Autonomous driving vehicle and control method thereof

Abstract

The disclosure relates to a method of controlling driving of a vehicle. The method includes, during a smart cruise control (SCC) operation, comparing a road condition of a road on which the vehicle is traveling based on control information of the vehicle with a preset standard road condition. The method further comprises the steps that when the road condition is different from the preset standard road condition, whether operation data of a driver is detected or not is judged; changing a control condition for controlling driving of the vehicle based on a result of the determination; and controlling driving of the vehicle based on the changed control condition.

Description

Technical Field

[0001] This disclosure relates to an autonomous vehicle and a control method thereof. Background Technology

[0002] The matters described in this background section are only intended to enhance the understanding of the background of this disclosure and should not be construed as an admission that they correspond to prior art known to those skilled in the art.

[0003] To ensure driver safety, various convenient systems can be installed in vehicles, such as anti-lock braking system (ABS), electronic stability control system (ESC), intelligent cruise control system (SCC), and advanced driver assistance system (ADAS).

[0004] These various convenient systems control vehicle behavior while taking road conditions into account to achieve optimal performance. Here, road conditions can refer to high-friction roads such as dry asphalt and dry concrete roads, as well as low-friction roads such as wet, snowy, and dusty roads.

[0005] Existing road identification methods include: methods that determine whether a road is a high-friction road or a low-friction road based on dynamic data such as wheel speed, engine torque, and vehicle speed; and methods that determine whether a road is a high-friction road or a low-friction road based on various sensors such as road-oriented ultrasonic sensors or microphones.

[0006] Road assessment methods based on dynamic data can identify whether a road surface is of high or low friction by analyzing slippage phenomena occurring in the vehicle. However, if the vehicle is traveling on a road with a specific pattern of neither rapid acceleration nor rapid deceleration, this method may fail to determine whether the road surface is of high or low friction.

[0007] Furthermore, the road determination method based on road orientation ultrasonic sensors has the following problem: it increases vehicle production costs because additional sensors need to be installed. Summary of the Invention

[0008] The effects that can be obtained from this disclosure are not limited to those described above. Other effects not described in this disclosure will be clearly understood by those skilled in the art based on the following description.

[0009] This disclosure relates to a method for controlling vehicle driving, comprising: during intelligent cruise control (SCC) operation, determining the road conditions of the road on which the vehicle is traveling based on vehicle control information; when the road conditions differ from preset standard road conditions, determining whether driver operation data is detected; changing the control conditions for controlling vehicle driving based on the determination result; and controlling vehicle driving based on the changed control conditions.

[0010] In the method, changing the control conditions may include one of the following: setting a first control condition when driver operation data is detected; or setting a second control condition when driver operation data is not detected.

[0011] The method may further include: controlling the vehicle's powertrain system using a first control condition to control the vehicle's driving.

[0012] In the method, controlling the powertrain system may include: controlling the acceleration of the vehicle using the second gear of the powertrain system; or, controlling the powertrain system by disabling the interaction between SCC and ISG (Idle Stop & Start).

[0013] In the method, controlling the powertrain system may include: controlling the powertrain system to raise the vehicle's shift mode to the downshift line.

[0014] In the method, controlling the powertrain system may further include: controlling the powertrain system to reduce the shift mode in advance before the vehicle stops.

[0015] In the method, controlling the driving of the vehicle may include: controlling the driving of the vehicle under a second control condition based on SCC and in a manner that meets a preset standard range, wherein the satisfaction of the preset standard range is determined based on sensor information from the vehicle and navigation information from the navigation server.

[0016] In the method, controlling the driving of the vehicle may include: controlling the driving of the vehicle based on the SCC, thereby reducing the required acceleration and the slope of the required acceleration of the SCC.

[0017] In the method, the sensor information may include information from the vehicle's lighting sensor and information from the vehicle's ambient temperature sensor.

[0018] This disclosure also relates to a non-transitory computer-readable recording medium storing instructions, wherein the instructions are configured to:

[0019] When the instruction is executed by one or more processors, the instruction causes the one or more processors to perform the following operations:

[0020] During Smart Cruise Control (SCC) operation, the road conditions of the road on which the vehicle is traveling are determined based on the vehicle's control information; when the road conditions differ from preset standard road conditions, it is determined whether driver operation data is detected; based on the determination result, the control conditions used to control the vehicle's driving are changed; and the vehicle's driving is controlled based on the changed control conditions.

[0021] In the non-transitory computer-readable recording medium, the instructions are configured to, when executed by the one or more processors, cause the one or more processors to control the driving of the vehicle based on the SCC, thereby reducing the required acceleration and the slope of the required acceleration of the SCC.

[0022] This disclosure also relates to an apparatus for controlling the driving of a vehicle, the apparatus including: one or more processors for executing instructions, and a memory for storing instructions, the instructions being configured such that, when executed by the one or more processors, the instructions cause the apparatus to perform the following operations:

[0023] During Smart Cruise Control (SCC) operation, the road conditions of the road on which the vehicle is traveling are determined based on the vehicle's control information; when the road conditions differ from preset standard road conditions, it is determined whether driver operation data has been detected; and based on the determination result, the control conditions used to control the vehicle's driving are changed; and the vehicle's driving is controlled based on the changed control conditions.

[0024] In the device, the instruction is configured such that, when executed by the one or more processors, the instruction causes the device to perform the following operations: when driver operation data is detected, setting a first control condition for the control condition; and when driver operation data is not detected, setting a second control condition for the control condition.

[0025] In the device, the instructions are configured such that, when executed by the one or more processors, the instructions cause the device to control the powertrain of the vehicle under the first control conditions, so as to control the driving of the vehicle.

[0026] In the device, the instructions are configured such that, when executed by the one or more processors, the instructions cause the device to control at least one of the following operations: controlling the acceleration of the vehicle using the second gear of the powertrain; or controlling the powertrain by disabling the interaction between the SCC and ISG (Idle Stop & Start).

