System and method for controlling autonomous vehicle

Abstract

The present disclosure relates to a system and method for controlling an autonomous vehicle capable of expanding the scope of sensing data and transmitting data to and receiving data from surrounding vehicles. According to one embodiment of the present disclosure, the perception accuracy and range of an autonomous vehicle can be effectively expanded by fusing a plurality of pieces of sensor data. In addition, more precise positioning information and a wide range of environmental information can be obtained through data sharing between vehicles, thereby greatly improving the safety and reliability of an autonomous driving system.

Description

Control system and method for autonomous vehicles

[0001] The present disclosure relates to a control system and method for an autonomous vehicle capable of expanding sensing data and transmitting and receiving data with surrounding vehicles.

[0002] Autonomous driving technology refers to a technology that enables a vehicle to independently perceive its surroundings, plan a driving route, and control the vehicle to reach its destination without driver intervention. To achieve this, autonomous vehicles utilize various sensors, such as LiDAR, radar, cameras, and ultrasonic sensors, to collect data on the surrounding environment. Based on this collected data, they recognize objects or analyze road conditions. Furthermore, autonomous vehicles combine high-precision maps and GPS data to determine the vehicle's current location and generate an optimal driving route through path planning algorithms.

[0003] However, autonomous vehicles based on conventional technology perceived the surrounding environment using only data from onboard sensors; consequently, the perception range was limited, and there was a problem where perception performance deteriorated in environments such as adverse weather.

[0004] One objective of the present disclosure is to overcome perception limitations and expand the perception range by fusing sensor data obtained from a plurality of sensors to obtain accurate and robust perception data.

[0005] Another objective of the present disclosure is for an autonomous vehicle to acquire accurate positioning information and acquire a wider range of environmental information by sharing sensor data, location data, or driving state data, etc., with other vehicles in real time.

[0006] However, the technical problems that this embodiment aims to solve are not limited to the technical problems described above, and other technical problems may exist.

[0007] A control system for an autonomous vehicle according to one embodiment of the present disclosure comprises: a sensor module comprising at least one environment sensor configured to acquire first environment data related to the surrounding environment of the vehicle and at least one position sensor configured to acquire first position data related to the position or movement of the vehicle; a communication module configured to receive data from another vehicle located around the vehicle or to transmit data to the other vehicle; at least one processor connected to the sensor module and the communication module to enable data transmission and reception; and at least one memory comprising computer program code. The at least one memory and the computer program code may be configured such that, through the at least one processor, the control system acquires the first environment data and the first position data through the sensor module, generates positioning data of the vehicle or an object located in the surrounding environment based on the acquired first environment data and the first position data, acquires shared data from the other vehicle through the communication module, and corrects the positioning data based on the acquired shared data.

[0008] A control method for an autonomous vehicle according to one embodiment of the present disclosure may include: acquiring first environmental data related to the surrounding environment of the vehicle and first position data related to the position or movement of the vehicle through a sensor module comprising at least one environmental sensor and at least one position sensor; generating positioning data of the vehicle or an object located in the surrounding environment based on the acquired first environmental data and the first position data; acquiring shared data from another vehicle through a communication module configured to receive data from another vehicle located around the vehicle or to transmit data to the other vehicle; and correcting the positioning data based on the acquired shared data.

[0009] According to one embodiment of the present disclosure, the perception accuracy and range of an autonomous vehicle can be effectively expanded by fusing a plurality of sensor data.

[0010] In addition, data sharing between vehicles enables the acquisition of more precise positioning information and a wider range of environmental information, which can significantly improve the safety and reliability of the autonomous driving system.

[0011] FIG. 1 illustrates the architecture of a control system for an autonomous vehicle according to one embodiment of the present disclosure.

[0012] FIG. 2a illustrates the arrangement and detection range of a LiDAR sensor according to one embodiment of the present disclosure.

[0013] FIG. 2b illustrates the arrangement and detection range of a camera sensor according to one embodiment of the present disclosure.

[0014] FIG. 2c illustrates the arrangement and detection range of a radar sensor according to one embodiment of the present disclosure.

