Control method and device and vehicle

Abstract

The invention provides a control method and device and a vehicle. The method can be applied to the field of intelligent driving. The method comprises the following steps: acquiring information of obstacles around a vehicle and road environment information; determining the speed of the vehicle according to the obstacle information and the road environment information; and controlling the vehicle according to the speed. The embodiment of the invention can be applied to an intelligent automobile or an electric automobile, and the driving experience of a user can be improved by outputting the speed which better conforms to the current scene of the automobile.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving, and more specifically, to a control method, device and vehicle. Background Art

[0002] During vehicle driving, the road speed limit value can usually be directly provided by map data or obtained through real-time perception of speed limit sign information. However, the map data may have the problem of incorrectly marked road speed limit values, and even if the map data is correctly marked, or the correct speed limit information is directly perceived in real time, it may not match the current scene of the vehicle (or, not match the driving habits of people). For example, on some highway ramps, driving at the speed marked on the map may cause traffic congestion.

[0003] Therefore, how to determine a speed that is more in line with the vehicle's current scenario (or more human-like) becomes a problem that needs to be solved urgently. Summary of the invention

[0004] The present application provides a control method, device and vehicle, which can output a speed that matches the current scenario of the vehicle (or is more human-like), helping to improve the user's driving experience.

[0005] In a first aspect, the present application provides a control method, which includes: acquiring 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.

[0006] Based on the above technical solution, the vehicle can determine the speed of the vehicle according to the information of obstacles and road environment information, and thus control the vehicle's driving according to the speed of the vehicle. In this way, the speed of the vehicle is combined with the information of surrounding obstacles and road environment information, so that the speed is more consistent with the current scene of the vehicle, or more similar to the actual speed in the human driving scene, which helps to improve the user's driving experience.

[0007] The above first speed may be a first speed limit value of the vehicle corresponding to the information of the first obstacle and the first road environment information, or the first speed limit value may be determined by the first speed.

[0008] In some possible implementations, the information of the first obstacle 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 first obstacle. The above first obstacle may include one or more obstacles. Exemplarily, the first obstacle may be other vehicles around the vehicle.

[0009] 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.

[0010] In some possible implementations, controlling the vehicle includes: controlling a prompting device in the vehicle to prompt the first speed.

[0011] In some possible implementations, the first obstacle may be an obstacle in a region of interest (ROI) of the vehicle. Alternatively, the first obstacle may include obstacles around the vehicle that have not been screened.

[0012] 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.

[0013] In some possible implementations, controlling the vehicle according to the first speed includes: controlling the vehicle to travel at the first speed.

[0014] In combination with the first aspect, in certain implementations of the first aspect, determining a first speed of the vehicle based on information about a 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.

[0015] Based on the above technical solution, by inputting obstacle information and road environment information into the model, the vehicle speed can be output. In this way, the generalization of speed determination can be improved; at the same time, the output speed information is combined with the surrounding obstacle information and road environment information, so that the speed is more consistent with the current scene of the vehicle, or more similar to the actual speed in the human driving scene, which helps to improve the user's driving experience.

[0016] 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 made lower than the speed limit indicated by the map data, thereby ensuring that the vehicle passes the curve safely.

[0017] 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; determining the first speed based on the weight of the first obstacle and the speed of the first obstacle.

[0018] Based on the above technical solution, the speed of the vehicle can be determined by combining the weight of the obstacle and the speed of the obstacle. In this way, the speed of the vehicle combines the weight of the surrounding obstacles and the speed of the obstacles, making the speed more consistent with the current scene of the vehicle, or more similar to the actual speed in the human driving scene, which helps to improve the user's driving experience.

[0019] 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.

[0020] Based on the above technical solution, the first obstacle can be screened out from multiple obstacles through the first road environment information and / or the information of each obstacle. In this way, it can be ensured that the screened out first obstacle is an obstacle that affects the speed of the vehicle, which helps to improve the accuracy of the vehicle speed.