[0027] In the device, the instruction is configured such that, when executed by the one or more processors, the instruction causes the device to control the powertrain system, thereby raising the vehicle's shift mode to a downshift line.

[0028] In the device, the instruction is configured such that when the instruction is executed by the one or more processors, the instruction causes the device to control the powertrain system to reduce the shift mode in advance before the vehicle stops.

[0029] In the device, the instructions are configured such that, when executed by the one or more processors, the instructions cause the device to control the driving of the vehicle under a second control condition based on the SCC and in a manner that satisfies a preset standard range, wherein the satisfaction of the preset standard range is determined based on sensor information from the vehicle and navigation information provided from a navigation server.

[0030] In the device, the instruction is configured such that, when executed by the one or more processors, the instruction causes the device to control the driving of the vehicle, thereby reducing the required acceleration of the SCC and the slope of the required acceleration of the SCC.

[0031] In the device, the sensor information may include information from the vehicle's lighting sensor and information from the vehicle's ambient temperature sensor. Attached Figure Description

[0032] Figure 1 An example of an autonomous vehicle according to an embodiment of the present disclosure is shown.

[0033] Figure 2 An example of a control method for an autonomous vehicle according to an embodiment of the present disclosure is shown.

[0034] Figure 3 , Figure 4 and Figure 5 It is used to explain in Figure 2 A schematic diagram illustrating the control of an autonomous vehicle under the first control condition.

[0035] Figure 6 and Figure 7 It is used to explain in Figure 2 A schematic diagram illustrating the control of an autonomous vehicle under the second control condition. Detailed Implementation

[0036] In the following detailed description, embodiments of the present disclosure are given with reference to the accompanying drawings to facilitate implementation by those skilled in the art to which this disclosure pertains. However, the present disclosure may be obtained in various different forms and is not limited to the embodiments described herein. Furthermore, for clarity, portions unrelated to the description have been omitted from the drawings, and the same reference numerals have been used to denote the same parts throughout the specification.

[0037] Throughout this specification, when a section “includes” an element, unless otherwise stated, it does not mean that the section excludes other elements, but rather that the section may further include other elements. Furthermore, elements indicated by the same reference numerals denote the same element.

[0038] Furthermore, the terms "unit" and "control unit" in names such as Vehicle Control Unit (VCU) are simply widely used terms in the names of controllers that control specific vehicle functions and do not imply a generic function unit. For example, each controller may include: a communication device that communicates with other controllers or sensors to control its functions; a memory that stores the operating system, logic commands, or input / output information; and one or more processors that perform the judgments, calculations, decisions, etc., required to control its functions.

[0039] For the purposes of this application and claims, the exemplary phrases “at least one of A; B; or C” or “at least one of A, B, or C” are used, which means “at least one A, or at least one B, or at least one C, or any combination of at least one A, at least one B, and at least one C.” Furthermore, exemplary phrases used herein such as “A, B, and C,” “A, B, or C,” “at least one of A, B, and C,” and “at least one of A, B, or C” mean each of the listed items or all possible combinations of the listed items. For example, “at least one of A or B” could refer to: (1) at least one A; (2) at least one B; or (3) at least one A and at least one B.

[0040] According to the standards proposed by the Society of Automotive Engineers (SAE), the automation level of autonomous vehicles can be classified into the following categories. At Level 0 of Automation, the SAE classification standard corresponds to "No Automation." In this case, the autonomous driving system temporarily intervenes in emergency situations (e.g., automatic emergency braking) and / or only provides warnings (e.g., blind spot warning, lane departure warning, etc.), and expects the driver to operate the vehicle. At Level 1 of Automation, the SAE classification standard corresponds to "Driver Assistance." In this case, when the driver operates the vehicle in normal driving conditions, the system performs some driving functions (e.g., steering, acceleration, braking, lane centering, adaptive cruise control, etc.), and expects the driver to determine the system's operating status and / or timing, perform other driving functions, and respond to (e.g., resolve) emergency situations. At Level 2 of Automation, the SAE classification standard corresponds to "Partial Automation." In this case, the system performs steering, acceleration, and / or braking under the driver's supervision, and expects the driver to determine the system's operating status and / or timing, perform other driving functions, and respond to (e.g., resolve) emergency situations. At Level 3 of autonomous driving, the SAE classification standard can correspond to "conditional automation," in which the system drives the vehicle under limited conditions (e.g., performing driving functions such as steering, acceleration, and / or braking), but transfers driving control to the driver if the required conditions are not met. The driver is expected to determine the system's operating state and / or timing, and take over control in emergency situations, but not otherwise operate the vehicle (e.g., steering, acceleration, and / or braking). At Level 4 of autonomous driving, the SAE classification standard can correspond to "high automation," in which the system performs all driving functions, and the driver is expected to take over control of the vehicle only in emergency situations. At Level 5 of autonomous driving, the SAE classification standard can correspond to "full automation," in which the system performs all driving functions without any driver assistance (including in emergency situations), and the driver is expected to perform no driving functions other than determining the system's operating state. Although this disclosure applies the SAE classification standard to the classification of autonomous driving, other classification methods and / or algorithms may be used in one or more configurations described herein.

[0041] One or more features associated with autonomous driving control can be activated based on configured autonomous driving control settings (e.g., based on at least one of the following: autonomous driving classification, selection of vehicle autonomous driving level, etc.). Vehicle operation can be controlled based on one or more features described herein (e.g., features that change control conditions based on changes in road conditions). Vehicle control can include various operational controls related to the vehicle (e.g., autonomous driving control, sensor control, braking control, braking time control, acceleration control, rate of change of acceleration control, alarm timing control, forward collision warning timing control, etc.).

