KR20200133852A - Autonomous driving apparatus and method - Google Patents

Abstract

The present invention relates to an autonomous driving apparatus and a method thereof. According to the present invention, the autonomous driving apparatus comprises: a sensor unit which detects surrounding vehicles of a user vehicle autonomously driving, and the status of a passenger in the user vehicle; a driving information detection unit which detects the driving information on the driving status of the user vehicle; a memory which stores map information; and a processor which controls the autonomous driving of the user vehicle based on the map information stored in the memory. The memory stores a lane change rate which is determined based on the driver′s lane change patterns, which are analyzed based on the driving information of the user vehicle when the user vehicle changes the lane, and the condition information of the road when the user vehicle changes the lane, and indicates the speed of the lane change of the user vehicle. The processor controls the autonomous driving of the user vehicle in accordance with a first expected driving trajectory which is generated based on the map information and the lane change rate stored in the memory and the driving information of the user vehicle detected by the driving information detection unit, and controls the autonomous driving of the user vehicle by selectively applying the first expected driving trajectory and a second expected driving trajectory in accordance with the status of the passenger detected by the sensor unit. The second expected driving trajectory is generated by reflecting a corrected lane change rate which is obtained by correcting the lane change rate stored in the memory. The autonomous driving apparatus and the method thereof are able to improve driving stability.

Description

Autonomous driving device and method {AUTONOMOUS DRIVING APPARATUS AND METHOD}

The present invention relates to an autonomous driving apparatus and method applied to an autonomous driving vehicle.

Today's automotive industry is moving toward implementing autonomous driving that minimizes driver intervention in vehicle driving. An autonomous vehicle refers to a vehicle that independently determines a driving route by recognizing the surrounding environment through a function of sensing and processing external information during driving, and independently driving by using its own power.

Autonomous vehicles can drive themselves to their destination by preventing collisions with obstacles on the driving path and adjusting the vehicle speed and driving direction according to the shape of the road, even if the driver does not operate the steering wheel, accelerator pedal, or brake. have. For example, acceleration may be performed on a straight road and deceleration may be performed while changing a driving direction in response to a curvature of the road on a curved road.

In order to ensure stable driving of an autonomous vehicle, it is necessary to accurately measure the driving environment through each sensor mounted on the vehicle, and control the driving according to the measured driving environment by continuously monitoring the driving state of the vehicle. To this end, various sensors, such as a lidar sensor, a radar sensor, an ultrasonic sensor, and a camera sensor, are applied to the autonomous vehicle as a sensor for detecting surrounding objects such as surrounding vehicles, pedestrians, and fixed facilities. Data output from these sensors is used to determine information about the driving environment, such as the location, shape, movement direction, and movement speed of surrounding objects.

Furthermore, autonomous vehicles use pre-stored map data to determine and correct the location of the vehicle to optimally determine the driving route and driving lane, control the driving of the vehicle so as not to deviate from the determined path and lane, and abruptly It also provides the function of performing defense and avoidance driving against dangers existing on the vehicle entering or the driving path.

Background art of the present invention is disclosed in Korean Patent Application Publication No. 10-1998-0068399 (published on October 15, 1998).

An object according to an aspect of the present invention is to improve the autonomous driving stability of the vehicle and at the same time improve the vehicle's autonomous driving stability by controlling the lateral driving of the vehicle at a speed determined in consideration of the state of the driver and passengers in the process of controlling the autonomous driving of the vehicle. It is to provide an autonomous driving device and method that enables follow-up measures suitable for the state of

An autonomous driving apparatus according to an aspect of the present invention includes a sensor unit for detecting a state of a vehicle surrounding the host vehicle and a passenger in the vehicle, and driving information for detecting driving information on the driving state of the host vehicle. A detection unit, a memory for storing map information, and a processor for controlling autonomous driving of the host vehicle based on the map information stored in the memory, wherein the memory includes driving information of the host vehicle when a lane of the host vehicle is changed. A lane change rate determined based on the driver's lane change pattern analyzed based on and the state information of the road at the time of changing the lane of the own vehicle and indicating the speed of the lane change of the own vehicle is stored, and the processor, The autonomous driving of the host vehicle is controlled according to a first predicted driving trajectory generated based on the map information and the lane change rate stored in the memory and the driving information of the host vehicle detected by the driving information detection unit, and the sensor unit Controls autonomous driving of the own vehicle by selectively applying the first and second predicted driving trajectories according to the state of the occupant detected by the vehicle, wherein the second predicted driving trajectory is a lane change rate stored in the memory. It is characterized in that the corrected correction difference is generated by reflecting the change rate.

In the present invention, the lane change rate is mapped with an entry steering angle and an entry speed for entering a target lane when the lane of the host vehicle is changed, and is stored in the memory, and the processor includes the first predicted driving trajectory. When controlling the autonomous driving of the host vehicle, the autonomous driving of the host vehicle is controlled according to an entry steering angle and an entry speed mapped to the lane change rate.

In the present invention, the processor, when controlling the autonomous driving of the host vehicle according to the second predicted driving trajectory, determines an entry steering angle and an entry speed having a value greater than an entry steering angle and an entry speed mapped to the lane change rate. Through it, it is characterized in that the autonomous driving of the own vehicle is controlled.

In the present invention, the processor, when a passenger other than the driver is not in the host vehicle, if the driving concentration of the driver determined based on the state of the driver acquired by the sensor unit is greater than or equal to a preset threshold concentration And controlling autonomous driving of the own vehicle according to the first predicted driving trajectory.

In the present invention, the processor, when it is determined that an emergency situation has occurred in the driver based on the state of the driver acquired by the sensor unit, when a passenger other than the driver is not boarded in the host vehicle, the second prediction It is characterized in that the autonomous driving of the host vehicle is controlled according to a driving trajectory.

In the present invention, the processor, when it is determined that an emergency situation has occurred in the passenger based on the state of the passenger obtained by the sensor unit when a passenger other than the driver boards the host vehicle, the second expected driving It is characterized in that the autonomous driving of the host vehicle is controlled according to a trajectory.

The present invention further includes an output unit, wherein the processor includes a case in which the driving concentration of the driver of the host vehicle is less than a preset threshold concentration, and when it is determined that an emergency situation has occurred in the driver or passenger of the host vehicle. If applicable, a warning is output through the output unit.

In an autonomous driving method according to an aspect of the present invention, a processor controls autonomous driving of the own vehicle according to a first predicted driving trajectory generated based on map information and a lane change rate stored in a memory, and driving information of the own vehicle. As a first control step, the lane change rate is based on a driver's lane change pattern analyzed based on driving information of the own vehicle when the vehicle's lane is changed, and road condition information when the vehicle's lane is changed. It is determined and is stored in the memory as an indicator of the speed of change in the lane of the own vehicle, and the processor comprises the control unit according to the state of the occupant in the own vehicle detected by the sensor unit. 1 As a second control step of controlling autonomous driving of the host vehicle by selectively applying an expected driving trajectory and a second predicted driving trajectory, the second predicted driving trajectory is changed to a correction lane in which the lane change rate stored in the memory is corrected. It characterized in that it comprises a step that is generated by reflecting the rate.

