repetition parameter is set to 'ON', and in the Tx beam sweeping process of the BS, the repetition parameter is set to 'OFF'.

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

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

The UE repeats signals on 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 transport filter) of the BS Receive.

The UE determines its Rx beam.

UE skips CSI reporting. That is, when the mall RRC parameter 'repetition' is set to 'ON', the UE may omit CSI reporting.

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

-The UE receives an NZP CSI-RS resource set IE including an RRC parameter regarding '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 transport 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 its RSRP to the BS.

Next, look at the UL BM process using the SRS.

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

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, SRS-SpatialRelation Info is set for each SRS resource and indicates whether to apply the same beamforming used for 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 set in the SRS resource, the UE transmits the SRS through the Tx beamforming determined by arbitrarily determining the Tx beamforming.

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

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

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

URLLC transmissions defined by NR include (1) relatively low traffic size, (2) relatively low arrival rate, (3) extremely low latency requirements (e.g., 0.5, 1 ms), (4) relatively short transmission duration (eg, 2 OFDM symbols), and (5) urgent service / message transmission. For UL, transmissions for certain types of traffic (eg URLLC) must be multiplexed with other previously scheduled transmissions (eg eMBB) to meet stringent latency requirements. Needs to be. In this regard, as one method, it informs the previously scheduled UE that it will be preemulated for a specific resource, and allows the URLLC UE to use the UL resource for the 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 UE has been partially punctured, and the UE may not be able to decode the PDSCH due to corrupted coded bits. In view of this, NR provides a preemption indication. The preemption indication may be referred to as an interrupted transmission indication.

In connection with the preemption indication, the UE receives the Downlink Preemption IE via RRC signaling from the BS. If the UE is provided with a DownlinkPreemption IE, the UE is set with the INT-RNTI provided by the parameter int-RNTI in the DownlinkPreemption IE for monitoring of the PDCCH that carries DCI format 2_1. The UE is additionally set with the set of serving cells by INT-ConfigurationPerServing Cell including the set of serving cell indices provided by servingCellID and the corresponding set of positions for fields in DCI format 2_1 by positionInDCI, dci-PayloadSize Is configured with the information payload size for DCI format 2_1, and is set with the indication granularity of time-frequency resources by timeFrequencySect.

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

If the UE detects a DCI format 2_1 for a serving cell in a set of serving cells, the UE selects the DCI format of the set of PRBs and the set of symbols of the last monitoring period of 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 a DL transmission scheduled to it and decodes the data based on the signals received in the remaining resource region.

E. mMTC  (massive MTC )

Massive Machine Type Communication (mMTC) is one of the 5G scenarios for supporting hyperconnected services that communicate with a large number of UEs simultaneously. In this environment, the UE communicates intermittently with very low transmission speed and mobility. Therefore, mMTC aims to be able to run the UE for a long time at low cost. Regarding the mMTC technology, 3GPP deals with MTC and Narrow Band (IB) -IoT.

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

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

F. 5G  Basic operation between autonomous vehicles using communication

3 illustrates 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. The 5G network may determine whether to remotely control the vehicle (S2). Here, the 5G network may include a server or a module for performing autonomous driving-related remote control. In addition, the 5G network may transmit information (or a signal) related to a remote control to the autonomous vehicle (S3).

G. 5G  Application Behavior Between Autonomous Vehicles and 5G Networks in Communication Systems

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

First, a basic procedure of an application operation to which the method proposed by the present invention to be described below and 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, information, and the like with the 5G network, the autonomous vehicle has an initial access procedure with the 5G network before step S1 of FIG. 3. And 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. In the initial access procedure, a beam management (BM) process and a beam failure recovery process may be added, and in the process of receiving a signal from a 5G network by an autonomous vehicle, a quasi-co location ) Relationships can be added.

In addition, autonomous vehicles perform random access procedures with 5G networks for UL synchronization acquisition and / or UL transmission. 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. The 5G network transmits a DL grant to the autonomous vehicle to schedule transmission of a 5G processing result for the specific information. Accordingly, the 5G network may transmit information (or a signal) related to remote control to the autonomous vehicle based on the DL grant.

Next, a basic procedure of an application operation to which the method proposed by the present invention to be described below and the URLLC technology of 5G communication are applied will be described.

As described above, after the autonomous vehicle performs an initial access procedure and / or random access procedure with the 5G network, the autonomous vehicle may receive a Downlink Preemption IE from the 5G network. The autonomous vehicle receives DCI format 2_1 from the 5G network that includes a pre-emption indication based on the Downlink Preemption IE. In addition, the autonomous vehicle does not perform (or expect or assume) reception of eMBB data in resources (PRB and / or OFDM symbols) indicated by a 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, a basic procedure of an application operation to which the method proposed by the present invention to be described below and the mMTC technology of 5G communication is applied will be described.

Of the steps of Figure 3 will be described in terms of parts that vary with the application of the mMTC technology.

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

H. 5G  Autonomous Driving Between Vehicles Using Communication

4 illustrates an example of a basic operation between a vehicle and a vehicle 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) resource allocation of the specific information, the response to the specific information of the vehicle-to-vehicle application operation The configuration may vary.

Next, the application operation between the vehicle using the 5G communication will be described.

First, a method in which a 5G network is directly involved in resource allocation of signal transmission / reception between vehicles is described.

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

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

The first vehicle senses the resource for mode 4 transmission in the first window. 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 on the PSCCH to the second vehicle for scheduling of specific information transmission based on the selected resource. The first vehicle transmits specific information to the second vehicle on the PSSCH.

Salping 5G communication technology may be applied in combination with the methods proposed in the present invention to be described later, or may be supplemented to specify or clarify the technical features of the methods proposed in the present invention.

Driving

(1) vehicle exterior

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

Referring to FIG. 5, a vehicle 10 according to an embodiment of the present invention is defined as a transportation means for traveling on a road or a track. The vehicle 10 is a concept including a car, a train and a motorcycle. The vehicle 10 may be a concept including both an internal combustion engine vehicle 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) the components of the vehicle

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

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

1) user interface device

The user interface device 200 is a device for communicating with the vehicle 10 and the user. The user interface device 200 may receive a user input and provide the user with information generated by the vehicle 10. 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 detecting 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 whether an object exists, location information of the object, distance information between the vehicle 10 and the object, and relative speed information between the vehicle 10 and the object. . The object detecting apparatus 210 may detect an object outside the vehicle 10. The object detecting apparatus 210 may include at least one sensor capable of detecting an object outside the vehicle 10. The object detecting apparatus 210 may include at least one of a camera, a radar, a lidar, an ultrasonic sensor, and an infrared sensor. The object detecting apparatus 210 may provide data on the object generated based on the sensing signal generated by the sensor to at least one electronic device included in the vehicle.