[0015] FIG. 3 is a power supply diagram of a control system for an autonomous vehicle according to one embodiment of the present disclosure.

[0016] FIG. 4 is a flowchart for generating positioning data by fusing a plurality of sensor data according to one embodiment of the present disclosure.

[0017] FIGS. 5a and 5b are diagrams of the state in which a vehicle joins a road on which another vehicle is traveling, according to one embodiment of the present disclosure.

[0018] FIG. 6 is a diagram showing the state when an emergency vehicle approaches a vehicle or another vehicle according to one embodiment of the present disclosure.

[0019] FIG. 7 is a diagram of the state when a vehicle or another vehicle according to one embodiment of the present disclosure approaches a toll electronic payment system or a gate of a highway toll booth.

[0020] FIG. 8 is a flowchart of a control method for an autonomous vehicle according to one embodiment of the present disclosure.

[0021] Embodiments of the present invention are described below with reference to the attached drawings to enable those skilled in the art to easily implement the invention. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.

[0022] Throughout this specification, when a component is described as being located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components.

[0023] Throughout this specification, when a part is described as "comprising" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0024] Throughout this specification, terms of degree such as “about,” “substantially,” etc., are used to mean at or near the stated value when inherent manufacturing and material tolerances are presented in the stated meaning, and are used to prevent unscrupulous infringers from unfairly exploiting the disclosure in which precise or absolute values ​​are mentioned to aid in understanding this specification. Throughout this specification, terms of degree such as “step” or “step of” do not mean “step for”.

[0025] Throughout this specification, the term “combination(s) of these” included in the Markush-type expression means one or more mixtures or combinations selected from the group consisting of the components described in the Markush-type expression, and means including one or more selected from the group consisting of said components.

[0026] Throughout this specification, the description "A and / or B" means "A or B, or A and B".

[0027] Hereinafter, embodiments and examples of the present invention will be described in detail with reference to the attached drawings. However, the present invention may not be limited to these embodiments and examples and drawings.

[0028] FIG. 1 illustrates the architecture of a control system (100) of an autonomous vehicle (V) according to one embodiment of the present disclosure. FIG. 2a illustrates the arrangement and detection range (FOV1, FOV2) of a LiDAR sensor (112) according to one embodiment of the present disclosure. FIG. 2b illustrates the arrangement and detection range (FOV3) of a camera sensor (114) according to one embodiment of the present disclosure. FIG. 2c illustrates the arrangement and detection range (FOV4) of a radar sensor (116) according to one embodiment of the present disclosure.

[0029] A control system for an autonomous vehicle (V) according to one embodiment may include a sensor module (112, 114, 116, 118) comprising at least one environment sensor (112, 114, 116) configured to acquire first environment data related to the surrounding environment of the vehicle (V) and at least one position sensor (118) configured to acquire first position data related to the position or movement of the vehicle (V); a communication module (120) configured to receive data from another vehicle located around the vehicle (V) or to transmit data to another vehicle; at least one processor (130) connected to the sensor module (112, 114, 116, 118) and the communication module (120) to enable the transmission and reception of data; and at least one memory (not shown) comprising computer program code.

[0030] In one embodiment, the control system (100) of an autonomous vehicle (V) may include hardware and software that detect the surrounding environment of the vehicle, acquire location data, and exchange data with other vehicles to enable more accurate autonomous driving. This control system (100) can recognize the surrounding environment, generate positioning data, and establish and execute a driving plan by utilizing various sensors and communication modules mounted on the vehicle through a processor (130).

[0031] In one embodiment, the sensor module (112, 114, 116, 118) may include at least one environment sensor (112, 114, 116) that acquires first environment data related to the surrounding environment of the vehicle (V), and at least one position sensor (118) that acquires first position data related to the position or movement of the vehicle.

[0032] In one embodiment, at least one environmental sensor (112, 114, 116) may include a lidar sensor (112), a camera sensor (114), and a radar sensor (116).

[0033] In one embodiment, the communication module (120) may be configured to receive shared data from another vehicle or transmit data to another vehicle. In one embodiment, the communication module (120) may cooperate with multiple autonomous vehicles to share a wider range of environmental information. Accordingly, the control system (100) of the autonomous vehicle (V) can generate more accurate positioning data.