According to an aspect of the present invention, the present invention provides a first predicted driving trajectory and a lane change according to a lane change rate predetermined based on a driver's lane change pattern and road state information according to the state of a driver and a passenger. By selectively applying a second predicted driving trajectory according to the rate of change of the corrected lane rate, the autonomous driving of the vehicle is controlled, thereby improving the autonomous driving stability of the vehicle and enabling follow-up measures appropriate to the occupant's condition. have.

1 is an overall block diagram of an autonomous driving control system to which an autonomous driving device according to an embodiment of the present invention can be applied.
2 is a block diagram showing a detailed configuration of an integrated autonomous driving control unit in an autonomous driving device according to an embodiment of the present invention.
3 is an exemplary view showing an example in which an autonomous driving device according to an embodiment of the present invention is applied to a vehicle.
4 is an exemplary view showing an example of an internal structure of a vehicle to which an autonomous driving device according to an embodiment of the present invention is applied.
5 is an exemplary view showing an example of a set distance and a horizontal angle of view at which a lidar sensor, a radar sensor, and a camera sensor can detect surrounding objects in an autonomous driving apparatus according to an embodiment of the present invention.
6 is an exemplary view showing an example in which a sensor unit detects a nearby vehicle in an autonomous driving device according to an embodiment of the present invention.
7 is a block diagram showing a process in which a lane change rate is stored in a memory and converted into a database in an autonomous driving apparatus according to an embodiment of the present invention.
8 and 9 are flowcharts illustrating an autonomous driving method according to an embodiment of the present invention.

Hereinafter, embodiments of an autonomous driving apparatus and method according to the present invention will be described with reference to the accompanying drawings. In this process, the thickness of the lines or the size of components shown in the drawings may be exaggerated for clarity and convenience of description. In addition, terms to be described later are terms defined in consideration of functions in the present invention and may vary according to the intention or custom of users or operators. Therefore, definitions of these terms should be made based on the contents throughout the present specification.

1 is an overall block diagram of an autonomous driving control system to which an autonomous driving device according to an embodiment of the present invention can be applied, and FIG. 2 is a detailed diagram of an autonomous driving integrated control unit in an autonomous driving device according to an embodiment of the present invention. A block diagram showing the configuration, FIG. 3 is an exemplary view showing an example in which an autonomous driving device according to an embodiment of the present invention is applied to a vehicle, and FIG. 4 is an autonomous driving device according to an embodiment of the present invention applied. It is an exemplary view showing an example of the internal structure of the vehicle, and FIG. 5 is a set distance and a horizontal angle of view at which a lidar sensor, a radar sensor, and a camera sensor can detect surrounding objects in an autonomous driving device according to an embodiment of the present invention. 6 is an exemplary diagram showing an example in which a sensor unit detects nearby vehicles in an autonomous driving device according to an embodiment of the present invention, and FIG. 7 is an autonomous driving according to an embodiment of the present invention. This is a block diagram showing a process in which the change rate of the difference in the device is stored in memory and converted into a database.

First, the structure and function of the autonomous driving control system to which the autonomous driving apparatus according to the present embodiment can be applied will be described with reference to FIGS. 1 and 3. As shown in FIG. 1, the autonomous driving control system is used to control autonomous driving of a vehicle through a driving information input interface 101, a driving information input interface 201, a passenger output interface 301, and a vehicle control output interface 401. It may be implemented around the autonomous driving integrated control unit 600 that transmits and receives necessary data.

The integrated autonomous driving control unit 600 may obtain driving information according to a user's manipulation of the user input unit 100 in the autonomous driving mode or the manual driving mode of the vehicle through the driving information input interface 101. The user input unit 100 includes a driving mode switch 110 and a user terminal 120 (e.g., a navigation terminal mounted on a vehicle, a smartphone or a tablet PC, etc.) possessed by a passenger, as shown as an example in FIG. 1 Accordingly, the driving information may include driving mode information and navigation information of the vehicle. For example, the driving mode of the vehicle determined according to the operation of the occupant on the driving mode switch 110 (i.e., autonomous driving mode/manual driving mode, or sports mode/eco mode/safety A mode (Safe Mode)/Normal Mode) may be transmitted to the autonomous driving integrated control unit 600 through the driving information input interface 101 as the driving information described above. In addition, navigation information such as the passenger's destination input through the user terminal 120 by the occupant and the route to the destination (the shortest route selected by the occupant among candidate routes to the destination, or a preferred route, etc.) is driving information as described above. It may be transmitted to the integrated autonomous driving controller 600 through the input interface 101. Meanwhile, the user terminal 120 may be implemented as a control panel (eg, a touch screen panel) that provides a user interface (UI) for the driver to input or modify information for autonomous driving control of the vehicle. In this case, the driving mode switch 110 described above may be implemented as a touch button on the user terminal 120.

In addition, the autonomous driving integrated control unit 600 may obtain driving information indicating the driving state of the vehicle through the driving information input interface 201. The driving information includes the steering angle formed as the passenger manipulates the steering wheel, the accelerator pedal stroke or the brake pedal stroke formed by depressing the accelerator pedal or the brake pedal, and the vehicle speed, acceleration, yaw, and pitch as the behavior formed in the vehicle. And various information indicating the driving state and behavior of the vehicle such as a roll, and each of the driving information includes a steering angle sensor 210, an APS (Accel Position Sensor)/PTS (Pedal Travel Sensor) as shown in FIG. 220, the vehicle speed sensor 230, the acceleration sensor 240, it may be detected by the driving information detection unit 200 including the yaw / pitch / roll sensor 250. Furthermore, the driving information of the vehicle may include location information of the vehicle, and the location information of the vehicle may be obtained through the GPS (Global Positioning Sysetm) receiver 260 applied to the vehicle. The driving information may be transmitted to the integrated autonomous driving controller 600 through the driving information input interface 201 and used to control the driving of the vehicle in the autonomous driving mode or the manual driving mode.

In addition, the integrated autonomous driving controller 600 may transmit driving state information provided to the occupant in the autonomous driving mode or the manual driving mode of the vehicle to the output unit 300 through the occupant output interface 301. That is, the integrated autonomous driving control unit 600 transmits the driving state information of the vehicle to the output unit 300, so that the occupant can drive the vehicle autonomously or manually based on the driving state information output through the output unit 300. The state may be checked, and the driving state information may include various information indicating the driving state of the vehicle, such as a current driving mode, a shift range, and a vehicle speed. In addition, the autonomous driving integrated control unit 600 outputs warning information through the occupant output interface 301 when it is determined that the driver needs a warning in the autonomous driving mode or the manual driving mode of the vehicle together with the driving status information. By passing it to 300, the output unit 300 may output a warning to the driver. In order to audibly and visually output such driving state information and warning information, the output unit 300 may include a speaker 310 and a display device 320 as shown in FIG. 1. In this case, the display device 320 may be implemented as the same device as the user terminal 120 described above, or may be implemented as a separate and independent device.

