WO2020241971A1

WO2020241971A1 - Traffic accident management device and traffic accident management method

Info

Publication number: WO2020241971A1
Application number: PCT/KR2019/010738
Authority: WO
Inventors: 이한성
Original assignee: 엘지전자 주식회사
Priority date: 2019-05-30
Filing date: 2019-08-23
Publication date: 2020-12-03

Abstract

The present invention relates to a traffic accident management method comprising the steps in which: at least one processor obtains data relating to a context of an autonomous vehicle; at least one processor determines whether or not an accident of the autonomous vehicle has occurred on the basis of the data; and at least one processor determines the matter of responsibility of the accident by means of an artificial intelligence algorithm. A traffic accident management device can manage a traffic accident of the autonomous vehicle. The autonomous vehicle can be linked to a robot. The traffic accident management device can be implemented by means of the artificial intelligence (AI) algorithm. The traffic accident management device can generate augmented reality (AR) content.

Description

Traffic accident handling device and traffic accident handling method

The present invention relates to a traffic accident handling apparatus and a traffic accident handling method.

A vehicle is a device that moves in a direction desired by a boarding user. A typical example is a car. Autonomous vehicle refers to a vehicle that can be driven automatically without human driving operation.

Accidents can also occur in autonomous vehicles due to sensor malfunction or the like. When an accident occurs between autonomous vehicles, it is necessary to determine where the responsibility for the accident is.

In order to solve the above problems, an object of the present invention is to provide a traffic accident processing apparatus and a traffic accident processing method for determining the responsibility of a traffic accident of an autonomous vehicle.

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

In order to achieve the above object, a traffic accident processing method according to an embodiment of the present invention includes, by at least one processor, acquiring data on a situation of an autonomous vehicle; Determining, by at least one processor, whether an accident has occurred in the autonomous vehicle based on the data; And determining, by at least one processor, a person responsible for the accident using an artificial intelligence algorithm.

A traffic accident processing apparatus according to an embodiment of the present invention acquires data on a situation of an autonomous vehicle, determines whether an accident has occurred in the autonomous vehicle based on the data, and uses an artificial intelligence algorithm, It includes; a processor for determining the responsibility of the accident.

Details of other embodiments are included in the detailed description and drawings.

According to the present invention, there are one or more of the following effects.

First, there is an effect of providing safe and convenient accident handling services to stakeholders of autonomous vehicles.

Second, there is an effect of preventing secondary accidents occurring in the process of handling accidents and improving traffic congestion.

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

1 is a diagram referenced to describe a system according to an embodiment of the present invention.

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

3 is a view referenced to explain a traffic accident processing apparatus according to an embodiment of the present invention.

4 to 8 are views referenced for explaining a traffic accident handling method according to an embodiment of the present invention.

Hereinafter, exemplary embodiments disclosed in the present specification will be described in detail with reference to the accompanying drawings, but identical or similar elements are denoted by the same reference numerals regardless of reference numerals, and redundant descriptions thereof will be omitted. The suffixes "module" and "unit" for components used in the following description are given or used interchangeably in consideration of only the ease of preparation of the specification, and do not have meanings or roles that are distinguished from each other by themselves. In addition, in describing the embodiments disclosed in the present specification, when it is determined that a detailed description of related known technologies may obscure the subject matter of the embodiments disclosed in the present specification, the detailed description thereof will be omitted. In addition, the accompanying drawings are for easy understanding of the embodiments disclosed in the present specification, and the technical idea disclosed in the present specification is not limited by the accompanying drawings, and all modifications included in the spirit and scope of the present invention It should be understood to include equivalents or substitutes.

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

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

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

In the present application, terms such as "comprises" or "have" are intended to designate the presence of features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, but one or more other features. It is to be understood that the presence or addition of elements or numbers, steps, actions, components, parts, or combinations thereof, does not preclude in advance.

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

Referring to FIG. 1, the system 1 may provide a vehicle 10 to a user. The system 1 may include a traffic accident handling device 2, at least one road side unit 3 and at least one vehicle 10.

The traffic accident processing apparatus 2 may be implemented with at least one server. The traffic accident processing apparatus 2 may perform traffic accident processing when a traffic accident occurs in the autonomous vehicle 10. In this specification, the traffic accident handling apparatus 2 is described as an electronic device separate from the vehicle 10, but may be an electronic device included in the vehicle 10. In this case, each vehicle 10 may include a traffic accident handling device 2.

The road side unit (RSU) 3 may be understood as a structure disposed around a road on which the vehicle 10 travels. The road side unit 3 may communicate with at least one of the autonomous vehicle 10 and the traffic accident processing device 2. The road side unit 3 may be provided with a sensing device that senses a road condition.

The vehicle 10 may be at least one of a manual driving vehicle and an autonomous driving vehicle. The vehicle 10 is defined as a means of transport running on a road or track. The vehicle 10 is a concept including a car, a train, and a motorcycle. The vehicle 10 may be a concept including both an internal combustion engine vehicle including an engine as a power source, a hybrid vehicle including an engine and an electric motor as a power source, and an electric vehicle including an electric motor as a power source.

The electronic device 100 may be included in the vehicle 10. The electronic device 100 may be provided in a vehicle for interaction with the traffic accident handling device 2.

Meanwhile, the vehicle 10 may interact with at least one robot. The robot may be an Autonomous Mobile Robot (AMR) capable of driving by magnetic force. The mobile robot is capable of moving by itself and is free to move, and is provided with a plurality of sensors to avoid obstacles while driving, so that it can travel avoiding obstacles. The mobile robot may be a flying robot (eg, a drone) having a flying device. The mobile robot may be a wheel-type robot that includes at least one wheel and is moved through rotation of the wheel. The mobile robot may be a legged robot that has at least one leg and is moved using the leg.

