Automatic driving control data determination method, device, equipment and storage medium

Abstract

This disclosure provides a method, apparatus, device, storage medium, program product, and autonomous vehicle for determining autonomous driving control data, relating to the field of computer technology, and particularly to the fields of artificial intelligence, autonomous driving, and intelligent transportation. The specific implementation scheme is as follows: based on the initial steering control data of the target vehicle, determine the target scene category corresponding to the initial steering control data and the target control threshold corresponding to the target scene category; based on the initial steering control data and the target control threshold, determine the initial steering control detection result; and based on the initial steering control detection result, determine the target steering control data used to control the driving of the target vehicle.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and more particularly to the fields of artificial intelligence, autonomous driving and intelligent transportation technology, specifically to a method, apparatus, device, storage medium, program product and autonomous vehicle for determining autonomous driving control data. Background Technology

[0002] Autonomous driving is an application branch of artificial intelligence technology, and how to control vehicle driving safely and efficiently is a major challenge we face today. Summary of the Invention

[0003] This disclosure provides a method, apparatus, device, storage medium, program product, and autonomous vehicle for determining autonomous driving control data.

[0004] According to one aspect of this disclosure, an autonomous driving control data determination method is provided, comprising: determining a target scene category corresponding to the initial steering control data and a target control threshold corresponding to the target scene category based on the initial steering control data of the target vehicle; determining an initial steering control detection result based on the initial steering control data and the target control threshold; and determining target steering control data for controlling the driving of the target vehicle based on the initial steering control detection result.

[0005] According to another aspect of this disclosure, an autonomous driving control data determination device is provided, comprising: a target control threshold determination module, an initial steering control detection result determination module, and a target steering control data determination module. The target control threshold determination module is used to determine, based on the initial steering control data of the target vehicle, a target scene category corresponding to the initial steering control data and a target control threshold corresponding to the target scene category; the initial steering control detection result determination module is used to determine an initial steering control detection result based on the initial steering control data and the target control threshold; and the target steering control data determination module is used to determine target steering control data for controlling the driving of the target vehicle based on the initial steering control detection result.

[0006] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor and a memory communicatively connected to the at least one processor. The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the methods of embodiments of this disclosure.

[0007] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing a computer to perform the methods of embodiments of this disclosure.

[0008] According to another aspect of this disclosure, a computer program product is provided, including a computer program stored on at least one of a readable storage medium and an electronic device, wherein the computer program, when executed by a processor, implements the methods of embodiments of this disclosure.

[0009] According to another aspect of this disclosure, an autonomous vehicle is provided, including electronic devices, wherein the target vehicle drives according to target steering control data.

[0010] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0011] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0012] Figure 1 This illustration schematically depicts an application scenario of the autonomous driving control data determination method and apparatus according to embodiments of the present disclosure;

[0013] Figure 2 A flowchart illustrating an autonomous driving control data determination method according to an embodiment of the present disclosure is shown schematically.

[0014] Figure 3 The illustration shows specific examples of scenario categories including lane change category B, abnormal turn category A, and normal turn category C;

[0015] Figure 4A This diagram illustrates the correlation between speed, steering control data, and steering acceleration when the steering control data is a steering wheel angle value.

[0016] Figure 4B This diagram illustrates the correlation between speed, steering control data, and steering acceleration when the steering control data is a numerical value representing the steering wheel angle rate.

[0017] Figure 4C The illustration shows the second steering acceleration threshold corresponding to the road curvature data when the steering control data is the steering wheel angle value. The second steering acceleration threshold corresponds to the steering wheel angle acceleration.

[0018] Figure 4D The illustration shows the second steering acceleration threshold corresponding to the road curvature data when the steering control data is the steering wheel angular rate value. The second steering acceleration threshold corresponds to the steering wheel angular rate acceleration.

[0019] Figure 5 The illustration shows a specific example of a lane-changing turning path that satisfies minimum curvature according to yet another embodiment of the present disclosure;

[0020] Figure 6 This schematically illustrates a control flow diagram of an autonomous driving control data determination method according to yet another embodiment of the present disclosure;

[0021] Figure 7 A block diagram of an autonomous driving control data determination device according to an embodiment of the present disclosure is schematically shown; and

[0022] Figure 8 A block diagram of an electronic device that can implement the autonomous driving control data determination method of the embodiments of the present disclosure is shown schematically. Detailed Implementation

[0023] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0024] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] 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 are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0026] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0027] Autonomous driving is an application branch of artificial intelligence technology, and how to control vehicle driving safely and efficiently is a major challenge we face today.

