Vehicle control method, device, electronic device, medium and autonomous driving vehicle

Abstract

The present disclosure provides a vehicle control method, apparatus, device, medium, product, and autonomous driving vehicle, relating to the fields of intelligent transportation technology, specifically autonomous driving, machine learning, and other technical fields. The vehicle control method includes: obtaining a driving mode transition probability for an obstructing vehicle based on historical driving data and first current perception data; in response to determining that the driving mode transition probability is less than a preset probability, processing second current perception data to obtain a driving mode transition result for the obstructing vehicle; and in response to determining that the driving mode transition result indicates that the obstructing vehicle is about to switch driving modes, controlling the driving state of a target vehicle relative to the obstructing vehicle.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent transportation, specifically to technical fields such as autonomous driving and machine learning, and more specifically to a vehicle control method, device, electronic device, medium, program product, and autonomous driving vehicle. Background Art

[0002] During driving, the target vehicle often needs to adjust its driving state in real time based on the driving behavior of the obstructing vehicle to ensure driving safety. For example, when the obstructing vehicle changes lanes, the target vehicle needs to slow down, brake, or yield. Therefore, real-time monitoring of the obstructing vehicle's driving behavior is necessary. Summary of the Invention

[0003] The present disclosure provides a vehicle control method, device, electronic device, storage medium, program product, and autonomous driving vehicle.

[0004] According to one aspect of the present disclosure, a vehicle control method is provided, comprising: obtaining a driving mode transition probability for an obstacle vehicle based on historical driving data and first current perception data; in response to determining that the driving mode transition probability is less than a preset probability, processing second current perception data to obtain a driving mode transition result for the obstacle vehicle; and in response to determining that the driving mode transition result indicates that the obstacle vehicle is about to switch driving modes, controlling the driving state of a target vehicle relative to the obstacle vehicle.

[0005] According to another aspect of the present disclosure, a vehicle control device is provided, comprising: an acquisition module, a processing module, and a control module. The acquisition module is configured to obtain a driving mode transition probability for an obstructing vehicle based on historical driving data and first current perception data; the processing module is configured to, in response to determining that the driving mode transition probability is less than a preset probability, process the second current perception data to obtain a driving mode transition result for the obstructing vehicle; and the control module is configured to, in response to determining that the driving mode transition result indicates that the obstructing vehicle is about to switch driving modes, control the driving state of a target vehicle relative to the obstructing vehicle.

[0006] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the aforementioned vehicle control method.

[0007] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the above-mentioned vehicle control method.

[0008] According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program / instruction, which implements the steps of the above vehicle control method when executed by a processor.

[0009] According to another aspect of the present disclosure, an autonomous driving vehicle is provided, comprising the electronic device as described above.

[0010] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0012] Figure 1 A vehicle control application scenario is schematically shown;

[0013] Figure 2 A flow chart schematically illustrates a vehicle control method according to an embodiment of the present disclosure;

[0014] Figure 3 A flow chart schematically illustrates a vehicle control method according to another embodiment of the present disclosure;

[0015] Figure 4 A schematic diagram schematically illustrates a method for obtaining a driving mode conversion result according to an embodiment of the present disclosure;

[0016] Figure 5 Schematically shows a schematic diagram of obtaining a driving mode conversion result according to another embodiment of the present disclosure;

[0017] Figure 6 Schematically shows a schematic diagram of obtaining a driving mode conversion result according to another embodiment of the present disclosure;

[0018] Figure 7 Schematically shows a schematic diagram of obtaining a driving mode conversion result according to another embodiment of the present disclosure;

[0019] Figure 8 A block diagram schematically shows a vehicle control device according to an embodiment of the present disclosure; and

[0020] Figure 9 It is a block diagram of an electronic device for implementing an embodiment of the present disclosure and performing vehicle control. DETAILED DESCRIPTION

[0021] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0022] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0023] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0024] When expressions such as "at least one of A, B and C, etc." are used, they should generally be interpreted in accordance with the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0025] Figure 1 The following schematically shows an application scenario of vehicle control. It should be noted that Figure 1 The examples shown are merely examples of application scenarios in which the embodiments of the present disclosure can be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure cannot be used in other devices, systems, environments or scenarios.

