Vehicle intelligent control method and device based on driving scene and vehicle

Abstract

The invention provides a vehicle intelligent control method and device based on a driving scene and a vehicle. The method comprises the following steps: acquiring scene decision data of a vehicle; wherein the scene decision data indicates a vehicle state and / or an environment state; determining a current driving scene of the vehicle according to the scene decision data; and controlling at least one vehicle-mounted device of the vehicle to work in the target state according to the driving scene. The method is used for achieving the effect of improving the interaction efficiency between the user and the vehicle-mounted equipment, and then the vehicle driving safety of the user is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a vehicle intelligent control method, control device and vehicle based on driving scenarios. Background Technology

[0002] With the rapid development of the logistics industry, heavy-duty trucks have become the core carriers for transporting goods. When driving heavy-duty trucks, users typically need to interact with and control the onboard equipment.

[0003] In some technologies, users locate and interact with target in-vehicle devices based on the actual driving scenario. However, these technologies require users to assess the scenario and locate the target in-vehicle device while driving. This approach is inefficient and compromises driving safety.

[0004] Therefore, there is an urgent need for a solution that can automatically determine the actual driving scenario and intelligently control the on-board equipment in the vehicle. Summary of the Invention

[0005] The vehicle intelligent control method, control device, and vehicle based on driving scenarios provided in this application are intended to improve the interaction efficiency between users and in-vehicle devices, thereby enhancing the safety of users driving vehicles.

[0006] In a first aspect, embodiments of this application provide a vehicle intelligent control method based on driving scenarios, including:

[0007] Acquire vehicle scenario decision data; wherein the scenario decision data indicates the vehicle status and / or environmental status;

[0008] Based on scenario decision data, determine the current driving scenario of the vehicle; and based on the driving scenario, control at least one on-board device of the vehicle to operate in the target state.

[0009] In one possible implementation, the scenario decision data includes one or more of the following: driving data, cargo data, and environmental data.

[0010] In one possible implementation, if the scenario decision data includes cargo data, which includes cargo box load and cargo box door status, then the current driving scenario of the vehicle is determined based on the scenario decision data, including:

[0011] If it is determined that the cargo box load is less than or equal to the first threshold, and the cargo box door status indicates that the cargo box door is open, then the current driving scenario of the vehicle is determined to be a cargo loading scenario.

[0012] If the cargo box load is determined to be greater than the first threshold, and the cargo box door status indicates that the cargo box door is open, then the current driving scenario of the vehicle is determined to be a cargo unloading scenario.

[0013] In one possible implementation, if the scene decision data includes driving data and environmental data, where the driving data includes driving duration and vehicle speed, and the environmental data includes light intensity, then determining the vehicle's current driving scenario based on the scene decision data includes:

[0014] If it is determined that the light intensity is less than the second threshold and the vehicle speed is less than the third threshold, then the current driving scenario of the vehicle is determined to be a nighttime parking scenario.

[0015] If it is determined that the light intensity is less than the second threshold, the vehicle speed is greater than or equal to the third threshold, and the driving duration is greater than or equal to the fourth threshold, then the current driving scenario of the vehicle is determined to be a nighttime cruise scenario.

[0016] If the light intensity is greater than or equal to the second threshold, the vehicle speed is greater than or equal to the third threshold, and the driving duration is greater than or equal to the fourth threshold, then the current driving scenario of the vehicle is determined to be a daytime cruise scenario.

[0017] In one possible implementation, if the scenario decision data includes environmental data, such as rainfall, ambient temperature, and ambient humidity, then the current driving scenario of the vehicle is determined based on the scenario decision data, including:

[0018] If the rainfall is greater than or equal to the fifth threshold, the current driving scenario of the vehicle is determined to be a driving scenario in the rain.

[0019] If the outside temperature and humidity meet the first preset condition, then the current driving scenario of the vehicle is determined to be a driving scenario in fog; where the first preset condition indicates that the external environment in which the vehicle is located is in a foggy state.

[0020] In one possible implementation, depending on the driving scenario, controlling at least one onboard device of the vehicle to operate in a target state includes:

[0021] Based on the driving scenario, determine the control parameters corresponding to the driving scenario; and based on the control parameters, control at least one on-board device of the vehicle to operate in the target state.

[0022] In one possible implementation, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in a target state, including:

[0023] When the driving scenario is a cargo loading or unloading scenario, a first display command and a first seat adjustment parameter are determined. The first display command controls the screen of the intelligent device in the vehicle to display the image captured by the camera in the cargo box, and the first seat adjustment parameter controls the seat of the vehicle to be adjusted to an upright angle.

[0024] In one possible implementation, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in a target state, including:

[0025] When the driving scenario is a nighttime parking scenario, a second display command and a first activation command are determined. The second display command controls the screen of the intelligent device in the vehicle to display the image captured by the vehicle's external camera, and the first activation command controls the vehicle's external warning lights to turn on.

[0026] When the driving scenario is a nighttime cruise scenario, the first control parameter and the second control parameter are determined. The cabin ambient light in the vehicle is controlled to enter the warm light mode according to the first control parameter, and the rearview mirror of the vehicle is controlled to enter the anti-glare mode according to the second control parameter.

[0027] When the driving scenario is daytime cruise, a third control parameter is determined, and the vibration module on the vehicle's steering wheel is controlled to provide vibration feedback at a preset frequency based on the third control parameter.

[0028] In one possible implementation, when the driving scenario is a nighttime parking scenario, a nighttime cruising scenario, or a daytime cruising scenario, the method further includes:

[0029] Acquire fatigue data of users in the vehicle; wherein the fatigue data includes at least one of the following: pressure distribution data of the vehicle's seats, user's heart rate data, and user's blood oxygen data;

[0030] Determine the user's fatigue level based on fatigue data;

[0031] When a fatigue condition indicates that the user is in a fatigued state, control parameters for at least one of the following in-vehicle devices are determined based on the fatigue condition: the vehicle seat, the vehicle air conditioner, the vehicle dashboard, and the vehicle smart device; and at least one in-vehicle device is controlled to operate in a target state based on the control parameters.

[0032] In one possible implementation, when a fatigue condition indicates that the user is in a state of fatigue, the fatigue condition includes the user's fatigue level.

[0033] Based on fatigue conditions, determine control parameters for at least one of the following vehicle-mounted devices; and based on these control parameters, control at least one vehicle-mounted device to operate in a target state, including:

[0034] When the fatigue level is mild fatigue, determine the second seat adjustment parameters, and control the vehicle seat adjustment to the preset angle according to the second seat adjustment parameters;

[0035] When the fatigue level is moderate, determine the third seat adjustment parameters and the air conditioning start command, and control the vehicle seat to adjust to the preset angle according to the third seat adjustment parameters, and control the inflation of the lumbar airbag of the vehicle seat; control the vehicle air conditioning to turn on according to the air conditioning start command.

[0036] When the fatigue level is severe fatigue, the system determines the warning parameters and controls the vehicle's dashboard to display fatigue warning information based on these parameters, as well as controls the vehicle's intelligent devices to send fatigue driving information to the management platform.

[0037] In one possible implementation, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in a target state, including:

[0038] When the driving scenario is driving in the rain, a second activation command is determined, and the vehicle's windshield wipers are activated according to the second activation command.

[0039] When driving in fog, a third activation command is determined, and the vehicle's fog lights are activated according to the third activation command.

[0040] In one possible implementation, the method further includes:

[0041] Acquire user interaction data; wherein, the interaction data includes at least one of the following: voice data, gesture data, and gaze data;

[0042] Determine the user's interaction intent based on the interaction data;

[0043] Based on the interaction intent, control at least one in-vehicle device of the vehicle to operate in a target state; wherein the interaction intent is used to instruct the user to interact with the in-vehicle device, and the target state of the in-vehicle device.

[0044] Secondly, embodiments of this application provide a vehicle intelligent control device based on driving scenarios, comprising:

[0045] The acquisition module is used to acquire scene decision data of the vehicle; wherein, the scene decision data indicates the vehicle status and / or environmental status;

[0046] The processing module is used to determine the current driving scenario of the vehicle based on scenario decision data; and to control at least one on-board device of the vehicle to work in the target state according to the driving scenario.

[0047] In one possible implementation, the scenario decision data includes one or more of the following: driving data, cargo data, and environmental data.

[0048] In one possible implementation, if the scenario decision data includes cargo data, which includes cargo box load and cargo box door status, then the current driving scenario of the vehicle is determined based on the scenario decision data, and the processing module is used to:

[0049] If it is determined that the cargo box load is less than or equal to the first threshold, and the cargo box door status indicates that the cargo box door is open, then the current driving scenario of the vehicle is determined to be a cargo loading scenario.