[0028] For example, steering control can be understood as the lateral control of a vehicle, and steering control is very important for vehicle safety.

[0029] Vehicle steering control is related to the steering wheel angle and the steering wheel angle rate. For autonomous vehicles, the safety boundary of steering control defines the safe range of steering control for autonomous vehicles.

[0030] Some implementations impose limits on steering control data (e.g.) Where u represents steering control data. This is the upper limit of the output. (This is for the lower limit of the output). This implementation method is relatively simple, directly limiting the upper and lower limits of steering control data such as steering wheel angle. Although it can achieve a certain steering control safety monitoring purpose, this implementation method is too general and simple, and cannot adapt to different operating conditions and constantly changing vehicle states. It may result in: failing to achieve the steering control safety monitoring effect under some operating conditions; and restricting the control function of the control system under other operating conditions.

[0031] Figure 1 The illustration schematically depicts an application scenario of an autonomous driving control data determination method, apparatus, and autonomous vehicle according to an embodiment of the present disclosure. It should be noted that... Figure 1 The examples shown are merely examples of application scenarios that can be applied to the embodiments of this disclosure, in order to help those skilled in the art understand the technical content of this disclosure, but do not mean that the embodiments of this disclosure cannot be used in other devices, systems, environments or scenarios.

[0032] like Figure 1 As shown, the application scenario 100 of this disclosure includes multiple vehicles 101, 102, and 103, and a server 104. Vehicles 101, 102, and 103 can be autonomous vehicles.

[0033] In one embodiment, vehicles 101, 102, and 103 can interact with server 104. For example, vehicles 101, 102, and 103 can transmit initial steering control data of the target vehicle to server 104. Server 104 then determines target steering control data for controlling the driving of the target vehicle based on the initial steering control data. The target steering control data can, for example, be used to control the vehicle's chassis drive-by-wire system.

[0034] In another example, vehicles 101, 102, and 103 can perform data processing. Specifically, the vehicle's infotainment system can have data processing capabilities, allowing it to determine target steering control data for controlling the target vehicle's movement based on the target vehicle's initial steering control data.

[0035] For example, the vehicle includes electronic devices, which include, but are not limited to, vehicle infotainment systems. These electronic devices can execute the autonomous driving control data determination method according to embodiments of this disclosure. The vehicle infotainment system may include a chassis drive-by-wire system.

[0036] The vehicle can be an autonomous vehicle.

[0037] It should be understood that Figure 1 The number of vehicles and servers shown is merely illustrative. Any number of vehicles and servers can be used depending on implementation needs.

[0038] It should be noted that the collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution disclosed herein all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0039] In the technical solution disclosed herein, the user's authorization or consent is obtained before acquiring or collecting the user's personal information.

[0040] This disclosure provides a method for determining autonomous driving control data, which will be described below in conjunction with... Figure 1 The system architecture, referencing Figures 2-6 This describes an autonomous driving control data determination method according to exemplary embodiments of the present disclosure. The autonomous driving control data determination method of the embodiments of the present disclosure may, for example, be derived from... Figure 1 The server 104 shown is used to execute this.

[0041] Figure 2 A flowchart illustrating an autonomous driving control data determination method according to an embodiment of the present disclosure is shown.

[0042] like Figure 2 As shown, the autonomous driving control data determination method 200 of this embodiment may include, for example, operations S210 to S230.

[0043] In operation S210, based on the initial steering control data of the target vehicle, the target scene category corresponding to the initial steering control data and the target control threshold corresponding to the target scene category are determined.

[0044] Initial steering control data can be understood as control data used to control the steering of the target vehicle.

[0045] For example, the initial steering control data may include at least one of steering wheel angle data and steering wheel angle rate data.

[0046] The target scene category can be, for example, one that matches the initial steering control data from a plurality of predefined scene categories.

[0047] Each scene category can be mapped to a corresponding control threshold, thereby determining the target control threshold corresponding to the target scene category based on the target scene category.

[0048] The target control threshold can characterize the safety boundary of whether the initial steering control data is safe under the target scene category.

[0049] In operation S220, the initial steering control detection result is determined based on the initial steering control data and the target control threshold.

[0050] For example, the initial steering control data can be compared with the target control threshold. If the initial steering control data exceeds the target control threshold, the initial steering control detection result can characterize that the initial steering control data exceeds the safety boundary, that is, it is defined as "unsafe" steering.