[0042] For example, one or more auxiliary devices (e.g., engine brakes, exhaust brakes, hydraulic reducers, electric reducers, regenerative brakes, etc.) can also be controlled based on one or more features described herein (e.g., features that change control conditions based on changes in road conditions).

[0043] For example, one or more communication devices (e.g., modems, network adapters, radio transceivers, antennas, etc., capable of communicating via one or more wired or wireless communication protocols, such as Ethernet, Wi-Fi, Near Field Communication (NFC), Bluetooth, Long Term Evolution (LTE), 5G New Radio (NR), Vehicle-to-Everything (V2X) etc.) can also be controlled based on one or more features described herein (e.g., features that change control conditions based on changes in road conditions).

[0044] For example, minimum risk maneuver (MRM) operations can also be controlled based on one or more features described herein (e.g., features that change control conditions based on changes in road conditions). A minimum risk maneuver (e.g., a minimum risk maneuver) can be a vehicle maneuver that minimizes (e.g., reduces) the risk of collision with surrounding vehicles, thereby achieving a lower (e.g., lowest) risk state. If the driver is unable to respond to an intervention request, the minimum risk maneuver may be an operation activated during autonomous driving. During a minimum risk maneuver, one or more processors in the vehicle can control the vehicle's driving operations for a set time period.

[0045] For example, biased driving operations can also be controlled based on one or more features described herein (e.g., features that change control conditions based on changes in road conditions). The drive control unit can perform biased driving control. To perform biased driving, the drive control unit can control the vehicle's movement within the lane by maintaining a lateral distance between the vehicle's center position and the lane center. For example, the drive control unit can control the vehicle to remain in the lane, but not in the center of the lane. The drive control unit can identify or determine a target lateral distance for biased driving control. For example, the target lateral distance can include: an intentionally adjusted lateral distance maintained by the vehicle from a reference point (such as the center of the lane or another vehicle) during maneuvers such as lane changes. This adjustment can be made to improve the vehicle's stability, safety, and / or performance under different driving conditions. For example, during a lane change, the drive control system may bias the lateral distance to maintain a safer distance from adjacent vehicles, taking into account factors such as vehicle speed, road conditions, and / or the presence of obstacles.

[0046] For example, one or more sensors (e.g., IMU sensors, cameras, LiDAR, RADAR, blind spot monitoring sensors, lane departure warning sensors, parking sensors, light sensors, rain sensors, traction control sensors, anti-lock braking system sensors, tire pressure monitoring sensors, seat belt sensors, airbag sensors, fuel sensors, emission sensors, throttle position sensors, inverters, converters, motor controllers, power distribution units, high-voltage wiring and connectors, auxiliary power modules, charging interfaces, etc.) can be controlled based on one or more features described herein (e.g., features that change control conditions based on changes in road conditions). Operational control for autonomous driving can include various driving controls of the vehicle by the vehicle control unit (e.g., acceleration, deceleration, steering control, gear shifting control, braking system control, traction control, stability control, cruise control, lane keeping assist control, collision avoidance system control, emergency braking assist control, traffic sign recognition control, adaptive headlight control, etc.).

[0047] Figure 1 An example of an autonomous vehicle according to an embodiment of the present disclosure is shown.

[0048] like Figure 1 As shown, an autonomous vehicle (100) according to an embodiment of the present disclosure may include a processor (110), a sensor module (120), a camera (130), a communication module (140), a braking module (150), a storage unit (160), and a display unit (170).

[0049] The processor (110) is disposed in the autonomous vehicle (100). The processor (110) is electrically connected to at least one or more components, modules, etc. installed on the autonomous vehicle (100), and performs overall control of the autonomous vehicle (100) while exchanging various data or signals with at least one or more electrically connected components, modules, etc. via wired / wireless communication.

[0050] For example, the components of the autonomous vehicle (100) can exchange signals or data under the control of the processor (110) via an internal communication module (141) which is the communication module (140) of the autonomous vehicle (100). For example, the internal communication module (141) of the autonomous vehicle (100) may include at least one communication protocol (e.g., CAN, LIN, FlexRay, MOST, Ethernet, etc.).

[0051] The processor (110) can perform control of the autonomous vehicle (100) by controlling other components installed in the autonomous vehicle (100). For example, the processor (110) can perform at least one function of the engine management system (EMS), electronic stability control (ESC), electronic stability program (ESP), vehicle dynamic control (VDC), lane keeping assist system (LKAS), smart cruise control (SCC), adaptive cruise control (ACC), autonomous emergency braking (AEB), forward collision avoidance assist (FCA), highway driving assist (HDA), highway driver assist (HDP), lane departure warning (LDW), driver awareness warning (DAW), driver status warning (DSW), or traction control system (TCS). The aforementioned functions may be referred to as advanced driver assistance systems (ADAS). SCC can be an advanced driver assistance system that can automatically manage speed and distance while driving. SCC uses sensors such as radar and cameras to adjust the vehicle speed to maintain a safe following distance from the vehicle in front, and can even bring the vehicle to a complete stop in traffic and automatically resume driving. SCC can reduce driver fatigue, improve safety by minimizing human error, and improve fuel efficiency by optimizing acceleration and braking. While SCC is very effective on highways and in heavy traffic, it may rely on clear road conditions and requires driver supervision in complex situations.

[0052] The processor (110) receives at least one or more sensor information from a sensor module (120) installed in the autonomous vehicle (100), identifies the driving state or driving condition of the autonomous vehicle (100) based on the sensor information, and predicts the condition of the road on which the autonomous vehicle (100) is driving.

[0053] For example, when Smart Cruise Control (SCC) is activated, the processor (110) can analyze the road conditions on which the autonomous vehicle (100) is traveling using the vehicle's control information. As a result of the processor's (110) analysis, if the road conditions differ from preset standard road conditions, it can determine whether driver operation data has been detected. Based on the determination result, the processor (110) can set different control conditions (under which the autonomous vehicle (100) is controlled).