[0026] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a target vehicle 101 and obstacle vehicles 102 and 103 .

[0027] For example, the target vehicle 101 may be an autonomous driving vehicle, and the obstacle vehicles 102 and 103 may also be autonomous driving vehicles.

[0028] The target vehicle 101 has a data processing function, for example, the target vehicle 101 has electronic equipment, and the data processing is performed by the electronic equipment. The target vehicle 101 may also include a data sensing device, which senses the surrounding traffic conditions to obtain sensing data, and processes the sensing data through the electronic equipment to obtain the driving conditions of the obstacle vehicles 102 and 103.

[0029] After the target vehicle 101 learns about the driving conditions of the obstructing vehicles 102 and 103, the target vehicle 101 can control its own driving state. For example, when the target vehicle learns that the obstructing vehicle 103 is about to change lanes, the target vehicle 101 can perform operations such as deceleration, braking, and yielding to ensure driving safety.

[0030] Therefore, in order to more accurately control the driving state of the target vehicle, it is necessary to know the driving status of the obstacle vehicle in real time.

[0031] The following combination Figure 1 For application scenarios, refer to Figures 2 to 7 The vehicle control method according to the exemplary embodiment of the present disclosure is described. The vehicle control method of the embodiment of the present disclosure can be, for example, Figure 1 The target vehicle shown is used for execution, and specifically can be executed by an electronic device of the target vehicle, such as the electronic device that is the same as or similar to the electronic device described below.

[0032] Figure 2 The flowchart of a vehicle control method according to an embodiment of the present disclosure is schematically shown.

[0033] like Figure 2 As shown, the vehicle control method 200 of the embodiment of the present disclosure may include, for example, operations S210 to S230.

[0034] In operation S210 , a driving mode transition probability for an obstacle vehicle is obtained based on historical driving data and first current perception data.

[0035] For example, the driving mode transition of an obstructing vehicle may represent a transition from its current driving mode to a lane change, deceleration, acceleration, braking, or other mode. This embodiment of the disclosure uses the example of a driving mode transition in which the obstructing vehicle switches from a straight-ahead mode to a lane change mode. Therefore, the driving mode transition probability represents the probability that the obstructing vehicle will change lanes.

[0036] Historical driving data typically represents past lane change scenarios involving multiple vehicles. The first current perception data, for example, includes current surrounding data perceived by the target vehicle via a perception device. Because historical driving data typically represents past lane change scenarios, the current driving mode transition probability of the obstructing vehicle can be predicted based on the first current perception data, using the historical driving data as a reference. The driving mode transition probability, for example, indicates the likelihood of the obstructing vehicle changing lanes.

[0037] In operation S220 , in response to determining that the driving mode conversion probability is less than the preset probability, the second current perception data is processed to obtain a driving mode conversion result for the obstacle vehicle.

[0038] If the driving mode transition probability is less than the preset probability, it indicates that the probability of the obstructing vehicle currently executing a lane change is low, as predicted by the first current perception data based on historical driving data. To avoid inaccurate predictions of the driving mode transition probability based on historical driving data, the second current perception data is used as an aid to further determine whether the obstructing vehicle needs to change lanes if the driving mode transition probability is inaccurate. The second current perception data, for example, includes surrounding data perceived by the target vehicle through a sensing device. The second current perception data can be the same as or different from the first current perception data. The preset probability can be set based on actual circumstances.

[0039] Exemplarily, by processing the second current perception data, a driving mode conversion result for the obstructing vehicle can be obtained. The driving mode conversion result, for example, represents the intention of the obstructing vehicle to convert the driving mode. The intention to convert the driving mode includes whether there is an intention to change lanes.

[0040] In operation S230 , in response to determining that the driving mode conversion result indicates that the obstacle vehicle is to convert the driving mode, the driving state of the target vehicle relative to the obstacle vehicle is controlled.

[0041] If the driving mode transition result indicates that the obstructing vehicle is about to change driving modes, it can be determined that the obstructing vehicle is about to change lanes. In this case, the target vehicle's driving state relative to the obstructing vehicle can be controlled. For example, when the obstructing vehicle is about to change lanes, the target vehicle can be controlled to decelerate, brake, or yield to the obstructing vehicle.

