Steering assisting method and vehicle

Abstract

The invention relates to a steering assisting method and a vehicle. The steering assisting method comprises the steps that a steering assisting function is started in response to a vehicle, steering information corresponding to all wheels of the vehicle is obtained, and the steering information corresponding to all the wheels is used for representing the steering state of all the wheels; predicting a steering path of the vehicle at least according to the steering information corresponding to each wheel to obtain a predicted steering path; and displaying the predicted steering path. According to the steering assisting method and the vehicle, the safety, stability and reliability of steering operation of a driver can be improved, the interactive experience of the driver is improved, and then the steering experience of the driver is improved.

Description

Technical Field

[0001] This disclosure relates to the field of vehicle technology, and more specifically, to a steering assistance method and a vehicle. Background Technology

[0002] With the development of vehicle intelligence, intelligent assisted steering functions are also evolving. These functions assist drivers in steering maneuvers in certain situations, enhancing the driving experience. Summary of the Invention

[0003] The purpose of this disclosure is to provide a steering assistance method and vehicle that can improve the safety, stability and reliability of the driver's steering operation, as well as improve the driver's interactive experience, thereby enhancing the driver's steering experience.

[0004] To achieve the above objectives, in a first aspect, this disclosure provides a steering assist method, comprising: in response to a vehicle activating a steering assist function, acquiring steering information corresponding to each wheel of the vehicle, the steering information corresponding to each wheel being used to characterize the steering state of each wheel; predicting the steering path of the vehicle based at least on the steering information corresponding to each wheel, thereby obtaining a predicted steering path; and displaying the predicted steering path.

[0005] Optionally, each wheel is equipped with an angle module, and obtaining the steering information corresponding to each wheel of the vehicle includes: obtaining the status information of the angle module configured on each wheel; and determining the steering information corresponding to each wheel based on the status information of the angle module configured on each wheel.

[0006] Optionally, the steering assistance method further includes: acquiring the vehicle's state perception information and environmental perception information, wherein the state perception information is used to characterize the vehicle's motion state and the environmental perception information is used to characterize the vehicle's surrounding environment; and predicting the vehicle's steering path based at least on the steering information corresponding to each wheel to obtain a predicted steering path, which includes: predicting the vehicle's steering path based on the steering information corresponding to each wheel, the state perception information, and the environmental perception information to obtain a predicted steering path.

[0007] Optionally, the steering assistance method further includes: obtaining the steering type of the vehicle, the steering type being used to characterize the steering scenario in which the vehicle is located; generating prompt information based on the steering type; and displaying the prompt information.

[0008] Optionally, the steering assistance method further includes: detecting obstacles around the vehicle during the vehicle's steering process to obtain obstacle detection information; predicting the collision risk of the vehicle based on the obstacle detection information to obtain collision risk prediction information, wherein the collision risk prediction information is used to characterize whether a collision risk exists; and displaying collision risk warning information when the collision risk prediction information characterizes the existence of a collision risk.

[0009] Optionally, each wheel is equipped with an angle module, and the steering assistance method further includes: acquiring the vehicle's state perception information and driver demand information, wherein the state perception information is used to characterize the vehicle's motion state and the driver demand information is used to characterize the vehicle's steering state as required by the driver; determining the control information of the angle modules configured for each wheel based on the state perception information and the driver demand information; and performing steering assistance control on the vehicle based on the control information of the angle modules configured for each wheel.

[0010] Optionally, determining the control information of the angle module configured for each wheel based on the state perception information and the driver's demand information includes: determining the slip ratio of each wheel based on the state perception information; determining the target steering angle of each wheel based on the state perception information and the driver's demand information; and determining the control information of the angle module configured for each wheel based on the state perception information, the slip ratio of each wheel, and the target steering angle.

[0011] Optionally, determining the target steering angle of each wheel based on the state perception information and the driver demand information includes: inputting the state perception information and the driver demand information into a pre-configured Ackerman optimization controller to obtain the target steering angle of each wheel output by the pre-configured Ackerman optimization controller; wherein the optimization objective of the pre-configured Ackerman optimization controller includes: minimizing the deviation between the vehicle steering state required by the driver and the actual vehicle steering state; and setting constraint information in the pre-configured Ackerman optimization controller, the constraint information including at least constraint information for the steering angle.

[0012] Optionally, displaying the predicted steering path includes: displaying the predicted steering path via the vehicle's head-up display; wherein the vehicle's head-up display displays vehicle outline information and vehicle surrounding environment information, and the display position of the predicted steering path is related to the display position of the vehicle outline information and vehicle surrounding environment information.

[0013] In a second aspect, embodiments of this disclosure provide a vehicle comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to execute the executable instructions to implement the steering assistance method as described in the first aspect of this disclosure.

[0014] The above technical solution improves the intelligent assisted steering function by adding steering path prediction and visualization. This allows the driver to understand the vehicle's actual steering situation. Under extreme steering conditions, the driver can execute steering operations more safely, reliably, and stably based on the predicted steering path. Under non-extreme steering conditions, the intuitive steering path display enhances the driver's interactive experience. Therefore, this technical solution provides drivers with more intuitive and effective steering assistance in various steering scenarios, thereby improving the safety, stability, and reliability of the driver's steering operations, enhancing the driver's interactive experience, and ultimately improving the overall steering experience.

[0015] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description

[0016] The accompanying drawings are provided to further illustrate the present disclosure and form part of the specification. They are used together with the following detailed description to explain the present disclosure, but do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating a steering assistance method according to an exemplary embodiment.

