Vehicle steering control method and device, electronic equipment and vehicle

Abstract

The embodiment of the invention provides a vehicle steering control method and device, electronic equipment and a vehicle. The method comprises the steps that under the condition that the yaw velocity deviation of a vehicle is larger than a set threshold value, vehicle driving state parameters, the road curvature of a current lane where the vehicle is located, the distance between the vehicle and a boundary line of the current lane, the steering angle of a steering wheel and vehicle steering parameters are obtained; and determining whether to trigger a lane keeping assist system of the vehicle at a predicted time based on the vehicle driving state parameter, the road curvature, the spacing, the steering wheel angle, and the vehicle steering parameter. According to the method, the specific time when the lane keeping auxiliary system should be triggered can be more accurately judged, the problem that the normal driving operation of a driver is affected due to frequent false triggering of the lane keeping auxiliary system in the returning process after the driver actively steers is avoided, and the vehicle safety is improved.

Description

Technical Field

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

[0002] Lane Keeping Assist System (LKAS) is an important component of intelligent driving assistance systems. It uses cameras to recognize lane markings in real time and automatically adjusts the steering wheel when the vehicle deviates from the lane to help the vehicle stay stable in the center of the lane.

[0003] Currently, existing lane keeping assist systems mainly use yaw rate to predict the vehicle's driving radius and assume that the vehicle moves in a circle along this radius. If the vehicle deviates from the lane during the movement, the lane keeping assist system is activated.

[0004] However, the existing technology is not accurate enough in predicting vehicle trajectory, which makes LKAS prone to frequent intervention, interfering with the driver's normal driving operations and easily causing safety accidents. Summary of the Invention

[0005] This application provides a vehicle steering control method, device, electronic device, and vehicle that can accurately determine the triggering timing of LKAS, avoiding frequent intervention of LKAS that interferes with the driver's normal driving operations and causes safety accidents.

[0006] In a first aspect, embodiments of this application provide a vehicle steering control method, including:

[0007] When the yaw rate deviation of the vehicle is greater than a set threshold, the vehicle driving status parameters, the road curvature of the current lane where the vehicle is located, the distance between the vehicle and the current lane boundary line, the steering wheel angle and the vehicle steering parameters are obtained.

[0008] Based on the vehicle driving state parameters, the road curvature, the spacing, the steering wheel angle, and the vehicle steering parameters, it is determined whether the vehicle's lane keeping assist system should be triggered at the predicted time.

[0009] In this embodiment, by introducing the steering wheel angle and combining it with vehicle driving state parameters, vehicle steering parameters, road curvature, and the distance between the vehicle and the lane boundary line, it is possible to accurately determine the triggering timing of the lane keeping assist system based on the driver's steering behavior, thereby improving the safety of the vehicle driving process and avoiding the problem of frequent triggering of the lane keeping assist system, interfering with the driver's normal driving operation, and causing safety accidents.

[0010] In one possible implementation, the vehicle driving state parameters include vehicle speed and the heading angle of the vehicle relative to the current lane;

[0011] The step of determining whether to trigger the vehicle's lane keeping assist system at a predicted time based on the vehicle's driving state parameters, road curvature, spacing, steering wheel angle, and vehicle steering parameters includes:

[0012] Get the steering wheel rotation rate;

[0013] Based on the steering wheel angle, the vehicle steering parameters, the road curvature, and the steering wheel rotation rate, determine the current steering angle of the vehicle's front wheels and the return time of the front wheel steering angle;

[0014] Based on the return-to-center time, the vehicle steering parameters, the road curvature, the current turning angle, the steering wheel rotation rate, and the vehicle speed, the future driving path of the vehicle is predicted;

[0015] Based on the future driving path, the heading angle, the road curvature, and the spacing, it is determined whether the lane keeping assist system should be triggered at the predicted time.

[0016] In this embodiment, by using the steering wheel angle to determine the steering wheel return time, and combining the vehicle steering parameters, road curvature, the current steering angle of the vehicle's front wheels, the steering wheel rotation rate, and the vehicle speed, the future driving path of the vehicle can be predicted. This can improve the accuracy of predicting the future position of the vehicle, thereby providing a more accurate triggering time for the lane keeping assist system. This avoids the prediction error caused by using a fixed yaw rate to predict the vehicle's driving radius in traditional solutions and assuming that the vehicle moves in a circle along this radius.

[0017] In one possible implementation, the steering parameters include the steering ratio of the vehicle steering system and the wheelbase of the vehicle;

[0018] The determination of the current steering angle of the vehicle's front wheels and the front wheel steering angle return time based on the steering wheel angle, the vehicle steering parameters, the road curvature, and the steering wheel rotation rate includes:

[0019] Based on the steering wheel angle and the steering ratio, determine the current steering angle of the vehicle's front wheels;

[0020] Based on the road curvature and the wheelbase, determine the desired steering angle of the vehicle's front wheels;

[0021] The return-to-center time is determined based on the current steering angle, the desired steering angle, and the steering wheel rotation rate.

[0022] In this embodiment of the application, by calculating the return time of the front wheel steering angle, the change of the vehicle's driving path during the steering wheel return process can be simulated more realistically. Based on this, the prediction accuracy of the future driving path can be improved, thereby further enhancing the accuracy of judging the triggering time of the lane keeping assist system.

[0023] In one possible implementation, predicting the future driving path of the vehicle based on the return-to-center time, the vehicle steering parameters, the road curvature, the current turning angle, the steering wheel rotation rate, and the vehicle speed includes:

[0024] Based on the return time and the vehicle speed, determine the arc length of the vehicle's return travel path;

[0025] Based on the vehicle steering parameters, the vehicle speed, the current steering angle and the steering wheel rotation rate, the first curvature of the vehicle after traveling the first path arc length is predicted, where the first path arc length is less than the straightening path arc length.

