Control method and device, and vehicle

Abstract

A control method and device, and a vehicle. The method can be applied to the field of intelligent driving. The method comprises: acquiring information of an obstacle around a vehicle and road environment information; determining the speed of the vehicle on the basis of the information of the obstacle and the road environment information; and controlling the vehicle on the basis of the speed. The method can be applied to an intelligent vehicle or an electric vehicle, and the driving experience of a user can be improved by outputting a speed better matching the current scene of the vehicle.

Description

Control method, device and vehicle

[0001] This application claims priority to the Chinese patent application filed with the State Intellectual Property Office of China on July 28, 2023, with application number 202310945780.6 and invention name “Control Method, Device and Vehicle”, the entire contents of which are incorporated by reference into this application. Technical Field

[0002] The present application relates to the field of intelligent driving, and more specifically, to a control method, device and vehicle. Background Art

[0003] While a vehicle is in motion, the road speed limit is typically provided directly by map data or obtained through real-time sensing of speed limit signs. However, map data may contain incorrectly labeled speed limits. Even if the map data is correctly labeled, or the correct speed limit information is directly sensed in real time, it may not match the vehicle's current location (or, may not be consistent with driving habits). For example, on some highway ramps, driving at the speed marked on the map may result in traffic congestion.

[0004] Therefore, how to determine a speed that is more in line with the vehicle's current scenario (or more human-like) becomes an urgent problem to be solved.

[0005] Summary of the Invention

[0006] The present application provides a control method, device, and vehicle that can output a speed that is consistent with the vehicle's current scenario (or more human-like), helping to improve the user's driving experience.

[0007] In a first aspect, the present application provides a control method, which includes: obtaining information about a first obstacle and first road environment information around a vehicle; determining a first speed of the vehicle based on the information about the first obstacle and the first road environment information; and controlling the vehicle based on the first speed.

[0008] Based on the above technical solution, the vehicle can determine its speed based on information about obstacles and the road environment, thereby controlling its travel accordingly. This incorporates information about surrounding obstacles and the road environment, making the vehicle's speed more consistent with the vehicle's current situation or more similar to the actual speed experienced by a human, thus improving the user's driving experience.

[0009] The first speed may be a first speed limit of the vehicle corresponding to the first obstacle information and the first road environment information, or the first speed limit may be determined by the first speed.

[0010] In some possible implementations, the first obstacle information includes one or more of the first obstacle's position, speed, acceleration, heading angle, type (e.g., truck, bus, or car), size, and turn signal information. The first obstacle may include one or more obstacles. For example, the first obstacle may be other vehicles surrounding the vehicle.

[0011] In some possible implementations, the first road environment information includes the number of lanes on the road where the vehicle is located, the attributes or topological structure of the lanes, etc.

[0012] In some possible implementations, controlling the vehicle includes: controlling a prompting device in the vehicle to prompt the first speed.

[0013] In some possible implementations, the first obstacle may be an obstacle in a region of interest (ROI) of the vehicle, or may include obstacles around the vehicle that have not been screened.

[0014] In some possible implementations, when there are no obstacles around the vehicle, the vehicle may be controlled according to a speed limit value indicated by map data.

[0015] In some possible implementations, controlling the vehicle according to the first speed includes: controlling the vehicle to travel at the first speed.

[0016] In combination with the first aspect, in certain implementations of the first aspect, determining the first speed of the vehicle based on information about the first obstacle and first road environment information includes: inputting the information about the first obstacle and the first road environment information into a model to obtain the first speed of the vehicle, the model being trained by a sample data set, the sample data set including information about obstacles around a sample vehicle, road environment information around the sample vehicle, and the speed of the sample vehicle.

[0017] Based on the above technical solution, by inputting obstacle information and road environment information into the model, the vehicle's speed can be output. This improves the generalization of speed determination. At the same time, the output speed information combines information about surrounding obstacles and the road environment, making it more consistent with the vehicle's current situation or more similar to the actual speed of a human driver, helping to improve the user's driving experience.

[0018] For example, at a curve where the speed limit indicated by the map data is high and the curvature is relatively large, by combining information about surrounding obstacles and road environment information, the output speed can be lowered than the speed limit indicated by the map data, thereby ensuring that the vehicle safely passes through the curve.

[0019] In combination with the first aspect, in certain implementations of the first aspect, the information of the first obstacle includes the speed of the first obstacle, and determining the first speed of the vehicle based on the information of the first obstacle and the first road environment information includes: determining the weight of the first obstacle based on the first road environment information and / or the information of the first obstacle; and determining the first speed based on the weight of the first obstacle and the speed of the first obstacle.

[0020] Based on the above technical solution, the vehicle's speed can be determined by combining the weights of the obstacles and their speeds. This combines the weights of the surrounding obstacles and their speeds, making the vehicle's speed more consistent with the vehicle's current situation or more similar to the actual speed of a human driver, thus improving the user's driving experience.

[0021] In combination with the first aspect, in certain implementations of the first aspect, obtaining information about a first obstacle around the vehicle includes: obtaining multiple obstacles around the vehicle; and determining the first obstacle from the multiple obstacles based on the first road environment information and / or information about each of the multiple obstacles.

[0022] Based on the above technical solution, the first obstacle can be screened out from multiple obstacles using the first road environment information and / or the information of each obstacle. In this way, the screened out first obstacle can be guaranteed to be an obstacle that affects the vehicle's speed, which helps to improve the accuracy of the vehicle's speed.

[0023] In some possible implementations, determining the first obstacle from the multiple obstacles based on the first road environment information includes: determining the first obstacle from the multiple obstacles based on attribute information of multiple lanes in the road where the vehicle is located (for example, whether the lane is a straight lane, a left turn lane, a right turn lane, a straight plus left turn lane, a straight plus right turn lane, or a U-turn lane, etc.).

[0024] In some possible implementations, determining the first obstacle from the multiple obstacles based on information about each obstacle in the multiple obstacles includes: determining the first obstacle from the multiple obstacles based on a heading angle of the vehicle and a heading angle of each obstacle in the multiple obstacles, and / or a speed of each obstacle.

[0025] In combination with the first aspect, in certain implementations of the first aspect, the information of the first obstacle and the first road environment information are information obtained at a first moment, and the method also includes: before the first moment, obtaining information of a second obstacle and second road environment information around the vehicle; determining a second speed of the vehicle based on the information of the second obstacle and the second road environment information; wherein, controlling the vehicle based on the first speed includes: determining a third speed based on the first speed and the second speed; and controlling the vehicle based on the third speed.

[0026] Based on the above technical solution, when controlling a vehicle, the third speed can be determined by combining the current speed output with the speed output at one or more previous times. This can stabilize the output speed for autonomous vehicles, helping to avoid sudden changes in vehicle behavior due to speed fluctuations, thereby improving the user's driving experience. For manually driven vehicles, this helps avoid frequent speed fluctuations that can distract the driver.