[0051] In operation S230, target steering control data for controlling the driving of the target vehicle is determined based on the initial steering control detection results.

[0052] For example, if the initial steering control detection result indicates that the initial steering control data exceeds the target control threshold, the manual intervention information can be determined as the target steering control data, which can prompt the driver to manually take over the target vehicle. Conversely, if the initial steering control detection result indicates that the initial steering control data does not exceed the target control threshold, the initial steering control data can be used as the target steering control data to control the driving of the target vehicle and ensure its steering safety.

[0053] According to the autonomous driving control data determination method of this disclosure, by determining the target scene category corresponding to the initial steering control data and the target control threshold corresponding to the target scene category based on the initial steering control data of the target vehicle, the target control threshold can be used as the safety boundary of the target vehicle in a scenario-by-scenario and fine-grained manner. By determining the initial steering control detection result based on the initial steering control data and the target control threshold, the safety of the initial steering control data of the target vehicle can be detected. The safety represented by the initial steering control detection result is more accurate. Therefore, the target steering control data determined based on the initial steering control detection result can safely and accurately control the driving of the target vehicle.

[0054] For example, in the autonomous driving control data determination method according to another embodiment of the present disclosure, the target scene category is one of a plurality of scene categories that matches the initial steering control data.

[0055] The scenario category includes at least one of the following: lane change turning category, abnormal turning category, and normal turning category.

[0056] Lane-changing steering can be understood as steering for the purpose of changing lanes. Abnormal steering can be understood as steering defined as abnormal, and normal steering can be understood as steering defined as normal.

[0057] For example, each scenario category may have a pre-determined relevant control threshold.

[0058] According to the autonomous driving control data determination method of this disclosure, vehicle steering can be refined into multiple scenarios through various scenario categories, such as lane-changing steering category, abnormal steering category, and normal steering category. For example, control thresholds related to each scenario category can be determined based on the characteristics of each scenario category. This allows for the application of different control thresholds to perform safety checks on initial steering control data for each scenario category, thereby improving the steering safety of the target vehicle. Under each scenario category, the corresponding control threshold can serve as the safety boundary for that scenario category.

[0059] For example, Figure 3 The illustration shows specific examples of scenario categories including lane change steering category B, abnormal steering category A, and normal steering category C.

[0060] For example, for abnormal steering category A, the vehicle dynamics control system (VDC) may perform a safety check on the initial steering control data in the case of abnormal steering category.

[0061] For example, for abnormal steering category A, abnormal steering conditions can be predefined. If the initial steering control data triggers this abnormal steering condition, the initial steering control detection result can be determined to be abnormal steering.

[0062] For example, the following embodiment can be used to implement a specific example of determining the initial steering control detection result based on the initial steering control data and the target control threshold: when the target scene category is an abnormal steering category, in response to the initial steering control data triggering an abnormal steering condition, the initial steering control detection result is determined to be an abnormal steering.

[0063] For example, abnormal steering conditions may include at least one of understeer and oversteer. For instance, when the initial steering control data includes a steering wheel angle value or a steering wheel angle rate value, a steering wheel angle threshold or steering wheel angle rate threshold corresponding to the understeer or oversteer condition can be predetermined. Taking the understeer condition as an example, if the steering wheel angle value or steering wheel angle rate value of the initial steering control data is lower than the steering wheel angle threshold or steering wheel angle rate threshold corresponding to the understeer condition, the initial steering control data meets the understeer condition, thus triggering this abnormal steering condition. Therefore, the initial steering control detection result can be determined to be abnormal steering.

[0064] For example, for lane change steering category B, safety checks of the initial steering control data in lane change steering category cases can be performed by an electric power steering system (EPS).

[0065] For example, for the normal steering category, safety checks of the initial steering control data in the normal steering category case can be performed by the autonomous driving system.

[0066] It should also be noted that the aforementioned lane change steering category B, abnormal steering category A, and normal steering category C can, for example, be performed by the same actuator of the autonomous driving system to perform safety checks on the initial steering control data.

[0067] For example, in another embodiment of the autonomous driving control data determination method according to the present disclosure, the initial steering control data includes the target vehicle speed and steering control data, and the steering control data includes at least one of the steering wheel angle value and the steering wheel angle rate value.

[0068] For example, the following embodiment can be used to implement a specific example of determining the target control threshold corresponding to the initial steering control data based on the initial steering control data of the target vehicle: when the target scene category is normal steering category, the steering control data associated with the target vehicle speed and the first steering acceleration threshold is determined as the target control threshold based on the speed-steering control data-steering acceleration association data, wherein the first steering acceleration threshold is associated with the normal steering category.

