WO2017018842A1

WO2017018842A1 - Apparatus and method for controlling an autonomous driving vehicle

Info

Publication number: WO2017018842A1
Application number: PCT/KR2016/008324
Authority: WO
Inventors: 크로닌존; 디안드레아미카엘
Original assignee: 삼성전자 주식회사
Priority date: 2015-07-30
Filing date: 2016-07-29
Publication date: 2017-02-02

Abstract

Provided are a device and a method which determine an optimal driving mode for a passenger, on the basis of at least one of context information on the passenger of the autonomous driving vehicle and environment information of the autonomous driving vehicle, and control the autonomous driving vehicle in accordance with the determined driving mode.

Description

[Correction 24.08.2016 by Rule 26] 장치 Apparatus and method for controlling autonomous vehicles

The present disclosure relates to an apparatus and method for controlling an autonomous vehicle.

Recently, interest in autonomous vehicles is increasing. In particular, various additional functions related to autonomous driving have been continuously developed to alleviate traffic congestion caused by increasing demand for automobiles and to safely avoid obstacles such as people and other vehicles. For example, there are many algorithms associated with lane keeping systems.

In addition, as internet connectivity expands, the amount of data generated from various devices and automobiles is rapidly increasing, and various services using the same are emerging.

Accordingly, there is a need for a method and an autonomous vehicle that can provide a familiar autonomous driving experience to the occupant using various data.

An apparatus and method for controlling an autonomous vehicle is provided. The technical problem to be achieved by the present embodiment is not limited to the technical problems as described above, and further technical problems can be inferred from the following embodiments.

According to an aspect, an apparatus for controlling an autonomous vehicle includes an interface for obtaining at least one of context information about a passenger of an autonomous vehicle and surrounding environment information of the autonomous vehicle; And a processor configured to determine a driving mode optimized for the occupant based on the obtained at least one information and to control the autonomous driving vehicle according to the determined driving mode.

According to another aspect, a method of controlling an autonomous vehicle includes: obtaining at least one of context information about an occupant of an autonomous vehicle and surrounding environment information of the autonomous vehicle; Determining a driving mode optimized for the occupant based on the obtained at least one information; And controlling the autonomous vehicle according to the determined driving mode.

According to another aspect, there is provided a computer readable recording medium having recorded thereon a program for implementing the method.

According to yet another aspect, a method of controlling an autonomous vehicle includes: obtaining user environment information; Determining a driving style based on user environment information; And setting driving parameters of the vehicle according to the driving style.

According to the present embodiment, a driving mode suitable for a situation in which an occupant of an autonomous vehicle is located provides a passenger-friendly driving environment.

1 illustrates an example of an autonomous vehicle.

2 is a diagram for describing an autonomous vehicle.

3 is a diagram for describing a vehicle control apparatus for controlling an autonomous vehicle.

4 illustrates a vehicle control apparatus for determining a driving mode based on destination information of a passenger.

5 illustrates an example of an association relationship between passenger destination information and a driving mode.

6 illustrates an example in which the vehicle control apparatus determines a driving mode based on destination information of a passenger.

7 illustrates an example in which the vehicle controller controls the autonomous vehicle in the acceleration driving mode.

8 illustrates a vehicle control apparatus for determining a driving mode based on passenger schedule information.

9 illustrates an example in which the vehicle control apparatus determines a driving mode based on schedule information of a passenger.

10 illustrates a vehicle control apparatus for determining a driving mode based on body state information of a passenger.

11 illustrates an example in which the vehicle control apparatus determines a driving mode based on the sleep state information of the occupant and controls the autonomous driving vehicle according to the determined driving mode.

12 illustrates an example in which the vehicle control apparatus controls the autonomous vehicle in a calm driving mode.

13 illustrates an example in which the vehicle control apparatus determines the driving mode based on the concentrated state information of the occupant, and controls the autonomous driving vehicle according to the determined driving mode.

14 illustrates an example in which the vehicle control apparatus determines a driving mode based on emergency state information of a passenger.

15 illustrates an example of a vehicle control apparatus for determining a driving mode based on identification information of a passenger.

16 illustrates an example in which the vehicle control apparatus determines a driving mode based on identification information of a passenger.

17 illustrates an example of a vehicle control apparatus for determining a driving mode based on location information of an autonomous vehicle.

18 illustrates an example in which the vehicle control apparatus determines a driving mode based on the highway position information, and controls the autonomous driving vehicle according to the determined driving mode.

19 illustrates an example in which the vehicle control apparatus determines a driving mode based on location information in a city center, and controls the autonomous driving vehicle in the determined driving mode.

20 illustrates an example in which the vehicle controller controls the autonomous vehicle in the eco driving mode.

FIG. 21 illustrates an example in which the vehicle control apparatus determines a driving mode based on landmark position information and controls the autonomous driving vehicle in the determined driving mode.

22 illustrates an example of a vehicle control apparatus for determining a driving mode based on traffic information.

FIG. 23 illustrates an embodiment in which the vehicle control apparatus determines a driving mode based on the traffic information and controls the autonomous driving vehicle according to the determined driving mode.

24 illustrates an example in which the vehicle control apparatus provides a graphical user interface (GUI) related to traffic congestion.

25 illustrates an example of a vehicle control apparatus for determining a driving mode based on weather information.

FIG. 26 illustrates an embodiment in which the vehicle control apparatus determines a driving mode based on weather information and controls the autonomous driving vehicle according to the determined driving mode.

27 illustrates an example of a vehicle control apparatus for determining a driving mode based on road state information.

28 illustrates an example in which the vehicle control apparatus determines a driving mode based on road state information and controls the autonomous driving apparatus according to the determined driving mode.

29 illustrates an example in which the vehicle control apparatus provides a GUI associated with a dangerous road.

30 shows an example of information on a dangerous road.

31 illustrates an example in which the vehicle control device controls the autonomous vehicle according to the degree of danger of the dangerous road.

32 illustrates an example in which the vehicle controller changes the driving mode based on other surrounding environment information.

33 illustrates an example in which the vehicle control apparatus changes the driving mode based on other context information.

34 shows an example of a priority setting menu.

FIG. 35 illustrates an example in which the vehicle control apparatus determines a driving mode optimized for a passenger based on priorities among acquired context information and surrounding environment information.

36 is a diagram for describing an example of a vehicle control apparatus communicating with a mobile device.

37 illustrates an example of driving information that can be set in the mobile device.

38 shows an example of a driving profile provided by the vehicle control apparatus.

39 shows an example of a menu for setting a driving mode.

40 illustrates an example of a menu for selecting a passenger from among a plurality of passengers in the autonomous vehicle.

41 shows an example of a method of controlling an autonomous vehicle.

In addition, the interface may obtain at least one information of the other context information and the other environment information, and the processor may change the driving mode based on at least one of the other context information and the other environment information.

In addition, the interface obtains at least two or more different information included in the context information and the surrounding environment information, and the processor, based on a preset priority and at least two different information obtained, the driving optimized for the occupant The mode can be determined.

In addition, the processor may determine a driving mode corresponding to the obtained at least one information based on a correlation between the obtainable context information and the driving mode applicable to the autonomous vehicle.

In addition, the association may be predetermined by the occupant or learned by the occupant's driving history to be determined.

In addition, the processor may control the driving parameter of the autonomous vehicle according to the determined driving mode.

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. The following examples are only for the purpose of embodying the spirit of the present invention, and of course, it does not limit or limit the scope of rights. From the detailed description and examples, what can be easily inferred by those skilled in the art is interpreted as belonging to the scope of rights.

As used herein, the term “consisting of” or “comprising” should not be construed as including all of the various elements, or steps, described in the specification, and some or some of them may be included. Should not be included, or should be construed to further include additional components or steps. In addition, the terms "... unit", "module", etc. described in the specification mean a unit for processing at least one function or operation, which may be implemented in hardware or software or a combination of hardware and software. .

In addition, terms including ordinal numbers, such as “first” or “second”, as used herein may be used to describe various components, but these terms may distinguish one component from another or It can be used for convenience.

Hereinafter, with reference to the drawings will be described embodiments of the present invention;

1 illustrates an example of an autonomous vehicle.

The autonomous vehicle 1 may refer to a vehicle that can drive itself without intervention of a passenger. The autonomous vehicle 1 may obtain driving context information. The driving context information may refer to user environment information describing what kind of situation the user of the autonomous vehicle 1 is in. As another example, the driving context information may include information affecting the driving of the autonomous vehicle 1.

The driving context information may include at least one of context information about the occupant and surrounding environment information of the autonomous vehicle 1. The term referred to hereinafter as "context information" may mean "context information about a passenger."

The context information about the occupant may refer to information representing a state of the occupant or a situation in which the occupant is located. For example, the context information may include at least one of destination information of the passenger, schedule information of the passenger, physical condition information of the passenger, and identification information of the passenger. As another example, the context information for the occupant may include information about the occupant at the present time or in the future.

The surrounding environment information of the autonomous vehicle 1 may mean information indicating an environment around the autonomous vehicle 1. For example, the surrounding environment information may include weather information, traffic information, road condition information, location information of the autonomous vehicle 1, current time information on which the autonomous vehicle is operating, and day of the week information. It may include at least one of the date information.

The autonomous vehicle 1 may determine the driving mode optimized for the occupant based on the obtained at least one driving context information. The driving mode may indicate a driving style of the autonomous vehicle 1. For example, when the driving mode is the acceleration driving mode, the autonomous vehicle 1 may travel in terms of increasing the acceleration performance. When the driving mode is the eco driving mode, the autonomous driving vehicle 1 may have fuel efficiency. You can drive in terms of savings. According to the driving mode, it is possible to determine which driving characteristics or driving characteristics of the autonomous vehicle 1 are to be driven.

