KR20240038680A - Autonomous driving apparatus and method - Google Patents

Abstract

The present invention relates to an autonomous driving device and method, which includes a sensor unit that detects vehicles around a self-driving vehicle, a memory that stores map information, and a processor that controls autonomous driving of the self-vehicle based on the map information stored in the memory. The processor generates an actual driving trajectory of surrounding vehicles based on driving information of surrounding vehicles detected by the sensor unit, generates an expected driving trajectory of surrounding vehicles based on map information stored in a memory, and a memory. An expected driving trajectory of the own vehicle is generated based on the map information stored in the host vehicle, and when it is determined that correction of the expected driving trajectory of the own vehicle is necessary through comparison between the actual driving trajectory and the expected driving trajectory of surrounding vehicles, the target is transferred from the own vehicle. It is characterized by correcting the expected driving trajectory of the own vehicle based on the degree of risk according to the distance to surrounding vehicles.

Description

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

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

Today's automobile industry is moving toward implementing autonomous driving that minimizes driver intervention in vehicle driving. An autonomous vehicle refers to a vehicle that recognizes the surrounding environment through external information detection and processing functions when driving, determines its own driving path, and drives independently using its own power.

Self-driving vehicles can drive to their destination on their own, preventing collisions with obstacles in the driving path and adjusting vehicle speed and driving direction according to the shape of the road, without the driver having to operate the steering wheel, accelerator pedal, or brakes. there is. For example, acceleration can be performed on a straight road, and deceleration can be performed on a curved road by changing the driving direction in response to the curvature of the road.

In order to ensure stable driving of an autonomous vehicle, the driving environment must be accurately measured through each sensor mounted on the vehicle, and the driving status of the vehicle must be continuously monitored to control driving according to the measured driving environment. To this end, various sensors such as Lidar sensors, radar sensors, ultrasonic sensors, and camera sensors are applied to autonomous vehicles to detect 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 status information such as the location, shape, direction of movement, and speed of surrounding objects.

Furthermore, autonomous vehicles use pre-stored map data to determine and correct the vehicle's position to optimally determine the driving path and driving lane, control the vehicle's driving so that it does not deviate from the determined path and lane, and control the vehicle's driving to prevent sudden changes in the surrounding area. It also provides functions to perform defense and avoidance operations against incoming vehicles or hazards present on the driving path.

The background technology of the present invention is disclosed in Republic of Korea Patent Publication No. 10-1998-0068399 (published on October 15, 1998).

An object according to one aspect of the present invention is an autonomous driving device for improving the driving stability and driving accuracy of an autonomous vehicle by correcting the driving trajectory of the autonomous vehicle in consideration of the risk according to the distance between the autonomous vehicle and surrounding vehicles, and It provides a method.

An autonomous driving device according to one aspect of the present invention includes a sensor unit that detects vehicles around a self-driving host vehicle, a memory that stores map information, and a processor that controls autonomous driving of the host vehicle based on the map information stored in the memory. It includes: the processor generates an actual driving trajectory of the surrounding vehicle based on the driving information of the surrounding vehicle detected by the sensor unit, and an expected driving trajectory of the surrounding vehicle based on the map information stored in the memory. Generates an expected driving trajectory of the host vehicle based on map information stored in the memory, and requires correction of the expected driving trajectory of the host vehicle through comparison between the actual driving trajectory and the expected driving trajectory of the surrounding vehicles. If it is determined that this is the case, the expected driving trajectory of the host vehicle is corrected based on the degree of risk according to the distance from the host vehicle to the target surrounding vehicles.

In the present invention, the processor is characterized in that it determines that correction of the expected driving trajectory of the own vehicle is necessary when the trajectory error between the actual driving trajectory and the expected driving trajectory of the surrounding vehicle is more than a preset threshold.

In the present invention, the target surrounding vehicle includes first and second target surrounding vehicles driving on the left and right sides of the host vehicle, respectively, and the processor determines the lateral distance between the host vehicle and the first target surrounding vehicle. And, based on the lateral distance between the host vehicle and the second target surrounding vehicle, the expected driving trajectory of the host vehicle is corrected in a direction where the driving risk of the host vehicle is low.

In the present invention, the processor determines a primary shift value for correcting the expected driving trajectory of the host vehicle in a direction with a low driving risk of the host vehicle, and the host vehicle determines the first and second target surrounding vehicles. After determining the final shift value by correcting the first shift value through a weight indicating the proximity risk for the case of approaching, the expected driving trajectory of the host vehicle is corrected according to the determined final shift value. .

An autonomous driving method according to an aspect of the present invention includes the steps of a processor controlling autonomous driving of a host vehicle based on map information stored in a memory, the processor controlling the driving of vehicles around the host vehicle detected by a sensor unit. generating an actual driving trajectory of the surrounding vehicle based on information, the processor generating an expected driving trajectory of the surrounding vehicle based on map information stored in the memory, the processor generating a map stored in the memory Generating an expected driving trajectory of the host vehicle based on information, the processor comparing the actual driving trajectory and the expected driving trajectory of the surrounding vehicles to determine whether correction of the expected driving trajectory of the host vehicle is necessary. , and when it is determined that correction of the expected driving trajectory of the host vehicle is necessary, the processor corrects the expected driving trajectory of the host vehicle based on the degree of risk according to the distance from the host vehicle to the target surrounding vehicle. It is characterized by including.