[0026] Based on the road curvature, the second curvature of the vehicle after traveling the second path arc length is determined, wherein the second path arc length is greater than or equal to the straightening travel path arc length;

[0027] Construct the curve equation based on the first curvature and the second curvature;

[0028] Based on the curve equation, the future driving path of the vehicle is predicted.

[0029] In this embodiment, by utilizing vehicle steering parameters, vehicle speed, current steering angle, and steering wheel rotation rate, the first curvature of the vehicle after completing the first path arc length is predicted, achieving high-precision prediction of the future driving path of the vehicle during the steering wheel return process. This further improves the accuracy of judging the triggering timing of the lane keeping assist system and avoids the problem of inaccurate judgment of the triggering timing of the lane keeping assist system caused by the use of constant yaw rate in traditional technical solutions.

[0030] In one possible implementation, obtaining the steering wheel rotation rate includes: obtaining sample steering rates of at least one driver while driving the vehicle; and determining the steering wheel rotation rate based on the sample steering rates of each driver.

[0031] In this embodiment of the application, by sampling the steering rates of multiple different drivers, the calculated steering rate can more closely resemble the steering wheel rotation rate in real driving scenarios, making the subsequent calculated return-to-center time more accurate and reliable, further improving the accuracy of predicting future driving paths, and further improving the accuracy of judging the triggering timing of the lane keeping assist system.

[0032] In one possible implementation, determining whether to trigger the lane keeping assist system at the predicted time based on the future driving path, the heading angle, the road curvature, and the spacing includes:

[0033] Based on the future driving path, determine the location of the vehicle at the predicted time;

[0034] Based on the vehicle's location at the predicted time, the heading angle, the road curvature, and the spacing, the left lane boundary and the right lane boundary are obtained;

[0035] Based on the location point, the left boundary of the lane, and the right boundary of the lane, determine whether to trigger the lane keeping assist system.

[0036] In this embodiment of the application, by utilizing the vehicle's future driving path to predict the vehicle's location in the future, and combining the left and right boundaries of the lane, the timing of the lane keeping assist system's activation can be accurately predicted, so that the vehicle can take timely steering measures before deviating from the lane, thereby improving the driver's safety during the driving process.

[0037] In one possible implementation, the spacing includes a first distance between the vehicle and the left boundary line of the current lane, and a second distance between the vehicle and the right boundary line of the current lane;

[0038] The process of obtaining the left and right boundaries of the lane based on the vehicle's location at the predicted time, the heading angle, the road curvature, and the spacing includes:

[0039] A two-dimensional coordinate system is established with the current vehicle orientation as the horizontal axis and the direction perpendicular to the vehicle orientation as the vertical axis, and the horizontal coordinate value corresponding to the position point is obtained.

[0040] The left boundary of the lane is determined based on the heading angle, the x-coordinate value, the road curvature, and the first distance.

[0041] The right boundary of the lane is determined based on the heading angle, the x-coordinate value, the road curvature, and the second distance.

[0042] In this embodiment, by introducing a two-dimensional coordinate system centered on the vehicle, the complex problem of the relationship between the vehicle's driving path and the lane lines is transformed into a simple local geometric calculation problem. This can improve the efficiency of determining the triggering time and quickly provide the determination result, so that the vehicle can take corresponding measures in a timely manner based on the determination result, thereby improving safety and avoiding the problem of the vehicle leaving the lane and causing a traffic accident due to excessively long determination time.

[0043] In one possible implementation, determining whether to trigger the lane keeping assist system based on the location point, the left boundary of the lane, and the right boundary of the lane includes:

[0044] Obtain the ordinate value corresponding to the location point;

[0045] If the ordinate value is between the right edge of the lane and the right edge of the lane, the lane keeping assist system will not be triggered;

[0046] If the ordinate value is not between the left boundary and the right boundary of the lane, the lane keeping assist system is triggered.

[0047] In this embodiment, by comparing the ordinate value of the location point with the positional relationship of the two boundary lines, the computational cost is smaller and the speed is faster, which improves the real-time performance of the lane keeping assist system's trigger timing determination and avoids the problem of the vehicle leaving the lane and causing traffic accidents due to excessively long determination time.

[0048] In one possible implementation, the predicted time is the shortest time before the lane keeping assist system is triggered before the vehicle leaves the current lane.

[0049] In this embodiment of the application, by defining the prediction time as the shortest time before the vehicle leaves the current lane, the lane keeping assist system will not be triggered, thus ensuring the safety of the vehicle during its current driving process.

[0050] Secondly, embodiments of this application provide a vehicle steering control device, comprising:

[0051] The data acquisition module is used to acquire vehicle driving status parameters, road curvature of the current lane where the vehicle is located, distance between the vehicle and the current lane boundary line, steering wheel angle and vehicle steering parameters when the yaw rate deviation of the vehicle is greater than a set threshold.

[0052] The trigger determination module is used to determine whether to trigger the vehicle's lane keeping assist system at a predicted time based on the vehicle driving state parameters, the road curvature, the spacing, the steering wheel angle, and the vehicle steering parameters.

[0053] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0054] The memory stores computer-executed instructions;

[0055] The processor executes computer execution instructions stored in the memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0056] Fourthly, embodiments of this application provide a vehicle, including a vehicle body and an electronic device disposed on the vehicle body as described above. Attached Figure Description

[0057] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0058] Figure 1 This is a schematic diagram of the prediction method for a traditional lane keeping assist system.