[0017] Figure 2 This is a schematic diagram of an analysis process based on an example illustrating a large-angle turning intention.

[0018] Figure 3 This is an example diagram illustrating a corner module wheel end structure according to an exemplary embodiment.

[0019] Figure 4 This is a processing flowchart illustrating a different steering scenario according to an exemplary embodiment.

[0020] Figure 5 This is an example diagram illustrating a dynamic display rule according to an exemplary embodiment.

[0021] Figure 6 This is an example diagram illustrating a normal display interface in a U-shaped extreme turn mode according to an exemplary embodiment.

[0022] Figure 7 This is an example diagram illustrating a collision risk warning display interface according to an exemplary embodiment.

[0023] Figure 8 This is a schematic diagram illustrating a steering assistance process according to an exemplary embodiment.

[0024] Figure 9 This is a structural block diagram illustrating a steering assist device according to an exemplary embodiment.

[0025] Figure 10 This is a functional block diagram of a vehicle according to an exemplary embodiment. Detailed Implementation

[0026] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit this disclosure.

[0027] In this disclosure, unless otherwise stated, directional terms such as "up," "down," "left," "right," "front," and "back" are used only for the convenience of describing this disclosure and for simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure.

[0028] As mentioned in the background section, vehicles generally have intelligent assisted steering functions, which are used to assist the driver in performing steering operations in steering scenarios to improve the driver's steering experience.

[0029] The intelligent steering assist function of related technologies focuses on assisting vehicle steering control, such as controlling the vehicle's driving status. This intelligent steering assist function is only suitable for relatively normal steering conditions. In some extreme steering conditions, it cannot provide the driver with intuitive and effective steering assistance, resulting in a poor steering experience for the driver.

[0030] Based on this, this disclosure provides a technical solution to improve the intelligent assisted steering function by adding steering path prediction and visualization. Through steering path prediction and visualization, the driver can understand the actual steering situation of the vehicle. Under extreme steering conditions, the driver can perform steering operations more safely, reliably, and stably based on the predicted steering path. Under non-extreme steering conditions, the intuitive steering path display enhances the driver's interactive experience.

[0031] Therefore, this technical solution can provide drivers with more intuitive and effective steering assistance in various steering scenarios of the vehicle, thereby improving the safety, stability and reliability of the driver's steering operation, as well as the driver's interactive experience, and thus improving the driver's steering experience.

[0032] The technical solutions of this disclosure can be applied to various steering scenarios. As an example, they can be applied to large-angle steering scenarios. In large-angle steering scenarios, the vehicle can achieve large-angle steering. Through the technical solutions of this disclosure, a more intuitive and effective assisted steering effect can be provided in large-angle steering scenarios, adapting to the vehicle's steering needs and thus improving the steering experience.

[0033] Regarding vehicles with large-angle steering, taking a four-wheeled vehicle as an example, each of its four wheels can be equipped with a corner module. Since each of the four wheels is equipped with a corner module, the four wheels can be controlled independently, thereby enabling large-angle steering.

[0034] Figure 1 This is a flowchart illustrating a steering assistance method according to an exemplary embodiment, such as... Figure 1 As shown, the steering assistance method may include the following steps: Step S11: In response to the vehicle activating the steering assist function, the steering information corresponding to each wheel of the vehicle is obtained. The steering information corresponding to each wheel is used to characterize the steering state of each wheel.

[0035] Step S12: Based on the steering information corresponding to each wheel, predict the steering path of the vehicle to obtain the predicted steering path.

[0036] Step S13: Display the predicted turning path.

[0037] In one implementation, the steering assist function can be activated based on whether the driver intends to make a large turn.

[0038] Figure 2 This is a schematic diagram illustrating an analysis process for a large-angle steering intention, as shown in the example. Figure 2 As shown, after the vehicle's cornering module is activated, the vehicle's status is monitored in real time. When the steering wheel speed is detected to be greater than or equal to a speed threshold, and / or the steering angle is detected to be greater than or equal to a steering angle threshold, it is determined that the driver intends to turn at a large angle, and the steering assist function is activated. The speed threshold and steering angle threshold can be configured according to different vehicle conditions and are not limited here.

[0039] Therefore, after the vehicle activates the steering assist function, the steering information corresponding to each wheel can be obtained, and this steering information can be used to predict the vehicle's steering path.

[0040] In one implementation, if the vehicle does not activate the assisted steering function, the vehicle's display (e.g., head-up display) can use a normal display mode, that is, display some conventional display content.

[0041] In addition, the vehicle can also have a fault detection function, which can perform a self-check of the vehicle system when the steering assist function is on; if sensor abnormalities are detected, it will enter a safe mode and continue vehicle monitoring; if an abnormality is displayed, it will enter a voice prompt mode and continue vehicle monitoring.

[0042] In scenarios involving large-angle steering, each wheel can be controlled independently, resulting in different steering states for each wheel.

[0043] The steering information for each wheel is used to characterize the steering state of each wheel. As an example, steering information may include wheel speed and wheel steering angle, etc., which are not limited here.

[0044] In one embodiment, when each wheel is equipped with an angle module, obtaining the steering information corresponding to each wheel of the vehicle may include: obtaining the status information of the angle module configured on each wheel; and determining the steering information corresponding to each wheel based on the status information of the angle module configured on each wheel.

[0045] In this implementation, since the steering state of each wheel can be controlled by the angle module, the steering information corresponding to the wheel can be determined by the working state of the angle module.

[0046] In one implementation, the wheel rotation speed information can be obtained from the hub motor at the wheel end, and the wheel steering angle can be calculated based on the data collected by the steering angle sensor in the steering motor at the wheel end.