[0021] In some possible implementations, the first obstacle is determined from the multiple obstacles based on the first road environment information, including: based on attribute information of multiple lanes in the road where the vehicle is located (for example, the lane is a straight lane, a left turn lane, a right turn lane, a straight plus a left turn lane, a straight plus a right turn lane, or a U-turn lane, etc.), determining the first obstacle from the multiple obstacles.

[0022] In some possible implementations, 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 heading angle of the vehicle and a heading angle of each obstacle in the multiple obstacles, and / or a speed of each obstacle.

[0023] 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 acquired at a first moment, and the method further includes: before the first moment, acquiring 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.

[0024] Based on the above technical solution, when controlling the vehicle, the third speed can be determined by combining the speed output at the current moment with the speed output at one or more moments before the current moment. In this way, for vehicles in an automatic driving state, the output speed can be made more stable, which helps to avoid a sudden change in the behavior of the vehicle 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's attention.

[0025] In some possible implementations, controlling the vehicle includes: controlling a prompting device in the vehicle to prompt the third speed.

[0026] In some possible implementations, the vehicle is controlled according to the third speed: the vehicle is controlled to travel at the third speed.

[0027] 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.

[0028] Based on the above technical solution, the determined speed can be processed in steps. In this way, for vehicles in the automatic driving state, the determined speed can be made more stable, which helps to avoid the sudden change of vehicle behavior due to speed jump, thereby improving the user's driving experience; for manually driven vehicles, it helps to avoid the frequent jump of speed and distraction of the driver's attention.

[0029] In some possible implementations, controlling the vehicle according to the fourth speed includes: controlling the vehicle to travel at the fourth speed.

[0030] 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, the preset speed range being determined by a speed limit value indicated by map data.

[0031] 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, which can avoid the safety hazard caused by excessively high speed, resulting in the vehicle speeding. At the same time, it can also avoid traffic congestion caused by vehicles driving too slowly due to too low speed.

[0032] 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.

[0033] 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.

[0034] In combination with the first aspect, in some implementations of the first aspect, the method further includes: controlling a prompting device to prompt the fourth speed.

[0035] Based on the above technical solution, by prompting the user with the fourth speed, the user can clearly know the actual speed of the current vehicle. For a vehicle in an automatic driving state, the user can determine whether to take over the vehicle according to the speed prompted by the prompt device; for a manually driven vehicle, the user can control the vehicle to accelerate or decelerate according to the speed prompted by the prompt device.

[0036] In combination with the first aspect, in certain implementations of the first aspect, the method also includes: obtaining information on 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 on the third obstacle and the third road environment information; and controlling the vehicle based on the fifth speed.

[0037] 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.

[0038] In some possible implementations, controlling the vehicle according to the fifth speed includes: controlling the vehicle to travel at the fifth speed.

[0039] In a second aspect, a control method is provided, the method comprising: obtaining information of multiple obstacles around a vehicle; determining a target obstacle set from the multiple obstacles based on road environment information of the vehicle and / or information of each of the multiple obstacles; performing weighted processing on the speed of each obstacle in the target obstacle set to obtain a first speed; and controlling the vehicle based on the first speed.

[0040] 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.

[0041] For example, the farther the distance between the vehicle and the i-th obstacle is, the greater the weight w iThe lower the distance between the vehicle and the i-th obstacle, the greater the weight w i The higher.

[0042] For example, the smaller the TTC between the vehicle and the i-th obstacle, the greater the weight w i The higher the TTC between the vehicle and the i-th obstacle, the greater the weight w i The lower.

[0043] 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.

[0044] 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.

[0045] 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 a sample vehicle, road environment information around the sample vehicle, and the speed of the sample vehicle.

[0046] 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.

[0047] In combination with the third aspect, in certain implementations of the third aspect, the acquisition unit 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.

[0048] 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 acquired at a first moment, and the acquisition unit is further used to: acquire the information of the second obstacle and the second road environment information around the vehicle before the first moment; the determination unit is further used to determine the 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.

[0049] In combination with the third aspect, in certain implementations of the third aspect, the control unit is used to: perform step-wise processing on the first speed to obtain a fourth speed; and control the vehicle according to the fourth speed.

[0050] 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, and the preset speed range is determined by a speed limit value indicated by map data.

[0051] 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.