[0059] Figure 2 This is a schematic diagram of the lane keeping assist system framework provided in an embodiment of this application;

[0060] Figure 3 A schematic flowchart illustrating the vehicle steering control method provided in an embodiment of this application;

[0061] Figure 4 A framework diagram of a vehicle steering control system provided in an embodiment of this application;

[0062] Figure 5 This is a flowchart illustrating the trigger determination process of the lane keeping assist system provided in an embodiment of this application.

[0063] Figure 6 This is a schematic diagram illustrating the process for determining the front wheel steering angle return time in an embodiment of this application.

[0064] Figure 7 This is a schematic diagram illustrating the predicted future driving path provided in an embodiment of this application;

[0065] Figure 8 This is a flowchart illustrating the trigger determination process of the lane keeping assist system provided in an embodiment of this application.

[0066] Figure 9 A schematic diagram of the vehicle steering control device provided in this application;

[0067] Figure 10 A schematic diagram of the structure of the electronic device provided in this application.

[0068] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0069] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0070] Lane Keeping Assist System (LKAS) is an important component of intelligent driving assistance systems. As a Level 2 Advanced Driver Assistance System (ADAS) function, LKAS's core objective is to reduce driver workload and improve driving safety by automatically controlling the steering system. Its operation relies on the coordinated processing of data from cameras (for lane marking), the steering system (for steering control), and sensors such as vehicle speed and yaw rate.

[0071] The traditional LKAS core algorithm predicts the vehicle path based on yaw rate. The specific steps are as follows: (1) The intelligent driving assistance system obtains the left and right distances of the lane lines, the vehicle heading angle, and the road curvature from the camera, as well as the vehicle speed and yaw rate from the sensor; (2) Assuming that the vehicle is driving at a constant current yaw rate, calculate the vehicle position at a future time point (determined by the calibration value Δt); (3) Compare the predicted position with the lane boundary (the area defined by the left and right lane lines). If the predicted position exceeds the lane boundary, LKAS intervention is triggered; if the predicted position does not exceed the lane boundary, no intervention is triggered.

[0072] The traditional LKAS (Lane Knock Assurance System) suffers from an oversimplified path prediction model, assuming a constant yaw rate and that the vehicle moves along a fixed-radius circle. However, in real-world driving, driver intervention (such as lane changes) causes sudden changes in yaw rate, which traditional LKAS fails to account for, leading to predictions that deviate from the actual path. For instance, when a driver actively turns the steering wheel, the yaw rate may briefly increase, but the steering wheel is subsequently straightened, and the vehicle's actual path gradually corrects. Traditional LKAS may incorrectly interpret this active steering as an unintended deviation, intervening prematurely or frequently and disrupting the driver's normal driving.

[0073] For example, Figure 1 This is a schematic diagram of the prediction method for traditional lane keeping assist systems, such as... Figure 1 As shown, traditional LKAS assumes a constant yaw rate, uses this yaw rate to predict the vehicle's travel radius R, and assumes the vehicle moves in a circle along this radius R. LKAS is triggered if the predicted position is about to deviate from the left or right boundary of the lane.

[0074] The method for calculating the predicted location is as follows:

[0075]

[0076]

[0077] In the above formula, X and Y are the two-dimensional coordinates of the predicted position, Vehicle Speed ​​is the vehicle speed, Yaw Rate is the yaw rate, and Prediction Time is a pre-calibrated value.

[0078] To address the aforementioned issues, this application provides a vehicle steering control method, device, equipment, and vehicle. By introducing the steering wheel angle as an input parameter for determining the timing of LKAS intervention, it is possible to dynamically predict the future driving path of the vehicle based on the driver's steering behavior, preventing misjudgments and making the triggering timing of LKAS more accurate.

[0079] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0080] Figure 2 This is a schematic diagram of the lane keeping assist system framework provided in an embodiment of this application, as shown below. Figure 2 As shown, the core algorithm of LKAS is integrated into an electronic control unit (which can be a standalone controller or share a controller with other functions). LKAS reads lane information provided by the camera and sends commands to the steering system (steering wheel). At the same time, LKAS also obtains the necessary vehicle information (such as vehicle speed) from other systems through the vehicle communication network.

[0081] LKAS is not always activated; it is only activated and provides steering assistance when it detects that the vehicle is about to deviate from its lane, so that the vehicle can continue to stay in the current lane.

[0082] In addition, the timing of LKAS activation is very important. If it is activated too late, LKAS may not be able to prevent unexpected lane departures. If LKAS is activated too early, it may be activated too frequently, affecting the driver's normal driving and even easily causing safety accidents.

[0083] Figure 3 This is a flowchart illustrating a vehicle steering control method provided in an embodiment of this application. This method can be applied to electronic control units in vehicles, such as... Figure 3 As shown, it includes the following steps:

[0084] Step 310: When the vehicle's yaw rate deviation is greater than a set threshold, acquire the vehicle's driving status parameters, the road curvature of the current lane where the vehicle is located, the distance between the vehicle and the current lane boundary line, the steering wheel angle, and the vehicle's steering parameters.

[0085] For example, vehicle driving state parameters may include vehicle speed and the vehicle's heading angle relative to the current lane. The distance between the vehicle and the current lane boundary lines may include the distance between the vehicle and the left boundary line of the current lane, and the distance between the vehicle and the right boundary line of the current lane. Vehicle steering parameters may include the steering ratio of the vehicle's steering system and the vehicle's wheelbase.

[0086] In other embodiments, the vehicle steering parameters may also include the steering wheel steering rate.

[0087] Step 320: Based on vehicle driving state parameters, road curvature, spacing, steering wheel angle, and vehicle steering parameters, determine whether to trigger the vehicle's lane keeping assist system at the predicted time.