[0069] For example, the first steering acceleration threshold can be 0.6g, where g can represent the numerical value of gravitational acceleration.

[0070] For example, Figure 4AThe illustration shows the correlation between speed, steering control data, and steering acceleration when the steering control data is a steering wheel angle value. The first angular acceleration threshold is 0.6g. In the example in Figure 4, the horizontal axis x represents the target vehicle speed, and the vertical axis y represents the steering wheel angle value. It can be understood that for each target vehicle speed value, the corresponding steering wheel angle value can be used as the target control threshold (in...). Figure 4A In the example, the target control threshold is the steering wheel angle safety threshold, which satisfies the first angular acceleration threshold.

[0071] For example, Figure 4B This diagram illustrates the correlation between speed, steering control data, and steering acceleration when the steering control data is a numerical value representing the steering wheel angle rate. Figure 4B In the example above, the target control threshold is the steering wheel angle rate safety threshold. Similar examples where steering control data is the steering wheel angle value will not be repeated here.

[0072] According to the autonomous driving control data determination method of this disclosure, for the case where the target scenario category is normal steering category, the steering control data determined based on speed-steering control data-steering acceleration correlation data and the steering control data associated with the target vehicle speed and the first steering acceleration threshold is used as the target control threshold. The first steering acceleration threshold can be used as a safety indicator to measure the normal steering category. The target control threshold determined in this way can adapt to the current target vehicle speed and the first steering acceleration threshold. In the case of normal steering category, the initial steering control detection result obtained by detecting the initial steering control data based on the target control threshold is more accurate, and the subsequent vehicle driving control based on the initial steering control detection result is safer.

[0073] For example, in another embodiment of the autonomous driving control data determination method according to the present disclosure, the initial steering control data further includes road curvature data.

[0074] The method for determining autonomous driving control data according to another embodiment of the present disclosure may further include: when the target scene category is a normal steering category, determining a second steering acceleration threshold corresponding to the road curvature data based on the road curvature data; and updating the first steering acceleration threshold using the second steering acceleration threshold when the second steering acceleration threshold is less than the first steering acceleration threshold.

[0075] Figure 4C This illustration schematically shows the second steering acceleration threshold corresponding to road curvature data when the steering control data is a steering wheel angle value. The second steering acceleration threshold corresponds to the steering wheel angle acceleration. Figure 4CIn the example, the horizontal axis R represents the radius corresponding to the road curvature, and the vertical axis a represents the second steering acceleration threshold.

[0076] Figure 4D This illustration schematically depicts the second steering acceleration threshold corresponding to road curvature data when the steering control data is a steering wheel angular rate value. The second steering acceleration threshold corresponds to the steering wheel angular rate acceleration. Figure 4D In the example, the horizontal axis R represents the radius corresponding to the road curvature, and the vertical axis j represents the second steering acceleration threshold.

[0077] For example, road curvature can be detected by relevant detection equipment for the target vehicle.

[0078] Road curvature also affects the steering safety of the target vehicle. According to the autonomous driving control data determination method of this disclosure, when the target scene category is normal steering category, a second steering acceleration threshold corresponding to the road curvature data is determined based on the road curvature data. Road curvature can be used as a parameter to measure the steering safety of the target vehicle. When the second steering acceleration threshold is less than the first steering acceleration threshold, the first steering acceleration threshold is updated using the second steering acceleration threshold, and a safer first steering acceleration threshold adapted to the road curvature data can be obtained.

[0079] For example, the second steering acceleration threshold corresponding to the speed-steering control data-steering acceleration correlation data and the road curvature data can be determined by at least one of software simulation or actual vehicle operation experiments.

[0080] For example, according to another embodiment of the autonomous driving control data determination method of this disclosure, the following specific example can be used to determine the target control threshold corresponding to the initial steering control data of the target vehicle: when the target scenario category is lane change steering category, the steering control data corresponding to the target vehicle speed and the third steering acceleration threshold is determined as the initial control threshold based on the speed-steering control data-steering acceleration association data; the target control threshold is determined based on the lane change parameter threshold and the lane change parameter data corresponding to the initial control threshold.