[0054] For example, when the result of the judgment is that the driver's operation data is detected, the processor (110) sets the control condition to the first control condition; when the result of the judgment is that the driver's operation data is not detected, the processor (110) sets the control condition to the second control condition, which is different from the first control condition.

[0055] Here, the driver's operational data can include information reflecting the driver's intentions. The driver can activate functions by clicking buttons (e.g., terrain mode or snow switch) to reflect active control over driving the autonomous vehicle (100).

[0056] For example, when set as the first control condition, the processor (110) can control the powertrain of the autonomous vehicle (100) and, based on this, control the driving of the autonomous vehicle (100). For example, the processor (110) can control the powertrain of the autonomous vehicle (100) and control the two-stage acceleration of the autonomous vehicle (100), and control it in a way that makes intelligent cruise control and ISG (Idle Stop & Start) incompatible with each other. ISG is a system designed to improve fuel efficiency and reduce emissions by automatically shutting off the engine when the vehicle is stationary, such as at traffic lights or in traffic jams, and restarting the engine if the driver is ready to move. The system keeps auxiliary functions such as air conditioning and lights working normally while the engine is off. By eliminating unnecessary idling, ISG saves fuel, reduces CO2 emissions, and improves energy efficiency, especially under urban driving conditions. It relies on an enhanced starter motor, a powerful battery system, and sensors to manage frequent engine restarts. While ISG is beneficial, it may present challenges in terms of compatibility with other systems and may increase wear on starter components.

[0057] The processor (110) can control the powertrain of the autonomous vehicle (100) by raising the shift mode of the autonomous vehicle (100) to the downshift line and by lowering the shift mode in advance before the autonomous vehicle (100) stops.

[0058] In contrast, when the second control condition is set, the processor (110) receives at least one or more sensor information from the sensor module (120) installed in the autonomous vehicle (100) and navigation information provided from the navigation server, and analyzes this information. If the result of the analysis meets the preset standard range, the autonomous vehicle (100) can be controlled to drive based on the activated intelligent cruise control (SCC).

[0059] For example, the processor (110) can control the driving of the autonomous vehicle (100) based on intelligent cruise control (SCC) and control it in a way that reduces the required acceleration of intelligent cruise control (SCC) and the slope of the required acceleration of intelligent cruise control (SCC).

[0060] The sensor module (120) is installed in the autonomous vehicle (100) and is able to sense the driving information, driving status, and surrounding environment of the autonomous vehicle (100) driving on the road.

[0061] For example, the sensor module (120) can sense lighting sensor information about the surroundings of the autonomous vehicle (100) by using a lighting sensor, and sense ambient temperature sensor information about the external environment of the autonomous vehicle (100) by using an ambient temperature sensor.

[0062] Alternatively or additionally, under the control of the processor (110), the sensor module (120) can accurately detect changes in the driving conditions of the autonomous vehicle (100) by using at least one or more sensors. Here, the at least one or more sensors may include radar (RADAR) sensors, light detection and ranging (LiDAR) sensors, infrared sensors, ultrasonic sensors, laser sensors, etc. For example, a laser sensor can accurately sense or identify vehicle control information related to the driving of the autonomous vehicle (100) by using time-of-flight (TOF), phase shift, etc., based on laser signal phase shift methods.

[0063] For example, under the control of the processor (110), the sensor module (120) can sense or identify target objects standing in front of the autonomous vehicle (100), objects driving in front of the autonomous vehicle (100), lanes on the road the vehicle is driving on, and the surrounding environment of the road the vehicle is driving on.

[0064] The aforementioned at least one or more sensors may include a heading sensor, a yaw sensor, a gyroscope sensor, a vehicle forward / backward travel sensor, a wheel sensor, a vehicle speed sensor, a vehicle slope detection sensor, a battery sensor, a fuel sensor, a tire sensor, a sensor for steering via a handle rotation, a vehicle interior temperature sensor, a vehicle interior humidity sensor, or a door sensor.

[0065] The camera (130) can collect images of the surroundings of the autonomous vehicle (100) or images of the interior of the autonomous vehicle (100). At least one or more cameras (130) are installed in the autonomous vehicle (100) and can collect images of the front area, rear area and side area of ​​the autonomous vehicle (100).

[0066] The camera (130) can provide the collected images to the processor (110). For example, the processor (110) can process still images or videos by analyzing the images collected by the camera (130) and extract image information from the processed still images or videos.

[0067] For example, the camera (130) may include a charge-coupled device (CCD) image sensor or a complementary metal-oxide-semiconductor (CMOS) image sensor. The camera (130) may also include a three-dimensional spatial sensing sensor, such as a KINECT (RGB-D sensor), a structured light sensor (TOF), and a stereo camera (130).

[0068] The communication module (140) can communicate with at least one or more base stations, external devices, or other vehicles. Here, other vehicles may include vehicles in front of, behind, and to the sides of the autonomous vehicle (100) that is in motion.

[0069] The communication module (140) can receive driving information from other vehicles under the control of the processor (110). The driving information may include the position, speed, acceleration, direction, predicted path, path history, or forward collision-avoidance assist (FCA) signals of other vehicles.

[0070] For example, the communication module (140) may include an internal communication module (141) and an external communication module (142).

[0071] The internal communication module (141) can perform transmission or reception using various communication protocols present within the autonomous vehicle (100). Here, the communication protocol may include at least one of Controller Area Network (CAN), CAN with Flexible Data Rate (CAN FD), Ethernet, Local Interconnect Network (LIN), and FlexRay. Other protocols may be included for enabling communication between various devices mounted in the vehicle.

[0072] The external communication module (142) can perform vehicle-to-vehicle (V2V) communication with other vehicles or vehicle-to-infrastructure (V2I) communication with infrastructure. Here, the infrastructure can be a roadside unit or server that periodically transmits traffic information through interoperability with a transportation information system (TIS) or an intelligent transportation system (ITS).