[0050] If the cargo box load is determined to be greater than the first threshold, and the cargo box door status indicates that the cargo box door is open, then the current driving scenario of the vehicle is determined to be a cargo unloading scenario.

[0051] In one possible implementation, if the scene decision data includes driving data and environmental data, where the driving data includes driving duration and vehicle speed, and the environmental data includes light intensity, then the current driving scene of the vehicle is determined based on the scene decision data, and the processing module is used to:

[0052] If it is determined that the light intensity is less than the second threshold and the vehicle speed is less than the third threshold, then the current driving scenario of the vehicle is determined to be a nighttime parking scenario.

[0053] If it is determined that the light intensity is less than the second threshold, the vehicle speed is greater than or equal to the third threshold, and the driving duration is greater than or equal to the fourth threshold, then the current driving scenario of the vehicle is determined to be a nighttime cruise scenario.

[0054] If the light intensity is greater than or equal to the second threshold, the vehicle speed is greater than or equal to the third threshold, and the driving duration is greater than or equal to the fourth threshold, then the current driving scenario of the vehicle is determined to be a daytime cruise scenario.

[0055] In one possible implementation, if the scene decision data includes environmental data, such as rainfall, ambient temperature, and ambient humidity, then the current driving scenario of the vehicle is determined based on the scene decision data, and the processing module is used to:

[0056] If the rainfall is greater than or equal to the fifth threshold, the current driving scenario of the vehicle is determined to be a driving scenario in the rain.

[0057] If the outside temperature and humidity meet the first preset condition, then the current driving scenario of the vehicle is determined to be a driving scenario in fog; where the first preset condition indicates that the external environment in which the vehicle is located is in a foggy state.

[0058] In one possible implementation, depending on the driving scenario, at least one onboard device of the vehicle is controlled to operate in a target state, and the processing module is used to:

[0059] Based on the driving scenario, determine the control parameters corresponding to the driving scenario; and based on the control parameters, control at least one on-board device of the vehicle to operate in the target state.

[0060] In one possible implementation, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in a target state. The processing module is used for:

[0061] When the driving scenario is a cargo loading or unloading scenario, a first display command and a first seat adjustment parameter are determined. The first display command controls the screen of the intelligent device in the vehicle to display the image captured by the camera in the cargo box, and the first seat adjustment parameter controls the seat of the vehicle to be adjusted to an upright angle.

[0062] In one possible implementation, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in a target state. The processing module is used for:

[0063] When the driving scenario is a nighttime parking scenario, a second display command and a first activation command are determined. The second display command controls the screen of the intelligent device in the vehicle to display the image captured by the vehicle's external camera, and the first activation command controls the vehicle's external warning lights to turn on.

[0064] When the driving scenario is a nighttime cruise scenario, the first control parameter and the second control parameter are determined. The cabin ambient light in the vehicle is controlled to enter the warm light mode according to the first control parameter, and the rearview mirror of the vehicle is controlled to enter the anti-glare mode according to the second control parameter.

[0065] When the driving scenario is daytime cruise, a third control parameter is determined, and the vibration module on the vehicle's steering wheel is controlled to provide vibration feedback at a preset frequency based on the third control parameter.

[0066] In one possible implementation, when the driving scenario is a nighttime parking scenario, a nighttime cruising scenario, or a daytime cruising scenario, the processing module is further configured to:

[0067] Acquire fatigue data of users in the vehicle; wherein the fatigue data includes at least one of the following: pressure distribution data of the vehicle's seats, user's heart rate data, and user's blood oxygen data;

[0068] Determine the user's fatigue level based on fatigue data;

[0069] When a fatigue condition indicates that the user is in a fatigued state, control parameters for at least one of the following in-vehicle devices are determined based on the fatigue condition: the vehicle seat, the vehicle air conditioner, the vehicle dashboard, and the vehicle smart device; and at least one in-vehicle device is controlled to operate in a target state based on the control parameters.

[0070] In one possible implementation, when a fatigue condition indicates that the user is in a state of fatigue, the fatigue condition includes the user's fatigue level.

[0071] Based on fatigue conditions, control parameters for at least one of the following vehicle-mounted devices are determined; and based on these control parameters, at least one vehicle-mounted device is controlled to operate in a target state. The processing module is used for:

[0072] When the fatigue level is mild fatigue, determine the second seat adjustment parameters, and control the vehicle seat adjustment to the preset angle according to the second seat adjustment parameters;

[0073] When the fatigue level is moderate, determine the third seat adjustment parameters and the air conditioning start command, and control the vehicle seat to adjust to the preset angle according to the third seat adjustment parameters, and control the inflation of the lumbar airbag of the vehicle seat; control the vehicle air conditioning to turn on according to the air conditioning start command.

[0074] When the fatigue level is severe fatigue, the system determines the warning parameters and controls the vehicle's dashboard to display fatigue warning information based on these parameters, as well as controls the vehicle's intelligent devices to send fatigue driving information to the management platform.

[0075] In one possible implementation, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in a target state. The processing module is used for:

[0076] When the driving scenario is driving in the rain, a second activation command is determined, and the vehicle's windshield wipers are activated according to the second activation command.

[0077] When the driving scenario is driving in fog, a third activation command is determined, and the vehicle's fog lights are activated according to the third activation command.

[0078] In one possible implementation, the processing module is further configured to:

[0079] Acquire user interaction data; wherein, the interaction data includes at least one of the following: voice data, gesture data, and gaze data;

[0080] Determine the user's interaction intent based on the interaction data;

[0081] Based on the interaction intent, control at least one in-vehicle device of the vehicle to operate in a target state; wherein the interaction intent is used to instruct the user to interact with the in-vehicle device, and the target state of the in-vehicle device.

[0082] Thirdly, embodiments of this application provide a control device, including: a memory and a processor;

[0083] The memory stores the instructions that the computer executes;

[0084] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0085] Fourthly, embodiments of this application provide a vehicle including the control device described in the third aspect above.

[0086] Fifthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0087] In a sixth aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0088] The vehicle intelligent control method, control device, and vehicle based on driving scenarios provided in this application collect scene decision data from various sensors on the vehicle, determine the actual driving scenario of the vehicle based on the scene decision data, and control the on-board equipment in the vehicle to operate according to the actual driving scenario. Different driving scenarios correspond to target states for the on-board equipment to operate. This aims to improve the interaction efficiency between the user and the on-board equipment, thereby improving the safety of the user while driving the vehicle. Attached Figure Description

[0089] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0090] Figure 1 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 1 ;

[0091] Figure 2 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 2 ;

[0092] Figure 3A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 3 ;

[0093] Figure 4 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 4 ;

[0094] Figure 5 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 5 ;

[0095] Figure 6 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 6 ;

[0096] Figure 7 A schematic diagram of the structure of the vehicle intelligent control device based on driving scenarios provided in this application;

[0097] Figure 8 A schematic diagram of the control device provided in this application.

[0098] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0099] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0100] First, let me explain the terms used in this application:

[0101] Driving scenario: refers to the driving situation obtained by the user (driver) in the process of driving the vehicle, taking into account the vehicle's status and the external environment.

[0102] Scene decision data refers to multiple types of data used to determine the current driving scenario of a vehicle.

[0103] With the rapid development of the social economy, the logistics industry is also developing rapidly. As the core carrier of freight transportation, heavy-duty trucks are experiencing increasingly rapid technological advancements. Users drive heavy-duty trucks to transport goods and perform other operations. While driving heavy-duty trucks, users typically need to interact with and control the onboard equipment.

[0104] In some embodiments, the user (driver) locates the target in-vehicle device to be interacted with and controlled in the vehicle, or locates a physical button in the vehicle to control the target in-vehicle device, based on the actual driving scenario, thereby enabling interaction and control of the target in-vehicle device.

[0105] For example, during loading and unloading, users can select to display images of the cargo container captured by a camera inside the container via a touchscreen on a smart device.

[0106] For example, during long-distance driving, you can use the control buttons on the vehicle to control the air conditioning or adjust the angle of the driver's seat.

[0107] In the above embodiments, users need to assess the driving scenario while driving and need to look away from their line of sight to locate the target in-vehicle device or the physical button used to control it. This process results in low efficiency in user interaction and control of the in-vehicle device, and also lowers driving safety.

[0108] The vehicle intelligent control method based on driving scenarios provided in this application collects scenario decision data from various sensors on the vehicle, determines the actual driving scenario based on this data, and controls the on-board equipment to operate according to the actual driving scenario. Different driving scenarios correspond to different target states for the on-board equipment to operate. This improves the efficiency of interaction between the user and the on-board equipment. Users do not need to shift their gaze to find the target on-board equipment or use physical buttons to control it, achieving fast and efficient interaction and automated control, thereby improving the safety of the user while driving.