[0081] The correlation data between speed, steering control data, and steering acceleration is similar to that described above. Figure 4A and Figure 4B The relevant explanations for the examples will not be repeated here. Figure 4A , Figure 4B Unlike the normal steering category, in the case of the lane change steering category, the third steering acceleration threshold for assessing the safety of the lane change steering category is less than or equal to the first steering acceleration threshold for assessing the safety of the normal steering category.

[0082] The lane change parameter thresholds include at least one of the lane change stability threshold and the lane change time threshold; the third steering acceleration threshold is associated with the lane change steering category.

[0083] The lane change stability threshold can be understood as a threshold used to characterize the stability parameters of a target vehicle under lane change steering categories. The lane change stability threshold can, for example, be predetermined.

[0084] For example, the stability parameter may include the road surface friction coefficient. For example, when the stability parameter includes the road surface friction coefficient, the lane-changing stability threshold may be 0.4.

[0085] For example, the third steering acceleration threshold can be in the range of 0.2g to 0.6g, where g can represent the numerical value of gravitational acceleration.

[0086] The autonomous driving control data determination method according to embodiments of this disclosure, in addition to using a third steering acceleration threshold to evaluate the safety of the target vehicle corresponding to the lane-changing steering category, also uses a lane-changing parameter threshold to evaluate the safety of the target vehicle corresponding to the lane-changing steering category. It can be adapted to various lane-changing steering category scenarios, comprehensively and accurately determining the target control threshold corresponding to the lane-changing steering category using multiple parameters. For example, the target control threshold can be determined by comprehensively considering at least one of the road surface friction coefficient and lane-changing time, adapting to lane-changing steering categories.

[0087] For example, the autonomous driving control data determination method according to another embodiment of the present disclosure further includes: when the lane change parameter threshold includes a lane change time threshold, determining a first minimum lane change turning radius that matches the vehicle speed and a third steering acceleration threshold; and determining the lane change time threshold based on the vehicle speed and the first minimum lane change turning radius.

[0088] For example, the first minimum lane-changing turning radius For example, it can be obtained using the following formula (1):

[0089] (1)

[0090] in, Characterizing the speed of the target vehicle, Characterizes the third steering acceleration threshold.

[0091] According to the autonomous driving control data determination method of this disclosure, when the lane change parameter thresholds include a lane change time threshold, a first minimum lane change turning radius that matches the target vehicle speed and a third steering acceleration threshold is determined; based on the target vehicle speed and the first minimum lane change turning radius, the determined lane change time threshold can match the target vehicle speed and satisfy the third steering acceleration threshold, thereby enabling more accurate and safer target vehicle steering detection.

[0092] For example, according to another embodiment of the present disclosure, the method for determining autonomous driving control data can be implemented using the following specific example to determine the initial steering control detection result based on the initial steering control data and the target control threshold: when the lane change parameter threshold includes a lane change time threshold, a second minimum lane change turning radius that matches the target vehicle speed and lane change turning path is determined; and the initial steering control detection result for the lane change time threshold is determined based on the lane change time corresponding to the lane change time threshold and the second minimum lane change turning radius.

[0093] The lane-changing and turning path satisfies the minimum curvature.

[0094] Figure 5 The illustration shows a specific example of a lane-changing turning path that satisfies the minimum curvature.

[0095] For example, the speed of the target vehicle can be determined using the following formula (2). The second minimum lane-changing turning radius matched with lane-changing turning path ABC :

[0096] (2)

[0097] For example, this can be achieved by comparing the first minimum lane-changing turning radius. With the second minimum lane-changing turning radius This provides a specific example of determining the initial steering control detection result for the lane-changing time threshold based on the lane-changing time threshold and the lane-changing time corresponding to the second minimum lane-changing turning radius.

[0098] For example, the lane-changing time threshold can be predetermined, and can be selected within the range of 5 to 7 seconds. This threshold is verified through simulation and real-vehicle operation, and meets the following requirements. Under the premise that lane changing time can be achieved The lane change time is less than 50% of the lane change time threshold (5~7s) corresponding to the lane change steering category, which can be considered as leaving sufficient margin for lane change steering.

[0099] It should be noted that, for lane-change steering, the second minimum lane-change steering radius corresponds to initial steering control data that can achieve the lane-change steering purpose but requires larger values ​​for steering wheel angle and steering rate. To ensure steering safety, the initial steering control data corresponding to the second minimum lane-change steering radius can be used as a comparison benchmark. Provided that the initial steering control data corresponding to the second minimum lane-change steering radius does not exceed the target control threshold, other radii that can satisfy lane-change steering can also meet the steering safety requirements of the target vehicle.

