Intelligent driving method, intelligent driving device, and intelligent driving apparatus

Abstract

An intelligent driving method, comprising: acquiring first road information, wherein the first road information indicates that a negative obstacle having a negative height appears on a road surface of a target traveling area of an intelligent driving apparatus, and the distance between the target traveling area and the current position of the intelligent driving apparatus is less than or equal to a first distance threshold; and on the basis of the first road information, executing at least one of the following: controlling the intelligent driving apparatus to decelerate, or controlling a prompt device of the intelligent driving apparatus to prompt at least one of the following: the negative obstacle appears in the target traveling area, a driving suggestion for the negative obstacle, or an action that the intelligent driving apparatus is executing or is about to execute for the negative obstacle. The method can be applied to the field of intelligent vehicles such as new energy vehicles and electric vehicles. For negative obstacles such as potholes, collapses, and fractures appearing on a road, a driver can be prompted in time and / or a vehicle can be controlled to decelerate, thereby reducing the probability that the vehicle falls into the negative obstacles. In addition, the present invention also relates to an intelligent driving device and an intelligent driving apparatus.

Description

Intelligent driving method, device and intelligent driving equipment

[0001] The present application claims priority to the Chinese patent application No. 202410722770.0, filed on June 5, 2024, and entitled "Intelligent driving method, device and intelligent driving equipment", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the field of intelligent driving, and more particularly, to an intelligent driving method, device and intelligent driving equipment. BACKGROUND

[0003] During driving, vehicles inevitably encounter some unexpected situations, which will affect the driving experience of the vehicle passengers, and in severe cases, affect the physical and mental health of the passengers or even endanger their lives. For example, when the road surface is damaged due to natural disasters, if the vehicle or the driver cannot obtain the information of the road in front of the vehicle in time, the vehicle cannot take evasive measures in time, which will result in damage to the vehicle and even endanger the health of the passengers.

[0004] In view of this, an intelligent driving scheme capable of responding to unexpected disasters and improving driving safety is urgently needed. SUMMARY

[0005] The present application provides an intelligent driving method, device and intelligent driving equipment, which can control the intelligent driving equipment to slow down when there is a negative obstacle in the target driving area of the intelligent driving equipment, and / or control the intelligent driving equipment to prompt the driver with information related to the negative obstacle, thereby reducing the probability of the intelligent driving equipment falling into the negative obstacle and improving driving safety.

[0006] In a first aspect, an intelligent driving method is provided, which can be executed by an intelligent driving equipment, for example, can be executed by a computing platform of the intelligent driving equipment, or by a chip or circuit for the intelligent driving equipment.

[0007] The method comprises: obtaining first road information, the first road information indicating that a first event affecting road safety occurs in a target driving area of the intelligent driving device, the first event including that a negative obstacle satisfying a first condition appears on a road surface of the target driving area, the first condition including that a negative height of the negative obstacle is greater than or equal to a height threshold, the height threshold being associated with a chassis height of the intelligent driving device, and a distance between the target driving area and a current position of the intelligent driving device being less than or equal to a first distance threshold; and performing at least one of the following according to the first road information: controlling the intelligent driving device to slow down, or controlling a prompt device of the intelligent driving device to prompt at least one of the following: the first event, a driving suggestion for the first event, or an action being performed or about to be performed by the intelligent driving device for the first event.

[0008] In some implementations, the first road information is information that a road surface of a road collapses or a road surface of a road breaks, so that the intelligent driving device determines that a negative obstacle exists in front of the road according to the information; or the first road information can also indicate that a negative obstacle satisfying the first condition exists in front of the road.

[0009] In the above technical solution, during driving of the intelligent driving device, information of a negative obstacle such as a collapsed road surface, a broken road surface, or a large pit in a driving direction can be obtained in a timely manner, so that the intelligent driving device is controlled to slow down, and / or a prompt device is controlled to prompt related information of the negative obstacle, so as to reduce a probability that the intelligent driving device falls into the negative obstacle, thereby improving driving safety and driving experience.

[0010] In a second aspect, an intelligent driving method is provided, which can be executed by an intelligent driving device, for example, can be executed by a computing platform of the intelligent driving device, or by a chip or circuit for the intelligent driving device.

[0011] The method comprises: obtaining first road information, the first road information indicating that a negative obstacle with a negative height greater than or equal to a height threshold appears on a road surface of a target driving area of the intelligent driving device, the height threshold being associated with a chassis height of the intelligent driving device, and a distance between the target driving area and a current position of the intelligent driving device being less than or equal to a first distance threshold; and performing at least one of the following according to the first road information: controlling the intelligent driving device to slow down, or controlling a prompt device of the intelligent driving device to prompt at least one of the following: that the target driving area has the negative obstacle, a driving suggestion for the negative obstacle, or an action being performed or about to be performed by the intelligent driving device for the negative obstacle.

[0012] In some implementations of the first aspect or the second aspect, the method further comprises: obtaining vehicle motion state information, the vehicle motion state information indicating position information of at least one vehicle in the first road perceived by the intelligent driving device, the distance between the at least one vehicle and the current position of the intelligent driving device being less than or equal to the second distance threshold; and obtaining the first road information, including at least one of: determining the first road information when the change rate of the visible part of the vehicle greater than or equal to the first quantity threshold in the at least one vehicle is less than or equal to the first change rate threshold; determining the first road information when the height change rate of the vehicle greater than or equal to the second quantity threshold in the at least one vehicle is greater than or equal to the second change rate threshold; or determining the first road information when the vehicle greater than or equal to the third quantity threshold in the at least one vehicle disappears within the first time length.

[0013] In the above technical solution, according to the change rate of the visible part of the other vehicle or the number of the disappeared other vehicle, it can be determined whether the target driving area has a negative obstacle with large negative height, which will cause the intelligent driving device to fall and affect the driving safety; according to the height change rate of the other vehicle, it can be determined whether the target driving area has a negative obstacle with small negative height and small length or width, which will cause the vehicle chassis to be scratched and thus the vehicle to be damaged. Compared with directly detecting whether the negative obstacle exists on the road surface by the perception system of the intelligent driving device, when the negative obstacle is far away from the intelligent driving device, the technical solution can also determine the existence of the negative obstacle, so as to control the intelligent driving device in advance and / or prompt the driver of the intelligent driving device, so as to reduce the probability of the intelligent driving device driving into the negative obstacle, thereby improving the driving safety and the driving experience.

[0014] In some implementations of the first aspect, the method further comprises: obtaining second road information, the second road information indicating the slope and the curvature of the first road of the target driving area, the first road being the road on which the first event occurs; and determining the first road information, including: determining the first road information when the slope change rate of the first road is less than or equal to a third change rate threshold, and / or the curvature of the first road is less than or equal to a curvature threshold.

[0015] In some implementations of the second aspect, the method further comprises: obtaining second road information, the second road information indicating the slope and the curvature of the first road of the target driving area, the first road being the road on which the negative obstacle appears; and determining the first road information, including: determining the first road information when the slope change rate of the first road is less than or equal to a third change rate threshold, and / or the curvature of the first road is less than or equal to a curvature threshold.

[0016] When the slope change rate or the curvature of the road is too large, the intelligent driving device may make a judgment that the preceding vehicle disappears, due to the limited sensing range of the sensing system of the intelligent driving device, even if there is no negative obstacle in front of the road. Therefore, in the technical solution described above, in combination with the slope change rate and the curvature of the road, it can be determined whether the other vehicle disappears due to a negative obstacle or the visible part change rate is too large, which helps to reduce the probability of false triggering of the intelligent driving device control deceleration and / or prompt, thereby improving the robustness of the intelligent driving system.

[0017] In combination with the first aspect or the second aspect, in some implementations of the first aspect or the second aspect, the method further comprises: obtaining environment information, the environment information indicating at least one of the following: whether there is weather affecting road safety in the target driving area within a second time period, or whether there is an earthquake higher than or equal to a level threshold within the second time period, wherein the starting time of the second time period is earlier than the current time, and the end time of the second time period is the current time or later than the current time; the first road information further indicates the risk level of the first event, and determining the first road information comprises: when the environment information indicates at least one of the following: there is weather affecting road safety in the target driving area within the second time period, or there is an earthquake higher than or equal to the level threshold within the second time period, determining that the risk level is a first risk level; or when the environment information indicates that there is no weather affecting road safety in the target driving area within the second time period, and there is no earthquake higher than or equal to the level threshold within the second time period, determining that the risk level is a second risk level, and the impact of the first risk level on road safety is higher than the impact of the second risk level on road safety.

[0018] In combination with the first aspect or the second aspect, in some implementations of the first aspect or the second aspect, according to the first road information, the intelligent driving device is controlled to decelerate, comprising: when the risk level is the first risk level, the intelligent driving device is controlled to decelerate at a first deceleration; or when the risk level is the second risk level, the intelligent driving device is controlled to decelerate at a second deceleration; wherein the absolute value of the first deceleration is greater than the absolute value of the second deceleration.

[0019] In the technical solution described above, according to the environment information, the risk level of the negative obstacle can be determined, so that the intelligent driving device is controlled to decelerate at different decelerations, which can ensure driving safety when the risk level is high, and ensure driving comfort when the risk level is low.

[0020] In combination with the first aspect or the second aspect, in some implementations of the first aspect or the second aspect, obtaining the first road information comprises: obtaining sensing information of the target driving area collected by the sensor of the intelligent driving device; and determining at least the negative height of the negative obstacle according to the sensing information.

[0021] Exemplarily, the sensor can be a camera, the perception information can be a road surface image of a target driving area, the image can be a depth image, and processing the image can determine the position and size of the negative obstacle.

[0022] In the technical solution described above, the height of the negative obstacle is determined according to the perception system of the intelligent driving device, which can reduce the processing complexity in the process of determining the negative obstacle, thereby reducing the consumption of computing power.

[0023] With reference to the first aspect or the second aspect, in some implementations of the first aspect or the second aspect, the first road information further indicates a damage degree of the road of the target driving area, the damage degree being associated with a passable part of the road; and according to the first road information, the intelligent driving device is controlled to decelerate, including: when the damage degree is a first degree, the intelligent driving device is controlled to decelerate at a third deceleration; or when the damage degree is a second degree, the intelligent driving device is controlled to decelerate at a fourth deceleration; wherein the passable part corresponding to the first degree is smaller than the passable part corresponding to the second degree, and the absolute value of the third deceleration is greater than the absolute value of the fourth deceleration.

[0024] In the technical solution described above, the intelligent driving device is controlled to decelerate at different decelerations according to the damage degree of the road, which helps to balance the driving comfort and the driving safety.

[0025] With reference to the first aspect or the second aspect, in some implementations of the first aspect or the second aspect, the first road information is acquired by: receiving light signal information, the light signal information indicating the quality of a signal transmitted by a first optical fiber, the first optical fiber being arranged below the road surface of the target driving area, or the first optical fiber being arranged on a first object in the target driving area, the first object affecting the safety of the road surface of the target driving area; and determining the damage degree according to the light signal information.

[0026] In the technical solution described above, in the case that the intelligent driving device does not have a perception capability or the perception capability is destroyed, the existence of road damage in the driving direction can also be determined according to the light signal, so as to control the intelligent driving device to decelerate and / or control the driver to be prompted.