[0047] It is understandable that hub motors, steering motors, etc., are part of the corner module. Therefore, the status information of the corner module can include the operating status information of these components.

[0048] Figure 3 This is an example diagram illustrating a corner module wheel end structure according to an exemplary embodiment. Figure 3 The wheel end structure of the corner module includes: upper control arm 1, shock absorber 2, lower control arm 3, steering knuckle 4, steering motor 5, rocker arm 6, tie rod 7, wheel hub motor 8, and brake caliper 9.

[0049] Large-angle steering based on angular modules relies on an L-shaped steering system layout, in which a two-stage reducer (harmonic reducer and planetary reducer) can amplify motor torque, save space and increase kingpin caster angle, thereby improving the vehicle's self-centering performance and driving stability.

[0050] When steering, torque can be applied to the fixed surface of the steering knuckle, achieving efficient transmission. The steering motor drives the harmonic reducer, and then transmits torque through the four gear shafts of the planetary reducer (shaft 1 receives the torque, shafts 2 and 3 amplify it, and shaft 4 connects the hub motor and the steering knuckle through a universal joint), causing the wheels to rotate.

[0051] Universal joints and suspension systems (including upper / lower control arms) ensure a wide range of wheel steering angles and stability, while integrating the braking system to save space.

[0052] use Figure 3 The angular module wheel end structure shown allows each wheel of the vehicle to achieve a large-angle steering of -50° to 50°.

[0053] In step S12, the steering information of each wheel can be used to predict the vehicle's steering path. For example, based on the steering angle and wheel speed of each wheel, the overall steering of the vehicle is analyzed to generate the corresponding steering path.

[0054] To more accurately predict steering paths, in addition to the steering information of each wheel, more information can be incorporated.

[0055] Therefore, in one embodiment, the steering assistance method further includes: acquiring vehicle state perception information and environmental perception information, wherein the state perception information is used to characterize the vehicle's motion state and the environmental perception information is used to characterize the vehicle's surrounding environment.

[0056] Furthermore, the vehicle's steering path is predicted based on the steering information corresponding to each wheel, and the predicted steering path can be obtained by: predicting the vehicle's steering path based on the steering information corresponding to each wheel, state perception information, and environmental perception information.

[0057] In this implementation, in addition to the steering information of each wheel, vehicle state perception information and environmental perception information are also introduced. State perception information allows analysis of the vehicle's motion state; environmental perception information allows analysis of the vehicle's surrounding environment. Furthermore, integrating these three types of information to predict the steering path yields a more accurate steering path.

[0058] In one implementation, the vehicle's state perception information may include: the vehicle's longitudinal acceleration, lateral acceleration, and direction of movement; the vehicle's environmental perception information may include: the area where the vehicle is located, and information about obstacles around the vehicle.

[0059] In one implementation, environmental perception information can be acquired through relevant sensors on the vehicle.

[0060] As an example, a vehicle includes at least: forward-facing, side-facing, and rear-facing cameras, and a lidar sensor. The cameras can be used to identify lane lines and road edge types (such as curbs); the lidar can be used to accurately detect the three-dimensional position, shape, and distance of obstacles around the vehicle.

[0061] Optionally, the vehicle may also include a high-precision positioning system, millimeter-wave radar, etc., which can obtain more reliable environmental perception information by fusing a large amount of data.

[0062] In one implementation, the vehicle's state perception information can be acquired in different ways. For example, longitudinal acceleration and lateral acceleration can be acquired using an acceleration sensor; vehicle speed can be acquired using a positioning system, global sensors, etc., without limitation.

[0063] In one implementation, an initial steering path can be generated first based on the vehicle's state perception information, and then adjusted according to the steering information of each wheel and environmental perception information. For example, the direction, length, and radian of the initial steering path can be adjusted, but this is not limited here.

[0064] After obtaining the predicted steering path, the predicted steering path can be displayed. In one implementation, the predicted steering path can be displayed via the vehicle's head-up display.

[0065] The vehicle's head-up display shows vehicle outline information and information about the vehicle's surrounding environment. The display position of the predicted steering path is related to the display position of the vehicle outline information and information about the vehicle's surrounding environment.

[0066] By combining and displaying predicted steering paths, vehicle outline information, and information about the vehicle's surrounding environment, the interactive experience can be improved.

[0067] In one implementation, in addition to displaying the predicted steering path, further prompts may be displayed, which may be related to the specific steering scenario of the vehicle.

[0068] Therefore, the steering assistance method may further include: acquiring the vehicle's steering type, which is used to characterize the steering scenario in which the vehicle is located; generating prompt information based on the steering type; and displaying the prompt information.

[0069] In this implementation, the steering type can be determined by analyzing factors such as the steering angle and the vehicle's surrounding environment, thus characterizing the steering scenario the vehicle is in. Different prompting methods can be used in different steering scenarios, therefore, different prompting messages can be generated and displayed accordingly.

[0070] Figure 4This is a processing flowchart illustrating different steering scenarios according to an exemplary embodiment, such as... Figure 4 As shown, after confirming the activation of the large-angle steering assist function, the steering scenario is analyzed to identify the specific steering type. Specifically, this may involve the following steering types and corresponding display methods: In narrow road U-turn scenarios, activating the "U-turn mode" will display information such as: 360° rotation prediction trajectory, virtual lane boundaries, tire angle visualization information, progress indicator, and safety zone markers.