[0052] In combination with the third aspect, in certain implementations of the third aspect, the acquisition unit is further used to: obtain information on a third obstacle around the vehicle and third road environment information after a preset time has elapsed since the first speed; the determination unit is further used to determine a fifth speed based on the information on the third obstacle and the third road environment information; the control unit is further used to: control the vehicle based on the fifth speed.

[0053] In a fourth aspect, a control device is provided, which includes: an acquisition unit for acquiring information of multiple obstacles around a vehicle; a determination unit for determining a target obstacle set from multiple obstacles based on road environment information of the vehicle and / or information of 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.

[0054] 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 perform any possible control method in the first aspect or the second aspect.

[0055] 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.

[0056] In combination with the sixth aspect, in certain implementations of the sixth aspect, the control system also includes a display device.

[0057] In combination with the sixth aspect, in certain implementations of the sixth aspect, the computing platform is located in a cloud server.

[0058] 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.

[0059] In an eighth aspect, the present application provides a server, which includes any possible control device in the third aspect, the fourth aspect or the fifth aspect.

[0060] 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.

[0061] 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.

[0062] In a tenth aspect, the present application provides a computer-readable medium storing a program code, and when the computer program code runs on a computer, the computer executes any possible control method in the first aspect or the second aspect.

[0063] In an eleventh aspect, the present application provides a chip, comprising a circuit, wherein the circuit is used to execute any possible control method in the first aspect or the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is a functional block diagram of a vehicle provided in an embodiment of the present application.

[0065] Figure 2 It is a schematic flow chart of the control method provided in the embodiment of the present application.

[0066] Figure 3 It is a schematic diagram of an application scenario provided by an embodiment of the present application.

[0067] Figure 4 It is another schematic diagram of the application scenario provided by the embodiment of the present application.

[0068] Figure 5 It is a schematic diagram of the model reasoning process provided in the embodiment of the present application.

[0069] Figure 6 It is a schematic diagram of the stepped processing provided in an embodiment of the present application.

[0070] Figure 7It is the human-machine interface HMI provided in the embodiment of the present application.

[0071] Figure 8 It is a schematic flow chart of the control method provided in the embodiment of the present application.

[0072] Fig. 9 It is a schematic block diagram of a control device provided in an embodiment of the present application.

[0073] Fig.10 It is another schematic block diagram of the control device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] 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. Among them, 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 description of the association relationship of associated objects, indicating that three relationships can exist, 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 three relationships can exist, 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.

[0075] 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 limitation 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 no unnecessary limitation should be constituted due to the use of such prefixes. In addition, in the description of the present embodiment, unless otherwise specified, the meaning of "multiple" is two or more.

[0076] Figure 11 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 around the vehicle 100. For example, the perception system 110 may include a positioning system, and the positioning system may be a global positioning system (GPS), or 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.

[0077] Some or all functions of the vehicle 100 may be controlled by the computing platform 120. The computing platform 120 may include one or more processors, such as processors 121 to 12n (n is a positive integer). The processor is a circuit with signal processing capability. In one implementation, the processor may be a circuit with instruction reading and execution capability, 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 relationship of a hardware circuit, and the logical relationship of the hardware circuit is fixed or reconfigurable, such as a hardware circuit implemented by a processor as 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 document 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 may 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, the memory is used to store instructions, and some or all of the processors 121 to 12n may call instructions in the memory to implement corresponding functions.

[0078] The vehicles involved in the embodiments of the present application are vehicles in a broad sense, which may be means of transportation (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 mowers, harvesters, etc.), amusement equipment, toy vehicles, etc. The embodiments of the present application do not specifically limit the type of vehicles.

[0079] As mentioned above, during vehicle driving, the road speed limit value can usually be directly provided by map data or obtained through real-time perception of speed limit sign information. However, the map data may have the problem of incorrectly marked road speed limit values, and even if the map data is correctly marked, or the correct speed limit information is directly perceived in real time, it may not match the current scene of the vehicle (or, not match the driving habits of people).

[0080] 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.

[0081] For another 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, it may cause congestion.

[0082] When dealing with the above 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 scene 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.