[0073] The external communication module (142) is not limited to this and can perform vehicle-to-vehicle (V2X) communication. The external communication module (142) can use various communication methods, such as vehicular ad hoc network (VANET), wireless access in vehicular environments (WAVE), dedicated short range communication (DSRC), communication access in land mobile (CALM), vehicle-to-network (V2N), wireless LAN (WLAN), Wi-Fi, wireless broadband (WiBro), long term evolution (LTE), long term evolution-advanced (LTE-A), 5G, 6G, ultra-wideband (UWB), and ZigBee communication. ) It communicates with Near Field Communication (NFC).

[0074] The communication module (140) may include at least one of a transmitting antenna, a receiving antenna, and a radio frequency (RF) circuit or RF element capable of implementing various communication protocols.

[0075] Alternatively or additionally, the communication module (140) may perform communication with the driver's or passenger's smart device.

[0076] The braking module (150) can brake the autonomous vehicle (100) while it is being driven, under the control of the processor (110). Under the control of the processor (110), when a braking signal is provided, the braking module (150) can brake suddenly or gradually in response to the braking signal. Here, the braking signal may include a time-of-collision (TTC) signal (hereinafter referred to as the TTC signal), which indicates the estimated or predicted time of collision between the autonomous vehicle (100) and a vehicle in front or behind.

[0077] The braking module (150) can, under the control of the processor (110), slowly reduce the speed of the autonomous vehicle (100) or suddenly stop the vehicle based on the braking signal.

[0078] For example, the braking module (150) may include multiple wheel brakes (FL, FR, RL and RR).

[0079] For example, multiple wheel brakes (FL, FR, RL and RR) may include: a first wheel brake (FL) that brakes the left front wheel of the autonomous vehicle (100), a second wheel brake (FR) that brakes the right front wheel of the autonomous vehicle (100), a third wheel brake (RL) that brakes the left rear wheel of the autonomous vehicle (100), and a fourth wheel brake (RR) that brakes the right rear wheel of the autonomous vehicle (100).

[0080] Multiple wheel brakes can be installed corresponding to each wheel of the autonomous vehicle (100). For example, each of the multiple wheel brakes (FL, FR, RL, and RR) can brake independently and can generate braking force on each wheel.

[0081] The storage unit (160) can be installed inside or separate from the autonomous vehicle (100). The storage unit (160) can store programs or information used to control advanced driver assistance systems (ADAS).

[0082] The storage unit (160) can store information sensed by the sensor module (120) (e.g., one or more sensors), image information collected by the camera (130), information generated by the processor (110), or information received by the communication module (140). The storage unit (160) is not limited thereto. Here, the storage unit (160) can be referred to as a memory.

[0083] The display unit (170) can be installed inside the autonomous vehicle (100). The display unit (170) can display driving assistance systems related to the autonomous vehicle (100) under the control of the processor (110). For example, the display unit (170) may include a cluster.

[0084] As described above, according to an embodiment of the present disclosure, an autonomous vehicle (100) under the control of a processor (110) analyzes the road conditions on which the autonomous vehicle (100) is driving by using the vehicle's control information when the intelligent cruise control (SCC) is activated. When the analysis result is that the road conditions are different from the preset standard road conditions, it is determined whether driver operation data has been detected. Based on this, the driving of the autonomous vehicle is controlled by actively or passively distinguishing the driving of the autonomous vehicle, thereby ensuring the reliability and ease of use of SCC control.

[0085] Figure 2 A schematic diagram of a control method for an autonomous vehicle according to an embodiment of the present disclosure is shown. Figures 3 to 5 It is used to explain in Figure 2 A schematic diagram illustrating the driving of an autonomous vehicle under the first control condition. Figure 6 and Figure 7 It is used to explain in Figure 2 A schematic diagram illustrating the control of an autonomous vehicle under the second control condition.

[0086] For convenience, Figure 2 The steps are described by way of an embodiment in which the steps are performed by a processor (e.g., control circuitry). Figure 2 One, some, or all of the steps, or a portion thereof, may be performed by one or more other circuits. Figure 2 One or more steps may be omitted, may be performed in a different order and / or modified in a different manner, and / or one or more additional steps may be added.

[0087] like Figure 2 As shown, a control method for an autonomous vehicle (100) including a processor (110) according to an embodiment of the present disclosure is as follows.

[0088] Under the control of the processor (110), the autonomous vehicle (100) can typically drive on the road.

[0089] When intelligent cruise control (SCC) is activated (S12), the autonomous vehicle (100) can analyze the road conditions on which the autonomous vehicle (100) is driving by using the vehicle's control information under the control of the processor (110) (S13).

[0090] For example, the processor (110) can analyze control states related to ESC, TCS, and ABS by using vehicle control information. For example, the processor (110) can analyze intervention states related to chassis control by comparing and analyzing actual vehicle speed and reference vehicle speed.

[0091] Here, the actual vehicle speed (Vreal) can be determined by providing information about the wheel speed (Whl Spd) via CAN signals and obtaining the average value of the provided wheel speeds. The reference vehicle speed (Vref) can be determined as the target vehicle speed based on longitudinal control.

[0092] The autonomous vehicle (100) compares and analyzes the road conditions and preset standard road conditions under the control of the processor (110) (S14). If the road conditions are basically the same as the standard road conditions, it can be determined that the road is a high-friction road, so it can continue to maintain normal driving (S14, "No").

[0093] In contrast, the autonomous vehicle (100) compares and analyzes the road conditions with the preset standard road conditions under the control of the processor (110) (S14). If the road conditions are different from the standard road conditions, it can be determined that the road is a low-friction road (S14, "yes").