In addition, the autonomous driving integrated control unit 600 may transmit control information for driving control of the vehicle in the autonomous driving mode or the manual driving mode of the vehicle to the lower control system 400 applied to the vehicle through the vehicle control output interface 401. have. The lower control system 400 for driving control of a vehicle may include an engine control system 410, a braking control system 420, and a steering control system 430 as shown in FIG. 1, and an integrated autonomous driving control unit 600 may transmit engine control information, braking control information, and steering control information as the control information to each of the sub-control systems 410, 420, and 430 through the vehicle control output interface 401. Accordingly, the engine control system 410 may increase or decrease the fuel supplied to the engine to control the vehicle speed and acceleration, and the braking control system 420 may control the braking of the vehicle by adjusting the braking force of the vehicle. In addition, the steering control system 430 may control the steering of the vehicle through a steering device applied to the vehicle (eg, a Motor Driven Power Steering (MDPS) system).

As described above, the autonomous driving integrated control unit 600 according to the present embodiment provides driving information according to the driver's manipulation and driving information indicating the driving state of the vehicle through the driving information input interface 101 and the driving information input interface 201, respectively. The driving state information and warning information generated according to the autonomous driving algorithm acquired and processed by the internal processor 610 may be transmitted to the output unit 300 through the occupant output interface 301, and the internal processor ( Control information generated according to the autonomous driving algorithm processed by 610 may be transmitted to the lower control system 400 through the vehicle control output interface 401 to perform driving control of the vehicle.

Meanwhile, in order to ensure stable autonomous driving of the vehicle, it is necessary to continuously monitor the driving state by accurately measuring the driving environment of the vehicle and control the driving according to the measured driving environment. To this end, the autonomous driving apparatus of this embodiment As shown in FIG. 1, it may include a sensor unit 500 for detecting objects around the vehicle, such as surrounding vehicles, pedestrians, roads, or fixed facilities (eg, traffic lights, milestones, traffic signs, construction fences, etc.). The sensor unit 500 may include one or more of a lidar sensor 510, a radar sensor 520, and a camera sensor 530 to detect surrounding objects outside the vehicle as shown in FIG. 1.

The lidar sensor 510 can detect surrounding objects outside the vehicle by transmitting a laser signal around the vehicle and receiving a signal that is reflected and returned to the object. It is possible to detect surrounding objects located within a vertical field of view and a set horizontal field of view. The lidar sensor 510 may include a front lidar sensor 511, an upper lidar sensor 512, and a rear lidar sensor 513 respectively installed at the front, upper and rear of the vehicle, but the installation position thereof And the number of installations is not limited to a specific embodiment. The threshold value for determining the validity of the laser signal reflected and returned from the object may be stored in advance in the memory 620 of the integrated autonomous driving controller 600, and the processor 610 of the integrated autonomous driving controller 600 Is to determine the position (including the distance to the object), speed, and movement direction of the object through a method of measuring the time when the laser signal transmitted through the lidar sensor 510 is reflected on the object and returns. I can.

The radar sensor 520 can detect surrounding objects outside the vehicle by emitting electromagnetic waves around the vehicle and receiving a signal reflected back from the object, and a preset distance, a set vertical angle of view, and Nearby objects located within the set horizontal angle of view can be detected. The radar sensor 520 includes a front radar sensor 521, a left radar sensor 521, a right radar sensor 522, and a rear radar sensor 523 respectively installed on the front, left, right and rear of the vehicle. However, the installation location and the number of installations are not limited to specific embodiments. The processor 610 of the autonomous driving integrated control unit 600 analyzes the power of the electromagnetic waves transmitted and received through the radar sensor 520 to determine the location of the object (including the distance to the object) and speed. And the moving direction can be determined.

The camera sensor 530 may detect surrounding objects outside the vehicle by photographing the surroundings of the vehicle, and may detect surrounding objects located within a preset distance, a set vertical angle of view, and a set horizontal angle of view according to the specifications. . The camera sensor 530 includes a front camera sensor 531, a left camera sensor 532, a right camera sensor 533, and a rear camera sensor 534 respectively installed on the front, left, right and rear of the vehicle. However, the installation location and the number of installations are not limited to specific embodiments. The processor 610 of the autonomous driving integrated control unit 600 applies a predefined image processing processing to the image captured through the camera sensor 530, so that the location of the object (including the distance to the object) and speed And the moving direction can be determined. In addition, an internal camera sensor 535 for capturing the inside of the vehicle may be mounted at a predetermined position (eg, a rear view mirror) inside the vehicle, and the processor 610 of the autonomous driving integrated control unit 600 is an internal camera A guide or warning may be output to the occupant through the above-described output unit 300 by monitoring the occupant's behavior and condition based on the image acquired through the sensor 535.

In addition to the lidar sensor 510, the radar sensor 520, and the camera sensor 530, the sensor unit 500 may further include an ultrasonic sensor 540 as shown in FIG. Various types of sensors for detecting surrounding objects may be further employed in the sensor unit 500. 3 is a front lidar sensor 511 or a front radar sensor 521 installed on the front of the vehicle, and a rear lidar sensor 513 or a rear radar sensor 524 is installed at the rear of the vehicle to aid understanding of this embodiment. The front camera sensor 531, the left camera sensor 532, the right camera sensor 533, and the rear camera sensor 534 are respectively installed on the front, left, right and rear sides of the vehicle. , As described above, the installation position and number of each sensor are not limited to a specific embodiment. FIG. 5 shows an example of a set distance and a horizontal angle of view at which the lidar sensor 510, the radar sensor 520, and the camera sensor 530 can detect surrounding objects in front, and FIG. 6 is An example of detecting an object is shown. 6 is only an example of detecting surrounding objects, and a method of detecting surrounding objects is determined depending on the installation location and number of sensors. According to the configuration of the sensor unit 500 described above, surrounding vehicles and surrounding objects in an omnidirectional region of the host vehicle may be detected.

Furthermore, the sensor unit 500 detects voice and bio signals of the occupant (eg, heart rate, electrocardiogram, respiration, blood pressure, body temperature, brain waves, blood flow (pulse wave), blood sugar, etc.) to determine the state of the occupant in the vehicle. It may further include a microphone and a biometric sensor, and the biometric sensor includes a heart rate sensor, an electrocardiogram sensor, a breathing sensor, a blood pressure sensor, a body temperature sensor, an electroencephalogram sensor, a photoplethysmography sensor, and a blood sugar sensor. This can be.

4 shows an example of the internal structure of the vehicle, and the state of the vehicle is controlled by the operation of the occupant, such as the driver or passenger of the vehicle, to facilitate the driver's driving or convenience (e.g., rest, entertainment activities, etc.) Internal devices may be installed to support. Such an internal device may include a vehicle seat (S) on which the occupant is seated, a lighting device such as an interior light and mood lamp (L), the aforementioned user terminal 120 and a display device 320, an interior table, and the like. The state of the internal device may be controlled by the processor 610.