[0027] In some possible implementations, controlling the vehicle includes: controlling a prompting device in the vehicle to prompt the third speed.

[0028] In some possible implementations, the vehicle is controlled according to the third speed: the vehicle is controlled to travel at the third speed.

[0029] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: performing step-wise processing on the first speed to obtain a fourth speed; and controlling the vehicle according to the fourth speed.

[0030] Based on the above technical solution, the determined speed can be processed in steps. This can make the determined speed more stable for vehicles in autonomous driving mode, helping to avoid sudden changes in vehicle behavior due to speed jumps, thereby improving the user's driving experience; for manually driven vehicles, it helps to avoid frequent speed jumps that distract the driver.

[0031] In some possible implementations, controlling the vehicle according to the fourth speed includes: controlling the vehicle to travel at the fourth speed.

[0032] In combination with the first aspect, in certain implementations of the first aspect, before controlling the vehicle according to the fourth speed, the method further includes: determining that the fourth speed is within a preset speed range, where the preset speed range is determined by a speed limit value indicated by map data.

[0033] Based on the above technical solution, before controlling the vehicle, it can be determined that the fourth speed obtained by post-processing is within the preset speed range, thereby avoiding safety hazards caused by excessive speed, resulting in excessive speed. At the same time, it can also avoid traffic congestion caused by vehicles traveling too slowly due to excessive speed.

[0034] Exemplarily, the maximum value of the preset speed range may be the speed limit value indicated by the map data plus the preset value, or the maximum value of the preset speed range may be the speed limit value indicated by the map data multiplied by a preset coefficient.

[0035] In some possible implementations, when the fourth speed is greater than a maximum value of the preset speed range, the fourth speed is determined as the maximum value; or, when the fourth speed is less than a minimum value of the preset speed range, the fourth speed is determined as the minimum value.

[0036] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: controlling a prompting device to prompt the fourth speed.

[0037] Based on the above technical solution, by prompting the user with the fourth speed, the user can clearly understand the actual current vehicle speed. For vehicles in autonomous driving mode, the user can determine whether to take over the vehicle based on the speed indicated by the prompt device. For vehicles in manual driving mode, the user can control the acceleration or deceleration of the vehicle based on the speed indicated by the prompt device.

[0038] In combination with the first aspect, in certain implementations of the first aspect, the method further includes: obtaining information about a third obstacle and third road environment information around the vehicle after obtaining a preset time from the first speed; determining a fifth speed based on the information about the third obstacle and the third road environment information; and controlling the vehicle based on the fifth speed.

[0039] Based on the above technical solution, the vehicle can keep its speed unchanged for a preset period of time after outputting the first speed, which can avoid the vehicle frequently switching speeds and affecting the user's driving experience.

[0040] In some possible implementations, controlling the vehicle according to the fifth speed includes: controlling the vehicle to travel at the fifth speed.

[0041] In a second aspect, a control method is provided, comprising: obtaining information about multiple obstacles surrounding a vehicle; determining a target obstacle set from the multiple obstacles based on information about a road environment in which the vehicle is located and / or information about each of the multiple obstacles; weighting the speed of each obstacle in the target obstacle set to obtain a first speed; and controlling the vehicle based on the first speed.

[0042] In conjunction with the second aspect, in certain implementations of the second aspect, the weight w of each obstacle in the target obstacle set is i The distance d between the vehicle and each obstacle can be i , the time to collision (TTC) between the vehicle and each obstacle, and one or more determinations of the lanes where each obstacle is located.

[0043] For example, the farther the distance between the vehicle and the i-th obstacle, the greater the weight w i The lower the distance between the vehicle and the i-th obstacle, the greater the weight w i The higher.

[0044] For example, the smaller the TTC between the vehicle and the i-th obstacle, the greater the weight w i The higher the weight w is, the greater the TTC between the vehicle and the i-th obstacle. i The lower.

[0045] For another example, the weight of an obstacle in the same lane as the vehicle is higher than the weight of an obstacle in a different lane from the vehicle.

[0046] In a third aspect, a control device is provided, which includes: an acquisition unit for acquiring information about a first obstacle and first road environment information around a vehicle; a determination unit for determining a first speed of the vehicle based on the information about the first obstacle and the first road environment information; and controlling the vehicle based on the first speed.

[0047] In combination with the third aspect, in certain implementations of the third aspect, the determination unit is used to input the information of the first obstacle and the first road environment information into a model to obtain the first speed, and the model is trained by a sample data set, which includes information about obstacles around the sample vehicle, road environment information around the sample vehicle, and the speed of the sample vehicle.

[0048] In combination with the third aspect, in certain implementations of the third aspect, the information of the first obstacle includes the speed of the first obstacle, and the determination unit is used to determine the weight of the first obstacle based on the first road environment information and / or the information of the first obstacle; and determine the first speed based on the weight of the first obstacle and the speed of the first obstacle.

[0049] In combination with the third aspect, in certain implementations of the third aspect, the acquisition unit is used to: obtain information about multiple obstacles around the vehicle; and determine the first obstacle from the multiple obstacles based on the first road environment information and / or information about each of the multiple obstacles.

[0050] In combination with the third aspect, in certain implementations of the third aspect, the information of the first obstacle and the first road environment information are information obtained at a first moment, and the acquisition unit is further used to: obtain information of a second obstacle and second road environment information around the vehicle before the first moment; the determination unit is further used to determine a second speed of the vehicle based on the information of the second obstacle and the second road environment information; wherein the control unit is used to: determine a third speed based on the first speed and the second speed; and control the vehicle based on the third speed.

[0051] In combination with the third aspect, in certain implementations of the third aspect, the control unit is configured to: perform step-wise processing on the first speed to obtain a fourth speed; and control the vehicle according to the fourth speed.

[0052] In combination with the third aspect, in certain implementations of the third aspect, the device further includes: a determination unit, configured to determine that the fourth speed is within a preset speed range before the control unit controls the vehicle according to the fourth speed, the preset speed range being determined by a speed limit value indicated by map data.

[0053] In combination with the third aspect, in certain implementations of the third aspect, the control unit is used to: control the prompt device to prompt the fourth speed.

[0054] In combination with the third aspect, in certain implementations of the third aspect, the acquisition unit is further used to: obtain information about a third obstacle around the vehicle and third road environment information after obtaining a preset time from the first speed; the determination unit is further used to determine a fifth speed based on the information about the third obstacle and the third road environment information; and the control unit is further used to: control the vehicle based on the fifth speed.