[0109] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0110] Figure 1 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 1 ,like Figure 1 As shown, the method includes:

[0111] Step 101. Obtain vehicle scenario decision data.

[0112] Among them, the scenario decision data indicates the vehicle status and / or environmental status.

[0113] For example, vehicle scenario decision data is used to make driving scenario judgments, and vehicle scenario decision data indicates vehicle status and / or environmental status.

[0114] Specifically, different types of sensors can be installed inside and / or outside the vehicle. Sensors inside the vehicle are used to collect data on the vehicle's internal status, while sensors outside the vehicle are used to collect data on the environmental conditions outside the vehicle.

[0115] For example, sensors inside a vehicle may include: door status sensors, load sensors, temperature sensors, and six-axis attitude sensors, among which the six-axis attitude sensors can be used to collect information such as vehicle speed and acceleration; sensors outside the vehicle may include: ultraviolet intensity sensors, temperature sensors, humidity sensors, and air quality sensors.

[0116] Step 102. Based on the scenario decision data, determine the current driving scenario of the vehicle; and based on the driving scenario, control at least one on-board device of the vehicle to work in the target state.

[0117] For example, the current driving scenario of the vehicle is determined based on scenario decision data. Specifically, the driving scenario is determined based on scenario decision data, indicating the environment and driving conditions under which the vehicle is actually driving.

[0118] For example, if the scenario decision data indicates that the environmental state is nighttime, then the current driving scenario of the vehicle is determined to be a nighttime driving scenario; if the scenario decision data indicates that the vehicle state is long-distance driving, then the current driving scenario of the vehicle is determined to be a long-distance cruising scenario.

[0119] For example, taking a heavy truck as an example, if the scenario decision data indicates that the vehicle status is that the cargo box door is open, then the current driving scenario of the vehicle is determined to be a loading and unloading scenario.

[0120] Furthermore, based on the determined driving scenario, the vehicle's onboard equipment is controlled to operate.

[0121] Specifically, each driving scenario has a corresponding preset control strategy. The control strategy for each driving scenario indicates one or more target on-board devices in the vehicle that need to be controlled, as well as the target state that the target on-board devices need to operate.

[0122] In one possible implementation, control parameters for the on-board equipment are determined based on the driving scenario, and the corresponding on-board equipment is controlled to operate in the target state based on the control parameters.

[0123] The vehicle intelligent control method based on driving scenarios provided in this application collects scenario decision data from various sensors on the vehicle, determines the actual driving scenario based on this data, and controls the on-board equipment to operate according to the actual driving scenario. Different driving scenarios correspond to different target states for the on-board equipment to operate. This improves the efficiency of interaction between the user and the on-board equipment. Users do not need to shift their gaze to find the target on-board equipment or use physical buttons to control it, achieving fast and efficient interaction and automated control, thereby improving the safety of the user while driving.

[0124] In one example, the scenario decision data includes one or more of the following: driving data, cargo data, and environmental data.

[0125] For example, taking heavy-duty trucks as an example, the scenario decision data may include: driving data, cargo data, and environmental data.

[0126] Among them, driving data represents the state of the user driving the heavy truck, and / or the state of the heavy truck. For example, the state of the user driving the heavy truck may include the user's driving time; the state of the heavy truck may include: vehicle speed data, acceleration data, and steering wheel angle data.

[0127] Among them, cargo data represents the status of the cargo box doors in heavy-duty trucks, and / or cargo box load data.

[0128] Among them, environmental data characterizes the external environment conditions of heavy-duty trucks during operation. For example, ambient temperature, ambient humidity, light intensity, and rainfall.

[0129] In the above example, by collecting different types of scenario decision data on the vehicle, it is possible to combine one or more scenario decision data to accurately determine the actual driving scenario of the current user driving the vehicle, laying the foundation for subsequent control of the vehicle's on-board equipment based on the driving scenario.

[0130] Based on the aforementioned embodiments, in one example, the scenario decision data includes cargo data, which includes cargo box load and cargo box door status.

[0131] For example, taking a heavy-duty truck as an example, the scenario decision data may include cargo data, which includes cargo box load and cargo box door status.

[0132] Specifically, the cargo box load can be measured and calculated by a pressure sensor installed inside the cargo box of a heavy-duty truck; the cargo box door status can be determined by setting a limit switch in the closed position of the cargo box door of the heavy-duty truck, based on the feedback signal of the limit switch.

[0133] Furthermore, Figure 2 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 2 ,like Figure 2 As shown, if the scenario decision data includes cargo data, which includes cargo box load and cargo box door status, then the process of determining the current driving scenario of the vehicle may include:

[0134] Step 201. If it is determined that the cargo box load is less than or equal to the first threshold, and the cargo box door status indicates that the cargo box door is in the open state, then the current driving scenario of the vehicle is determined to be a cargo loading scenario.

[0135] For example, if the cargo box load is less than or equal to a first threshold, it indicates that the cargo box of the heavy-duty truck is not yet loaded with goods. Meanwhile, if the cargo box door status indicates that the cargo box door is open, it means that goods need to be loaded into the cargo box of the heavy-duty truck. Therefore, the current driving scenario of the vehicle is determined to be a cargo loading scenario.

[0136] Step 202. If it is determined that the cargo box load is greater than the first threshold and the cargo box door status indicates that the cargo box door is open, then the current driving scenario of the vehicle is determined to be a cargo unloading scenario.

[0137] For example, if the cargo box load exceeds a first threshold, it indicates that the cargo box of the heavy truck is already loaded with goods. Simultaneously, if the cargo box door status indicates that the cargo box door is open, it means that unloading of goods from the cargo box of the central truck is now required. Therefore, the current driving scenario of the vehicle is determined to be a cargo unloading scenario.

[0138] It should be noted that steps 201 and 202 above can be executed simultaneously or sequentially, and the order of execution is not limited.

[0139] It should also be noted that the first threshold mentioned above can be set according to the actual application of heavy-duty trucks.

[0140] Furthermore, based on the driving scenario, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in the target state.

[0141] For example, based on the currently determined driving scenario, control parameters corresponding to the current driving scenario are determined, and based on these control parameters, the on-board devices in the vehicle are controlled to operate. These control parameters indicate the target on-board device in the vehicle that needs to be controlled and interacted with, as well as the target state of the target on-board device's operation. It should be noted that control commands corresponding to the driving scenario can also be generated, and based on these control commands, the on-board devices in the vehicle are controlled to operate. These control commands indicate the target on-board device in the vehicle that needs to be controlled and interacted with, as well as the target state of the target on-board device's operation.

[0142] Specifically, when the driving scenario is a cargo loading or unloading scenario, a first display command and a first seat adjustment parameter are determined. The screen of the intelligent device in the vehicle is controlled to display the image captured by the camera in the cargo box according to the first display command, and the seat of the vehicle is adjusted to an upright angle according to the first seat adjustment parameter.

[0143] For example, taking heavy trucks as an example, when the driving scenario is a cargo loading or unloading scenario, the user (driver) usually needs to monitor the cargo loading and unloading situation.

[0144] Specifically, a first display command can be automatically generated. Based on this first display command, the screens of intelligent devices in the vehicle are controlled to display images of the cargo box, which are captured by cameras installed in the vehicle's cargo box. The intelligent devices in the vehicle can include, but are not limited to: a center console, a vehicle-mounted infotainment system, a head-up display (HUD), etc. Optionally, the HUD can also be an augmented reality head-up display (AR-HUD).

[0145] Specifically, the first seat adjustment parameters can be determined automatically. Based on these parameters, the driver's seat can be adjusted to an upright position.

[0146] Through the two methods described above, users can use the vehicle's smart devices to broadcast the cargo status inside the cargo box during loading or unloading scenarios, and by adjusting the driver's seat to an upright position, users can more easily observe the loading and unloading process.

[0147] In the above embodiments, the current driving scenario of the vehicle is determined based on the cargo data in the scenario decision data. When the driving scenario is cargo loading or unloading, control parameters are automatically determined. These control parameters include a first display command and a first seat adjustment parameter. Based on the first display command, the intelligent device in the vehicle is controlled to display an image inside the cargo box, and based on the first seat adjustment parameter, the driver's seat is controlled. This ensures that during the loading and unloading process of a heavy truck, the driver can automatically monitor the cargo status and automatically adjust the seat through the intelligent device in the vehicle without manual interaction, improving the efficiency of interactive control and enhancing the intelligence of vehicle interactive control.