[0042] According to embodiments of the present disclosure, the probability of an obstructing vehicle switching driving modes can be predicted based on historical driving data. If the probability is low, the target vehicle generally does not need to change its driving state. However, to avoid the potential risk of traffic accidents caused by the target vehicle not changing its driving state due to inaccurate predictions of driving mode switching probabilities based on historical driving data, embodiments of the present disclosure further determine the driving mode switching result of the obstructing vehicle based on the second current perception data. This allows for further determination of whether the obstructing vehicle will change lanes when the driving mode switching probability is low. This allows for more accurate understanding of the obstructing vehicle's lane change status, enabling timely control of the target vehicle's driving state and improving driving safety.

[0043] Figure 3 A flow chart of a vehicle control method according to another embodiment of the present disclosure is schematically shown.

[0044] like Figure 3 As shown, the trained deep learning model 302 is obtained based on historical driving data. For example, the historical driving data is used as a training sample to train the deep learning model 301 to obtain the trained deep learning model 302.

[0045] After obtaining the trained deep learning model 302, the first current perception data is processed based on the trained deep learning model 302 to obtain a driving mode transition probability for the obstructing vehicle. For example, the first current perception data is input into the trained deep learning model 302 for prediction, and the driving mode transition probability is output. The first current perception data may include, for example, driving data of the target vehicle, driving data of the obstructing vehicle, and surrounding traffic data.

[0046] After the predicted driving mode transition probability is obtained, if the driving mode transition probability is greater than or equal to a preset probability, it indicates that the obstructing vehicle is about to change lanes. In this case, the target vehicle is controlled to perform maneuvers such as deceleration, braking, and yielding. If the driving mode transition probability is less than the preset probability, it indicates that the obstructing vehicle has no intention of changing lanes. To avoid inaccurate prediction results, the driving mode transition result can be further determined based on the second current perception data.

[0047] The driving mode transition result may include, for example, whether the obstructing vehicle has no lane change intention or is about to change lanes. If the driving mode transition result indicates that the obstructing vehicle has no lane change intention, the target vehicle is controlled to maintain its current driving state. If the driving mode transition result indicates that the obstructing vehicle is about to change lanes, the target vehicle is controlled to decelerate, brake, or yield.

[0048] According to an embodiment of the present disclosure, after the probability of the obstructing vehicle changing its driving mode is predicted based on a deep learning model, in order to avoid safety hazards caused by inaccurate prediction results, the second current perception data is further used to assist in determining the driving mode change result of the obstructing vehicle, so that when the driving mode change probability is not accurate enough, it is determined whether the obstructing vehicle is going to change lanes based on the driving mode change result, thereby more accurately knowing the lane change intention of the obstructing vehicle and timely controlling the driving state of the target vehicle.

[0049] The following combination Figure 4-Figure 7 To describe how to determine the driving mode conversion result based on the second current perception data in different instances.

[0050] Figure 4 A schematic diagram of obtaining a driving mode conversion result according to an embodiment of the present disclosure is schematically shown.

[0051] like Figure 4 As shown, the second current perception data includes, for example, image data and point cloud data collected by the target vehicle 401. Based on the second current perception data, a first speed V of the obstructing vehicle 402, a first deflection angle θ of the obstructing vehicle 402, and turn signal information 4021 of the obstructing vehicle 402 are determined.

[0052] If the first driving speed V is less than the first preset speed, the driving mode conversion result of the obstacle vehicle 402 can be determined based on the first deflection angle θ and the turn signal information 4021. The first preset speed can be set according to actual conditions.

[0053] For example, first deflection angle θ is the angle between the current driving direction of obstructing vehicle 402 and the road ahead. If it is determined that first deflection angle θ is greater than a first preset angle and turn signal information 4021 indicates that obstructing vehicle 402 is about to change lanes, then it is determined that obstructing vehicle 402 is about to change driving modes, and a determination result is obtained. The determination result is used as the driving mode change result. The first preset angle can be set according to actual circumstances.