[0088] The vehicle steering control method provided in this application can identify a driver-initiated steering scenario when a large deviation in the vehicle's yaw rate is detected. By introducing the steering wheel angle and combining it with vehicle driving state parameters, vehicle steering parameters, road curvature, and the distance between the vehicle and the lane boundary line, the method can accurately determine the triggering timing of the lane keeping assist system based on the driver's steering behavior. This improves the safety of vehicle driving and avoids the lane keeping assist system from frequently triggering, interfering with the driver's normal driving operations, and causing safety accidents.

[0089] In this embodiment, the entity executing the above-described vehicle steering control method can be an electronic control unit. For example, Figure 4 A framework diagram of the vehicle steering control system provided in the embodiments of this application is shown below. Figure 4 As shown, the vehicle can be equipped with cameras, image processors, vehicle speed sensors, yaw rate sensors, and steering wheel angle sensors, etc.

[0090] The image data collected by the camera is processed by the image processor to obtain the distance between the vehicle and the left edge of the current lane (hereinafter referred to as the first distance) and the distance between the vehicle and the right edge of the current lane (hereinafter referred to as the second distance), as well as the heading angle of the vehicle relative to the current lane and the curvature of the road, and then input to the electronic control unit.

[0091] The LKAS algorithm in the electronic control unit uses the first distance, the second distance, the heading angle, the road curvature, the vehicle speed, the yaw rate, and the steering wheel angle as input parameters. The LKAS algorithm calculates the triggering time and triggers LKAS when the triggering time arrives.

[0092] Regarding step 310 above, the threshold can be a pre-configured value, which serves as a parameter for determining whether the driver actively steers. When the vehicle's yaw rate deviation is greater than the set threshold, it is confirmed that the driver is actively turning the steering wheel.

[0093] Among them, the yaw rate can be collected in real time by the yaw rate sensor, the vehicle speed can be collected in real time by the vehicle speed sensor, and the steering wheel angle can be collected in real time by the steering wheel angle sensor.

[0094] In addition, the electronic control unit can also acquire first distance, second distance, heading angle and road curvature through the camera.

[0095] Regarding step 320 above, when the steering wheel angle is detected to increase, it indicates that the driver is performing a steering operation, and when the steering wheel angle is detected to decrease, it indicates that the driver is performing a steering wheel straightening operation.

[0096] In real-world scenarios, if the yaw rate deviation is large, it is considered to be caused by the driver actively steering. In this case, the driver is likely to straighten the steering wheel afterward. If LKAS is triggered in this situation, it may affect the driver's normal driving. Therefore, the triggering time of LKAS needs to be delayed.

[0097] In this embodiment, the predicted time can be a pre-defined time. For example, the predicted time is the shortest time before the lane keeping assist system is triggered before the vehicle leaves the current lane.

[0098] In this scenario, if it is predicted that the vehicle will leave the current lane at a future time point T1, and further assumed that the lane keeping assist system will be triggered at another future time point T2 (time point T2 is before time point T1), then the time from the current time point to time point T2 can be used as the predicted time, that is, the shortest time during which the lane keeping assist system will not be triggered.

[0099] In this embodiment, the future driving path of the vehicle can be predicted by using the steering wheel angle. Then, by combining the vehicle speed, first distance, second distance, heading angle, vehicle steering parameters and road curvature, it can be determined whether the vehicle is still in the current lane at the predicted time, thereby determining whether LKAS should be triggered.

[0100] Among them, vehicle steering parameters may include the steering ratio of the vehicle's steering system and the vehicle's wheelbase.

[0101] The above text mentions that traditional LKAS (Lane Knock Assurance System) assumes a constant yaw rate, then uses this yaw rate to predict the vehicle's radius, and assumes the vehicle moves in a circle along this radius to predict its path. This method leads to frequent interventions by LKAS. To address this, Figure 5 The flowchart for the lane keeping assist system trigger determination provided in the embodiments of this application is as follows: Figure 5 As shown, it includes the following steps:

[0102] Step 510: Obtain the vehicle's steering wheel rotation rate;

[0103] Step 520: Based on the steering wheel angle, vehicle steering parameters, road curvature, and steering wheel rotation rate, determine the current steering angle of the vehicle's front wheels and the return-to-center time of the front wheel steering angle;

[0104] Step 530: Based on the return-to-center time, vehicle steering parameters, road curvature, current steering angle, steering wheel rotation rate, and vehicle speed, predict the vehicle's future driving path;

[0105] Step 540: Based on the future driving path, heading angle, road curvature, and spacing, determine whether to trigger the lane keeping assist system at the predicted time.

[0106] The spacing can include the distance between the vehicle and the left edge of the current lane (i.e., the first distance mentioned above), and the distance between the vehicle and the right edge of the current lane (i.e., the second distance mentioned above).

[0107] In this embodiment, the large deviation in yaw rate is mainly caused by the driver's active steering. In this case, the driver is likely to turn the steering wheel back to the correct angle. Therefore, to prevent LKAS from being triggered, the steering wheel angle parameter needs to be considered.

[0108] The LKAS trigger determination method provided in this application determines the steering wheel return time by using the steering wheel angle, and combines the vehicle steering parameters, road curvature, the current steering angle of the vehicle's front wheels and the vehicle speed to predict the vehicle's future driving path. This avoids the prediction error caused by using a fixed yaw rate to predict the vehicle's driving radius in traditional solutions and assuming that the vehicle moves in a circle along this radius, thus improving the accuracy of predicting the vehicle's future position and providing a more accurate LKAS trigger timing.