In the case of the vehicle seat S, the angle can be adjusted by the processor 610 (or by manual operation of the occupant), and the vehicle seat S is the front row seat S1 and the rear row seat S2 If it is composed of, only the angle of the front row seat (S1) can be adjusted. If the rear row seat (S2) is not provided and the front row seat (S1) is divided into a seat structure and a footrest structure, the seat structure of the front row seat (S1) is physically separated from the footrest structure. It can be implemented so that the angle is adjusted. In addition, an actuator (eg, a motor) for adjusting the angle of the vehicle seat S may be provided. In the case of the lighting device L, the on/off may be controlled by the processor 610 (or by manual operation of the occupant), and the lighting device L may operate a plurality of lighting units such as internal lights and mood lights. When included, each lighting unit can be independently controlled on and off. The user terminal 120 or the display device 320 may adjust the angle according to the occupant's viewing angle by the processor 610 (or by the occupant's manual operation), for example, the screen in the direction of the occupant's gaze. The angle can be adjusted so that there is. In this case, an actuator (eg, a motor) for adjusting the angles of the user terminal 120 and the display device 320 may be provided.

The integrated autonomous driving controller 600 may communicate with the server 700 through a network as shown in FIG. 1. As a network method between the autonomous driving integrated control unit 600 and the server 700, various communication methods such as a wide area network (WAN), a local area network (LAN), or a personal area network (PAN) may be employed. In addition, in order to secure wide network coverage, LPWAN (Low Power Wide Area Network, a network with very wide coverage among Internet of Things, including commercialized technologies such as LoRa, Sigfox, Ingenu, LTE-M, NB-IOT, etc.) communication The scheme can be employed. For example, LoRa (low-power communication is possible, but has a wide coverage of up to 20Km), or Sigfox (which has a coverage of 10Km (city) to 30Km (outskirts outside the city) depending on the environment) is adopted. In addition, 3GPP (3rd Generation) such as LTE-MTC (Machine-type Communications) (or LTE-M), NB (Narrowband) LTE-M, and NB IoT having a power saving mode (PSM) Partnership Project) Release 12, 13-based LTE network technology may be employed. The server 700 may provide map information that is kept up to date (various map information such as two-dimensional navigation map data, three-dimensional remote map data, or three-dimensional high-precision electronic map data may correspond), and further It can also provide various information such as accident information, road control information, traffic volume information and weather information. The autonomous driving integrated control unit 600 may receive the latest map information from the server 700 and update the map information stored in the memory 620, and receive accident information, road control information, traffic volume information, and weather information to It can also be used for autonomous driving control.

Next, the structure and function of the integrated autonomous driving controller 600 according to the present embodiment will be described with reference to FIG. 2. As shown in FIG. 2, the integrated autonomous driving controller 600 may include a processor 610 and a memory 620.

The memory 620 may store basic information necessary for the autonomous driving control of the vehicle, or may store information generated in the process of controlling the autonomous driving of the vehicle by the processor 610, and the processor 610 may store the memory 620 ), you can control the autonomous driving of the vehicle by accessing (read, access) the information stored in it. The memory 620 may be implemented as a computer-readable recording medium and may operate to allow the processor 610 to access it. Specifically, the memory 620 is a hard drive, magnetic tape, memory card, read-only memory (ROM), random-access memory (RAM), digital video disc (DVD), or optical disk. It may be implemented as an optical data storage device such as.

Map information required for autonomous driving control by the processor 610 may be stored in the memory 620. The map information stored in the memory 620 may be a navigation map (a numerical topographic map) that provides road-level information, but a precision road map that provides road information in units of lanes to improve the precision of autonomous driving control, That is, it may be desirable to be implemented as 3D high-precision electronic map data. Accordingly, the map information stored in the memory 620 is dynamic and necessary for autonomous driving control of the vehicle, such as lanes, lane center lines, regulatory lines, road boundaries, road center lines, traffic signs, road signs, road shapes and heights, and lane widths. Static information can be provided.

In addition, the memory 620 may store an autonomous driving algorithm for autonomous driving control of a vehicle. The autonomous driving algorithm is an algorithm (recognition, determination and control algorithm) that recognizes the surroundings of an autonomous vehicle, determines its state, and controls the driving of the vehicle according to the determination result, and the processor 610 is an autonomous vehicle stored in the memory 620 By executing the driving algorithm, it is possible to perform active autonomous driving control in the surrounding environment of the vehicle.

The processor 610 includes driving information and driving information respectively input from the driving information input interface 101 and the driving information input interface 201 described above, information on surrounding objects detected through the sensor unit 500, and memory. The autonomous driving of the vehicle may be controlled based on the map information stored in 620 and the autonomous driving algorithm. The processor 610 may be implemented as a dedicated semiconductor circuit such as an embedded processor such as a complex instruction set computer (CISC) or a reduced instruction set computer (RISC), or an application specific integrated circuit (ASIC). .

In this embodiment, the processor 610 may control autonomous driving of the own vehicle by analyzing each driving trajectory of the own vehicle and the surrounding vehicle. To this end, as shown in FIG. 2, the processor 610 includes a sensor processing module ( 611), a driving trajectory generation module 612, a driving trajectory analysis module 613, a driving control module 614, a trajectory learning module 615, and a passenger status determination module 616. Although FIG. 2 shows each module as an independent block according to a function, each module may be integrated into one module to be implemented in a configuration in which each function is integrated.

The sensor processing module 611 includes driving information of the surrounding vehicle (that is, the location of the surrounding vehicle, and includes the location of the surrounding vehicle and the speed of the surrounding vehicle along with the location) based on the result of detecting the surrounding vehicle of the host vehicle through the sensor unit 500. It may further include a moving direction). That is, the position of the surrounding vehicle is determined based on the signal received through the lidar sensor 510, the position of the surrounding vehicle is determined based on the signal received through the radar sensor 520, or the camera sensor 530 The location of the surrounding vehicle may be determined based on the image captured through the device, or the location of the surrounding vehicle may be determined based on a signal received through the ultrasonic sensor 540. To this end, the sensor processing module 611 as shown in FIG. 1 may include a lidar signal processing module 611a, a radar signal processing module 611b, and a camera signal processing module 611c (ultrasonic signal processing A module may be further added to the sensor processing module 611). A method of determining a location of a nearby vehicle using the lidar sensor 510, the radar sensor 520, and the camera sensor 530 is a specific embodiment, and the implementation method is not limited thereto. In addition, the sensor processing module 611 may determine attribute information such as the size and type of the surrounding vehicle as well as the location, speed, and movement direction of the surrounding vehicle, and the position, speed, movement direction, and size of the surrounding vehicle as described above. And an algorithm for determining information such as type may be predefined.

The driving trajectory generation module 612 can generate the actual driving trajectory and the expected driving trajectory of the surrounding vehicle, and the actual driving trajectory of the own vehicle, and for this purpose, as shown in FIG. 2, the surrounding vehicle driving trajectory generation module 612a And a driving trajectory generation module 612b of the own vehicle.

First, the surrounding vehicle driving trajectory generation module 612a may generate an actual driving trajectory of the surrounding vehicle.