[0083] In response to the above problems, the embodiments of the present application provide a control method, device and vehicle, which imitate the practices of human drivers when dealing with unreasonable road speed limits, refer to the speed of surrounding traffic, and output a more human-like speed, thereby helping to improve the user's driving experience.

[0084] Figure 2 FIG. 2 shows a schematic flow chart of a control method 200 provided in an embodiment of the present application. The method 200 may be Figure 1 The method 200 may be performed by the vehicle 100 shown, or the method 200 may be performed by the computing platform 120, or the method 200 may be performed by a system consisting of the computing platform 120 and a sensor, or the method 200 may be performed by a system-on-chip (SoC) in the computing platform 120, or the method 200 may be performed by a processor, circuit or chip in the computing platform 120, or the method 200 may be performed by a server. The method 200 includes:

[0085] S210, obtaining information of a first obstacle and first road environment information around the vehicle.

[0086] 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.

[0087] The first obstacle may include one or more obstacles. Exemplarily, the first obstacle may be other vehicles around the vehicle.

[0088] Optionally, if the executor of method 200 is a vehicle, obtaining the information of the first obstacle and the first road environment information around the vehicle includes: determining the information of the first obstacle and the first road environment information based on data collected by a sensor (for example, one or more of a camera, a lidar, and a millimeter-wave radar).

[0089] 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.

[0090] 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.

[0091] Optionally, 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 the position of the obstacle relative to the vehicle. Exemplarily, an obstacle located in the ROI may be determined as the first obstacle.

[0092] For example, Figure 3 A schematic diagram of an application scenario provided by an embodiment of the present application is shown. The area around the vehicle includes obstacles 1, 2, 3, 4, and 5, wherein obstacles 1, 2, 3, and 4 are located in the ROI area. The vehicle may determine obstacles 1, 2, 3, and 4 as the first obstacle.

[0093] Alternatively, the vehicle may also screen obstacles based on obstacle 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 in obstacles 1, 2, and 3 and the heading angle of the vehicle 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 heading angle of the vehicle is greater than 10°, then the vehicle may screen obstacles 1, 2, and 3 from multiple obstacles, or obstacles 1, 2, and 3 are obstacles that can be used to determine the speed of the vehicle.

[0094] 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.

[0095] For example, Figure 4 A schematic diagram of an application scenario provided by an embodiment of the present application is shown. 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.

[0096] Alternatively, if Figure 4 As shown, if the distance between obstacle 1 and the intersection is greater than or equal to a preset distance (for example, 50 meters), then obstacle 1, obstacle 2 and obstacle 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 obstacle 2 and obstacle 3 can be determined as obstacles that can be used to determine the speed of the vehicle.

[0097] Optionally, determining the first obstacle from the multiple obstacles according to information of each obstacle in the multiple obstacles includes: determining the first obstacle from the multiple obstacles according to a speed and / or a heading angle of each obstacle in the multiple obstacles.

[0098] 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.

[0099] Exemplarily, the preset speed is 5 km / h, and the preset difference is 10°.

[0100] Optionally, the vehicle determines the first obstacle from the multiple obstacles based on the attributes of each lane and information of each obstacle in the multiple obstacles.

[0101] For example, Figure 4 As shown, 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°, 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.

[0102] S220: Determine a first speed of the vehicle according to the information about the first obstacle and the first road environment information.

[0103] The first speed determined above may be the speed limit of the vehicle, or the first speed may be used to determine the current speed limit of the vehicle. For example, when the first speed is determined to be 50 km / h based on the information of the first obstacle and the first road environment information, the current speed limit of the vehicle may be determined to be 50 km / h, or the current maximum speed of the vehicle may be determined to be less than or equal to the first speed plus a preset speed (e.g., 20 km / h), that is, the current maximum speed of the vehicle may be less than or equal to 70 km / h.

[0104] Optionally, determining a 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 a sample vehicle, road environment information around the sample vehicle, and the speed of the sample vehicle.

[0105] Figure 5 FIG. 1 is a schematic diagram showing the model reasoning process provided by the embodiment of the present application. Figure 5 As shown, the input of the model 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 obstacles around the vehicle. The output of the model may include the speed of the vehicle.