[0109] Regarding step 510 above, the steering wheel rotation rate can be predicted based on a neural network model. For example, by collecting massive amounts of sample data (including the steering wheel rotation rates of different drivers), the neural network model can be trained, and the current driver's steering wheel rotation rate can be predicted based on the trained neural network model.

[0110] Alternatively, in some implementations, sample steering rates of at least one driver while driving the vehicle can be obtained; then, the steering wheel rotation rate can be determined based on the sample steering rates of each driver.

[0111] Among them, the sample steering rate of each driver is different. For example, some drivers apply more force when turning the steering wheel, and the corresponding sample steering rate is larger, while some drivers apply less force when turning the steering wheel, and the corresponding sample steering rate is relatively smaller.

[0112] The average steering rate can be obtained by averaging the steering rates of each driver's samples, and this average can be used as the steering wheel rotation rate of the current driver.

[0113] In this embodiment, by sampling at least one driver and determining the steering wheel rotation rate of the current driver based on the sample steering rate of each driver, the accuracy of the steering wheel rotation rate can be guaranteed, thereby improving the accuracy of the subsequent return-to-center time calculation, so that the predicted future driving path is more accurate.

[0114] In addition, in some embodiments, a driver's hand position sensor can be introduced, combined with steering wheel angle data, to determine whether the driver is actively turning the steering wheel, thereby solving the problem that traditional solutions cannot recognize the driver's active steering, causing LKAS to be falsely triggered.

[0115] Specifically, when the driver's hands are detected touching the steering wheel and the rate of change of the steering wheel angle exceeds a threshold, it can be determined that the driver is actively steering.

[0116] In the scenario where the driver actively turns the steering wheel, LKAS needs to be delayed to avoid conflict with the driver's intention and improve LKAS's fault tolerance in active steering scenarios.

[0117] Regarding step 520 above, when the driver turns the steering wheel, the front wheels of the vehicle will turn accordingly. The steering angle of the front wheels can be determined by the angle of the steering wheel, and the return time of the front wheels can be further determined.

[0118] Exemplary, in some embodiments, Figure 6 This is a schematic diagram of the process for determining the front wheel steering angle return time provided in an embodiment of this application, as shown below. Figure 6 As shown, it includes the following steps:

[0119] Step 610: Determine the current steering angle of the vehicle's front wheels based on the steering wheel angle and steering ratio;

[0120] Step 620: Determine the desired steering angle of the vehicle's front wheels based on road curvature and wheelbase;

[0121] Step 630: Determine the return-to-center time based on the current steering angle, the desired steering angle, and the steering wheel rotation rate.

[0122] Regarding step 610 above, as mentioned earlier, vehicle steering parameters include the steering ratio of the vehicle's steering system and the vehicle's wheelbase. Based on this, the current steering angle of the vehicle's front wheels can be calculated using the following formula:

[0123]

[0124] In the above formula, Current Front Wheel Angle represents the current steering angle of the front wheel, Steering Handwheel Angle represents the steering wheel angle, and Steering Ratio is the steering ratio determined by the vehicle steering system, which is a constant.

[0125] Regarding step 620 above, the desired steering angle of the vehicle's front wheels can be determined using the following formula:

[0126]

[0127] In the above formula, Desired Front Wheel Angle is the desired turning angle, Wheelbase is the wheelbase, and RoadCurvature is the road curvature.

[0128] Regarding step 630 above, if the driver's steering wheel rotation rate is Steering Rate, the time required for the vehicle's front wheels to reach the desired steering angle can be determined as follows:

[0129]

[0130] In the above formula, CutOff Time is the time to return to center.

[0131] The front wheel steering angle return time determination method provided in this application can more realistically simulate the changes in the vehicle's driving path during the steering wheel return process (such as gradually transitioning from a large radius steering to the desired radius) by calculating the front wheel steering return time, thereby more accurately predicting the future driving path.

[0132] Regarding step 530 above, the traditional LKAS system uses a fixed constant yaw rate to predict the vehicle's driving radius and assumes that the vehicle moves in a circle along this radius to predict the vehicle's driving path. This method will cause LKAS to be triggered frequently.

[0133] In the LKAS trigger determination method provided in this embodiment, the future driving path of the vehicle can be predicted by using the return time, vehicle steering parameters, road curvature, the current turning angle of the front wheels of the vehicle and the actual speed of the vehicle. This can fit the scenario in which the driver actively turns the steering wheel in real-world situations and improve the accuracy of LKAS triggering.

[0134] For example, in some embodiments, the future driving path can be predicted through the following steps:

[0135] Step (1): Determine the arc length of the vehicle's return-to-center travel path based on the return-to-center time and vehicle speed.

[0136] Step (2): Based on the vehicle steering parameters, vehicle speed, current steering angle of the front wheels and steering wheel rotation rate, predict the first curvature of the vehicle after traveling the first path arc length.

[0137] The arc length of the first path is less than the arc length of the path for returning to the starting position.

[0138] Step (3): Based on the road curvature, determine the second curvature of the vehicle after it has traveled the second path arc length. The second path arc length is greater than or equal to the arc length of the return travel path.

[0139] Step (4): Construct the curve equation based on the first curvature and the second curvature;

[0140] Step (5): Based on the curve equation, predict the future driving path of the vehicle.

[0141] In this embodiment, reference is continued to be made to the above. Figure 1Traditional Lane Keeping Assist (LKAS) assumes the vehicle moves in a circle along radius R, meaning the driving path is a semicircle. It doesn't consider the driver's active steering wheel movements, leading to prediction points consistently deviating from the lane and frequent LKAS triggers. The driving path prediction method provided in this embodiment adjusts the path prediction from a fixed semicircle to a spiral, solving the problem of frequent LKAS penalties caused by ignoring steering wheel centering behavior in traditional solutions.