Specifically, the surrounding vehicle driving trajectory generation module 612a is based on the driving information of the surrounding vehicle detected by the sensor unit 500 (that is, the position of the surrounding vehicle determined by the sensor processing module 611). You can create an actual driving trajectory. In this case, in order to generate the actual driving trajectory of the surrounding vehicle, the surrounding vehicle driving trajectory generation module 612a may refer to the map information stored in the memory 620, and the position of the surrounding vehicle detected by the sensor unit 500 And a random location on the map information stored in the memory 620 may be cross-referenced to generate an actual driving trajectory of the surrounding vehicle. For example, when a nearby vehicle is detected at a specific point by the sensor unit 500, the surrounding vehicle driving trajectory generation module 612a is a location of the detected surrounding vehicle and an arbitrary location on the map information stored in the memory 620. By cross-referencing, it is possible to specify the location of the currently detected surrounding vehicle on the map information, and by continuously monitoring the location of the surrounding vehicle as described above, the actual driving trajectory of the surrounding vehicle can be generated. That is, the surrounding vehicle driving trajectory generation module 612a maps and accumulates the location of the surrounding vehicle detected by the sensor unit 500 to the location on the map information stored in the memory 620 based on the cross reference. The actual driving trajectory of the vehicle can be created.

Meanwhile, the actual driving trajectory of the surrounding vehicle may be compared with the predicted driving trajectory of the surrounding vehicle to be described later, and used to determine whether the map information stored in the memory 620 is incorrect. In this case, when comparing the actual driving trajectory of a specific surrounding vehicle with the predicted driving trajectory, a problem of erroneously determining that the map information is accurate but inaccurate may arise. For example, when the actual driving trajectory of a plurality of surrounding vehicles and the predicted driving trajectory coincide with each other, and the actual driving trajectory and the predicted driving trajectory of a specific surrounding vehicle are different, only the actual driving trajectory of the specific surrounding vehicle is matched with the expected driving trajectory. In comparison, even though the map information is accurate, it can be misjudged as inaccurate. Therefore, there is a need to determine whether the tendency of the actual driving trajectory of the plurality of surrounding vehicles deviates from the expected driving trajectory, and for this purpose, the driving trajectory generation module 612a of the surrounding vehicles generates the actual driving trajectories of the plurality of surrounding vehicles, respectively. You may. Furthermore, considering that the driver of the surrounding vehicle tends to move the steering wheel to the left and right during the driving process for a straight path driving, the actual driving trajectory of the surrounding vehicle may be generated in a curved shape rather than a straight line. In order to calculate the error between the predicted driving trajectories, the driving trajectory generation module 612a of the surrounding vehicle may generate the actual driving trajectory in the form of a straight line by applying a predetermined smoothing technique to the original actual driving trajectory that is generated in a curved form. Various techniques such as interpolation for each position of the surrounding vehicle can be employed as the smoothing technique.

Also, the surrounding vehicle driving trajectory generation module 612a may generate a predicted driving trajectory of the surrounding vehicle based on map information stored in the memory 620.

As described above, the map information stored in the memory 620 may be 3D high-precision electronic map data. Therefore, the map information includes lanes, lane center lines, regulatory lines, road boundaries, road center lines, traffic signs, road signs, and road shapes. And it is possible to provide dynamic and static information necessary for autonomous driving control of the vehicle, such as height and lane width. In general, considering that the vehicle runs in the center of the lane, it may be expected that the surrounding vehicles running in the vicinity of the own vehicle also travel in the center of the lane. Therefore, the surrounding vehicle driving trajectory generation module 612a The vehicle's predicted driving trajectory can be generated as a lane center line reflected in the map information.

The own vehicle driving trajectory generation module 612b may generate an actual driving trajectory that the own vehicle has traveled to date based on the driving information of the own vehicle acquired through the driving information input interface 201 described above.

Specifically, the host vehicle driving trajectory generation module 612b includes a location of the host vehicle obtained through the driving information input interface 201 (that is, location information of the host vehicle obtained through the GPS receiver 260) and a memory 620 ), it is possible to create an actual driving trajectory of the own vehicle by cross-referencing an arbitrary location on the map information stored in ). For example, by cross-referencing the location of the own vehicle acquired through the driving information input interface 201 and an arbitrary location on the map information stored in the memory 620, the current location of the own vehicle can be specified on the map information, As described above, by continuously monitoring the position of the own vehicle, an actual driving trajectory of the own vehicle can be generated. That is, the host vehicle driving trajectory generation module 612b maps and accumulates the location of the host vehicle acquired through the driving information input interface 201 to the location on the map information stored in the memory 620 based on the cross reference. By doing so, it is possible to generate an actual traveling trajectory of the own vehicle.

In addition, the own vehicle driving trajectory generation module 612b may generate a predicted driving trajectory in which the own vehicle should travel to the destination based on map information stored in the memory.

That is, the own vehicle driving trajectory generation module 612b is stored in the current position of the own vehicle obtained through the driving information input interface 201 (that is, the current position information of the own vehicle obtained through the GPS receiver 260) and the memory. An estimated driving trajectory to the destination can be generated using the stored map information, and the predicted driving trajectory of the own vehicle will be generated as a center line of the lane reflected in the map information stored in the memory 620 like the predicted driving trajectory of nearby vehicles. I can.

The driving trajectory generated by the surrounding vehicle driving trajectory generating module 612a and the own vehicle driving trajectory generating module 612b may be stored in the memory 620, and the autonomous driving of the own vehicle is controlled by the processor 610. It can be used for various purposes in the process.

The driving trajectory analysis module 613 is generated by the driving trajectory generation module 612 and stored in the memory 620 (that is, the actual driving trajectory and the expected driving trajectory of the surrounding vehicle, the actual driving trajectory of the own vehicle). By analyzing, it is possible to diagnose the reliability of autonomous driving control for the current own vehicle. The reliability diagnosis of autonomous driving control may be performed by analyzing a trajectory error between an actual driving trajectory and an expected driving trajectory of nearby vehicles.

The driving control module 614 may perform a function of controlling autonomous driving of the own vehicle, and specifically, driving information and driving information input from the driving information input interface 101 and the driving information input interface 201 described above, respectively. Wow, the autonomous driving algorithm is processed by comprehensively using information on surrounding objects detected through the sensor unit 500 and map information stored in the memory 620, and control information is transmitted through the vehicle control output interface 401. It is possible to allow the lower control system 400 to control the autonomous driving of the own vehicle by transmitting the vehicle, and also transmit the driving state information and warning information of the own vehicle to the output unit 300 through the occupant output interface 301 to the driver Can be made aware of. In addition, when the driving control module 614 integrally controls the autonomous driving as described above, the own vehicle analyzed by the sensor processing module 611, the driving trajectory generation module 612, and the driving trajectory analysis module 613 And by controlling the autonomous driving in consideration of the driving trajectory of the surrounding vehicle, the precision of the autonomous driving control may be improved and the stability of the autonomous driving control may be improved.

The trajectory learning module 615 may learn or correct the actual driving trajectory of the own vehicle generated by the own vehicle driving trajectory generation module 612b. For example, if the trajectory error between the actual driving trajectory of the surrounding vehicle and the expected driving trajectory is greater than or equal to a preset threshold, it is determined that the map information stored in the memory 620 is inaccurate, and it is determined that the actual driving trajectory of the own vehicle needs to be corrected. Accordingly, a lateral shift value for correcting the actual traveling trajectory of the own vehicle may be determined to correct the traveling trajectory of the own vehicle.