The autonomous driving vehicle 1 may determine a situation in which the occupant is placed based on at least one of context information and surrounding environment information about the occupant in order to determine a driving mode optimized for the occupant, and the determined situation It is possible to determine the driving mode suitable for. That is, the autonomous vehicle 1 may determine a driving mode suitable for a situation in which the occupant is in the plurality of driving modes. For example, the autonomous vehicle 1 may determine the driving mode optimized for the occupant as the acceleration driving mode when the situation in which the occupant is in need of the acceleration driving. According to another example, the autonomous vehicle 1 may determine the speed limit mode as the driving mode optimized for the occupant when the situation in which the occupant is in need of safe driving.

The autonomous vehicle 1 can travel in accordance with the determined driving mode without any intervention of the occupant. For example, the autonomous vehicle 1 may travel by adjusting driving parameters according to the determined driving mode. In detail, the autonomous vehicle 1 may drive by adjusting a driving parameter according to the determined driving mode. Examples of travel parameters include brake sensitivity, steering sensitivity, acceleration and deceleration degree, maximum speed, G-force, throttle, suspension frequency, and the like. That is, at least one driving parameter may be set differently according to the driving mode.

2 is a diagram for describing an autonomous vehicle.

The autonomous vehicle 1 includes a power supply device 299, a communication device 250, an input device 260, an output device 280, a storage device 270, a travel device 220, and a sensing device 230. , Peripheral device 240, and control device 290. In the autonomous vehicle 1 shown in FIG. 2, only components related to the present embodiment are shown. Accordingly, it will be understood by those skilled in the art that other general purpose components may be further included in addition to the components shown in FIG. 2.

The propulsion device 210 may include an engine / motor 211, an energy source 212, a transmission 213 and a wheel / tire 214.

The engine / motor 211 may be any combination between an internal combustion engine, an electric motor, a steam engine, and a stirling engine. For example, when the autonomous vehicle 1 is a gas-electric hybrid car, the engine / motor 211 may be a gasoline engine and an electric motor.

Energy source 212 may be a source of energy that powers the engine / motor 211 in whole or in part. That is, engine / motor 211 may be configured to convert energy source 212 into mechanical energy. Examples of energy sources 212 may be at least one of gasoline, diesel, propane, other compressed gas based fuels, ethanol, solar panels, batteries, and other electrical power sources. Alternatively, the energy source 212 may be at least one of a fuel tank, a battery, a capacitor, and a flywheel. According to one example, the energy source 212 can provide energy to the systems and devices of the autonomous vehicle 1.

Transmission 213 may be configured to transfer mechanical power from engine / motor 211 to wheel / tire 214. For example, the transmission 213 may include at least one of a gearbox, a clutch, a differential, and a drive shaft. When the transmission 213 includes drive shafts, the drive shafts may include one or more axles configured to be coupled to the wheel / tire 214.

The wheel / tire 214 may be configured in a variety of formats, including a unicycle, a bike / motorcycle, a tricycle, or a four wheel type of a car / truck. For example, other wheel / tire types may be possible, such as including six or more wheels. The wheel / tire 214 includes at least one wheel fixedly attached to the transmission 213 and at least one tire coupled to a rim of the wheel that can contact the driving surface. can do.

The traveling device 220 may include a brake unit 221, a steering unit 222, and a throttle 223.

The steering unit 222 may be a combination of mechanisms configured to adjust the direction of the autonomous vehicle 1.

The throttle 223 may be a combination of mechanisms configured to control the speed of operation of the engine / motor 211 to control the speed of the autonomous vehicle 1. In addition, the throttle 223 may adjust the amount of throttle opening to adjust the amount of mixed gas of fuel air flowing into the engine / motor 211, and may control power and thrust by adjusting the throttle opening.

The brake unit 221 may be a combination of mechanisms configured to decelerate the autonomous vehicle 1. For example, the brake unit 221 may use friction to reduce the speed of the wheel / tire 214.

The sensing device 230 may include a plurality of sensors configured to sense information about the environment in which the autonomous vehicle 1 is located, as well as one or more actuators configured to modify the position and / or orientation of the sensors. Can include them. For example, sensing device 230 may include a Global Positioning System (GPS) 224, an Inertial Measurement Unit (IMU) 225, a RADAR unit 226, a LIDAR unit 227, and a camera 228. Can be. In addition, the sensing device 230 may include a geomagnetic sensor 229, an acceleration sensor 231, a temperature / humidity sensor 232, an infrared sensor 233, a gyroscope sensor 234, and atmospheric pressure. The sensor 235 may include at least one of the proximity sensor 236 and the RGB sensor 237, but is not limited thereto. Since functions of the respective sensors can be intuitively deduced by those skilled in the art from the names, detailed descriptions thereof will be omitted.

The GPS 224 may be a sensor configured to estimate the geographic location of the autonomous vehicle 1. That is, the GPS 224 may include a transceiver configured to estimate the position of the autonomous vehicle 1 with respect to the earth.

The IMU 225 may be a combination of sensors configured to detect positional and orientation changes of the autonomous vehicle 1 based on inertial acceleration. For example, the combination of sensors may include accelerometers and gyroscopes.

The RADAR unit 226 may be a sensor configured to detect objects in the environment in which the autonomous vehicle 1 is located using a wireless signal. In addition, the RADAR unit 226 can be configured to sense the speed and / or direction of the objects.

The LIDAR unit 227 may be a sensor configured to detect objects in the environment in which the autonomous vehicle 1 is located using a laser. More specifically, LIDAR unit 227 may include a laser light source and / or laser scanner configured to emit a laser, and a detector configured to detect reflection of the laser. The LIDAR unit 227 may be configured to operate in coherent (eg, using hetirodyne detection) or noncoherent detection mode.

The camera 228 may be a still camera or a video camera configured to record three-dimensional images of the interior of the autonomous vehicle 1. For example, the camera 228 may include a plurality of cameras, and the plurality of cameras may be disposed at a plurality of positions on the inside and outside of the autonomous vehicle 1.

The peripheral device 240 may include a navigation 241, a light 242, a turn signal 243, a wiper 244, an interior light 245, a heater 246, and an air conditioner 247.

The navigation 241 may be a system configured to determine a travel route for the autonomous vehicle 1. The navigation 241 may be configured to dynamically update the travel route while the autonomous vehicle 1 is traveling. For example, the navigation 241 may use data from the GPS 224 and maps to determine the route of travel for the autonomous vehicle 1.

The storage device 270 may include a magnetic disk drive, an optical disk drive, and a flash memory. Alternatively, the storage device 270 may be a portable USB data storage device. Storage device 270 may store system software for executing examples related to the present disclosure. System software for carrying out the examples relating to the present disclosure may be stored on a portable storage medium.

The communication device 250 may include at least one antenna for wirelessly communicating with another device. For example, communication device 250 may be used to communicate with a cellular network or other wireless protocols and systems wirelessly via Wi-Fi or Bluetooth. The communication device 250 controlled by the control device 290 may transmit and receive a wireless signal. For example, the control device 290 may execute a program included in the storage device 270 in order for the communication device 250 to transmit and receive a wireless signal with the cellular network.

The input device 260 means a means for inputting data for controlling the autonomous vehicle 1. For example, the input device 260 may include a key pad, a dome switch, a touch pad (contact capacitive type, pressure resistive type, infrared sensing type, surface ultrasonic conduction type, and integral type). Tension measurement method, piezo effect method, etc.), a jog wheel, a jog switch, and the like, but are not limited thereto. In addition, the input device 260 may include a microphone, which may be configured to receive audio (eg, voice commands) from the occupant of the autonomous vehicle 1.

The output device 280 may output an audio signal or a video signal, and the output device 280 may include a display 281 and a sound output unit 282.

The display unit 281 may be a liquid crystal display, a thin film transistor-liquid crystal display, an organic light-emitting diode, a flexible display, or a three-dimensional display. 3D display, an electrophoretic display. In addition, depending on the implementation form of the output device 280, the output device 280 may include two or more display units 281.

The sound output unit 282 outputs audio data received from the communication device 250 or stored in the storage device 270. In addition, the sound output unit 282 may include a speaker, a buzzer, and the like.

The input device 260 and the output device 280 may include a network interface, and may be implemented as a touch screen.

The control device 290 typically controls the overall operation of the autonomous vehicle 1. For example, the control device 290 executes the programs stored in the storage device 270, such that the propulsion device 210, the traveling device 220, the sensing device 230, the peripheral device 240, and the communication device ( 250, the input device 260, the storage device 270, the output device 280, and the power supply 299 may be controlled overall.

The power supply 299 may be configured to provide power to some or all of the components of the autonomous vehicle 1. For example, power supply 299 may comprise a rechargeable lithium ion or lead-acid battery.

The vehicle control device 100 may be included in the autonomous vehicle 1, and the control device 290, the communication device 250, the input device 260, the output device 280, and the sensing device 230 of FIG. 2. ) May include at least one, and thus descriptions of overlapping contents will be omitted.

The vehicle control apparatus 100 may include an interface 110 and a processor 120. In the autonomous vehicle 1 shown in FIG. 2, only components related to the present embodiment are shown. Accordingly, it will be understood by those skilled in the art that other general purpose components may be further included in addition to the components shown in FIG. 2.

The interface 110 may obtain at least one of context information about the occupant and surrounding environment information of the autonomous vehicle 1. As an example, the interface 110 may obtain at least one of context information about the occupant and surrounding environment information of the autonomous vehicle 1 from an external device. For example, the communication device 250 may acquire at least one of context information about the occupant and surrounding environment information of the autonomous vehicle 1 from an external device, and transmit the obtained information to the interface 110. . As another example, the interface 110 may obtain at least one of context information about the occupant stored in the storage device 270 and surrounding environment information of the autonomous vehicle 1. As another example, the interface 110 may obtain at least one of context information about the occupant and surrounding environment information of the autonomous vehicle 1 from the sensing device 230. For example, the sensing device 230 may obtain body state information of the occupant and transmit the obtained information to the interface 110. As another example, the interface 110 may obtain at least one of context information about the occupant and surrounding environment information of the autonomous vehicle 1 from the input device 260. For example, the occupant may input context information about the occupant and surrounding environment information of the autonomous vehicle 1 through the input device 260, and the input device 260 may input the user interface to the interface 110. Information can be sent.