[0054] In other words, taking the case where the obstructing vehicle 402 is located on the right side of the target vehicle 401 as an example, when the first deflection angle θ indicates that the obstructing vehicle 402 is deflecting to the left, and the turn signal information 4021 indicates that the left turn signal of the obstructing vehicle 402 is on, indicating that the obstructing vehicle 402 is about to change lanes to the left, the determined driving mode conversion result includes information indicating that the obstructing vehicle 402 is about to change lanes to the left.

[0055] According to an embodiment of the present disclosure, when an obstructing vehicle is traveling at a low speed, it is possible to determine whether the obstructing vehicle is about to change lanes based on both the deflection angle and the turn signal of the obstructing vehicle, thereby improving the accuracy of the driving mode conversion result.

[0056] In another example, when the first driving speed V of the obstacle vehicle 402 is greater than or equal to the first preset speed, it means that the obstacle vehicle 402 is traveling at a high speed. Figure 5 Determine whether the obstructing vehicle is about to change lanes.

[0057] Figure 5 A schematic diagram of obtaining a driving mode conversion result according to another embodiment of the present disclosure is schematically shown.

[0058] like Figure 5 As shown, for target vehicle 501 and obstacle vehicle 502, if it is determined that first speed V of obstacle vehicle 502 is greater than or equal to a first preset speed, it indicates that obstacle vehicle 502 is traveling at a high speed. If obstacle vehicle 502 needs to change lanes while traveling at a high speed, a relatively small deflection angle is typically sufficient. Therefore, first deflection angle θ is typically relatively small. Determining whether obstacle vehicle 502 has changed lanes based on first deflection angle θ results in low accuracy.

[0059] To improve accuracy, a velocity component V' can be determined based on the first deflection angle θ and the first driving speed V. The velocity component V' can be perpendicular to the direction of the road ahead. When the first driving speed V is high, the velocity component V' is typically high even if the first deflection angle θ of the obstructing vehicle 502 is low. For example, in one case, the velocity component V' can be directly derived based on the second current perception data. In another case, the velocity component V' = V × sinθ.

[0060] Next, the driving mode conversion result of the obstacle vehicle 502 may be determined based on the speed component V′ and the turn signal information.

[0061] For example, when speed component V' is greater than a preset speed component and turn signal information 5021 indicates that obstructing vehicle 502 is about to change lanes, it is determined that obstructing vehicle 502 is about to change driving mode. For example, when speed component V' is greater than a preset speed component and is pointing to the left, and turn signal information 5021 indicates that obstructing vehicle 502's left turn signal is on, indicating that obstructing vehicle 502 is about to change lanes to the left, the determined driving mode change result includes information indicating that obstructing vehicle 502 is about to change lanes to the left. The preset speed component can be set based on actual circumstances.

[0062] According to an embodiment of the present disclosure, when an obstructing vehicle is traveling at high speed, whether the obstructing vehicle is about to change lanes can be determined based on both the speed component and the turn signal of the obstructing vehicle, thereby improving the accuracy of the driving mode conversion result.

[0063] Figure 6 A schematic diagram of obtaining a driving mode conversion result according to another embodiment of the present disclosure is schematically shown.

[0064] like Figure 6 As shown, for the target vehicle 601 and the obstacle vehicle 602, the target vehicle 601 collects the second current perception data, and the target vehicle 601 determines the second deflection angle θ of the obstacle vehicle 602 and the relative position of the obstacle vehicle 602 and the lane line 603 based on the second current perception data. Then, based on the second deflection angle θ and the relative position, the driving mode conversion result of the obstacle vehicle 602 is determined.

[0065] For example, if it is determined that the second deflection angle θ is greater than the second preset angle and the relative position indicates that the obstructing vehicle 602 has crossed the lane line 603, then it is determined that the obstructing vehicle 602 is about to switch driving modes. For example, if the second deflection angle θ indicates that the obstructing vehicle 602 has deflected to the left, and the relative position indicates that the front of the obstructing vehicle 602 has crossed the lane line 603, then the determined driving mode switch result includes that the obstructing vehicle 602 is about to switch driving modes. The second preset angle can be set based on actual circumstances.