The occupant state determination module 616 may determine the occupant's state and behavior based on the occupant's state and bio-signals detected by the aforementioned internal camera sensor 535 and the biometric sensor. The state of the occupant determined by the occupant state determination module 616 may be used in the process of controlling the autonomous driving of the own vehicle or outputting a warning to the occupant.

Based on the above, hereinafter, according to the state of the occupant, the first predicted driving trajectory according to the lane change rate predetermined based on the driver's lane change pattern and road state information, and the lane change rate are corrected. An embodiment in which autonomous driving of a vehicle is controlled by selectively applying a second expected driving trajectory according to a correction lane change rate will be described.

Basically, the processor 610 controls the autonomous driving of the own vehicle according to the map information and the lane change rate stored in the memory 620 and the predicted driving trajectory (first predicted driving trajectory) generated based on the driving information of the own vehicle. can do. Here, the lane change rate is determined based on the driver's lane change pattern analyzed based on the driving information of the own vehicle when the own vehicle's lane changes, and the road condition information when the own vehicle's lane changes. As an indicator of the speed, it may be stored in the memory 620.

When the lane change rate employed in the present embodiment will be described in detail, as described above, the lane change rate refers to a parameter indicating the speed of change of the lane of the host vehicle. The speed of lane change is the entry steering angle for entering the target lane when the vehicle's lane is changed (the steering angle of the host vehicle that determines the angle between the entry direction to the target lane and the target lane) and the entry speed (the side of the host vehicle). Direction can mean speed). In other words, a small lane change rate means that the entry steering angle and entry speed are small, so that the lane can be changed slowly, and a large lane change rate means that the entry steering angle and entry speed are large, so that the lane can be changed quickly. do.

This lane change rate is the driver's lane change pattern analyzed based on the driving information of the own vehicle acquired by the driving information detection unit 200 when the vehicle's lane changes due to the driver's manual driving, and when the vehicle's lane is changed. May be determined based on the state information of the road ahead (e.g., the width, curvature, slope and number of lanes, etc. of the road ahead, which may be obtained by the sensor unit 500), and a database based on the driving history of the own vehicle And stored in the memory 620.

FIG. 7 shows a process in which the lane change rate is stored in the memory 620 and converted into a database, and the processor 610 of the present embodiment is shown in FIG. 2 as shown in FIG. In addition to each of the modules, a lane change pattern analysis module 617, a road condition determination module 618, and a lane change rate determination module 619 may be included.

The lane change pattern analysis module 617 is the driving information of the own vehicle detected by the driving information detection unit 200, and the steering angle when the lane of the host vehicle changes (that is, the steering angle formed as the driver operates the steering wheel), The driver's lane change pattern can be analyzed based on the time it took for the own vehicle to complete the lane change and the speed at which it enters the target lane. For example, the lane change pattern analysis module 617 includes the first steering angle at the time when the lane change of the host vehicle is started, the second steering angle at the time when the vehicle lane change is completed, and the time required to complete the lane change. Through this, it is possible to analyze the lane change pattern as to how much time the driver took to change the lane at what size and how much time. In this case, the starting point of the lane change is a time when the steering angle of the host vehicle reaches a preset critical steering angle, or when the display direction of the host vehicle's direction indicator and the direction of the vehicle's steering angle coincide, or In a state where the display direction and the direction of the vehicle's steering angle coincide, the steering angle of the host vehicle may be a time point when it is determined that it is equal to or greater than a preset threshold steering angle, and the completion time of the lane change may be a time point when the direction indicator of the host vehicle is off.

The road condition grasp module 618 can grasp the condition of the road when the vehicle's lane is changed (eg, the width, curvature, slope, and number of lanes of the road ahead). For example, the sensor unit 500 The state of the road may be identified through a method of analyzing the result of detecting the road among surrounding objects of the vehicle, or the memory 620 based on the current position of the vehicle positioned from the GPS receiver 260 of the driving information detector 200. ), it is possible to grasp the state of the road by extracting the state information of the road from the map information stored in ).

The lane change rate determination module 619 determines the lane change rate based on the driver's lane change pattern analyzed by the lane change pattern analysis module 617 and the road state information identified by the road state determination module 618. You can decide. For example, the lane change rate determination module 619 calculates a lane change pattern index indicating the speed of lane change based on the analysis result of the lane change pattern, and then calculates the calculated lane change pattern index into the state of the road (road One or more of the width, curvature, gradient, and number of lanes may be used) to determine the lane change rate by increasing or decreasing the number of lanes. This lane change rate may be converted into a database according to the state of the road and stored in the memory 620, and the processor 610 may determine the map information stored in the memory 620 and the driving information of the host vehicle detected by the detection unit 200. A first predicted driving trajectory for controlling autonomous driving of the own vehicle may be generated by reflecting the lane change rate stored in the memory 620 together with the driving information. Accordingly, when the processor 610 controls autonomous driving of the own vehicle according to the first predicted driving trajectory (that is, when changing a lane in the process of autonomous driving of the own vehicle), the processor 610 is reflected in the first predicted driving trajectory. It is possible to control the autonomous driving of the own vehicle according to the entry steering angle and entry speed mapped to the change rate of the existing lane (ie, it is possible to control the change of the lane of the own vehicle).

Meanwhile, in the process of controlling the autonomous driving of the own vehicle through the first predicted driving trajectory according to the lane change rate stored in the memory 620, it may be necessary to correct the change rate of the lane of the own vehicle according to the state of the occupant. . For example, when an emergency situation occurs to the occupant, the host vehicle needs to change lanes more quickly to perform emergency driving or to quickly move to the shoulder. To this end, in the present embodiment, in the process of controlling the autonomous driving of the own vehicle according to the first expected driving trajectory, the processor 610 performs autonomous driving control according to the first predicted driving trajectory in which the lane change rate is reflected according to the state of the occupant. It is possible to determine whether to maintain or switch to autonomous driving control according to the second predicted driving trajectory in which the corrected lane change rate is reflected. That is, the processor 610 selectively applies the first predicted driving trajectory (reflecting the lane change rate) and the second predicted driving trajectory (reflecting the correction lane change rate) according to the state of the occupant detected by the sensor unit 500. You can control the autonomous driving of your own vehicle. The processor 610 uses a method of mapping the entry steering angle and entry speed having a value greater than the entry steering angle mapped to the lane change rate and the entry speed in order to perform a faster lane change compared to the lane change rate stored in the memory 620. The corrected lane change rate can be determined through the correction lane. Accordingly, when controlling the autonomous driving of the own vehicle according to the second expected driving trajectory, the processor 610 has a value greater than the entry steering angle and entry speed mapped to the lane change rate. By controlling the autonomous driving of the own vehicle through the entry steering angle and entry speed, faster lane changes can be made. The entry steering angle of the corrected lane change rate versus the lane change rate and the increase in the entry speed may be designed in advance according to the intention of the designer.

Whether the processor 610 controls the autonomous driving of the own vehicle using which of the first predicted driving trajectory reflecting the lane change rate and the second predicted driving trajectory reflecting the corrected lane change rate is determined by passengers other than the driver. It is determined according to whether or not the vehicle is on board and the state of the occupant, and hereinafter, each autonomous driving control process classified according to the state of the occupant will be described.