[0106] The above model can be a neural network (NN), such as transformer, multi-layer perceptron (MLP), residual neural network (ResNet), recurrent neural network (RNN); or, it can also be other machine learning algorithms, such as support vector machine (SVM) or decision tree.

[0107] The above model can be obtained by training the information of obstacles around the sample vehicle, the road environment information around the sample vehicle and the speed of the sample vehicle, wherein the speed of the sample vehicle is a marked speed. For example, the speed of the sample vehicle can be the real speed of the sample vehicle, or the speed of the sample vehicle can be the average speed or maximum speed of the sample vehicle within a preset time before the information of obstacles around the sample vehicle and the road environment information are collected.

[0108] During the model training phase, model training can be performed using the following two sample training data sets.

[0109] (1) The obstacles around the sample vehicle may include all obstacles in the ROI determined by the location of the sample vehicle.

[0110] If the sample data set used in the model training phase includes information about all obstacles in the ROI, then in the model inference phase, the vehicle can input information about all obstacles in the vehicle's ROI and road environment information into the model. Figure 3 As shown, in the model reasoning stage, the information of obstacle 1, obstacle 2, obstacle 3 and obstacle 4 and the road environment information can be input into the model to obtain the speed of the vehicle.

[0111] (2) The obstacles around the sample vehicle may include obstacles screened based on road environment information and / or obstacle information.

[0112] If the sample data set used in the model training phase includes obstacles that have been screened based on road environment information and / or obstacle information, then in the model inference phase, the vehicle can first screen the obstacles in the ROI based on the vehicle's road environment information and / or obstacle information to obtain screened obstacles. The vehicle can input the screened obstacle information and road environment information into the model. For example, Figure 4 As shown, in the model reasoning stage, the information of obstacle 2 and obstacle 3 and the road environment information can be input into the model to obtain the speed of the vehicle.

[0113] In the above model training stage, the loss function of the model can be a regression loss function such as mean squared error (MSE); or, the loss function can be a classification loss function such as cross entropy loss (cross entropy loss).

[0114] Optionally, the sample training data set also includes the speed limit value indicated by the map data and the reason why the speed of the sample vehicle differs greatly from the speed limit value indicated in the map data. For example, the road environment information and obstacle information collected in a certain frame in the sample training data set can be labeled, and the label indicates that the sample vehicle is located in a ramp of a highway, and the reason why the speed of the sample vehicle differs greatly from the speed limit value of the ramp indicated in the map data is that a too low speed limit value will cause congestion; or, the label indicates that the sample vehicle is located at a curve with a large curvature radius of the highway, and the reason why the speed of the sample vehicle differs greatly from the speed limit value of the ramp indicated in the map data is that a too high speed limit value will affect the driving safety of the user. In this way, when the speed limit value output by the model differs greatly from the speed limit value indicated by the map data, the vehicle can also output the reason why the speed of the vehicle differs greatly from the speed limit value indicated by the map data.

[0115] Optionally, determining a first speed of the vehicle based on information about the first obstacle and first road environment information includes: determining a weight of the first obstacle based on the first road environment information and / or information about the first obstacle; and determining the first speed based on the weight of the first obstacle and the speed of the first obstacle.

[0116] 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 of the first obstacle; the first speed of the vehicle can be determined based on the weight of each obstacle in the multiple obstacles and the speed of each obstacle.

[0117] 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 first road environment information includes: determining an average value of the speeds of the plurality of obstacles as the first speed.

[0118] 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 of 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.

[0119] S230: Control the vehicle according to the first speed.

[0120] The embodiments of the present application can be applied to vehicles in an automatic driving state. For example, when it is detected that the vehicle has turned on the navigation cruise assistant (NCA) and the integrated cruise assistant (ICA) functions, it can be determined that the vehicle is in an automatic driving state. At this time, the vehicle can make decisions and plans for the vehicle's behavior based on the first speed. For example, the decision to give way, the decision to change lanes, the speed planning, the control prompt device to prompt the user, etc.

[0121] The embodiments of the present application may also be applied to vehicles that are manually driven. For example, after the speed of the vehicle is determined, a prompt may 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.