[0066] According to the embodiments of the present disclosure, whether the obstructing vehicle needs to change lanes can be determined based on both the deflection angle of the obstructing vehicle and the relative position of the obstructing vehicle and the lane line, which more intuitively reflects whether the obstructing vehicle needs to change lanes.

[0067] In another example, a second driving speed V of obstructing vehicle 602 can be determined based on the second current perception data. If the second driving speed V is determined to be less than a second preset speed, a driving mode transition result of obstructing vehicle 602 is determined based on the second deflection angle θ and relative position. For example, obstructing vehicle 602 crossing lane marking 603 to change lanes typically occurs during low-speed driving. Therefore, embodiments of the present disclosure can further determine whether the second deflection angle θ is greater than a second preset angle and whether the relative position indicates that obstructing vehicle 602 has crossed lane marking 603, even when the second driving speed V of obstructing vehicle 602 is relatively low. If the second deflection angle θ is greater than the second preset angle and the relative position indicates that obstructing vehicle 602 has crossed lane marking 603, it indicates that obstructing vehicle 602 is about to change lanes. The second preset speed and the second preset angle can be set based on actual conditions.

[0068] It can be seen that, based on the premise that the obstructing vehicle is traveling at a low speed, the lane change situation of the obstructing vehicle is determined based on the deflection angle and the relative position of the obstructing vehicle and the lane line, which further improves the accuracy of lane change recognition.

[0069] Figure 7 A schematic diagram of obtaining a driving mode conversion result according to another embodiment of the present disclosure is schematically shown.

[0070] like Figure 7As shown, for target vehicle 701 and obstacle vehicle 702, target vehicle 701 collects second current perception data. Based on this second current perception data, target vehicle 701 determines a third deflection angle θ of obstacle vehicle 702 and the lane type of lane 703 in which obstacle vehicle 702 is located. If lane 703 is a preset lane type and third deflection angle θ is greater than a third preset angle, it is determined that obstacle vehicle 702 is about to switch driving modes, and the determination that obstacle vehicle 702 is about to switch driving modes is used as the driving mode switch result. The third preset angle can be set based on actual conditions.

[0071] For example, if obstructing vehicle 702 is a motor vehicle, the preset lane type includes a non-motor vehicle lane. If obstructing vehicle 702 is in the non-motor vehicle lane, it is likely to change lanes to a motor vehicle lane. Therefore, if the third deflection angle θ is greater than the third preset angle, it indicates that obstructing vehicle 702 is about to change lanes to the lane where target vehicle 701 is located. Target vehicle 701 is a motor vehicle, and the lane where target vehicle 701 is located is a motor vehicle lane.

[0072] According to an embodiment of the present disclosure, when an obstructing vehicle is located in a non-motorized vehicle lane, the possibility of the obstructing vehicle changing lanes is high. Therefore, whether the obstructing vehicle is about to change lanes can be determined based on the deflection angle of the obstructing vehicle, so as to timely and accurately know the obstructing vehicle's lane change intention.

[0073] The above describes various strategies for identifying lane-changing intentions, including determining the obstructing vehicle's lane-changing intention based on the deflection angle and turn signal at low speeds, determining the obstructing vehicle's lane-changing intention based on the speed component and turn signal at high speeds, determining the obstructing vehicle's lane-changing intention based on the deflection angle and whether the obstructing vehicle has crossed the lane line, and determining the obstructing vehicle's lane-changing intention based on the lane type and deflection angle of the lane in which the obstructing vehicle is located. If any of these strategies is met, it can be determined that the obstructing vehicle is about to change lanes. At this time, the target vehicle can be controlled to perform operations such as deceleration, braking, and yielding to ensure driving safety.

[0074] Figure 8 A block diagram of a vehicle control device according to an embodiment of the present disclosure is schematically shown.

[0075] like Figure 8 As shown, the vehicle control device 800 of the embodiment of the present disclosure includes, for example, an acquisition module 810 , a processing module 820 and a control module 830 .

[0076] The acquisition module 810 can be used to obtain the driving mode conversion probability for the obstacle vehicle based on the historical driving data and the first current perception data. According to the embodiment of the present disclosure, the acquisition module 810 can, for example, execute the above reference Figure 2 The operation S210 described above will not be repeated here.