[0071] In right-angle turns or sharp turns, activating the "sharp turn mode" will display information such as: curve perspective guide lines, apex indicator markers, speed suggestions, tire load visualization, and exit path prediction.

[0072] When parking and turning, activating the "Extreme Parking Mode" will display information such as vehicle outline projection, turning stage prompts, centimeter-level distance prompts, four-wheel trajectory prediction, and safety stop lines on the corresponding extreme parking interface.

[0073] In lateral movement and steering scenarios, activating the "lateral movement mode" will display information such as: lateral movement trajectory lines, vehicle alignment guide lines, lateral distance indicator, front and rear wheel synchronization indicator, and lateral obstacle warning.

[0074] In one implementation, information related to assisted steering can be transmitted to the head-up display controller and dynamically displayed in the driver's line of sight by projecting light onto the driver's side window.

[0075] When displaying information dynamically, you can use some corresponding prompt message types and dynamic display rules.

[0076] Figure 5 This is an example diagram illustrating a dynamic display rule according to an exemplary embodiment, such as... Figure 5 As shown, the types of prompts and dynamic display rules involved may include: Interface gradient transition: When switching scenes, dynamic screen transitions are used to achieve a smooth display effect.

[0077] AR (Augmented Reality) lane marking overlay: The turn signal guide lines are projected onto the driver's side window, blending seamlessly with the real road markings in the driver's field of vision for guidance.

[0078] Dynamic color coding: Areas farther from obstacles are displayed in a lighter green, while areas closer to obstacles are displayed in a brighter red, serving as a warning to the driver and guiding them towards a safer area, thus improving safety during steering.

[0079] Real-time distance display: Calculates the distance between the vehicle and obstacles in real time and displays the distance on the driver's side screen to provide a prompt to the driver.

[0080] Steering Progress Bar: Provides real-time updates on the vehicle's steering progress. The system continuously monitors whether the steering operation is complete. If not, the bar continuously updates and returns to the judgment status. If the steering operation is complete, the display fades out, then returns to standard mode, and finally resumes real-time vehicle status monitoring.

[0081] Understandable. Figure 4 and Figure 5 The display method shown is merely an example and does not constitute a limitation on the embodiments of this disclosure.

[0082] In one embodiment, the steering assistance method may further include: detecting obstacles around the vehicle during the vehicle's steering process to obtain obstacle detection information; predicting the vehicle's collision risk based on the obstacle detection information to obtain collision risk prediction information, the collision risk prediction information being used to characterize whether a collision risk exists; and displaying collision risk warning information when the collision risk prediction information characterizes the existence of a collision risk.

[0083] In this implementation, obstacles around the vehicle are detected in real time, collision risks are predicted based on obstacle detection information, and corresponding collision risk prediction information is displayed when a collision risk exists.

[0084] The methods for determining obstacle detection information can be found in mature obstacle detection technologies in this field, and will not be described in detail here.

[0085] As an example of collision risk prediction, a limit distance between an obstacle and a vehicle is pre-set. If the distance between the obstacle and the vehicle is less than this limit distance, it can be considered that there is a collision risk.

[0086] If there is no risk of collision, the currently displayed content can continue to be shown, such as displaying prompts or predicting steering paths.

[0087] In cases where there is a risk of collision, collision risk warning information will be displayed first, with this collision risk prediction information having a higher priority than the currently displayed content.

[0088] In one implementation, the collision risk warning information is displayed via a head-up display; additionally, an audio prompt may be added, such as playing a warning tone, etc., which is not limited here.

[0089] Figure 6 This is an example diagram illustrating a normal display interface in a U-shaped extreme turn mode according to an exemplary embodiment, such as... Figure 6 As shown, different information is displayed in different display areas of the display interface.

[0090] In the central area, the vehicle outline and predicted steering path (i.e., predicted trajectory line) are displayed, along with information such as safety zones and obstacles. Through AR image fusion technology, the predicted driving trajectory line is blended with the road surface to guide the driver's operation.

[0091] On the left side of the central area is a display area for vehicle status and road information. It shows the steering status of the four wheels of the vehicle, with square graphics indicating the current wheel's steering angle relative to the maximum steering angle, and arrows indicating the direction of wheel steering (left arrow for left turn, right arrow for right turn). Numbers represent the specific steering angle. Below this, the current road surface adhesion and condition are displayed.

[0092] On the right side of the central area are the driver's operation instructions, which can show the operation that needs to be completed and the instructions for the next operation. Below, the suggested steering speed and the estimated driving distance required to complete the steering can be displayed, and the progress of this step can be shown below.

[0093] Below the central area, the progress of the entire steering process is displayed, with the progress of each stage shown separately.

[0094] At the bottom of the central area, the safe distances for the vehicle in all directions are displayed.

[0095] Figure 7 This is an example diagram illustrating a collision risk warning display interface according to an exemplary embodiment, such as... Figure 7 As shown, when the vehicle gets too close to an obstacle, the driver is warned to stop and continue driving according to the guidance of the assisted steering system after moving away from the obstacle.

[0096] The foregoing embodiments introduced various visual display functions within the steering assist feature to enhance the driver's interactive experience and assist the driver in performing steering operations more reliably, stably, and safely. To further improve the safety of steering operations and reduce vehicle slippage, corresponding steering assist control can also be implemented during steering.

[0097] Therefore, as an optional implementation, when each wheel is equipped with an angle module, the steering assistance method further includes: acquiring vehicle state perception information and driver demand information, wherein the state perception information is used to characterize the vehicle's motion state and the driver demand information is used to characterize the vehicle's steering state as required by the driver; determining the control information of the angle module configured for each wheel based on the state perception information and the driver demand information; and performing steering assistance control on the vehicle based on the control information of the angle module configured for each wheel.