[0055] In a fourth aspect, a control device is provided, comprising: an acquisition unit for acquiring information about multiple obstacles around a vehicle; a determination unit for determining a target obstacle set from multiple obstacles based on road environment information in which the vehicle is located and / or information about each of the multiple obstacles; a data processing unit for performing weighted processing on the speed of each obstacle in the target obstacle set to obtain a first speed of the vehicle; and a control unit for controlling the vehicle based on the first speed.

[0056] In a fifth aspect, the present application provides a control device, which includes a processing unit and a storage unit, wherein the storage unit is used to store instructions, and the processing unit executes the instructions stored in the storage unit to enable the device to execute any possible control method in the first aspect or the second aspect.

[0057] In a sixth aspect, the present application provides a control system comprising a sensor and a computing platform, wherein the computing platform comprises any possible control device in the third aspect, or comprises the control device described in the fourth aspect, or comprises the control device described in the fifth aspect.

[0058] In combination with the sixth aspect, in certain implementations of the sixth aspect, the control system further includes a display device.

[0059] In combination with the sixth aspect, in certain implementations of the sixth aspect, the computing platform is located in a cloud server.

[0060] In a seventh aspect, the present application provides a vehicle comprising any possible control device in the third aspect, or comprising the control device described in the fourth aspect, or comprising the control device described in the fifth aspect, or comprising the control system described in the sixth aspect.

[0061] In an eighth aspect, the present application provides a server comprising any possible control device in the third aspect, the fourth aspect or the fifth aspect.

[0062] In a ninth aspect, the present application provides a computer program product, comprising: a computer program code, which, when executed on a computer, enables the computer to execute any possible control method in the first or second aspect.

[0063] It should be noted that the above-mentioned computer program code can be stored in whole or in part on the first storage medium, wherein the first storage medium can be packaged together with the processor or separately packaged with the processor, and the embodiments of the present application do not specifically limit this.

[0064] In a tenth aspect, the present application provides a computer-readable medium storing a program code. When the computer program code runs on a computer, the computer executes any possible control method in the first or second aspect.

[0065] In an eleventh aspect, the present application provides a chip comprising a circuit for executing any possible control method in the first or second aspect above. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] FIG1 is a functional block diagram of a vehicle provided in an embodiment of the present application.

[0067] FIG2 is a schematic flow chart of a control method provided in an embodiment of the present application.

[0068] FIG3 is a schematic diagram of an application scenario provided by an embodiment of the present application.

[0069] FIG4 is another schematic diagram of an application scenario provided by an embodiment of the present application.

[0070] FIG5 is a schematic diagram of the model reasoning process provided in an embodiment of the present application.

[0071] FIG6 is a schematic diagram of the step-by-step processing provided in an embodiment of the present application.

[0072] FIG7 is a human-machine interface HMI provided in an embodiment of the present application.

[0073] FIG8 is a schematic flow chart of a control method provided in an embodiment of the present application.

[0074] FIG9 is a schematic block diagram of a control device provided in an embodiment of the present application.

[0075] FIG10 is another schematic block diagram of the control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0076] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B; "and / or" in this article is only a way to describe the association relationship of associated objects, indicating that there can be three kinds of relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. "At least one" means one or more. For example, "at least one of A and B" is similar to "A and / or B", describing the association relationship of associated objects, indicating that there can be three kinds of relationships, for example, at least one of A and B can mean: A exists alone, A and B exist at the same time, and B exists alone.

[0077] In the embodiments of the present application, prefixes such as "first" and "second" are used only to distinguish different description objects and have no limiting effect on the position, order, priority, quantity or content of the described objects. The use of prefixes such as ordinal numbers to distinguish description objects in the embodiments of the present application does not constitute a restriction on the described objects. For the statement of the described objects, please refer to the description in the context of the claims or embodiments, and the use of such prefixes should not constitute an unnecessary restriction. In addition, in the description of this embodiment, unless otherwise specified, the meaning of "plurality" is two or more.

[0078] FIG1 is a functional block diagram of a vehicle 100 provided in an embodiment of the present application. The vehicle 100 may include a perception system 110 and a computing platform 120, wherein the perception system 110 may include one or more sensors for sensing information about the environment surrounding the vehicle 100. For example, the perception system 110 may include a positioning system, which may be a global positioning system (GPS), a BeiDou system, or other positioning systems. The perception system 110 may also include one or more of an inertial measurement unit (IMU), a laser radar, a millimeter-wave radar, an ultrasonic radar, and a camera device.

[0079] Some or all functions of the vehicle 100 may be controlled by a computing platform 120. The computing platform 120 may include one or more processors, such as processors 121 to 12n (n is a positive integer). A processor is a circuit capable of processing signals. In one implementation, the processor may be a circuit capable of reading and executing instructions, such as a central processing unit (CPU), a microprocessor, a graphics processing unit (GPU) (which can be understood as a microprocessor), or a digital signal processor (DSP). In another implementation, the processor may implement certain functions through the logical relationships of a hardware circuit. The logical relationships of the hardware circuit may be fixed or reconfigurable. For example, the processor may be a hardware circuit implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD), such as a field programmable gate array (FPGA). In a reconfigurable hardware circuit, the process of the processor loading a configuration file to implement the hardware circuit configuration can be understood as the process of the processor loading instructions to implement the functions of some or all of the above units. In addition, the processor may also be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), a tensor processing unit (TPU), a deep learning processing unit (DPU), etc. In addition, the computing platform 120 may also include a memory for storing instructions, and some or all of the processors 121 to 12n may call the instructions in the memory to implement corresponding functions.

[0080] The vehicles involved in the embodiments of the present application are vehicles in a broad sense, which can be transportation vehicles (such as commercial vehicles, passenger cars, motorcycles, flying cars, trains, etc.), industrial vehicles (such as forklifts, trailers, tractors, etc.), engineering vehicles (such as excavators, bulldozers, cranes, etc.), agricultural equipment (such as lawn mowers, harvesters, etc.), amusement equipment, toy vehicles, etc. The embodiments of the present application do not specifically limit the type of vehicles.

[0081] As previously mentioned, while a vehicle is driving, road speed limits are typically provided directly by map data or obtained through real-time sensing of speed limit signs. However, map data may contain incorrectly labeled speed limits. Even if the map data is correctly labeled or the correct speed limit information is obtained directly through real-time sensing, it may not match the vehicle's current location (or may not be consistent with human driving habits).

[0082] For example, on some highways with large curvatures, the speed limit value marked on the map data is higher. If a vehicle in automatic driving mode drives according to the speed limit value marked on the map data, it may cause the vehicle to lose control.

[0083] For example, on a highway ramp, the speed limit value marked on the map data is low. If a vehicle in autonomous driving mode drives according to the speed limit value marked on the map data, congestion may occur.