[0027] With reference to the first aspect or the second aspect, in some implementations of the first aspect or the second aspect, the first object includes at least one of the following: a mountain, a tunnel.

[0028] In the technical solution described above, for the influence on the safety of the road surface caused by non-negative obstacles, such as the risk of tunnel deformation and mountain landslide in the driving direction, a warning can also be given in advance, thereby improving the driving safety.

[0029] In a third aspect, an intelligent driving apparatus is provided, which includes an obtaining unit and a processing unit, wherein the obtaining unit is configured to: obtain first road information, the first road information indicating that a first event affecting road safety occurs in a target driving area of the intelligent driving device, the first event including a negative obstacle on a road surface of the target driving area satisfying a first condition, the first condition including a negative height of the negative obstacle being greater than or equal to a height threshold value, the height threshold value being associated with a chassis height of the intelligent driving device, a distance between the target driving area and a current position of the intelligent driving device being less than or equal to a first distance threshold value; and the processing unit is configured to: according to the first road information, perform at least one of the following: controlling the intelligent driving device to slow down, or controlling a prompting apparatus of the intelligent driving device to prompt at least one of the following: the first event, a driving suggestion for the first event, or an action being performed or about to be performed by the intelligent driving device for the first event.

[0030] In a fourth aspect, an intelligent driving apparatus is provided, which includes an obtaining unit and a processing unit, wherein the obtaining unit is configured to: obtain first road information, the first road information indicating that a negative obstacle satisfying a negative height greater than or equal to a height threshold value occurs on a road surface of a target driving area of the intelligent driving device, the height threshold value being associated with a chassis height of the intelligent driving device, a distance between the target driving area and a current position of the intelligent driving device being less than or equal to a first distance threshold value; and the processing unit is configured to: according to the first road information, perform at least one of the following: controlling the intelligent driving device to slow down, or controlling a prompting apparatus of the intelligent driving device to prompt at least one of the following: the existence of the negative obstacle in the target driving area, a driving suggestion for the negative obstacle, or an action being performed or about to be performed by the intelligent driving device for the negative obstacle.

[0031] In combination with the third aspect or the fourth aspect, in some implementations of the third aspect or the fourth aspect, the obtaining unit is further configured to: obtain vehicle motion state information, the vehicle motion state information indicating position information of at least one vehicle in the first road perceived by the intelligent driving device, a distance between the at least one vehicle and the current position of the intelligent driving device being less than or equal to a second distance threshold value; and the processing unit is configured to perform at least one of the following: determining the first road information when a change rate of a visible part of a vehicle greater than or equal to a first number threshold value in the at least one vehicle is less than or equal to a first change rate threshold value; determining the first road information when a height change rate of a vehicle greater than or equal to a second number threshold value in the at least one vehicle is greater than or equal to a second change rate threshold value; or determining the first road information when a vehicle greater than or equal to a third number threshold value in the at least one vehicle disappears within a first time length.

[0032] With reference to the third aspect, in some implementations of the third aspect, the obtaining unit is further configured to: obtain second road information, the second road information indicating a slope and a curvature of a first road of the target driving area, the first road being a road on which the first event occurs; and the processing unit is configured to: determine the first road information when a slope change rate of the first road is less than or equal to a third change rate threshold and / or a curvature of the first road is less than or equal to a curvature threshold.

[0033] With reference to the fourth aspect, in some implementations of the fourth aspect, the obtaining unit is configured to: obtain second road information, the second road information indicating a slope and a curvature of a first road of the target driving area, the first road being a road on which the negative obstacle appears; and the processing unit is configured to: determine the first road information when a slope change rate of the first road is less than or equal to a third change rate threshold and / or a curvature of the first road is less than or equal to a curvature threshold.

[0034] With reference to the third aspect or the fourth aspect, in some implementations of the third aspect or the fourth aspect, the obtaining unit is further configured to: obtain environment information, the environment information indicating at least one of: whether there is weather affecting road safety in the target driving area within a second time length, or whether there is an earthquake higher than or equal to a level threshold within the second time length, wherein a start time of the second time length is earlier than the current time, and an end time of the second time length is the current time or later than the current time; and the first road information further indicates a risk level of the first event, the processing unit is configured to: determine the risk level as a first risk level when the environment information indicates at least one of: there is weather affecting road safety in the target driving area within the second time length, or there is an earthquake higher than or equal to the level threshold within the second time length; or determine the risk level as a second risk level when the environment information indicates that there is no weather affecting road safety in the target driving area within the second time length, and there is no earthquake higher than or equal to the level threshold within the second time length, the impact of the first risk level on road safety being higher than the impact of the second risk level on road safety.

[0035] With reference to the third aspect or the fourth aspect, in some implementations of the third aspect or the fourth aspect, the processing unit is further configured to: control the intelligent driving device to decelerate at a first deceleration when the risk level is the first risk level; or control the intelligent driving device to decelerate at a second deceleration when the risk level is the second risk level; wherein an absolute value of the first deceleration is greater than an absolute value of the second deceleration.

[0036] With reference to the third aspect or the fourth aspect, in some implementations of the third aspect or the fourth aspect, the obtaining unit is configured to: obtain perception information of the target driving area collected by a sensor of the intelligent driving device; and determine at least a negative height of the negative obstacle according to the perception information.

[0037] In some implementations of the third aspect or the fourth aspect, in combination with the third aspect or the fourth aspect, the first road information further indicates a damage degree of the road of the target driving area, the damage degree being associated with a passable part of the road; and the processing unit is configured to: control the intelligent driving device to decelerate at a third deceleration when the damage degree is a first degree; or control the intelligent driving device to decelerate at a fourth deceleration when the damage degree is a second degree; wherein the passable part corresponding to the first degree is smaller than the passable part corresponding to the second degree, and the absolute value of the third deceleration is greater than the absolute value of the fourth deceleration.

[0038] In some implementations of the third aspect or the fourth aspect, in combination with the third aspect or the fourth aspect, the obtaining unit is configured to: receive light signal information, the light signal information indicating a quality of a signal transmitted by the first optical fiber, the first optical fiber being arranged below a road surface of the target driving area, or the first optical fiber being arranged on a first object of the target driving area, the first object affecting the safety of the road surface of the target driving area; and the processing unit is configured to: determine the damage degree according to the light signal information.

[0039] In some implementations of the third aspect or the fourth aspect, in combination with the third aspect or the fourth aspect, the first object includes at least one of: a mountain, a tunnel.

[0040] In a fifth aspect, an intelligent driving device is provided, which includes a processor configured to execute a computer program stored in a memory to cause the device to perform the method in any possible implementation of the first aspect or the second aspect.

[0041] In some implementations of the fifth aspect, in combination with the fifth aspect, the intelligent driving device further includes the memory.

[0042] In a sixth aspect, an intelligent driving device is provided, which includes the device in any possible implementation of the third aspect to the fifth aspect.

[0043] In some implementations of the sixth aspect, in combination with the sixth aspect, the intelligent driving device is a vehicle.

[0044] In a seventh aspect, a computer program product is provided, which includes computer program code configured to cause a computer or a processor to perform the method in any possible implementation of the first aspect or the second aspect when the computer program code is run on the computer or the processor.

[0045] It should be noted that the computer program code can be stored in whole or in part on a storage medium, wherein the storage medium can be packaged together with the processor or packaged separately from the processor.

[0046] In an eighth aspect, a computer readable medium is provided, and the computer readable medium stores instructions which, when executed by a processor, cause the processor to implement the method in any possible implementation of the first aspect or the second aspect.

[0047] In a ninth aspect, a chip is provided, and the chip comprises circuitry configured to perform the method in any possible implementation of the first aspect or the second aspect.

[0048] The beneficial effects not described in the third aspect to the ninth aspect can be referred to the description in the first aspect and the second aspect, and will not be described here again. BRIEF DESCRIPTION OF DRAWINGS

[0049] FIG. 1 is a functional schematic block diagram of an intelligent driving device according to an embodiment of the present application;

[0050] FIG. 2 is a schematic diagram of an autonomous driving system architecture according to an embodiment of the present application;

[0051] FIG. 3 is a schematic flowchart of an intelligent driving method according to an embodiment of the present application;

[0052] FIG. 4 is a schematic diagram of an application scenario of the intelligent driving method according to an embodiment of the present application;

[0053] FIG. 5 is a GUI according to an embodiment of the present application;

[0054] FIG. 6 is another schematic flowchart of an intelligent driving method according to an embodiment of the present application;

[0055] FIG. 7 is still another schematic flowchart of an intelligent driving method according to an embodiment of the present application;

[0056] FIG. 8 is still another schematic diagram of an application scenario of the intelligent driving method according to an embodiment of the present application;

[0057] FIG. 9 is still another schematic flowchart of an intelligent driving method according to an embodiment of the present application;

[0058] FIG. 10 is a schematic block diagram of an intelligent driving device according to an embodiment of the present application;

[0059] FIG. 11 is another schematic block diagram of an intelligent driving device according to an embodiment of the present application. DETAILED DESCRIPTION

[0060] With the development of intelligent and automatic vehicles, more and more vehicles are equipped with intelligent driving systems, such as advanced driving assistant system (ADAS), to reduce driving stress and improve safety. The ADAS system includes many active safety functions, such as autonomous emergency braking (AEB) function, lane departure warning (LDW), evasive steering assist (ESA) function, etc., which can improve driving safety. Current AEB mainly relies on the detection of positive obstacles to control the vehicle to stop, and negative obstacles (such as road collapse) cannot trigger AEB activation. That is, in the case of road collapse caused by sudden disasters, etc., when the vehicle and / or the driver do not obtain relevant information in time, it may not be possible to take evasive measures in time, resulting in the vehicle falling into the collapsed position and causing personal and property losses.

[0061] In view of this, the embodiments of the present application provide an intelligent driving method, device and equipment, which can obtain information about road collapse in the driving direction in time during vehicle driving, so as to control the vehicle to slow down and / or control the prompt device to prompt the road collapse related information, so as to reduce the probability of the vehicle falling into the collapsed position, thereby improving the driving safety. The intelligent driving scheme provided by the present application can be arranged in the computing platform of the vehicle as a new active safety function. Exemplarily, the new active safety function can be regarded as a new AEB function or an AEB enhanced function.

[0062] In order to facilitate understanding of the technical solutions of the present application, the concepts involved in the present application are introduced as follows.

[0063] Positive obstacle: an obstacle with a highest point higher than the plane on which the vehicle is located.

[0064] Negative obstacle: an obstacle with a highest point lower than the plane on which the vehicle is located, and the average distance or maximum distance between the bottom (i.e. the position near the highest point) of the negative obstacle and the plane on which the vehicle is located is called negative height. For example, the negative obstacle is a pit, and the bottom of the pit is the bottom of the negative obstacle, and the highest point of the bottom of the pit is regarded as the highest point of the negative obstacle. In some scenarios, the negative obstacle involved in the present application has a size (i.e. area) in the direction perpendicular to the height (i.e. parallel to the direction of the road on which the vehicle is located) greater than a certain threshold, which is sufficient to accommodate one or more wheels of the vehicle in the direction perpendicular to the height; in addition, the size (i.e. negative height) of the negative obstacle in the direction parallel to the height can be a part or all of the wheel diameter, or greater than the wheel diameter.

[0065] The technical solutions in the present application will be described below with reference to the drawings.