[0094] If the road conditions differ from standard road conditions, the autonomous vehicle (100), under the control of the processor (110), determines whether driver operation data has been detected (S15). For example, under the control of the processor (110), the autonomous vehicle (100) sets different control conditions (S16, S17) for driving the autonomous vehicle (100) based on the determination result. Here, the driver operation data can be data related to the terrain (snow) mode.

[0095] For example, when driver operation data is detected, the processor (110) can determine that the terrain (snow) mode is activated. Conversely, if no driver operation data is detected, the processor (110) can determine that the terrain (snow) mode is not activated.

[0096] Under the control of the processor (110), when the driver's operation data is detected, the autonomous vehicle (100) sets the control condition to the first control condition and controls the driving of the autonomous vehicle (100) based on this condition (S16).

[0097] like Figures 3 to 5 As shown, if the first control condition is set, the autonomous vehicle (100) controls the power transmission system of the autonomous vehicle (100) under the control of the processor (110), and controls the driving of the autonomous vehicle (100) based on this (S16).

[0098] For example, under the control of the processor (110), the autonomous vehicle (100) controls the power transmission system of the autonomous vehicle (100), distinguishes the conditions into the stop & start conditions of SCC and the normal driving conditions of SCC, and actively controls the autonomous vehicle (100) based on this, thereby distinguishing the driving of the autonomous vehicle (100) (S161, S162).

[0099] For example, if the condition is a stop & start condition for the SCC, the processor (110) reduces the required acceleration of the SCC or reduces the slope of the required acceleration of the SCC by using an advanced driver assistance system (ADAS) (S161a, S161b).

[0100] If the condition is a stop & start condition for SCC, the processor (110) controls the two-stage acceleration of the autonomous vehicle (100) by using the powertrain of the autonomous vehicle (100) in a manner that prevents SCC and IGS (idle stop & start) from communicating with each other, or in a manner that prevents the communication control between SCC and ISG (idle stop & start) from being activated.

[0101] For example, if the conditions are normal driving conditions for the SCC, the processor (110) controls the SCC by using an advanced driver assistance system (ADAS) to reduce the required acceleration of the SCC or the slope of the required acceleration of the SCC (S162a, S162b).

[0102] If the conditions are normal driving conditions for the SCC, the processor (110) optimizes the shift mode of the acceleration conditions by using the powertrain of the autonomous vehicle (100). For example, if the conditions are normal driving conditions for the SCC, the processor (110) controls the process by using the powertrain of the autonomous vehicle (100) to raise the downshift line of the autonomous vehicle (100) (S161c).

[0103] When displayed as a line graph, such as Figure 5 As shown. In Figure 5 In the diagram, the horizontal direction represents vehicle speed, and the vertical direction represents APS.

[0104] If the conditions are normal driving conditions for SCC, the processor (110) raises the downshift line of the autonomous vehicle (100) by controlling the powertrain of the autonomous vehicle (100), thereby driving at low friction in SCC. By driving at low friction in SCC under the control of the processor (110), the autonomous vehicle (100) is able to prevent frequent downshifts due to APS operation.

[0105] Alternatively, if the conditions are normal driving conditions for the SCC, the processor (110) can apply engine braking under deceleration conditions by using the powertrain of the autonomous vehicle (100). For example, if the conditions are normal driving conditions for the SCC, the processor (110) can add a mapping before control, in which a deceleration criterion is added to the shift mode of the SCC, thereby downshifting in advance and storing the mapping.

[0106] Alternatively, if no driver operation data is detected, the autonomous vehicle (100) may set a second control condition different from the first control condition under the control of the processor (110), and may control the driving of the autonomous vehicle (100) based on the second control condition (S17).

[0107] If a second control condition is set, under the control of the processor (110), the autonomous vehicle (100) can receive and analyze information from at least one or more sensor modules installed in the autonomous vehicle (100) and navigation information provided from the navigation server.

[0108] If the analysis results meet the preset standard range, then under the control of the processor (110), the autonomous vehicle (100) can control the driving of the autonomous vehicle (100) based on the activated intelligent cruise control (SCC).

[0109] For example, such as Figure 6 and Figure 7 As shown, under the control of the processor (110), the autonomous vehicle (100) can determine the day / night flow state by using lighting sensor information provided from the lighting sensor (S171). If the state is a night flow state, the autonomous vehicle (100) can determine that it meets a preset standard range under the control of the processor (110).

[0110] Under the control of the processor (110), the autonomous vehicle (100) can determine whether there is a side road leading to the highway or a tunnel in the national highway (S172) by using navigation information provided by the navigation server. If there is a side road leading to the tunnel, the autonomous vehicle (100) can determine that it meets the preset standard range under the control of the processor (110).

[0111] Under the control of the processor (110), the autonomous vehicle (100) can determine whether the temperature is equal to or lower than a specific temperature by using information from the ambient temperature sensor provided by the ambient temperature sensor (S173). If the temperature is below zero degrees, the autonomous vehicle (100) can determine that it meets the preset standard range under the control of the processor (110).

[0112] Under the control of the processor (110), if the analysis result meets the preset standard range, the autonomous vehicle (100) can predict that the road it is driving is a low-friction road with black ice, etc., and passively control the autonomous vehicle (100) based on this, thereby distinguishing the driving of the autonomous vehicle (100) (S174).

[0113] For example, if a preset standard range is met, the processor (110) can control the driving of the autonomous vehicle (100) based on intelligent cruise control (SCC), and can control it by using an advanced driver assistance system (ADAS) to reduce the required acceleration of SCC or the slope of the required acceleration of SCC (S174a, S174b).

[0114] As described above, according to an embodiment of the present disclosure, an autonomous vehicle (100) under the control of a processor (110) can analyze the road conditions on which the autonomous vehicle (100) is driving by using the vehicle's control information when intelligent cruise control (SCC) is activated. If the analysis result is that the road conditions are different from the preset standard road conditions, it is determined whether driver operation data has been detected, and based on this, the driving of the autonomous vehicle is controlled by actively or passively distinguishing the driving of the autonomous vehicle, thereby ensuring the reliability and ease of use of SCC control.