[0077] The processing module 820 can be used to process the second current perception data in response to determining that the driving mode conversion probability is less than the preset probability, and obtain the driving mode conversion result for the obstacle vehicle. According to the embodiment of the present disclosure, the processing module 820 can, for example, execute the above reference Figure 2 Operation S220 described above will not be repeated here.

[0078] The control module 830 can be used to control the driving state of the target vehicle relative to the obstacle vehicle in response to determining that the driving mode conversion result indicates that the obstacle vehicle is about to change the driving mode. According to the embodiment of the present disclosure, the control module 830 can, for example, execute the above reference Figure 2 The operation S230 described above will not be repeated here.

[0079] According to an embodiment of the present disclosure, processing module 820 includes: a first determination submodule, configured to determine a first driving speed of the obstructing vehicle, a first deflection angle of the obstructing vehicle, and turn signal information of the obstructing vehicle based on the second current perception data; and a second determination submodule, configured to determine a driving mode transition result of the obstructing vehicle based on the first deflection angle and turn signal information in response to determining that the first driving speed is less than a first preset speed.

[0080] According to an embodiment of the present disclosure, the second determination submodule is further used to: in response to determining that the first deflection angle is greater than the first preset angle and the turn signal information indicates that the obstructing vehicle is about to change lanes, determine that the obstructing vehicle is about to change driving mode as a driving mode conversion result.

[0081] According to an embodiment of the present disclosure, processing module 820 further includes: a third determining submodule for determining a speed component based on the first deflection angle and the first speed in response to determining that the first speed is greater than or equal to the first preset speed; and a fourth determining submodule for determining a driving mode transition result of the obstructing vehicle based on the speed component and turn signal information.

[0082] According to an embodiment of the present disclosure, the fourth determining submodule is further configured to: in response to determining that the speed component is greater than a preset speed component and the turn signal information indicates that the obstructing vehicle is about to change lanes, determine that the obstructing vehicle is about to change driving modes as a driving mode conversion result.

[0083] According to an embodiment of the present disclosure, processing module 820 includes: a fifth determination submodule and a sixth determination submodule. The fifth determination submodule is configured to determine a second deflection angle of the obstructing vehicle and a relative position between the obstructing vehicle and the lane line based on the second current perception data; and the sixth determination submodule is configured to determine a driving mode transition result of the obstructing vehicle based on the second deflection angle and the relative position.

[0084] According to an embodiment of the present disclosure, the processing module 820 also includes: a seventh determination submodule, used to determine the second driving speed of the obstructing vehicle based on the second current perception data; wherein the sixth determination submodule is further used to: in response to determining that the second driving speed is less than the second preset speed, determine the driving mode conversion result of the obstructing vehicle based on the second deflection angle and relative position.

[0085] According to an embodiment of the present disclosure, the sixth determination submodule is further used to: in response to determining that the second deflection angle is greater than the second preset angle and the relative position indicates that the obstacle vehicle has crossed the lane line, determine that the obstacle vehicle needs to change the driving mode as a driving mode change result.

[0086] According to an embodiment of the present disclosure, processing module 820 includes an eighth determination submodule and a ninth determination submodule. The eighth determination submodule is configured to determine, based on the second current perception data, a third deflection angle of the obstructing vehicle and a lane type of the lane in which the obstructing vehicle is located; and the ninth determination submodule is configured to determine, in response to determining that the lane type is a preset lane type and the third deflection angle is greater than the third preset angle, that the obstructing vehicle needs to switch driving modes as a driving mode switch result.

[0087] According to an embodiment of the present disclosure, the acquisition module 810 is further used to: process the first current perception data based on a deep learning model to obtain a driving mode conversion probability for the obstacle vehicle, wherein the deep learning model is obtained based on historical driving data.

[0088] According to an embodiment of the present disclosure, the obstacle vehicle's need to change driving mode includes the obstacle vehicle's need to change lanes; the control module 830 is further configured to control the target vehicle to perform at least one of deceleration, braking, and yielding relative to the obstacle vehicle.