The processor 120 may determine a driving mode optimized for the occupant based on the obtained at least one information. That is, the processor 120 may determine the situation in which the occupant is faced without the intervention of the occupant based on at least one of context information and surrounding environment information about the occupant, and determine a driving mode suitable for the determined situation. .

The driving mode may be, for example, an acceleration driving mode for increasing the acceleration performance of the autonomous vehicle 1, an eco driving mode for saving fuel economy of the autonomous vehicle 1, vibration and acceleration of the autonomous vehicle 1. It may include a calm driving mode for minimizing the speed, a speed limit mode for driving below a certain speed, a terrain mode suitable for the terrain on which the autonomous vehicle 1 travels, and an emergency driving mode for an emergency of a passenger. The name of the driving mode described above is an example, and is not limited to the name of the driving mode described above. In addition, each driving mode described above may be applied to a case of manual driving for driving under the driver's driving control, and may also be applied to an autonomous driving without the driver's driving control. For example, even when the occupant directly drives the autonomous vehicle 1, the autonomous vehicle 1 may travel in a predetermined eco driving mode, and the occupant does not directly drive the autonomous vehicle 1. Even in this case, the autonomous vehicle 1 may travel in a predetermined acceleration driving mode.

The processor 120 may control the autonomous vehicle 1 according to the determined driving mode. For example, the processor 120 may adjust the driving parameter according to the determined driving mode. In addition, the processor 120 may control the propulsion device 210 or the peripheral device 240 of the autonomous vehicle 1 according to the determined driving mode.

When the driving mode is the acceleration driving mode, the processor 120 may adjust the driving parameter in terms of increasing the acceleration performance of the autonomous vehicle 1. For example, the processor 120 may increase the amount of opening of the throttle, the suspension frequency, and the suspension stiffness. In addition, the processor 120 may allow the autonomous vehicle 1 to react quickly by using a large torque output to enable agile driving.

When the driving mode is the eco driving mode, the processor 120 may adjust the driving parameters in terms of improving fuel efficiency of the autonomous vehicle 1. For example, the processor 120 may set the throttle opening amount and the acceleration of the autonomous vehicle 1 to a minimum value. In addition, the processor 120 may increase fuel efficiency by allowing the autonomous vehicle 1 to maintain a low revolution per minute (RPM) through a faster shift.

When the driving mode is the calm driving mode, the processor 120 may adjust the driving parameter in terms of minimizing vibration and acceleration of the autonomous vehicle 1. For example, processor 120 may reduce suspension stiffness and suspension frequency. In addition, the processor 120 may control a damping ratio of shock absorbers for suppressing vibrations applied to the tire generated by the road surface reaction and vibrations on the suspension spring.

When the driving mode is the speed limit mode, the processor 120 may limit the speed at which the autonomous vehicle 1 can travel to a predetermined value. For example, the processor 120 may limit the maximum speed at which the autonomous vehicle 1 can travel to 60 km / h.

When the driving mode is the emergency driving mode, the processor 120 may control the autonomous vehicle 1 to travel for a shortest time to a predetermined destination. For example, the processor 120 may control the autonomous vehicle 1 to autonomously travel for the shortest time to the hospital nearest to the current location.

When the driving mode is the terrain mode, the processor 120 may adjust the driving parameters of the autonomous vehicle 1 according to the terrain state in which the autonomous vehicle 1 travels. For example, when the terrain on which the autonomous vehicle 1 runs is mountainous terrain with a lot of gravel / sand, the processor 120 may raise the suspension, and switch to four-wheel drive to a ratio of the front wheel and the rear wheel at a certain ratio. The driving force can be distributed.

The interface 110 may obtain destination information of the passenger as context information about the passenger. For example, the interface 110 may directly receive destination information from the occupant. As an example, the interface 110 may obtain destination information of the passenger through a voice signal of the passenger representing the destination information. As another example, the interface 110 can obtain destination information from the occupant's device via the communication device 250.

The processor 120 may determine a driving mode optimized for the occupant based on the obtained destination information of the occupant. For example, the processor 120 may determine the situation of the occupant based on the destination information of the occupant, and determine a driving mode suitable for the determined situation. If the destination information of the occupant is 'work', the processor 120 determines that the occupant's situation is 'urgent situation in which traffic congestion is possible' because the current time is a commute time, and accelerates the driving mode optimized for the occupant. You can decide in mode. As an example, whether the current time or traffic congestion is imminent may be obtained through an in-vehicle device, an out-of-vehicle device, a server, and the like through the interface 110. Of course, even if other information such as current time or traffic jam is not directly input through the interface 110, the processor 120 may infer or assume other information based on the destination information. For example, when a workplace is input as the destination information, the processor 120 determines that the occupant has boarded the autonomous vehicle 1 for the purpose of going to work, and assumes that the current time is the time of commencement. May be determined.

In addition, the processor 120 may determine a driving mode corresponding to the destination information of the passenger based on the association between the destination information of the passenger and the driving mode. A more detailed example of the association between the destination information of the passenger and the driving mode will be described below with reference to FIG. 5.

The processor 120 may control the autonomous vehicle 1 according to the determined driving mode.

According to an example, the table 510 illustrated in FIG. 5 represents an association relationship between the destination information of the passenger and the driving mode.

When obtaining the destination information of the occupant, the processor 120 may determine a driving mode optimized for the occupant based on the table 510. For example, when the destination information of the occupant is the 'rest area', the processor 120 may determine the driving mode optimized for the occupant as the calm driving mode with reference to the table 510 since the current time is 'weekend'. . According to another example, when the destination information of the occupant is 'coastal road', since the current time is 'dawn time zone', the processor 120 accelerates the driving mode optimized for the occupant with reference to the table 510. You can decide in mode.

An association such as the table 510 may be preset by the occupant. As an example, the passenger in advance may input information for setting the table 510 through the input device 260 of the autonomous vehicle 1 in advance. As another example, the occupant may preset the table 510 within the mobile device. The interface 110 may then receive the table 510 from the mobile device, and the processor 120 may determine the driving mode corresponding to the passenger's destination using the received table 510.

In addition, an association relationship such as the table 510 may be learned and determined by a passenger's past driving history. For example, the processor 120 may determine an association, such as the table 510, based on the passenger's past driving history. If the occupant directly determines the driving mode of the autonomous vehicle 1, the processor 120 determines an association relationship such as the table 510 based on the destination of the occupant, the driving time zone, and the determined driving mode. You can update it.

The occupant 610 may speak the destination information as 'company' in the autonomous vehicle 1. Subsequently, the vehicle control apparatus 100 may sense a voice signal of the occupant 610 to obtain destination information as a 'company'.

The vehicle control apparatus 100 may determine the driving mode optimized for the occupant 610 as the acceleration driving mode since the current time is the commute time zone based on the 'company', which is the destination information. That is, the vehicle control apparatus 100 determines that the destination information of the occupant 610 is 'company' and the current time is at the commute time, so that the situation of the occupant 610 is 'urgent situation in which traffic congestion is possible'. Can be determined as the acceleration driving mode.

Therefore, the vehicle control apparatus 100 may control the autonomous vehicle 1 in the determined acceleration driving mode.

The vehicle control apparatus 100 may control the autonomous vehicle 1 based on the parameter setting menu 710 for the acceleration driving mode. For example, the vehicle control apparatus 100 may provide a menu 710 to a passenger in advance, and receive parameter values of the menu 710 from the passenger. That is, as shown in FIG. 7, the occupant is configured to maximize the amount of opening of the throttle, maximum suspension stiffness, maximum suspension frequency, and lateral force. G-force) to the maximum value, turning speed can be set to the maximum value. For example, the maximum throttle opening can mean more than 70% of the wide-open state, the maximum suspension frequency can mean between 1.25 hz and 2.5 hz, and the maximum lateral force is 0.9 at 0.7 G. Can mean G. However, the maximum value of the throttle opening amount, the maximum value of the suspension frequency, and the maximum value of the lateral force are not limited to the numerical values described above.

As another example, the vehicle control apparatus 100 may receive the menu 710 from the passenger's mobile device and control the autonomous vehicle 1 according to the received menu 710. The occupant may input a parameter value for the accelerated driving mode through the menu 710 of the occupant's mobile device.

The interface 110 may obtain schedule information of the occupant as context information about the occupant. For example, the interface 110 may obtain the schedule information of the passenger stored in the mobile device of the passenger through the communication device 250. In detail, the interface 110 may obtain information about a destination and a target arrival time of the passenger from among schedule information of the passenger.

The processor 120 may determine a driving mode optimized for the occupant based on the acquired schedule information of the occupant. For example, the processor 120 may determine a driving mode optimized for the occupant based on the information on the occupant's destination and the target arrival time of the occupant's schedule information.

In detail, the processor 120 may determine an estimated arrival time at which the autonomous vehicle 1 arrives at the passenger's destination based on the information on the passenger's destination. Subsequently, the processor 120 may compare the target arrival time and the estimated arrival time of the passenger to determine how much time the passenger has. That is, when the difference between the target arrival time and the scheduled arrival time is shorter than the predetermined time, or when the target arrival scheduled time is after the target arrival time, the processor 120 determines that the passenger lacks the time, and the driving optimized for the passenger The mode can be determined as the acceleration driving mode. In addition, when the target arrival time is before the target arrival time, when the difference between the target arrival time and the scheduled arrival time is longer than the predetermined time, the processor 120 determines that the passengers have enough time, and the driving optimized for the passengers. The mode can be determined as the eco driving mode.