[0142] For example, Figure 7 This is a schematic diagram of the predicted future driving path provided in the embodiments of this application, such as... Figure 7 As shown, assuming the driver actively steers, the vehicle suddenly turns left. After a period of time, the driver quickly straightens the steering wheel, and the vehicle then turns right. During this process, the vehicle's path is a spiral rather than a traditional semicircle.

[0143] Therefore, once the driver is detected to begin actively steering, it is necessary to reasonably predict the vehicle's future driving path in subsequent moments.

[0144] Specifically, for step (1) above, the arc length of the return-to-center driving path = Vehicle Speed ​​* CutOff Time. Vehicle Speed ​​is the vehicle speed, and CutOff Time is the return-to-center time.

[0145] During the steering wheel return process, the vehicle's path is a spiral rather than a regular semicircle. If the arc length traversed by the vehicle does not exceed the arc length of this return path, then the vehicle is still in the process of steering wheel return.

[0146] Regarding step (2) above, the formula for calculating the first curvature can be as follows:

[0147]

[0148] In the above formula, Predicted Path Curvature(s) represents the curvature at the first path arc length s, i.e., the first curvature, s=Vehicle Speed*(t-t0), t0 can be the current time, at the current time t0, the vehicle speed direction can be Current Front Wheel Angle, t can be a future time, and is less than CutOff Time, Steering Rate represents the steering wheel rotation rate, Vehicle Speed ​​represents the vehicle speed, Wheel Base represents the wheelbase, Current Front Wheel Angle represents the current steering angle of the front wheel, and CutOff Time is the return-to-center time.

[0149] Regarding step (3) above, if the arc length of the vehicle's travel path is greater than or equal to the arc length of the return travel path (Vehicle Speed ​​* CutOff Time), that is, the arc length of the vehicle's travel path at this time is the second arc length, and the corresponding formula for calculating the second curvature is as follows:

[0150]

[0151] In the above formula, Predicted Path Curvature(s) represents the curvature at the second path arc length s, i.e., the second curvature, and Road Curvature is the road curvature.

[0152] Regarding steps (4) and (5) above, after obtaining the curvature, the curvature equation (X(t), Y(t)) can be obtained by combining the relevant mathematical equations, where X(t) represents the horizontal coordinate of the vehicle at time t and Y(t) represents the vertical coordinate of the vehicle at time t.

[0153] Based on X(t) and Y(t), it is possible to determine whether a vehicle is outside the left or right boundary of the lane.

[0154] Regarding step 540 above, after predicting the future driving path, the vehicle's position at the predicted time can be determined based on the future driving path. Combined with the heading angle, road curvature, first distance, and second distance, it can be further determined whether the position is outside the lane boundary, thereby determining whether LKAS should be triggered.

[0155] The Lane Keeping Assist System (LKAS) triggering determination method provided in this application predicts the future driving path of the vehicle in subsequent moments by using the steering wheel angle and steering wheel rotation rate, avoiding false triggering of LKAS due to the assumption of constant yaw rate, and improving the accuracy of LKAS triggering.

[0156] The following describes in detail, through some examples, how to determine whether the lane keeping assist system is triggered.

[0157] Figure 8 The flowchart for the lane keeping assist system trigger determination provided in the embodiments of this application is as follows: Figure 8 As shown, it includes the following steps:

[0158] Step 810: Based on the future travel path, determine the vehicle's location at the predicted time;

[0159] Step 820: Based on the vehicle's location, heading angle, road curvature, and spacing at the predicted time, obtain the left and right boundaries of the lane;

[0160] Step 830: Based on the location point, the left edge of the lane, and the right edge of the lane, determine whether to trigger the lane keeping assist system.

[0161] The spacing includes the first distance and the second distance mentioned above.

[0162] Regarding step 810 above, taking the prediction time as T as an example, based on the curve equation mentioned above, the location of the vehicle at the prediction time T can be determined as (X(T), Y(T)).

[0163] Regarding step 820 above, in some implementations, images of the lanes around the vehicle can be captured by a camera or the like, and the left and right boundaries of the lanes can be determined based on the image information captured by the camera.

[0164] Alternatively, in some embodiments, the left and right boundaries of the lane can also be determined in the following manner:

[0165] Step 1: Establish a two-dimensional coordinate system with the current vehicle orientation as the horizontal axis and the direction perpendicular to the vehicle orientation as the vertical axis, and obtain the horizontal coordinate value corresponding to the position point.

[0166] Step 2: Determine the left boundary of the lane based on the heading angle, x-coordinate value, road curvature, and first distance;

[0167] Step 3: Determine the right boundary of the lane based on the heading angle, x-coordinate value, road curvature, and second distance.

[0168] In this embodiment, reference is continued to be made to the above. Figure 7 A two-dimensional coordinate system can be established by taking the vehicle's direction (i.e., the direction the vehicle is facing) as the X-axis (i.e., the horizontal axis) and the direction perpendicular to the vehicle's direction as the Y-axis (i.e., the vertical axis).

[0169] The calculation method for the left boundary of the lane is as follows:

[0170]

[0171] In the above formula, Y Left The vertical coordinate represents the left edge of the lane, the road curvature represents the road curvature, the vehicle heading represents the heading angle, X represents the horizontal coordinate of the vehicle's position, and the left distance represents the first distance.

[0172] The calculation method for the right boundary of the lane is as follows:

[0173]

[0174] In the above formula, Y RightThe vertical coordinate represents the right boundary of the lane, the road curvature represents the road curvature, the vehicle heading represents the heading angle, X represents the horizontal coordinate of the vehicle's position, and the right distance represents the second distance.