[0034] In one embodiment, the communication module (120) may be connected to the processor (130) via Ethernet, or connected to the processor (130) or the vehicle control unit (150) via CAN communication.

[0035] In one embodiment, the processor (130) can transmit and receive data to and from the vehicle control unit (150) via CAN (Controller Area Network) communication. In one embodiment, the processor (130) can correspond to the CADM (Core Architecture Data Model) of the control system of the autonomous vehicle (V). In one embodiment, the memory (not shown) can store computer program code, or store volatile data or non-volatile data.

[0036] In one embodiment, the processor (130) can generate positioning data of an object located in a vehicle or surrounding environment by processing first environment data and first location data obtained through sensor modules (112, 114, 116, 118). Additionally, the processor (130) can correct the positioning data by utilizing shared data obtained through a communication module (120). In one embodiment, the processor (130) can correct the error in the positioning data by applying an extended Kalman filter algorithm.

[0037] In one embodiment, the processor (130) can set a driving plan for the vehicle based on corrected positioning data and can control the vehicle control unit (150) based on the set driving plan.

[0038] In one embodiment, the vehicle control unit (150) can control the steering device (157), power device (153), or braking device (155) of the vehicle (V), and can control the steering device (157), power device (153), or braking device (155) according to a driving plan set by the processor (130). In one embodiment, the vehicle control unit (150) can control the acceleration, deceleration, and / or steering of the vehicle (V) by controlling the steering device (157), power device (153), or braking device (155) according to the driving plan.

[0039] In one embodiment, the lidar sensor (112) may be configured to acquire three-dimensional point cloud data of the surrounding environment. In one embodiment, the lidar sensor (112) may be a sensor that emits a laser beam and receives a reflected signal to generate a three-dimensional map of the environment around the vehicle. In one embodiment, the lidar sensor (112) is capable of high-resolution distance measurement and may be used primarily for obstacle detection and environment mapping.

[0040] In one embodiment, spatial data can be acquired through a LiDAR sensor (112), and this can be utilized for cooperative positioning as a multi-layer map such as Simultaneous Localization and Mapping (SLAM) or a High Definition Map (HD Map).

[0041] In one embodiment, the lidar sensor (112) may be a rotating type that rotates so that the detection range (FOV1, FOV2) moves, or a fixed type in which the detection range (FOV1, FOV2) is fixed. In one embodiment, the lidar sensor (112) may include a detection range (FOV1) facing forward or backward and a detection range (FOV2) facing left or right, and the angles of the detection ranges (FOV1, FOV2) may differ from each other. For example, the lidar sensor (112) may have a relatively larger angle when the detection range (FOV2) faces left or right than when the detection range (FOV1) faces forward or backward.

[0042] In one embodiment, the lidar sensors (112) may be connected to each other via Ethernet or connected to a processor (130) via Ethernet to transmit and receive data.

[0043] In one embodiment, the Ethernet system may include a debugging Ethernet LAN cable (e.g., CAT7 STP) placed at the passenger seat of the vehicle.

[0044] In one embodiment, the camera sensor (114) may be configured to acquire image data of the surrounding environment. In one embodiment, the camera sensor (114) may be a sensor capable of visually analyzing the surrounding environment of a vehicle by acquiring an optical image. In one embodiment, the camera sensor (114) is useful for detecting information such as color, shape, traffic signals, and road signs, and can identify and recognize objects in combination with an image processing algorithm.

[0045] In one embodiment, an object image acquired through a camera sensor (114) can be utilized as a multivision for object recognition by overlaying it on sensor data or positioning data.

[0046] In one embodiment, the camera sensor (114) may include a sensor facing forward according to the vehicle's direction of movement, a sensor facing left or right, and a sensor facing rear. For example, the camera sensor (114) may be provided with a plurality of sensors facing forward according to the vehicle's direction of movement, each having at least some different detection ranges (FOV3), and their detection ranges (FOV3) may partially overlap each other.

[0047] In one embodiment, camera sensors (114) may be connected to each other via GMSL2 (Gigabit Multimedia Serial Link) or connected to a processor (130) via GMSL2 to enable data transmission and reception.