2.1) camera

The camera may generate information about an object outside the vehicle 10 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 generates data about an object based on the processed signal.

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

The camera may be mounted at a position capable of securing a field of view (FOV) in the vehicle to photograph the outside of the vehicle. The camera may be disposed in close proximity to the front windshield, in the interior of the vehicle, to obtain an image in front of the vehicle. The camera may be disposed around the front bumper or radiator grille. The camera may be disposed in close proximity to the rear glass in the interior of the vehicle to obtain an image of the rear of the vehicle. The camera may be disposed around the rear bumper, trunk or tail gate. The camera may be disposed in close proximity to at least one of the side windows in the interior of the vehicle to acquire an image of the vehicle side. Alternatively, the camera may be arranged around a side mirror, fender or door.

2.2) Radar

The radar may generate information about an object outside the vehicle 10 by using radio waves. The radar may include at least one processor electrically connected to the electromagnetic wave transmitter, the electromagnetic wave receiver, and the electromagnetic wave transmitter and the electromagnetic wave receiver to process the received signal and generate data for the 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 radio wave firing principle. The radar may be implemented in a frequency modulated continuous wave (FMCW) method or a frequency shift keyong (FSK) method according to a signal waveform among continuous wave radar methods. The radar detects an object based on a time of flight (TOF) method or a phase-shift method based on electromagnetic waves, and detects a position of the detected object, a distance from the detected object, and a relative speed. Can be. 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 rider may generate information about an object outside the vehicle 10 using the laser light. The lidar may include at least one processor electrically connected to the optical transmitter, the optical receiver and the optical transmitter, and the optical receiver to process the received signal and generate data for the object based on the processed signal. . The rider may be implemented in a time of flight (TOF) method or a phase-shift method. The lidar may be implemented driven or non-driven. When implemented in a driven manner, the lidar may be rotated by a motor and detect an object around the vehicle 10. When implemented in a non-driven manner, the lidar may detect an object located within a predetermined range with respect to the vehicle by the optical steering. The vehicle 10 may include a plurality of non-driven lidars. The lidar detects an object based on a time of flight (TOF) method or a phase-shift method using laser light, and detects the position of the detected object, the distance to the detected object, and the relative velocity. Can be detected. The rider may be placed at a suitable location outside of the vehicle to detect objects located in front, rear or side of the vehicle.

3) communication device

The communication device 220 may exchange signals with a device located outside the vehicle 10. The communication device 220 may exchange signals with at least one of an infrastructure (for example, a server and 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 device may exchange signals with an external device based on Cellular V2X (C-V2X) technology. For example, C-V2X technology may include LTE based sidelink communication and / or NR based sidelink communication. Details related to the C-V2X will be described later.

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

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

4) driving operation device

The driving manipulation apparatus 230 is a device that receives a user input for driving. In the manual mode, the vehicle 10 may be driven based on a signal provided by the driving manipulation apparatus 230. The driving manipulation apparatus 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 overall operations of at least one electronic device included in the vehicle 10.

6) drive control device

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

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

The driving control device 250 may control the vehicle driving device based on the signal received by the autonomous driving device 260. For example, the drive control device 250 may control the power train, the steering device, and the brake device based on the 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 route. The autonomous driving device 260 may generate a signal for controlling the movement of the vehicle according to the driving plan. The autonomous driving device 260 may provide the generated signal to the driving control device 250.

The autonomous driving device 260 may implement at least one ADAS (Advanced Driver Assistance System) function. ADAS includes Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), Foward Collision Warning (FCW), Lane Keeping Assist (LKA) ), Lane Change Assist (LCA), Target Following Assist (TFA), Blind Spot Detection (BSD), Adaptive High Beam Assist (HBA) , Auto Parking System (APS), Pedestrian Collision Warning System (PD Collision Warning System), Traffic Sign Recognition System (TSR), Trafffic Sign Assist (TSA), Night Vision System At least one of (NV: Night Vision), Driver Status Monitoring System (DSM), and 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 switches the mode of the vehicle 10 from the autonomous driving mode to the manual driving mode or from the manual driving mode based on the signal received from the user interface device 200. You can switch to

8) Sensing Part

The sensing unit 270 may sense a 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, a vehicle, and a vehicle. 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 may be included. Meanwhile, the inertial measurement unit (IMU) sensor may include one or more of an acceleration sensor, a gyro sensor, and a magnetic sensor.

The sensing unit 270 may generate state data of the vehicle based on a signal generated by at least one sensor. The vehicle state data may be information generated based on data sensed by various sensors provided in 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 tilt data, vehicle forward / reverse data, vehicle weight data, battery data, fuel data, tire inflation pressure data, vehicle interior temperature data, vehicle interior humidity data, steering wheel rotation angle data, vehicle exterior illuminance Data, pressure data applied to the accelerator pedal, pressure data applied to the brake pedal, and the like can be generated.

9) Position data generator

The position data generator 280 may generate position data of the vehicle 10. The position data generating device 280 may include at least one of a global positioning system (GPS) and a differential global positioning system (DGPS). The location data generation device 280 may generate location data of the vehicle 10 based on a signal generated by at least one of the GPS and the DGPS. According to an embodiment, the position data generating apparatus 280 may correct the position data based on at least one of an IMU (Inertial Measurement Unit) of the sensing unit 270 and a camera of the object detection apparatus 210. The location data generation device 280 may be referred to as a global navigation satellite system (GNSS).

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

(3) the components of the autonomous vehicle

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

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

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

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

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

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

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

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

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

(4) operation of the autonomous vehicle

8 is a signal flowchart of an autonomous vehicle according to an embodiment of the present invention.

1) Receive operation

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

2) Processing / Judgement Actions

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

2.1) Driving  Plan data generation behavior

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

Electronic horizon data may include horizon map data and horizon pass data.

2.1.1) Horizon  Map data

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

Topology data can be described as maps created by connecting road centers. The topology data is suitable for roughly indicating the position of the vehicle and may be in the form of data mainly used in navigation for the driver. The topology data may be understood as data about road information excluding information about lanes. The topology data may be generated based on the data received at the external server through the communication device 220. The topology data may be based on data stored in at least one memory included in the vehicle 10.