[0089] In the technical solution disclosed herein, the collection, storage, use, processing, transmission, provision, disclosure and application of user personal information involved comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good morals.

[0090] In the technical solution disclosed herein, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0091] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0092] According to an embodiment of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, where the computer instructions are used to cause a computer to execute the vehicle control method described above.

[0093] According to an embodiment of the present disclosure, a computer program product is provided, including a computer program / instruction, which implements the vehicle control method described above when the computer program / instruction is executed by a processor.

[0094] According to an embodiment of the present disclosure, an autonomous driving vehicle is provided, comprising the electronic device described below.

[0095] Figure 9 It is a block diagram of an electronic device for implementing an embodiment of the present disclosure and performing vehicle control.

[0096] Figure 9 A schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure is shown. The electronic device 900 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0097] like Figure 9 As shown, the device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. Various programs and data required for the operation of the device 900 can also be stored in the RAM 903. The computing unit 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0098] Various components in the device 900 are connected to the I / O interface 905, including an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a magnetic disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0099] The computing unit 901 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above, such as the vehicle control method. For example, in some embodiments, the vehicle control method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the vehicle control method described above can be performed. Alternatively, in other embodiments, the computing unit 901 can be configured to perform the vehicle control method by any other appropriate means (e.g., by means of firmware).

[0100] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0101] The program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. Such program code may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable vehicle control device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0103] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0104] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0105] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0106] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0107] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A vehicle control method, comprising: Obtaining a driving mode transition probability for the obstacle vehicle based on historical driving data and the first current perception data, wherein the historical driving data includes historical lane change information of a plurality of historical vehicles; In response to determining that the driving mode transition probability is less than a preset probability, processing the second current perception data to obtain a driving mode transition result for the obstacle vehicle, wherein the driving mode transition result indicates whether the obstacle vehicle has an intention to change lanes; In response to determining that the driving mode conversion result indicates that the obstacle vehicle has a lane change intention, controlling the driving state of the target vehicle relative to the obstacle vehicle; The processing of the second current perception data to obtain a driving mode conversion result for the obstacle vehicle includes: determining a second traveling speed of the obstacle vehicle based on the second current perception data; In response to the second driving speed being less than a second preset speed, determining a second deflection angle of the obstacle vehicle and a relative position of the obstacle vehicle and a lane line; and In response to the second deflection angle being greater than a second preset angle and the relative position indicating that the front end of the obstructing vehicle has crossed a lane line, it is determined that the obstructing vehicle has an intention to change lanes.

2. The method according to claim 1, wherein The processing of the second current perception data to obtain a driving mode conversion result for the obstacle vehicle includes: determining, based on the second current perception data, a first traveling speed of the obstructing vehicle, a first deflection angle of the obstructing vehicle, and turn signal information of the obstructing vehicle; and In response to determining that the first driving speed is less than a first preset speed, a driving mode conversion result of the obstacle vehicle is determined based on the first deflection angle and the turn signal information.

3. The method according to claim 2, wherein: The determining, based on the first deflection angle and the turn signal information, a driving mode conversion result of the obstacle vehicle includes: In response to determining that the first deflection angle is greater than a first preset angle and the turn signal information indicates that the obstructing vehicle is about to change lanes, determining that the obstructing vehicle has a lane-changing intention is used as the driving mode conversion result.

4. The method according to claim 2, wherein: The processing of the second current perception data to obtain a driving mode conversion result for the obstacle vehicle further includes: In response to determining that the first travel speed is greater than or equal to a first preset speed, determining a speed component based on the first yaw angle and the first travel speed; and Based on the speed component and the turn signal information, a driving mode conversion result of the obstacle vehicle is determined.

5. The method according to claim 4, wherein The determining, based on the speed component and the turn signal information, a driving mode conversion result of the obstacle vehicle includes: In response to determining that the speed component is greater than a preset speed component and the turn signal information indicates that the obstacle vehicle is about to change lanes, it is determined that the obstacle vehicle has a lane change intention as the driving mode conversion result.