The present invention is characterized in that in the determining step, the processor determines that correction of the expected driving trajectory of the own vehicle is necessary when the trajectory error between the actual driving trajectory and the expected driving trajectory of the surrounding vehicle is more than a preset threshold. Do it as

In the present invention, the vehicles around the target include first and second vehicles around the target running on the left and right sides of the host vehicle, respectively, and in the step of correcting, the processor Characterized by correcting the expected driving trajectory of the host vehicle in a direction with a low driving risk of the host vehicle based on a first lateral distance between surrounding vehicles and a second lateral distance between the host vehicle and the second target surrounding vehicle. Do it as

In the present invention, in the correction step, the processor compares the first and second lateral distances to determine a direction with a low driving risk of the host vehicle, and determines the expected driving trajectory of the host vehicle in the determined direction. Determining a primary shift value for correction, wherein the processor corrects the primary shift value through a weight indicating a proximity risk when the host vehicle approaches the first and second target surrounding vehicles. It is characterized in that it includes a step of determining a final shift value, and a step of correcting, by the processor, an expected driving trajectory of the host vehicle according to the determined final shift value.

According to one aspect of the present invention, the present invention determines the need for correction of the driving trajectory of an autonomous vehicle, and according to the judgment result, corrects the driving trajectory of the autonomous vehicle in consideration of the degree of risk according to the distance between surrounding vehicles, thereby autonomously driving the vehicle. The driving stability and driving accuracy of a driving vehicle can be improved.

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.
Figure 2 is a block diagram showing the specific configuration of the autonomous driving integrated control unit in the autonomous driving device according to an embodiment of the present invention.
Figure 3 is an exemplary diagram showing an example of an autonomous driving device being applied to a vehicle according to an embodiment of the present invention.
Figure 4 is an exemplary diagram showing an example of the internal structure of a vehicle to which an autonomous driving device according to an embodiment of the present invention is applied.
Figure 5 is an exemplary diagram showing an example of a set distance and 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.
Figure 6 is an exemplary diagram showing an example of a sensor unit detecting surrounding vehicles in an autonomous driving device according to an embodiment of the present invention.
7 and 8 are flowcharts for explaining an autonomous driving method according to an embodiment of the present invention.

Hereinafter, embodiments of the autonomous driving device and method according to the present invention will be described with reference to the attached drawings. In this process, the thickness of lines or sizes of components shown in the drawing may be exaggerated for clarity and convenience of explanation. In addition, the terms described below are terms defined in consideration of functions in the present invention, and may vary depending on the intention or custom of the user or operator. Therefore, definitions of these terms should be made based on the content throughout this specification.

Figure 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 Figure 2 is a detailed view of the autonomous driving integrated control unit in an autonomous driving device according to an embodiment of the present invention. It is a block diagram showing the configuration, Figure 3 is an example diagram showing an example of the self-driving device according to an embodiment of the present invention being applied to a vehicle, and Figure 4 is an example of the self-driving device being applied to a vehicle according to an embodiment of the present invention. It is an exemplary diagram showing an example of the internal structure of a vehicle, and Figure 5 shows the set distance and horizontal angle of view at which the lidar sensor, radar sensor, and camera sensor can detect surrounding objects in the autonomous driving device according to an embodiment of the present invention. This is an exemplary diagram showing an example, and Figure 6 is an exemplary diagram showing an example of a sensor unit detecting surrounding vehicles in an autonomous driving device according to an embodiment of the present invention.

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

The autonomous driving integrated control unit 600 may obtain driving information according to the passenger's manipulation of the user input unit 100 in the vehicle's autonomous driving mode or manual driving mode 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 tablet PC carried by a passenger, etc.) 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 occupant's operation of the driving mode switch 110 (i.e., autonomous driving mode/manual driving mode, or Sport Mode/Eco Mode/Safety) Mode (Safe Mode/Normal Mode) can be transmitted to the autonomous driving integrated control unit 600 through the driving information input interface 101 as the above-described driving information. In addition, navigation information such as the passenger's destination and the route to the destination (such as the shortest route or preferred route selected by the passenger among candidate routes to the destination) that the passenger inputs through the user terminal 120 is the driving information. It may be transmitted to the autonomous driving integrated control unit 600 through the input interface 101. Meanwhile, the user terminal 120 may be implemented as a control panel (e.g., a touch screen panel) that provides a UI (User Interface) for the driver to input or modify information for autonomous driving control of the vehicle. , In this case, the aforementioned driving mode switch 110 may be implemented as a touch button on the user terminal 120.