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

The HD map data may include detailed lane-level topology information of the road, connection information of each lane, and feature information for localization of the vehicle (eg, traffic signs, lane marking / properties, road furniture, etc.). Can be. The HD map data may be based on data received at an external server through the communication device 220.

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

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

2.1.2) Horizon  Pass data

The horizon pass data may be described as a trajectory that the vehicle 10 may take within a range from the point where the vehicle 10 is located to the horizon. The horizon pass data may include data indicative of a relative probability of selecting any road at a decision point (eg, fork, intersection, intersection, etc.). Relative probabilities may be calculated based on the time it takes to arrive at the final destination. For example, if the decision point selects the first road and the time it takes to reach the final destination is smaller than selecting the second road, the probability of selecting the first road is greater than the probability of selecting the second road. Can be calculated higher.

Horizon pass data may include a main path and a sub path. The main pass can be understood as a track connecting roads with a relatively high probability of being selected. The sub path may branch at least one decision point on the main path. The sub path may be understood as a track connecting at least one road having a relatively low probability of being selected at least one decision point on the main path.

3) Control signal generation operation

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

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

9 illustrates a type of V2X application.

Four types of V2X applications can use "co-operative awareness" to provide more intelligent services for end users. This allows knowledge such as vehicles, roadside infrastructure, application servers, and pedestrians to process and share that knowledge to provide more intelligent information, such as cooperative collision warnings or autonomous driving, such as other vehicles in close proximity. Or information received from sensor equipment).

These intelligent transport services and related message sets are defined in automotive Standards Developing Organizations (SDOs) outside 3GPP.

Three basic classes for providing ITS services: road safety, traffic efficiency and other applications are described, for example, in ETSI TR 102 638 V1.1.1: "Vehicular Communications; Basic Set of Applications; Definitions".

The radio protocol architecture for the user plane for V2X communication and the control plane for the control plane for V2X communication may be basically the same as the protocol stack structure for sidelinks. have. The radio protocol structure for the user plane includes Packet Data Convergence Protocol (PDCP), Radio Link Control (RLC), Medium Access Control (MAC), and Physical Layer (PHY), and the radio protocol structure for the control plane is RRC ( radio resource control), RLC, MAC, and physical layer. For a more detailed description of the protocol stack for V2X communication may refer to 3GPP TS 23.303, 3GPP TS 23.285, 3GPP TS 24.386 and the like.

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

In sidelink, different physical sidelink control channels (PSCCHs) are allocated in the frequency domain and different sidelink shared channels (PSSCHs) are spaced apart from each other in the frequency domain as shown in FIG. Can be assigned. Alternatively, as shown in (b) of FIG. 10, different PSCCHs may be allocated in the frequency domain in succession, and PSSCHs may be allocated in the frequency domain in succession.

In time division multiple access (TDMA) and frequency division multiple access (FDMA) systems, accurate time and frequency synchronization is essential. If time and frequency synchronization is not accurate, intersymbol interference (ISI) and intercarrier interference (ICI) are caused and system performance is degraded. The same applies to V2X. In V2X, the side layer synchronization signal (SLSS) is used in the physical layer, and the master information block-sidelink-V2X (MIB-SL-V2X) is used in the RLC (radio link control) layer. Can be used.

Describes the source of synchronization or the criteria of synchronization in V2X. The user equipment (UE) may obtain information on time / frequency synchronization from at least one of a global navigation satellite systems (GNSS), a base station (BS), or other neighboring UEs.

In particular, the UE may be synchronized directly to the GNSS or to another UE time / frequency synchronized to the GNSS. When GNSS is set as a synchronization source, the UE can calculate the DFN and subframe number using Coordinated Universal Time (UTC) and (preset) DFN offset.

Alternatively, the UE may be synchronized directly to the BS or to another UE time / frequency synchronized to the BS. For example, if the UE is in network coverage, the UE may receive synchronization information provided by the BS and may be synchronized directly to the BS. The synchronization information may then be provided to other neighboring UEs. If BS timing is set as the basis of synchronization, for synchronization and downlink measurements, the UE may select a cell associated with that frequency (if within cell coverage at that frequency), a primary cell or a serving cell (out of cell coverage at that frequency). Can be followed).

The serving cell (BS) may provide synchronization settings for the carrier used for V2X sidelink communication. In this case, the UE may follow the synchronization setting received from the BS. If no cell is detected in the carrier used for the V2X sidelink communication and no synchronization setting is received from the serving cell, the UE may follow a preset synchronization setting.

Or, the UE may be synchronized to another UE that has not obtained synchronization information either directly or indirectly from the BS or GNSS. The source and preference of synchronization may be preset to the UE or may be set via a control message provided by the BS.

The SLSS is a sidelink specific sequence, and may include a primary sidelink synchronization signal (PSSS) and a secondary sidelink synchronization signal (SSSS).

Each SLSS may have a physical layer sidelink synchronization identity, and the value may be any one of 0 to 335, for example. The synchronization source may be identified depending on which of the above values is used. For example, 0, 168, and 169 may mean Global Navigation Satellite System (GNSS), 1 to 167 are BS, and 170 to 335 are out of coverage. Alternatively, among the values of the physical layer sidelink synchronization ID, 0 to 167 may be values used by the network, and 168 to 335 may be values used outside the network coverage.

A UE providing synchronization information to another UE may be regarded as operating as a synchronization reference. The UE may additionally provide information on synchronization with a SLSS through a sidelink broadcast channel (SL-BCH).

Sidelinks have transport modes 1, 2, 3, and 4.

In transmission mode 1/3, the BS performs resource scheduling on UE 1 through PDCCH (more specifically, DCI), and UE 1 performs D2D / V2X communication with UE 2 according to the resource scheduling. UE 1 may transmit sidelink control information (SCI) to UE 2 through a physical sidelink control channel (PSCCH) and then transmit data based on the SCI through a physical sidelink shared channel (PSSCH). Transmission mode 1 may be applied to D2D, and transmission mode 3 may be applied to V2X.

The transmission mode 2/4 may be referred to as a mode in which the UE schedules itself. In more detail, transmission mode 2 is applied to D2D, and a UE may select a resource by itself in a configured resource pool to perform a D2D operation. The transmission mode 4 is applied to the V2X, and after performing a sensing process, the UE may select a resource by itself in a selection window and perform a V2X operation. UE 1 may transmit SCI to UE 2 through PSCCH and then transmit data based on the SCI through PSSCH. Hereinafter, the transmission mode can be abbreviated as mode.