The robot may function as a device that complements the user's convenience of the vehicle 10. For example, the robot may perform a function of moving the luggage loaded in the vehicle 10 to the user's final destination. For example, the robot may perform a function of guiding a user who gets off the vehicle 10 to a final destination. For example, the robot may perform a function of transporting a user who gets off the vehicle 10 to a final destination.

At least one electronic device included in the vehicle may communicate with the robot through the communication device 220.

At least one electronic device included in the vehicle may provide the robot with data processed by at least one electronic device included in the vehicle. For example, at least one electronic device included in the vehicle may provide at least one of object data, HD map data, vehicle state data, vehicle location data, and driving plan data to the robot.

At least one electronic device included in the vehicle may receive data processed by the robot from the robot. At least one electronic device included in the vehicle may receive at least one of sensing data generated by the robot, object data, robot state data, robot position data, and movement plan data of the robot.

At least one electronic device included in the vehicle may generate a control signal further based on data received from the robot. For example, at least one electronic device included in the vehicle compares the information on the object generated by the object detection device 210 with the information on the object generated by the robot, and based on the comparison result, a control signal Can be created. At least one electronic device included in the vehicle may generate a control signal so that interference between the movement path of the vehicle 10 and the movement path of the robot does not occur.

At least one electronic device included in the vehicle may include a software module or a hardware module (hereinafter, referred to as an artificial intelligence module) that implements artificial intelligence (AI). At least one electronic device included in the vehicle may input acquired data to an artificial intelligence module and use data output from the artificial intelligence module.

The artificial intelligence module may perform machine learning on input data using at least one artificial neural network (ANN). The artificial intelligence module may output driving plan data through machine learning on input data.

At least one electronic device included in the vehicle may generate a control signal based on data output from the artificial intelligence module.

According to an embodiment, at least one electronic device included in the vehicle may receive data processed by artificial intelligence from an external device through the communication device 220. At least one electronic device included in the vehicle may generate a control signal based on data processed by artificial intelligence.

Referring to FIG. 2, the vehicle 10 includes an electronic device 100 for a vehicle, a user interface device 200, an object detection device 210, a communication device 220, a driving operation device 230, and a main ECU 240. ), a vehicle driving device 250, a driving system 260, a sensing unit 270, and a location data generating device 280.

The vehicle electronic device 100 may exchange signals, information, or data with the traffic accident processing device 2 through the communication device 220. The vehicle electronic device 100 may provide a signal, information, or data received from the traffic accident processing device 2 to another electronic device in the vehicle 10.

The user interface device 200 is a device for communicating with the vehicle 10 and a user. The user interface device 200 may receive a user input and provide information generated in the vehicle 10 to the user. The vehicle 10 may implement a user interface (UI) or a user experience (UX) through the user interface device 200.

The object detection device 210 may detect an object outside the vehicle 10. The object detection device 210 may include at least one sensor capable of detecting an object outside the vehicle 10. The object detection device 210 may include at least one of a camera, a radar, a lidar, an ultrasonic sensor, and an infrared sensor. The object detection device 210 may provide data on an object generated based on a sensing signal generated by a sensor to at least one electronic device included in the vehicle.

The camera may generate information on an object outside the vehicle 10 by using the image. The camera may include at least one lens, at least one image sensor, and at least one processor that is electrically connected to the image sensor and processes a received signal, and generates data about an object based on the processed signal.

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

The camera may be mounted in a position where field of view (FOV) can be secured in the vehicle in order to photograph the outside of the vehicle. The camera may be placed in the interior of the vehicle, close to the front windshield, to acquire an image of the front of the vehicle. The camera can be placed around the front bumper or radiator grille. The camera may be placed in the interior of the vehicle, close to the rear glass, in order to acquire an image of the rear of the vehicle. The camera can be placed around the rear bumper, trunk or tailgate. The camera may be disposed in proximity to at least one of the side windows in the interior of the vehicle in order to acquire an image of the side of the vehicle. Alternatively, the camera may be disposed around a side mirror, a fender, or a door.

The radar may generate information on an object outside the vehicle 10 using radio waves. The radar may include at least one processor that is electrically connected to the electromagnetic wave transmitter, the electromagnetic wave receiver, and the electromagnetic wave transmitter and the electromagnetic wave receiver, processes a received signal, and generates data for an object based on the processed signal. The radar may be implemented in a pulse radar method or a continuous wave radar method according to the principle of radio wave emission. The radar may be implemented in a frequency modulated continuous wave (FMCW) method or a frequency shift keyong (FSK) method according to a signal waveform among continuous wave radar methods. The radar detects an object by means of an electromagnetic wave, a time of flight (TOF) method or a phase-shift method, and detects the position of the detected object, the distance to the detected object, and the relative speed. I can. The radar may be placed at a suitable location outside of the vehicle to detect objects located in front, rear or side of the vehicle.

The lidar may generate information on an object outside the vehicle 10 using laser light. The radar may include at least one processor that is electrically connected to the optical transmitter, the optical receiver, and the optical transmitter and the optical receiver, processes a received signal, and generates data for an object based on the processed signal. . The rider may be implemented in a TOF (Time of Flight) method or a phase-shift method. The lidar can be implemented either driven or non-driven. When implemented as a drive type, the lidar is rotated by a motor, and objects around the vehicle 10 can be detected. When implemented in a non-driven manner, the lidar can detect an object located within a predetermined range with respect to the vehicle by optical steering. The vehicle 100 may include a plurality of non-driven lidars. The radar detects an object based on a time of flight (TOF) method or a phase-shift method by means of a laser light, and determines the position of the detected object, the distance to the detected object, and the relative speed. Can be detected. The lidar may be placed at an appropriate location outside the vehicle to detect objects located in front, rear or side of the vehicle.