[0066] FIG. 1 is a functional block diagram of an intelligent driving device according to an embodiment of the present application. As shown in FIG. 1, the intelligent driving device 100 can include a perception system 120, a prompting device 130, and a computing platform 150. The perception system 120 can include several sensors for sensing information about the environment around the intelligent driving device 100. For example, the perception system 120 can include a positioning system, which can be a global positioning system (GPS), a Beidou system, or other positioning systems. For another example, the perception system 120 can also include one or more of an inertial measurement unit (IMU), a laser radar, a millimeter wave radar, an ultrasonic radar, and a camera.

[0067] The prompting device 130 can include any of a sound-emitting device and a display device. The sound-emitting device can include a loudspeaker, a sound box, etc. The display device mainly includes two types, a first type being a vehicle-mounted display screen, and a second type being a projection display screen, such as a head up display (HUD). The vehicle-mounted display screen is a physical display screen and is an important component of a vehicle information entertainment system. The vehicle-mounted display screen can include a human machine interface (HMI). The head up display, also known as a head up display system, is mainly used to display driving information such as speed and navigation on a display device (such as a windshield) in front of a user, so as to reduce the time of the user's visual line shifting and avoid pupil changes caused by the user's visual line shifting, thereby improving driving safety and comfort.

[0068] Optionally, the prompting device 130 can also include a light device for displaying light. The light device can be an atmosphere lamp or a breathing lamp composed of light emitting diode (LED) lamp beads or lamp strips, wherein the lamp strips can include a plurality of LED lamp beads; or the light device can also be other types of lamps. The light device can be arranged at positions such as an instrument panel and a central control screen. The light device can also be arranged around the display screens such as the instrument panel and the central control screen, or can also be arranged at other positions convenient for prompting information to the driver, such as a steering wheel.

[0069] Some or all functions of the intelligent driving device 100 can be controlled by the computing platform 150. The computing platform 150 can include processors 151-15n, which are circuits having a processing capability for signals. In one implementation, the processor can be a circuit having an instruction reading and running capability, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a kind of microprocessor), or a digital signal processor (DSP), etc. In another implementation, the processor can implement certain functions through a logical relationship of hardware circuits, which is fixed or can be reconfigured, such as an application-specific integrated circuit (ASIC) or a programmable logic device (PLD) implemented hardware circuit, such as a field programmable gate array (FPGA). In the reconfigurable hardware circuit, the processor loads a configuration document to implement the hardware circuit configuration, which can be understood as the process of the processor loading instructions to implement the functions of the above part or all units. In addition, the processor can also be a hardware circuit designed for artificial intelligence, which can be understood as a kind of ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc. In addition, the computing platform 150 can also include a memory for storing instructions, and some or all of the processors 151-15n can call the instructions in the memory to implement corresponding functions.

[0070] The intelligent driving device 100 can include an ADAS that uses various sensors (including but not limited to laser radar, millimeter wave radar, camera, ultrasonic sensor, global positioning system, inertial measurement unit) on the intelligent driving device to obtain information from the surroundings of the intelligent driving device, and analyzes and processes the obtained information to implement functions such as obstacle perception, target recognition, intelligent driving device positioning, path planning, driver monitoring / reminding, etc., thereby improving the safety, automation level and comfort of the intelligent driving device driving.

[0071] From the logical function, the ADAS system generally includes three main functional modules: a perception module, a decision module and an execution module. The perception module perceives the environment around the vehicle body through sensors, inputs corresponding real-time data to the decision layer processing center, and mainly includes vehicle-mounted cameras, ultrasonic radars, millimeter wave radars, laser radars, etc. The decision module makes corresponding decisions using computing devices and algorithms based on the information obtained by the perception module. The execution module takes corresponding actions such as driving, lane changing, steering, braking, warning, etc. after receiving the decision signal from the decision module.

[0072] At different automatic driving levels (L0-L5), ADAS can achieve different levels of automatic driving assistance based on artificial intelligence algorithms and information obtained by multiple sensors. The above automatic driving levels (L0-L5) are based on the classification standard of the Society of Automotive Engineers (SAE). Among them, L0 level is non-automation; L1 level is driving support; L2 level is partial automation; L3 level is conditional automation; L4 level is high automation; L5 level is complete automation. The tasks of monitoring the road conditions and making responses at L1 to L3 levels are completed by the driver and the system together, and the driver needs to take over the dynamic driving task. L4 and L5 levels can make the driver completely change to the role of a passenger. Currently, the functions that ADAS can achieve mainly include but are not limited to: adaptive cruise control, automatic emergency braking, automatic parking, blind spot monitoring, front intersection traffic warning / braking, rear intersection traffic warning / braking, front vehicle collision warning, lane departure warning, lane keeping assistance, rear vehicle collision warning, traffic sign recognition, traffic congestion assistance, highway assistance, etc. It should be understood that the above various functions can have specific modes at different automatic driving levels (L0-L5), and the higher the automatic driving level, the more intelligent the corresponding mode.

[0073] In the embodiments of the present application, the computing platform 150 can determine whether an event affecting road safety occurs in the road in the driving direction according to one or more of the road information in the driving direction, the motion state information of the other vehicle in the driving direction, and the environmental information. When different degrees of events affecting road safety occur in the road in the driving direction, the prompting device 130 is controlled to prompt, and / or the braking system of the intelligent driving device is controlled to brake.

[0074] FIG. 2 shows a schematic diagram of an automatic driving system architecture provided by an embodiment of the present application. As shown in FIG. 2, the system includes an other vehicle information acquisition module 210, a road information acquisition module 220, a natural information acquisition module 230, a road state detection module 240, a regulation and control module 250, and a prompting module 260. The roles of the modules in the system shown in FIG. 2 are described as follows (1) to (6).

[0075] The he-vehicle information acquisition module 210 is configured to acquire motion state information of a he-vehicle in the driving direction of the intelligent driving device. The motion state information can include the position of the he-vehicle, or can further include at least one of the distance between the he-vehicle and the intelligent driving device, the speed of the he-vehicle, and the acceleration of the he-vehicle. When the intelligent driving device drives in the head direction, the he-vehicle in the driving direction of the intelligent driving device is a front vehicle. When the intelligent driving device drives in the tail direction, the he-vehicle in the driving direction of the intelligent driving device is a rear vehicle.

[0076] The road information acquisition module 220 is configured to acquire road information in the driving direction of the intelligent driving device. The road information indicates the road surface of the road. The road information can include the curvature of the road, the slope of the road, and the slope change rate of the road. Alternatively, the road information can further include road damage information, which indicates road damage and / or road damage degree information.

[0077] The natural information acquisition module 230 is configured to acquire environmental information of a target region in a past time period. The target region can include a portion of the road on which the intelligent driving device drives and a range within a certain distance (e.g., 1 km, 2 km, or another distance) from the intelligent driving device. The past time period can be a time period before the current time. The time period can be 0.5 days, 1 day, or 3 days, or another time period. In addition, the past time period can include the current time or can not include the current time. The environmental information includes weather information and / or earthquake information. The weather information can indicate whether there is weather that can cause damage to the road surface in the target region in the past time period. The weather that can cause damage to the road surface can include rainfall exceeding a preset threshold. The earthquake information can indicate whether there is an earthquake with a magnitude greater than or equal to a magnitude threshold in the target region in the past time period. The magnitude threshold can be 6.5, or 7, or another magnitude.

[0078] The road state detection module 240 is configured to determine whether an event that affects the safety of the road surface occurs in the road in the driving direction of the intelligent driving device according to one or more of the motion state of the he-vehicle in the driving direction of the intelligent driving device, the road information, the weather information of the target region, and the earthquake information of the target region. Alternatively, the road state detection module 240 can determine the degree of influence of the event on the safety of the road surface. Furthermore, the road state detection module 240 sends the information about the event that affects the safety of the road surface to the control module 250.

[0079] (five) the control module 250 is configured to control the braking of the intelligent driving device and / or control the prompt module 260 to prompt the relevant information in response to the road safety event occurred in the road where the intelligent driving device is driving. Alternatively, the control module 250 is configured to: according to the different degrees of the road safety event occurred in the road, control the actuator to execute different control amount (such as the opening degree of the accelerator pedal, the opening degree of the deceleration pedal, etc.) to brake, and / or control the prompt module 260 to prompt the relevant information.

[0080] (six) the prompt module 260 is configured to prompt at least one of the following: the road safety event occurred in the current driving road, the driving suggestion for the road safety event occurred in the current driving road, or the measure being taken or having been taken for the road safety event occurred in the current driving road.

[0081] In actual implementation, part or all of the functions of the ego vehicle information acquisition module 210, the road information acquisition module 220 and the natural information acquisition module 230 can be implemented by the perception system 120 shown in FIG. 1. For example, the ego vehicle information acquisition module 210 can acquire the motion state information of the ego vehicle through the sensors in the perception system 120; the road information acquisition module 220 can acquire the road information through the sensors in the perception system 120; the natural information acquisition module 230 can acquire the weather information of the location where the ego vehicle is located at the current time and in the past time period through the sensors in the perception system 120. In some implementations, the ego vehicle information acquisition module 210, the road information acquisition module 220 and the natural information acquisition module 230 can also acquire the relevant information through the communication system of the intelligent driving device. For example, the ego vehicle information acquisition module 210 can acquire the motion state information of the ego vehicle from the ego vehicle through vehicle to vehicle (V2V) communication, or from the road side device (RSU) through vehicle to infrastructure (V2I) communication, or through other ways to acquire the motion state information of the ego vehicle in real time; the road information acquisition module 220 can acquire the road information from the third party (such as map software, road monitoring device, etc.); the natural information acquisition module 230 can acquire the environmental information from the third party (such as weather forecast software, earthquake detection software, etc.).

[0082] Exemplarily, the road state detection module 240 and the control module 250 can respectively include one or more processors in the computing platform 150 shown in FIG. 1; the prompt module 260 can include the prompt device 130 shown in FIG. 1; and the actuator can include the braking control system in the intelligent driving device 100.

[0083] It should be understood that the above modules are only an example, and in actual applications, the above modules can be added or deleted according to actual needs. For example, in the system architecture shown in FIG. 2, the other-vehicle information acquisition module 210, the road information acquisition module 220, and the natural information acquisition module 230 can be combined into one module. For another example, in the system architecture shown in FIG. 2, the road state detection module 240 and the control module 250 can be combined into one module.

[0084] The above introduces the automatic driving system architecture provided by the embodiments of the present application, and the following introduces in detail the process of the intelligent driving method provided by the embodiments of the present application based on the automatic driving system shown in FIG. 2.

[0085] The intelligent driving device related by the embodiments of the present application can include a road vehicle, a water vehicle, an air vehicle, an industrial device, an agricultural device, or an entertainment device, etc. For example, the intelligent driving device can be a vehicle, which is a vehicle in a broad sense, and can be a vehicle (such as a commercial vehicle, a passenger vehicle, a motorcycle, a flying vehicle, a train, etc.), an industrial vehicle (such as a forklift, a trailer, a tractor, etc.), an engineering vehicle (such as an excavator, a bulldozer, a crane, etc.), an agricultural device (such as a mower, a harvester, etc.), an amusement device, a toy vehicle, etc., and the embodiments of the present application do not specifically limit the type of the vehicle. For ease of understanding, the following takes the vehicle as an example for description.