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

Additionally, the autonomous driving integrated control unit 600 may transmit driving status information provided to the passenger in the vehicle's autonomous driving mode or manual driving mode to the output unit 300 through the passenger output interface 301. That is, the autonomous driving integrated control unit 600 transmits the vehicle's driving status information to the output unit 300, so that the occupants can select the vehicle's autonomous driving status or manual driving status based on the driving status information output through the output unit 300. The status can be checked, and the driving status information may include various information indicating the driving status of the vehicle, such as the current vehicle driving mode, shift range, and vehicle speed. In addition, when the autonomous driving integrated control unit 600 determines that a warning is required to the driver in the vehicle's autonomous driving mode or manual driving mode along with the driving status information, the autonomous driving integrated control unit 600 outputs warning information through the passenger output interface 301. It can be transmitted to 300 so that the output unit 300 outputs a warning to the driver. In order to output such driving status information and warning information audibly and visually, the output unit 300 may include a speaker 310 and a display device 320 as shown in FIG. 1 . At this time, 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 can transmit control information for driving control of the vehicle in the vehicle's autonomous driving mode or manual driving mode to the lower control system 400 applied to the vehicle through the vehicle control output interface 401. there is. The sub-control system 400 for driving control of the 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 autonomous driving integrated control unit. 600 can transmit engine control information, braking control information, and steering control information as the control information to each lower control system 410, 420, and 430 through the vehicle control output interface 401. Accordingly, the engine control system 410 can control the vehicle speed and acceleration of the vehicle by increasing or decreasing the fuel supplied to the engine, and the braking control system 420 can control the braking of the vehicle by adjusting the braking force of the vehicle. The steering control system 430 may control the steering of the vehicle through a steering device applied to the vehicle (e.g., Motor Driven Power Steering (MDPS) system).

As described above, the autonomous driving integrated control unit 600 of this embodiment provides driving information according to the driver's operation 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 status information and warning information generated according to the autonomous driving algorithm obtained and processed by the internal processor 610 can be transmitted to the output unit 300 through the occupant output interface 301, and the internal processor ( The 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 control driving 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 driving according to the measured driving environment. To this end, the autonomous driving device of this embodiment is 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 (e.g., traffic lights, signposts, traffic signs, construction fences, etc.). As shown in FIG. 1, 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.

The LiDAR sensor 510 can detect surrounding objects outside the vehicle by transmitting a laser signal to the surroundings of the vehicle and receiving a signal that is reflected and returned by the object, and has a predefined set distance and set vertical according to its specifications. It is possible to detect surrounding objects located within the vertical field of view and setting 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, which are installed at the front, top, and rear of the vehicle, respectively, but their installation locations and number of installations are not limited to specific embodiments. The threshold for determining the validity of the laser signal reflected by the object and returned may be pre-stored in the memory 620 of the autonomous driving integrated control unit 600, and the processor 610 of the autonomous driving integrated control unit 600 Determines the location (including the distance to the object), speed, and direction of movement of the object by measuring the time when the laser signal transmitted through the LIDAR sensor 510 is reflected by the object and returns. You can.

The radar sensor 520 can detect surrounding objects outside the vehicle by radiating electromagnetic waves around the vehicle and receiving signals that are reflected and returned by the object, and has a predefined set distance, set vertical angle of view, and Surrounding objects located within the set horizontal angle of view can be detected. The radar sensor 520 may include a front radar sensor 521, a left radar sensor 521, a right radar sensor 522, and a rear radar sensor 523, which are installed on the front, left side, right side, and rear of the vehicle, respectively. However, the installation location and number of installations are not limited to specific embodiments. The processor 610 of the autonomous driving integrated control unit 600 analyzes the power of electromagnetic waves transmitted and received through the radar sensor 520 to determine the location (including the distance to the object) and speed of the object. and direction of movement can be determined.

The camera sensor 530 can detect surrounding objects outside the vehicle by capturing images around the vehicle, and can detect surrounding objects located within a predefined range of a set distance, a set vertical angle of view, and a set horizontal angle of view according to its specifications. . The camera sensor 530 may include a front camera sensor 531, a left camera sensor 532, a right camera sensor 533, and a rear camera sensor 534 installed on the front, left side, right side, and rear of the vehicle, respectively. However, the installation location and number of installations are not limited to specific embodiments. The processor 610 of the autonomous driving integrated control unit 600 applies predefined image processing to the image captured through the camera sensor 530 to determine the location (including the distance to the object) and speed of the object. and direction of movement can be determined. Additionally, an internal camera sensor 535 for capturing images of the inside of the vehicle may be mounted at a predetermined location inside the vehicle (e.g., a rearview mirror), and the processor 610 of the autonomous driving integrated control unit 600 may use the internal camera. Based on the image acquired through the sensor 535, the occupant's behavior and status may be monitored and guidance or warnings may be output to the occupant through the above-described output unit 300.

In addition to the lidar sensor 510, radar sensor 520, and camera sensor 530, the sensor unit 500 may further include an ultrasonic sensor 540 as shown in FIG. 1, along with the vehicle's Various types of sensors for detecting surrounding objects may be further employed in the sensor unit 500. 3 shows that the front lidar sensor 511 or the front radar sensor 521 is installed at the front of the vehicle, and the rear lidar sensor 513 or rear radar sensor 524 is installed at the rear of the vehicle to help understand this embodiment. It is installed in, and shows an example where the front camera sensor 531, left camera sensor 532, right camera sensor 533, and rear camera sensor 534 are installed on the front, left side, right side, and rear of the vehicle, respectively. , As described above, the installation location and number of each sensor are not limited to a specific embodiment. Figure 5 shows an example of the set distance and horizontal angle of view at which the lidar sensor 510, radar sensor 520, and camera sensor 530 can detect surrounding objects in front, and Figure 6 shows each sensor's surrounding objects. An example of detecting an object is shown. Figure 6 is only an example of surrounding object detection, and the surrounding object detection method 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 the omnidirectional area of the host vehicle can be detected.