[0100] In addition, the lane-changing turning path corresponding to the second minimum lane-changing turning radius can still achieve the shortest path for lane changing. By using lane-changing parameter thresholds, including lane-changing time thresholds, the second minimum lane-changing turning radius that matches the target vehicle speed and lane-changing turning path is determined. Based on the lane-changing time threshold and the lane-changing time corresponding to the second minimum lane-changing turning radius, the initial steering control detection result for the lane-changing time threshold is determined, and lane-changing turning safety can be detected from the lane-changing time.

[0101] Figure 6 The diagram illustrates a control flow diagram of an autonomous driving control data determination method according to yet another embodiment of the present disclosure.

[0102] like Figure 6 As shown, the autonomous driving control data determination method according to embodiments of this disclosure can sequentially determine whether the initial steering control data corresponds to an abnormal steering category, a lane-changing steering category, and a normal steering category based on the initial steering control data. For example, if the initial steering control data corresponds to an abnormal steering category, the driver can be notified to take over and exit autonomous driving control. If the initial steering control data corresponds to a lane-changing steering category, it can be detected whether the initial steering control data exceeds the target control threshold corresponding to the lane-changing steering category. If it exceeds, the current lane-changing steering is considered unsafe, and autonomous driving control can be exited. If the initial steering control data does not correspond to a lane-changing steering category, it can be assumed that the initial steering control data corresponds to a normal steering category. It can be detected whether the initial steering control data exceeds the target control threshold corresponding to the normal steering category. If it exceeds, the current normal steering is considered unsafe, and autonomous driving control can be exited.

[0103] In summary, the autonomous driving control data determination method according to the embodiments of this disclosure can effectively ensure the safety of autonomous driving steering control. While considering safety measures such as lane changes or emergency lane changes in high-level autonomous driving, it avoids imposing overly stringent limitations on steering control solely for comfort reasons, thus ensuring the controllable range of steering control. Furthermore, it can better adapt to different operating conditions and constantly changing vehicle states. For example, considering the control requirements under different autonomous driving scenarios and road adhesion conditions such as different road friction coefficients, it can perform steering control comprehensively and stably, thereby improving the adaptability and safety of autonomous driving steering control.

[0104] Figure 7 A block diagram of an automatic driving control data determination device according to an embodiment of the present disclosure is shown schematically.

[0105] like Figure 7 As shown, the autonomous driving control data determination device 700 of this embodiment includes, for example, a target control threshold determination module 710, an initial steering control detection result determination module 720, and a target steering control data determination module 730.

[0106] The target control threshold determination module 710 is used to determine the target scene category corresponding to the initial steering control data and the target control threshold corresponding to the target scene category based on the initial steering control data of the target vehicle.

[0107] The initial steering control detection result determination module 720 is used to determine the initial steering control detection result based on the initial steering control data and the target control threshold.

[0108] The target steering control data determination module 730 is used to determine target steering control data for controlling the driving of the target vehicle based on the initial steering control detection results.

[0109] For example, the target scenario category is one of a plurality of scenario categories that matches the initial steering control data; the scenario category includes at least one of the following: lane change steering category, abnormal steering category, normal steering category.

[0110] For example, the initial steering control data includes the target vehicle speed and steering control data, the steering control data including at least one of the steering wheel angle value and the steering wheel angle rate value; the target control threshold determination module includes: a first determination submodule, used to determine the steering control data associated with the target vehicle speed and the first steering acceleration threshold as the target control threshold based on the speed-steering control data-steering acceleration association data when the target scenario category is normal steering category, wherein the first steering acceleration threshold is associated with the normal steering category.

[0111] For example, the initial steering control data also includes road curvature data; the device further includes: a second steering acceleration threshold determination module, used to determine a second steering acceleration threshold corresponding to the road curvature data based on the road curvature data when the target scene category is normal steering category; and an update module, used to update the first steering acceleration threshold using the second steering acceleration threshold when the second steering acceleration threshold is less than the first steering acceleration threshold.

[0112] For example, the target control threshold determination module includes: a second determination submodule, used to determine, when the target scenario category is lane change steering category, steering control data corresponding to the target vehicle speed and a third steering acceleration threshold as an initial control threshold based on speed-steering control data-steering acceleration association data; a third determination submodule, used to determine the target control threshold based on lane change parameter threshold and lane change parameter data corresponding to the initial control threshold, wherein the lane change parameter threshold includes at least one of a lane change stability threshold and a lane change time threshold; the third steering acceleration threshold is associated with the lane change steering category, and the third steering acceleration threshold is less than or equal to the first steering acceleration threshold.