[0148] Based on the foregoing embodiments, in one example, the scene decision data includes driving data and environmental data. The driving data includes driving time and vehicle speed, and the environmental data includes light intensity.

[0149] For example, taking a heavy-duty truck as an example, the scene decision data may include driving data and environmental data. The driving data includes driving time and vehicle speed; the environmental data includes light intensity.

[0150] Specifically, driving time can be obtained by timing the timer in the vehicle controller of the central truck; vehicle speed can be measured by the vehicle speed sensor inside the vehicle, or read directly from the vehicle's dashboard.

[0151] Specifically, the light intensity can be measured using light sensors installed on the exterior of the heavy-duty truck.

[0152] Furthermore, Figure 3 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 3 ,like Figure 3 As shown, if the scenario decision data includes driving data and environmental data, where driving data includes driving duration and vehicle speed, and environmental data includes light intensity, then the process of determining the vehicle's current driving scenario can include:

[0153] Step 301. If it is determined that the light intensity is less than the second threshold and the vehicle speed is less than the third threshold, then the current driving scenario of the vehicle is determined to be a nighttime parking scenario.

[0154] For example, when the light intensity is less than the second threshold, it indicates that the ambient light intensity is low during the heavy truck's operation, suggesting that it is already evening or late at night. Simultaneously, if the heavy truck's speed is less than the third threshold, it indicates that the heavy truck may be in a parking or halting state. Therefore, the current driving scenario is determined to be a nighttime parking scenario.

[0155] Step 302. If it is determined that the light intensity is less than the second threshold, the vehicle speed is greater than or equal to the third threshold, and the driving duration is greater than or equal to the fourth threshold, then the current driving scenario of the vehicle is determined to be a nighttime cruise scenario.

[0156] For example, when the light intensity is less than the second threshold, it indicates that the ambient light intensity is low during the heavy truck's operation, suggesting that it is already evening or late at night. Meanwhile, if the heavy truck's speed is greater than or equal to the third threshold, it indicates that the heavy truck is likely currently in motion. Furthermore, if the driving duration is determined to be greater than or equal to the fourth threshold, it means that the user has been driving the heavy truck continuously for a period of time. Therefore, the current driving scenario is determined to be a nighttime cruising scenario.

[0157] Step 303. If it is determined that the light intensity is greater than or equal to the second threshold, the vehicle speed is greater than or equal to the third threshold, and the driving duration is greater than or equal to the fourth threshold, then the current driving scenario of the vehicle is determined to be a daytime cruise scenario.

[0158] For example, when the light intensity is greater than or equal to the second threshold, it indicates that the ambient light intensity is high during the heavy truck's operation, suggesting daytime or daytime conditions. Simultaneously, if the heavy truck's speed is greater than or equal to the third threshold, it indicates that the heavy truck is likely currently in motion. Furthermore, if the driving duration is determined to be greater than or equal to the fourth threshold, it means the user has been driving the heavy truck continuously for a period of time. Therefore, the current driving scenario is determined to be a daytime cruising scenario.

[0159] It should be noted that steps 301 to 303 above can be executed simultaneously or sequentially, and the order of execution is not limited.

[0160] It should also be noted that the second, third, and fourth thresholds mentioned above can be set according to the actual application of heavy-duty trucks.

[0161] Furthermore, based on the driving scenario, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in the target state.

[0162] For example, based on the currently determined driving scenario, control parameters corresponding to the current driving scenario are determined, and based on these control parameters, the on-board devices in the vehicle are controlled to operate. These control parameters indicate the target on-board device in the vehicle that needs to be controlled and interacted with, as well as the target state of the target on-board device's operation. It should be noted that control commands corresponding to the driving scenario can also be generated, and based on these control commands, the on-board devices in the vehicle are controlled to operate. These control commands indicate the target on-board device in the vehicle that needs to be controlled and interacted with, as well as the target state of the target on-board device's operation.

[0163] Specifically, in one example, when the driving scenario is a nighttime parking scenario, a second display command and a first activation command are determined. The second display command controls the screen of the smart device in the vehicle to display the image captured by the vehicle's external camera, and the first activation command controls the vehicle's external warning lights to turn on.

[0164] For example, taking heavy-duty trucks as an example, due to their long body, they are prone to blind spots. Therefore, in nighttime parking scenarios, it is usually necessary to show the user (driver) what is in the blind spots outside the heavy-duty truck, and to use warning lights installed on the outside of the heavy-duty truck to warn other vehicles or people in the vicinity.

[0165] Specifically, a second display command can be automatically generated. Based on this second display command, the screen of the intelligent device in the vehicle is controlled to display images of the blind spots outside the vehicle. These images of the blind spots outside the vehicle are captured by external cameras installed on the exterior of the vehicle.

[0166] Specifically, a first activation command can be automatically generated. Based on this first activation command, the external warning lights located on the exterior of the vehicle are activated.

[0167] Specifically, in one example, when the driving scenario is a nighttime cruise scenario, a first control parameter and a second control parameter are determined, and the cabin ambient light in the vehicle is controlled to enter a warm light mode according to the first control parameter, and the rearview mirror of the vehicle is controlled to enter an anti-glare mode according to the second control parameter.

[0168] For example, taking heavy-duty trucks as an example, since drivers often spend a considerable amount of time driving at night, adjusting the ambient lighting in the vehicle's cabin can alleviate driving stress. Additionally, during nighttime driving, when meeting oncoming traffic or overtaking, the headlights of other vehicles may create a glare effect in the rearview mirror. This glare effect occurs because the light from other vehicles' headlights is reflected through the rearview mirror, blurring the driver's vision. This poses a driving safety risk; therefore, it is necessary to switch the rearview mirror to anti-glare mode.

[0169] Specifically, a first control parameter can be automatically determined. Based on this first control parameter, the ambient lighting in the vehicle's cabin can be controlled to enter a warm light mode. Optionally, the ambient lighting can be composed of light sources of different colors. Based on the first control parameter, the intensity of different colors of light in the ambient lighting can be controlled to achieve the goal of controlling the ambient lighting to enter a warm light mode.

[0170] Specifically, a second control parameter can be automatically determined. Based on this second control parameter, the vehicle's rearview mirror is controlled to enter anti-glare mode. Optionally, the rearview mirror can enter anti-glare mode by adjusting the lens to reduce the light reflectivity of the lens, or the rearview mirror can enter anti-glare mode by applying a low-voltage current to the photochromic material in the lens layer of the rearview mirror, causing the color of the photochromic material to change, thereby changing the lens color of the rearview mirror.

[0171] Specifically, in one example, when the driving scenario is a daytime cruise scenario, a third control parameter is determined, and the vibration module on the vehicle's steering wheel is controlled to provide vibration feedback at a preset frequency based on the third control parameter.

[0172] For example, taking a heavy-duty truck as an example, since the user (driver) has been driving for a long time during daytime cruising, the vibration module on the vehicle's steering wheel can provide vibration feedback to remind the user to concentrate on driving the vehicle.

[0173] Specifically, a third control parameter can be automatically determined. Based on this third control parameter, the vibration module on the vehicle's steering wheel is controlled to provide vibration feedback at a preset frequency. The preset frequency can be set to vibrate for 2 seconds every 2 minutes.

[0174] In the above embodiments, the current driving scenario of the vehicle is determined based on driving data and environmental data in the scenario decision data. When the driving scenario is nighttime parking, nighttime cruising, or daytime cruising, control parameters are automatically determined. Based on these control parameters, the vehicle's intelligent devices are controlled to display images of the vehicle's exterior, and the vehicle's external warning lights are activated. This ensures that surrounding vehicles and pedestrians are alerted during nighttime parking, improving driving safety. Based on these control parameters, the vehicle's interior lighting atmosphere is adjusted, and the vehicle's rearview mirrors are put into anti-glare mode. This prevents driver fatigue during nighttime cruising and prevents momentary strong glare caused by reflections of oncoming headlights in the rearview mirrors, improving driving safety. Based on these control parameters, the steering wheel's vibration feedback module is controlled to provide vibration feedback prompts, which also helps prevent driver fatigue and improves driving safety. Furthermore, all of the above operations can be automated, eliminating the need for manual interaction by the driver, thus improving the efficiency of interactive control and enhancing the intelligence of vehicle interactive control.

[0175] Building upon the aforementioned embodiments, drivers are often prone to fatigue when driving at night or on long distances. This can pose a danger to the vehicle. Therefore, driver fatigue can be monitored in fatigue-prone driving scenarios, and timely intervention strategies can be implemented to address fatigue-related driving issues.