[0084] When dealing with the above-mentioned problems, human drivers usually refer to the speed of the traffic around their own vehicles and drive at a similar speed to avoid the problem of speeding too fast or too slow. Therefore, the handling method of human drivers can be referred to, and the speed of the vehicle can be dynamically corrected based on the speed of the surrounding traffic, so that the behavior of the vehicle in the automatic driving state is more human-like, or the behavior of the vehicle in the automatic driving state is more in line with the current scenario of the vehicle. The existing method of determining the vehicle speed only considers the speed limit value indicated by the map data or the road speed limit value determined by the data collected by the sensor, and does not consider the traffic information (or the information of obstacles around the vehicle). Therefore, it is difficult to form a human-like speed (or road speed limit value) correction, and the application scenarios are limited.

[0085] To address the above issues, the embodiments of the present application provide a control method, device, and vehicle. By imitating the practices of human drivers when dealing with unreasonable road speed limits, referring to the speed of surrounding traffic, and outputting a more human-like speed, the method helps to improve the user's driving experience.

[0086] FIG2 shows a schematic flow chart of a control method 200 provided in an embodiment of the present application. The method 200 may be executed by the vehicle 100 shown in FIG1 , or the method 200 may be executed by the computing platform 120 described above, or the method 200 may be executed by a system consisting of the computing platform 120 and sensors, or the method 200 may be executed by a system-on-chip (SoC) in the computing platform 120, or the method 200 may be executed by a processor, circuit, or chip in the computing platform 120, or the method 200 may be executed by a server. The method 200 includes:

[0087] S210: Acquire information about a first obstacle and first road environment information around the vehicle.

[0088] Optionally, the information of the first obstacle includes one or more of the position, speed, acceleration, heading angle, type (eg, truck, bus, or car), size, and turn signal information of the first obstacle.

[0089] The first obstacle may include one or more obstacles. For example, the first obstacle may be other vehicles around the vehicle.

[0090] Optionally, if the executor of method 200 is a vehicle, obtaining information about the first obstacle and the first road environment information around the vehicle includes: determining the information about the first obstacle and the first road environment information based on data collected by sensors (for example, one or more of a camera, a lidar, and a millimeter-wave radar).

[0091] Optionally, if the executor of method 200 is a vehicle, obtaining information about the first obstacle and the first road environment information around the vehicle includes: the vehicle sending data collected by the vehicle's sensors to a cloud server; and the vehicle receiving information about the first obstacle and the first road environment information determined by the cloud server based on the data.

[0092] Optionally, the first road environment information includes the number of lanes on the road where the vehicle is located, the attributes or topological structure of the lanes, etc.

[0093] Alternatively, the ROI may be determined based on the position of the vehicle, and whether an obstacle around the vehicle is located in the ROI is determined by its position relative to the vehicle. For example, an obstacle located in the ROI may be determined as the first obstacle.

[0094] For example, Figure 3 shows a schematic diagram of an application scenario provided by an embodiment of the present application. The area surrounding a vehicle includes obstacles 1, 2, 3, 4, and 5, wherein obstacles 1, 2, 3, and 4 are located within the ROI. The vehicle may identify obstacles 1, 2, 3, and 4 as the first obstacle.

[0095] Alternatively, the vehicle can filter obstacles based on their information. For example, if the speeds of obstacles 1, 2, and 3 are all greater than 5 km / h, and the difference between the heading angle of each obstacle and the vehicle's heading angle is less than 10°, and the speed of obstacle 4 is greater than 5 km / h, and the difference between the heading angle of obstacle 4 and the vehicle's heading angle is greater than 10°, the vehicle can filter out obstacles 1, 2, and 3 from the multiple obstacles. Alternatively, obstacles 1, 2, and 3 can be used to determine the vehicle's speed.

[0096] Optionally, obtaining information about a first obstacle around the vehicle includes: obtaining multiple obstacles around the vehicle; and determining the first obstacle from the multiple obstacles based on the first road environment information and / or information about the vehicle and each of the multiple obstacles.

[0097] Exemplarily, FIG4 shows a schematic diagram of an application scenario provided by an embodiment of the present application. The first road environment information includes the attributes of each lane (for example, lane 1 is a left-turn lane, and lanes 2 and 3 are through lanes). At this time, the vehicle determines the first obstacle from the multiple obstacles based on the attributes of each lane. For example, obstacle 1 is in lane 1, the vehicle and obstacle 2 are in lane 2, and obstacle 3 is in lane 3. Since lane 1 is a left-turn lane, obstacle 1 is about to turn left in the future. At this time, the vehicle can filter out obstacle 2 and obstacle 3 from multiple obstacles, or obstacle 2 and obstacle 3 are obstacles that can be used to determine the speed of the vehicle.

[0098] Optionally, as shown in Figure 4, if the distance between obstacle 1 and the intersection is greater than or equal to a preset distance (for example, 50 meters), then obstacles 1, 2 and 3 can be determined as obstacles that can be used to determine the speed of the vehicle; if the distance between obstacle 1 and the intersection is less than the preset distance, then obstacles 2 and 3 can be determined as obstacles that can be used to determine the speed of the vehicle.

[0099] Optionally, determining the first obstacle from the multiple obstacles based on information of each obstacle in the multiple obstacles includes: determining the first obstacle from the multiple obstacles based on a speed and / or heading angle of each obstacle in the multiple obstacles.

[0100] Exemplarily, the first obstacle is an obstacle among the multiple obstacles whose speed is greater than or equal to a preset speed, and / or the first obstacle is an obstacle among the multiple obstacles whose heading angle difference with the heading angle of the vehicle is less than or equal to a preset difference.

[0101] Exemplarily, the preset speed is 5 km / h, and the preset difference is 10°.

[0102] Optionally, the vehicle determines the first obstacle from the multiple obstacles based on the attributes of each lane and information about each obstacle in the multiple obstacles.

[0103] For example, as shown in FIG4 , if the speed of obstacle 2 is 50 km / h and the difference between the heading angle of obstacle 2 and the vehicle is less than or equal to 10°, and the speed of obstacle 3 is 0 and the difference between the heading angle of obstacle 3 and the vehicle is less than or equal to 10°, then the vehicle can select obstacle 2 from multiple obstacles, or obstacle 2 is an obstacle that can be used to determine the speed.

[0104] S220: Determine a first speed of the vehicle based on the information about the first obstacle and the first road environment information.

[0105] The first speed determined above may be the vehicle's speed limit, or the first speed may be used to determine the vehicle's current speed limit. For example, if the first speed is determined to be 50 km / h based on the first obstacle information and the first road environment information, the current vehicle speed limit may be determined to be 50 km / h. Alternatively, the vehicle's current maximum speed may be determined to be less than or equal to the first speed plus a preset speed (e.g., 20 km / h), i.e., the vehicle's current maximum speed may be less than or equal to 70 km / h.