The communication device 220 may exchange signals with devices located outside the vehicle 10. The communication device 220 may exchange signals with at least one of an infrastructure (eg, a server, a broadcasting station) and another vehicle. The communication device 220 may include at least one of a transmission antenna, a reception antenna, a radio frequency (RF) circuit capable of implementing various communication protocols, and an RF element to perform communication.

The communication device 220 may communicate with a device located outside the vehicle 10 using a 5G (for example, new radio, NR) method. The communication device 220 may implement V2X (V2V, V2D, V2P, V2N) communication using a 5G method.

The driving operation device 230 is a device that receives a user input for driving. In the case of the manual mode, the vehicle 10 may be driven based on a signal provided by the driving operation device 230. The driving operation device 230 may include a steering input device (eg, a steering wheel), an acceleration input device (eg, an accelerator pedal), and a brake input device (eg, a brake pedal).

The main ECU 240 may control the overall operation of at least one electronic device provided in the vehicle 10.

The drive control device 250 is a device that electrically controls various vehicle drive devices in the vehicle 10. The drive control device 250 may include a power train drive control device, a chassis drive control device, a door/window drive control device, a safety device drive control device, a lamp drive control device, and an air conditioning drive control device. The power train drive control device may include a power source drive control device and a transmission drive control device. The chassis drive control device may include a steering drive control device, a brake drive control device, and a suspension drive control device.

Meanwhile, the safety device driving control device may include a safety belt driving control device for controlling the safety belt.

The vehicle drive control device 250 may be referred to as a control Electronic Control Unit (ECU).

The driving system 260 may control a movement of the vehicle 10 or generate a signal for outputting information to a user based on data on an object received by the object detection device 210. The driving system 260 may provide the generated signal to at least one of the user interface device 200, the main ECU 240, and the vehicle driving device 250.

The driving system 260 may be a concept including ADAS. ADAS 260 includes an adaptive cruise control system (ACC), an automatic emergency braking system (AEB), a forward collision warning system (FCW), and a lane maintenance assistance system (LKA: Lane Keeping Assist), Lane Change Assist (LCA), Target Following Assist (TFA), Blind Spot Detection (BSD), Adaptive High Beam Control System (HBA: High) Beam Assist), Auto Parking System (APS), PD collision warning system, Traffic Sign Recognition (TSR), Traffic Sign Assist (TSA), At least one of a night vision system (NV: Night Vision), a driver status monitoring system (DSM), and a traffic jam assistance system (TJA) may be implemented.

The driving system 260 may include an autonomous driving electronic control unit (ECU). The autonomous driving ECU may set an autonomous driving route based on data received from at least one of other electronic devices in the vehicle 10. The autonomous driving ECU is based on data received from at least one of the user interface device 200, the object detection device 210, the communication device 220, the sensing unit 270, and the location data generating device 280, You can set an autonomous driving route. The autonomous driving ECU may generate a control signal so that the vehicle 10 travels along the autonomous driving path. The control signal generated by the autonomous driving ECU may be provided to at least one of the main ECU 240 and the vehicle driving device 250.

The sensing unit 270 may sense the state of the vehicle. The sensing unit 270 includes an inertial navigation unit (IMU) sensor, a collision sensor, a wheel sensor, a speed sensor, a tilt sensor, a weight detection sensor, a heading sensor, a position module, and a vehicle. At least one of forward/reverse sensor, battery sensor, fuel sensor, tire sensor, steering sensor by steering wheel rotation, vehicle interior temperature sensor, vehicle interior humidity sensor, ultrasonic sensor, illuminance sensor, accelerator pedal position sensor, and brake pedal position sensor It may include. Meanwhile, the inertial navigation unit (IMU) sensor may include one or more of an acceleration sensor, a gyro sensor, and a magnetic sensor.

The sensing unit 270 may generate state data of the vehicle based on a signal generated by at least one sensor. The sensing unit 270 includes vehicle attitude information, vehicle motion information, vehicle yaw information, vehicle roll information, vehicle pitch information, vehicle collision information, vehicle direction information, vehicle angle information, and vehicle speed. Information, vehicle acceleration information, vehicle tilt information, vehicle forward/reverse information, battery information, fuel information, tire information, vehicle ramp information, vehicle internal temperature information, vehicle internal humidity information, steering wheel rotation angle, vehicle exterior illuminance, accelerator pedal It is possible to acquire a sensing signal for the pressure applied to the brake pedal and the pressure applied to the brake pedal.

In addition, the sensing unit 270 includes an accelerator pedal sensor, a pressure sensor, an engine speed sensor, an air flow sensor (AFS), an intake air temperature sensor (ATS), a water temperature sensor (WTS), and a throttle position sensor. (TPS), a TDC sensor, a crank angle sensor (CAS), and the like may be further included.

The sensing unit 270 may generate vehicle state information based on the sensing data. The vehicle status information may be information generated based on data sensed by various sensors provided inside the vehicle.

For example, the vehicle status information includes vehicle attitude information, vehicle speed information, vehicle tilt information, vehicle weight information, vehicle direction information, vehicle battery information, vehicle fuel information, vehicle tire pressure information, It may include vehicle steering information, vehicle interior temperature information, vehicle interior humidity information, pedal position information, vehicle engine temperature information, and the like.