[0176] In one example, Figure 4 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 4 ,like Figure 4 As shown, when the driving scenario is a nighttime parking scenario, a nighttime cruise scenario, or a daytime cruise scenario, the method also includes:

[0177] Step 401. Obtain fatigue data of users in the vehicle.

[0178] The fatigue data includes at least one of the following: pressure distribution data of the vehicle's seats, user's heart rate data, and user's blood oxygen data.

[0179] For example, a pressure sensor matrix consisting of multiple pressure sensors is installed on the vehicle seat, through which pressure distribution data of the vehicle seat can be measured.

[0180] The vehicle's steering wheel is equipped with a physiological indicator detection module, which is used to measure the user's heart rate and / or blood oxygen data.

[0181] It should be noted that before the physiological indicator detection module collects the user's physiological data, there are obvious prompt sounds and messages in the vehicle; and an authorization interface is provided for the user to choose whether to allow the vehicle to collect physiological data.

[0182] Step 402. Determine the user's fatigue level based on the fatigue data.

[0183] For example, fatigue data can be used to determine a user's fatigue level. Specifically, a user's fatigue level can include whether the user is in a non-fatigued state or a fatigued state. Furthermore, the user's fatigue level can be determined based on a fatigue index.

[0184] Furthermore, the user's fatigue index can be obtained using the following formula (1):

[0185] (1)

[0186] In formula (1), This indicates the user's fatigue index; This represents the maximum value of the user's heart rate data. This represents the average value of the user's heart rate data. This represents the user's blood oxygen data; , and All three are weight parameters, and the sum of the three weight parameters is 1.

[0187] When a user's fatigue index is less than the fatigue threshold, the user is considered to be in a non-fatigue state.

[0188] When a user's fatigue index is greater than or equal to the fatigue threshold, the user is considered to be in a state of fatigue.

[0189] Step 403. When the fatigue condition indicates that the user is in a fatigued state, determine the control parameters of at least one of the following in-vehicle devices based on the fatigue condition: the seat in the vehicle, the air conditioner in the vehicle, the dashboard in the vehicle, and the smart device in the vehicle; and control at least one in-vehicle device to operate in a target state based on the control parameters.

[0190] For example, when a fatigue indicator shows that a user is fatigued, it means that the current user (driver) is driving while fatigued. An intervention strategy for fatigued driving is needed.

[0191] Specifically, based on the user's fatigue level, control parameters for the on-board equipment are determined when the user is fatigued. Based on these control parameters, the on-board equipment in the vehicle is controlled to operate. It should be noted that control commands for the on-board equipment when the user is fatigued can also be generated, and the on-board equipment in the vehicle is controlled to operate based on these control commands.

[0192] For example, when a fatigue condition indicates that a user is in a state of fatigue, the in-vehicle equipment that needs to be controlled includes at least one of the following: a seat in the vehicle, an air conditioner in the vehicle, an instrument panel in the vehicle, and a smart device in the vehicle.

[0193] The control parameter indicates that at least one of the aforementioned vehicle-mounted devices is the target vehicle-mounted device to be controlled, and the target state in which the target vehicle-mounted device is operating. Alternatively, the control command indicates that at least one of the aforementioned vehicle-mounted devices is the target vehicle-mounted device to be controlled, and the target state in which the target vehicle-mounted device is operating.

[0194] In the above example, when the driving scenario is any one of nighttime parking, nighttime cruise, or daytime cruise, considering that the user may be driving while fatigued, fatigue data of the user is obtained, and the user's fatigue status is judged based on the fatigue data. If the fatigue status indicates that the user is already in a fatigued state, further relevant intervention measures can be taken to improve driving safety.

[0195] Furthermore, in one example, when a fatigue condition indicates that a user is in a state of fatigue, the fatigue condition includes the user's fatigue level.

[0196] For example, as can be seen from the previous examples, when a user's fatigue index is greater than or equal to the fatigue threshold, the user is in a state of fatigue.

[0197] When a fatigue status indicates that a user is in a state of fatigue, the fatigue status includes the user's fatigue level. Specifically, the user's fatigue level can be determined based on the range of the fatigue index in the previous example.

[0198] For example, if the fatigue index falls within the first range, the fatigue level is determined to be mild fatigue; if the fatigue index falls within the second range, the fatigue level is determined to be moderate fatigue; and if the fatigue index falls within the third range, the fatigue level is determined to be severe fatigue.

[0199] Among them, the lower limit of the first numerical range is greater than or equal to the fatigue threshold, the upper limit of the first numerical range is less than or equal to the lower limit of the second numerical range, and the upper limit of the second numerical range is less than or equal to the lower limit of the third numerical range.

[0200] Specifically, in conjunction with the aforementioned example, when a fatigue condition indicates that the user is in a fatigued state, different in-vehicle devices can be controlled according to the fatigue level.

[0201] In one example, when the fatigue level is mild fatigue, a second seat adjustment parameter is determined, and the vehicle seat is adjusted to a preset angle according to the second seat adjustment parameter.

[0202] For example, when the fatigue level is mild, adjusting the driver's seat angle can moderately alleviate the user's fatigue. Therefore, a second seat adjustment parameter can be automatically determined. Based on this second seat adjustment parameter, the vehicle's seat is controlled to adjust to a preset angle.

[0203] Optionally, the preset angle can be an ergonomically comfortable angle or a user-defined angle.

[0204] In one example, when the fatigue level is moderate fatigue, the third seat adjustment parameters and the air conditioning start command are determined. Based on the third seat adjustment parameters, the vehicle's seat is adjusted to a preset angle, and the lumbar airbag of the vehicle's seat is inflated. Based on the air conditioning start command, the vehicle's air conditioning is turned on.

[0205] For example, when the fatigue level is moderate, the lumbar airbag can be inflated by adjusting the driver's seat angle to provide external support for the user's lower back. Therefore, a third seat adjustment parameter can be automatically determined. Based on this parameter, the vehicle's seat is adjusted to a preset angle, and the lumbar airbag is inflated.

[0206] For example, when the fatigue level is moderate, the vehicle's air conditioning system can be turned on to regulate the temperature inside the cabin and moderately alleviate the user's fatigue. Therefore, the air conditioning activation command can also be automatically determined. Based on this command, the vehicle's air conditioning can be controlled to turn on.

[0207] Optionally, the air conditioner start command can also indicate the air conditioner's operating mode and operating temperature, so that after the air conditioner is turned on, it is controlled to operate in the mode and operating temperature indicated by the air conditioner start command.

[0208] In one example, when the fatigue level is severe fatigue, a warning prompt parameter is determined, and the vehicle's dashboard is controlled to display fatigue warning information based on the warning prompt parameter, and the vehicle's smart devices are controlled to send fatigue driving information to the management platform.

[0209] For example, when the fatigue level is severe fatigue, it indicates that the user (driver) is no longer suitable to continue driving a heavy truck, so the user needs to be advised to take a rest.

[0210] Specifically, warning parameters can be determined automatically. Based on these parameters, the vehicle's dashboard is controlled to display fatigue warning information, and the vehicle's intelligent devices send fatigue driving information to the management platform via their built-in communication modules.

[0211] Optionally, the fatigue warning information displayed on the dashboard can be indicated by flashing a preset icon.

[0212] Optionally, during the process of smart devices sending fatigue driving information to the management platform through their built-in communication modules, communication technologies such as the Internet of Things (IoT) and 5G can be used. Taking heavy-duty trucks as an example, the management platform can refer to a fleet command platform for heavy-duty trucks, a freight dispatcher management platform, or a freight receiver management platform.

[0213] It should be noted that if the fatigue data indicates that the user is not fatigued, then it is only necessary to control the in-vehicle equipment according to the control method for the nighttime parking scenario, the control method for the nighttime cruise scenario, or the control method for the daytime cruise scenario, as described in the previous example.

[0214] In the example above, when the fatigue indicator shows the user is fatigued, different fatigue intervention strategies are implemented based on the different levels of fatigue. For instance, in cases of mild fatigue, the driver's seat angle is adjusted to alleviate muscle stiffness caused by prolonged driving; in cases of moderate fatigue, the lumbar airbag support is activated, and the air conditioning is turned on to prevent decreased body support due to lumbar muscle fatigue and to regulate the cabin temperature for a more comfortable environment; in cases of severe fatigue, timely warnings are issued to prevent driving safety issues caused by severe fatigue driving and improve driving safety. Furthermore, this process is automated through monitoring and interactive control, eliminating the need for manual adjustment and control by the user, thus improving the interactive efficiency of the vehicle's onboard equipment.