[0115] Processor 150 may be a central processing unit (CPU) or a semiconductor device that processes instructions stored in memory and / or storage devices. Memory and storage devices may include various types of volatile or non-volatile storage media. For example, memory may include read-only memory (ROM) and random access memory (RAM).

[0116] Therefore, the operation of the methods or algorithms described in conjunction with the embodiments disclosed in this specification can be directly implemented using hardware modules, software modules, or a combination of hardware and software modules executed by a processor. The software modules may reside on a storage medium (i.e., memory and / or storage device), such as RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disk, removable disk, or CD-ROM.

[0117] An exemplary storage medium can be coupled to a processor. The processor can read information from the storage medium and write information to it. Alternatively, the storage medium can be integrated with the processor. The processor and storage medium can reside in an application-specific integrated circuit (ASIC). The ASIC can reside within the user terminal. In another scenario, the processor and storage medium can reside as separate components in the user terminal.

[0118] The purpose of this disclosure is to provide an autonomous vehicle and a control method thereof, which can ensure the reliability and ease of use of SCC control by differentiating the control of the autonomous vehicle according to road conditions during SCC control.

[0119] The technical objectives to be achieved by this disclosure are not limited to those described above, and other undescribed technical objectives will be clearly understood by those skilled in the art to which this disclosure pertains.

[0120] To achieve the above-mentioned technical objectives, this disclosure provides a vehicle control method. The vehicle includes a memory storing instructions and one or more processors configured to execute the instructions. The method includes: in intelligent cruise control (SCC), the one or more processors executing the instructions determine the road conditions on which the vehicle is traveling based on vehicle control information; if the road conditions are different from preset standard road conditions, the one or more processors executing the instructions determine whether driver operation data is detected; the one or more processors executing the instructions set control conditions for controlling the driving of the vehicle based on the determination result; and controlling the driving of the vehicle based on the changed control conditions.

[0121] Alternatively, setting the control conditions differently may include: setting a first control condition if driver operation data is detected; and setting a second control condition if driver operation data is not detected.

[0122] Alternatively, the control method may further include controlling the vehicle's powertrain in response to detected driver operation data.

[0123] Alternatively, controlling the powertrain may include: controlling the acceleration of the vehicle using the second gear of the powertrain, or controlling it in a manner that prevents the SCC and ISG (idle stop & start) from communicating with each other.

[0124] Alternatively, controlling the powertrain may include controlling it in a manner that causes the vehicle's shift mode to shift to a downshift line.

[0125] Alternatively, controlling the powertrain may further include controlling it in a manner that causes the shift mode to be reduced in advance before the vehicle stops.

[0126] Alternatively, the control method may further include: when it is determined, based on sensor information from sensor modules at the vehicle and navigation information provided by a navigation server, that a preset standard range is met, controlling the driving of the vehicle based on the SCC.

[0127] Alternatively, controlling the driving of the vehicle based on the SCC may include controlling it in a manner that reduces the desired acceleration and the slope of the desired acceleration of the SCC.

[0128] Alternatively, the sensor information may include information from a lighting sensor installed in the vehicle and information from an ambient temperature sensor installed in the vehicle.

[0129] Alternatively, this disclosure may include a computer-readable recording medium having a program on which the control method described above is executed.

[0130] Alternatively, to achieve the above-mentioned technical objectives, a vehicle according to an embodiment of the present disclosure is provided, the vehicle including a memory storing instructions and one or more processors configured to execute instructions, wherein, when executed by the one or more processors, the instructions cause the processor to determine, in intelligent cruise control (SCC) based on the vehicle's control information, the road conditions of the road on which the vehicle is traveling, and if the road conditions are different from preset standard road conditions, to determine whether driver operation data has been detected, to set control conditions for controlling the driving of the vehicle based on the result of the determination, and to control the driving of the vehicle based on the changed control conditions.

[0131] Alternatively, setting the control conditions differently may include: setting a first control condition if driver operation data is detected; and setting a second control condition if driver operation data is not detected.

[0132] Alternatively, the instructions may further cause the processor to control the vehicle's powertrain in response to detected driver operation data.

[0133] Alternatively, controlling the powertrain may include: controlling the acceleration of the vehicle using the second gear of the powertrain, or controlling it in a manner that prevents the SCC and ISG (idle stop & start) from communicating with each other.

[0134] Alternatively, controlling the powertrain may include controlling it in a manner that causes the vehicle's shift mode to shift to a downshift line.

[0135] Alternatively, controlling the powertrain may further include controlling it in a manner that causes the shift mode to be reduced in advance before the vehicle stops.

[0136] Alternatively, if a preset standard range is determined based on sensor information from sensor modules at the vehicle and navigation information provided by a navigation server, the instructions may further enable the processor to control the driving of the vehicle based on the SCC.

[0137] Alternatively, controlling the driving of the vehicle based on the SCC may include controlling it in a manner that reduces the desired acceleration of the SCC and the slope of the desired acceleration of the SCC.

[0138] Alternatively, the sensor information may include information from a lighting sensor installed in the vehicle and information from an ambient temperature sensor installed in the vehicle.

[0139] The vehicle and control method of this disclosure configured as described above have the following effects.

[0140] By differentiating vehicle control based on road conditions during SCC control, the reliability and ease of use of SCC control can be ensured.

[0141] Passive control allows for the differentiation of low-friction conditions that are difficult for the driver to recognize during SCC control.

[0142] If the driver directly activates the terrain mode or snow switch when driving conditions are met, the situation can be distinguished through active control.