Furthermore, the sensor unit 500 detects the occupant's voice and biosignals (e.g., heart rate, electrocardiogram, respiration, blood pressure, body temperature, brain wave, blood flow (pulse wave), and blood sugar, etc.) to determine the condition of the occupant in the vehicle. It may further include a microphone and a biometric sensor, and the biometric sensors include a heart rate sensor, electrocardiogram sensor, respiration sensor, blood pressure sensor, body temperature sensor, electroencephalogram sensor, blood flow sensor, and blood sugar sensor. This can be.

Figure 4 shows an example of the internal structure of a vehicle, and the state of the interior of the vehicle is controlled by the operation of the vehicle's occupants, such as the driver or passenger, to facilitate the occupant's driving or convenience (e.g., rest, entertainment activities, etc.). Internal devices may be installed to support it. These internal devices may include the vehicle seat (S) on which the occupants sit, lighting devices (L) such as interior lights and mood lights, the user terminal 120 and display device 320 described above, internal tables, etc. 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 passenger), and the vehicle seat (S) is divided into the front row seat (S1) and the rear row seat (S2). If configured, only the angle of the front row seat (S1) can be adjusted. In the case where 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. The angle may be implemented to be adjusted. Additionally, an actuator (eg, motor) may be provided to adjust the angle of the vehicle seat (S). In the case of the lighting device (L), its on/off may be controlled by the processor 610 (or by manual operation of the passenger), and the lighting device (L) may operate a plurality of lighting units, such as an interior light and a mood light. When included, each lighting unit can be independently controlled on and off. The angle of the user terminal 120 or the display device 320 may be adjusted by the processor 610 (or by the passenger's manual operation) according to the passenger's viewing angle, for example, the screen may be adjusted in the passenger's gaze direction. The angle can be adjusted so that there is. In this case, an actuator (eg, motor) may be provided to adjust the angle of the user terminal 120 and the display device 320.

The autonomous driving integrated control unit 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 WAN (Wide Area Network), LAN (Local Area Network), or PAN (Personal Area Network) may be adopted. In addition, in order to secure wide network coverage, LPWAN (Low Power Wide Area Network, a network with very wide coverage among the Internet of Things, including commercialized technologies such as LoRa, Sigfox, Ingenu, LTE-M, and NB-IOT) communication method can be adopted. For example, communication methods such as LoRa (which enables low-power communication and has a wide coverage of up to 20 km) or Sigfox (which has a coverage of 10 km (in the city center) to 30 km (outside the city center) depending on the environment) are adopted. 3GPP (3rd Generation) such as LTE-MTC (Machine-type Communications) (or LTE-M) with Power Saving Mode (PSM), NB (Narrowband) LTE-M, and NB IoT. Partnership Project) Release 12 and 13-based LTE network technology may be adopted. The server 700 can provide map information that maintains up-to-dateness (various map information may apply, such as 2D navigation map data, 3D isolated map data, or 3D high-precision electronic map data), and further provides road It can also provide various information such as accident information, road control information, traffic information, and weather information. The autonomous driving integrated control unit 600 receives the latest map information from the server 700 and can update the map information stored in the memory 620, and receives 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 autonomous driving integrated control unit 600 of this embodiment will be described with reference to FIG. 2. As shown in FIG. 2 , the autonomous driving integrated control unit 600 may include a processor 610 and a memory 620 .

The memory 620 may store basic information necessary for 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, accessing) the information stored in ). The memory 620 may be implemented as a computer-readable recording medium so that the processor 610 can 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 disc. It can be implemented as an optical data storage device such as.

The memory 620 may store map information required for autonomous driving control by the processor 610. The map information stored in the memory 620 may be a navigation map (digital topographic map) that provides road-level information, but in order to improve the precision of autonomous driving control, a precision road map that provides lane-level road information, In other words, it may be desirable to implement it as 3D high-precision electronic map data. Accordingly, the map information stored in the memory 620 includes dynamic and Static information can be provided.

Additionally, the memory 620 may store an autonomous driving algorithm for controlling the autonomous driving of a vehicle. The autonomous driving algorithm is an algorithm (recognition, judgment and control algorithm) that recognizes the surroundings of the autonomous vehicle, determines its status, and controls the driving of the vehicle according to the judgment result. The processor 610 uses the autonomous driving algorithm stored in the memory 620. By executing driving algorithms, active autonomous driving control can be performed based on the vehicle's surrounding environment.