[0086] FIG. 3 shows a schematic flowchart of the intelligent driving method provided by the embodiments of the present application. The method can be applied to the intelligent driving device shown in FIG. 1, or the method can be executed by the system shown in FIG. 2. More specifically, the method can be executed by the control module 250, and the method can include:

[0087] S301, acquiring motion state information of at least one vehicle in direction 1 and road information 1 of direction 1.

[0088] Exemplarily, the direction 1 can be the driving direction of the ego vehicle, for example, the ego vehicle drives in the vehicle head direction, and then the direction 1 is the front of the vehicle; the ego vehicle drives in the vehicle tail direction, and then the direction 1 is the rear of the vehicle. The at least one vehicle can be understood as the other vehicle relative to the ego vehicle. The following takes the front of the vehicle as an example for description.

[0089] In some implementations, the motion state information can indicate the position information of the vehicle (the other vehicle), or the motion state information can also indicate other motion state information of the vehicle, such as speed, acceleration, etc. Among them, the position information can indicate the accurate position (such as coordinates) of the other vehicle, or the position information can also indicate the position (such as the direction relative to the ego vehicle) of the other vehicle relative to the ego vehicle.

[0090] Exemplarily, the motion state information can be determined according to information collected by a perception system of the ego vehicle. For example, the ego vehicle can collect an image in direction 1 by using a camera, and the image includes pixels corresponding to the at least one vehicle. Then, the position information of each vehicle in the at least one vehicle can be determined according to the pixels corresponding to each vehicle. Alternatively, in a poor visibility scenario at night, the image includes pixels corresponding to the tail lights of each vehicle in the at least one vehicle. Then, the position information of each vehicle in the at least one vehicle can be determined according to the pixels corresponding to the tail lights of each vehicle. For another example, the ego vehicle can collect a laser point cloud in direction 1 by using a laser radar, and the position information of each vehicle in the at least one vehicle can be determined according to the laser point cloud.

[0091] In some implementations, the road information 1 can indicate the curvature and slope information of a target driving road of the ego vehicle in direction 1. The slope information can indicate the slope of the road and / or the slope change rate, which can indicate the change of the slope with space, for example, the slope increase or decrease per meter relative to the last meter in the road. The road information 1 can be determined according to information collected by a perception system of the ego vehicle, such as an image in direction 1 collected by a camera of the ego vehicle, which includes pixels of the target driving road. Then, the curvature of the target driving road can be determined according to the pixels of the target driving road. Alternatively, the road information 1 can be obtained from a third party, such as a map software, an RSU, a cloud server, and the like.

[0092] In S302, it is determined whether the following condition is met: the road curvature is less than or equal to a curvature threshold, or the slope change rate is less than or equal to a change rate threshold.

[0093] It should be noted that, in this application, the slope is a positive number representing an uphill, and the slope is a negative number representing a downhill. For example, the slope change amplitude with space is a positive number, indicating that this place is located at the connection between the downhill and the uphill, or this place is located at the connection between the flat road and the uphill, i.e., the road trend is downhill or flat road first and then uphill. The slope change amplitude with space is a negative number, indicating that this place is located at the connection between the uphill and the downhill, or this place is located at the connection between the uphill and the flat road, i.e., the road trend is uphill first and then downhill or flat road. The slope change rate in this application can be understood as the absolute value of the slope change amplitude with space. Exemplarily, the curvature threshold can be 2 cm -1 , or can be 2.5 cm -1 , or can be other numerical values; and the change rate threshold can be 5% / m (i.e., the slope change amplitude per meter is 5%), or can be 10% / m, or can be other numerical values.

[0094] It can be understood that if the road curvature is greater than the curvature threshold value and / or the slope change rate is greater than the change rate threshold value, even if the vehicle in front of the ego vehicle is normally driving in the road, the ego vehicle can not be able to detect the front vehicle. For example, as shown in (a) of FIG. 4, the road curvature is too large, which can cause the ego vehicle to be unable to obtain the position information of the front vehicle. In some scenarios, when the road curvature is less than or equal to the curvature threshold value, but there is an obstruction on the inner side of the curved part of the road, the ego vehicle can also be unable to detect the position information of the other vehicle. For example, as shown in (b) of FIG. 4, there is a mountain on the inner side of the curved part of the road, which limits the sensing range of the ego vehicle sensing system, so that the ego vehicle can not be able to obtain the position information of the vehicle in front. If the road curvature is less than or equal to the curvature threshold value, and there is no obstruction on the inner side of the curved part of the road, the ego vehicle can still obtain the position information of the other vehicle. When the slope change rate of the road is greater than or equal to the change rate threshold value, the ego vehicle can not be able to detect the position information of the other vehicle. For example, as shown in (c) of FIG. 4, the road in front of the ego vehicle includes a connection between an uphill and a downhill, when the ego vehicle drives to the uphill and the other vehicle drives to the downhill, the slope limits the sensing range of the ego vehicle sensing system, so that the ego vehicle can not be able to obtain the position information of the other vehicle. For another example, as shown in (d) of FIG. 4, the road in front of the ego vehicle includes an uphill, when the ego vehicle drives to the flat road and the other vehicle drives to the uphill, the slope limits the sensing range of the ego vehicle sensing system, so that the ego vehicle can not be able to obtain the position information of the other vehicle.

[0095] Therefore, if there is no obstruction on the inner side of the curved part of the road, when the slope change rate is less than or equal to the change rate threshold value, S303 is performed; otherwise, S301 is performed. If there is an obstruction on the inner side of the curved part of the road, when the road curvature is less than or equal to the curvature threshold value and the slope change rate is less than or equal to the change rate threshold value, S303 is performed; otherwise, S301 is performed.

[0096] S303, determining whether there is a vehicle greater than or equal to the number threshold value disappearing in the time period 1 within the range of the direction 1 and the distance 1 from the ego vehicle.

[0097] Exemplarily, the distance 1 can be 1 kilometer, or 1.5 kilometers, or can also be other values, for example, the distance 1 can be determined according to the speed of the ego vehicle, and the distance 1 can increase with the increase of the speed of the ego vehicle. The time period 1 can be 5 seconds, or 10 seconds, or other values. The starting time of the time period 1 can be any time after the at least one vehicle is detected. The quantity threshold can be 3, or can also be 5, or can also be other values, for example, the quantity threshold can be determined according to the total number of the at least one vehicle, the quantity threshold is the upward / downward rounding of 30% of the total number of the at least one vehicle, or the upward / downward rounding of 50% of the total number of the at least one vehicle, or can also be determined according to other proportions of the total number of the at least one vehicle. For example, taking the downward rounding of 30% of the total number of the at least one vehicle as the quantity threshold as an example, when the total number of the at least one vehicle is 9 (that is, the at least one vehicle includes 9 vehicles), the quantity threshold is 3.

[0098] Wherein, the disappearance of a vehicle (or the loss of the position information of the vehicle) can be understood as that the ego vehicle cannot obtain the position information of the vehicle. For example, as shown in (e) of FIG. 4, if the road in front of the ego vehicle collapses (such as position a), and the ego vehicle and the other vehicle are located at positions ① and ③ at time 1 respectively, and are located at positions ② and ④ at time 2 respectively, that is, the other vehicle in front of the ego vehicle falls into the collapsed position, the ego vehicle will not be able to obtain the position information of the other vehicle that falls into the collapsed position. It should be noted that the time 2 is later than the time 1.

[0099] More specifically, if there are more than or equal to the quantity threshold vehicles disappearing in the time period 1 within the range of the distance 1 from the direction 1 to the ego vehicle, S304 is performed; otherwise, S301 is performed.

[0100] Optionally, S303 can also be: determining whether the rate of change of the visible part of the vehicle is less than or equal to a threshold 1, in the range of direction 1 and distance 1 from the ego vehicle, for the vehicle greater than or equal to a quantity threshold. Illustratively, the visible part can be the visible part of the rear of the front vehicle. For example, when the ego vehicle and the front vehicle are at the same level, the visible part of the rear of the front vehicle is 100%. When the level of the ego vehicle is higher than the level of the front vehicle, the visible part of the rear of the front vehicle can be less than 100%. As shown in (f) of FIG. 4, the front of the front vehicle falls into a collapsed position, and the ego vehicle can still detect the position information of the front vehicle, but the visible part of the front vehicle is less than 100%. More specifically, when the visible part of the front vehicle is less than 100%, the entire rear part of the front vehicle can be recovered according to the existing visible part, and the specific value (percentage) of the visible part of the front vehicle can be determined according to the proportion of the existing visible part in the entire rear part. Illustratively, the threshold 1 can be -5% / frame (i.e. the visible part of the current frame image is reduced by 5% compared with the previous frame image), or can also be -10% / frame, or can also be other values. It can be understood that the smaller the rate of change of the visible part, the greater the absolute value of the rate of change, i.e. the greater the change of the visible part of the vehicle.

[0101] In some implementations, the disappearance of the vehicle can be understood as: the rate of change of the visible part of the vehicle is greater than or equal to -100% / n frames, where n is a positive integer, and the value of n can be determined according to the height of the ego vehicle and the frame rate of the camera device. For example, when the height of the ego vehicle is 1.68 meters and the frame rate of the camera device is 25 frames per second, n frames can be , where represents rounding up, g is the acceleration of gravity, and g is taken as 10 m / s 2 . More specifically, in a good light environment (such as daytime), when the camera device of the ego vehicle can collect the entire image of the rear of the front vehicle, the visible part can be determined according to the image pixels of the rear of the front vehicle; in a poor light environment (such as night), when the camera device of the ego vehicle can collect the image of the tail light of the front vehicle, the visible part can be determined according to the image pixels of the tail light of the front vehicle. When the pixels including the tail light of the front vehicle in the image collected by the ego vehicle become pixels not including the tail light of the front vehicle, it can be determined that the rate of change of the visible part of the vehicle is greater than or equal to -100% / n frames.

[0102] Further, when the rate of change of the visible part of the vehicle is greater than or equal to the threshold 1, for the vehicle greater than or equal to the quantity threshold, in the range of direction 1 and distance 1 from the ego vehicle, S304 is performed; otherwise, S301 is performed.

[0103] Optionally, S303 can further determine whether the rate of change of height of the vehicles greater than or equal to the quantity threshold is greater than or equal to a threshold 2 within the range of direction 1 and distance 1 from the ego vehicle. The rate of change of height can be understood as the absolute value of the change of the highest point of the vehicle over time. For example, the threshold 2 can be 0.5 m / s (the height changes more than 0.5 meters in one second), or 0.6 m / s, or other values. It can be understood that when the negative height of the negative obstacle is small, the height of the vehicle can change greatly when part of the wheels of the vehicle enter and exit the negative obstacle. Therefore, when the rate of change of height of the front vehicle is greater than or equal to the threshold 2, it is determined that there is a negative obstacle in front of the road of the ego vehicle. Further, when the rate of change of height of the vehicles greater than or equal to the quantity threshold is greater than or equal to the threshold 2 within the range of direction 1 and distance 1 from the ego vehicle, S304 is performed; otherwise, S301 is performed.

[0104] S304, prompting relevant information 1.

[0105] For example, the relevant information 1 can include at least one of the following: the road in direction 1 has an anomaly, a specific category of the anomaly of the road in direction 1, and a driving suggestion for the anomaly of the road in direction 1.