[0143] Because vehicle behavior is unstable under operating conditions, control differentiation can provide reliable performance before intervening in other controls to enable the convenience of autonomous driving.

[0144] The above disclosure can be implemented as computer-readable code on a medium on which a program is recorded. Such computer-readable medium includes various recording devices storing data that can be read by a computer system. Examples of computer-readable media include hard disk drives (HDDs), solid-state drives (SSDs), silicon disk drives (SDDs), ROMs, RAMs, CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, etc.

[0145] Therefore, the above detailed description should not be construed as restrictive, but rather as exemplary. The scope of this disclosure should be determined by a reasonable interpretation of the appended claims, and all modifications within the equivalent scope of this disclosure are included within its scope.

Claims

1. A method for controlling the driving of a vehicle, in, include: During Smart Cruise Control (SCC) operation, the road conditions of the road on which the vehicle is traveling are determined based on the vehicle's control information. When the road conditions differ from the preset standard road conditions, determine whether driver operation data has been detected. Based on the judgment result, the control conditions used to control the driving of the vehicle are changed; and The driving of the vehicle is controlled based on the changed control conditions.

2. The method according to claim 1, wherein, Changing the control conditions includes one of the following: When the driver's operation data is detected, a first control condition is set for the control conditions; or When no driver operation data is detected, a second control condition is set for the control condition.

3. The method according to claim 1, wherein, Further includes: The vehicle's powertrain is controlled in response to detected driver operation data.

4. The method according to claim 3, wherein, Controlling the power transmission system includes: The acceleration of the vehicle is controlled by the second gear of the powertrain; or The powertrain is controlled by disabling the interaction between the SCC and the idle stop and start ISG.

5. The method according to claim 3, wherein, Controlling the power transmission system includes: Control the powertrain system to raise the vehicle's shift mode to the downshift line.

6. The method according to claim 5, wherein, Controlling the power transmission system further includes: Control the powertrain system to reduce the shift mode in advance before the vehicle stops.

7. The method according to claim 1, wherein, Controlling the driving of the vehicle includes: The vehicle is controlled to drive based on the SCC and in a manner that satisfies a preset standard range, wherein the satisfaction of the preset standard range is determined based on sensor information from the vehicle and navigation information from the navigation server.

8. The method according to claim 1, wherein, Controlling the driving of the vehicle includes: The vehicle is driven based on the SCC, thereby reducing the required acceleration and the slope of the required acceleration of the SCC.

9. The method according to claim 7, wherein, The sensor information includes information from the vehicle's lighting sensors and information from the vehicle's ambient temperature sensors.

10. A non-transitory computer-readable recording medium storing instructions, wherein, The instruction is configured as follows: When the instruction is executed by one or more processors, the instruction causes the one or more processors to perform the following operations: During Smart Cruise Control (SCC) operation, the road conditions of the road on which the vehicle is traveling are determined based on the vehicle's control information. When the road conditions differ from the preset standard road conditions, determine whether driver operation data has been detected. Based on the judgment result, the control conditions used to control the driving of the vehicle are changed; and The driving of the vehicle is controlled based on the changed control conditions.

11. The non-transitory computer-readable recording medium according to claim 10, wherein, The instruction is configured as follows: When the instruction is executed by the one or more processors, the instruction causes the one or more processors to control the driving of the vehicle based on the SCC, thereby reducing the required acceleration and the slope of the required acceleration of the SCC.

12. A device for controlling the driving of a vehicle, in, include: One or more processors that execute instructions; and The memory that stores the instructions. The instructions are configured such that, when executed by the one or more processors, the instructions cause the device to perform the following operations: During Smart Cruise Control (SCC) operation, the road conditions of the road on which the vehicle is traveling are determined based on the vehicle's control information. When the road conditions differ from the preset standard road conditions, it is determined whether driver operation data has been detected; and Based on the judgment result, the control conditions used to control the driving of the vehicle are changed; and The driving of the vehicle is controlled based on the changed control conditions.

13. The apparatus according to claim 12, wherein, The instruction is configured to, When the instruction is executed by the one or more processors, the instruction causes the device to perform the following operations: When the driver's operation data is detected, a first control condition is set for the control conditions; and When no driver operation data is detected, a second control condition is set for the control condition.

14. The apparatus according to claim 12, wherein, The instruction is configured as follows: When the instructions are executed by the one or more processors, the instructions cause the device to control the vehicle's powertrain in response to detected driver operation data.

15. The apparatus according to claim 14, wherein, The instruction is configured as follows: When the instruction is executed by the one or more processors, the instruction causes the device to control at least one of the following operations: The acceleration of the vehicle is controlled by the second gear of the powertrain; or The powertrain is controlled by disabling the interaction between the SCC and the idle stop and start ISG.

16. The apparatus according to claim 14, wherein, The instruction is configured as follows: When the instruction is executed by the one or more processors, the instruction causes the device to control the powertrain system, thereby raising the vehicle's shift mode to the downshift line.

17. The apparatus according to claim 16, wherein, The instruction is configured as follows: When the instruction is executed by the one or more processors, the instruction causes the device to control the powertrain system, thereby reducing the shift mode in advance before the vehicle stops.

18. The apparatus according to claim 12, wherein, The instruction is configured as follows: When the instruction is executed by the one or more processors, the instruction causes the device to control the driving of the vehicle based on the SCC and in a manner that satisfies a preset standard range, wherein the satisfaction of the preset standard range is determined based on sensor information from the vehicle and navigation information provided from a navigation server.

19. The apparatus according to claim 12, wherein, The instruction is configured as follows: When the instructions are executed by the one or more processors, the instructions cause the device to control the driving of the vehicle, thereby reducing the required acceleration of the SCC and the slope of the required acceleration of the SCC.

20. The apparatus according to claim 18, wherein, The sensor information includes information from the vehicle's lighting sensors and information from the vehicle's ambient temperature sensors.

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