While control information transmitted by the BS to the UE through the PDCCH is called downlink control information (DCI), control information transmitted by the UE to another UE through the PSCCH may be referred to as SCI. SCI may carry sidelink scheduling information. There may be various formats of SCI, for example, there may be SCI format 0 and SCI format 1.

SCI format 0 may be used for scheduling of PSSCH. In SCI format 0, the frequency hopping flag (1 bit), resource block allocation and hopping resource allocation fields (the number of bits may vary depending on the number of resource blocks in the sidelink), time resource pattern, MCS (modulation and a coding scheme, a time advance indication, a group destination ID, and the like.

SCI format 1 may be used for scheduling of PSSCH. In SCI format 1, priority, resource reservation, frequency resource location of initial transmission and retransmission (the number of bits may vary depending on the number of subchannels in the sidelink), and time gap between initial transmission and retransmission gap between initial transmission and retransmission), MCS, retransmission index, etc.

SCI format 0 may be used for transmission modes 1 and 2, and SCI format 1 may be used for transmission modes 3 and 4.

Hereinafter, resource allocation in mode 3 and mode 4 applied to V2X will be described in more detail. First, mode 3 will be described.

Mode 3 may be referred to as scheduled resource allocation. The UE may be in an RRC_CONNECTED state to transmit data.

10 illustrates a case where a UE performs a mode 3 operation.

The UE requests a transmit / receive resource from the BS, and the BS may schedule resource (s) related to the transmission / reception of sidelink control information and / or data to the UE. At this time, the sidelink SPS may be supported for scheduled resource allocation. The UE may transmit / receive sidelink control information and / or data with another UE by using the allocated resource.

The UE requests a transmit / receive resource from the BS, and the BS may schedule resource (s) related to the transmission / reception of sidelink control information and / or data to the UE. At this time, the sidelink SPS may be supported for scheduled resource allocation. The UE may transmit / receive sidelink control information and / or data with another UE by using the allocated resource.

Mode 4 may be referred to as UE autonomous resource selection. The UE may perform sensing for (re) selection of sidelink resources. Based on the sensing result, the UE may arbitrarily select / reserve sidelink resources among the remaining resources except for a specific resource. The UE may perform up to two parallel independent resource reservation processes.

As described above, the UE may perform sensing to select a mode 4 transmission resource.

For example, the UE identifies transmission resources reserved by another UE or resources used by another UE through sensing in a sensing window, and excludes resources among the remaining resources after excluding them in the selection window. You can select resources at random.

For example, in the sensing window, the UE may decode a PSCCH including information on a period of reserved resources and measure a PSSCH RSRP in resources determined periodically based on the PSCCH. Resources whose PSSCH RSRP value exceeds a threshold may be excluded in the selection window. Thereafter, the sidelink resource may be arbitrarily selected from the remaining resources in the selection window.

Received signal strength indication (RSSI) of periodic resources in the sensing window is measured to identify, for example, low interference resources corresponding to the lower 20%. The sidelink resource may be randomly selected from among resources included in the selection window among the periodic resources. For example, this method can be used when decoding of the PSCCH fails.

Salping 5G communication technology may be applied in combination with the methods proposed in the present invention to be described later, or may be supplemented to specify or clarify the technical features of the methods proposed in the present invention.

Hereinafter, an autonomous driving control method and an autonomous driving system using the same according to the input signal priority according to an embodiment of the present invention will be described in detail.

For example, when a collision with a pedestrian in front is imminent, simply processing one radar data cannot avoid an emergency, and signals from multiple sensors, such as camera data, front and / or rear radar data, vehicle speed information, vehicle The posture data must be combined and fusion processed.

However, if the processor processes signals, data, information, or messages in the order in which they are input, it can cause problems where important and urgent information needs to be processed later, while waiting for the processing of non-critical information that was entered first. . In order to solve this problem, the concept of scheduling has been introduced in the past, and the scheduling function has been introduced in autonomous vehicles, but the scheduling optimized for a special situation called autonomous driving is required.

In addition, in a situation where various types of data or information are simultaneously input from various sensors, the processor must perform various operations with different computational loads such as sensor signal reception, signal processing, situation determination, and control signal generation. However, performing these various operations on a single processor is not efficient for processor design and for rapid processing of data.

The present invention provides a preprocessor (or preprocessor) between the main processor and the sensor separately from the main processor (or main processor) that calculates and processes the input signal of the sensor, and processes the data input from the various sensors first. By determining the number of data to be processed, the order of data to be processed first, and the like, the data determined to be processed first is transmitted to the main processor at high speed, so that the main processor can effectively respond to an emergency even with limited computing power.

11 is a diagram illustrating a connection between an autonomous driving device and a sensing system according to an exemplary embodiment of the present invention.

As shown in FIG. 7, the autonomous driving device is a functional block unit, the autonomous driving device 260 may include a memory 140, a processor 170, an interface unit 180, and a power supply unit 190. Of these, the memory 140 and the processor 170 are directly involved in arithmetic operations.

In FIG. 11, the sensing system 290 is a block including sensors that output sensor data on which the processor 170 determines the current driving situation. In FIG. 6, the sensing system 210 and the communication device ( 220, the sensing unit 270, and the location data generating device 280 may be included therein.

In FIG. 11, the interface unit 180 provides a path for connecting the processor 170 and the sensing system 290 to exchange signals or data by wire or wirelessly. The interface unit 180 transmits a signal, data, or message mainly sensed from the sensing system 290 to the processor 170, and mainly transmits a control command from the processor 170 to the sensing system 29.

In FIG. 11, the memory 140 manages a database 141 that stores statistical data related to a traffic accident, and the database 141 may transfer statistical data corresponding to a current driving situation to the processor 170.

In FIG. 11, the processor 170 receives a data output from the sensors and determines a processing priority of the sensor data. The processor 170 determines a current driving situation of the vehicle based on the sensor data, and determines the preprocessor 175. A main processor 176 that feeds back a current driving situation, and a control signal for generating a vehicle control signal suitable for the current driving situation, but for a sensor determined to have a high priority to sample data at a high speed, and outputting it to the corresponding sensor. The controller 177 may be configured to.

The preprocessor 175 receives sensor data from the sensors of the sensing system 290 through the interface unit 180, receives traffic accident statistics data from the database 141, and currently receives the data from the main processor 176. Get feedback about your driving situation (for example, driving straight, lane change, reverse parking, etc.).

Such feedback information and statistical data may be used to determine the maximum number of sensors and priority processing order that the preprocessor 175 should process first. That is, the preprocessor 175 determines the processing priority of the sensor data based on traffic accident statistical data according to the current situation of the own vehicle. For example, when the lane is changed to the right side, the right side blind spot of the own vehicle is determined. Based on the statistical data that the traffic accident rate due to collision with other cars entering the road is high, the processing priority may be increased in the data of the sensors detecting the right side.