When a passenger other than the driver is not in the host vehicle, the processor 610 determines the first predicted driving trajectory if the driver's driving concentration determined based on the driver's state acquired by the sensor unit 500 is greater than or equal to a preset threshold concentration. Accordingly, the autonomous driving of the own vehicle can be controlled. Here, the driver's driving concentration is a numerical value of the driver's state acquired by the sensor unit 500, and may be, for example, a numerical parameter based on whether the driver is constantly looking ahead. In the occupant state determination module 616 of 610, a predetermined algorithm for calculating the driving concentration by quantifying the driver's state may be preset. In addition, the critical concentration is a value that serves as a reference for determining whether the driver concentrates on driving, and may be selected as an appropriate value according to the intention of the designer and may be preset in the occupant state determination module 616.

Depending on the level of autonomous driving, supervision of the driver's autonomous driving control may be required (e.g., levels 1 to 3), so if the driver's driving concentration is above the critical concentration (e.g., the driver is looking ahead), the driver As a normal state in which an emergency situation has not occurred, it is desirable to maintain the lane change according to the lane change rate stored in the memory 620 in order to secure autonomous driving stability, so the processor 610 It can maintain autonomous driving control of the vehicle.

Meanwhile, if the driver's driving concentration is less than the critical concentration (eg, the driver is not looking ahead), the processor 610 may output a warning through the output unit 300. After the warning is output, when the driver's driving concentration recovers above the threshold concentration, the processor 610 performs autonomous driving control of the own vehicle according to the first predicted driving trajectory, and after the warning is output, the driver's driving concentration does not recover above the threshold concentration. Otherwise, the autonomous driving mode can be turned off with the driver's approval to change the driving mode.

Next, the processor 610 determines that an emergency situation has occurred in the driver based on the driver's state acquired by the sensor unit 500 when no passengers other than the driver board the vehicle, and the second expected driving trajectory is Accordingly, autonomous driving of the own vehicle can be controlled. That is, in order to determine the occurrence of an emergency situation of the occupant, such as difficulty breathing or cardiac arrest, the driver detects not only the internal camera sensor 535 of the sensor unit 500 but also biometric information such as the occupant's heart rate, pulse and blood pressure. If it is determined that a biometric sensor may be used), it is necessary to prioritize the rapid movement of the own vehicle for emergency treatment to the driver, so that the processor 610 autonomously drives the own vehicle according to the second expected driving trajectory. By controlling it, it is possible to induce a rapid emergency driving of the own vehicle or a rapid movement to the shoulder.

Next, the processor 610 determines that an emergency situation has occurred in the passenger based on the state of the passenger obtained by the sensor unit 500 when a passenger other than the driver boards the own vehicle, according to the second expected driving trajectory. You can control the autonomous driving of your own vehicle. That is, in this case, it is necessary to prioritize the rapid movement of the own vehicle for emergency treatment for the passenger, so the processor 610 controls the autonomous driving of the own vehicle according to the second expected driving trajectory, and Can induce rapid movement of

If an emergency situation does not occur to the driver and passengers, it is desirable to maintain the lane change according to the lane change rate stored in the memory 620 in order to secure autonomous driving stability, so that the processor 610 uses the first expected driving trajectory. It is possible to maintain autonomous driving control of the own vehicle according to the following. In addition, when it is determined that an emergency situation has occurred to the driver or passenger, the processor 610 may output a warning through the output unit 300.

8 and 9 are flowcharts for explaining an autonomous driving method according to an exemplary embodiment of the present invention. Referring to FIG. 8, the autonomous driving method according to the present exemplary embodiment includes a first control step (S100) and a second control step. It may include (S200).

In the first control step (S100), the processor 610 controls the autonomous driving of the own vehicle according to the map information stored in the memory 620 and the lane change rate, and a first predicted driving trajectory generated based on the driving information of the own vehicle. do. As described above, the lane change rate is determined based on the driver's lane change pattern analyzed based on the driving information of the own vehicle when the own vehicle's lane changes, and the road condition information when the own vehicle's lane changes. It is stored in the memory 620 as an indicator of the speed of change.

In the second control step (S200), the processor 610 selectively applies the first predicted driving trajectory and the second predicted driving trajectory according to the state of the occupant in the own vehicle detected by the sensor unit 500 Controls autonomous driving. As described above, the correction lane change rate in which the lane change rate stored in the memory 620 is corrected is reflected in the second expected driving trajectory.

The lane change rate is mapped with the entry steering angle and entry speed for entering the target lane when the lane of the host vehicle is changed, and is stored in the memory 620, and accordingly, the processor 610 of the host vehicle according to the first expected driving trajectory When controlling autonomous driving, the autonomous driving of the own vehicle is controlled according to the entry steering angle and entry speed mapped to the lane change rate.

In addition, when controlling the autonomous driving of the own vehicle according to the second predicted driving trajectory, the processor 610 determines the entry steering angle and entry speed mapped to the lane change rate. Controls autonomous driving.

Assuming the above-described contents, step S200 will be described in detail with reference to FIG. 9, the processor 610 is a driver acquired by the sensor unit 500 when a passenger other than the driver is not boarded in the own vehicle (S201). If the driving concentration of the driver determined based on the state of is equal to or greater than the preset threshold concentration (S202), autonomous driving of the own vehicle is controlled according to the first expected driving trajectory (S203). If the driver's driving concentration is less than the preset threshold concentration (S202), the processor 610 outputs a warning through the output unit 300 (S204), and after the warning is output, the driver's driving concentration is restored to a threshold concentration or higher. (S205) Autonomous driving control of the own vehicle is performed according to the first predicted driving trajectory (S203), and if the driver's driving concentration does not recover above the critical concentration level after the warning is output (S205), autonomous driving under the driver's approval of switching to driving mode The mode is turned off (S206).

In addition, when a passenger other than the driver is not in the vehicle (S201), the processor 610 outputs when it is determined that an emergency situation has occurred to the driver based on the driver's state acquired by the sensor unit 500 (S207). After outputting a warning through the unit 300 (S208), autonomous driving of the own vehicle is controlled according to the second expected driving trajectory (S209).

And, when a passenger other than the driver boards the own vehicle (S201), if it is determined that an emergency situation has occurred in the passenger based on the state of the passenger acquired by the sensor unit 500 (S210), through the output unit 300 After the warning is output (S211), the autonomous driving of the own vehicle is controlled according to the second expected driving trajectory (S212).

If an emergency situation does not occur to the driver in step S207 or an emergency situation does not occur to the passenger in step S210, autonomous driving control of the own vehicle is performed according to the first predicted driving trajectory (S203).

As described above, according to the present embodiment, according to the state of the driver and passenger, the first expected driving trajectory and the lane change rate are corrected according to the lane change rate predetermined based on the driver's lane change pattern and road state information. By selectively applying the second predicted driving trajectory according to the lane change rate to control the autonomous driving of the vehicle, it is possible to improve the autonomous driving stability of the vehicle and to enable follow-up measures suitable for the condition of the occupant.