[0106] Optionally, determining the first speed of the vehicle based on the information of the first obstacle and the first road environment information includes: inputting the information of the first obstacle and the first road environment information into a model to obtain the first speed, the model being trained by a sample data set, the sample data set including information of obstacles around the sample vehicle, road environment information around the sample vehicle, and the speed of the sample vehicle.

[0107] Figure 5 shows a schematic diagram of the model reasoning process provided by an embodiment of the present application. As shown in Figure 5, the model input may include obstacle information and road environment information, wherein the obstacle information includes one or more of the position, speed, acceleration, heading angle, type (e.g., truck, bus, or car), size, and turn signal information of the obstacle around the vehicle. The model output may include the vehicle's speed.

[0108] The above models can be neural networks (NN), such as transformer, multi-layer perceptron (MLP), residual neural network (ResNet), recurrent neural network (RNN); or they can be other machine learning algorithms, such as support vector machine (SVM) or decision tree.

[0109] The above model can be trained using information about obstacles around the sample vehicle, information about the road environment around the sample vehicle, and the speed of the sample vehicle, where the speed of the sample vehicle is a labeled speed. For example, the speed of the sample vehicle can be the actual speed of the sample vehicle, or the speed of the sample vehicle can be the average speed or maximum speed of the sample vehicle during a preset period of time before the information about obstacles around the sample vehicle and the road environment is collected.

[0110] During the model training phase, model training can be performed using the following two sample training data sets.

[0111] (1) The obstacles around the sample vehicle may include all obstacles in the ROI determined by the location of the sample vehicle.

[0112] If the sample dataset used in the model training phase includes information about all obstacles in the ROI, then during the model inference phase, the vehicle can input information about all obstacles in the vehicle's ROI, as well as road environment information, into the model. For example, as shown in Figure 3, during the model inference phase, information about obstacles 1, 2, 3, and 4, as well as road environment information, can be input into the model to obtain the vehicle's speed.

[0113] (2) The obstacles around the sample vehicle may include obstacles that are screened based on road environment information and / or obstacle information.

[0114] If the sample dataset used in the model training phase includes obstacles filtered based on road environment information and / or obstacle information, then during the model inference phase, the vehicle can first filter obstacles in the ROI based on the vehicle's road environment information and / or obstacle information to obtain the filtered obstacles. The vehicle can then input the filtered obstacle information and road environment information into the model. For example, as shown in Figure 4, during the model inference phase, information about obstacles 2 and 3, along with the road environment information, can be input into the model to obtain the vehicle's speed.

[0115] In the above model training phase, the loss function of the model can be a regression loss function such as the mean squared error loss function (MSE); or, the loss function can be a classification loss function such as the cross entropy loss function.

[0116] Optionally, the sample training dataset also includes the speed limit indicated by the map data and the reason why the sample vehicle's speed differs significantly from the speed limit indicated in the map data. For example, the road environment information and obstacle information captured in a frame in the sample training dataset can be labeled, with the label indicating that the sample vehicle is located on a highway ramp and the reason why the sample vehicle's speed differs significantly from the ramp's speed limit indicated in the map data is that an excessively low speed limit can cause congestion. Alternatively, the label indicates that the sample vehicle is located on a curve with a large curvature radius on the highway and the reason why the sample vehicle's speed differs significantly from the ramp's speed limit indicated in the map data is that an excessively high speed limit can affect the user's driving safety. In this way, when the speed limit output by the model differs significantly from the speed limit indicated by the map data, the vehicle can also output the reason why the vehicle's speed differs significantly from the speed limit indicated by the map data.

[0117] Optionally, determining the first speed of the vehicle based on the information of the first obstacle and the first road environment information includes: determining the weight of the first obstacle based on the first road environment information and / or the information of the first obstacle; and determining the first speed based on the weight of the first obstacle and the speed of the first obstacle.

[0118] Exemplarily, the first obstacle includes multiple obstacles, and the weight of each obstacle in the multiple obstacles can be determined based on the first road environment information and / or information about the first obstacle; the first speed of the vehicle is determined based on the weight of each obstacle in the multiple obstacles and the speed of each obstacle.

[0119] Exemplarily, the first obstacle includes a plurality of obstacles, and determining the first speed of the vehicle according to information about the first obstacle and the first road environment information includes: determining an average value of the speeds of the plurality of obstacles as the first speed.

[0120] Exemplarily, determining a first speed of the vehicle based on information about a first obstacle and first road environment information includes: when it is determined based on the first road environment information that the vehicle is currently in a preset scene (for example, a ramp on a highway, a curve with a curvature radius greater than a preset radius, etc.), determining the first speed based on the speed of the first obstacle.

[0121] S230: Control the vehicle according to the first speed.

[0122] The embodiments of the present application can be applied to vehicles in autonomous driving mode. For example, if the vehicle detects that the navigation cruise assistant (NCA) or integrated cruise assistant (ICA) functions are enabled, the vehicle can be determined to be in autonomous driving mode. In this case, the vehicle can make decisions and plans for its behavior based on the first speed. For example, decisions to yield, lane change, speed planning, and control prompts to the user can be made.

[0123] The embodiments of the present application can also be applied to vehicles that are manually driven. For example, after determining the speed of the vehicle, a prompt can be given to the user, so that the user can control the acceleration or deceleration of the vehicle according to the speed prompted by the prompt device.

[0124] Optionally, controlling the vehicle based on the first speed includes: obtaining information about a second obstacle and second road environment information around the vehicle; determining a second speed of the vehicle based on the information about the second obstacle and the second road environment information; determining a third speed based on the first speed and the second speed; and controlling the vehicle to travel based on the third speed.

[0125] In an embodiment of the present application, after determining the speed of the vehicle, the speed may be filtered to reduce fluctuations in the output result over time.

[0126] For example, the filtered speed can be determined by the following formula (1):

[0127] in, is the filtered speed (or speed limit), t is the current time, v limit,t-τ The speed (or speed limit) at each moment output by the model, or the speed before filtering (or speed limit), is T, which is a preset value. τ can vary according to a preset step size. For example, if T is 10, it means that the speed is averaged over 10 moments, including the current moment and the 9 moments before it. τ represents each of these 10 moments.

[0128] Optionally, the method 200 further includes: performing a stepwise process on the first speed to obtain a fourth speed; and controlling the vehicle to travel according to the fourth speed. For example, FIG6 shows a schematic diagram of the stepwise process provided by an embodiment of the present application. For example, when the speed output by the model is between (45 km / h, 50 km / h), the final speed can be determined to be 50 km / h.