[0122] 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.

[0123] In the 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.

[0124] Exemplarily, the filtered speed can be determined by the following formula (1):

[0125]

[0126] in, is the filtered speed (or speed limit), t is the current time, v limit,t-τ It is the speed (or speed limit) at each moment output by the model or the speed (or speed limit) before filtering. T is a preset value, and τ can change according to the preset step size. For example, T is 10, which means that the speed of 10 moments needs to be averaged. These 10 moments can include the current moment and the 9 moments before the current moment. τ represents each moment in these 10 moments.

[0127] 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. Figure 6 The schematic diagram of the step-by-step processing provided by the embodiment of the present application is shown. For example, when the speed output by the model is between (45km / h, 50km / h], it can be determined that the final speed is 50km / h.

[0128] 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, wherein the preset speed range is determined by a speed limit value indicated by map data.

[0129] For example, Figure 6 As shown, the processed speed is within the preset speed limit value range [40km / h, 60km / h]. If the speed determined in S220 is 65km / h, the processed speed may be 60km / h; if the speed determined in S220 is 35km / h, the processed speed may be 40km / h.

[0130] Exemplarily, the preset speed range may be determined by the speed limit indicated by the map data. For example, the maximum value of the preset speed range may be the speed limit indicated by the map plus the 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).

[0131] 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].

[0132] Optionally, the method 200 also includes: obtaining information on a third obstacle and third road environment information around the vehicle after a preset time from obtaining the first speed, the second moment being a moment after the first moment; determining a fifth speed based on the information on the third obstacle and the third road environment information; and controlling the vehicle based on the fifth speed.

[0133] The above post-processing method for the speed output by the model can enable a vehicle in an autonomous driving state to control the vehicle's driving at a more stable speed; for manually driven vehicles, it can avoid frequent speed switching that distracts the driver's attention.

[0134] Optionally, controlling the vehicle includes: controlling a prompting device to prompt the first speed.

[0135] Exemplarily, the prompting device includes but is not limited to a display device, a speaker, and the like.

[0136] 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 command based on the first speed and send the vehicle control command to the vehicle.

[0137] Figure 7 The human machine interface (HMI) provided by the embodiment of the present application is shown.

[0138] like Figure 7 As shown in the figure, the map data indicates that the speed limit of the ramp is 40km / h. After the vehicle enters the ramp, the vehicle speed can be obtained to be 50km / h based on the information of surrounding obstacles and road environment information. At the same time, a prompt message "The speed limit indicated by the map is too low, which may cause congestion, and the speed has been increased to 50km / h" can also be displayed.

[0139] Figure 8 FIG. 8 is a schematic flow chart of a control method 800 provided in an embodiment of the present application. The method 800 may be Figure 1 The method 800 may be performed by the vehicle 100 shown in the figure, or the method 800 may be performed by the computing platform 120, or the method 800 may be performed by a system consisting of the computing platform 120 and a sensor, or the method 800 may be performed by a SoC in the computing platform 120, or the method 800 may be performed by a processor, circuit or chip in the computing platform 120, or the method 800 may be performed by a server. The method 800 includes:

[0140] S810, obtaining information of multiple obstacles around the vehicle.

[0141] 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.

[0142] The process of the vehicle determining the target obstacle set from the multiple obstacles according to the road environment information and / or the information of each obstacle in the multiple obstacles in S820 above can refer to the description in S210 above, which will not be repeated here.

[0143] S830: Perform weighted processing on the speeds of obstacles in the target obstacle set to obtain a first speed of the vehicle.

[0144] For example, the speeds of obstacles in the target obstacle set can be weighted averaged by the following formula (2):

[0145]

[0146] Among them, v limit 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 i It can be determined by the distance d between the vehicle and the i-th obstacle 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 is, 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 is i It can be determined by the following formula (3):

[0150]

[0151] For example, the smaller the TTC between the vehicle and the i-th obstacle, the greater the weight w i The higher the TTC between the vehicle and the i-th obstacle, the greater the weight w i The lower.

[0152] 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.

[0153] S840: Control the vehicle according to the first speed.