The processor 610 includes driving information and driving information input from the driving information input interface 101 and the driving information input interface 201, information on surrounding objects detected through the sensor unit 500, and a memory. The autonomous driving of the vehicle can be controlled based on the map information and autonomous driving algorithm stored in 620. The processor 610 may be implemented as an embedded processor such as a Complex Instruction Set Computer (CISC) or Reduced Instruction Set Computer (RISC), or a dedicated semiconductor circuit such as an Application Specific Integrated Circuit (ASIC). .

In this embodiment, the processor 610 can control the autonomous driving of the own vehicle by analyzing each driving trajectory of the own vehicle and surrounding vehicles. 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 an occupant status determination module 616. Figure 2 shows each module as an independent block according to its function, but each module may be integrated into one module and implemented in a configuration that performs each function in an integrated manner.

The sensor processing module 611 includes driving information (i.e., the location of the surrounding vehicle) of the surrounding vehicle based on the result of detecting the vehicle surrounding the own vehicle through the sensor unit 500, and includes the location, speed and (may also include the direction of movement) can be determined. That is, the location of surrounding vehicles is determined based on the signal received through the lidar sensor 510, the location of the surrounding vehicle is determined based on the signal received through the radar sensor 520, or the camera sensor 530 The location of surrounding vehicles can be determined based on images captured through, or the location of surrounding vehicles can be determined based on signals received through the ultrasonic sensor 540. To this end, as shown in FIG. 1, the sensor processing module 611 may include a lidar signal processing module 611a, a radar signal processing module 611b, and a camera signal processing module 611c (ultrasonic signal processing Additional modules may be added to the sensor processing module 611). The method of determining the location of surrounding vehicles using the lidar sensor 510, radar sensor 520, and camera sensor 530 is not limited in implementation to a specific embodiment. In addition, the sensor processing module 611 may determine attribute information such as the size and type of surrounding vehicles as well as the location, speed, and direction of movement of surrounding vehicles. An algorithm for determining information such as and type may be predefined.

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

First, the surrounding vehicle driving trajectory generation module 612a can generate the actual driving trajectory of surrounding vehicles.

Specifically, the surrounding vehicle driving trajectory generation module 612a determines the surrounding vehicle driving information (i.e., the location of the surrounding vehicle determined by the sensor processing module 611) of the surrounding vehicle detected by the sensor unit 500. Actual driving trajectories can be generated. 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 location of the surrounding vehicle detected by the sensor unit 500. The actual driving trajectory of surrounding vehicles can be generated by cross-referencing arbitrary locations on the map information stored in the memory 620. For example, when a surrounding vehicle is detected at a specific point by the sensor unit 500, the surrounding vehicle driving trace generation module 612a selects the location of the detected surrounding vehicle and a random location on the map information stored in the memory 620. By cross-referencing, the location of the surrounding vehicle currently detected on the map information can be specified, and the actual driving trajectory of the surrounding vehicle can be generated by continuously monitoring the location of the surrounding vehicle as described above. That is, the surrounding vehicle driving trace generation module 612a maps and accumulates the locations of surrounding vehicles detected by the sensor unit 500 to the locations on the map information stored in the memory 620, based on the above cross-reference. The actual driving trajectory of the vehicle can be generated.

Meanwhile, the actual driving trajectories of surrounding vehicles can be compared with the expected driving trajectories of surrounding vehicles, which will be described later, and used to determine whether the map information stored in the memory 620 is inaccurate. In this case, when comparing the actual driving trajectory of a specific nearby vehicle with the expected driving trajectory, a problem may arise where the map information is misjudged as inaccurate even though it is accurate. For example, if the actual and expected driving trajectories of a number of surrounding vehicles are identical, and the actual and expected driving trajectories of a specific surrounding vehicle are different, only the actual driving trajectory of the specific surrounding vehicle is the expected driving trajectory and Comparison may lead to misjudgment that the map information is inaccurate even though it is accurate. Therefore, there is a need to determine whether the tendency of the actual driving trajectories of a plurality of surrounding vehicles deviates from the expected driving trajectory, and for this purpose, the surrounding vehicle driving trajectory generation module 612a generates the actual driving trajectories of a plurality of surrounding vehicles, respectively. You may. Furthermore, considering that drivers of surrounding vehicles tend to move the steering wheel somewhat to the left and right during the driving process to drive on a straight path, the actual driving trajectory of surrounding vehicles may be generated in a curved form rather than a straight line, which will be described later. In order to calculate the error between the expected driving trajectories, the surrounding vehicle driving trajectory generation module 612a may apply a predetermined smoothing technique to the original actual driving trajectory generated in a curved shape to generate an actual driving trajectory in a straight shape. As a smoothing technique, various techniques such as interpolation for each position of surrounding vehicles can be employed.

Additionally, the surrounding vehicle driving trajectory generation module 612a may generate an expected driving trajectory of surrounding vehicles 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, and therefore the map information includes lanes, lane center lines, regulation lines, road boundaries, road center lines, traffic signs, road surface signs, and road shapes. It can provide dynamic and static information necessary for autonomous driving control of vehicles, such as height and lane width. Considering that vehicles generally drive in the center of the lane, surrounding vehicles driving around the own vehicle can also be expected to drive in the center of the lane, and therefore the surrounding vehicle driving trajectory generation module 612a The vehicle's expected driving trajectory can be generated as the lane center line reflected in map information.