[0215] Based on the aforementioned embodiments, in one example, the scene decision data includes environmental data, which includes rainfall, ambient temperature, and ambient humidity.

[0216] For example, taking heavy-duty trucks as an example, the scenario decision-making data may include environmental data. This environmental data includes rainfall, ambient temperature, and ambient humidity.

[0217] Specifically, rainfall can be measured using a rain sensor located on the exterior of the heavy-duty truck. Ambient temperature can be measured using a temperature sensor located on the exterior of the heavy-duty truck. Ambient humidity can be measured using a humidity sensor located on the exterior of the heavy-duty truck.

[0218] Furthermore, Figure 5 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 5 ,like Figure 5 As shown, if the scenario decision data includes environmental data, such as rainfall, outside temperature, and outside humidity, then the process of determining the vehicle's current driving scenario can include:

[0219] Step 501. If the rainfall is greater than or equal to the fifth threshold, then the current driving scenario of the vehicle is determined to be a driving scenario in the rain.

[0220] For example, when the rainfall is greater than or equal to the fifth threshold, the weather in the external environment is considered to be rainy while the heavy truck is in motion. Therefore, the current driving scenario of the vehicle is determined to be a driving scenario in the rain.

[0221] Step 502. If the ambient temperature and ambient humidity meet the first preset condition, then the current driving scenario of the vehicle is determined to be a driving scenario in fog; wherein, the first preset condition indicates that the external environment in which the vehicle is located is in a foggy state.

[0222] For example, when the ambient temperature and humidity meet the first preset conditions, it indicates that the weather in the external environment is foggy or foggy during the heavy truck's operation. Therefore, the current driving scenario of the vehicle is determined to be a driving scenario in fog.

[0223] The first preset condition indicates that the external environment in which the vehicle is located is foggy.

[0224] Specifically, the first preset condition can be that the ambient humidity is greater than a preset humidity threshold and the ambient temperature is less than or equal to a preset dew point temperature. When the ambient temperature and ambient humidity meet the first preset condition, the ambient environment is in a foggy state.

[0225] It should be noted that steps 501 and 502 can be executed simultaneously or sequentially, and the order of execution is not limited.

[0226] It should also be noted that the aforementioned fifth threshold, preset humidity threshold, and preset dew point temperature can be set according to the actual application of heavy-duty trucks.

[0227] Furthermore, based on the driving scenario, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in the target state.

[0228] For example, based on the currently determined driving scenario, control parameters corresponding to the current driving scenario are determined, and based on these control parameters, the on-board devices in the vehicle are controlled to operate. These control parameters indicate the target on-board device in the vehicle that needs to be controlled and interacted with, as well as the target state of the target on-board device's operation. It should be noted that control commands corresponding to the driving scenario can also be generated, and based on these control commands, the on-board devices in the vehicle are controlled to operate. These control commands indicate the target on-board device in the vehicle that needs to be controlled and interacted with, as well as the target state of the target on-board device's operation.

[0229] Specifically, in one example, when the driving scenario is driving in the rain, a second activation command is determined, and the vehicle's windshield wipers are activated according to the second activation command.

[0230] For example, in a driving scenario involving rain, it's typically necessary to turn on the windshield wipers to ensure the windshield remains clear for the driver's cabin. Therefore, a second activation command can be automatically determined. Based on this second activation command, the vehicle's windshield wipers are then activated.

[0231] Optionally, the second activation command can also indicate the operating frequency of the wipers, so that after the wipers are activated, the wipers are controlled to operate at the operating frequency indicated by the second activation command.

[0232] Specifically, in one example, when the driving scenario is driving in fog, a third activation command is determined, and the vehicle's fog lights are activated according to the third activation command.

[0233] For example, in a driving scenario involving fog, fog lights typically need to be turned on. Therefore, a third activation command can be automatically determined. Based on this third activation command, the vehicle's fog lights can be activated.

[0234] In the above embodiments, the current driving scenario of the vehicle is determined based on environmental data in the scenario decision data. When the driving scenario is driving in the rain, a second activation command is automatically determined. Based on this second activation command, the windshield wipers in the vehicle are controlled to turn on. This enables automatic activation of the windshield wipers in adverse weather conditions (rain) without requiring manual operation by the user, thus improving interaction efficiency. When the driving scenario is driving in fog, a third activation command is automatically determined. Based on this third activation command, the fog lights in the vehicle are controlled to turn on. This enables automatic activation of the fog lights in adverse weather conditions (dense fog or low visibility) without requiring manual operation by the user, further improving interaction efficiency and enhancing the intelligence of vehicle interaction control. Simultaneously, since users typically need to concentrate more on the road conditions ahead in adverse weather, the automated interaction and control of in-vehicle equipment eliminates the need for manual operation by the user, improving driving safety. Furthermore, turning on the windshield wipers in the rain ensures clear visibility through the windshield, and turning on the fog lights in fog improves visibility and warns other vehicles, further enhancing driving safety.

[0235] Based on any of the foregoing embodiments, the interaction intent can be determined by combining the user's actual interaction operations, and the in-vehicle equipment in the vehicle can be controlled according to the interaction intent. Figure 6 A flowchart illustrating the vehicle intelligent control method based on driving scenarios provided in this application. Figure 6 ,like Figure 6 As shown, the method may further include:

[0236] Step 601. Obtain user interaction data.

[0237] The interactive operation data includes at least one of the following: voice data, gesture data, and gaze data.

[0238] For example, user interaction data can include various types of data, such as voice data, gesture data, and gaze data.

[0239] Specifically, voice data is collected through an array of noise-canceling microphones deployed inside the vehicle's cockpit; gesture data is collected through capacitive sensors deployed on the vehicle's steering wheel; and gaze data is collected through binocular eye-tracking cameras deployed inside the vehicle's cockpit.

[0240] Step 602. Determine the user's interaction intent based on the interaction operation data.

[0241] For example, the user's interaction intent can be determined based on the user's interaction data.

[0242] For example, the user's voice data is "turn on the air conditioner to cooling mode". Based on this voice data, it can be determined that the user's interaction intent is "to control the air conditioner in the vehicle to turn on, and to control the air conditioner in the vehicle to work in cooling mode".

[0243] For another example, the user's gesture data is a swipe to the right. Based on this gesture data, it can be determined that the user's interaction intent is "to control the smart device in the vehicle to play the next song of the current song".

[0244] For another example, user gaze data indicates that the user's gaze is focused near the drive mode switching button, which can determine that the user's interaction intention is "to control the switching of the vehicle's drive mode".

[0245] It should be noted that the examples above do not represent a limitation on the interaction intent corresponding to voice data, gesture data, and gaze data. In practical applications, the user's interaction intent can be determined based on one or more of these data.

[0246] Optionally, determining the user's interaction intent may require multiple types of interaction data. Therefore, each type of interaction data can be assigned a corresponding weight value. Based on the intent corresponding to each type of interaction data and its weight value, the user's interaction intent is comprehensively determined. Furthermore, the weight values ​​can be flexibly adjusted. For example, when the vehicle's external sensors detect significant road roughness, the weight value corresponding to the gesture data is reduced. This is because significant road roughness can cause vehicle vibration, and the user's continued grip on the steering wheel may cause the capacitive sensors on the steering wheel to misinterpret the gesture data.

[0247] Step 603. Based on the interaction intent, control at least one on-board device of the vehicle to operate in the target state.

[0248] Interaction intent refers to the in-vehicle device that the user interacts with, as well as the target state of the in-vehicle device.

[0249] For example, based on the determined interaction intent, control parameters and / or control commands for the in-vehicle device are determined. At least one in-vehicle device of the vehicle is then controlled to operate in a target state based on the control parameters, and / or based on the control commands.

[0250] To illustrate with the example above, when the user's interaction intent is "to control the air conditioner in the vehicle to turn on and to control the air conditioner in the vehicle to work in cooling mode", an air conditioner control command is generated, and the air conditioner in the vehicle is controlled to turn on and work in cooling mode based on the air conditioner control command.

[0251] To illustrate with the example above, when the user's interaction intent is "to control the smart device in the vehicle to play the next song of the current song", a music control command is generated, and the smart device in the vehicle is controlled to play the next song of the current song based on the music control command.

[0252] To illustrate with the example above, when the user's interaction intent is "to control the switching of the vehicle's drive mode", a button control command is generated. Based on this button control command, the indicator light corresponding to the drive mode switching button is turned on, thereby illuminating the drive mode switching button and facilitating the user's button interaction.