Meanwhile, the sensing unit may include a tension sensor. The tension sensor may generate a sensing signal based on a tension state of the seat belt.

The location data generating device 280 may generate location data of the vehicle 10. The location data generating apparatus 280 may include at least one of a Global Positioning System (GPS) and a Differential Global Positioning System (DGPS). The location data generating apparatus 280 may generate location data of the vehicle 10 based on a signal generated by at least one of GPS and DGPS. According to an embodiment, the location data generating apparatus 280 may correct the location data based on at least one of an IMU (Inertial Measurement Unit) of the sensing unit 270 and a camera of the object detection apparatus 210.

The location data generating device 280 may be referred to as a location positioning device. The location data generating device 280 may be referred to as a Global Navigation Satellite System (GNSS).

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

3 is a control block diagram of an electronic device according to an embodiment of the present invention.

Referring to FIG. 3, the traffic accident processing apparatus 2 may include a communication device 320, a memory 340, a processor 370, an interface unit 180, and a power supply unit 390.

The communication device 320 may exchange signals with the vehicle 10 and a communication device outside the vehicle. The vehicle external communication device may include a load side unit 3 and an external server.

The communication device 320 may include at least one of a transmission antenna, a reception antenna, a radio frequency (RF) circuit capable of implementing various communication protocols, and an RF element to perform communication.

The communication device 320 may communicate with the vehicle 10 and an external communication device outside the vehicle using a 5G (for example, new radio (NR)) method. Hereinafter, a vehicle external communication device according to an embodiment of the present invention may be described based on the road side unit 3.

The memory 340 is electrically connected to the processor 370. The memory 340 may store basic data for a unit, control data for controlling the operation of the unit, and input/output data. The memory 340 may store data processed by the processor 370. In terms of hardware, the memory 340 may be configured with at least one of ROM, RAM, EPROM, flash drive, and hard drive. The memory 340 may store various data for the overall operation of the traffic accident processing apparatus 2, such as a program for processing or controlling the processor 370. The memory 340 may be implemented integrally with the processor 370. Depending on the embodiment, the memory 340 may be classified as a sub-element of the processor 370.

The interface unit 180 may exchange signals with at least one electronic device provided in the vehicle 10 by wire or wirelessly. The interface unit 180 includes a user interface device 200, an object detection device 210, a communication device 220, a driving operation device 230, a main ECU 240, a vehicle driving device 250, a driving system ( 260), the sensing unit 270, and the location data generating device 280 may exchange signals with at least one of wired or wirelessly. The interface unit 280 may be configured with at least one of a communication module, a terminal, a pin, a cable, a port, a circuit, an element, and a device.

The power supply unit 390 can supply power to the traffic accident handling apparatus 2. The power supply unit 390 may receive power from a power source (eg, a battery) included in the vehicle 10 and supply power to each unit of the traffic accident handling apparatus 2. The power supply unit 390 may be operated according to a control signal provided from the main ECU 340. The power supply unit 390 may be implemented as a switched-mode power supply (SMPS).

The processor 370 may be electrically connected to the memory 340, the interface unit 180, and the power supply unit 390 to exchange signals. The processor 370 includes application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, and controllers. It may be implemented using at least one of (controllers), micro-controllers, microprocessors, and electrical units for performing other functions.

The processor 370 may be driven by power provided from the power supply unit 390. The processor 370 may receive data, process data, generate a signal, and provide a signal while power is supplied by the power supply unit 390.

The processor 370 may receive information from another electronic device in the vehicle 10 through the interface unit 180. The processor 370 may provide a control signal to another electronic device in the vehicle 10 through the interface unit 180.

The processor 370 may acquire data on the situation of the autonomous vehicle 10. The processor 370 may obtain first data from at least one electronic device included in the autonomous vehicle 10. For example, the processor 370 may obtain the first data from at least one of the object detection device 210 and the sensing unit 270 provided in the autonomous vehicle 10. The first data may include at least one of location information, speed information, heading information, acceleration information, deceleration information, steering information, brake information, and impact amount information of the autonomous vehicle 10. The first data may be data on objects around the autonomous vehicle 10. The processor 370 may acquire second data from at least one electronic device provided in another vehicle located around the autonomous vehicle. The other vehicle may be another autonomous vehicle. For example, the processor 370 may obtain second data from at least one of an object detection device and a sensing unit provided in another autonomous vehicle. The second data may include sensing data sensed by the autonomous vehicle 10. The second data may include at least one of location information, speed information, heading information, acceleration information, deceleration information, steering information, brake information, and impact amount information of another autonomous vehicle.

The processor 370 may acquire third data from at least one external communication device located around the autonomous vehicle 10. The third data may be sensing data sensed by the vehicle 10 acquired by the road side unit 3. The third data may be sensing data sensed by the vehicle 10 acquired from the load side unit 3 by an external server.

The processor 370 may obtain third data from at least one road side unit 3 located around the autonomous vehicle 10. The third data may include at least one of sensing data (eg, image data), traffic light data, and map data sensed by the autonomous vehicle 10.

The processor 370 may determine whether an accident has occurred in the autonomous vehicle based on data on the situation of the autonomous vehicle 10. The processor 370 is based on at least one of the first data obtained from the autonomous vehicle 10, the second data obtained from another autonomous vehicle, and the third data obtained from the road side unit, It is possible to determine whether an accident in (10) has occurred. For example, on the basis of speed information, acceleration information, deceleration information, brake information, impact amount information of the autonomous vehicle 10 and image data acquired from other autonomous vehicles or roadside units, the autonomous vehicle 10 It is possible to determine whether an accident has occurred.