The host vehicle driving trajectory generation module 612b may generate the actual driving trajectory that the host vehicle has driven to date based on the driving information of the host vehicle obtained through the driving information input interface 201 described above.

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

Additionally, the host vehicle driving trajectory generation module 612b may generate an expected driving trajectory along which the host vehicle should drive to the destination based on map information stored in the memory.

That is, the host vehicle driving trajectory generation module 612b stores the current location of the host vehicle acquired through the driving information input interface 201 (i.e., the current location information of the host vehicle acquired through the GPS receiver 260) and the memory. The expected driving trajectory to the destination can be generated using the stored map information, and the expected driving trajectory of the own vehicle can be generated as the lane center line reflected in the map information stored in the memory 620, like the expected driving trajectory of surrounding vehicles. You can.

The driving trajectory generated by the surrounding vehicle driving trajectory generation 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 during the process.

The driving trajectory analysis module 613 analyzes each driving trajectory generated by the driving trajectory generation module 612 and stored in the memory 620 (i.e., the actual driving trajectory and expected driving trajectory of surrounding vehicles, and the actual driving trajectory of the own vehicle). Through analysis, the reliability of autonomous driving control for the current vehicle can be diagnosed. Reliability diagnosis of autonomous driving control can be carried out through the process of analyzing trajectory errors between the actual driving trajectories of surrounding vehicles and the expected driving trajectories.

The driving control module 614 can perform the function of controlling the autonomous driving of the own vehicle, and specifically, the driving information and driving information input from the driving information input interface 101 and the driving information input interface 201, respectively. The autonomous driving algorithm is processed by comprehensively using information about surrounding objects detected through the sensor unit 500 and map information stored in the memory 620, and control information is provided through the vehicle control output interface 401. This can be transmitted to the lower control system 400 to control the autonomous driving of the own vehicle, and the driving status information and warning information of the own vehicle can be transmitted to the output unit 300 through the occupant output interface 301 to the driver. can be recognized. In addition, when the driving control module 614 comprehensively controls autonomous driving as described above, the host vehicle analyzed by the above-described sensor processing module 611, driving trajectory generation module 612, and driving trajectory analysis module 613 And by controlling autonomous driving by considering the driving trajectories of surrounding vehicles, the precision of autonomous driving control can be improved and autonomous driving control stability can be improved.

The trajectory learning module 615 may learn or correct the actual driving trajectory of the host vehicle generated by the host vehicle driving trajectory creation module 612b. For example, if the trajectory error between the actual driving trajectory and the expected driving trajectory of surrounding vehicles is greater than a preset threshold, the map information stored in the memory 620 is judged to be inaccurate and correction of the actual driving trajectory of the own vehicle is determined to be necessary. Accordingly, the driving trajectory of the own vehicle can be corrected by determining the lateral shift value for correcting the actual driving trajectory of the own vehicle.

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

Based on the foregoing, an embodiment of correcting the driving trajectory of an autonomous vehicle will be described below in detail.

As described above, the processor 610 (driving trajectory generation module 612) of this embodiment can generate the actual driving trajectory of the surrounding vehicle based on the driving information of the surrounding vehicle detected by the sensor unit 500. there is. That is, when a surrounding vehicle is detected at a specific point by the sensor unit 500, the processor 610 cross-references the location of the detected surrounding vehicle with the location on the map information stored in the memory 620 to detect the current location on the map information. The location of surrounding vehicles can be specified, and the actual driving trajectory of surrounding vehicles can be generated by continuously monitoring the locations of surrounding vehicles as described above.

Additionally, the processor 610 (driving trajectory generation module 612) may generate an expected driving trajectory of a surrounding vehicle based on the map information stored in the memory 620. In this case, the processor 610 may generate an expected driving trajectory of the surrounding vehicle. The expected driving trajectory can be generated as the lane center line reflected in the map information stored in the memory 620.

In addition, the processor 610 (the driving trajectory generation module 612) may generate an expected driving trajectory of the host vehicle based on the map information stored in the memory 620. In this case, the processor 610 may generate the expected driving trajectory of the host vehicle. The expected driving trajectory can be created as the lane center line reflected in map information.

When the actual and expected driving trajectories of the surrounding vehicles and the expected driving trajectory of the own vehicle are generated, the processor 610 (trajectory learning module 615) compares the actual and expected driving trajectories of the surrounding vehicles to determine the own vehicle. If it is determined that correction of the expected driving trajectory of the vehicle is necessary, the expected driving trajectory of the own vehicle may be corrected based on the degree of risk according to the distance from the own vehicle to the target surrounding vehicles. Here, the target surrounding vehicle may include first and second target surrounding vehicles running on the left and right sides of the host vehicle, respectively. Hereinafter, the case where the host vehicle is traveling between the first and second target surrounding vehicles will be described. Assume. In addition, in this embodiment, the term 'target surrounding vehicle' is used to explain that it refers to the surrounding vehicle that serves as a correction standard for the expected driving trajectory of the own vehicle, but the target surrounding vehicle is the surrounding vehicle driving trajectory generation module described above ( It may refer to the same vehicle as the surrounding vehicle for which the actual driving trajectory and the expected driving trajectory are calculated by 612a).