The present invention has been described with reference to the embodiments shown in the drawings, but these are only exemplary, and those of ordinary skill in the art to which the present technology pertains, various modifications and other equivalent embodiments are possible. I will understand. Therefore, the true technical protection scope of the present invention should be determined by the following claims.

100: user input unit 101: driving information input interface
110: drive mode switch 120: user terminal
200: driving information detection unit 201: driving information input interface
210: steering angle sensor 220: APS/PTS
230: vehicle speed sensor 240: acceleration sensor
250: yaw/pitch/roll sensor 260: GPS receiver
300: output unit 301: occupant output interface
310: speaker 320: display device
400: sub-control system 401: vehicle control output interface
410: engine control system 420: braking control system
430: steering control system 500: sensor unit
510: lidar sensor 511: front lidar sensor
512: upper lid sensor 513: rear lid sensor
520: radar sensor 521: front radar sensor
522: left radar sensor 523: right radar sensor
524: rear radar sensor 530: camera sensor
531: front camera sensor 532: left camera sensor
533: right camera sensor 534: rear camera sensor
535: internal camera sensor 540: ultrasonic sensor
600: autonomous driving integrated control unit 610: processor
611: sensor processing module 611a: lidar signal processing module
611b: radar signal processing module 611c: camera signal processing module
612: driving trajectory generation module 612a: driving trajectory generation module of surrounding vehicles
612b: host vehicle driving trajectory generation module 613: driving trajectory analysis module
614: driving control module 615: trajectory learning module
616: occupant status determination module 617: lane change pattern analysis module
618: road condition identification module 619: lane change rate determination module
620: memory 700: server

Claims (14)

A sensor unit detecting a state of a vehicle surrounding the own vehicle and a passenger in the vehicle;
A driving information detection unit that detects driving information on a driving state of the host vehicle;
A memory for storing map information; And
A processor for controlling autonomous driving of the own vehicle based on the map information stored in the memory; and
The memory,
When the lane of the host vehicle is changed, it is determined based on the driver’s lane change pattern analyzed based on the driving information of the host vehicle, and the road condition information when the vehicle’s lane is changed, and the speed of the lane change of the host vehicle is determined. It stores the rate of change to the index lane,
The processor,
Controls autonomous driving of the host vehicle according to a first predicted driving trajectory generated based on map information and a lane change rate stored in the memory, and driving information of the host vehicle detected by the driving information detection unit,
Controls autonomous driving of the own vehicle by selectively applying the first and second predicted driving trajectories according to the state of the occupant detected by the sensor unit, and the second predicted driving trajectory is stored in the memory. An autonomous driving device that is generated by reflecting the corrected lane change rate for which the stored lane change rate is corrected.
The method of claim 1,
The lane change rate is mapped with an entry steering angle and an entry speed for entering a target lane when the lane of the host vehicle is changed, and is stored in the memory,
The processor,
When controlling the autonomous driving of the host vehicle according to the first predicted driving trajectory, the autonomous driving apparatus of the host vehicle is controlled according to an entry steering angle and an entry speed mapped to the lane change rate.
The method of claim 2,
The processor,
When controlling the autonomous driving of the host vehicle according to the second predicted driving trajectory, autonomous driving of the host vehicle is performed through an entry steering angle mapped to the lane change rate and an entry steering angle and an entry speed having a larger value than the entry speed. Autonomous driving device, characterized in that to control.
The method of claim 3,
The processor,
When a passenger other than the driver is not in the host vehicle, the driving concentration of the driver determined based on the state of the driver obtained by the sensor unit is greater than or equal to a preset threshold concentration, according to the first expected driving trajectory. An autonomous driving device, characterized in that controlling autonomous driving of the own vehicle.
The method of claim 3,
The processor,
When it is determined that an emergency situation has occurred in the driver based on the driver's condition acquired by the sensor unit when no passengers other than the driver board the host vehicle, the host vehicle is displayed according to the second expected driving trajectory. An autonomous driving device, characterized in that controlling autonomous driving.
The method of claim 3,
The processor,
When it is determined that an emergency situation has occurred in the passenger based on the state of the passenger obtained by the sensor unit when a passenger other than the driver boards the own vehicle, the autonomous vehicle is autonomous according to the second expected driving trajectory. An autonomous driving device, characterized in that it controls driving.
The method of claim 3,
It further includes an output unit,
The processor,
When the driving concentration of the driver of the host vehicle is less than a preset threshold concentration, or when it is determined that an emergency situation has occurred to the driver or passenger of the host vehicle, a warning is output through the output unit. Autonomous driving device.
A first control step in which a processor controls autonomous driving of the host vehicle according to a first predicted driving trajectory generated based on map information and a lane change rate stored in a memory, and driving information of the host vehicle, wherein the lane change rate is When the lane of the host vehicle is changed, it is determined based on the driver’s lane change pattern analyzed based on the driving information of the host vehicle, and the road condition information when the vehicle’s lane is changed, and the speed of the lane change of the host vehicle is determined. The index is stored in the memory; And
The second processor controls autonomous driving of the own vehicle by selectively applying the first predicted driving trajectory and the second predicted driving trajectory according to the state of the occupant in the own vehicle detected by the sensor unit. A control step, wherein the second predicted driving trajectory is generated by reflecting a correction lane change rate in which a lane change rate stored in the memory is corrected;
Autonomous driving method comprising a.
The method of claim 8,
The lane change rate is mapped with an entry steering angle and an entry speed for entering a target lane when the lane of the host vehicle is changed, and is stored in the memory,
The processor,
When controlling the autonomous driving of the host vehicle according to the first predicted driving trajectory, the autonomous driving method of the host vehicle is controlled according to an entry steering angle and an entry speed mapped to the lane change rate.
The method of claim 9,
The processor,
When controlling the autonomous driving of the host vehicle according to the second predicted driving trajectory, autonomous driving of the host vehicle is performed through an entry steering angle mapped to the lane change rate and an entry steering angle and an entry speed having a larger value than the entry speed. Autonomous driving method, characterized in that to control.
The method of claim 10,
In the second control step, the processor,
When a passenger other than the driver is not in the host vehicle, the driving concentration of the driver determined based on the state of the driver obtained by the sensor unit is greater than or equal to a preset threshold concentration, according to the first expected driving trajectory. An autonomous driving method, characterized in that controlling the autonomous driving of the own vehicle.
The method of claim 10,
In the second control step, the processor,
When it is determined that an emergency situation has occurred in the driver based on the driver's condition acquired by the sensor unit when no passengers other than the driver board the host vehicle, the host vehicle is displayed according to the second expected driving trajectory. An autonomous driving method, characterized in that controlling autonomous driving.
The method of claim 10,
In the second control step, the processor,
When it is determined that an emergency situation has occurred in the passenger based on the state of the passenger obtained by the sensor unit when a passenger other than the driver boards the own vehicle, the autonomous vehicle is autonomous according to the second expected driving trajectory. An autonomous driving method, characterized in that controlling driving.
The method of claim 10,
In the second control step, the processor,
An autonomous driving method comprising outputting a warning when the driving concentration of the driver of the host vehicle is less than a preset threshold concentration or when it is determined that an emergency situation has occurred to the driver or passenger of the host vehicle .

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