[0098] In this implementation, the control information for each corner module is determined by combining state perception information and driver demand information, thereby enabling steering assistance control of the vehicle.

[0099] Regarding the method for acquiring state perception information, please refer to the description in the foregoing embodiments. The state perception information required for steering assist control may include: vehicle position, yaw angle, longitudinal acceleration, lateral acceleration, yaw rate, longitudinal vehicle speed, and lateral vehicle speed.

[0100] In one implementation, state perception information at multiple time points can be acquired, and then the extended Kalman filter algorithm can be used to average the state perception information at multiple time points to obtain more accurate and representative perception information of the global state. It can be understood that the extended Kalman filter algorithm is used to process different state perception information separately.

[0101] The extended Kalman filter algorithm is a mature technology in this field and will not be described in detail here. In this algorithm, information from multiple time points is averaged by means of observation noise, observation model, etc. The various parameters used in this algorithm can be adaptively adjusted according to the accuracy of the sensor used to collect the data.

[0102] Regarding driver demand information, this can characterize the vehicle steering state as requested by the driver, and may include information such as steering wheel angle and desired yaw rate. The steering wheel angle can be determined by detecting the steering wheel's state; the desired yaw rate can be input by the driver or determined through analysis of the driver's historical driving data and driving habits, and is not limited here.

[0103] In one embodiment, the control information of the angle modules configured for each wheel is determined based on state perception information and driver demand information, including: determining the slip ratio of each wheel based on state perception information; determining the target steering angle of each wheel based on state perception information and driver demand information; and determining the control information of the angle modules configured for each wheel based on state perception information, slip ratio of each wheel, and target steering angle.

[0104] In this implementation, based on the corresponding information in the state perception information, the slip ratio of each wheel can be determined first; and, by combining the state perception information and the driver's demand information, the target steering angle of each wheel can be determined; finally, by integrating the state perception information, the slip ratio of each wheel and the target steering angle, the control information of the angle module configured for each wheel can be determined.

[0105] In one implementation, the wheel slip ratio can be calculated based on the wheel's actual steering angle, the longitudinal distance from the wheel center to the vehicle's center of gravity, the wheel's rotational angular velocity, the wheel's effective rolling radius, the wheel's longitudinal velocity, and the wheel's position.

[0106] As an example, the slip ratio of any wheel i can be expressed as: s_i=(ω_i×r-v_{x,i}) / max(|v_{x,i}|,1e-3), where s_i: slip ratio; ω_i: the rotational angular velocity of the wheel measured by the sensor of the hub motor; r: the effective rolling radius of the wheel; v_{x,i}: the longitudinal velocity of the wheel center, which can be calculated based on the longitudinal and lateral velocities of the vehicle's center of mass in the vehicle coordinate system, the vehicle's yaw rate, and the vehicle's position.

[0107] In one embodiment, determining the target steering angle of each wheel based on state perception information and driver demand information includes: inputting the state perception information and driver demand information into a pre-configured Ackerman optimization controller to obtain the target steering angle of each wheel output by the pre-configured Ackerman optimization controller; wherein the optimization objective of the pre-configured Ackerman optimization controller includes: minimizing the deviation between the vehicle steering state required by the driver and the actual vehicle steering state; and setting constraint information in the pre-configured Ackerman optimization controller, the constraint information including at least constraint information for the steering angle.

[0108] In this implementation, the optimal target steering angle is determined by an Ackermann optimization controller based on the input state perception information and driver demand information, using the principle of optimization control.

[0109] In the Ackermann optimization controller, a variety of optimization objectives can be configured. One of them is the optimization objective for the deviation between the vehicle steering state required by the driver and the actual vehicle steering state. This deviation needs to be minimized so that the actual vehicle motion follows the desired motion.

[0110] Therefore, in Ackermann optimal controllers, the objective function can involve functions related to this deviation.

[0111] Besides this optimization objective, other optimization objectives may also include minimizing drastic changes in control variables, which are not limited here.

[0112] In one implementation, the Ackerman optimization controller also includes constraint information to determine the optimal target steering angle for each wheel by solving a constrained quadratic programming problem.

[0113] Regarding constraint information, it should at least include constraint information for the steering angle, such as the range of steering angle values, which can be set according to the hardware limits of the steering angle.

[0114] In one implementation, the constraint information may involve physical constraints, tire adhesion constraints, and kinematic compatibility relaxation constraints, etc.

[0115] As an example, constraint information may also include: constraint information related to road surface friction coefficient, vertical load, longitudinal force of wheel, lateral force, front and rear axle distance, wheel track, kinematic relaxation, and allowable geometric deviation due to tire slip, etc., which are not limited here.

[0116] As an example, the optimization variables of an Ackermann optimal controller can be represented as: U_δ = [δ_fl_opt, δ_fr_opt, δ_rl_opt, δ_rr_opt] T δ_i_opt is the optimal target steering angle of the i-th wheel to be determined, fl: left front wheel, fr: right front wheel, rl: left rear wheel, rr: right rear wheel.

[0117] The objective function to be minimized can be expressed as: J = w_track × J_track + w_smooth × J_smooth + w_act × J_act.

[0118] In this objective function, J_track=(ω_z_des-ω_z_pred)^2, J_smooth=Σ_i(δ_i_opt-δ_i_prev)^2, J_act=Σ_i(δ_i_opt-δ_i_ackermann)^2.