[0048] In one embodiment, the radar sensor (116) may be configured to detect or track location data of an object located in the surrounding environment. In one embodiment, the radar sensor (116) may be a sensor that measures the distance, speed, and direction of an object present in the surrounding environment using electromagnetic waves. In one embodiment, the radar sensor (116) may have robust characteristics against weather conditions and may be capable of stable detection even at night or in bad weather.

[0049] In one embodiment, a plurality of radar sensors (116) with at least some different detection ranges (FOV4) may be arranged along the perimeter of the vehicle. In one embodiment, the radar sensors (116) may be placed at each corner of the vehicle (e.g., left front corner, left rear corner, right front corner, right rear corner, etc.). In one embodiment, the detection range (FOV4) of the radar sensors (116) may overlap at least partially with the detection ranges (FOV4) of adjacent sensors.

[0050] In one embodiment, a portion of the radar sensor (116) may be positioned to face forward according to the direction of movement of the vehicle. In one embodiment, the radar sensor (116) may include a dummy sensor for debugging positioned to face forward according to the direction of movement of the vehicle.

[0051] In one embodiment, the radar sensors (116) may be connected to each other via CAN communication (e.g., CAN-FD) or connected to the processor (130) via CAN communication to transmit and receive data.

[0052] In one embodiment, at least one position sensor (118) may include a Global Positioning System (GPS) sensor or an Inertia Measurement Unit (IMU) sensor. In one embodiment, at least one position sensor (118) may detect the latitude, longitude, altitude, acceleration, and angular velocity of the vehicle.

[0053] In one embodiment, the GPS sensor may be a sensor that receives satellite signals to measure the basic absolute position of the vehicle. In one embodiment, the GPS sensor provides latitude, longitude, and altitude information and may be utilized in combination with the vehicle's overall location information and navigation system. In one embodiment, the GPS sensor may be a Real-Time Kinematic (RTK) device that measures real-time dynamic position.

[0054] In one embodiment, the IMU sensor may be a sensor that measures the acceleration and angular velocity of a vehicle to analyze the vehicle's attitude and movement state. In one embodiment, the IMU sensor may include an accelerometer and a gyroscope to precisely measure the vehicle's movement and, when combined with a GPS sensor, provide more accurate position data.

[0055] In one embodiment, the IMU sensor may be an Inertial Navigation System (INS) device. For example, the IMU sensor can detect the position of a vehicle by analyzing the vehicle's attitude and movement state in a state where satellite signals cannot be received from a GPS sensor (e.g., a GPS blind spot).

[0056] In one embodiment, at least one position sensor (118) can be connected to a processor (130) via Ethernet.

[0057] FIG. 3 is a power supply diagram of a control system (100) of an autonomous vehicle (V) according to one embodiment of the present disclosure.

[0058] Referring further to FIG. 3, a control system (100) of an autonomous vehicle (V) according to one embodiment may include a power supply (310), a plurality of low-voltage batteries (320, 330) and a DC power isolator (340, DC Isolator).

[0059] In one embodiment, the power supply unit (310) may include a high-voltage battery, a 12V battery (12V Batt), and a DC / DC converter (LDC). In one embodiment, the DC / DC converter (LDC) may convert power stored in the high-voltage battery (High-Volt Battery) to a low voltage (e.g., 12V) to charge the 12V battery (12V Batt) or provide it to the control system (100) of the autonomous vehicle (V).

[0060] In one embodiment, power provided from a power supply unit (310) can be provided to and charged by a plurality of low-voltage batteries (320, 330). In one embodiment, a DC power separator (340) is disposed between the power supply unit (310) and the plurality of low-voltage batteries (320, 330), and the plurality of low-voltage batteries (320, 330) can be connected in parallel to the DC power separator (340).

[0061] In one embodiment, the first low-voltage battery (320) may be connected to supply power to the lidar sensor (112), the radar sensor (116), at least one position sensor (118), and the communication module (120).

[0062] In one embodiment, the second low-voltage battery (330) may be connected to supply power to the processor (130). In one embodiment, the processor (130) may be connected to supply a portion of the power supplied from the second low-voltage battery (330) to the camera sensor (114).