The vehicle control apparatus 100 may receive schedule information 920 stored in the passenger's mobile device 910 from the mobile device 910. That is, based on the communication between the vehicle control apparatus 100 and the mobile device 910, the vehicle control apparatus 100 may receive the passenger schedule information 920 from the mobile device 910.

The vehicle control apparatus 100 may determine a driving mode optimized for the occupant based on the received schedule information 920 of the occupant. More specifically, the vehicle control apparatus 100 may obtain information about 'office A', which is the passenger's destination, and information about '10: 00 ', which is a target arrival time, through the schedule information 920 of the passenger. . Subsequently, the vehicle control apparatus 100 may determine an estimated arrival time for reaching the destination 'A office' based on the current location and the current time. For example, the vehicle control apparatus 100 may determine an estimated arrival time for reaching the destination 'A office' using the navigation 241 of the autonomous vehicle 1. Subsequently, the vehicle control apparatus 100 may compare the determined estimated arrival time with the target arrival time '10: 00 'to determine how much time the passenger has time, and the vehicle control apparatus 100 based on the determination The driving mode optimized for the occupant may be determined.

For example, when the determined estimated arrival time is 09:50, since the difference from the target arrival time '10: 00 'is within a preset time of 20 minutes, the vehicle control apparatus 100 indicates that the passenger lacks time. By determining, the driving mode optimized for the occupant may be determined as the acceleration driving mode. Therefore, the vehicle control apparatus 100 may control the autonomous driving vehicle 1 according to the determined acceleration driving mode. According to another example, when the determined estimated arrival time is 09:20, since the difference from the target arrival time '10: 00 'is 20 minutes or more, which is a preset time, the vehicle control apparatus 100 may allow time for the occupant. Is determined to be sufficient, the driving mode optimized for the occupant can be determined as the eco driving mode.

Therefore, the vehicle control apparatus 100 may control the autonomous vehicle 1 according to the determined eco driving mode.

The interface 110 may obtain body state information of the occupant as context information about the occupant. The passenger's physical condition information includes information about the occupant's heart rate, blood pressure, breathing, blood alcohol level, body temperature, blood sugar, as well as whether the occupant is sleeping, the occupant is concentrating, or the occupant's health is critical. Information about whether or not the state is included. As an example, the sensing device 230 may sense the physical state information of the occupant and transmit the sensed information to the interface 110. As another example, the interface 110 may obtain body state information of the occupant from the communication device 250. That is, the communication device 250 may obtain body state information of the occupant from an external device capable of sensing the body state of the occupant, and transmit the obtained information to the interface 110.

The processor 120 may determine a driving mode optimized for the occupant based on the acquired physical condition information of the occupant. As an example, when the physical condition information of the occupant is information indicating the sleep state of the occupant, the processor 120 may determine the calmness driving mode that is optimized for the occupant to minimize the disturbance of the occupant's sleep. have. As another example, when the occupant's physical state information is in a state where the occupant is concentrating, the processor 120 may determine the occupant-optimized driving mode as the calm driving mode in order to remove an obstacle to the occupant's concentration. An example of a state in which the occupant is concentrating may be a case in which the occupant is looking at the tablet PC for a predetermined time or more. As another example, when the physical condition information of the occupant is in an emergency state of the occupant, the processor 120 may determine a mode optimized for the occupant as the emergency driving mode.

The wearable device 1110 may determine whether the occupant is in the sleep state. For example, the wearable device 1110 may photograph an occupant's eye through a camera, and determine whether the occupant is in a sleeping state by photographing the occupant's eye. In detail, the wearable device 1110 may determine that the occupant is in the sleep state when the occupant's eyes are closed more than a reference ratio compared to the normal state, or when the occupant's eyes are closed for a predetermined time or more. Subsequently, the wearable device 1110 may transmit sleep state information of the occupant to the vehicle control apparatus 100.

The vehicle control apparatus 100 may obtain the sleep state information of the occupant from the wearable device 1110 as the body state information of the occupant. The vehicle control apparatus 100 may determine a driving mode optimized for the occupant based on the sleep state information of the occupant. That is, since the vehicle control apparatus 100 is sleeping, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant as the calm driving mode in order to minimize the disturbance of the occupant's sleep.

Therefore, the vehicle control apparatus 100 may control the autonomous vehicle 1 according to the determined calm driving mode. In addition, when the vehicle control apparatus 100 controls the autonomous vehicle 1 in the calm driving mode, the vehicle control device 100 may also control the peripheral device 240 of the autonomous vehicle 1. For example, the vehicle control apparatus 100 may adjust the internal lighting 245 to a predetermined brightness.

The vehicle control apparatus 100 may control the autonomous vehicle 1 based on the parameter setting menu 1210 for the calm driving mode. For example, the vehicle control apparatus 100 may provide the passenger with the menu 1210 in advance, and receive parameter values of the menu 1210 from the passenger. That is, as shown in FIG. 12, the occupant has a minimum amount of opening of the throttle, a minimum of suspension stiffness, a minimum of suspension frequency, and a lateral force. G-force) can be set to minimum and turning speed to minimum. For example, the minimum value of the throttle opening may mean less than 30% of the wide-open state, the minimum value of the suspension frequency may mean between 1.0 and 1.2 hz, and the minimum of the lateral force is 0.5 to 0.3 G. Can mean G.

As another example, the vehicle control apparatus 100 may receive the menu 1210 from the passenger's mobile device, and control the autonomous vehicle 1 according to the received menu 1210. The occupant may input a parameter value for the calm driving mode through the menu 1210 of the occupant's mobile device.

The occupant 1310 may perform a predetermined task through the tablet PC 1320. That is, the occupant 1310 may concentrate on the tablet PC 1320. The tablet PC 1320 may photograph the eyes of the occupant 1310 through a camera, and may determine whether the occupant 1310 is in a concentrated state by photographing the eyes of the occupant 1310. For example, when the occupant 1310 looks at the tablet PC 1320 for a predetermined time or more, the tablet PC 1320 may determine that the occupant 1310 is in a concentrated state. Subsequently, the tablet PC 1320 may transmit the concentrated state information of the occupant 1310 to the vehicle control apparatus 100.

The vehicle control apparatus 100 may obtain the concentrated state information of the occupant from the tablet PC 1320 as the physical state information of the occupant. The vehicle control apparatus 100 may determine a driving mode optimized for the occupant 1310 based on the concentrated state information of the occupant 1310. That is, since the occupant 1310 is concentrating on a predetermined task, the vehicle control apparatus 100 sets the driving mode optimized for the occupant 1310 to the calm driving mode in order to minimize the disturbance of the occupant 1310. You can decide.

Therefore, the vehicle control apparatus 100 may control the autonomous vehicle 1 according to the determined calm driving mode.

The wearable device 1420 may periodically detect a health state of the occupant 1410. For example, the wearable device 1420 may periodically detect heart rate, blood pressure, respiration, body temperature, and the like of the occupant 1410 through a sensor. Accordingly, the wearable device 1420 may periodically detect a health state of the occupant 1410 and determine whether the occupant 1410 is in an emergency state. For example, the wearable device 1420 may detect a heart rate of the occupant 1410 to determine whether the occupant 1410 is a heart attack, and the wearable device 1420 may detect a body temperature of the occupant 1410. The occupant 1410 may determine whether there is a high temperature symptom. According to another example, when the occupant 1410 has a predetermined disease, the wearable device 1420 may determine whether the illness of the occupant 1410 is worsened. When the occupant 1410 is in an emergency state, the wearable device 1420 may transmit emergency state information of the occupant 1410 to the vehicle control apparatus 100.

The vehicle control apparatus 100 may obtain emergency state information of the occupant 1410 from the wearable device 1420 as body state information of the occupant 1410. The vehicle control apparatus 100 may determine a driving mode optimized for the occupant 1410 based on the emergency state information of the occupant 1410. That is, since the health of the occupant 1410 is in an emergency state, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant 1410 as the emergency driving mode in order to recover the health of the occupant 1410.

Therefore, the vehicle control apparatus 100 may control the autonomous vehicle 1 according to the determined emergency driving mode. For example, the vehicle control apparatus 100 may control the autonomous vehicle 1 to autonomously travel for the shortest time to the hospital closest to the current location.

The interface 110 may obtain identification information of the passenger as context information about the passenger. For example, the identification information of the occupant may indicate whether the occupant is a 'child' or 'elderly person'. As an example, the interface 110 may obtain identification information of the passenger from a device capable of identifying the passenger. Here, the device capable of identifying the occupant may determine whether the occupant is a child according to whether the child is in the child car seat. As another example, the device capable of identifying the occupant may identify the occupant's voice to determine whether the occupant is a child or an elderly person.

The processor 120 may determine a driving mode optimized for the occupant based on the acquired identification information of the occupant. For example, if the identification information of the occupant is information representing the elderly or the child, the processor 120 may determine the driving mode optimized for the occupant as the calm driving mode to protect the elderly or the child.

The occupant information detector 1610 may acquire child occupant information when a child rides in the child car seat. Subsequently, the vehicle control apparatus 100 may obtain child occupant information from the occupant information detector 1610.

The vehicle control apparatus 100 may determine the driving mode optimized for the occupant as the calm driving mode based on the acquired child occupant information. That is, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant as the calm driving mode to protect the child.

The interface 110 may obtain location information of the autonomous vehicle 1 as the environment information of the autonomous vehicle 1. According to one example, the interface 110 may obtain location information of the autonomous vehicle 1 from the GPS 224 of the autonomous vehicle 1, and according to another example, the interface 110 may be a mobile device of a passenger. The position information of the autonomous vehicle 1 can be obtained from the GPS of the device.