[0119] Wherein, J_track: enables the vehicle's actual motion to track the desired outcome; ω_z_pred: the instantaneous yaw rate predicted by the linear vehicle model based on the current state and candidate steering angle U_δ; ω_z_des: the desired yaw rate; J_smooth: minimizes drastic changes in the control input; δ_i_prev: the steering angle of the wheel in the previous control cycle; δ_i_ackermann: the theoretical steering angle of the wheel calculated using the classical Ackermann geometry formula; J_act: minimizes the deviation between theory and reality; w_track, w_smooth, and w_act represent the weights of J_track, J_smooth, and J_act, respectively.

[0120] As an example, the application of physical constraints, tire adhesion constraints, and kinematic compatibility relaxation constraints can be expressed as: δ_min≤δ_i_opt≤δ_max,for all i |δ_i_opt-δ_i_prev| ≤Δδ_max×Δt,for all i sqrt(F_{x,i}^2+F_{y,i}^2)≤μ_i×F_{z,i},for all i |(v_y+l_f×ω_z) / (v_x±W / 2×ω_z)-tan(δ_{f,opt})|≤ε_f |(v_y-l_r×ω_z) / (v_x ±W / 2×ω_z)-tan(δ_{r,opt})|≤ε_r Wherein, δ_min, δ_max: steering angle hardware limits (e.g., ±90°); Δδ_max: steering angular velocity limit; μ_i, F_{z,i}: road friction coefficient and vertical load at the wheel contact point; F_{x,i}, F_{y,i}: longitudinal and lateral forces of the tire, which are functions of α_i (tire slip angle) and s_i; W: wheelbase; l_f, l_r: distance from the center of gravity to the front and rear axles; ε_f, ε_r: kinematic relaxation of the front and rear axles, allowing for geometric deviations caused by tire slip; v_x, v_y, and ω_z represent: the longitudinal and lateral velocities of the vehicle's center of gravity in the vehicle coordinate system, and the vehicle's yaw rate, respectively; Δt: time variation; δ_{f,opt}: front axle steering angle; δ_{r,opt}: rear axle steering angle.

[0121] Furthermore, based on the slip ratio and target steering angle of each wheel, as well as the state perception information, the control information of the angle module configured for each wheel can be determined.

[0122] The control information for the corner module may include the drive information for the steering motor and the drive information for the hub motor.

[0123] Steering angle can be controlled by driving the steering motor; wheel speed can be controlled by driving the hub motor.

[0124] When determining the control information of the corner module, the state perception information that needs to be applied may include: vehicle speed, longitudinal acceleration, lateral acceleration, etc.

[0125] Furthermore, after obtaining the control information of each corner module, distributed independent control is performed on each corner module so that each corner module can perform fast and accurate closed-loop tracking control according to its assigned target.

[0126] In one implementation, a distributed tracking controller can be used to drive the actuator motor within the corner module for precise tracking based on the optimal target for each wheel issued from the upper layer. Specifically, a PID (Proportion-Integration-Differential) controller can be applied to achieve error tracking.

[0127] As an example, it can be represented as: δ_i_cmd =PID_δ(δ_i_opt,δ_i_actual) +δ_i_ff.

[0128] Where δ_i_cmd is the angle command sent to the steering motor of the i-th wheel; PID_δ(...) is an independent PID controller that handles the angle tracking error e_δ=δ_i_opt-δ_i_actual for that wheel; and δ_i_ff is the feedforward compensation angle based on the vehicle state and tire model, used to improve response speed. δ_i_opt represents the optimal target steering angle, and δ_i_actual represents the actual steering angle.

[0129] Furthermore, the wheel speed and torque of the hub motor are controlled. The speed command can be determined by the vehicle speed requirement, steering angle, and slip ratio control. Specifically: v_{x,i}_des=(v_x_des-ω_z_des×y_i)×cos(δ_i_opt)+(v_y_des+ω_z_des×x_i)×sin(δ_i_opt);ω_i_des_ base=v_{x,i}_des / r; ω_i_opt=ω_i_des_base×(1+λ_des); T_i_cmd=PID_T(ω_i_opt,ω_i_actual)+T_i_ff.

[0130] Where, v_{x,i}_des: desired wheel speed; (x_i,y_i): the position of the wheel in the vehicle coordinate system; λ_des: optimal slip ratio setting to maximize longitudinal force; ω_i_opt: target rotational angular velocity of the wheel; T_i_cmd: torque command sent to the i-th hub motor; PID_T(...): independent PID controller to handle the wheel's speed tracking error; T_i_ff: feedforward compensation torque based on drag, slope, etc. v_x_des: desired longitudinal velocity; v_y_des: desired lateral velocity; r: rolling radius; ω_i_des_base: desired rotational angular velocity; ω_i_actual: actual rotational angular velocity; ω_z_des: desired yaw rate.

[0131] Figure 8This is a schematic diagram illustrating a steering assistance process according to an exemplary embodiment. Figure 8 The text shows various functions of the steering assist.

[0132] Data fusion and state estimation involve the combined application of driving mode selection and the environmental perception layer. Driving mode selection can determine the driver's needs and the current steering scenario; environmental perception data can be analyzed using the perception data from the environmental perception layer.

[0133] Furthermore, based on data from multiple sources, fusion processing can be performed to obtain vehicle status, including vehicle speed, yaw angle, and position.

[0134] Regarding Ackerman optimization controllers, it may involve Ackerman geometric models and actuator constraint databases. The Ackerman geometric model can solve for the initial solution, and then the constraint data is used to apply physical constraints to obtain the optimization solution.