[0154] S840 may refer to the description of S230 above, which will not be repeated here.

[0155] Fig. 9 FIG. 1 shows a schematic block diagram of a control device 900 provided in an embodiment of the present application. Fig. 9 As shown, the device 900 includes: an acquisition unit 910, used to acquire information about a first obstacle and first road environment information around a vehicle; a determination unit 920, used to determine a first speed of the vehicle based on the information about the first obstacle and the first road environment information; and a control unit 930, used to control the vehicle based on the first speed.

[0156] 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, and the model is trained by a sample data set, which includes information about obstacles around a sample vehicle, road environment information around the sample vehicle, and the speed of the sample vehicle.

[0157] Optionally, the information of the first obstacle includes the speed of the first obstacle, and the determination unit 920 is used to determine the weight of the first obstacle according to the first road environment information and / or the information of the first obstacle; and determine the first speed according to the weight of the first obstacle and the speed of the first obstacle.

[0158] 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.

[0159] Optionally, the information of the first obstacle and the first road environment information are information acquired at a first moment, and the acquisition unit 910 is also used to: acquire the information of the second obstacle and the second road environment information around the vehicle before the first moment; the determination unit 920 is also 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.

[0160] Optionally, the control unit 930 is used to: perform step-wise processing on the first speed to obtain a fourth speed; and control the vehicle according to the fourth speed.

[0161] Optionally, the device 900 further includes: a determination 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.

[0162] Optionally, the control unit 930 is used to: control a prompting device to prompt the fourth speed.

[0163] Optionally, the acquisition unit 910 is also used to: obtain information on a third obstacle around the vehicle and third road environment information after obtaining a preset time from the first speed; the determination unit 920 is also used to determine a fifth speed based on the information on the third obstacle and the third road environment information; the control unit 930 is also used to: control the vehicle based on the fifth speed.

[0164] For example, the acquisition unit 910 may be Figure 1 The computing platform or the processing circuit, processor or controller in the computing platform. Taking the acquisition unit 910 as the processor 121 in the computing platform as an example, the processor 121 can acquire data collected by the vehicle sensor and determine the information of the first obstacle around the vehicle and the first road environment information according to the data collected by the sensor.

[0165] For another example, the determining unit 920 may be Figure 1 The computing platform or the processing circuit, processor or controller in the computing platform. Taking the determination unit 920 as the processor 122 in the computing platform as an example, the processor 122 can determine the first speed of the vehicle according to the information of the first obstacle around the vehicle and the first road environment information determined by the processor 121.

[0166] For another example, the control unit 930 may be Figure 1 The computing platform in the computing platform or the processing circuit, processor or controller in the computing platform. Taking the control unit 930 as the processor 123 in the computing platform as an example, the processor 123 can control the vehicle according to the first speed determined by the processor 122. For example, the processor 123 can generate a vehicle control instruction according to the first speed and send the vehicle control instruction to the chassis controller. For another example, the processor 123 can control a prompt device (e.g., a display screen, a speaker, etc.) to prompt the user with the first speed.

[0167] 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 application does not limit this.

[0168] Fig.10 FIG. 1 is a schematic block diagram of a control device 1000 provided in an embodiment of the present application. Fig.10 As shown, the device 1000 includes: an acquisition unit 1010, used to acquire information of multiple obstacles around a vehicle; a determination unit 1020, used to 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; a data processing unit 1030, used to perform 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, used to control the vehicle according to the first speed.

[0169] For example, the acquisition unit 1010 may be Figure 1The computing platform or the processing circuit, processor or controller in the computing platform. Taking the acquisition unit 1010 as the processor 121 in the computing platform as an example, the processor 121 can acquire data collected by the vehicle sensor and determine the information of multiple obstacles around the vehicle according to the data collected by the sensor.

[0170] For another example, the determining unit 1020 may be Figure 1 The computing platform in the computing platform or the processing circuit, processor or controller in the computing platform. Taking the determination unit 1020 as the processor 122 in the computing platform as an example, the processor 122 can determine the target obstacle set from the multiple obstacles obtained by the processor 121 according to the road environment information of the vehicle and / or the information of each obstacle in the multiple obstacles.