6. The method according to claim 1, wherein The processing of the second current perception data to obtain a driving mode conversion result for the obstacle vehicle includes: determining, based on the second current perception data, a third deflection angle of the obstructing vehicle and a lane type of the lane in which the obstructing vehicle is located; and In response to determining that the lane type is a preset lane type and the third deflection angle is greater than a third preset angle, it is determined that the obstacle vehicle has a lane change intention as the driving mode conversion result.

7. The method according to claim 1, wherein The obtaining of the driving mode conversion probability for the obstacle vehicle based on the historical driving data and the first current perception data includes: Based on the deep learning model, the first current perception data is processed to obtain the driving mode conversion probability for the obstacle vehicle. Wherein, the deep learning model is obtained based on the historical driving data.

8. The method according to any one of claims 1 to 7, wherein: The driving state of the controlled target vehicle relative to the obstacle vehicle includes: The target vehicle is controlled to perform at least one of deceleration, braking, and yielding relative to the obstacle vehicle.

9. A vehicle control device comprising: an obtaining module, configured to obtain a driving mode transition probability for an obstacle vehicle based on historical driving data and the first current perception data, wherein the historical driving data includes historical lane change information of a plurality of historical vehicles; a processing module, configured to, in response to determining that the driving mode transition probability is less than a preset probability, process the second current perception data to obtain a driving mode transition result for the obstacle vehicle, wherein the driving mode transition result indicates whether the obstacle vehicle has an intention to change lanes; a control module configured to control a driving state of the target vehicle relative to the obstacle vehicle in response to determining that the driving mode conversion result indicates that the obstacle vehicle has an intention to change lanes; The processing module is further configured to determine a second traveling speed of the obstacle vehicle based on the second current sensing data; In response to the second driving speed being less than a second preset speed, determining a second deflection angle of the obstacle vehicle and a relative position of the obstacle vehicle and a lane line; and In response to the second deflection angle being greater than a second preset angle and the relative position indicating that the front end of the obstructing vehicle has crossed a lane line, it is determined that the obstructing vehicle has an intention to change lanes.

10. The device according to claim 9, wherein The processing module includes: a first determining submodule, configured to determine a first traveling speed of the obstructing vehicle, a first deflection angle of the obstructing vehicle, and turn signal information of the obstructing vehicle based on the second current sensing data; and The second determining submodule is configured to determine, in response to determining that the first driving speed is less than a first preset speed, a driving mode conversion result of the obstacle vehicle based on the first deflection angle and the turn signal information.

11. The device according to claim 10, wherein The second determining submodule is further configured to: In response to determining that the first deflection angle is greater than a first preset angle and the turn signal information indicates that the obstructing vehicle is about to change lanes, determining that the obstructing vehicle has a lane-changing intention is used as the driving mode conversion result.

12. The device according to claim 10, wherein The processing module further includes: a third determining submodule, configured to determine a speed component based on the first deflection angle and the first traveling speed in response to determining that the first traveling speed is greater than or equal to a first preset speed; and The fourth determining submodule is configured to determine a driving mode conversion result of the obstacle vehicle based on the speed component and the turn signal information.

13. The device according to claim 12, wherein The fourth determining submodule is further configured to: In response to determining that the speed component is greater than a preset speed component and the turn signal information indicates that the obstacle vehicle is about to change lanes, it is determined that the obstacle vehicle has a lane change intention as the driving mode conversion result.

14. The device according to claim 9, wherein The processing module includes: an eighth determining submodule, configured to determine a third deflection angle of the obstructing vehicle and a lane type of the lane in which the obstructing vehicle is located based on the second current perception data; and a ninth determining submodule configured to, in response to determining that the lane type is a preset lane type and the third deflection angle is greater than a third preset angle, determine that the obstacle vehicle has a lane change intention as the driving mode conversion result.

15. The device according to claim 9, wherein The acquisition module is further used for: Based on the deep learning model, the first current perception data is processed to obtain the driving mode conversion probability for the obstacle vehicle. Wherein, the deep learning model is obtained based on the historical driving data.

16. The device according to any one of claims 9 to 15, wherein: The control module is further configured to: The target vehicle is controlled to perform at least one of deceleration, braking, and yielding relative to the obstacle vehicle.

17. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 8.

19. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

20. An autonomous driving vehicle comprising: The electronic device according to claim 17.

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