[0129] Optionally, before controlling the vehicle to travel according to the fourth speed, the method further includes: determining that the fourth speed is within a preset speed range, where the preset speed range is determined by a speed limit value indicated by map data.

[0130] For example, as shown in FIG6 , the processed speed is within the preset speed limit range [40 km / h, 60 km / h]. If the speed determined in S220 is 65 km / h, the processed speed may be 60 km / h; if the speed determined in S220 is 35 km / h, the processed speed may be 40 km / h.

[0131] For example, the preset speed range may be determined by a speed limit indicated by map data. For example, the maximum value of the preset speed range may be the speed limit indicated by the map plus a preset value, or the maximum value may be the speed limit indicated by the map multiplied by a preset coefficient (e.g., 1.2). The minimum value of the preset speed range may be the speed limit indicated by the map data, or the minimum value may also be a preset value (e.g., 40 km / h).

[0132] For example, if the speed limit indicated by the map data is 40km / h, then the preset speed range may be [40km / h, 60km / h]; for another example, if the speed limit indicated by the map data is 100km / h, then the preset speed range may be [100km / h, 120km / h].

[0133] Optionally, the method 200 further includes: obtaining information about a third obstacle and third road environment information around the vehicle after a preset time period from obtaining the first speed, the second moment being a moment after the first moment; determining a fifth speed based on the information about the third obstacle and the third road environment information; and controlling the vehicle based on the fifth speed.

[0134] The above post-processing method of the speed output by the model can enable vehicles in autonomous driving state to control vehicle driving at a more stable speed; for manually driven vehicles, it can avoid frequent speed switching that distracts the driver's attention.

[0135] Optionally, controlling the vehicle includes: controlling a prompting device to prompt the first speed.

[0136] Exemplarily, the prompting device includes but is not limited to a display device, a speaker, and the like.

[0137] Optionally, if the executor of method 200 is a server, the server can obtain data collected by sensors sent by the vehicle and obtain information about a first obstacle and first road environment information around the vehicle based on the data collected by the sensors; the server can determine a first speed of the vehicle based on the information about the first obstacle and the first road environment information; the server can generate a vehicle control instruction based on the first speed and send the vehicle control instruction to the vehicle.

[0138] FIG7 shows a human machine interface (HMI) provided in an embodiment of the present application.

[0139] As shown in Figure 7, the map data indicates that the speed limit on the ramp is 40 km / h. After entering the ramp, the vehicle can determine its speed is 50 km / h based on information about surrounding obstacles and the road environment. A prompt message may also be displayed, stating that the speed limit indicated on the map is too low and may cause congestion. The speed has been increased to 50 km / h.

[0140] FIG8 shows a schematic flow chart of a control method 800 provided in an embodiment of the present application. The method 800 can be executed by the vehicle 100 shown in FIG1 , or the method 800 can be executed by the computing platform 120, or the method 800 can be executed by a system consisting of the computing platform 120 and sensors, or the method 800 can be executed by the SoC in the computing platform 120, or the method 800 can be executed by a processor, circuit or chip in the computing platform 120, or the method 800 can be executed by a server. The method 800 includes:

[0141] S810: Obtain information about multiple obstacles around the vehicle.

[0142] S820: Determine a target obstacle set from the multiple obstacles based on the road environment information of the vehicle and / or the information of each obstacle in the multiple obstacles.

[0143] The process of the vehicle determining the target obstacle set from the multiple obstacles based on the road environment information and / or the information of each obstacle in the multiple obstacles in S820 above can be referred to the description in S210 above, and will not be repeated here.

[0144] S830: Perform weighted processing on the speeds of obstacles in the target obstacle set to obtain a first speed of the vehicle.

[0145] For example, the speed of obstacles in the target obstacle set can be weighted averaged using the following formula (2):

[0146] Among them, vlimit represents the speed of the vehicle (or the speed limit), N is the number of obstacles in the target obstacle set and N is a positive integer, v i represents the speed of the i-th obstacle in the target obstacle set, w i Represents the weight of the i-th obstacle in the target obstacle set.

[0147] Optionally, the weight w of the i-th obstacle in the target obstacle set is i The distance d between the vehicle and the i-th obstacle can be i , the TTC between the vehicle and the i-th obstacle, and the lane information where the i-th obstacle is located, one or more of which are determined.

[0148] For example, the farther the distance between the vehicle and the i-th obstacle, the greater the weight w i The lower the distance between the vehicle and the i-th obstacle, the greater the weight w i The higher.

[0149] For example, the weight w of the i-th obstacle i It can be determined by the following formula (3):

[0150] For example, the smaller the TTC between the vehicle and the i-th obstacle, the greater the weight w i The higher the weight w is, the greater the TTC between the vehicle and the i-th obstacle. i The lower.

[0151] For another example, the weight of an obstacle in the same lane as the vehicle is higher than the weight of an obstacle in a different lane from the vehicle.

[0152] S840: Control the vehicle according to the first speed.

[0153] S840 can refer to the description of S230 above, which will not be repeated here.

[0154] Figure 9 shows a schematic block diagram of a control device 900 provided in an embodiment of the present application. As shown in Figure 9, the device 900 includes: an acquisition unit 910 for acquiring information about a first obstacle and first road environment information surrounding a vehicle; a determination unit 920 for determining a first speed of the vehicle based on the first obstacle information and the first road environment information; and a control unit 930 for controlling the vehicle based on the first speed.

[0155] Optionally, the determination unit 920 is used to input the information of the first obstacle and the first road environment information into a model to obtain the first speed. The model is trained by a sample data set, and the sample data set includes information about obstacles around the sample vehicle, road environment information around the sample vehicle, and the speed of the sample vehicle.

[0156] Optionally, the information of the first obstacle includes the speed of the first obstacle. The determination unit 920 is used to determine the weight of the first obstacle based on the first road environment information and / or the information of the first obstacle; and determine the first speed based on the weight of the first obstacle and the speed of the first obstacle.

[0157] Optionally, the acquisition unit 910 is used to: acquire information of multiple obstacles around the vehicle; and determine the first obstacle from the multiple obstacles based on the first road environment information and / or information of each obstacle in the multiple obstacles.

[0158] Optionally, the information of the first obstacle and the first road environment information are information obtained at a first moment, and the acquisition unit 910 is further used to: obtain the information of the second obstacle and the second road environment information around the vehicle before the first moment; the determination unit 920 is further used to obtain the second speed of the vehicle based on the information of the second obstacle and the second road environment information; wherein, the control unit 930 is used to: determine a third speed based on the first speed and the second speed; and control the vehicle based on the third speed.

[0159] Optionally, the control unit 930 is configured to: perform step-wise processing on the first speed to obtain a fourth speed; and control the vehicle according to the fourth speed.