The processor 370 may use an artificial intelligence algorithm to determine the responsibility of the accident.

The processor 370 is based on at least one of the first data obtained from the autonomous vehicle 10, the second data obtained from another autonomous vehicle, and the third data obtained from the road side unit, and the accident occurrence time point The situation of can be reconstructed by simulation.

For example, the processor 370 synthesizes and edits the image data acquired from the autonomous vehicle 10, the image data acquired from another autonomous vehicle, and the image data acquired from the road side unit, The situation can be reconstructed into a simulation.

For example, the processor 370 may map the autonomous driving vehicle 10 and surrounding objects of the autonomous driving vehicle on the map in chronological order. The processor 370 may reconstruct a situation at the time of an accident in a simulation through mapping of the autonomous driving vehicle 10 and surrounding objects of the autonomous driving vehicle. On the other hand, the information on the surrounding objects of the autonomous vehicle 10 is, among the first data obtained from the autonomous vehicle 10, the second data obtained from the other autonomous vehicle, and the third data obtained from the road side unit. It may be generated based on at least any one.

For example, the processor 370 may be based on at least one of first data obtained from the autonomous vehicle 10, second data obtained from another autonomous vehicle, and third data obtained from the road side unit. , A top view image based on the image of the autonomous vehicle 10 may be generated. The processor 370 may reconfigure the situation at the time of the accident by generating a top view image into a simulation. The top view image may be a video including a posture image of a steering wheel of an autonomous vehicle.

The processor 370 inputs data on the situation of the autonomous vehicle 10 and traffic law data into an artificial intelligence (AI) algorithm, and based on the result of machine learning, determines the responsibility of the accident. I can judge. The processor 370 may further input past traffic accident history data to an artificial intelligence algorithm and determine the person responsible for the accident based on the result of machine learning.

The artificial intelligence algorithm may perform machine learning on input data using at least one artificial neural network (ANN).

The processor 370 may execute a routine for preventing a secondary accident after the accident has occurred.

After the accident, the processor 370 may determine whether the autonomous vehicle 10 can be moved. When it is determined that the autonomous driving vehicle 10 is movable, the processor 370 may generate an avoidance path for the autonomous driving vehicle 10. The processor 370 may provide a control signal to drive the autonomous vehicle 10 along the avoidance path.

The processor 370 may calculate a collision prediction time with another vehicle following the autonomous vehicle 10. The processor 370 may compare the collision prediction time and the avoidance time of the autonomous vehicle 10 according to the avoidance movement path at the accident point. When the avoidance time is greater than or equal to the collision prediction time, the processor 370 may provide a control signal so that the autonomous vehicle 10 travels along the avoidance path.

The traffic accident handling apparatus 2 may include at least one printed circuit board (PCB). The memory 140, the interface unit 180, the power supply unit 190, and the processor 370 may be electrically connected to a printed circuit board.

4 is a view referenced for explaining a traffic accident handling method (S400) according to an embodiment of the present invention.

Referring to FIG. 4, the processor 370 may acquire data on the situation of the autonomous vehicle 10 (S410 ). In the step of obtaining data (S410), the at least one processor 370 is a step of obtaining first data from at least one electronic device provided in the autonomous vehicle 10, the at least one processor 370 , Obtaining second data from at least one electronic device provided in another vehicle located around the autonomous vehicle 10, and at least one processor, from at least one road side unit located around the autonomous vehicle It may include obtaining third data.

The processor 370 may determine whether an accident has occurred in the autonomous vehicle 10 based on data on the situation of the autonomous vehicle 10 (S420).

When it is determined that an accident has occurred in the autonomous vehicle 10, the processor 370 may determine the responsibility of the accident using an artificial intelligence algorithm (S430).

In the step of determining the responsible position (S430), the at least one processor 370 includes first data obtained from the autonomous vehicle 10, second data obtained from other autonomous vehicles, and the road side unit. Based on at least one of the third data, it may include reconfiguring the situation at the time of the accident in a simulation. For example, the reconfiguration may include a step of at least one processor 370 mapping the autonomous driving vehicle 10 and surrounding objects of the autonomous driving vehicle 10 on a map in chronological order. In this case, information on the surrounding objects of the autonomous vehicle 10 may be generated based on at least one of the first data, the second data, and the third data. For example, the step of reconfiguring, at least one processor 370, based on at least one of the first data, the second data, and the third data, based on the image of the autonomous vehicle 10 It may include the step of generating a top view image. Here, the top view image may be a video including an image of the attitude of the steering wheel of the autonomous vehicle 10.

In the step of determining the responsible person (S430), the at least one processor 370 inputs data on the situation of the autonomous vehicle 10 and traffic regulation data into an artificial intelligence algorithm, and based on the result of machine running, You can determine where you are responsible for the accident. In the step of determining the responsible person (S430), the at least one processor 370 may further input past traffic accident history data to an artificial intelligence algorithm and determine the person responsible for the accident based on the result of machine learning. .

When it is determined that an accident has occurred in the autonomous vehicle 10, the processor 370 may execute a routine for preventing secondary accidents after the accident (S430).

In the step of executing the secondary accident prevention routine (S430), the at least one processor 370 determines whether the autonomous driving vehicle 10 can be moved after the accident occurs, and the at least one processor 370 , Generating an avoidance movement path of the autonomous vehicle 10, and at least one processor 370 providing a control signal to allow the autonomous driving vehicle 10 to travel along the avoidance movement path. .