[0253] In the above example, in addition to automatically controlling the in-vehicle equipment, actual user interaction data can be collected, such as voice commands, gesture commands, and eye contact. This interaction data allows for the determination of the user's interaction intent, and the system can control the indicated target in-vehicle equipment to operate in the desired state based on that intent. This further improves the efficiency of user-in-vehicle interaction control. By using various types of user interaction data, it avoids requiring the user to take their eyes off the road during actual interaction, thus further enhancing driving safety.

[0254] The vehicle intelligent control method based on driving scenarios provided in this application collects scenario decision data from various sensors on the vehicle, determines the actual driving scenario based on this data, and controls the on-board equipment to operate according to the actual driving scenario. Different driving scenarios correspond to different target states for the on-board equipment to operate. This improves the efficiency of interaction between the user and the on-board equipment. Users do not need to shift their gaze to find the target on-board equipment or use physical buttons to control it, achieving fast and efficient interaction and automated control, thereby improving the safety of the user while driving.

[0255] Figure 7 A schematic diagram of the structure of the vehicle intelligent control device based on driving scenarios provided in this application is shown below. Figure 7 As shown, the vehicle intelligent control device 70 based on driving scenarios provided in this embodiment includes:

[0256] The acquisition module 701 is used to acquire scene decision data of the vehicle; wherein, the scene decision data indicates the vehicle status and / or environmental status;

[0257] The processing module 702 is used to determine the current driving scenario of the vehicle based on scenario decision data; and to control at least one on-board device of the vehicle to work in a target state based on the driving scenario.

[0258] In one possible implementation, the scenario decision data includes one or more of the following: driving data, cargo data, and environmental data.

[0259] In one possible implementation, if the scenario decision data includes cargo data, which includes cargo box load and cargo box door status, then the current driving scenario of the vehicle is determined based on the scenario decision data, and the processing module 702 is used for:

[0260] If it is determined that the cargo box load is less than or equal to the first threshold, and the cargo box door status indicates that the cargo box door is open, then the current driving scenario of the vehicle is determined to be a cargo loading scenario.

[0261] If the cargo box load is determined to be greater than the first threshold, and the cargo box door status indicates that the cargo box door is open, then the current driving scenario of the vehicle is determined to be a cargo unloading scenario.

[0262] In one possible implementation, if the scene decision data includes driving data and environmental data, where the driving data includes driving duration and vehicle speed, and the environmental data includes light intensity, then the current driving scene of the vehicle is determined based on the scene decision data, and the processing module 702 is used to:

[0263] If it is determined that the light intensity is less than the second threshold and the vehicle speed is less than the third threshold, then the current driving scenario of the vehicle is determined to be a nighttime parking scenario.

[0264] If it is determined that the light intensity is less than the second threshold, the vehicle speed is greater than or equal to the third threshold, and the driving duration is greater than or equal to the fourth threshold, then the current driving scenario of the vehicle is determined to be a nighttime cruise scenario.

[0265] If the light intensity is greater than or equal to the second threshold, the vehicle speed is greater than or equal to the third threshold, and the driving duration is greater than or equal to the fourth threshold, then the current driving scenario of the vehicle is determined to be a daytime cruise scenario.

[0266] In one possible implementation, if the scene decision data includes environmental data, such as rainfall, ambient temperature, and ambient humidity, then the current driving scenario of the vehicle is determined based on the scene decision data, and the processing module 702 is used for:

[0267] If the rainfall is greater than or equal to the fifth threshold, the current driving scenario of the vehicle is determined to be a driving scenario in the rain.

[0268] If the outside temperature and humidity meet the first preset condition, then the current driving scenario of the vehicle is determined to be a driving scenario in fog; where the first preset condition indicates that the external environment in which the vehicle is located is in a foggy state.

[0269] In one possible implementation, depending on the driving scenario, at least one onboard device of the vehicle is controlled to operate in a target state, and the processing module 702 is used to:

[0270] Based on the driving scenario, determine the control parameters corresponding to the driving scenario; and based on the control parameters, control at least one on-board device of the vehicle to operate in the target state.

[0271] In one possible implementation, based on the driving scenario, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in a target state. The processing module 702 is used for:

[0272] When the driving scenario is a cargo loading or unloading scenario, a first display command and a first seat adjustment parameter are determined. The first display command controls the screen of the intelligent device in the vehicle to display the image captured by the camera in the cargo box, and the first seat adjustment parameter controls the seat of the vehicle to be adjusted to an upright angle.

[0273] In one possible implementation, based on the driving scenario, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in a target state. The processing module 702 is used for:

[0274] When the driving scenario is a nighttime parking scenario, a second display command and a first activation command are determined. The second display command controls the screen of the intelligent device in the vehicle to display the image captured by the vehicle's external camera, and the first activation command controls the vehicle's external warning lights to turn on.

[0275] When the driving scenario is a nighttime cruise scenario, the first control parameter and the second control parameter are determined. The cabin ambient light in the vehicle is controlled to enter the warm light mode according to the first control parameter, and the rearview mirror of the vehicle is controlled to enter the anti-glare mode according to the second control parameter.

[0276] When the driving scenario is daytime cruise, a third control parameter is determined, and the vibration module on the vehicle's steering wheel is controlled to provide vibration feedback at a preset frequency based on the third control parameter.

[0277] In one possible implementation, when the driving scenario is a nighttime parking scenario, a nighttime cruising scenario, or a daytime cruising scenario, the processing module 702 is further configured to:

[0278] Acquire fatigue data of users in the vehicle; wherein the fatigue data includes at least one of the following: pressure distribution data of the vehicle's seats, user's heart rate data, and user's blood oxygen data;

[0279] Determine the user's fatigue level based on fatigue data;

[0280] When a fatigue condition indicates that the user is in a fatigued state, control parameters for at least one of the following in-vehicle devices are determined based on the fatigue condition: the vehicle seat, the vehicle air conditioner, the vehicle dashboard, and the vehicle smart device; and at least one in-vehicle device is controlled to operate in a target state based on the control parameters.

[0281] In one possible implementation, when a fatigue condition indicates that the user is in a state of fatigue, the fatigue condition includes the user's fatigue level.

[0282] Based on fatigue conditions, control parameters for at least one of the following vehicle-mounted devices are determined; and based on the control parameters, at least one vehicle-mounted device is controlled to operate in a target state. The processing module 702 is used for:

[0283] When the fatigue level is mild fatigue, determine the second seat adjustment parameters, and control the vehicle seat adjustment to the preset angle according to the second seat adjustment parameters;

[0284] When the fatigue level is moderate, determine the third seat adjustment parameters and the air conditioning start command, and control the vehicle seat to adjust to the preset angle according to the third seat adjustment parameters, and control the inflation of the lumbar airbag of the vehicle seat; control the vehicle air conditioning to turn on according to the air conditioning start command.

[0285] When the fatigue level is severe fatigue, the system determines the warning parameters and controls the vehicle's dashboard to display fatigue warning information based on these parameters, as well as controls the vehicle's intelligent devices to send fatigue driving information to the management platform.

[0286] In one possible implementation, based on the driving scenario, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in a target state. The processing module 702 is used for:

[0287] When the driving scenario is driving in the rain, a second activation command is determined, and the vehicle's windshield wipers are activated according to the second activation command.

[0288] When the driving scenario is driving in fog, a third activation command is determined, and the vehicle's fog lights are activated according to the third activation command.

[0289] In one possible implementation, the processing module 702 is further configured to:

[0290] Acquire user interaction data; wherein, the interaction data includes at least one of the following: voice data, gesture data, and gaze data;

[0291] Determine the user's interaction intent based on the interaction data;

[0292] Based on the interaction intent, control at least one in-vehicle device of the vehicle to operate in a target state; wherein the interaction intent is used to instruct the user to interact with the in-vehicle device, and the target state of the in-vehicle device.

[0293] The vehicle intelligent control device based on driving scenarios provided in this embodiment can execute the methods provided in the above method embodiments. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0294] Figure 8 A schematic diagram of the control device provided in this application. Figure 8 As shown, the control device 80 provided in this embodiment includes at least one processor 801 and a memory 802. Optionally, the control device 80 further includes a communication component 803. The processor 801, memory 802, and communication component 803 are connected via a bus 804.

[0295] In a specific implementation, at least one processor 801 executes computer execution instructions stored in memory 802, causing at least one processor 801 to perform the above-described method.

[0296] The specific implementation process of processor 801 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0297] It should be noted that the processor in the control device provided in this application embodiment can integrate a microcontroller unit (MCU) and a neural processing unit (NPU). The MCU is responsible for real-time control logic, while the NPU is responsible for recognizing driving scenarios and user interaction intentions.