The processor 610 may determine that correction of the expected driving trajectory of the own vehicle is necessary when the trajectory error between the actual driving trajectory and the expected driving trajectory of the surrounding vehicle is greater than or equal to a preset threshold. That is, as described above, if the trajectory error between the actual driving trajectory and the expected driving trajectory of the surrounding vehicle is greater than the threshold, the map information stored in the memory 620 may be determined to be inaccurate, and therefore the map information stored in the memory 620 The expected driving trajectory of the own vehicle generated based on this also needs to be corrected.

When it is determined that the expected driving trajectory of the host vehicle needs to be corrected as described above, the processor 610 calculates the lateral distance between the host vehicle and the first target surrounding vehicle, and the lateral distance between the host vehicle and the second target surrounding vehicle. Based on the distance, the expected driving trajectory of the own vehicle can be corrected in a direction with a lower driving risk. When the lateral distance between the host vehicle and the vehicle surrounding the first target is defined as the first lateral distance, and the lateral distance between the host vehicle and the vehicle surrounding the second target is defined as the second lateral distance, the first and second The lateral distance may refer to the distance between a straight line extending in the driving direction of the host vehicle and the first and second surrounding vehicles. The processor 610 may compare the first and second lateral distances and determine that the risk of driving in the left direction is lower when the first lateral distance is larger, and when the second lateral distance is larger, the risk of driving in the right direction is lower. The risk of driving in that direction can be judged to be lower.

In this case, the processor 610 corrects the expected driving trajectory of the host vehicle by determining a shift value for driving the host vehicle by shifting it in the lateral direction (i.e., to correct the expected driving trajectory of the host vehicle). can do. That is, the processor 610 determines a primary shift value for correcting the expected driving trajectory of the host vehicle in a direction with a low driving risk of the host vehicle, and when the host vehicle approaches the first and second target vehicles, After determining the final shift value by correcting the first shift value through a weight indicating the proximity risk, the expected driving trajectory of the own vehicle can be corrected according to the determined final shift value.

Specifically, the processor 610 may determine a primary shift value for correcting the expected driving trajectory of the host vehicle in a direction where the driving risk of the host vehicle is low. For example, when the first lateral distance is greater than the second lateral distance, a primary shift value for shifting the expected driving trajectory of the host vehicle to the left can be determined, and the size of the primary shift value is, e.g. For example, it may be determined as 1/2 of the value obtained by subtracting the second lateral distance from the first lateral distance (i.e., the size of the first shift value so that the host vehicle travels in the center of the first and second target surrounding vehicles) can be determined). Likewise, when the second lateral distance is greater than the first lateral distance, a primary shift value for shifting the expected driving trajectory of the host vehicle to the right can be determined, and the size of the primary shift value is, for example, It may be determined as 1/2 of the value obtained by subtracting the first lateral distance from the second lateral distance. In addition, the sign of the primary shift value indicates the shift direction for the vehicle's expected driving trajectory (e.g., (-) sign is left, (+) sign is right), and its absolute value indicates the size of the shift value. It may be possible.

Thereafter, the processor 610 may determine the final shift value by correcting the first shift value through a weight indicating the proximity risk when the own vehicle approaches the first and second target surrounding vehicles. The weight indicating the proximity risk in the case where the own vehicle approaches the first and second target vehicles is, for example, a state in which the host vehicle is close to the target vehicle with a smaller volume (size) among the first and second target vehicles. It may refer to a parameter for correcting the primary shift value to allow the host vehicle to drive. For example, assume that the vehicle around the first target is a large car and the vehicle around the second target is a small car, and the second lateral distance is larger than the first lateral distance, so the first shift value is determined to be a value of (+). When doing so, the final shift value may be determined to have a value greater than the first shift value by applying the above-mentioned weight. The degree of increase or decrease of the first shift value, that is, the weight, for determining the final shift value may be selected in various ways according to the designer's intention and may be stored in advance in the memory 620.

Accordingly, the processor 610 can correct the expected driving trajectory of the host vehicle according to the final shift value. By correcting the expected driving trajectory of the host vehicle, the expected driving of the host vehicle is generated by the host vehicle driving trajectory generation module 612b in the process of controlling the autonomous driving of the host vehicle according to the map information stored in the memory 620. By shifting the trajectory by the final shift value compared to before correction, the autonomous driving stability of the own vehicle can be secured.

7 and 8 are flowcharts for explaining an autonomous driving method according to an embodiment of the present invention.

When explaining the autonomous driving method according to an embodiment of the present invention with reference to FIG. 7, first, the processor 610 controls autonomous driving of the host vehicle based on map information stored in the memory 620 (S100).

Thereafter, the processor 610 generates the actual driving trajectory of the surrounding vehicle based on the driving information of the surrounding vehicle detected by the sensor unit 500 during the autonomous driving process of the own vehicle (S200).

Next, the processor 610 generates an expected driving trajectory of surrounding vehicles based on the map information stored in the memory 620 (S300).