[0171] For another example, the data processing unit 1030 may be Figure 1 The computing platform in the computing platform or the processing circuit, processor or controller in the computing platform. Taking the data processing unit 1030 as the processor 123 in the computing platform as an example, the processor 123 can perform weighted processing on the speeds of obstacles in the target obstacle set determined by the processor 122, so as to determine the first speed.

[0172] For another example, the control unit 1040 may be Figure 1 The computing platform in the computing platform or the processing circuit, processor or controller in the computing platform. Taking the control unit 1040 as the processor 124 in the computing platform as an example, the processor 124 can control the vehicle according to the first speed determined by the processor 123. For example, the processor 124 can generate a vehicle control instruction according to the first speed and send the vehicle control instruction to the chassis controller. For another example, the processor 124 can control a prompt device (e.g., a display screen, a speaker, etc.) to prompt the user with the first speed.

[0173] 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 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 application is not limited to this.

[0174] It should be understood that the division of the units in the above device is only a division of logical functions. In actual implementation, they can be fully or partially integrated into one physical entity, or they can be physically separated. In addition, the units in the device can be implemented in the form of a processor calling software; for example, the device includes a processor, the processor is connected to a memory, and instructions are stored in the memory. The processor calls the instructions stored in the memory to implement any of the above methods or realize the functions of the units of the device, wherein the processor is, for example, a general-purpose processor, such as a CPU or a microprocessor, and the memory is a memory in the device or a memory outside the device. Alternatively, the units in the device can be implemented in the form of hardware circuits, and the functions of some or all of the units can be realized by designing the hardware circuits. The hardware circuit can 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 are realized by designing the logical relationship of the components in the circuit; for another example, in another implementation, the hardware circuit can be implemented by PLD. Taking FPGA as an example, it can include a large number of logic gate circuits, and the connection relationship between the logic gate circuits is configured through the configuration file, so as to realize the functions of some or all of the above units. All units of the above device may be implemented entirely in the form of a processor calling software, or entirely in the form of a hardware circuit, or partially in the form of a processor calling software and the rest in the form of a hardware circuit.

[0175] Each unit in the above device may be one or more processors (or processing circuits) configured to implement the above method, such as a CPU, a GPU, an NPU, a TPU, a DPU, a microprocessor, a DSP, an ASIC, an FPGA, or a combination of at least two of these processor forms.

[0176] In addition, all or part of the units in the above device can be integrated together, or can be implemented independently. In one implementation, these units are integrated together and implemented in the form of SoC. The SoC may include at least one processor for implementing any of the above methods or implementing the functions of each unit of the device. The type of the at least one processor may be different, for example, including CPU and FPGA, CPU and artificial intelligence processor, CPU and GPU, etc.

[0177] 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 executed by the above embodiment.

[0178] Alternatively, if the device is located in a vehicle, the processing unit may be Figure 1 Processors 121-12n are shown.

[0179] 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.

[0180] 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.

[0181] The embodiment of the present application further provides a server, which may include the above-mentioned device 900 or device 1000. For example, the above-mentioned acquisition unit 910 may acquire data collected by the sensor of the vehicle and determine the first road environment information and the first obstacle information according to the data. For another example, the above-mentioned control unit 930 may generate and send a vehicle control instruction to the vehicle according to the first speed, thereby controlling the vehicle to travel.

[0182] The embodiment of the present application further provides a computer program product, which includes: a computer program code, and when the computer program code is executed on a computer, the computer executes the above control method.

[0183] The 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 control method.

[0184] In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in a processor or an instruction in the form of software. The method disclosed in conjunction with the embodiment of the present application can be directly embodied as a hardware processor for execution, or a combination of hardware and software modules in a processor for execution. 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 a memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it is not described in detail here.

[0185] 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.

[0186] 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.

[0187] Those of ordinary skill 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 to be beyond the scope of this application.

[0188] Those skilled in the art can 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.

[0189] In the several embodiments provided in the present 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 only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, 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.

[0190] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] 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.

[0192] 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 can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0193] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be covered. Within the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope 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 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.

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 a speed limit value indicated by 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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