In the step of executing the secondary accident prevention routine (S430), the at least one processor 370 calculates a collision prediction time with another vehicle following the autonomous vehicle 10, at least one processor 370 ) May include comparing the collision prediction time and the avoidance time of the autonomous vehicle 10 according to the avoidance movement path at the accident point. In this case, in the providing of the control signal, the at least one processor 370 may provide the control signal when the avoidance time is greater than or equal to the attendance prediction time.

5 to 8 are views referenced for explaining a traffic accident handling method according to an embodiment of the present invention. 5 to 8 illustrate flow charts when the vehicle 10 includes a traffic accident processing apparatus.

Referring to FIG. 5, the processor 370 may determine whether accident data collected by the vehicle 10 is sufficient (S510). Whether or not the collected data of the vehicle 10 is sufficient may be determined based on whether or not the vehicle 10 and surrounding objects are collected for a certain period of time immediately before the accident, traffic light information, and the like. If it is determined that it is sufficient in step S510, the processor 370 may transmit the accident data collected by the vehicle 10 to the server 2, and execute a secondary accident prevention routine (S520). If it is determined that it is not sufficient in step S520, the processor 370 may determine whether additional data collection is possible (S530). If it is determined that collection is possible in step S530, the processor 370 may additionally collect data from other vehicles or roadside units, and execute a secondary accident prevention routine (S540). The processor 370 may transmit the data to the server 2 and execute a secondary accident prevention routine after the data addition collection is completed (S550).

The processor 370 may determine the responsibility of the accident (S560). Determination of the location of responsibility may be made by matching the collected accident data with the past judgment of the location of responsibility of the accident stored in the server. In step S560, if it is possible to determine the position of responsibility of the accident, the processor 370 may provide a result of the determination of the position of responsibility to the accident stakeholders and perform the accident (S570). In step S560, the processor 370, if it is impossible to determine the responsibility of the accident, may call the insurance company employee (S580). The called insurer employee can carry out subsequent incident handling.

Referring to FIG. 6, the processor 370 may determine whether the collected accident data is sufficient (S605). In step S605, if it is determined that it is not sufficient, the processor 370 may determine whether another vehicle exists (S610). In step S610, if it is determined that another vehicle exists, the processor 370 may determine whether the other vehicle satisfies a predetermined condition (S615). For example, the processor 370 may determine whether another vehicle is located within a predetermined radius of the vehicle. In step S615, when it is determined that the other vehicle does not satisfy the predetermined condition, the processor 370 obtains the time when the vehicle reaches the current position of the rear vehicle from the server, the driveable area to the safe area, and additionally to prevent a secondary accident. This necessary data may be received (S620). The processor 370 may calculate a movement path that enables the collection of necessary data within the time point of arrival within the area, and may move the vehicle 10 according to the path (S625). When the vehicle 10 moves, the processor 370 may stream and transmit sensor data including a movement trajectory (S630). The processor 370 may collect accident data and track vehicle movement from the accident site. The processor 370 may maintain a stopped state of the vehicle 10 and activate monitoring of the approaching direction and the avoidance direction of other vehicles in preparation for a possibility of a secondary accident (S635).

If it is determined that the data collected in step S605 is sufficient, the processor 370 may match the accident situation calculated from the collected accident data with the existing accident situation in the storage of the server 2 (S650). The processor 370 may calculate the contents of the accident situation in real time and transmit the calculated contents in real time when the degree of correspondence between the accident area, the damaged area, and the pre-accident operation is higher than a certain level (S655). The processor 370 may determine whether the accident parties are convinced of the calculated content (S660). If it is determined to be convinced in step S660, the processor 370 may determine whether the autonomous driving function can be maintained (S665). If it is determined that the autonomous driving function can be maintained in step S665, the processor 370 may automatically set a destination according to the vehicle accident damage situation and move the vehicle 10 to the destination. Here, the automatically set destination may be at least one of an existing destination, a repair shop, and an evacuation center. If it is determined in step S665 that the autonomous driving function cannot be maintained, a safe area (eg, a shoulder) may be set, and a control signal may be provided to move the vehicle to the corresponding area (S675).

On the other hand, if the accident parties are not convinced with the calculated content in step S660, the processor 370 may call an emergency service vehicle or an insurance company employee for tasks such as identifying the accident, collecting evidence, and calculating damage (S680). . An emergency service vehicle or an insurance company employee may wait for arrival (S685). The processor 370 may determine whether another vehicle exists (S690). If it is determined that another vehicle exists, the processor 370 may move to step S615. If it is determined that other vehicles do not exist, the processor 370 may move to step S685.

Meanwhile, in step S610, if it is determined that no other vehicle exists, the processor 370 may move to step S680.

Meanwhile, in step S615, when it is determined that the other vehicle satisfies the predetermined condition, the processor 370 may transmit a request signal for obtaining additional data to the other vehicle. The processor 370 may determine whether reception of the additional data is complete (S645). If it is determined in step S645 that the reception of the additional data is complete, the processor 370 may move to step S650.

Referring to FIG. 7, the processor 370 may record vehicle information and object information around the vehicle in the first database 701 at a predetermined period (S710). The first database 701 may be classified as a subordinate configuration of the memory 340. The vehicle information may be described as information related to a motion state of the vehicle, such as a position, speed, heading, and acceleration of the vehicle. Object information is related to the motion state of objects such as position, speed, heading, and acceleration of objects around the vehicle that are detected and tracked using sensors such as vehicles, motorcycles, pedestrians, and obstacles. It can be established as information. The processor 370 may record image information around the vehicle in the second database 702 at a predetermined period (S720). The second database 702 may be classified as a subordinate configuration of the memory 340. When there is a traffic light, the processor 370 may record the traffic light information in the third database 703 at a predetermined period using information from the camera or V2X (S730). The third database 703 may be classified as a sub configuration of the memory 340.