[0113] For example, the apparatus further includes: a first minimum lane-changing turning radius determination module, configured to determine a first minimum lane-changing turning radius that matches the vehicle speed and a third steering acceleration threshold when the lane-changing parameter thresholds include a lane-changing time threshold; and a lane-changing time threshold determination module, configured to determine a lane-changing time threshold based on the vehicle speed and the first minimum lane-changing turning radius.

[0114] For example, the initial steering control detection result determination module includes: a second minimum lane-changing turning radius determination submodule, used to determine a second minimum lane-changing turning radius that matches the vehicle speed and lane-changing turning path when the lane-changing parameter threshold includes a lane-changing time threshold, wherein the lane-changing turning path satisfies minimum curvature; and an initial steering control detection result determination submodule, used to determine the initial steering control detection result for the lane-changing time threshold based on the lane-changing time corresponding to the lane-changing time threshold and the second minimum lane-changing turning radius.

[0115] For example, the initial steering control detection result determination module includes an abnormal steering detection submodule, which is used to determine that the initial steering control detection result is abnormal steering in response to the abnormal steering condition triggered by the initial steering control data when the target scene category is an abnormal steering category.

[0116] It should be understood that the embodiments of the apparatus portion of this disclosure correspond to the same or similar embodiments of the method portion of this disclosure, and the technical problems solved and the technical effects achieved are also the same or similar. This disclosure will not repeat them here.

[0117] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0118] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device 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 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0119] like Figure 8 As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0120] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0121] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the autonomous driving control data determination method. For example, in some embodiments, the autonomous driving control data determination method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the autonomous driving control data determination method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the autonomous driving control data determination method by any other suitable means (e.g., by means of firmware).

[0122] Various embodiments of the systems and techniques described above herein 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), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0123] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0124] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0125] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0126] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0127] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0128] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0129] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for determining autonomous driving control data, comprising: Based on the initial steering control data of the target vehicle, determine the target scene category corresponding to the initial steering control data and the target control threshold corresponding to the target scene category; The initial steering control detection result is determined based on the initial steering control data and the target control threshold; as well as Based on the initial steering control detection results, target steering control data for controlling the driving of the target vehicle is determined; The initial steering control data includes the target vehicle speed and steering control data. Determining the target control threshold corresponding to the initial steering control data based on the target vehicle's initial steering control data includes: When the target scenario category is the normal steering category, the steering control data associated with the target vehicle speed and the first steering acceleration threshold is determined as the target control threshold based on the speed-steering control data-steering acceleration correlation data, wherein the first steering acceleration threshold is associated with the normal steering category.

2. The method of claim 1, wherein, The target scene category is one of multiple scene categories that matches the initial steering control data; The multiple scenario categories include: lane change turning category, abnormal turning category, and normal turning category.

3. The method of claim 2, wherein, The steering control data includes at least one of the steering wheel angle value and the steering wheel angle rate value.

4. The method of claim 3, wherein, The initial steering control data also includes road curvature data; the method further includes: when the target scene category is the normal steering category. Based on the road curvature data, a second steering acceleration threshold corresponding to the road curvature data is determined; If the second steering acceleration threshold is less than the first steering acceleration threshold, the first steering acceleration threshold is updated using the second steering acceleration threshold.

5. The method of claim 3, wherein, The step of determining the target control threshold corresponding to the initial steering control data based on the initial steering control data of the target vehicle includes: when the target scenario category is the lane change steering category, Based on the speed-steering control data-steering acceleration correlation data, the steering control data corresponding to the target vehicle speed and the third steering acceleration threshold is determined as the initial control threshold; The target control threshold is determined based on the lane change parameter threshold and the lane change parameter data corresponding to the initial control threshold, wherein the lane change parameter threshold includes at least one of the lane change stability threshold and the lane change time threshold; the third steering acceleration threshold is associated with the lane change steering category, and the third steering acceleration threshold is less than or equal to the first steering acceleration threshold.

6. The method according to claim 5, wherein, The method further includes, when the lane-changing parameter threshold includes the lane-changing time threshold. Determine a first minimum lane-changing turning radius that matches the target vehicle speed and the third steering acceleration threshold; and The lane-changing time threshold is determined based on the target vehicle speed and the first minimum lane-changing turning radius.