[0298] Optionally, the processor in the control device provided in this application embodiment may be configured with a fusion decision model. This fusion decision model can be constructed based on Dempster-Shafer theory and dynamic Bayesian networks.

[0299] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0300] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0301] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0302] Optionally, by combining the control device provided in the embodiments of this application, the sensors deployed in the vehicle, and the on-board equipment in the vehicle, a vehicle intelligent control system based on driving scenarios can be constructed.

[0303] In this system, the perception layer includes various types of sensors deployed in the vehicle to collect vehicle scene decision data, user fatigue data, and user interaction operation data. The control layer includes the control devices provided in the embodiments of this application. The execution layer includes various on-board devices in the vehicle.

[0304] The various types of sensors in the perception layer and the various vehicle-mounted devices in the execution layer are each connected to the control device in the control layer via a bus. This enables the control device in the control layer to acquire the data collected by the sensors and send the generated control parameters and / or control commands to each vehicle-mounted device, so that each vehicle-mounted device operates in the target state.

[0305] This application also provides a vehicle including the control device as described in the foregoing embodiments. This control device enables the execution of the vehicle intelligent control method based on driving scenarios provided in the foregoing method embodiments, thereby improving the interaction efficiency between the user and the vehicle's onboard equipment and thus enhancing driving safety.

[0306] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0307] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0308] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0309] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0310] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0311] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0312] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0313] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0314] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0315] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A vehicle intelligent control method based on driving scenarios, characterized in that, include: Acquire scene decision data for the vehicle; wherein the scene decision data indicates the vehicle status and / or environmental status; Based on the scenario decision data, the current driving scenario of the vehicle is determined; and based on the driving scenario, at least one on-board device of the vehicle is controlled to operate in a target state.

2. The method according to claim 1, characterized in that, The scenario decision data includes one or more of the following: driving data, cargo data, and environmental data.

3. The method according to claim 2, characterized in that, If the scenario decision data includes cargo data, and the cargo data includes cargo box load and cargo box door status, then based on the scenario decision data, the current driving scenario of the vehicle is determined, including: If it is determined that the cargo box load is less than or equal to the first threshold, and the cargo box door status indicates that the cargo box door is in the open state, then the current driving scenario of the vehicle is determined to be a cargo loading scenario. If it is determined that the cargo box load is greater than the first threshold, and the cargo box door status indicates that the cargo box door is open, then the current driving scenario of the vehicle is determined to be a cargo unloading scenario.

4. The method according to claim 2, characterized in that, If the scenario decision data includes driving data and environmental data, wherein the driving data includes driving duration and vehicle speed, and the environmental data includes light intensity, then the current driving scenario of the vehicle is determined based on the scenario decision data, including: If it is determined that the light intensity is less than the second threshold and the vehicle speed is less than the third threshold, then the current driving scenario of the vehicle is determined to be a nighttime parking scenario. If it is determined that the light intensity is less than the second threshold, the vehicle speed is greater than or equal to the third threshold, and the driving duration is greater than or equal to the fourth threshold, then the current driving scenario of the vehicle is determined to be a nighttime cruise scenario. If it is determined that the light intensity is greater than or equal to the second threshold, the vehicle speed is greater than or equal to the third threshold, and the driving duration is greater than or equal to the fourth threshold, then the current driving scenario of the vehicle is determined to be a daytime cruising scenario.

5. The method according to claim 2, characterized in that, If the scenario decision data includes environmental data, such as rainfall, ambient temperature, and ambient humidity, then the current driving scenario of the vehicle is determined based on the scenario decision data, including: If the rainfall is greater than or equal to the fifth threshold, then the current driving scenario of the vehicle is determined to be a driving scenario in the rain. If the ambient temperature and ambient humidity meet the first preset conditions, then the current driving scenario of the vehicle is determined to be a driving scenario in fog; wherein, the first preset conditions indicate that the external environment in which the vehicle is located is in a foggy state.

6. The method according to claim 1, characterized in that, Based on the driving scenario, controlling at least one onboard device of the vehicle to operate in a target state includes: Based on the driving scenario, control parameters corresponding to the driving scenario are determined; and based on the control parameters, at least one on-board device of the vehicle is controlled to operate in a target state.

7. The method according to claim 6, characterized in that, Based on the driving scenario, determine control parameters corresponding to the driving scenario; and based on the control parameters, control at least one on-board device of the vehicle to operate in a target state, including: When the driving scenario is a cargo loading scenario or a cargo unloading scenario, a first display command and a first seat adjustment parameter are determined, and the screen of the intelligent device in the vehicle is controlled to display the image captured by the camera in the cargo box of the vehicle according to the first display command, and the seat of the vehicle is adjusted to an upright angle according to the first seat adjustment parameter.

8. The method according to claim 6, characterized in that, Based on the driving scenario, determine control parameters corresponding to the driving scenario; and based on the control parameters, control at least one on-board device of the vehicle to operate in a target state, including: When the driving scenario is a nighttime parking scenario, a second display command and a first activation command are determined, and the screen of the intelligent device in the vehicle is controlled to display the image captured by the vehicle's external camera according to the second display command, and the external warning lights of the vehicle are controlled to be turned on according to the first activation command. When the driving scenario is a nighttime cruise scenario, a first control parameter and a second control parameter are determined, and the cabin ambient light in the vehicle is controlled to enter a warm light mode according to the first control parameter, and the rearview mirror of the vehicle is controlled to enter an anti-glare mode according to the second control parameter. When the driving scenario is a daytime cruise scenario, a third control parameter is determined, and the vibration module on the vehicle's steering wheel is controlled to provide vibration feedback at a preset frequency according to the third control parameter.

9. The method according to claim 6, characterized in that, When the driving scenario is a nighttime parking scenario, a nighttime cruising scenario, or a daytime cruising scenario, the method further includes: Acquire fatigue data of a user in a vehicle; wherein the fatigue data includes at least one of the following: pressure distribution data of the vehicle's seats, the user's heart rate data, and the user's blood oxygen data; Based on the fatigue data, the user's fatigue status is determined; When the fatigue condition indicates that the user is in a fatigued state, control parameters for at least one of the following in-vehicle devices are determined based on the fatigue condition: the vehicle seat, the vehicle air conditioner, the vehicle dashboard, and the vehicle smart device; and at least one in-vehicle device is controlled to operate in a target state based on the control parameters.

10. The method according to claim 9, characterized in that, When the fatigue condition indicates that the user is in a state of fatigue, the fatigue condition includes the user's fatigue level; Based on the fatigue condition, determine the control parameters of at least one of the following vehicle-mounted devices; And according to the control parameters, control at least one vehicle-mounted device to operate in a target state, including: When the fatigue level is mild fatigue, a second seat adjustment parameter is determined, and the vehicle seat is adjusted to a preset angle according to the second seat adjustment parameter. When the fatigue level is moderate fatigue, the third seat adjustment parameters and the air conditioning start command are determined, and the vehicle seat is adjusted to a preset angle and the lumbar airbag of the vehicle seat is inflated according to the third seat adjustment parameters; the vehicle air conditioning is turned on according to the air conditioning start command. When the fatigue level is severe fatigue, a warning parameter is determined, and the vehicle's dashboard is controlled to display fatigue warning information based on the warning parameter, and the vehicle's smart devices are controlled to send fatigue driving information to the management platform.

11. The method according to claim 6, characterized in that, Based on the driving scenario, determine control parameters corresponding to the driving scenario; and based on the control parameters, control at least one on-board device of the vehicle to operate in a target state, including: When the driving scenario is driving in the rain, a second activation command is determined, and the vehicle's windshield wipers are activated according to the second activation command. When the driving scenario is a foggy driving scenario, a third activation command is determined, and the vehicle's fog lights are activated according to the third activation command.

12. The method according to any one of claims 1-11, characterized in that, The method further includes: Acquire user interaction data; wherein the interaction data includes at least one of the following: voice data, gesture data, and gaze data; Based on the interaction data, determine the user's interaction intent; According to the interaction intent, at least one in-vehicle device of the vehicle is controlled to operate in a target state; wherein the interaction intent is used to instruct the user to interact with the in-vehicle device and the target state of the in-vehicle device.

13. A vehicle intelligent control device based on driving scenarios, characterized in that, include: An acquisition module is used to acquire scene decision data of the vehicle; wherein the scene decision data indicates the vehicle status and / or environmental status; The processing module is used to determine the current driving scenario of the vehicle based on the scenario decision data; and to control at least one on-board device of the vehicle to operate in a target state based on the driving scenario.

14. A control device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-12.

15. A vehicle, characterized in that, Includes the control device as described in claim 14.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1-12.

17. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method of any one of claims 1-12.

Images