[0160] Optionally, the apparatus 900 further includes: a determining unit configured to determine, before the control unit 930 controls the vehicle according to the fourth speed, that the fourth speed is within a preset speed range, where the preset speed range is determined by a speed limit value indicated by map data.

[0161] Optionally, the control unit 930 is used to control the prompting device to prompt the fourth speed.

[0162] Optionally, the acquisition unit 910 is further used to: obtain information about a third obstacle and third road environment information around the vehicle after a preset time period from the first speed is obtained; the determination unit 920 is further used to determine a fifth speed based on the information about the third obstacle and the third road environment information; the control unit 930 is further used to: control the vehicle based on the fifth speed.

[0163] For example, the acquisition unit 910 may be the computing platform in FIG1 or a processing circuit, processor, or controller in the computing platform. For example, if the acquisition unit 910 is the processor 121 in the computing platform, the processor 121 may acquire data collected by vehicle sensors and determine information about a first obstacle and first road environment information around the vehicle based on the data collected by the sensors.

[0164] For another example, the determining unit 920 may be the computing platform in FIG1 or a processing circuit, processor, or controller in the computing platform. For example, if the determining unit 920 is the processor 122 in the computing platform, the processor 122 may determine the first speed of the vehicle based on the information about the first obstacle around the vehicle and the first road environment information determined by the processor 121.

[0165] For another example, control unit 930 may be the computing platform shown in FIG1 , or a processing circuit, processor, or controller within the computing platform. For example, if control unit 930 is processor 123 within the computing platform, processor 123 may control the vehicle based on the first speed determined by processor 122. For example, processor 123 may generate a vehicle control command based on the first speed and send the command to a chassis controller. For another example, processor 123 may control a prompt device (e.g., a display screen, a speaker, etc.) to notify the user of the first speed.

[0166] The functions implemented by the acquisition unit 910, the functions implemented by the determination unit 920, and the functions implemented by the control unit 930 may be implemented by different processors, or may be implemented by the same processor, or some functions may be implemented by the same processor. This embodiment of the present application does not limit this.

[0167] Figure 10 shows a schematic block diagram of a control device 1000 provided in an embodiment of the present application. As shown in Figure 10, the device 1000 includes: an acquisition unit 1010 for acquiring information about multiple obstacles around a vehicle; a determination unit 1020 for determining a target obstacle set from the multiple obstacles based on information about the road environment in which the vehicle is located and / or information about each of the multiple obstacles; a data processing unit 1030 for performing weighted processing on the speed of each obstacle in the target obstacle set to obtain a first speed of the vehicle; and a control unit 1040 for controlling the vehicle based on the first speed.

[0168] For example, the acquisition unit 1010 may be the computing platform in FIG1 or a processing circuit, processor, or controller in the computing platform. For example, if the acquisition unit 1010 is the processor 121 in the computing platform, the processor 121 may acquire data collected by vehicle sensors and determine information about multiple obstacles around the vehicle based on the data collected by the sensors.

[0169] For another example, the determining unit 1020 may be the computing platform in FIG1 or a processing circuit, processor, or controller in the computing platform. For example, if the determining unit 1020 is the processor 122 in the computing platform, the processor 122 may determine the target obstacle set from the multiple obstacles obtained by the processor 121 based on the road environment information of the vehicle and / or the information of each of the multiple obstacles.

[0170] For another example, data processing unit 1030 may be the computing platform in FIG1 , or a processing circuit, processor, or controller within the computing platform. For example, if data processing unit 1030 is processor 123 within the computing platform, processor 123 may perform weighted processing on the speeds of obstacles in the target obstacle set determined by processor 122 to determine the first speed.

[0171] For another example, control unit 1040 may be the computing platform shown in FIG1 , or a processing circuit, processor, or controller within the computing platform. For example, if control unit 1040 is processor 124 within the computing platform, processor 124 may control the vehicle based on the first speed determined by processor 123. For example, processor 124 may generate a vehicle control command based on the first speed and send the command to a chassis controller. For another example, processor 124 may control a prompt device (e.g., a display screen, a speaker, etc.) to notify a user of the first speed.

[0172] The functions implemented by the acquisition unit 1010, the functions implemented by the determination unit 1020, the functions implemented by the data processing unit 1030, and the functions implemented by the control unit 1040 can be implemented by different processors, or can be implemented by the same processor, or can be partially implemented by the same processor. This embodiment of the present application is not limited to this.

[0173] It should be understood that the division of the various units in the above device is merely a division of logical functions. In actual implementation, they may be fully or partially integrated into a single physical entity, or they may be physically separated. Furthermore, the units in the device may be implemented in the form of a processor calling software; for example, the device includes a processor connected to a memory storing instructions, and the processor calls the instructions stored in the memory to implement any of the above methods or the functions of the various units in the device, where the processor is, for example, a general-purpose processor such as a CPU or a microprocessor, and the memory is a memory within the device or a memory external to the device. Alternatively, the units in the device may be implemented in the form of hardware circuits, and the functions of some or all of the units may be implemented through the design of the hardware circuits. The hardware circuits may be understood as one or more processors. For example, in one implementation, the hardware circuit is an ASIC, and the functions of some or all of the above units may be implemented through the design of the logical relationships between the components within the circuits. In another implementation, the hardware circuit may be implemented using a PLD, such as an FPGA, which may include a large number of logic gate circuits, and the connections between the logic gate circuits may be configured using a configuration file to implement the functions of some or all of the above units. All units of the above apparatus may be implemented entirely in the form of software called by a processor, or entirely in the form of hardware circuits, or partially in the form of software called by a processor and the rest in the form of hardware circuits.

[0174] Each unit in the above device can be one or more processors (or processing circuits) configured to implement the above method, such as: CPU, GPU, NPU, TPU, DPU, microprocessor, DSP, ASIC, FPGA, or a combination of at least two of these processor forms.

[0175] In addition, the various units in the above apparatus may be fully or partially integrated together, or may be implemented independently. In one implementation, these units are integrated together and implemented in the form of a system-on-chip (SoC). The SoC may include at least one processor for implementing any of the above methods or implementing the functions of the various units of the apparatus. The at least one processor may be of different types, for example, including a CPU and an FPGA, a CPU and an artificial intelligence processor, a CPU and a GPU, etc.

[0176] An embodiment of the present application also provides a device, which includes a processing unit and a storage unit, wherein the storage unit is used to store instructions, and the processing unit executes the instructions stored in the storage unit so that the device executes the method or steps performed by the above embodiment.

[0177] Optionally, if the device is located in a vehicle, the processing unit may be the processors 121 - 12n shown in FIG. 1 .

[0178] An embodiment of the present application further provides a control system, which includes a computing platform and one or more sensors, wherein the computing platform includes the above-mentioned device 900 or device 1000.