The processor 370 may determine whether an accident has occurred (S740). When it is determined that an accident has occurred, the processor 370 may reconstruct the accident into a simulation by mapping locations in chronological order using vehicle/object information, surrounding image information, traffic light information, etc. for a certain period of time just before the accident. Yes (S750). The processor 370 may receive map data from the map database 704 and reflect various types of information in the map data to reconstruct the accident into a simulation. The processor 370 may determine the responsibility of the accident (S760). The processor 370 may receive traffic laws from the traffic laws database 705 and determine the responsible position of the accident by referring to the traffic laws. The processor 370 may transmit the result of determining the location responsible for the accident to the relevant accident stakeholders (S770).

Referring to FIG. 8, the processor 370 may turn on the emergency light after an accident occurs (S810 ). Depending on the embodiment, the processor 370 may automatically install a tripod or a flame signal. The processor 370 may continue to transmit and receive data with an external device (eg, a server, another vehicle, or a user terminal) through V2X communication (S820). The processor 370 may periodically estimate a Time To Collision (TTC), a remaining time for data transmission/reception, and an avoidance time of a vehicle that follows. TTC can be described as the estimated time it takes for another vehicle to collide the vehicle. The remaining time for data transmission/reception may be described as an estimated remaining time for receiving accident-related data from another vehicle or a road side unit, or completing transmission to a server. The avoidance time may be described as the time it takes to move the vehicle so that the vehicle in which a collision is expected can be avoided. The avoidance time may be additionally given an extra time so that the vehicle can escape with a margin.

The processor 370 may determine whether the TTC is less than or equal to the sum of the remaining data transmission/reception time and the avoidance time (S840). When it is determined that the TTC is less than the sum of the data transmission/reception remaining time and the avoidance time, the processor 370 may determine whether the TTC is less than or equal to the avoidance time (S850). Since the TTC is variable according to the braking condition of the following vehicle, the processor 370 may continue to transmit and receive data while continuously monitoring the value of the TTC. In step S850, when it is determined that the TTC is less than the avoidance time, the processor 370 may evade the vehicle in an emergency (S860). When the vehicle moves, the processor 370 may stream and transmit sensor data included as a movement trajectory.

When an accident occurs, the accident vehicle may transmit vehicle information related to the accident to the server 2. The server 2 may be an insurance company server or a vehicle company server. The accident-related vehicle information includes the vehicle's collision location, collision intensity, sensing information of the driving unit of the vehicle, sensing information that senses the surrounding environment of the vehicle (front camera, black box, radar, lidar, etc.), and surrounding vehicles acquired through V2X. It may include one or more of one sensing information.

The server 2 may acquire data including before and after a certain point in time of the accident of each accident vehicle, and calculate an error rate based on the obtained data.

The fruit ratio can be determined based on the learning result of an artificial intelligence algorithm located on the server based on one or more of the obtained data, traffic law data, and accident DB, and information related to the determined fruit ratio is transmitted to each accident vehicle. can do. The vehicle 10 may provide the fruit ratio information received from the server 2 through an output unit located in the vehicle. The accident DB information may be information generated by an insurance company or an organization that collects related information. The accident DB may include accident history information of other vehicles, and the accident history information may include an error rate.

For example, when an accident occurs, the server can check whether there is accident history information of other vehicles similar to the accident information that occurred based on the accident history information of other vehicles obtained from the accident DB.

The output unit located in the vehicle may be a display (head up display, instrument panel, center information display) or an audio output unit.

The accident party can choose whether to be convinced of the percentage of negligence provided through the in-vehicle output. When it is selected to understand the negligence rate, information that the accident party has confirmed the negligence rate may be transmitted to the insurance company, and the insurance company may set the insurance amount based on the transmitted information. If the person involved in the accident chooses not to understand the rate of negligence, additional guidance information can be provided based on this. You can call an insurance company employee to request a direct visit to the accident location, or collect more information from nearby vehicles or objects and re-provide the negligence rate through secondary learning.

When calculating the error rate, the server 2 further acquires surrounding environment data including before and after a certain point in time of the accident of each accident vehicle, if the data for calculating the error rate is insufficient, and based on the obtained data. You can calculate the fruit ratio. Here, the surrounding environment data may be sensor information acquired by a nearby vehicle at a certain point in time when an accident occurs, or sensor information acquired by a surrounding sensing device (CCTV, RSU, etc.).

For example, if the insurance company server transmits the calculated error rate information to the accident vehicle, the vehicle may output the information received from the insurance company server on a center information display (CID) provided on the instrument panel of the vehicle. The CID may be formed integrally with the touch input unit or form a layer structure with the touch input unit to implement a touch screen, and may further include a gesture input unit or a voice input unit. The information output to the CID may be information for inquiring whether to understand the car's negligence rate, and the person involved in the accident may input a selection value as to whether or not to understand the negligence rate through the touch input unit of the CID.

Vehicle accident types can be largely classified into accidents, vehicle-to-vehicle accidents, vehicle-to-property accidents, and vehicle-to-pedestrian accidents. In the case of a driving vehicle, the ratio of responsibility between the person and the vehicle may vary according to the ratio of the driver's control rights.