[0106] For example, the specific category can include road collapse, road fracture, etc.

[0107] S305, determining whether the distance between the ego vehicle and the anomaly occurrence position is less than or equal to a distance threshold 1.

[0108] For example, the distance threshold 1 can be 100 meters, or 150 meters, or other values, for example, the distance threshold 1 can be determined according to the speed of the ego vehicle, and the distance threshold 1 decreases as the speed of the ego vehicle increases. The anomaly occurrence position can be the vehicle disappearance position, or the position where the rate of change of the visible part of the vehicle is less than or equal to a threshold 1.

[0109] When the distance between the ego vehicle and the anomaly occurrence position is less than or equal to the distance threshold 1, S306 is performed; otherwise, S307 is performed.

[0110] S306, determining whether a disaster affecting road safety occurs within a time length 2.

[0111] Exemplarily, the time length 2 can be 24 hours, or 36 hours, or can also be other time lengths, the starting moment of the time length 2 is earlier than the current moment, and the ending moment of the time length 2 is the current moment or later than the current moment. The disaster affecting the road surface safety can include an earthquake higher than or equal to a grade threshold, where the grade threshold can be 6.5, or can also be 7, or can also be other grades. Alternatively, the disaster affecting the road surface safety can also include weather that can cause damage to the road surface, where the weather that can cause damage to the road surface can include rainfall exceeding a preset threshold. The rainfall can be the total rainfall in the last 12 hours, or can also be the total rainfall in the last 24 hours. The preset threshold can be 100 mm, or can also be 75 mm, or can also be other numerical values.

[0112] Specifically, when the disaster affecting the road surface safety occurs in the time length 2, S308 is performed; otherwise, S309 is performed.

[0113] S307, confirming a low risk level, controlling the ego vehicle to keep the current speed and / or acceleration.

[0114] The low risk level can be understood as that the influence of the abnormality on the road on the driving safety of the ego vehicle is low, or the influence of the abnormality on the road on the driving safety of the ego vehicle is negligible. Controlling the ego vehicle to keep the current speed and / or acceleration can be understood as controlling the accelerator pedal and the brake pedal of the ego vehicle to keep the current state.

[0115] S308, confirming a high risk level, controlling the ego vehicle to decelerate at a deceleration 1.

[0116] S309, confirming a medium risk level, controlling the ego vehicle to decelerate at a deceleration 2.

[0117] Exemplarily, the high risk level can be understood as that the abnormality on the road has an influence on the driving safety of the ego vehicle, and the influence is large. The medium risk level can be understood as that the abnormality on the road has an influence on the driving safety of the ego vehicle, and the influence is lower than that of the high risk level.

[0118] The deceleration 1 and the deceleration 2 are both negative numbers, and the absolute value of the deceleration 1 is greater than the absolute value of the deceleration 2.

[0119] Exemplarily, the self-vehicle deceleration can be achieved by controlling at least one of the following of the self-vehicle: the opening degree of the accelerator pedal, the opening degree change rate of the accelerator pedal, the opening degree of the deceleration pedal, and the opening degree change rate of the deceleration pedal. More specifically, controlling the self-vehicle to decelerate at a deceleration of 2 can be achieved by controlling the accelerator pedal to decrease the opening degree at an opening degree change rate of 1; controlling the self-vehicle to decelerate at a deceleration of 2 can be achieved by controlling the accelerator pedal to decrease the opening degree at an opening degree change rate of 2 and controlling the deceleration pedal to increase the opening degree at an opening degree change rate of 3. The absolute value of the opening degree change rate 1 is less than or equal to the absolute value of the opening degree change rate 2.

[0120] Exemplarily, when controlling the self-vehicle to decelerate, the prompting device can be controlled to prompt the action being performed by the self-vehicle, such as the vehicle being controlled to decelerate; or before controlling the self-vehicle to decelerate, the prompting device can be controlled to prompt the action to be performed by the self-vehicle, such as the vehicle being controlled to decelerate.

[0121] In some implementations, the deceleration can also be determined according to the real-time speed of the self-vehicle and the distance between the self-vehicle and the abnormal position, and then the vehicle can be controlled to decelerate according to the deceleration.

[0122] The intelligent driving method provided by the embodiments of the present application can determine whether the driving direction of the self-vehicle has a negative obstacle such as road collapse or road rupture according to the state of the other vehicle in the driving direction of the self-vehicle, and can timely prompt the driver when it is determined that there is a negative obstacle in the driving direction of the self-vehicle that affects the driving safety, and directly control the self-vehicle to decelerate when the risk level is high, which helps to reduce the risk of the self-vehicle falling into the negative obstacle position and improve the driving safety.

[0123] In order to facilitate understanding of the specific implementation of the above-mentioned vehicle prompting related information, the following will illustrate the prompting manner of the vehicle by taking FIG. 5 as an example.

[0124] FIG. 5 shows a schematic diagram of a set of graphical user interfaces (GUIs) displayed on a vehicle display screen. As shown in FIG. 5, the vehicle instrument screen displays a display area 320, a display area 330, and a display area 340. In the display area 320, the current speed of the vehicle (e.g., 65 kilometers per hour (kph)) 321, gear information (e.g., the current vehicle is in D gear), the minimum speed limit of the road (e.g., 40 kph) 322, the maximum speed limit of the road (e.g., 80 kph) 323, the remaining battery level (e.g., 80%) and the range (e.g., 460 kilometers) can be displayed. The display area 330 can display a virtual scene generated by the data collected by the sensors, which includes the vehicle icon and the road environment information (e.g., information of other vehicles, information of lane lines, etc.) around the vehicle. Through the virtual scene, the user can confirm the relative position relationship between the vehicle and other vehicles. The display area 340 can display entertainment-related information, such as a music playing interface.

[0125] In some implementations, as shown in (a) of FIG. 5, when the vehicle determines that the change rate of the visible part of the vehicle is greater than or equal to the threshold value 1 for the number threshold value of vehicles within the range of distance 1 from the vehicle, or when the number threshold value of vehicles disappear within the time period 1, the vehicle can control the instrument screen to display a pop-up window 331 including the text “Road anomaly ahead, please slow down”. Alternatively, when the vehicle determines that the road anomaly ahead is a road collapse, the vehicle can also display “Please be aware that the road ahead has collapsed” or “Road collapse ahead, please slow down” in the pop-up window 331.

[0126] In some implementations, when it is determined that the vehicle needs to be controlled to slow down, the vehicle can also prompt the action being performed or about to be performed by the vehicle through a prompt device. As shown in (b) of FIG. 5, when the vehicle controls the vehicle to slow down (e.g., the vehicle speed has been reduced to 15 kph as shown by 324), the vehicle can also control the instrument screen to display a pop-up window 332 including the text “Road anomaly ahead, speed controlled”, or the text in the pop-up window 332 can also be “Road anomaly ahead, speed being controlled”. Alternatively, before the vehicle controls the vehicle to slow down, the vehicle can also control the pop-up window 332 to display the text “Road anomaly ahead, speed to be controlled”.

[0127] In some implementations, the related prompt can also be played through a sound device such as a loudspeaker, for example, the voice "abnormal road ahead, please drive carefully" is played through the loudspeaker at 350. Exemplarily, the prompt can also be combined with a light device, for example, when determining that the road is abnormal, the ambient light in the vehicle can be controlled to display warning lights such as red lights, when the risk level of the abnormal road is high, the ambient light can be controlled to flash at a certain frequency, and the flashing frequency can increase as the risk level increases; for another example, when determining that the road is abnormal, the instrument screen or the display area 330 around the display area 330 can be controlled to display warning lights such as red lights, for example, the effect shown in 333 in FIG. 5, when the risk level of the abnormal road is high, the lights around the instrument screen or the display area 330 can be controlled to flash at a certain frequency, and the flashing frequency can increase as the risk level increases.

[0128] It should be noted that the above-mentioned instrument screen, sound device, and light device can be regarded as some examples of the prompt device, and the content in the pop-up window 331 and the pop-up window 332 can be understood as some examples of the prompted information. Exemplarily, the "abnormal road ahead" displayed in the foregoing display screen or played by the sound device can be understood as an example of prompting that the road is abnormal; the foregoing prompt "the road ahead collapses" can be understood as an example of prompting the specific category of the abnormal road; the foregoing prompts "please slow down" or "please drive carefully" can be understood as an example of prompting the driving suggestion for the abnormal road in direction 1; the foregoing prompts "reducing speed is being controlled" and "speed reduction is about to be controlled" can be understood as an example of prompting the action being performed and about to be performed by the vehicle for the abnormal road in direction 1, respectively.

[0129] It should be further noted that the display area 330 shown in FIG. 5 is only described by taking a two-dimensional (2D) virtual scene as an example, and in actual implementation, a three-dimensional (3D) virtual scene can also be displayed in the display area 330, or other virtual scenes can also be displayed. In addition, each display area can also display other information, for example, the display area 320 can also display navigation information of the vehicle (for example, prompting the user to turn right at 2 kilometers ahead and the estimated driving time, etc.). In addition, in actual implementation, the instrument screen can also include more or fewer display areas.

[0130] FIG. 6 shows another exemplary flowchart of the intelligent driving method provided by the embodiments of the present application. The method can be applied to the intelligent driving device shown in FIG. 1, or the method can be executed by the system shown in FIG. 2. More specifically, the method can be executed by the control module 250, and the method can include:

[0131] S401, acquiring road information 1 of direction 1.

[0132] Exemplarily, the road information 1 comprises size information of the negative obstacle in the road, such as the negative height of the negative obstacle, or can also comprise the width and length of the negative obstacle. Wherein, the length of the negative obstacle refers to the size of the negative obstacle in the plane parallel to the vehicle and parallel to the driving direction of the vehicle (such as direction 1), which can be the maximum length or can also be the average length; the width of the negative obstacle refers to the size of the negative obstacle in the plane parallel to the vehicle and perpendicular to the driving direction of the vehicle, which can be the maximum width or the average width.

[0133] In some implementations, the information of the negative obstacle in the road of direction 1 can be collected by the perception system of the ego vehicle, or can also be received from other devices, such as from RSU, cloud server or other vehicles.

[0134] S402, determine whether the negative height at position 1 of the road is greater than or equal to the height threshold value.

[0135] In some implementations, the negative height at position 1 of the road can be understood as the negative height of the negative obstacle, such as the deep ditch, crack and the like caused by the road collapse.

[0136] In an example, when the negative height of the negative obstacle is greater than or equal to the height threshold value, S403 is executed; otherwise, S401 is executed.

[0137] In another example, when the negative height is greater than or equal to the height threshold value, the width of the negative obstacle is greater than or equal to the width threshold value, and the length of the negative obstacle is greater than or equal to the length threshold value, S403 is executed; otherwise, S401 is executed.

[0138] Exemplarily, the height threshold value can be 0.3 meters, or 0.5 meters, or can also be other numerical values; the width threshold value can be 1.5 meters, or 2 meters, or can also be other numerical values, for example, the width threshold value can be determined according to the road width, such as the width threshold value can be 50% of the one-way road width; the length threshold value can be 1 meter, or 1.5 meters, or can also be other numerical values, for example, the length threshold value can be determined according to the wheel diameter.

[0139] S403, determine whether the distance between the ego vehicle and position 1 is less than or equal to the distance threshold value 2.