[0175] Regarding step 830 above, after calculating (X(T), Y(T)) and Y... Left and Y Right Then, if (X(T), Y(T)) is in Y Left and Y Right If (X(T), Y(T)) is not in Y, then LKAS will not be triggered. Left and Y Right If this occurs, LKAS will be triggered.

[0176] To improve decision-making efficiency and ensure that the LKAS triggering timing is not delayed, Y(T) can be directly compared with the ordinate Y of the left boundary of the lane. Left The ordinate Y of the right edge of the lane Right For comparison, if Y(T) is located at the ordinate Y of the left boundary of the lane... Left The ordinate Y of the right edge of the lane Right If Y(T) is not within the range of the left boundary of the lane, the lane keeping assist system will not be triggered; Left The ordinate Y of the right edge of the lane Right If the lane is between these points, the lane keeping assist system will be triggered.

[0177] The above embodiments can accurately determine the timing of LKAS triggering by utilizing future driving paths, heading angles, road curvature, first distances, and second distances, thereby avoiding false triggering of LKAS and improving driver safety during vehicle operation.

[0178] Figure 9 A schematic diagram of the vehicle steering control device provided in this application is shown below. Figure 9 As shown, the vehicle steering control device 90 provided in this embodiment includes:

[0179] The data acquisition module 910 is used to acquire vehicle driving status parameters, road curvature of the current lane where the vehicle is located, distance between the vehicle and the current lane boundary line, steering wheel angle and vehicle steering parameters when the yaw rate deviation of the vehicle is greater than a set threshold.

[0180] The trigger determination module 920 is used to determine whether to trigger the vehicle's lane keeping assist system at a predicted time based on vehicle driving state parameters, road curvature, spacing, steering wheel angle, and vehicle steering parameters.

[0181] In one possible implementation, the trigger determination module can be used to: obtain the vehicle's steering wheel rotation rate; then, based on the steering wheel angle, vehicle steering parameters, road curvature, and steering wheel rotation rate, determine the current steering angle of the vehicle's front wheels and the return-to-center time of the front wheel steering angle; and based on the return-to-center time, vehicle steering parameters, road curvature, the current steering angle of the vehicle's front wheels, steering wheel rotation rate, and vehicle speed, predict the vehicle's future driving path; finally, based on the future driving path, heading angle, road curvature, and distance, determine whether to trigger the lane keeping assist system at the predicted time.

[0182] In one possible implementation, the vehicle steering parameters include the steering ratio of the vehicle's steering system and the vehicle's wheelbase. Accordingly, the trigger determination module can be specifically used to: determine the current steering angle of the vehicle's front wheels based on the steering wheel angle and steering ratio; then determine the desired steering angle of the vehicle's front wheels based on the road curvature and wheelbase; and finally determine the return-to-center time based on the current steering angle, the desired steering angle, and the steering wheel rotation rate.

[0183] In one possible implementation, the trigger determination module can be used to: determine the arc length of the vehicle's return-to-center driving path based on the return-to-center time and vehicle speed; then, based on the vehicle's steering parameters, vehicle speed, the current steering angle of the vehicle's front wheels, and the steering wheel rotation rate, predict the first curvature of the vehicle after traveling the first arc length; and based on the road curvature, determine the second curvature of the vehicle after traveling the second arc length, then construct a curve equation based on the first and second curvatures; finally, based on the curve equation, predict the vehicle's future driving path.

[0184] The arc length of the first path is less than the arc length of the straightening path, and the arc length of the second path is greater than or equal to the arc length of the straightening path.

[0185] In one possible implementation, the trigger determination module can be specifically used to: obtain sample steering rates of at least one driver while driving the vehicle; and determine the steering wheel rotation rate based on the sample steering rates of each driver.

[0186] In one possible implementation, the trigger determination module can be used to: determine the vehicle's location at the predicted time based on the future driving path; then obtain the left and right lane boundaries based on the vehicle's location, heading angle, road curvature, and spacing at the predicted time; and finally determine whether to trigger the lane keeping assist system based on the location, left lane boundary, and right lane boundary.

[0187] In one possible implementation, the trigger determination module can be used to: establish a two-dimensional coordinate system with the current vehicle orientation as the horizontal axis and the direction perpendicular to the vehicle orientation as the vertical axis, and obtain the horizontal coordinate value corresponding to the position point; then determine the left boundary of the lane based on the heading angle, the horizontal coordinate value, the road curvature and the first distance; and finally determine the right boundary of the lane based on the heading angle, the horizontal coordinate value, the road curvature and the second distance.

[0188] In one possible implementation, the trigger determination module can be used to: obtain the ordinate value corresponding to the location point; if the ordinate value is between the right edge of the lane and the right edge of the lane, then the lane keeping assist system is not triggered; if the ordinate value is not between the left edge of the lane and the right edge of the lane, then the lane keeping assist system is triggered.

[0189] In one possible implementation, the prediction time is the shortest time before the lane keeping assist system is triggered before the vehicle leaves the current lane.

[0190] The vehicle steering control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0191] Figure 10 A schematic diagram of the structure of the electronic device provided in this application. Figure 10 As shown, the electronic device 1000 provided in this embodiment includes at least one processor 1001 and a memory 1002. Optionally, the electronic device 1000 further includes a communication component 1003. The processor 1001, memory 1002, and communication component 1003 are connected via a bus.

[0192] In a specific implementation, at least one processor 1001 executes computer execution instructions stored in memory 1002, causing at least one processor 1001 to perform the above-described method.

[0193] The specific implementation process of processor 1001 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0194] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0195] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0196] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0197] This application also provides a vehicle, which includes a vehicle body and the aforementioned electronic equipment disposed on the vehicle body.