The maximum number of sensors capable of emergency transmission of sensor data can be defined at the system design stage in consideration of the autonomous driving function to be implemented in the autonomous driving system and the set of sensors to be installed.

Between the preprocessor 175 and the main processor 176, a high speed bus is formed separately from the general bus for transmitting general sensor data, thereby processing sensor data requiring high priority and requiring emergency processing. You can quickly forward to (176).

The main processor 176 checks the current autonomous driving environment based on the input sensor data and gives feedback to the preprocessor 175. The identifiable autonomous driving environment includes emergency situations, time-to-collision (TTC) for surrounding objects or objects, location of objects, types of objects (pedestrians, bicycles, vehicles, etc.), location of own vehicles, and movement of own vehicles (forward and backward). , Left turn, right turn, U-turn, stop, lane change, etc.).

The controller 177 receives the data processed by the main processor 176 from the main processor 176, generates a driving control signal based on the data, and provides the driving control signal to the driving control device 250 via the interface unit 180. Enable autonomous driving.

In addition, the controller 177 may control the sampling rate of the sensor necessary for more precisely controlling the vehicle in the emergency situation based on the driving environment data received by the main processor 176. In order to process the sensor data necessary for avoiding an emergency situation within the limited resources of the controller 177, a control signal for controlling the sampling rate at which the sensor samples the data is generated and passed through the interface unit 180 to sense the sensing system ( 290).

That is, the controller 177 may vary the data rate or the sampling rate for the plurality of sensors according to the situation determined by the main processor 176. For the sensors having a high priority and accurate data, the controller 177 raises the sampling rate first. For lower rank sensors, the sampling rate can be lowered.

On the other hand, the preprocessor 175 determines the total number and priority of the sensor data needing emergency treatment, the input supplied to the preprocessor 175, the operation performed by the preprocessor 175 and the preprocessor 175 The output is as follows:

First, the input supplied to the preprocessor 175 includes data of all sensors supplied by the sensing system 290.

Sensor data output from at least one of a camera, a radar, a lidar, an ultrasonic sensor, and an infrared sensor belonging to the object detecting device 210 for generating information about an object outside the vehicle is passed through the preprocessor (180). 175).

In addition, an inertial sensor, a collision sensor, a wheel sensor, a speed sensor, an inclination sensor, a weight sensor, a heading sensor, a position module, a vehicle forward / reverse sensor, a battery sensor, belonging to the sensing unit 270 for sensing a state of a vehicle, Sensor data output by at least one of 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 is input to the preprocessor 175 via the interface unit 180.

In addition, data output by the GPS or DGPS belonging to the location data generation device 280 for generating location data of the vehicle is input to the preprocessor 175 via the interface unit 180.

In addition, the preprocessor 175 may receive data, for example, map data, from an external server from the communication device 220.

In addition, the preprocessor 175 receives information related to the current autonomous driving situation from the main processor 176. The current autonomous driving situation information includes information related to the driving of the own vehicle, the number of detected surrounding objects (or objects), and specific data about each detected object.

Information related to the running of the vehicle includes information on the behavior of the vehicle, such as going straight, backward, left turn, right turn, U-turn, stop, lane change, and interruption, and also the position of the vehicle, the speed of the vehicle, the acceleration of the vehicle, Direction may also be included.

As the data for each detected object, TTC (Time To Collision) (time to collision) between the host vehicle and the object, the class of the object (for example, a walker, a bicycle, an animal, a vehicle (a sedan, a bus, Truck, ambulance, police car, etc.)), the location of the object, the speed of the object, the acceleration of the object, the direction of the object, and the like.

In addition, the preprocessor 175 receives traffic accident statistics data from the database 141.

On the other hand, the preprocessor 175 determines the total number of sensor data requiring emergency treatment, and determines the type and priority of sensors that need emergency treatment.

The preprocessor 175 determines the emergency processing priority of the sensor data using the traffic accident statistics data according to the behavior information of the own vehicle. For example, if the host vehicle changes lanes to the right, the statistical data indicates that there is a high rate of traffic accidents due to collisions with other vehicles entering the right side blind spot of the host vehicle. Raise the processing rank.

The output of the preprocessor 175 includes the total number of sensor data requiring emergency processing and the priority of sensor data requiring emergency processing.

Next, the criteria for determining the emergency processing priority of the sensor data by the preprocessor 175 will be described.

First, the preprocessor 175 selects a sensor for detecting a distance to an object existing in an area having the highest probability of a traffic accident as a first priority sensor.

Next, the preprocessor 175 selects a sensor that identifies the class of the object as the second rank sensor. When the first rank sensor is capable of class identification as well as distance detection, the second rank sensor It is assumed to be the same sensor as the first rank sensor.

If the class identification only needs to be tracked using the first priority sensor without continuing to identify after the initial identification, the class identification sensor may be excluded from the emergency processing priority after the class identification.

For example, if a vehicle that is stopped due to a failure in front of the vehicle is found to be an emergency situation within 3 seconds, the TTC detects the distance to the vehicle in front of the vehicle by using a radar, which is a first priority sensor, and then uses a camera that is a second priority sensor. After identifying the object's class as a vehicle, if it is no longer necessary to use the camera to identify the class, only the first rank sensor, radar, tracks the vehicle in front of the window and continues the distance. In addition, the main processor 176 and / or the controller 177 may perform distance monitoring for determining whether and when the vehicle is urgently braking.

The preprocessor 175 may further continue the prioritization only when necessary according to the behavior of the host vehicle.

The preprocessor 175 determines the total number and priority of the sensors to be processed first as follows based on feedback from each of the current actions of the autonomous vehicle from the main processor 176.

i) If the vehicle is straight or backward

The preprocessor 175 may determine the number of emergency treatment sensors up to two according to the statistics that there are many traffic accidents due to forward collisions when going straight and back collisions when going backward.

The preprocessor 175 selects a sensor that measures the distance to the target object in the forward or backward path with a first rank sensor, for example a radar or a lidar, and is in the forward or backward path with a second rank sensor. You can choose a sensor that identifies the class of the target object, for example a camera or a rider.

ii) if the vehicle turns left, turns right or u-turn

The preprocessor 175 may determine the number of emergency treatment sensors as a maximum of four according to the statistics that there are many traffic accidents due to side rear collisions opposite to the rotation direction, and the TTC with the front or rear rear objects is not below the threshold. It may be excluded from an emergency.