[0140] In some implementations, the distance threshold value 2 can be determined according to the speed of the ego vehicle, and the distance threshold value 2 decreases with the increase of the speed of the ego vehicle.

[0141] Exemplarily, the distance threshold value 2 and the aforementioned distance threshold value 1 can be the same numerical value, or can also be different numerical values.

[0142] S404, prompt the relevant information 2 and / or control the ego vehicle to decelerate at the deceleration 3.

[0143] In some implementations, the deceleration 3 can be the same as the aforementioned deceleration 1, or can also be different. In some implementations, the vehicle can control the ego vehicle to decelerate at different decelerations according to different sizes of the negative obstacle. For example, in the case that the negative height of the negative obstacle is greater than or equal to the height threshold, the width of the negative obstacle is greater than or equal to the width threshold, and the length of the negative obstacle is greater than or equal to the length threshold, the greater the size of one or more dimensions of the negative obstacle, the greater the deceleration at which the vehicle is controlled to decelerate.

[0144] Exemplarily, the relevant information 2 can include any of the following: the front road exists a negative obstacle, a driving suggestion for the front negative obstacle, or an action to be performed or being performed by the vehicle for the front negative obstacle. The specific implementation of prompting the relevant information 2 can refer to the corresponding description of FIG. 5, which will not be repeated here.

[0145] The intelligent driving method provided by the embodiments of the present application can determine whether there is a negative obstacle on the road in the driving direction of the ego vehicle according to the information perceived by the ego vehicle perception system. When it is determined that there is a negative obstacle in the driving direction of the ego vehicle that affects driving safety, the driver can be prompted in time, and when the risk level is high, the ego vehicle can be directly controlled to decelerate, which helps to reduce the risk of the ego vehicle falling into the position of the negative obstacle and improve driving safety.

[0146] FIG. 7 shows another exemplary flowchart of an intelligent driving method provided by the embodiments of the present application. The method can be applied to the intelligent driving device shown in FIG. 1, or the method can be executed by the system shown in FIG. 2. More specifically, the method can be executed by the regulation and control module 250, and the method can include:

[0147] S501, obtain the road information 2 of the direction 1, the road information 2 indicating that the road is damaged at the position 2 and the damage degree.

[0148] In some implementations, the road information 2 can include signals of the distributed optical fiber arranged in the road, i.e., the signals of the distributed optical fiber are used to determine whether damage occurs in the road and the damage degree. As shown in FIG. 8, the distributed optical fiber can be arranged under the road surface of the road, and parts such as the body of the mountain and the tunnel, to monitor whether the state of the road, the mountain, the tunnel, etc. is abnormal. For example, when the road is normally open to traffic, the signals of the optical fiber arranged under the road surface can change with the load of the road, and when the road collapses, the signals of the optical fiber can present different characteristics from when the road normally carries vehicles. Or, when the mountain is loose and there is a risk of landslide or a landslide has occurred, the signals of the optical fiber arranged in the mountain can present different characteristics from when the mountain is not loose. Or, when the body of the tunnel deforms, the deformation of the body will cause the stress borne by the optical fiber to change, thereby causing the signals of the optical fiber to present different characteristics from when the body is not deformed, so that the signals of the optical fiber can be used to determine whether damage occurs in the road and the damage degree. In some scenarios, the collapse of the road surface, the landslide of the mountain, the deformation of the tunnel, etc. can also cause the optical fiber to break, so that the signals in the optical fiber cannot be obtained. In this case, it can also be determined that the road is damaged, or that the damage degree of the road is high.

[0149] In yet other implementations, the road information 2 can also be information collected by the RSU, such as images of the road surface of direction 1 collected by the RSU and directly sent to the ego vehicle, or sent to the ego vehicle through a cloud server. The ego vehicle can determine whether the road surface collapses and the area and depth of the collapse according to the images of the road surface, and further determine the damage degree according to the area and depth of the collapse. Exemplarily, the relationship between the area and depth of the collapse of the road surface and the damage degree can be as shown in Table 1.

[0150] Table 1

[0151] Wherein, the area of the collapse of the road surface refers to the area of the collapsed part in the direction parallel to the road surface, and the depth of the collapse refers to the maximum negative height of the collapsed part perpendicular to the road surface. It should be understood that the values shown in Table 1 are only exemplary, and in actual implementation, the damage degree can be determined by other dimensions or data.

[0152] S502, determining whether the damage degree is greater than or equal to a damage threshold.

[0153] Exemplarily, the damage threshold can be level 1, or level 2, or other values.

[0154] S503, the distance between the ego vehicle and the position 2 is less than or equal to a distance threshold 3.

[0155] In some implementations, the distance threshold 3 can be determined according to the speed of the ego vehicle, and the distance threshold 3 decreases as the speed of the ego vehicle increases.

[0156] Exemplarily, the distance threshold 3 can be the same as the aforementioned distance threshold 1 or distance threshold 2, or can be different from the aforementioned distance threshold 1 or distance threshold 2.

[0157] S504, prompting the related information 3 and / or controlling the ego vehicle to decelerate at the deceleration 4.

[0158] In some implementations, the deceleration 4 can be the same as the aforementioned deceleration 1, or can be different from the aforementioned deceleration 1. In some implementations, the vehicle can control the ego vehicle to decelerate at different decelerations according to different damage degrees of the road. For example, the greater the damage degree of the road is, the greater the deceleration at which the vehicle is controlled to decelerate is.

[0159] Exemplarily, the related information 3 can include any one of the following: damage of the road in front, damage degree of the road in front, driving suggestion for the damage of the road in front, or action to be performed or being performed by the vehicle for the damage of the road in front. The specific implementation of prompting the related information 3 can refer to the corresponding description of FIG. 5, which is not described here again.

[0160] The intelligent driving method provided by the embodiments of the present application can receive information about the damage degree of the road in the driving direction of the ego vehicle from a third party, and when it is determined that the damage degree of the road in the driving direction of the ego vehicle is high, the driver can be prompted in time, and when the risk level is high, the ego vehicle is directly controlled to decelerate, which helps to reduce the risk of the ego vehicle driving into the road damage position and improve the driving safety.

[0161] FIG. 9 shows another schematic flowchart of an intelligent driving method provided by an embodiment of the present application. The method 900 can be applied to the intelligent driving device shown in FIG. 1, or the method can be executed by the system shown in FIG. 2. More specifically, the method 900 can include S910 and S920.

[0162] S910, obtaining first road information, the first road information indicating that a first event affecting road safety occurs in a target driving area of the intelligent driving device, the first event including that a negative obstacle satisfying a first condition appears on a road surface of the target driving area, the first condition including that a negative height of the negative obstacle is greater than or equal to a height threshold, the height threshold being associated with a chassis height of the intelligent driving device, and a distance between the target driving area and a current position of the intelligent driving device being less than or equal to a first distance threshold.

[0163] Exemplarily, the intelligent driving device can include the ego vehicle in the foregoing embodiments, or the intelligent driving device can also be other devices; the target driving area can be understood as an area that the intelligent driving device must pass through to drive to a destination, and the target driving area can include the road in the foregoing direction 1, and the destination can be the final destination of the intelligent driving device, or can be a certain intermediate point on the way to the final destination of the intelligent driving device. The first distance threshold can be the distance threshold 1 in S305, or can also be other numerical values.

[0164] Exemplarily, the first event affecting road safety can be understood as that the intelligent driving device such as a vehicle driving on the road, the first event can affect the driving safety or the personal safety of the driver and passengers. The first event can be any of the following: the road surface is cracked, the road surface is collapsed, and the road surface is dented. The negative obstacle can be any of the following: a road surface cracking area, a road surface collapse area, and a dented area.

[0165] In some implementations, the height threshold is associated with the chassis height of the intelligent driving device, which can be understood as that the height threshold is a value that affects the safety of the chassis of the intelligent driving device, for example, the height threshold is the height that causes the chassis of the intelligent driving device to be scratched; the height threshold is determined according to the chassis height of the intelligent driving device, for example, the height threshold is determined according to the chassis height of the intelligent driving device and the front and rear axle track. Exemplarily, the chassis height is h, and the front and rear wheel axle distance is L, and then the height threshold H can satisfy the following formula:

[0166] It can be understood that when the negative height of the negative obstacle is greater than or equal to the height threshold, the chassis of the intelligent driving device will be scratched; or because the length and width of the negative obstacle are too large and the speed of the intelligent driving device is too fast, the chassis of the intelligent driving device may fall into the negative obstacle before the chassis of the intelligent driving device is scratched. That is, the "height threshold is associated with the chassis height of the intelligent driving device" does not mean that the chassis of the intelligent driving device will be scratched, but it will bring the risk of the chassis of the intelligent driving device being scratched. When the intelligent driving device is a small car, the value of the height threshold can refer to the description in S402; when the intelligent driving device is a larger vehicle, the height threshold can take a larger value.

[0167] In some implementations, the first condition can further include that the width of the negative obstacle is greater than or equal to a width threshold, and the length of the negative obstacle is greater than or equal to a length threshold. The specific values of the width threshold and the length threshold can refer to the description in S402, which will not be described here.

[0168] In actual implementation, the first road information can be information that a road surface collapses or a road surface breaks, so that the intelligent driving device determines that there is a negative obstacle in front of the road according to the information; or the first road information can also indicate that there is a negative obstacle in front of the road that meets the first condition.

[0169] In some implementations, the first road information is collected by a perception system of the intelligent driving device or is determined according to information collected by the intelligent driving device.

[0170] In an example, the first road information is determined according to front vehicle information of a target driving area collected by the intelligent driving device. Specifically, S910 can be refined as: obtaining vehicle motion state information, the vehicle motion state information indicating position information of at least one vehicle in the first road perceived by the intelligent driving device, a distance between the at least one vehicle and a current position of the intelligent driving device being less than or equal to a second distance threshold; and obtaining the first road information, including: determining the first road information when a change rate of a visible part of a vehicle greater than or equal to a first quantity threshold in the at least one vehicle is less than or equal to a first change rate threshold; determining the first road information when a height change rate of a vehicle greater than or equal to a second quantity threshold in the at least one vehicle is greater than or equal to a second change rate threshold; or determining the first road information when a vehicle greater than or equal to a third quantity threshold in the at least one vehicle disappears within a first time length.

[0171] Exemplarily, the first quantity threshold, the second quantity threshold and the third quantity threshold can be the same or different, and specific values thereof can refer to the description in S303, which will not be repeated here. Specifically, the first change rate threshold is a negative number, for example, the first change rate threshold can be threshold 1 in S303; the height change rate is the absolute value of the height change amplitude of the vehicle, and the second change rate threshold is a positive number, which can be threshold 2 in S303. The first time length can be time period 1 in S303. For the above different scenarios, the first condition can be the same or different. For example, when the change rate of the visible part of the vehicle greater than or equal to the first quantity threshold in the at least one vehicle is less than or equal to the first change rate threshold, the first condition can be that the negative height of the negative obstacle is greater than or equal to 0.5 meters, the length of the negative obstacle is greater than or equal to 4 meters, and the width of the negative obstacle is greater than or equal to 2.2 meters; for another example, when the height change rate of the vehicle greater than or equal to the second quantity threshold in the at least one vehicle is greater than or equal to the second change rate threshold, the first condition can be that the negative height of the negative obstacle is greater than or equal to 0.3 meters, the length of the negative obstacle is greater than or equal to 1 meter, and the width of the negative obstacle is greater than or equal to 1 meter; for another example, when the vehicle greater than or equal to the third quantity threshold in the at least one vehicle disappears within the first time length, the first condition can be that the negative height of the negative obstacle is greater than or equal to 1.5 meters, the length of the negative obstacle is greater than or equal to 6 meters, and the width of the negative obstacle is greater than or equal to 2.5 meters.