[0198] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0199] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0200] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0201] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0202] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0203] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0204] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0205] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0206] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0207] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A vehicle steering control method, characterized in that, include: When the yaw rate deviation of the vehicle is greater than a set threshold, the vehicle driving status parameters, the road curvature of the current lane where the vehicle is located, the distance between the vehicle and the current lane boundary line, the steering wheel angle and the vehicle steering parameters are obtained. Based on the vehicle driving state parameters, the road curvature, the spacing, the steering wheel angle, and the vehicle steering parameters, it is determined whether the vehicle's lane keeping assist system should be triggered at the predicted time.

2. The method according to claim 1, characterized in that, The vehicle driving status parameters include vehicle speed and the heading angle of the vehicle relative to the current lane; The step of determining whether to trigger the vehicle's lane keeping assist system at a predicted time based on the vehicle's driving state parameters, road curvature, spacing, steering wheel angle, and vehicle steering parameters includes: Get the steering wheel rotation rate; Based on the steering wheel angle, the vehicle steering parameters, the road curvature, and the steering wheel rotation rate, determine the current steering angle of the vehicle's front wheels and the return time of the front wheel steering angle; Based on the return-to-center time, the vehicle steering parameters, the road curvature, the current turning angle, the steering wheel rotation rate, and the vehicle speed, the future driving path of the vehicle is predicted; Based on the future driving path, the heading angle, the road curvature, and the spacing, it is determined whether the lane keeping assist system should be triggered at the predicted time.

3. The method according to claim 2, characterized in that, The steering parameters include the steering ratio of the vehicle steering system and the wheelbase of the vehicle; The determination of the current steering angle of the vehicle's front wheels and the front wheel steering angle return time based on the steering wheel angle, the vehicle steering parameters, the road curvature, and the steering wheel rotation rate includes: Based on the steering wheel angle and the steering ratio, determine the current steering angle of the vehicle's front wheels; Based on the road curvature and the wheelbase, determine the desired steering angle of the vehicle's front wheels; The return-to-center time is determined based on the current steering angle, the desired steering angle, and the steering wheel rotation rate.

4. The method according to claim 2, characterized in that, The method of predicting the future driving path of the vehicle based on the return-to-center time, the vehicle steering parameters, the road curvature, the current turning angle, the steering wheel rotation rate, and the vehicle speed includes: Based on the return time and the vehicle speed, determine the arc length of the vehicle's return travel path; Based on the vehicle steering parameters, the vehicle speed, the current steering angle and the steering wheel rotation rate, the first curvature of the vehicle after traveling the first path arc length is predicted, where the first path arc length is less than the straightening path arc length. Based on the road curvature, the second curvature of the vehicle after traveling the second path arc length is determined, wherein the second path arc length is greater than or equal to the straightening travel path arc length; Construct the curve equation based on the first curvature and the second curvature; Based on the curve equation, the future driving path of the vehicle is predicted.

5. The method according to claim 2, characterized in that, The process of obtaining the steering wheel rotation rate includes: Obtain a sample steering rate of at least one driver on the steering wheel while driving the vehicle; The steering wheel rotation rate is determined based on the sample steering rate of each driver.

6. The method according to claim 2, characterized in that, The step of determining whether to trigger the lane keeping assist system at the predicted time based on the future driving path, the heading angle, the road curvature, and the spacing includes: Based on the future driving path, determine the location of the vehicle at the predicted time; Based on the vehicle's location at the predicted time, the heading angle, the road curvature, and the spacing, the left lane boundary and the right lane boundary are obtained; Based on the location point, the left boundary of the lane, and the right boundary of the lane, determine whether to trigger the lane keeping assist system.

7. The method according to claim 6, characterized in that, The spacing includes a first distance between the vehicle and the left boundary line of the current lane, and a second distance between the vehicle and the right boundary line of the current lane; The process of obtaining the left and right boundaries of the lane based on the vehicle's location at the predicted time, the heading angle, the road curvature, and the spacing includes: A two-dimensional coordinate system is established with the current vehicle orientation as the horizontal axis and the direction perpendicular to the vehicle orientation as the vertical axis, and the horizontal coordinate value corresponding to the position point is obtained. The left boundary of the lane is determined based on the heading angle, the x-coordinate value, the road curvature, and the first distance. The right boundary of the lane is determined based on the heading angle, the x-coordinate value, the road curvature, and the second distance.

8. The method according to claim 7, characterized in that, The step of determining whether to trigger the lane keeping assist system based on the location point, the left boundary of the lane, and the right boundary of the lane includes: Obtain the ordinate value corresponding to the location point; If the ordinate value is between the right edge of the lane and the right edge of the lane, the lane keeping assist system will not be triggered; If the ordinate value is not between the left boundary and the right boundary of the lane, the lane keeping assist system is triggered.

9. The method according to claim 1, characterized in that, The predicted time is the shortest time before the lane keeping assist system is triggered before the vehicle leaves the current lane.

10. A vehicle steering control device, characterized in that, include: The data acquisition module is used to acquire vehicle driving status parameters, road curvature of the current lane where the vehicle is located, distance between the vehicle and the current lane boundary line, steering wheel angle and vehicle steering parameters when the yaw rate deviation of the vehicle is greater than a set threshold. The trigger determination module is used to determine whether to trigger the vehicle's lane keeping assist system at a predicted time based on the vehicle driving state parameters, the road curvature, the spacing, the steering wheel angle, and the vehicle steering parameters.

11. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1-9.

12. A vehicle, characterized in that, It includes a vehicle body and an electronic device as described in claim 11 disposed on the vehicle body.

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