The preprocessor 175 selects a sensor for measuring the distance to the left and right target objects during the left turn, the right turn, and the U turn with the first rank sensor, and the class of the left and right target objects for the left, right and u turn with the second rank sensor. Select the sensor to identify the sensor, select the sensor to measure the distance to the front target object when the left turn, right turn, U-turn with the third rank sensor, and select the class of the forward target object when the left, right turn, U-turn with the fourth rank sensor You can choose which sensor to identify. In this case, the side rear sensor may select a sensor opposite to the rotation direction, for example, a right rear sensor in the case of left rotation.

iii) When own vehicle is stopped

The preprocessor 175 may determine the number of emergency treatment sensors as a maximum of two according to the statistics that there are many traffic accidents caused by other vehicles rushing in the rear while the vehicle is stopped.

The preprocessor 175 selects a sensor that measures a distance to a target object approaching the host vehicle stopped from the rear by the first rank sensor, and approaches the target object approaching the host vehicle stopped from the rear by the second rank sensor. You can select a sensor to identify the class of.

In this case, when a collision is expected, the controller 177 may generate and output a driving control signal for controlling an internal safety device such as an airbag to protect the occupant, or proceed with an emergency control processor to avoid rear collision.

iv) If the lane changes lanes

During the lane change, the preprocessor 175 may determine the number of emergency treatment sensors as a maximum of six according to the statistics that there are many traffic accidents in the order of side collision, collision with the rearward approaching vehicle, and collision with the front vehicle.

The preprocessor 175 selects a sensor for measuring the distance to the target object existing on the side of the direction in which the lane is to be changed by the first rank sensor, and is on the side of the direction in which the lane is to be changed by the second rank sensor. Select a sensor that identifies the class of the existing target object, select a sensor that measures the distance to the approaching target object from the side to the rear of the direction to change the lane to the third rank sensor, and lane to the fourth rank sensor Select a sensor that identifies the class of the target object approaching from the side to the rear of the direction to change, and select the sensor to measure the distance to the target object located in front of the traveling direction of the host vehicle when the lane change to the fifth rank sensor And selecting a sensor for identifying a class of the target object located in the forward direction of the host vehicle when the lane is changed to the sixth rank sensor. Can.

v) responds to the interruption of another vehicle

The preprocessor 175 may determine the number of emergency treatment sensors as a maximum of two according to statistics that there are many collision accidents in the front side, that is, the front right side or the front left side when another vehicle is interrupted.

The preprocessor 175 selects a sensor that senses a direction in which another vehicle enters the first ranking sensor, that is, a distance to a target object existing in a front right or front left area, and uses the second ranking sensor as a corresponding target object. You can select a sensor to identify the class of.

FIG. 12 shows an example of traffic accident statistics data and is obtained from an Autonomous Vehicle Test Development Symposium held in Stuttgart in 2018.

The memory 140 manages statistical data related to a traffic accident in the database 141. The risk factor is different from a situation in which a traffic accident occurs when driving a human being and autonomous driving.

The left graph of FIG. 12 shows a ratio of situations or risk factors that cause a traffic accident when a human driver drives. Obstacles, lane changes, and cut ins when a human driver drives. The following is the high rate of traffic accidents in order of following, weather and weather.

On the other hand, the graph on the right side of FIG. 12 shows risk factors when the vehicle is driven by automation, that is, ADAS or autonomous driving technology, and the order of the risk factors is the same as that of a human driver. While the accident rate is lower than that of the other driver, new automation errors such as sensor error or misinterpretation that the human driver did not drive are added and the ratio is relatively high. In addition, it can be seen that the autonomous driving technology gave an accident risk as much as a safety gain when the vehicle is driven, compared to when a human driver is driving.

13 is a flowchart illustrating a method of determining the number and priority of sensors requiring emergency transmission according to an embodiment of the present invention.

The main processor 176 determines whether it is an emergency (S110), and one of the methods for determining an emergency is TTC. That is, if the remaining time until the autonomous vehicle collides with the surrounding object is less than the threshold value, it may be determined as an emergency situation.

The main processor 176 transmits the current autonomous driving environment to the preprocessor 175 as a feedback (S120), and the identifiable autonomous driving environment includes an object such as a TTC, an object's location, a pedestrian, a bicycle, a vehicle, and the like for surrounding objects. The type of vehicle, the position of the own vehicle, go straight, backward, left turn, right turn, U-turn, stop, lane behavior, such as lane change.

The preprocessor 175 determines the total number and priority of sensors requiring emergency treatment based on the feedback data from the main processor 176 and the traffic accident statistics data from the database 141 (S130). The maximum number of sensors requiring emergency transmission can be defined at the system design stage in consideration of the autonomous driving function to be implemented in the autonomous driving system and the set of sensors to be installed.

The preprocessor 175 configures and transmits a high speed bus to transmit the sensor data necessary for emergency processing to the main processor 176 (S140).

The main processor 176 determines whether the TTC is less than the threshold (S150), where the threshold is a value used to determine whether to increase the sampling rate (SR) for a sensor requiring emergency transmission, and whether the emergency It may be less than or equal to the threshold used in step S110 to determine whether or not.

The controller 177 needs to have a higher rate of data output from the sensor in order to finely control the autonomous vehicle in an emergency situation. Accordingly, the controller 177 outputs a command to increase the sampling rate for the plurality of sensors requiring emergency transmission of the sensor data so that the emergency situation can be more accurately determined, and the main processor 176 outputs the sensor data of the high sampling rate. The internal clock speed may be increased to process (S160).

14 is a flowchart illustrating a method of setting a sampling rate of a sensor according to an embodiment of the present invention.

In order to control the vehicle more efficiently and safely for emergency situations within the resources that the main processor 176 and controller 177 allow in limited circumstances, the controller 177 can vary the sampling rate for a plurality of sensors. Can be operated with

First, the controller 177 may increase the sampling rate for a sensor selected as an emergency processing sensor requiring emergency processing, and reduce the sampling rate for other sensors. The controller 177 can also adjust the sampling rate differently according to the priority of the emergency treatment sensor.

That is, when the controller 177 enters a specific region or a specific driving condition, the controller 177 may increase the sampling rate of the high priority sensor, and conversely, reduce the sampling rate of the low priority sensor. In addition, the controller 177 adds a monitoring mechanism for a sensor whose sampling rate is reduced to stop data transmission or processing, so that an emergency situation can be responded to in an area detected by the sensor.