[0063] In one embodiment, the power supply (310) may be connected to supply power to an external device via V2L (Vehicle to Load).

[0064] FIG. 4 is a flowchart for generating positioning data by fusing a plurality of sensor data according to one embodiment of the present disclosure.

[0065] Referring to FIG. 4, a processor (130) according to one embodiment can acquire first environment data and first location data through sensor modules (410, 420, 112, 114, 116). Based on the acquired first environment data and first location data, the processor (130) according to one embodiment can generate positioning data of a vehicle (V) or an object located in the surrounding environment.

[0066] In one embodiment, the sensor module (410, 420, 112, 114, 116) can individually fuse the first environment data and the first location data obtained from at least one environment sensor (112, 114, 116) or at least one location sensor (410, 420), respectively, and transmit the fused data to the processor (130). In one embodiment, each sensor module (410, 420, 112, 114, 116) is equipped with a separate processing device so that the first environment data and the first location data obtained from each can be individually fused and processed. Accordingly, the efficiency of the control system (100) can be improved by distributing and processing a large amount of sensing data in real time.

[0067] In one embodiment, the processor (130) can apply fusion data obtained through sensor modules (410, 420, 112, 114, 116) to an Extended Kalman Filter (EKF) algorithm to correct errors in the first environment data and the first location data, thereby generating more reliable positioning data.

[0068] In one embodiment, the extended Kalman filter algorithm may consist of the following steps.

[0069] (1) System architecture: performs data collection and location information sharing through sensor modules (410, 420, 112, 114, 116) and communication modules (120), and may include a structure for data fusion and cooperative positioning.

[0070] (2) Initialization phase: Initial state estimates can be set and the covariance matrix determined. For example, initial state ( ) and covariance matrix( ) can be set as follows.

[0071]

[0072]

[0073] (3) Prediction Step: Predicts the future state based on the current state, and can perform state prediction and covariance prediction.

[0074] For example, state prediction can be set as follows.

[0075]

[0076] For example, covariance prediction can be set as follows.

[0077]

[0078] (4) Update Step: Calculate Kalman Gain to compare the predicted value with the actual measured value and calculate the optimal estimate, perform a state update to generate corrected positioning data, and update the covariance to adjust the confidence level to be used in the next prediction step.

[0079] For example, the Kalman gain (K_K) can be calculated as follows.

[0080]

[0081] For example, a state update can be performed as follows.

[0082]

[0083] For example, the covariance update can be performed as follows.

[0084]

[0085] In one embodiment, the positioning data may include first positioning data corresponding to the position of the vehicle (V) and second positioning data corresponding to an object located in the surrounding environment. Here, the object may be another vehicle, a lane, an obstacle, etc. For example, the first positioning data may be the absolute position of the vehicle (V) or a relative position from a reference point, and the second positioning data may be the absolute position of the object or a relative position from the vehicle (V).

[0086] In one embodiment, the processor (130) can obtain shared data from another vehicle through the communication module (120) and correct positioning data based on the obtained shared data.

[0087] In one embodiment, the shared data may include second environment data related to the surrounding environment of another vehicle, or second location data related to the location or movement of another vehicle.

[0088] In one embodiment, the processor (130) can correct the first positioning data corresponding to the position of the vehicle (V) and the second positioning data corresponding to an object located in the surrounding environment based on the second environment data received from another vehicle. Here, since the second environment data is sensed at a different location from the vehicle (V), the processor (130) can correct the second positioning data by applying the relative position of the vehicle (V) and the other vehicle.

[0089] In one embodiment, the processor (130) can correct data corresponding to another vehicle included in the second positioning data based on second position data received from another vehicle.

[0090] In one embodiment, the processor (130) can set a driving plan for the vehicle (V) based on corrected positioning data and control the vehicle control unit (150) based on the set driving plan.

[0091] In one embodiment, the vehicle control unit (150) can control the steering device (157), power device (153), or braking device (155) of the vehicle according to a driving plan set by the processor (130). This is explained below through a specific scenario.