The processor 120 may determine the driving mode optimized for the occupant based on the acquired position information of the autonomous vehicle 1. According to an example, when the location information of the autonomous vehicle 1 indicates the 'highway', the processor 120 may determine the driving mode optimized for the occupant as the acceleration driving mode. According to another example, when the location information of the autonomous vehicle 1 indicates 'in the city', the processor 120 may determine the driving mode optimized for the occupant as the eco driving mode. According to another example, when the location information of the autonomous vehicle 1 indicates 'around the landmark', the processor 120 may determine the speed limit mode as the driving mode optimized for the occupant.

The GPS 224 of the autonomous vehicle 1 may obtain current location information of the autonomous vehicle 1. Subsequently, the vehicle control apparatus 100 may obtain current location information of the autonomous vehicle 1 from the GPS 224.

Subsequently, the vehicle control apparatus 100 may recognize that the current location is 'highway' based on the current location information. Therefore, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant based on the current position 'highway'. That is, since the autonomous vehicle 1 is suitable for the high speed driving mode on the highway, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant as the high speed driving mode.

The occupant's mobile device 1910 may obtain current location information of the autonomous vehicle 1 using the GPS in the mobile device 1910. Subsequently, the vehicle control apparatus 100 may obtain current location information of the autonomous vehicle 1 from the mobile device 1910.

Subsequently, the vehicle control apparatus 100 may recognize that the current position of the autonomous vehicle 1 is within a city center based on the current position information. Therefore, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant based on the current position 'in the city'. That is, since the autonomous vehicle 1 stops a lot in the city, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant as the eco driving mode.

The vehicle control apparatus 100 may control the autonomous vehicle 1 based on the parameter setting menu 2010 for the eco driving mode. As an example, the vehicle control apparatus 100 may provide a menu 2010 to a passenger in advance, and receive parameter values of the menu 2010 from the passenger. That is, as shown in FIG. 20, the occupant may set the opening amount of the throttle to the minimum value and the acceleration value to the minimum value.

As another example, the vehicle control apparatus 100 may receive the menu 2010 from the passenger's mobile device, and control the autonomous vehicle 1 according to the received menu 2010. The occupant may input a parameter value for the eco driving mode through the menu 2010 of the occupant's mobile device.

The GPS 224 of the autonomous vehicle 1 may obtain current location information of the current autonomous vehicle 1. Subsequently, the vehicle control apparatus 100 may obtain current location information of the autonomous vehicle 1 from the GPS 224.

Subsequently, the vehicle control apparatus 100 may recognize that the current position of the autonomous vehicle 1 is around a specific landmark 2110 based on the obtained current position information. For example, when the current location is within a predetermined distance of the specific landmark 2110, the vehicle control apparatus 100 may recognize that the current location is around the specific landmark 2110. In addition, the vehicle control apparatus 100 may store location information of the specific landmark 2110. For example, the vehicle control apparatus 100 may store location information about landmarks that the occupant wants to visit in advance. Therefore, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant based on the current position around the specific landmark 2110. That is, when there is a landmark in the vicinity, the vehicle control apparatus 100 may determine the speed limit mode as a driving mode optimized for the occupant so that the occupant may easily view the landmark.

Therefore, the vehicle control apparatus 100 may control the autonomous vehicle 1 according to the determined speed limit mode.

The interface 110 may obtain traffic information as surrounding environment information of the autonomous vehicle 1. In addition, the interface 110 may acquire traffic information around the autonomous vehicle 1 based on the location information of the autonomous vehicle 1. For example, the traffic information around the autonomous vehicle 1 is not only information about the speed, position, etc. of the vehicle in front of the autonomous vehicle 1, but also information on whether the road on which the autonomous vehicle 10 is running is congested. It includes. As an example, the interface 110 may obtain traffic information around the autonomous vehicle 1 from an external traffic management system. As another example, the communication device 250 may obtain traffic information around the autonomous vehicle 1 from an external traffic management system and transmit the obtained information to the interface 110. As another example, the sensing device 230 may sense the speed of the vehicle in front of the autonomous vehicle 1 and transmit the sensed information to the interface 110. For example, the RADAR unit 226 of FIG. 2 may continue to sense the speed of the vehicle in front of the autonomous vehicle 1, and the RADAR unit 226 may provide information about the sensed speed to the interface 110. Can transmit Subsequently, the processor 120 may recognize traffic congestion when the speed of the front vehicle is maintained for a predetermined time or less based on the information on the speed transmitted to the interface 110.

The processor 120 may determine a driving mode optimized for the occupant based on the obtained traffic information. As an example, when the obtained traffic information is information indicating traffic congestion, since the autonomous vehicle 1 stops a lot, the processor 120 may determine the driving mode optimized for the passenger as the eco driving mode. have. As another example, when the obtained traffic information is information indicating that the traffic is smooth, the autonomous vehicle 1 may determine the driving mode optimized for the occupant as the acceleration driving mode.

In addition, the processor 120 may provide the occupant to the passenger through the output device 280 of FIG. 2, based on the traffic information acquired by the interface 110, that there will be a traffic congestion after a predetermined time. For example, the processor 120 may inform the occupant by a voice signal through the output device 280 that there will be a traffic jam after 15 seconds. In addition, since the traffic is congested after a predetermined time, the processor 120 may provide the passenger with information indicating that the current driving mode is stopped through the output device 280.

23 illustrates an example in which the vehicle control apparatus determines a driving mode based on the traffic information, and controls the autonomous driving vehicle according to the determined driving mode.

The vehicle control apparatus 100 may obtain traffic information around the vehicle control apparatus 100 from an external traffic information management system 2310. That is, the vehicle control device 100 may establish communication with the external traffic information management system 2310 to obtain traffic information around the vehicle control device 100 from the external traffic information management system 2310.

The vehicle control apparatus 100 may determine the driving mode optimized for the occupant based on the obtained traffic information. That is, since the traffic information is information representing the traffic congestion, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant as the eco driving mode.

The vehicle control apparatus 100 may control the autonomous vehicle 1 according to the determined driving mode.

The vehicle control apparatus 100 may provide a GUI 2410 to the occupant when traffic congestion approaches. That is, when the autonomous vehicle 1 reaches a traffic congestion situation after 15 seconds, the vehicle control apparatus 100 may provide the GUI 2410 to the occupant. When the occupant selects 'Auto Adjust in 15 seconds' in the GUI 2410, the vehicle control apparatus 100 may control the autonomous driving vehicle 1 in a preset driving mode. For example, the vehicle control apparatus 100 may change the driving mode of the autonomous vehicle 1 to the eco driving mode according to the traffic jam information. In addition, when the occupant selects 'Select New Route' in the GUI 2410, the vehicle control apparatus 100 may determine another driving route without traffic congestion, and drive the autonomous vehicle 1 in another driving route. Can be controlled. In addition, when the passenger selects 'Make a call', 'Send a text', or 'Send an email' in the GUI 2410, the vehicle control apparatus 100 may transmit a preset message or voice to a preset person. I can deliver it. For example, when the passenger selects 'Send a text' in the GUI 2410, the vehicle control apparatus 100 may transmit a message including the arrival time of the passenger to the person who is scheduled to meet.

The interface 110 may obtain weather information as surrounding environment information of the autonomous vehicle 1. As an example, the interface 110 may obtain weather information around the autonomous vehicle 1 from an external weather information management system. Also, as another example, the sensing device 230 may sense a surrounding road condition through a tire of the autonomous vehicle 1, and the processor 120 may detect the autonomous driving vehicle 1 based on the sensed surrounding road condition. ) May generate weather information around the interface, and the interface 110 may obtain weather information from the processor 120. For example, the processor 120 may recognize that the surrounding road condition is a rainy state, based on the information on the frictional force of the tire ground surface of the autonomous vehicle 1 sensed by the sensing device 230. It can generate weather information that it is raining. The interface 110 may then obtain weather information that it is raining from the processor 120.

The processor 120 may determine a driving mode optimized for the occupant based on the acquired weather information. For example, if the acquired weather information is information indicating that rain or snow is falling around the autonomous vehicle 1, for the safety of the occupant, the processor 120 may select the driving mode optimized for the occupant, the calm driving mode and the speed. You can decide to limit mode.

FIG. 26 illustrates an example in which the vehicle control apparatus determines a driving mode based on weather information and controls the autonomous driving vehicle according to the determined driving mode.

The vehicle control apparatus 100 may obtain traffic information around the vehicle control apparatus 100 from an external weather information management system 2510. That is, the vehicle control apparatus 100 may establish communication with the external weather information management system 2510 to obtain weather information around the vehicle control apparatus 100 from the external weather information management system 2510.

The vehicle control apparatus 100 may determine a driving mode optimized for the occupant based on the acquired weather information. That is, since the weather information is raining information, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant as the speed limit mode and the calm driving mode.

The interface 110 may obtain road state information as surrounding environment information of the autonomous vehicle 1. That is, the interface 110 may obtain road state information around the autonomous vehicle 1. For example, the interface 110 may obtain road state information from a device capable of sensing road state. In detail, the sensing device 230 may acquire road state information based on a vibration signal transmitted through the tire of the autonomous vehicle 1, and the interface 110 may acquire the road state obtained from the sensing device 230. You can get information.

The processor 120 may determine a driving mode optimized for the occupant based on the obtained road condition information. For example, when the obtained road condition information is information of mountainous terrain with a lot of gravel / sand, the processor 120 may optimize the passenger's driving mode in order to minimize the safety of the passenger and the vibration of the autonomous vehicle 1. To determine the terrain mode suitable for mountainous terrain.

In addition, the interface 110 may obtain information about the dangerous road. For example, the interface 110 may receive information about a dangerous road from an external traffic management system.

The processor 120 may determine whether the autonomous vehicle 1 is near a dangerous road based on the current location information. For example, the processor 120 may determine whether the autonomous vehicle 1 reaches the dangerous road after 15 seconds. When the autonomous vehicle 1 reaches the dangerous road, the processor 120 may control the autonomous vehicle 1 according to the degree of danger of the dangerous road. That is, the processor 120 may reduce the acceleration of the autonomous vehicle 1 and control the vehicle to run smoother according to the danger of the dangerous road. For example, if the risk of the dangerous road is low, the processor 120 may decrease the value of the throttle opening amount or the lateral force by 5% according to the preset driving mode.