[0135] Furthermore, based on the Ackermann optimized controller, steering control commands can be generated, which can then be sent to the execution layer for execution. Specifically, the individual corner modules are controlled separately through steering motor drive and wheel hub motor drive.

[0136] Furthermore, based on the predicted trajectory data, display information can be processed, and combined with the AR rendering engine and projection control information, visual cues can be provided to the driver.

[0137] This integrates driver vision and vehicle motion to achieve comprehensive, intuitive, and effective steering assistance.

[0138] Furthermore, the input from the driver's steering wheel can also be used as a form of sensory information for fusion processing.

[0139] This assisted steering method can predict the expected driving path of a vehicle with a large turning angle in real time at the current steering wheel angle, and present the path and potential collision risks to the driver intuitively through enhanced visualization, thereby greatly improving driving safety and human-vehicle interaction experience.

[0140] Figure 9 This is a structural block diagram of a steering assist device 200 according to an exemplary embodiment, such as... Figure 9 As shown, the steering assist device 200 includes: The acquisition module 201 is used to acquire the steering information corresponding to each wheel of the vehicle in response to the vehicle starting the steering assist function. The steering information corresponding to each wheel is used to characterize the steering state of each wheel.

[0141] The prediction module 202 is used to predict the steering path of the vehicle based on the steering information corresponding to each wheel, and obtain the predicted steering path.

[0142] Display module 203 is used to display the predicted turning path.

[0143] Optionally, the acquisition module 201 is further configured to: acquire the status information of the corner modules configured for each wheel; and determine the steering information corresponding to each wheel based on the status information of the corner modules configured for each wheel.

[0144] Optionally, the acquisition module 201 is further configured to: acquire the vehicle's state perception information and environmental perception information, wherein the state perception information is used to characterize the vehicle's motion state and the environmental perception information is used to characterize the vehicle's surrounding environment; the prediction module 202 is further configured to: predict the vehicle's steering path based on the steering information corresponding to each wheel, the state perception information, and the environmental perception information, thereby obtaining a predicted steering path.

[0145] Optionally, the acquisition module 201 is further configured to: acquire the steering type of the vehicle, the steering type being used to characterize the steering scenario in which the vehicle is located; generate prompt information based on the steering type; and the display module 203 is further configured to display the prompt information.

[0146] Optionally, the prediction module 202 is further configured to: detect obstacles around the vehicle during the vehicle's turning process to obtain obstacle detection information; predict the collision risk of the vehicle based on the obstacle detection information to obtain collision risk prediction information, wherein the collision risk prediction information is used to characterize whether there is a collision risk; the display module 203 is further configured to: display collision risk warning information when the collision risk prediction information characterizes the existence of a collision risk.

[0147] Optionally, the acquisition module 201 is further configured to: acquire the vehicle's state perception information and driver demand information, wherein the state perception information is used to characterize the vehicle's motion state and the driver demand information is used to characterize the vehicle's steering state as required by the driver; the device further includes a control module, configured to: determine the control information of the angle modules configured for each wheel based on the state perception information and the driver demand information; and perform steering assistance control on the vehicle based on the control information of the angle modules configured for each wheel.

[0148] Optionally, the control module is further configured to: determine the slip ratio of each wheel based on the state perception information; determine the target steering angle of each wheel based on the state perception information and the driver's demand information; and determine the control information of the angle module configured for each wheel based on the state perception information, the slip ratio of each wheel, and the target steering angle.

[0149] Optionally, the control module is further configured to: input the state perception information and the driver demand information into a pre-configured Ackerman optimization controller to obtain the target steering angle of each wheel output by the pre-configured Ackerman optimization controller; wherein the optimization objective of the pre-configured Ackerman optimization controller includes: minimizing the deviation between the vehicle steering state required by the driver and the actual vehicle steering state; and setting constraint information in the pre-configured Ackerman optimization controller, wherein the constraint information includes at least constraint information for the steering angle.

[0150] Optionally, the display module 203 is further configured to: display the predicted steering path via the vehicle's head-up display; wherein the vehicle's head-up display displays vehicle outline information and vehicle surrounding environment information, and the display position of the predicted steering path is related to the display position of the vehicle outline information and vehicle surrounding environment information.

[0151] Figure 10 This is a functional block diagram of a vehicle 300 according to an exemplary embodiment. The vehicle 300 may include various subsystems, such as an infotainment system 310, a perception system 320, a decision control system 330, a drive system 340, and a computing platform 350. The vehicle 300 may also include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and each component of the vehicle 300 can be interconnected via wired or wireless means.

[0152] In some embodiments, the infotainment system 310 may include a communication system, an entertainment system, and a navigation system, etc.

[0153] The perception system 320 may include several sensors for sensing information about the environment surrounding the vehicle 300. For example, the perception system 320 may include a global positioning system (which may be GPS, BeiDou, or other positioning systems), an inertial measurement unit (IMU), lidar, millimeter-wave radar, ultrasonic radar, and a camera device.

[0154] The decision control system 330 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

[0155] The drive system 340 may include components that provide powered motion to the vehicle 300. In one embodiment, the drive system 340 may include an engine, an energy source, a transmission system, and wheels. The engine may be one or a combination of internal combustion engines, electric motors, and compressed air engines. The engine is capable of converting energy provided by the energy source into mechanical energy.

[0156] Some or all of the functions of the vehicle 300 are controlled by a computing platform 350. The computing platform 350 may include at least one processor 351 and a memory 352, the processor 351 being able to execute instructions 353 stored in the memory 352.