Next, the processor 610 generates an expected driving trajectory of the host vehicle based on the map information stored in the memory 620 (S400).

Next, the processor 610 determines whether correction of the expected driving trajectory of the own vehicle is necessary by comparing the actual driving trajectory and the expected driving trajectory of surrounding vehicles (S500). In step S500, the processor 610 determines that correction of the expected driving trajectory of the own vehicle is necessary when the trajectory error between the actual driving trajectory and the expected driving trajectory of the surrounding vehicle is greater than or equal to a preset threshold.

If it is determined in step S500 that correction of the expected driving trajectory of the host vehicle is necessary, the processor 610 corrects the expected driving trajectory of the host vehicle based on the degree of risk according to the distance from the host vehicle to the target surrounding vehicle (S600). . In step S600, the processor moves the host vehicle in a direction with a low driving risk of the host vehicle based on the first lateral distance between the host vehicle and the first target surrounding vehicle and the second lateral distance between the host vehicle and the second target surrounding vehicle. Correct the expected driving trajectory.

Describing step S600 in detail with reference to FIG. 8, the processor 610 determines a direction with a low driving risk of the host vehicle by comparing the first and second lateral distances, and sets the expected driving trajectory of the host vehicle in the determined direction. Determine the first shift value to correct (S610).

Then, the processor 610 determines the final shift value by correcting the first shift value through a weight indicating the proximity risk when the own vehicle approaches the first and second target surrounding vehicles (S620).

Then, the processor 610 corrects the expected driving trajectory of the host vehicle according to the final shift value determined in step S620 (630).

When the expected driving trajectory of the host vehicle is corrected in step S600, the processor 610 normally controls autonomous driving (S700).

In this way, this embodiment determines the need for correction of the driving trajectory of the autonomous vehicle, and according to the judgment result, corrects the driving trajectory of the autonomous vehicle by considering the risk according to the distance between surrounding vehicles, thereby ensuring the driving stability of the autonomous vehicle. and driving accuracy can be improved.

The present invention has been described with reference to the embodiments shown in the drawings, but these are merely illustrative, and those skilled in the art will recognize that various modifications and other equivalent embodiments can be made therefrom. You will understand. Therefore, the true technical protection scope of the present invention should be determined by the scope of the patent claims below.

100: User input unit 101: Driving information input interface
110: Driving 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: passenger 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 LiDAR sensor 513: Rear LiDAR 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: Surrounding vehicle driving trajectory generation module
612b: Own vehicle driving trajectory generation module 613: Driving trajectory analysis module
614: Driving control module 615: Trajectory learning module
616: Occupant status determination module 620: Memory
700: Server

Claims (5)

Memory to store map information; and
A processor that controls autonomous driving of the own vehicle based on map information stored in the memory,
The processor,
Generating an actual driving trajectory of the surrounding vehicle based on driving information of the vehicle surrounding the own vehicle,
Generates an expected driving trajectory of the surrounding vehicle based on map information stored in the memory,
Generating an expected driving trajectory of the host vehicle based on map information stored in the memory,
If it is determined that correction of the expected driving trajectory of the host vehicle is necessary through comparison between the actual driving trajectory and the expected driving trajectory of the surrounding vehicles, correcting the expected driving trajectory of the host vehicle based on the risk of the own vehicle. Featured autonomous driving device.
According to paragraph 1,
The processor,
An autonomous driving device characterized in that it is determined that correction of the expected driving trajectory of the own vehicle is necessary when the trajectory error between the actual driving trajectory and the expected driving trajectory of the surrounding vehicle is more than a preset threshold.
According to paragraph 1,
The target surrounding vehicles include first and second target surrounding vehicles running on the left and right sides of the host vehicle, respectively,
The processor,
Based on the lateral distance between the host vehicle and the first target surrounding vehicle, and the lateral distance between the host vehicle and the second target surrounding vehicle, the expected driving trajectory of the host vehicle is determined in a direction with a low driving risk of the host vehicle. An autonomous driving device characterized by correction.
According to paragraph 3,
The processor,
Determine a primary shift value for correcting the expected driving trajectory of the host vehicle in a direction with a low driving risk of the host vehicle, and determine the proximity risk for the case where the host vehicle approaches the first and second target surrounding vehicles An autonomous driving device characterized in that the final shift value is determined by correcting the first shift value through a weight indicating , and then the expected driving trajectory of the own vehicle is corrected according to the determined final shift value.
A processor controlling autonomous driving of the own vehicle based on map information stored in a memory;
generating, by the processor, an actual driving trajectory of a surrounding vehicle based on driving information of the vehicle surrounding the own vehicle;
generating, by the processor, an expected driving trajectory of the surrounding vehicle based on map information stored in the memory;
generating, by the processor, an expected driving trajectory of the host vehicle based on map information stored in the memory;
determining, by the processor, whether correction of the expected driving trajectory of the host vehicle is necessary by comparing the actual driving trajectory and the expected driving trajectory of the surrounding vehicles; and
When it is determined that correction of the expected driving trajectory of the host vehicle is necessary, correcting, by the processor, the expected driving trajectory of the host vehicle based on the degree of risk of the host vehicle;
An autonomous driving method comprising:

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