[0172] It should be noted that the values corresponding to the first condition described above are only exemplary, and in actual implementation, the first condition can also correspond to other values, for example, for different vehicle models, the values in the first condition can be adjusted.

[0173] In yet another example, the first road information is determined according to road surface information of a target driving area collected by the intelligent driving device. Specifically, S910 can be refined as: obtaining perception information of the target driving area collected by a sensor of the intelligent driving device; and determining at least the negative height of the negative obstacle according to the perception information. Optionally, the length and / or width of the negative obstacle can also be determined according to the perception information.

[0174] In some scenarios, the method further includes: obtaining second road information, the second road information indicating a slope and a curvature of a first road of the target driving area, the first road being a road on which the first event occurs or on which the negative obstacle exists. The aforementioned determining the first road information includes: determining the first road information when a slope change rate of the first road is less than or equal to a third change rate threshold and / or a curvature of the first road is less than or equal to a curvature threshold. That is, when the slope change rate of the first road is less than or equal to the third change rate threshold and / or the curvature of the first road is less than or equal to the curvature threshold, and when any of the following conditions is met, the first road information is determined: a change rate of a visible part of a vehicle greater than or equal to a first quantity threshold in the at least one vehicle is less than or equal to a first change rate threshold, or a height change rate of a vehicle greater than or equal to a second quantity threshold in the at least one vehicle is greater than or equal to a second change rate threshold, or a vehicle greater than or equal to a third quantity threshold in the at least one vehicle disappears within a first time length. Exemplarily, the second road information can be determined according to information collected by a perception system of the intelligent driving device, or can be sent to the intelligent driving device by a third party such as an RSU or a cloud server. The third change rate threshold can be the change rate threshold in S302, and the curvature threshold can be the curvature threshold in S302.

[0175] In yet some scenarios, the method further includes: obtaining environment information, the environment information indicating at least one of the following: whether there is weather affecting road safety in the target driving area within a second time length, or whether there is an earthquake higher than or equal to a level threshold within the second time length, wherein a starting time of the second time length is earlier than the current time, and an ending time of the second time length is the current time or later than the current time. The first road information further includes a risk level of the first event or the negative obstacle, and determining the first road information includes: when there is weather affecting road safety in the target driving area within the second time length, or there is an earthquake higher than or equal to the level threshold within the second time length; or when the environment information indicates that there is no weather affecting road safety in the target driving area within the second time length, and there is no earthquake higher than or equal to the level threshold within the second time length, determining that the risk level is a second risk level, and a first risk level has a higher impact on road safety than the second risk level. Exemplarily, the environment information can be determined according to information collected by a perception system of the intelligent driving device, or can be obtained by a third party such as weather software. The second time length can include the time length 2 in S306. The weather affecting road safety can include rainy days with rainfall greater than or equal to a preset threshold, and the level threshold can be the level threshold in S306.

[0176] In yet some implementations, the first road information is obtained from other devices (such as an RSU, a cloud server, etc.).

[0177] In an example, S910 can be refined as: receiving light signal information, the light signal information indicating a quality of a signal transmitted by the first optical fiber, the first optical fiber being arranged under a road surface of the target driving area, or the first optical fiber being arranged on a first object of the target driving area, the first object affecting the road surface safety of the target driving area. The first object includes a mountain and / or a tunnel. Further, the method further includes: determining a damage degree of the road of the target driving area according to the light signal information, the damage degree being associated with a passable part of the road. The higher the damage degree, the smaller the passable part.

[0178] In another example, S910 can be refined as: receiving first road information from the RSU or the cloud server, the first road information indicating the damage degree of the target driving area.

[0179] In the implementation, the light signal information or the first road information can be the road information 2 in S501. The specific implementation of determining the damage degree according to the light signal information or the specific implementation of indicating the damage degree by the first road information can refer to the description in S501, which will not be repeated here.

[0180] S920, according to the first road information, performing at least one of the following: controlling the intelligent driving device to slow down, or controlling a prompt device of the intelligent driving device to prompt at least one of the following: the first event, a driving suggestion for the first event, or an action being performed or about to be performed by the intelligent driving device for the first event.

[0181] For example, the driving suggestion for the first event can include but is not limited to: suggesting cautious driving, suggesting slowing down, or suggesting changing a route. The action being performed or about to be performed by the intelligent driving device for the first event can include braking or slowing down.

[0182] In some implementations, according to the first road information, controlling the intelligent driving device to slow down includes: when the risk level is a first risk level, controlling the intelligent driving device to slow down at a first deceleration; or when the risk level is a second risk level, controlling the intelligent driving device to slow down at a second deceleration; wherein the absolute value of the first deceleration is greater than the absolute value of the second deceleration. For example, the first risk level can be the high risk level, and the second risk level can be the medium risk level. It can be understood that the first deceleration and the second deceleration are both negative numbers.

[0183] In some implementations, the first road information further indicates a damage degree of the road of the target driving area, the damage degree being associated with a passable part of the road; and the processing unit is configured to: control the intelligent driving device to decelerate at a third deceleration when the damage degree is a first degree; or control the intelligent driving device to decelerate at a fourth deceleration when the damage degree is a second degree; wherein the passable part corresponding to the first degree is smaller than the passable part corresponding to the second degree, and the absolute value of the third deceleration is greater than the absolute value of the fourth deceleration. For example, the first degree can be the third damage degree in the foregoing embodiments, and the second degree can be the first or second damage degree in the foregoing embodiments; or the first degree can be the fourth damage degree in the foregoing embodiments, and the second degree can be the first, second or third damage degree in the foregoing embodiments.

[0184] For the implementation of controlling the intelligent driving device to decelerate, refer to the description in the foregoing embodiments, which will not be repeated here.

[0185] The intelligent driving method provided by the embodiments of the present application can obtain the information of the road collapse in the driving direction in time during the driving of the vehicle, so as to control the vehicle to decelerate and / or control the prompt device to prompt the information related to the road collapse, so as to reduce the probability of the vehicle falling into the collapsed position, thereby improving the driving safety.

[0186] In the embodiments of the present application, the terms and / or descriptions of different embodiments are consistent and can be referred to each other if there is no special description and logical conflict, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0187] The intelligent driving method provided by the embodiments of the present application is described in detail above in combination with FIGS. 1 to 9. The device provided by the embodiments of the present application will be described in detail below in combination with FIGS. 10 and 11. It should be understood that the description of the device embodiments corresponds to the description of the method embodiments, and therefore, the content not described in detail can be referred to the method embodiments described above, which will not be repeated here for brevity.

[0188] FIG. 10 shows a schematic block diagram of the intelligent driving device 2000 provided by the embodiments of the present application, which can include units for performing the methods shown in FIGS. 3, 6, 7 and 9. Each unit in the device 2000 is configured to implement the corresponding flow in the above method embodiments. The device 2000 includes an acquisition unit 2010, which can be configured to implement the corresponding data acquisition or transceiving function. The device 2000 further includes a processing unit 2020, which can be configured to implement the corresponding processing function.

[0189] Optionally, the apparatus 2000 further includes a storage unit, which can be used to store instructions and / or data. The processing unit 2020 can read the instructions and / or data in the storage unit to enable the apparatus to implement the relevant actions in the foregoing various method embodiments.

[0190] It should be understood that the specific processes by which the units perform the corresponding steps described above have been described in detail in the foregoing method embodiments, and thus will not be described here again for brevity.

[0191] It should also be understood that the apparatus 2000 herein is embodied in the form of functional units. The term “module” or “unit” herein can refer to an application-specific ASIC, an electronic circuit, a processor (for example, a shared processor, a dedicated processor, or a group of processors, etc.) and a memory for executing one or more software or firmware programs, a combinational logic circuit, and / or other suitable components that support the described functions.

[0192] The apparatus of each of the above solutions has the function of implementing the corresponding steps performed by the computing platform 150 in the above method. The function can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions; for example, the acquisition unit 2010 can be replaced by a transceiver, and other units, such as the processing unit, etc., can be replaced by a processor, for performing the relevant processing operations in each method embodiment.

[0193] Illustratively, the acquisition unit 2010 and the processing unit 2020 can be arranged in the intelligent driving device 100 shown in FIG. 1, or can also be arranged in the system shown in FIG. 2. More specifically, the acquisition unit 2010 and the processing unit 2020 described above can be arranged in the regulation and control module 250. Illustratively, the operations performed by the acquisition unit 2010 and the processing unit 2020 described above can be performed by one processor, or can also be performed by different processors. In a specific implementation process, the one or more processors described above can be the processor arranged in the intelligent driving device 100 shown in FIG. 1; or the apparatus 2000 described above can be a chip arranged in the intelligent driving device 100.

[0194] In a specific implementation process, the units in the above apparatus can be integrated together or can also be independently implemented. In one implementation, the units are integrated together to be implemented in the form of a system on a chip (SoC).

[0195] Fig. 11 is another schematic block diagram of the intelligent driving apparatus provided in the embodiments of the present application. The intelligent driving apparatus 2100 shown in Fig. 11 can include a processor 2110, a transceiver 2120, and a memory 2130. The processor 2110, the transceiver 2120, and the memory 2130 are connected through internal connection paths. The memory 2130 is configured to store instructions, and the processor 2110 is configured to execute the instructions stored in the memory 2130 to implement the methods in the above embodiments. Alternatively, the memory 2130 can be coupled to the processor 2110 through an interface or integrated with the processor 2110.

[0196] It should be noted that the transceiver 2120 can include, but is not limited to, a transceiving device such as an input / output interface to enable communication between the apparatus 2100 and other devices or communication networks.

[0197] The memory 2130 can be a volatile memory and / or a non-volatile memory. The non-volatile memory can be a read-only memory (ROM), a programmable ROM (PROM), an erasable PROM (EPROM), an electrically EPROM (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM). For example, the RAM can be used as an external cache. By way of example and not limitation, the RAM includes the following various forms: a static RAM (SRAM), a dynamic RAM (DRAM), a synchronous DRAM (SDRAM), a double data rate SDRAM (DDR SDRAM), an enhanced SDRAM (ESDRAM), a synchlink DRAM (SLDRAM), and a direct rambus RAM (DR RAM).

[0198] The transceiver 2120 uses a transceiving device such as, but not limited to, a transceiver to enable communication between the apparatus 2100 and other devices or communication networks to receive / send data / information for implementing the methods in the above embodiments.

[0199] The embodiment of the present application further provides an intelligent driving device, which comprises the intelligent driving apparatus 2000 or the intelligent driving apparatus 2100 in the above embodiment.

[0200] The embodiment of the present application further provides a computer program product, which comprises computer program codes, and when the computer program codes are run on a computer, the computer program codes make the computer implement the method in the above embodiment of the present application.

[0201] The embodiment of the present application further provides a computer readable storage medium, which stores computer instructions, and when the computer instructions are run on a computer, the computer instructions make the computer implement the method in the above embodiment of the present application.