The controller 177 sets the sampling rate as an initial value for each sensor (S210), and the initial value may be stored in the memory 140.

The main processor 176 determines whether it is an emergency situation based on time (TTC) until the autonomous vehicle collides with the surrounding object (S220), and in case of an emergency situation (YES in S220), the controller 177 is a processor. That is, the sampling rate of the sensors is adjusted in consideration of the processing capability of the main processor 177 (S230).

The controller 177 may be based on traffic accident statistics, based on a recommended value for each area or condition received from a server, and / or based on a vehicle's driving speed, distance to a nearby vehicle, or risk. The sampling rate of the highest priority sensor, taking into account the processing power of the processor, adjusting or increasing the sampling rate of the next priority sensor, and lowering the priority. Alternatively, the sampling rate of sensors not related to emergency treatment can be lowered.

The controller 177 determines whether the sampling rate of the corresponding sensor is less than the threshold value for each sensor (S240), and if the sampling rate is set lower than the threshold value because the data of the corresponding sensor is meaningless in the current situation (S240). For example, to output a control command to deactivate the sensor so that the sensor does not transmit the sensor data, or feed back to the main processor 176 or the preprocessor 175 so as not to process the sensor data output by the sensor. It may be (S250).

The controller 177 determines whether the number of deactivated sensors is greater than or equal to a predetermined threshold value (S260). If there are too many deactivated sensors (YES in S260), the sampling rate of the higher priority sensor in the current driving condition is necessary. In operation S270, the sampling rate of the sensors is set again in consideration of the path, the speed, the distance to the surrounding vehicle, and the like, which are the calculation criteria of the sampling rate.

15 is a flowchart illustrating a method of processing an event when a sensor is deactivated according to an embodiment of the present invention.

Sensors that are pushed out of processing priority and whose sampling rate is lower than the threshold value are deactivated and are not set to transmit sensor data or output sensor data, but the main processor 176 or preprocessor 175 does not process the sensor data. .

However, if an emergency occurs in the area covered by the deactivated sensor, even if the deactivated sensor is not selected as the priority sensor requiring emergency transmission, the sensor may be activated for a predetermined time or the sampling rate may be increased.

The main processor 176 determines whether emergency processing is required for the deactivated sensor because an emergency situation occurs in an area that is in charge of the deactivated sensor (S310).

The controller 177 outputs a control command for activating a sensor disposed in the area where an emergency situation has occurred but deactivated for a predetermined time and increasing a sampling rate of the corresponding sensor (S320).

In addition, when the deactivated sensor samples the sensor data at a low sampling rate and transmits the sensor data, the preprocessor 175 monitors the output of the deactivated sensor and when an abnormal situation occurs or significant data is input to the sensor data, The notifying emergency message may be sent to the main processor 176.

Based on this, the main processor 176 and the controller 177 may activate the deactivation sensor for a period of time and increase the sampling rate of the sensor.

16A and 16B are diagrams illustrating a scenario in which an autonomous vehicle according to an embodiment of the present invention copes with a situation in which a collision with an pedestrian in front is imminent, and the autonomous vehicle is going straight and suddenly popped out of India. The collision with the situation is imminent and the TTC is calculated to be less than the threshold of 2 seconds.

The main processor 176 transmits the current autonomous driving environment to the preprocessor 175 as a feedback. In the current autonomous driving environment, the TTC is about 1.990 seconds, the position of the object is in front of the host vehicle, and the type of the object is a pedestrian. The car is approaching pedestrians at a speed of 60kph / h, 33.167m away from the pedestrians. The current road condition is ice and the friction coefficient is low, so even if AEB (Automatic Emergency Braking) is executed immediately, the braking distance is likely to be long.

The preprocessor 175 prepares not only the AEB but also evasive collision avoidance steering in preparation for the possibility that the braking distance cannot be secured due to the slippery road surface based on the data of the feedbacked autonomous driving environment. Decide The preprocessor 175 determines a total of three sensors for emergency transmission, and first determines the order of the front radar, the left rear radar, and the right rear radar. When the TTC is less than 1.5 seconds, the controller 177 instructs the radar to double the sampling rate of the radar.

As a result of sensing the pedestrian with the front radar after the AEB, the possibility of collision with the pedestrian was increased due to the slippery road surface as predicted. According to the data sensed by the left rear radar, there is no vehicle on the left lane, According to the right side of the motorcycle is approaching state, the controller 177 decides to avoid the collision avoidance steering to the left side, as shown in Figure 16b by controlling the steering device to change lanes to the left, avoid collision with pedestrians can do.

FIG. 17 is a diagram illustrating a scenario in which an autonomous vehicle according to an embodiment of the present invention copes with a lane change.

The autonomous vehicle (first vehicle), which is its own vehicle, attempts to change lanes to the right in order to exit to the right exit of the highway.

Although there is a second vehicle on the right rear side, it is determined that there is no risk of collision, the first vehicle turns on the right turn signal and initiates a lane change to the right.

At this time, the second vehicle suddenly accelerates and interferes with the lane change, so that the first vehicle increases the risk of collision with the second vehicle, and the TTC is dropped to less than two seconds. At the same time, the third vehicle from the left side approaches the high speed and detects that the lane is trying to change lanes to the lane where the vehicle was originally located.

The preprocessor 175 sets two sensors requiring emergency transmission, and determines the priority 1 as the right rear radar for monitoring the second vehicle and the priority 2 as the left rear radar for monitoring the third vehicle. Because it is a highway, it may not be necessary to select a camera for identifying an object as a sensor for emergency transmission.

When the TTC between the host vehicle and the second vehicle falls below 1.5 seconds, the controller 177 instructs to double the sampling rates of the right rear and left rear radars for more precise control, and the host vehicle controls the second vehicle and the first vehicle. 3 Avoid the collision by performing longitudinal and lateral control to avoid collision with the vehicle.

Various embodiments of the autonomous driving control method and the autonomous driving system according to the priority of the input signal of the present invention will be described briefly and clearly as follows.

The autonomous driving control method according to the input signal priority is based on the information on the autonomous driving situation of the own vehicle when the remaining time until the collision with the surrounding object is less than the first threshold value, and the priority of the processing of the emergency treatment sensor Determining; Transmitting sensor data of an emergency sensor through a high speed bus; And adjusting a sampling rate for one or more of the sensors for emergency treatment.

In an embodiment, the determining may include determining the maximum number of sensors for emergency treatment in consideration of the autonomous driving level implemented by the autonomous driving system and the sensor set installed in the autonomous system.