The road state sensing unit 2810 may sense a vibration signal transmitted through the tire of the autonomous vehicle 1, and the vehicle control apparatus 100 may determine that the road state is a mountainous terrain state based on the sensed vibration signal. Information can be obtained.

The vehicle control apparatus 100 may determine the driving mode optimized for the occupant based on the obtained road state information. That is, since the road state information is mountainous terrain, in order to minimize the safety of the occupants and the vibration of the autonomous vehicle 1, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant as the terrain mode suitable for the mountainous terrain. have.

The vehicle control apparatus 100 may control the autonomous vehicle 1 according to the determined terrain mode. For example, the vehicle control apparatus 100 may raise the suspension of the autonomous vehicle 1 and may switch to four-wheel drive to distribute the driving force at a ratio of 1: 1 to the front wheel and the rear wheel.

The vehicle control apparatus 100 may provide a GUI 2910 to the occupant when the dangerous road approaches. That is, when the autonomous vehicle 1 reaches the dangerous road after 15 seconds, the vehicle control apparatus 100 may provide the GUI 2910 to the occupant. The items 'High', 'Medium', and 'Low' shown in the GUI 2910 indicate how dangerous the roads to reach are. When the occupant selects 'Auto Adjust' in the GUI 2910, the vehicle control apparatus 100 may control the autonomous vehicle 1 in a preset driving mode. For example, the vehicle control apparatus 100 may partially adjust a parameter value corresponding to the preset driving mode according to the danger of the dangerous road. In addition, when the occupant selects 'Select New Route' in the GUI 2910, the vehicle control apparatus 100 may determine another driving route without a dangerous road, and drive the autonomous vehicle 1 in another driving route. Can be controlled. In addition, when the occupant selects 'Make a call', 'Send a text', or 'Send an email' in the GUI 2910, the vehicle control apparatus 100 may transmit a preset message or voice to a preset person. I can deliver it. For example, when the occupant selects 'Send a text' in the GUI 2910, the vehicle control apparatus 100 may transmit a message including the estimated arrival time of the occupant to the person who is supposed to meet on a schedule.

30 shows an example of information on a dangerous road.

The vehicle control apparatus 100 may obtain information 3010 about the dangerous road. The vehicle control apparatus 100 may obtain information 3010 on the dangerous road from the external traffic management system. As shown in FIG. 30, the vehicle control apparatus 100 includes the starting position information 'Mile 52' and '44 .0N: -72.6E 'of the dangerous road' Interstate 89 'and the ending position information' Mile 69 'and' 44.3: -72.7E '. In addition, the vehicle control apparatus 100 may obtain information that the risk of the dangerous road 'Interstate 89' is 'Low'. The degree of danger among the information about the dangerous road 3010 may be set based on the number of accidents occurring on the dangerous road. For example, if the accident rate on a dangerous road is 0% to 10% higher than the accident rate on the road on average, the risk of that dangerous road may be low, and on average the accident rate on the road. For example, if the accident rate on the dangerous road is 25% or higher, the risk of the dangerous road may be high.

The vehicle control apparatus 100 may control the autonomous vehicle 1 based on the table 3110. That is, when driving on a dangerous road with a low risk, the vehicle control apparatus 100 may reduce the maximum throttle opening amount, which is a driving parameter corresponding to the preset driving mode, by 5%, and decrease the maximum lateral force value by 5%. You can. Similarly, when driving on a dangerous road having a medium risk, the vehicle control apparatus 100 may reduce the maximum throttle opening amount, which is a driving parameter corresponding to the preset driving mode, by 10%, and decrease the maximum lateral force value by 10%. You can.

Referring to FIG. 3 again, the interface 110 may obtain at least one of other context information and other surrounding environment information in addition to the previously obtained context information or surrounding environment information. For example, while the autonomous vehicle 1 is driving in a preset driving mode, the interface 110 may obtain at least one of other context information and other surrounding environment information. Specifically, while the autonomous vehicle 1 is driving in the acceleration driving mode, the interface 110 may obtain traffic congestion information as other surrounding environment information.

The processor 120 may change the driving mode optimized for the occupant based on at least one of other context information and other surrounding environment information. That is, while the autonomous vehicle 1 is driving in the preset driving mode, as other context information or other surrounding environment information is generated, the processor 120 may change the driving mode optimized for the occupant. For example, based on the traffic congestion information acquired by the interface 110 while the autonomous vehicle 1 is driving in the high speed driving mode, the processor 120 may echo the driving mode optimized for the passenger in the high speed driving mode. You can change the mode.

The processor 120 may control the autonomous vehicle 1 based on the changed driving mode.

The vehicle control apparatus 100 may determine the driving mode as the acceleration driving mode based on the positional information indicating the high speed road, and the autonomous vehicle 1 may run in the acceleration driving mode under the control of the vehicle control apparatus 100. Can be.

While the autonomous vehicle 1 is driving in the acceleration driving mode, the vehicle control apparatus 100 may acquire position information that is around the landmark 3210. That is, the vehicle control apparatus 100 may obtain current position information from the GPS 224, and the vehicle control apparatus 100 may land the current position of the autonomous vehicle 1 based on the obtained current position information. It may be recognized that the mark 2110 is around. Therefore, the vehicle control apparatus 100 may change the driving mode optimized for the occupant from the accelerated driving mode to the limited speed mode based on the positional information of the vicinity of the landmark 2110.

Therefore, the vehicle control apparatus 100 may control the autonomous vehicle 1 in accordance with the changed speed limit mode.

While the autonomous vehicle 1 is driving in the acceleration driving mode, the vehicle control apparatus 100 may obtain the sleep state information of the occupant. That is, the vehicle control apparatus 100 may obtain the sleep state information of the occupant from the device 3310 capable of sensing the physical state of the occupant. Therefore, the vehicle control apparatus 100 may change the driving mode optimized for the occupant from the acceleration driving mode to the calm driving mode based on the sleep state information of the occupant.

Therefore, the vehicle control apparatus 100 may control the autonomous vehicle 1 in accordance with the changed calm driving mode.

Referring back to FIG. 3, the interface 110 may obtain at least two different information included in the context information and the surrounding environment information. For example, the interface 110 may obtain information that the current location is a highway and traffic jam information around the autonomous vehicle 1. Also, as another example, the interface 110 may obtain schedule information, traffic smoothness information, and passenger identification information of the passenger.

The processor 120 may determine a driving mode optimized for the occupant based on at least two different pieces of information obtained and preset priority. That is, the processor 120 may determine any one of at least two different pieces of information obtained in consideration of a preset priority, and determine a driving mode optimized for the occupant based on the determination result. For example, assuming that the information acquired by the interface 110 is high speed road location information and surrounding traffic congestion information, and at a preset priority, the surrounding traffic congestion information has priority over the high speed road location information, the processor 120 may determine the surroundings. According to the traffic jam information, the driving mode optimized for the occupant may be determined as the eco driving mode. That is, the processor 120 may determine that the eco-driving mode based on the surrounding traffic congestion information is a driving mode optimized for the occupant rather than the acceleration driving mode based on the high speed road location information. The priority of the context information and the surrounding environment information may be preset by the occupant.

34 shows an example of a priority setting menu.

For example, the vehicle control apparatus 100 may provide a priority setting menu 3410 to a passenger. For example, the vehicle control apparatus 100 may display a priority setting menu on the screen through the output device 280. The occupant may set a priority between context information and surrounding environment information through the priority setting menu 3410. As illustrated in FIG. 34, the occupant may set emergency state information among physical state information as first rank information, and the vehicle control apparatus 100 may obtain emergency state information among various acquired context information and surrounding environment information. First, the emergency driving mode may be determined as the driving mode optimized for the occupant.

As another example, the occupant's mobile device may provide a priority setting menu 3410 to the occupant. That is, the passenger may set the priority between the context information and the surrounding environment information through the priority setting menu 3410 provided by the mobile device, and the vehicle control apparatus 100 may obtain information about the priority from the passenger's mobile device. Can be obtained.

The vehicle control apparatus 100 may acquire the sleep state information as the body state information of the occupant and the landmark surrounding information as the surrounding environment information. For example, the vehicle control apparatus 100 may obtain sleep state information from the device 3510 capable of sensing the physical state of the occupant, and the vehicle control apparatus 100 may obtain the current position obtained from the GPS 224. The landmark may acquire the surrounding information based on the information.

Subsequently, the vehicle control apparatus 100 may preferentially set sleep state information among sleep state information and landmark surrounding information acquired based on the priority setting menu 3410 of FIG. 31. Therefore, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant as the calm driving mode based on the sleep state information.

Therefore, the vehicle control device 100 may control the autonomous vehicle 1 based on the calm driving mode.

The vehicle control apparatus 100 may interwork with the mobile device 3610 through a communication such as the Internet or Bluetooth. For example, the mobile device 3610 may be the device of the occupant in the autonomous vehicle 1.

The mobile device 3610 may include a communication interface 3611, a touch screen 3612, a power supply 3613, and a memory 3614. The memory 3614 may include driving information for setting a driving mode and driving parameters of the autonomous vehicle 1.

The occupant's mobile device 3610 may provide the occupant with driving information 3710 that sets the driving parameter of the autonomous vehicle 1. That is, the occupant may adjust driving parameters of the autonomous vehicle 1 through the driving information 3710 provided from the mobile device 3610. For example, the occupant's mobile device may provide driving information 3710 to the occupant via the touch screen. FIG. 37 illustrates driving information 3710 for setting parameters related to a maximum throttle opening, a maximum G-force limit, and a suspension frequency, according to an example.