7. The method according to claim 6, wherein, The step of determining the initial steering control detection result based on the initial steering control data and the target control threshold includes: when the lane change parameter threshold includes the lane change time threshold, Determine a second minimum lane-changing turning radius that matches the target vehicle speed and lane-changing turning path, wherein the lane-changing turning path satisfies a minimum curvature; and Based on the lane-change time threshold and the lane-change time corresponding to the second minimum lane-change turning radius, the initial steering control detection result for the lane-change time threshold is determined.

8. The method according to claim 2, wherein, The step of determining the initial steering control detection result based on the initial steering control data and the target control threshold includes: when the target scene category is the abnormal steering category, In response to the initial steering control data triggering an abnormal steering condition, the initial steering control detection result is determined to be abnormal steering.

9. An automatic driving control data determination device, comprising: The target control threshold determination module is used to determine the target scene category corresponding to the initial steering control data and the target control threshold corresponding to the target scene category based on the initial steering control data of the target vehicle. The initial steering control detection result determination module is used to determine the initial steering control detection result based on the initial steering control data and the target control threshold. as well as The target steering control data determination module is used to determine target steering control data for controlling the driving of the target vehicle based on the initial steering control detection results. The initial steering control data includes the target vehicle speed and steering control data, and the target control threshold determination module includes: The first determining submodule is used to determine, when the target scenario category is normal steering category, steering control data associated with the target vehicle speed and a first steering acceleration threshold as the target control threshold based on speed-steering control data-steering acceleration association data, wherein the first steering acceleration threshold is associated with the normal steering category.

10. The apparatus according to claim 9, wherein, The target scene category is one of multiple scene categories that matches the initial steering control data; The multiple scenario categories include: lane change turning category, abnormal turning category, and normal turning category.

11. The apparatus according to claim 10, wherein, The steering control data includes at least one of the steering wheel angle value and the steering wheel angle rate value.

12. The apparatus according to claim 11, wherein, The initial steering control data also includes road curvature data; the device also includes: The second steering acceleration threshold determination module is used to determine the second steering acceleration threshold corresponding to the road curvature data based on the road curvature data when the target scene category is the normal steering category. An update module is used to update the first steering acceleration threshold using the second steering acceleration threshold when the second steering acceleration threshold is less than the first steering acceleration threshold.

13. The apparatus according to claim 11, wherein, The target control threshold determination module includes: The second determining submodule is used to determine, when the target scenario category is the lane change steering category, the steering control data corresponding to the target vehicle speed and the third steering acceleration threshold as the initial control threshold based on the speed-steering control data-steering acceleration correlation data. The third determining submodule is used to determine the target control threshold based on the lane change parameter threshold and the lane change parameter data corresponding to the initial control threshold, wherein the lane change parameter threshold includes at least one of a lane change stability threshold and a lane change time threshold; the third steering acceleration threshold is associated with the lane change steering category, and the third steering acceleration threshold is less than or equal to the first steering acceleration threshold.

14. The apparatus according to claim 13, wherein, The device further includes: The first minimum lane-changing turning radius determination module is used to determine a first minimum lane-changing turning radius that matches the vehicle speed and the third steering acceleration threshold, provided that the lane-changing parameter thresholds include the lane-changing time threshold; and The lane-changing time threshold determination module is used to determine the lane-changing time threshold based on the vehicle speed and the first minimum lane-changing turning radius.

15. The apparatus according to claim 14, wherein, The initial steering control detection result determination module includes: The second minimum lane-changing turning radius determination submodule is used to determine a second minimum lane-changing turning radius that matches the vehicle speed and lane-changing turning path when the lane-changing parameter threshold includes the lane-changing time threshold, wherein the lane-changing turning path satisfies the minimum curvature. The initial steering control detection result determination submodule is used to determine the initial steering control detection result for the lane change time threshold based on the lane change time threshold and the lane change time corresponding to the second minimum lane change turning radius.

16. The apparatus according to claim 10, wherein, The initial steering control detection result determination module includes: An abnormal steering detection submodule is used to determine that the initial steering control detection result is an abnormal steering when the target scene category is the abnormal steering category, in response to the abnormal steering condition triggered by the initial steering control data.

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 to enable the at least one processor to perform the method of any one of claims 1-8.

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

19. A computer program product comprising a computer program stored on at least one of a readable storage medium and an electronic device, the computer program implementing the method according to any one of claims 1-8 when executed by a processor.

20. An autonomous vehicle, including the electronic equipment of claim 17, wherein the target vehicle drives according to the target steering control data.

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