[0179] An embodiment of the present application also provides a vehicle, which may include the above-mentioned device 900 or device 1000, or include the above-mentioned control system.

[0180] The present application also provides a server that may include the aforementioned apparatus 900 or apparatus 1000. For example, the acquisition unit 910 may acquire data collected by a vehicle sensor and determine first road environment information and first obstacle information based on the data. For another example, the control unit 930 may generate and send a vehicle control command to the vehicle based on the first speed, thereby controlling the vehicle's travel.

[0181] An embodiment of the present application further provides a computer program product, which includes: computer program code, which enables the computer to execute the above control method when the computer program code is run on the computer.

[0182] An embodiment of the present application further provides a computer-readable medium, wherein the computer-readable medium stores a program code. When the computer program code is executed on a computer, the computer executes the above-mentioned control method.

[0183] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or a power-on erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware. To avoid repetition, it will not be described in detail here.

[0184] It should be understood that in the embodiment of the present application, the memory may include a read-only memory and a random access memory, and provide instructions and data to the processor.

[0185] It should also be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0186] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0187] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0188] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0189] The units described as separate components may or may not be physically separate, and 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 these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0190] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0191] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0192] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be covered and fall within the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A control method, characterized in that: include: Acquiring information of a first obstacle and first road environment information around the vehicle; determining a first speed of the vehicle according to the information of the first obstacle and the first road environment information; The vehicle is controlled based on the first speed.

2. The method according to claim 1, characterized in that The determining the first speed of the vehicle according to the information of the first obstacle and the first road environment information includes: The information of the first obstacle and the first road environment information are input into a model to obtain the first speed, wherein the model is trained by a sample data set, and the sample data set includes information of obstacles around a sample vehicle, road environment information around the sample vehicle, and the speed of the sample vehicle.

3. The method according to claim 1, characterized in that The information of the first obstacle includes a speed of the first obstacle, and determining the first speed of the vehicle according to the information of the first obstacle and the first road environment information includes: determining a weight of the first obstacle according to the first road environment information and / or the information of the first obstacle; The first speed is determined according to the weight of the first obstacle and the speed of the first obstacle.

4. The method according to any one of claims 1 to 3, characterized in that The obtaining information of the first obstacle around the vehicle includes: Acquire multiple obstacles around the vehicle; The first obstacle is determined from the multiple obstacles according to the first road environment information and / or information of each obstacle among the multiple obstacles.

5. The method according to any one of claims 1 to 4, characterized in that The first obstacle information and the first road environment information are information acquired at a first moment, and the method further includes: Before the first moment, obtaining information of a second obstacle and second road environment information around the vehicle; determining a second speed of the vehicle according to the information of the second obstacle and the second road environment information; Wherein, controlling the vehicle according to the first speed includes: determining a third speed according to the first speed and the second speed; The vehicle is controlled according to the third speed.

6. The method according to any one of claims 1 to 4, characterized in that The method further comprises: Performing a stepwise process on the first speed to obtain a fourth speed; The vehicle is controlled according to the fourth speed.

7. The method according to claim 6, characterized in that Before controlling the vehicle according to the fourth speed, the method further includes: It is determined that the fourth speed is within a preset speed range, where the preset speed range is determined by a speed limit value indicated by map data.

8. The method according to claim 6 or 7, characterized in that: The method further comprises: The control prompting device prompts the fourth speed.

9. The method according to any one of claims 1 to 8, characterized in that The method further comprises: After obtaining the preset time from the first speed, obtaining information of a third obstacle around the vehicle and third road environment information; determining a fifth speed according to the information of the third obstacle and the third road environment information; The vehicle is controlled according to the fifth speed.

10. A control device, characterized in that: include: An acquisition unit, used to acquire information of a first obstacle and first road environment information around the vehicle; a determining unit, configured to determine a first speed of the vehicle according to the information of the first obstacle and the first road environment information; A control unit is used to control the vehicle according to the first speed.

11. The device according to claim 10, characterized in that The information of the first obstacle includes a speed of the first obstacle, and the determining unit is configured to: The information of the first obstacle and the first road environment information are input into the model to obtain the first speed. The model is composed of a sample This data set is obtained by training, and the sample data set includes information about obstacles around the sample vehicle, road environment information around the sample vehicle, and the speed of the sample vehicle.

12. The device according to claim 10, characterized in that The determining unit is configured to: determine the weight of the first obstacle according to the first road environment information and / or the information of the first obstacle; The first speed is determined according to the weight of the first obstacle and the speed of the first obstacle.

13. The device according to any one of claims 10 to 12, characterized in that The acquisition unit is used to: Acquiring information of multiple obstacles around the vehicle; The first obstacle is determined from the multiple obstacles according to the first road environment information and / or information of each obstacle among the multiple obstacles.

14. The device according to any one of claims 10 to 13, characterized in that The first obstacle information and the first road environment information are information acquired at the first moment, The acquisition unit is further used to: before the first moment, acquire information of a second obstacle around the vehicle and second road environment information; The determining unit is further used to determine a second speed of the vehicle according to the information of the second obstacle and the second road environment information; Wherein, the control unit is used for: determining a third speed according to the first speed and the second speed; The vehicle is controlled according to the third speed.

15. The device according to any one of claims 10 to 13, characterized in that The control unit is used for: Performing a stepwise process on the first speed to obtain a fourth speed; The vehicle is controlled according to the fourth speed.

16. The device according to claim 15, characterized in that The device also includes: The determining unit is used to determine that the fourth speed is within a preset speed range before the control unit controls the vehicle according to the fourth speed, and the preset speed range is determined by the speed limit value indicated by the map data.

17. The device according to claim 15 or 16, characterized in that The control unit is used for: The control prompting device prompts the fourth speed.

18. The device according to any one of claims 10 to 17, characterized in that The acquisition unit is further configured to: acquire information of a third obstacle around the vehicle and third road environment information after obtaining a preset time from the first speed; The determining unit is further configured to determine a fifth speed according to the information of the third obstacle and the third road environment information; The control unit is further used to control the vehicle according to the fifth speed.

19. A control device, characterized in that: include: Memory for storing computer programs; A processor, configured to execute the computer program stored in the memory, so that the apparatus performs the method according to any one of claims 1 to 9.

20. A control system, characterized in that: The device comprises a sensor and a computing platform, wherein the computing platform comprises a control device as claimed in any one of claims 10 to 19.

21. A vehicle, characterized in that: The method comprises a control device as claimed in any one of claims 10 to 19, or a control system as claimed in claim 20.

22. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a computer, the method according to any one of claims 1 to 9 is implemented.

23. A chip, characterized in that: include: A circuit for executing the method according to any one of claims 1 to 9.

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