[0092] FIGS. 5a and 5b are state diagrams of a case where a vehicle (V) joins a road on which another vehicle (O) is traveling according to one embodiment of the present disclosure.

[0093] A processor (130) according to one embodiment can obtain shared data including the location, speed, or path of the vehicle from another vehicle (O) through a communication module (120) when an autonomous vehicle (V) joins a road where another vehicle (O) is driving.

[0094] For example, an autonomous vehicle (V) can recognize the location and speed of another vehicle (O), and the other vehicle (O) can recognize the intention of the autonomous vehicle (V) to enter.

[0095] In one embodiment, the processor (130) can set a driving plan including acceleration based on the acquired shared data.

[0096] For example, the autonomous vehicle (V) can set an optimal path at the merging point and set a driving plan to accelerate or decelerate appropriately by taking into account the speed and position of the other vehicle (O). For example, the other vehicle (O) can set a driving plan to maintain speed or accelerate slightly to allow the autonomous vehicle (V) to merge.

[0097] In one embodiment, the processor (130) can transmit a set driving plan to another vehicle (O). In one embodiment, the autonomous vehicle (V) and the other vehicle (O) can share shared data including each other's location, speed, or driving path in real time through the communication module (120).

[0098] In one embodiment, when the autonomous vehicle (V) safely merges behind another vehicle (O), the autonomous vehicle (V) and the other vehicle (O) continue driving and can change lanes as needed. In one embodiment, the autonomous vehicle (V) and the other vehicle (O) can prevent a collision by immediately operating the braking device (155) in the event of an emergency.

[0099] In one embodiment, when an autonomous vehicle (V) is unable to merge onto a road, it can plan an alternative route and drive safely.

[0100] FIG. 6 is a diagram showing the state when an emergency vehicle (E) according to one embodiment of the present disclosure approaches a vehicle (V) or another vehicle (O).

[0101] Referring to FIG. 6, a processor (130) according to one embodiment can obtain shared data including the location, speed, or path of the emergency vehicle (E) from another vehicle (O) through a communication module (120) when the emergency vehicle (E) approaches an autonomous vehicle (V) or another vehicle.

[0102] In another embodiment, the processor (130) can obtain shared data including the location, speed, or route of an emergency vehicle (E) through a traffic infrastructure via a communication module (120).

[0103] A processor (130) according to one embodiment may set a driving plan including the path of a vehicle (V) based on acquired shared data. In one embodiment, the processor (130) may set a driving plan that changes lanes so that an emergency vehicle (E) can pass safely. In one embodiment, the processor (130) may set a driving plan that decelerates or accelerates so that an emergency vehicle (E) can pass safely.

[0104] A processor (130) according to one embodiment can transmit a set driving plan to another vehicle (O) through a communication module (120).

[0105] In one embodiment, the processor (130) can set a driving plan including an optimal return path to return to the original path after the emergency vehicle (E) has passed.

[0106] FIG. 7 is a diagram of the state when a vehicle (V) or another vehicle (O) according to one embodiment of the present disclosure approaches a gate of an electronic toll payment system or a highway toll booth.

[0107] Referring to FIG. 7, a processor (130) according to one embodiment can obtain shared data including the location or traffic status of the gate (G) from another vehicle (O) when a vehicle (V) or another vehicle (O) approaches a toll electronic payment system or a highway toll booth gate (G).

[0108] In another embodiment, the processor (130) can obtain shared data including the location or traffic status of the gate (G) from the traffic infrastructure.

[0109] In one embodiment, the processor (130) can obtain shared data including the speed or path of another vehicle (O).

[0110] A processor (130) according to one embodiment may set a driving plan including the speed of a vehicle (V) based on acquired shared data. In one embodiment, the processor (130) may set a driving plan to maintain or decelerate the speed of the vehicle in order to safely pass through a gate (G). In one embodiment, the processor (130) may set a driving plan to adjust the distance from another vehicle (O) in order to safely pass through a gate (G).

[0111] A processor (130) according to one embodiment can transmit a set driving plan to another vehicle (O) through a communication module (120).

[0112] The control system (100) of the autonomous vehicle according to the present embodiment can fuse various sensor data, perform more precise positioning through cooperation between vehicles, and enable safer and more reliable autonomous driving.