In one embodiment, the determining may further determine the total number and priority of the sensors for emergency treatment by further considering traffic accident statistics data.

In an embodiment, the determining may include determining a sensor that detects a distance to a first object existing in an area having the highest probability of a traffic accident as a first rank, and selecting a sensor that identifies a class of the first object. You can decide by 2 ranks.

In an embodiment, the determining may exclude the sensor determined as the second rank from the processing priority after identifying the class of the first object.

In one embodiment, the adjusting step takes into account the processing power of the processor to process the sensor data to generate information about the autonomous driving situation, thereby increasing the sampling rate of the sensors determined as the emergency treatment sensor, and sampling the other sensors. The rate can be lowered.

In one embodiment, the adjusting may deactivate the first sensor when the sampling rate of the first sensor is lower than the second threshold.

In one embodiment, the adjusting may reset the sampling rate of the sensors in consideration of the path, the speed, or the distance to the surrounding vehicle when the number of deactivated sensors is higher than the third threshold.

In an embodiment, the adjusting may activate the deactivated sensor for a predetermined time and increase the sampling rate of the sensor when an emergency occurs in an area in which the deactivated sensor is in charge.

An autonomous driving system includes: a sensing system including two or more sensors for outputting sensor data; A main processor which generates information on an autonomous driving situation of the own vehicle based on the sensor data; A preprocessor that receives sensor data from the sensing system and transmits the sensor data to the main processor and determines the total number and priority of the sensors for emergency treatment based on the information on the autonomous driving situation fed back by the main processor; And a controller that generates a vehicle control signal suitable for an autonomous driving situation and adjusts a sampling rate for one or more of the sensors for emergency treatment.

The present invention described above can be embodied as computer readable codes on a medium in which a program is recorded. The computer-readable medium includes all kinds of recording devices in which data that can be read by a computer system is stored. Examples of computer-readable media include hard disk drives (HDDs), solid state disks (SSDs), silicon disk drives (SDDs), ROMs, RAMs, CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, and the like. This also includes implementations in the form of carrier waves (eg, transmission over the Internet).

Accordingly, the above detailed description should not be construed as limiting in all aspects and should be considered as illustrative. The scope of the invention should be determined by reasonable interpretation of the appended claims, and all changes within the equivalent scope of the invention are included in the scope of the invention.

140: Memory 141: Database
175: preprocessor 176: main processor
177: controller 180: interface
290: sensing system

Claims (20)

Determining the total number and priority of the emergency treatment sensor based on the information on the autonomous driving situation of the own vehicle when the remaining time until the collision with the surrounding object is less than the first threshold value;
Transmitting sensor data of the sensor for emergency processing through a high speed bus; And
And adjusting a sampling rate for at least one of the sensors for emergency processing. According to claim 1,
The determining may include determining the maximum number of sensors for emergency treatment in consideration of the autonomous driving level implemented by the autonomous driving system and the sensor set installed in the autonomous system. Control method. According to claim 1,
In the determining, the autonomous driving control method according to the input signal priority may be further determined by considering traffic accident statistical data to determine the total number and priority of the sensors for emergency treatment. The method of claim 3, wherein
The determining may include determining, as a first rank, a sensor that detects a distance to a first object existing in a region having the highest probability of a traffic accident, and a sensor that identifies a class of the first object as a second rank. Autonomous driving control method according to the input signal priority, characterized in that for determining. The method of claim 4, wherein
The determining may include, after identifying the class of the first object, excluding the sensor determined as the second priority from the processing priority. The method of claim 3, wherein
In the adjusting, the sampling rate of the sensors determined as the emergency processing sensor is increased in consideration of the processing capability of the processor to process the sensor data to generate information on the autonomous driving situation, and the sampling rate of the other sensors. Autonomous driving control method according to the input signal priority, characterized in that to lower the. The method of claim 6,
The adjusting may include deactivating the first sensor when the sampling rate of the first sensor is lower than a second threshold value. The method of claim 7, wherein
The adjusting may include resetting the sampling rate of the sensors in consideration of a path, a speed, or a distance to a surrounding vehicle when the number of the deactivated sensors is higher than a third threshold value. Autonomous driving control method. The method of claim 7, wherein
The adjusting may include activating the deactivated sensor for a predetermined time and increasing a sampling rate of the corresponding sensor when an emergency occurs in an area in which the deactivated sensor is in charge. Way. According to claim 1,
Generating information on the autonomous driving situation based on the sensor data; And
And controlling the vehicle driving apparatus based on the information on the autonomous driving situation. A sensing system including two or more sensors to output sensor data;
A main processor which generates information on an autonomous driving situation of the own vehicle based on the sensor data;
A preprocessor that receives the sensor data from the sensing system and transmits the sensor data to the main processor and determines the total number and priority of the sensors for emergency processing based on the information on the autonomous driving situation fed back by the main processor; And
And a controller for generating a vehicle control signal suitable for the autonomous driving situation and adjusting a sampling rate for one or more of the sensors for emergency treatment. The method of claim 11, wherein
The preprocessor transmits the sensor data of the sensor for emergency processing to the main processor via a high-speed bus (BUS). The method of claim 11, wherein
The preprocessor determines the maximum number of sensors for emergency treatment in consideration of the autonomous driving level implemented by the autonomous driving system and the sensor set installed in the autonomous system. The method of claim 11, wherein
It further comprises a memory for storing traffic accident statistics data,
And the preprocessor determines the total number and priority of the sensors for the emergency treatment by further considering traffic accident statistics data provided by the memory. The method of claim 14,
The preprocessor determines that the sensor that detects the distance to the first object existing in the area having the highest probability of a traffic accident as a first rank and the sensor that identifies the class of the first object as the second rank. An autonomous driving system characterized by the above-mentioned. The method of claim 15,
And the preprocessor excludes the sensor determined as the second rank from the processing priority after identifying the class of the first object. The method of claim 14,
The controller may increase the sampling rate of the sensors determined as the emergency treatment sensor and reduce the sampling rate of the other sensors in consideration of the processing capability of the main processor. The method of claim 17,
And the controller deactivates the first sensor when the sampling rate of the first sensor is lower than the second threshold. The method of claim 18,
And the controller resets the sampling rate of the sensors in consideration of a path, a speed, or a distance to a surrounding vehicle when the number of the deactivated sensors is higher than a third threshold. The method of claim 18,
And the controller activates the deactivated sensor for a predetermined time and raises a sampling rate of the corresponding sensor when an emergency occurs in an area in which the deactivated sensor is in charge.

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