As disclosed in the driving information 3710, the default limit setting may set the maximum throttle opening amount to 70%, the maximum horizontal force to 0.7g, the suspension frequency to 1.25 Hz, and the maximum limit setting (Maximum). Limit Throttle), the maximum throttle opening amount is 100%, the maximum lateral force is 0.9g, the suspension frequency is 2.00Hz, the minimum throttle opening amount is 50%, the maximum lateral force is 0.5%. g, the suspension frequency may be set to 1.00 Hz. Each of the above values can be changed by the occupant.

In addition, the occupant may determine whether to set each of the maximum throttle opening amount, the maximum lateral force, and the suspension frequency to a maximum limit setting, a minimum limit setting, or a default limit setting. For example, as shown in FIG. 37, the occupant may set each of the maximum throttle opening amount, maximum lateral force, and suspension frequency to the maximum limit setting.

In addition, the occupant may set a driving parameter corresponding to the driving mode through the driving information 3710. That is, in the acceleration driving mode, the occupant may set the maximum throttle opening amount, the maximum lateral force, and the suspension frequency to the maximum limit setting. Similarly, each of the quiet driving mode and the normal driving mode may also be set as the driving information 3710.

Therefore, when the passenger sets the driving information 3710, the vehicle control apparatus 100 receives the parameter information set in the driving information 3710 from the passenger's mobile device 3610, and based on the driving information 3710. The autonomous vehicle 1 can be controlled.

The vehicle control apparatus 100 of FIG. 36 may include a driving profile for controlling the autonomous vehicle 1.

The driving profile can be set by the occupant and stored on the occupant's mobile device. The driving profile may include thresholds of driving parameters such as acceleration, brake force, suspension. The threshold value of the driving parameter may be set so as not to exceed the safety limit. The safety limit may be set in consideration of road conditions, traffic regulations, proximity to other vehicles on the road, technical capability of the autonomous vehicle 1, and the like. The driving profile can be updated by the occupant and can also be updated when the autonomous vehicle 1 is driving.

The driving profile may be downloaded from the occupant's mobile device 3610 to the vehicle control device 100. The communication between the mobile device 3610 and the vehicle control device 100 may be formed by a near field communication technology such as NFC or Bluetooth. In addition, the vehicle control apparatus 100 may use a wireless communication method such as Wi-Fi or cellular. For example, the vehicle control apparatus 100 may access a road history network through the Internet and collect information. The road history network can provide information on the risks associated with a particular road, and the road history network can be provided from an external traffic management system.

The driving profile can be adjusted based on the information provided in the road history network.

38 illustrates an example of a driving profile provided by the vehicle control apparatus.

The vehicle control apparatus 100 may provide a driver with a driving profile 3810 that sets driving parameters. That is, the occupant may adjust driving parameters of the autonomous vehicle 1 through the driving profile 3810 provided from the vehicle control apparatus 100. In FIG. 38, the maximum throttle opening, the maximum lateral force, the suspension frequency, the tire pressure, and the ride height, according to an example. A driving profile 3810 is set for setting parameters related to.

As disclosed in driving profile 3810, the default limit setting is 70% maximum throttle opening, 0.7 g maximum lateral force, 1.25 Hz suspension frequency, 32 psi tire pressure, and 7 inch ride height. The maximum limit setting and the minimum limit setting may also be set as shown in FIG. 38.

In addition, the occupant may determine whether to set each of the maximum throttle opening amount, the maximum lateral force, and the suspension frequency to a maximum limit setting, a minimum limit setting, or a default limit setting. For example, as shown in FIG. 38, the occupant may set the maximum throttle opening amount, the maximum lateral force, and the suspension frequency respectively to the maximum limit setting.

39 shows an example of a menu for setting a driving mode.

The vehicle control apparatus 100 may provide a menu 3910 for setting a driving mode to the occupant. That is, the occupant may set parameters of each driving mode through the menu 3910 provided by the vehicle control apparatus 100. For example, when the occupant selects the 'parameter setting' menu of the acceleration driving mode, the vehicle control apparatus 100 may additionally provide the passenger with a menu for setting parameter values for the acceleration driving mode. In addition, when the occupant selects the "default" menu of the acceleration driving mode, the vehicle control apparatus 100 may set a parameter value for the acceleration driving mode according to a preset setting.

In addition, the occupant may add a new driving mode through the menu 3910. In addition, the occupant may set driving parameters for the new driving mode through the menu 3910 and set context information or surrounding environment information requiring the new driving mode. For example, if the passenger wants the autonomous vehicle 1 to travel at a preset speed at night or at dawn, the passenger may add a new dawn driving mode. Therefore, when the current time is night or dawn time, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant as the dawn driving mode, and the speed at which the autonomous vehicle 1 is preset based on the determined dawn driving mode. It can be controlled to drive while maintaining.

As an example, when there are a plurality of passengers in the autonomous vehicle 1, the vehicle control apparatus 100 may provide a passenger with a menu 4010 for selecting which passenger to determine the driving mode based on. have. That is, since the optimized driving mode may be different for each of the plurality of passengers, the vehicle control apparatus 100 may determine the driving mode based on the passenger selected through the menu 4010.

As another example, the vehicle control apparatus 100 may detect mobile devices of each of the plurality of passengers in the autonomous vehicle 1 to recognize the presence of the plurality of passengers in the autonomous vehicle 1. Accordingly, the vehicle control apparatus 100 includes a plurality of passengers 'User 1', 'User 2', 'User 3'. The passenger may be provided with a menu 4010 inquiring to select a specific passenger's mobile device. Therefore, the vehicle control apparatus 100 may determine the driving mode based on the mobile device of the specific occupant selected through the menu 4010.

41 shows an example of a method of controlling an autonomous vehicle.

Since the method illustrated in FIG. 41 may be performed by the vehicle control apparatus 100 of FIGS. 3 to 40, redundant descriptions thereof will be omitted.

In operation S4110, the vehicle control apparatus 100 may obtain driving context information. The driving context information may refer to user environment information describing a situation in which the user of the autonomous vehicle 1 is in the bar, and the vehicle control apparatus 100 may obtain user environment information. The driving context information may include at least one of context information about the occupant and surrounding environment information of the autonomous vehicle 1, and the vehicle control apparatus 100 may include context information about the occupant in the autonomous vehicle 1 and At least one piece of information about the surrounding environment of the autonomous vehicle 1 may be obtained. The context information may include at least one of destination information of the passenger, schedule information of the passenger, physical condition information of the passenger, or identification information of the passenger. In addition, the surrounding environment information may include at least one of weather information, traffic information, road condition information, or location information of the autonomous vehicle 1 around the autonomous vehicle 1.

In addition, the vehicle control apparatus 100 may obtain at least one of other context information and other surrounding environment information in addition to the previously obtained context information or surrounding environment information. According to an example, while the autonomous vehicle 1 is driving in a preset driving mode, the vehicle control apparatus 100 may obtain at least one of other context information and other surrounding environment information.

In addition, the vehicle control apparatus 100 may obtain at least two different pieces of information included in the context information and the surrounding environment information. For example, the vehicle control apparatus 100 may obtain information that the current location is a highway and traffic jam information around the autonomous vehicle 1.

In operation S4120, the vehicle control apparatus 100 may determine a driving mode optimized for the occupant based on the information obtained in operation S4110. That is, the vehicle control apparatus 100 may determine the situation in which the occupant is faced without the intervention of the occupant based on at least one of context information and surrounding environment information about the occupant, and determine a driving mode suitable for the determined situation. Can be. The driving mode includes an acceleration driving mode for increasing the acceleration performance of the autonomous driving vehicle 1, an eco driving mode for saving fuel efficiency of the autonomous driving vehicle 1, and for minimizing vibration and acceleration of the autonomous driving vehicle 1. The vehicle may include a quiet driving mode, a speed limit mode for driving below a certain speed, a terrain mode optimized for a predetermined terrain, and an emergency driving mode for an emergency of a passenger. The driving mode optimized for the occupant may be changed based on at least one of other context information and other surrounding environment information in addition to the context information or surrounding environment information. That is, while the autonomous vehicle 1 is driving in the preset driving mode, as the other context information or other surrounding environment information is generated, the vehicle control apparatus 100 may change the driving mode optimized for the occupant.

In addition, the vehicle control apparatus 100 may determine the driving mode optimized for the occupant based on at least two different pieces of information and preset priorities obtained. That is, the vehicle control apparatus 100 may determine specific information among two types of information obtained in consideration of a preset priority, and determine a driving mode optimized for a passenger based on the determined specific information. For example, when the information obtained by the vehicle control apparatus 100 is the high speed road location information and the surrounding traffic congestion information, since the surrounding traffic congestion information has priority over the high speed road location information at the preset priority, the vehicle control device 100 ) May determine the driving mode optimized for the occupant as the eco driving mode according to the surrounding traffic congestion information. That is, the vehicle control apparatus 100 may determine that the eco driving mode based on the surrounding traffic congestion information is a driving mode optimized for the occupant rather than the acceleration driving mode based on the high speed road position information.

In addition, the vehicle control apparatus 100 may determine the driving style based on the user environment information acquired in s4110.

In operation S4130, the vehicle control apparatus 100 may control the autonomous vehicle 1 according to the driving mode determined in operation S4120. For example, the vehicle control apparatus 100 may adjust the driving parameter according to the determined driving mode. In addition, the vehicle control apparatus 100 may control the propulsion device or the peripheral device of the autonomous vehicle 1 according to the determined driving mode.

As another embodiment, the vehicle control apparatus 100 may provide the user with the driving mode determined in s4120 and control the autonomous driving vehicle 1 according to the driving mode selected in s4130 when a selection of the driving mode is input from the occupant. have.