[0113] FIG. 8 is a flowchart of a control method for an autonomous vehicle (V) according to one embodiment of the present disclosure.

[0114] Referring to FIG. 8, a control system (100) of an autonomous vehicle (V) according to one embodiment can obtain first environment data related to the surrounding environment of the vehicle (V) and first position data related to the position or movement of the vehicle through sensor modules (112, 114, 116, 118) in operation 810.

[0115] A control system (100) of an autonomous vehicle (V) according to one embodiment can generate positioning data of an object located in the vehicle (V) or surrounding environment based on first environment data and first location data obtained in operation 820.

[0116] A control system (100) of an autonomous vehicle (V) according to one embodiment can obtain shared data from another vehicle (O) through a communication module (120) in operation 830.

[0117] A control system (100) of an autonomous vehicle (V) according to one embodiment can correct positioning data based on shared data obtained in operation 840.

[0118] A control system (100) of an autonomous vehicle (V) according to one embodiment can set a driving plan for the vehicle based on corrected positioning data in operation 850.

[0119] A control system (100) of an autonomous vehicle (V) according to one embodiment can control a steering device (157), a power device (153), or a braking device (155) of the vehicle (V) based on a set driving plan in operation 860.

[0120] In the control system (100) of the autonomous vehicle described above, the control method of the autonomous vehicle may also be implemented in the form of a computer program stored on a computer-readable recording medium executed by a computer or in the form of a recording medium containing instructions executable by a computer. Additionally, in the control system (100) of the autonomous vehicle described above, the control method of the autonomous vehicle may also be implemented in the form of a computer program stored on a computer-readable recording medium executed by a computer.

[0121] A computer-readable recording medium may be any available medium accessible by a computer and includes both volatile and non-volatile media, and both removable and inremovable media. Additionally, a computer-readable recording medium may include a computer storage medium. A computer storage medium includes both volatile and non-volatile, removable and inremovable media implemented by any method or technique for storing information such as computer-readable instructions, data structures, program modules, or other data.

[0122] The functions realized by the components described herein may be implemented in a general-purpose processor, a specific-purpose processor, an integrated circuit, an Application Specific Integrated Circuit (ASIC), a Central Processing Unit (CPU), a circuit, and / or a combination thereof, which are programmed to realize the described functions. A processor may include transistors or other circuits and is considered to be a circuit or a processing circuit. A processor may be a programmed processor that executes a program stored in memory.

[0123] In this specification, circuits, parts, units, and means are hardware programmed to perform or execute the described functions. Such hardware may be any hardware disclosed in this specification or any hardware known to be programmed or execute the described functions.

[0124] If the hardware is a processor considered to be a circuit type, the circuit, the part, means, or unit is a combination of the hardware and the software used to constitute the hardware and / or processor.

[0125] The foregoing description of the present disclosure is for illustrative purposes only, and those skilled in the art will understand that other specific forms can be easily modified without altering the technical spirit or essential features of the present disclosure. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. For example, each component described as a single unit may be implemented in a distributed manner, and components described as distributed may likewise be implemented in a combined form.

[0126] The scope of the present disclosure is defined by the claims set forth below rather than by the detailed description above, and all modifications or variations derived from the meaning and scope of the claims and equivalent concepts thereof should be interpreted as being included within the scope of the present disclosure.

Claims

1. In a control system for an autonomous vehicle, A sensor module comprising at least one environment sensor configured to acquire first environment data related to the surrounding environment of the vehicle, and at least one position sensor configured to acquire first position data related to the position or movement of the vehicle; A communication module configured to receive data from another vehicle located around the vehicle or to transmit data to the other vehicle; At least one processor connected to the sensor module and the communication module to enable data transmission and reception; and It includes at least one memory containing computer program code, and The above at least one memory and the above computer program code are used by the control system through the above at least one processor: Through the sensor module above, the first environment data and the first location data are obtained, and Based on the acquired first environment data and first location data, positioning data of the vehicle or an object located in the surrounding environment is generated, and Through the communication module above, shared data is obtained from the other vehicle, and Based on the shared data obtained above, which is set to correct the positioning data, Control system.