The above-described present invention can be implemented as a computer-readable code on a medium on which a program is recorded. The computer-readable medium includes all types of recording devices that store data that can be read by a computer system. Examples of computer-readable media include HDD (Hard Disk Drive), SSD (Solid State Disk), SDD (Silicon Disk Drive), ROM, RAM, CD-ROM, magnetic tape, floppy disk, optical data storage device, etc. There is also a carrier wave (e.g., transmission over the Internet). Also, the computer may include a processor or a control unit. Therefore, the detailed description above should not be construed as restrictive in all respects and should be considered as illustrative. The scope of the present invention should be determined by rational interpretation of the appended claims, and all changes within the equivalent scope of the present invention are included in the scope of the present invention.

Claims

Obtaining, by at least one processor, data on a situation of the autonomous vehicle;

Determining, by at least one processor, whether an accident has occurred in the autonomous vehicle based on the data; And

At least one processor, using an artificial intelligence algorithm, determining the responsible position of the accident; traffic accident processing method comprising a.
The method of claim 1,

The step of obtaining the data,

Acquiring, by at least one processor, first data from at least one electronic device provided in the autonomous vehicle;

Obtaining, by at least one processor, second data from at least one electronic device provided in another vehicle located around the autonomous vehicle; And

At least one processor, obtaining third data from at least one road side unit (RSU) located around the autonomous vehicle.
The method of claim 2,

The step of determining the responsible person,

At least one processor, based on at least one of the first data, the second data, and the third data, reconstructing the situation at the time of the accident in a simulation; traffic accident processing method comprising a.
The method of claim 3,

The reconstructing step,

At least one processor, the step of mapping the autonomous driving vehicle and the surrounding objects of the autonomous driving vehicle on a map in chronological order; includes,

Information on the surrounding objects of the autonomous vehicle,

A traffic accident processing method generated based on at least one of the first data, the second data, and the third data.
The method of claim 3,

The reconstructing step,

At least one processor, based on at least one of the first data, the second data, and the third data, generating a top view image based on the image of the autonomous vehicle; includes,

The top view image,

A traffic accident processing method that is a video including an image of the attitude of the steering wheel of the autonomous vehicle.
The method of claim 1,

The step of determining the responsible person,

A traffic accident processing method in which at least one processor determines the responsibility of the accident based on a result of machine learning by inputting data on the situation of the autonomous vehicle and traffic regulation data into the artificial intelligence algorithm.
The method of claim 6,

The step of determining the responsible person,

At least one processor further inputs past traffic accident history data to the artificial intelligence algorithm and determines a responsible person for the accident based on a machine learning result.
The method of claim 1,

At least one processor, the step of executing a routine for preventing secondary accidents after the occurrence of the accident; traffic accident handling method further comprising.
The method of claim 8,

The step of executing the secondary accident prevention routine,

Determining, by at least one processor, whether the autonomous vehicle can be moved after the accident;

Generating, by at least one processor, an avoidance movement path of the autonomous vehicle; And

At least one processor, providing a control signal to drive the autonomous vehicle along the avoidance movement path; traffic accident processing method comprising a.
The method of claim 9,

The step of executing the secondary accident prevention routine,

Calculating, by at least one processor, a collision prediction time with another vehicle following the autonomous vehicle; And

Comprising, by at least one processor, comparing the collision prediction time with the avoidance time of the autonomous vehicle according to the avoidance movement path at the accident point; and

Providing the control signal,

At least one processor, when the avoidance time is greater than the collision prediction time, the traffic accident processing method for providing the control signal.
Acquires data on the situation of autonomous vehicles,

Based on the data, it is determined whether an accident has occurred in the autonomous vehicle,

A traffic accident processing apparatus comprising a; processor for determining the responsible position of the accident by using an artificial intelligence algorithm.
The method of claim 11,

The processor,

Obtaining first data from at least one electronic device provided in the autonomous vehicle,

Acquiring second data from at least one electronic device provided in another vehicle located around the autonomous vehicle,

A traffic accident processing apparatus for obtaining third data from at least one road side unit (RSU) located around the autonomous vehicle.
The method of claim 12,

The processor,

A traffic accident processing apparatus configured to reconstruct a situation at the time of the accident into a simulation based on at least one of the first data, the second data, and the third data.
The method of claim 13,

The processor,

Map the autonomous driving vehicle and surrounding objects of the autonomous driving vehicle on a map in chronological order,

Information on the surrounding objects of the autonomous vehicle,

A traffic accident processing apparatus generated based on at least one of the first data, the second data, and the third data.
The method of claim 13,

The processor,

Generating a top view image based on the image of the autonomous vehicle based on at least one of the first data, the second data, and the third data,

The top view image,

A traffic accident processing device that is a video including an image of the attitude of the steering wheel of the autonomous vehicle.
The method of claim 11,

The processor,

A traffic accident processing device that determines a responsible person for the accident based on a machine learning result output by inputting data on the situation of the autonomous vehicle and traffic regulation data into the artificial intelligence algorithm.
The method of claim 16,

The processor,

A traffic accident processing device that further inputs past traffic accident history data to the artificial intelligence algorithm and determines who is responsible for the accident based on machine learning results.
The method of claim 11,

The processor,

A traffic accident handling device that executes a routine for preventing secondary accidents after the accident.
The method of claim 18,

The processor,

After the accident, it is determined whether the autonomous vehicle can be moved,

Generating an evasive movement path of the autonomous vehicle,

Traffic accident processing apparatus for providing a control signal to drive the autonomous vehicle along the avoided movement path.
The method of claim 19,

The processor,

Calculate a collision prediction time with another vehicle following the autonomous vehicle,

Compare the collision prediction time with the avoidance time of the autonomous vehicle according to the avoidance movement path at the accident point,

When the avoidance time is greater than or equal to the collision prediction time, the traffic accident processing apparatus provides the control signal.