In this case, the vehicle control apparatus 100 may present one driving mode to the occupant, or may present a plurality of driving modes. When a plurality of driving modes are presented, priority information may be provided with respect to each driving mode in an order suitable for the current situation, or information about the driving mode having the highest priority may be provided together. When one driving mode is presented, the occupant may accept the presented driving mode or request another driving mode. Similarly, when a plurality of driving modes are presented, the occupant may select one of the presented driving modes or request another driving mode.

As another embodiment, the vehicle control apparatus 100 may provide the user with the driving mode determined in s4120, and if the driver does not select the driving mode from the occupant for a predetermined time, the autonomous driving vehicle 1 according to the driving mode determined in s4130. You can also control. If a plurality of driving modes are presented, if there is no driving mode selection for a predetermined time from the occupant, the vehicle control apparatus 100 may control the autonomous driving vehicle 1 in the driving mode having the highest priority.

The vehicle control apparatus 100 may present the driving mode to the user through an output device such as a speaker or a display, and receive a driving mode selection or another driving mode request from the user through the input device 260.

The embodiments described so far may be applied to manual driving vehicles as well as autonomous driving vehicles. For example, if a driving mode is determined based on context information or surrounding environment information of a passenger, the vehicle control apparatus 100 may drive driving parameters such as suspension and brake sensitivity according to the determined driving mode even when the passenger manually drives the vehicle. Can be set or changed. In this case, the occupant becomes a driver, and the driver may feel a different ride or driving feeling depending on the driving mode.

The device according to the embodiments described above includes a processor, a memory for storing and executing program data, a permanent storage such as a disk drive, a communication port for communicating with an external device, a touch panel, a key, a button, and the like. The same user interface device and the like. Methods implemented by software modules or algorithms may be stored on a computer readable recording medium as computer readable codes or program instructions executable on the processor. The computer-readable recording medium may be a magnetic storage medium (eg, read-only memory (ROM), random-access memory (RAM), floppy disk, hard disk, etc.) and an optical reading medium (eg, CD-ROM). ) And DVD (Digital Versatile Disc). The computer readable recording medium can be distributed over network coupled computer systems so that the computer readable code is stored and executed in a distributed fashion. The medium is readable by the computer, stored in the memory, and can be executed by the processor.

This embodiment can be represented by functional block configurations and various processing steps. Such functional blocks may be implemented in various numbers of hardware or / and software configurations that perform particular functions. For example, an embodiment may include an integrated circuit configuration such as memory, processing, logic, look-up table, etc. that may execute various functions by the control of one or more microprocessors or other control devices. You can employ them. Similar to the components that may be implemented as software programming or software elements, the present embodiment includes various algorithms implemented in C, C ++, Java (data structures, processes, routines or other combinations of programming constructs). It may be implemented in a programming or scripting language such as Java), an assembler, or the like. The functional aspects may be implemented with an algorithm running on one or more processors. In addition, the present embodiment may employ the prior art for electronic configuration, signal processing, and / or data processing. Terms such as "mechanism", "element", "means" and "configuration" can be used widely and are not limited to mechanical and physical configurations. The term may include the meaning of a series of routines of software in conjunction with a processor or the like.

Specific implementations described in this embodiment are examples, and do not limit the technical scope in any way. For brevity of description, descriptions of conventional electronic configurations, control systems, software, and other functional aspects of the systems may be omitted. In addition, the connection or connection members of the lines between the components shown in the drawings by way of example shows a functional connection and / or physical or circuit connections, in the actual device replaceable or additional various functional connections, physical It may be represented as a connection, or circuit connections.

In the present specification (particularly in the claims), the use of the term “above” and similar indicating terminology may correspond to both the singular and the plural. In addition, when a range is described, it includes the individual values which belong to the said range (if there is no description contrary to it), and it is the same as describing each individual value which comprises the said range in detailed description. Finally, if there is no explicit order or contrary to the steps constituting the method, the steps may be performed in a suitable order. It is not necessarily limited to the order of description of the above steps. The use of all examples or exemplary terms (eg, etc.) is for the purpose of describing technical concepts in detail and is not to be limited in scope by the examples or exemplary terms unless defined by the claims. In addition, one of ordinary skill in the art appreciates that various modifications, combinations and changes can be made depending on design conditions and factors within the scope of the appended claims or equivalents thereof.

Claims

In the apparatus for controlling an autonomous vehicle,

An interface for acquiring at least one of context information about a passenger of the autonomous vehicle and surrounding environment information of the autonomous vehicle; And

And a processor configured to determine a driving mode optimized for the occupant based on the obtained at least one information, and to control the autonomous driving vehicle according to the determined driving mode.
The method of claim 1,

The interface is,

Obtaining at least one of other context information and other surrounding environment information,

The processor,

And change the driving mode based on at least one of the other context information and other surrounding environment information.
The method of claim 1,

The interface is,

Obtaining at least two or more different information included in the context information and the surrounding environment information,

The processor,

And determine a driving mode optimized for the occupant based on a preset priority and the obtained at least two different pieces of information.
The method of claim 1,

The processor,

And determine the driving mode corresponding to the obtained at least one information based on an association between the obtainable context information and the driving mode applicable to the autonomous driving vehicle.
The method of claim 4, wherein

The correlation is

And predetermined by the occupant or learned by the occupant's driving history.
The method of claim 1,

The processor,

And according to the determined driving mode, controlling a driving parameter of the autonomous vehicle.
The method of claim 1,

The interface is,

Obtaining destination information of the occupant as the context information,

The processor,

And determine a driving mode of the autonomous vehicle based on the obtained destination information.
The method of claim 1,

The interface is,

Obtaining schedule information of the occupant as the context information,

The processor,

And determine a driving mode of the autonomous vehicle based on the obtained schedule information.
The method of claim 8,

The interface is,

Obtain the schedule information from the passenger's mobile device.
The method of claim 1,

The interface is,

Obtaining the physical state information of the occupant as the context information,

The processor,

And determine a driving mode of the autonomous vehicle based on the obtained body state information.
The method of claim 10,

The interface is,

And obtain the body state information from a device that senses the body state of the occupant.
The method of claim 1,

The interface is,

Obtaining occupant identification information of the autonomous vehicle as the context information,

The processor,

And determine a driving mode of the autonomous vehicle based on the obtained occupant identification information.
The method of claim 1,

The interface is,

Obtaining location information of the autonomous vehicle as the surrounding environment information;

The processor,

And determine a driving mode of the autonomous vehicle based on the obtained position information.
The method of claim 1,

The interface is,

Obtains traffic state information around the autonomous vehicle as the surrounding environment information,

The processor,

And determine a driving mode of the autonomous vehicle based on the obtained traffic state information.
The method of claim 1,

The interface is,

Obtain road condition information around the autonomous vehicle as the surrounding environment information;

The processor,

And determine a driving mode of the autonomous vehicle based on the obtained road condition information.
The method of claim 1,

The interface is,

Obtaining weather information around the autonomous vehicle as the surrounding environment information;

The processor,

And determine a driving mode of the autonomous vehicle based on the obtained weather information.
In a method for controlling an autonomous vehicle,

Acquiring at least one of context information of a passenger of the autonomous vehicle and surrounding environment information of the autonomous vehicle;

Determining a driving mode optimized for the occupant based on the obtained at least one information; And

Controlling the autonomous vehicle according to the determined driving mode.
The method of claim 17,

The obtaining step,

Obtaining at least one of other context information and other surrounding environment information,

The determining step,

And change the driving mode based on at least one of the other context information and other surrounding environment information.
The method of claim 17,

The obtaining step,

Obtaining at least two or more different information included in the context information and the surrounding environment information,

The determining step,

Determining a driving mode optimized for the occupant based on a preset priority and the obtained at least two different pieces of information.
The method of claim 17,

The determining step,

And determining the driving mode corresponding to the obtained at least one information based on a correlation between the obtainable context information and the driving mode applicable to the autonomous vehicle.
The method of claim 20,

The correlation is

Or predetermined by the occupant or learned by the occupant's driving history.
The method of claim 17,

The controlling step,

According to the determined driving mode, controlling a driving parameter of the autonomous vehicle.
The method of claim 17,

The obtaining step,

Obtaining destination information of the occupant as the context information,

The determining step,

Determining a driving mode of the autonomous vehicle based on the obtained destination information.
The method of claim 17,

The obtaining step,

Obtaining schedule information of the occupant as the context information,

The determining step,

Determining a driving mode of the autonomous vehicle based on the obtained schedule information.
The method of claim 24,

The obtaining step,

Obtaining the schedule information from the passenger's mobile device.
The method of claim 17,

The obtaining step,

Obtaining the physical state information of the occupant as the context information,

The determining step,

And determine a driving mode of the autonomous vehicle based on the obtained body state information.
The method of claim 26,

The obtaining step,

Obtaining the body state information from a device that senses the body state of the occupant.
The method of claim 17,

The obtaining step,

Obtaining occupant identification information of the autonomous vehicle as the context information,

The determining step,

And determine a driving mode of the autonomous vehicle based on the obtained occupant identification information.
The method of claim 17,

The obtaining step,

Obtaining location information of the autonomous vehicle as the surrounding environment information;

The determining step,

Determining a driving mode of the autonomous vehicle based on the obtained position information.
The method of claim 17,

The obtaining step,

Obtains traffic state information around the autonomous vehicle as the surrounding environment information,

The determining step,

Determining a driving mode of the autonomous vehicle based on the obtained traffic state information.
The method of claim 17,

The obtaining step,

Obtain road condition information around the autonomous vehicle as the surrounding environment information;

The determining step,

Determining a driving mode of the autonomous vehicle based on the obtained road condition information.
The method of claim 17,

The obtaining step,

Obtaining weather information around the autonomous vehicle as the surrounding environment information;

The determining step,

And determine a driving mode of the autonomous vehicle based on the obtained weather information.
A computer-readable recording medium having recorded thereon a program for executing the method of claim 17 on a computer.
Obtaining user environment information;

Determining a driving style based on the user environment information; And

Setting a driving parameter of the vehicle according to the driving style.