2. In Paragraph 1, The above at least one environment sensor includes a LiDAR sensor configured to acquire three-dimensional point cloud data of the surrounding environment, a camera sensor configured to acquire image data of the surrounding environment, or a radar sensor configured to detect or track location data of an object located in the surrounding environment. Control system.

3. In Paragraph 1, The above at least one position sensor includes a Global Positioning System (GPS) sensor configured to transmit or receive GPS signals regarding the latitude, longitude, or altitude of the vehicle, or an Inertia Measurement Unit (IMU) sensor configured to sense the acceleration or angular velocity of the vehicle. Control system.

4. In Paragraph 1, The sensor module is configured to individually fuse the first environment data and the first location data, respectively acquired from the at least one environment sensor or the at least one location sensor, and to transmit the fused data to the processor. Control system.

5. In Paragraph 4, The above at least one memory and the above computer program code are used by the control system through the above at least one processor: It is configured to apply the fused data to an extended Kalman filter algorithm to correct errors in the first environment data and the first location data to generate the positioning data. Control system.

6. In Paragraph 1, The above positioning data includes a first positioning data corresponding to the location of the vehicle, and a second positioning data corresponding to an object located in the surrounding environment. Control system.

7. In Paragraph 1, The shared data above includes second environmental data related to the surrounding environment of the other vehicle, or second location data related to the location or movement of the other vehicle. Control system.

8. In Paragraph 1, The above at least one memory and the above computer program code are used by the control system through the above at least one processor: Based on the above-mentioned corrected positioning data, a driving plan for the vehicle is set, and Based on the driving plan set above, configured to control the steering device, power device, or braking device of the vehicle, Control system.

9. In Paragraph 8, The above at least one memory and the above computer program code are used by the control system through the above at least one processor: In the case where the vehicle joins the road where the other vehicle is traveling, At least as part of the operation of acquiring the shared data, the shared data including a location, speed, or path corresponding to the other vehicle is acquired from the other vehicle, and At least as part of the operation of setting a driving plan for the above vehicle, a driving plan including the acceleration of the above vehicle is set based on the acquired shared data, and The above communication module is configured to transmit the above-set driving plan to the above-set other vehicle. Control system.

10. In Paragraph 8, The above at least one memory and the above computer program code are used by the control system through the above at least one processor: When an emergency vehicle approaches the said vehicle or the said other vehicle, At least as part of the operation of acquiring the above-mentioned shared data, the shared data including a location, speed, or route corresponding to the emergency vehicle is acquired, and At least as part of the operation of setting a driving plan for the above vehicle, a driving plan including a route of the above vehicle is set based on the acquired shared data, and The above communication module is configured to transmit the above-set driving plan to the above-set other vehicle. Control system.

11. In Paragraph 1, The above at least one memory and the above computer program code are used by the control system through the above at least one processor: When the above vehicle or the other vehicle approaches an Electronic Toll Collection system or a highway toll booth gate, At least as part of the operation of acquiring the above-mentioned shared data, the above-mentioned shared data including the location or traffic status of the gate is acquired, and At least as part of the operation of setting a driving plan for the above vehicle, a driving plan including the speed of the above vehicle is set based on the acquired shared data, and The above communication module is configured to transmit the above-set driving plan to the above-set other vehicle. Control system.

12. In a method for controlling an autonomous vehicle, An operation of acquiring first environmental data related to the surrounding environment of the vehicle and first position data related to the position or movement of the vehicle through a sensor module comprising at least one environmental sensor and at least one position sensor; An operation to generate positioning data of the vehicle or an object located in the surrounding environment based on the acquired first environment data and the first location data; The operation of acquiring shared data from another vehicle through a communication module configured to receive data from another vehicle located around the vehicle or to transmit data to the other vehicle; and The method includes an operation to correct the positioning data based on the shared data obtained above. Control method.

13. In Paragraph 12, The above at least one memory and the above computer program code are used by the control system through the above at least one processor: The operation of setting a driving plan for the vehicle based on the above-mentioned corrected positioning data; and The operation of controlling the steering device, power device, or braking device of the vehicle based on the driving plan set above, Control method.

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