[0202] The embodiment of the present application further provides a chip, which comprises a circuit, and is used for executing the method in the above embodiment of the present application.

[0203] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, the apparatus and the unit described above can refer to the corresponding process in the foregoing method embodiments, and will not be described here.

[0204] In the description of the embodiments of the present application, unless otherwise specified, " / " represents the meaning of or, for example, A / B can represent A or B; "and / or" in the present application is a description of the association relationship of the associated objects, which means that there can be three kinds of relationships, for example, A and / or B can represent: A exists alone, A and B exist together, and B exists alone. In the present application, "at least one" means one or more, and "multiple" means two or more. "At least one of the following" or similar expressions means any combination of these items, including any combination of single item or multiple items. For example, at least one of a, b, or c can represent: a, b, c, a-b, a-c, b-c, or a-b-c, where a, b, and c can be single or multiple.

[0205] In the embodiments of the present application, the prefix words such as "first", "second" are only used to distinguish different description objects, and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of ordinal words such as ordinal words in the embodiments of the present application does not constitute a limitation on the described objects, and the description of the described objects should refer to the description in the context of claims or embodiments, and should not constitute redundant limitations because of the use of such prefix words.

[0206] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other manners. For example, the division of the above-described device embodiments is only a logical function division, and there can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections between different units, or the among different units, can be indirect couplings or communication connections through some interfaces, devices or units, and can be in electrical, mechanical or other forms.

[0207] In each embodiment of the present application, the terms and / or descriptions between different embodiments are consistent and can be mutually referenced if there is no special description and logical conflict, and the technical features in different embodiments can be combined to form new embodiments according to their inherent logical relationship.

[0208] The units described as separated components can or can not be physically separated, and the components displayed as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0209] In addition, each functional unit in the embodiments of the present application can be integrated in one processing unit, or each unit can be a physically independent unit, or two or more units can be integrated in one unit.

[0210] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for intelligent driving, the method comprising: The method comprises: obtaining first road information, the first road information indicating that a target driving area of an intelligent driving device has a first event affecting road safety, the first event including a negative obstacle on a road surface of the target driving area meeting a first condition, the first condition including a negative height of the negative obstacle being greater than or equal to a height threshold value, the height threshold value being associated with a chassis height of the intelligent driving device, a distance between the target driving area and a current position of the intelligent driving device being less than or equal to a first distance threshold value; based on the first road information, performing at least one of the following: controlling the intelligent driving device to slow down, or controlling a prompt device of the intelligent driving device to prompt at least one of the following: the first event, a driving suggestion for the first event, or an action being performed or about to be performed by the intelligent driving device for the first event.

2. The method of claim 1, wherein, The method further comprises: obtaining vehicle motion state information, the vehicle motion state information indicating position information of at least one vehicle in the first road perceived by the intelligent driving device, a distance between the at least one vehicle and the current position of the intelligent driving device being less than or equal to a second distance threshold value; the obtaining of the first road information comprises at least one of the following: when a change rate of a visible part of a vehicle greater than or equal to a first quantity threshold value in the at least one vehicle is less than or equal to a first change rate threshold value, determining the first road information; when a height change rate of a vehicle greater than or equal to a second quantity threshold value in the at least one vehicle is greater than or equal to a second change rate threshold value, determining the first road information; or when a vehicle greater than or equal to a third quantity threshold value in the at least one vehicle disappears within a first time length, determining the first road information.

3. The method of claim 2, wherein, The method further comprises: obtaining second road information, the second road information indicating a slope and a curvature of a first road of the target driving area, the first road being a road where the first event occurs; the determining of the first road information comprises: when a slope change rate of the first road is less than or equal to a third change rate threshold value, and / or a curvature of the first road is less than or equal to a curvature threshold value, determining the first road information.

4. The method according to claim 2 or 3, characterized in that, The method further comprises: obtaining environment information, the environment information indicating at least one of the following: whether there is weather affecting road safety in the target driving area within a second time length, or whether there is an earthquake higher than or equal to a level threshold value within the second time length, wherein a starting time of the second time length is earlier than a current time, and an ending time of the second time length is the current time or later than the current time; the first road information further indicates a risk level of the first event, and the determining of the first road information comprises: when the environment information indicates at least one of the following: there is weather affecting road safety in the target driving area within the second time length, or there is an earthquake higher than or equal to the level threshold value within the second time length, determining the risk level as a first risk level; or In a case where the environmental information indicates that there is no weather affecting road safety in the target driving area in the second time length, and there is no earthquake higher than or equal to a grade threshold in the second time length, the risk grade is determined as a second risk grade, and the first risk grade has a higher impact on road safety than the second risk grade.

5. The method of claim 4, wherein, The control of the intelligent driving device to decelerate according to the first road information comprises: In a case where the risk grade is the first risk grade, the intelligent driving device is controlled to decelerate at a first deceleration; or In a case where the risk grade is the second risk grade, the intelligent driving device is controlled to decelerate at a second deceleration; The absolute value of the first deceleration is greater than the absolute value of the second deceleration.

6. The method of claim 1, wherein, The first road information is obtained by: obtaining perception information of the target driving area collected by a sensor of the intelligent driving device; determining a negative height of the negative obstacle according to the perception information.

7. The method according to any one of claims 1 to 6, characterized in that, The first road information further indicates a damage degree of a road of the target driving area, and the damage degree is associated with a passable part of the road; The control of the intelligent driving device to decelerate according to the first road information comprises: In a case where the damage degree is a first degree, the intelligent driving device is controlled to decelerate at a third deceleration; or In a case where the damage degree is a second degree, the intelligent driving device is controlled to decelerate at a fourth deceleration; The passable part corresponding to the first degree is smaller than the passable part corresponding to the second degree, and the absolute value of the third deceleration is greater than the absolute value of the fourth deceleration.

8. The method of claim 7, wherein, The first road information is obtained by: receiving optical signal information, the optical signal information indicating a quality of a signal transmitted by a first optical fiber, the first optical fiber being arranged below a road surface of the target driving area, or the first optical fiber being arranged on a first object in the target driving area, the first object affecting road safety of the target driving area; determining the damage degree according to the optical signal information.

9. The method of claim 8, wherein, The first object comprises at least one of a mountain and a tunnel.

10. An intelligent driving apparatus, characterized by comprising: The method comprises: obtaining a first road information, the first road information indicating a first event affecting road safety in a target driving area of an intelligent driving device, the first event comprising a negative obstacle on a road surface of the target driving area satisfying a first condition, the first condition comprising a negative height of the negative obstacle being greater than or equal to a height threshold, the height threshold being associated with a chassis height of the intelligent driving device, and a distance between the target driving area and a current position of the intelligent driving device being less than or equal to a first distance threshold; processing the first road information to perform at least one of the following: controlling the intelligent driving device to decelerate, or controlling a prompt device of the intelligent driving device to prompt at least one of the following: the first event, a driving suggestion for the first event, or an action being performed or about to be performed by the intelligent driving device for the first event.

11. The apparatus of claim 10, wherein, The obtaining unit is further configured to: acquire vehicle motion state information, the vehicle motion state information being indicative of position information of at least one vehicle in the first road perceived by the intelligent driving device, the at least one vehicle being at a distance less than or equal to a second distance threshold from a current position of the intelligent driving device; the processing unit is configured to determine the first road information when at least one of the following conditions is met: a change rate of a visible part of a vehicle in the at least one vehicle is less than or equal to a first change rate threshold, the vehicle being greater than or equal to a first quantity threshold; a height change rate of a vehicle in the at least one vehicle is greater than or equal to a second change rate threshold, the vehicle being greater than or equal to a second quantity threshold; or a vehicle in the at least one vehicle disappears within a first time length, the vehicle being greater than or equal to a third quantity threshold. the acquisition unit is further configured to:

12. The apparatus of claim 11, wherein, acquire second road information, the second road information being indicative of a slope and a curvature of a first road of the target driving area, the first road being a road where the first event occurs; the processing unit is configured to determine the first road information when at least one of the following conditions is met: a slope change rate of the first road is less than or equal to a third change rate threshold, and / or a curvature of the first road is less than or equal to a curvature threshold. the acquisition unit is further configured to:

13. The apparatus of claim 11 or 12, wherein, acquire environment information, the environment information being indicative of at least one of the following: whether there is weather affecting road safety in the target driving area within a second time length, or whether there is an earthquake higher than or equal to a level threshold within the second time length, wherein a start time of the second time length is earlier than a current time, and an end time of the second time length is the current time or later than the current time; the first road information is further indicative of a risk level of the first event, and the processing unit is configured to: determine the risk level as a first risk level when the environment information indicates at least one of the following: there is weather affecting road safety in the target driving area within the second time length, or there is an earthquake higher than or equal to the level threshold within the second time length; or determine the risk level as a second risk level when the environment information indicates that there is no weather affecting road safety in the target driving area within the second time length, and there is no earthquake higher than or equal to the level threshold within the second time length, the first risk level having a higher impact on road safety than the second risk level. the processing unit is configured to:

14. The apparatus of claim 13, wherein, control the intelligent driving device to decelerate at a first deceleration when the risk level is the first risk level; or control the intelligent driving device to decelerate at a second deceleration when the risk level is the second risk level; wherein an absolute value of the first deceleration is greater than an absolute value of the second deceleration. the acquisition unit is configured to:

15. The apparatus of claim 10, wherein, acquire perception information of the target driving area collected by a sensor of the intelligent driving device; and determine a negative height of the negative obstacle based on the perception information. the first road information is further indicative of a damage degree of a road of the target driving area, the damage degree being associated with a passable part of the road.

16. The apparatus of any one of claims 10 to 15, wherein, ​ The processing unit is configured to: control the intelligent driving device to decelerate at a third deceleration when the damage degree is a first degree; or control the intelligent driving device to decelerate at a fourth deceleration when the damage degree is a second degree; wherein the passable part corresponding to the first degree is less than the passable part corresponding to the second degree, and the absolute value of the third deceleration is greater than the absolute value of the fourth deceleration.

17. The apparatus of claim 16, wherein, The acquisition unit is configured to: receive optical signal information, the optical signal information indicating the quality of a signal transmitted by a first optical fiber, the first optical fiber being arranged below the road surface of the target driving area, or the first optical fiber being arranged on a first object in the target driving area, the first object affecting the safety of the road surface of the target driving area; The processing unit is configured to determine the damage degree according to the optical signal information.

18. The apparatus of claim 17, wherein, The first object includes at least one of the following: a mountain, a tunnel.

19. An intelligent driving apparatus, characterized by comprising: The apparatus comprises: a processor configured to execute a computer program stored in a memory to cause the apparatus to perform the method of any one of claims 1 to 9.

20. The apparatus of claim 19, wherein, The apparatus further comprises the memory. 21.An intelligent driving device, characterized in that, The apparatus comprises any one of claims 10 to 20.

22. A computer-readable storage medium, characterized in that, instructions stored thereon, which, when executed by a processor, implement the method of any one of claims 1 to 9.

23. A computer program product, characterised in that, The computer program product comprises computer program code which, when executed by a processor, implements the method of any one of claims 1 to 9.

24. A chip, characterized by The chip comprises a circuit configured to perform the method of any one of claims 1 to 9.

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