[0157] Processor 351 can be any conventional processor, such as a commercially available CPU. Processors may also include graphics processing units (GPUs), field-programmable gate arrays (FPGAs), systems-on-chips (SoCs), application-specific integrated circuits (ASICs), or combinations thereof.

[0158] The memory 352 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.

[0159] In addition to instruction 353, memory 352 can also store data, such as road maps, route information, vehicle position, direction, speed, and other data. The data stored in memory 352 can be used by computing platform 350.

[0160] In this embodiment of the disclosure, processor 351 may execute instruction 353 to complete all or part of the steps of the steering assistance method described above.

[0161] In another exemplary embodiment, a controller is also provided, which may be part of the aforementioned vehicle. The controller may be an integrated circuit (IC) or a chip, wherein the integrated circuit may be a single IC or a collection of multiple ICs; the chip may include, but is not limited to, the following types: GPU (Graphics Processing Unit), CPU (Central Processing Unit), FPGA (Field Programmable Gate Array), DSP (Digital Signal Processor), ASIC (Application Specific Integrated Circuit), SOC (System on Chip), etc. The aforementioned integrated circuit or chip may be used to execute executable instructions (or code) to implement the aforementioned steering assistance method. The executable instructions may be stored in the integrated circuit or chip, or obtained from other devices or equipment, such as the integrated circuit or chip including a processor, memory, and an interface for communicating with other devices. The executable instructions can be stored in the memory, and when the executable instructions are executed by the processor, the above-mentioned control method for rail transit vehicles can be implemented; or, the integrated circuit or chip can receive the executable instructions through the interface and transmit them to the processor for execution to implement the above-mentioned steering assistance method.

[0162] The preferred embodiments of this disclosure have been described in detail above with reference to the accompanying drawings. However, this disclosure is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this disclosure, various simple modifications can be made to the technical solutions of this disclosure, and these simple modifications all fall within the protection scope of this disclosure.

[0163] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, this disclosure will not describe the various possible combinations separately.

[0164] Furthermore, various different embodiments of this disclosure can be combined in any way, as long as they do not violate the spirit of this disclosure, they should also be regarded as the content disclosed in this disclosure.

Claims

1. A steering assistance method, characterized in that, include: In response to the vehicle activating the steering assist function, the steering information corresponding to each wheel of the vehicle is obtained, and the steering information corresponding to each wheel is used to characterize the steering state of each wheel. Based at least on the steering information corresponding to each wheel, the steering path of the vehicle is predicted to obtain the predicted steering path; The predicted steering path is displayed.

2. The steering assistance method according to claim 1, characterized in that, Each wheel is equipped with an angle module. The process of obtaining the steering information corresponding to each wheel of the vehicle includes: Obtain the status information of the corner modules configured for each wheel; Based on the status information of the angle modules configured for each wheel, the steering information corresponding to each wheel is determined.

3. The steering assistance method according to claim 1, characterized in that, The steering assistance method further includes: The vehicle's state perception information and environmental perception information are acquired. The state perception information is used to characterize the vehicle's motion state, and the environmental perception information is used to characterize the vehicle's surrounding environment. The step of predicting the steering path of the vehicle based at least on the steering information corresponding to each wheel, to obtain a predicted steering path, includes: Based on the steering information corresponding to each wheel, the state perception information, and the environment perception information, the steering path of the vehicle is predicted to obtain the predicted steering path.

4. The steering assistance method according to claim 1, characterized in that, The steering assistance method further includes: Obtain the steering type of the vehicle, which is used to characterize the steering scenario in which the vehicle is located; Based on the steering type, generate a prompt message; The aforementioned prompt message will be displayed.

5. The steering assistance method according to claim 1, characterized in that, The steering assistance method further includes: During the vehicle's turning process, obstacles around the vehicle are detected to obtain obstacle detection information; Based on the obstacle detection information, the collision risk of the vehicle is predicted to obtain collision risk prediction information, which is used to characterize whether there is a collision risk. If the collision risk prediction information indicates the existence of a collision risk, a collision risk warning message will be displayed.

6. The steering assistance method according to claim 1, characterized in that, Each wheel is equipped with an angle module, and the steering assistance method further includes: The vehicle's state perception information and driver demand information are acquired. The state perception information is used to characterize the vehicle's motion state, and the driver demand information is used to characterize the vehicle's steering state as required by the driver. Based on the state perception information and the driver's demand information, determine the control information of the corner modules configured for each wheel; The vehicle is subjected to steering assist control based on the control information of the corner modules configured for each wheel.

7. The steering assistance method according to claim 6, characterized in that, The step of determining the control information of the corner modules configured for each wheel based on the state perception information and the driver's demand information includes: Based on the state perception information, the slip ratio of each wheel is determined; Based on the state perception information and the driver's demand information, determine the target steering angle of each wheel; Based on the state perception information, the slip ratio of each wheel, and the target steering angle, the control information of the angle module configured for each wheel is determined.

8. The steering assistance method according to claim 7, characterized in that, Determining the target steering angle of each wheel based on the state perception information and the driver's demand information includes: The state perception information and the driver demand information are input into a pre-configured Ackerman optimization controller to obtain the target steering angle of each wheel output by the pre-configured Ackerman optimization controller. The optimization objectives of the pre-configured Ackerman optimization controller include: minimizing the deviation between the vehicle steering state required by the driver and the actual vehicle steering state; and setting constraint information in the pre-configured Ackerman optimization controller, wherein the constraint information includes at least constraint information for the steering angle.

9. The steering assistance method according to any one of claims 1 to 8, characterized in that, The display of the predicted turning path includes: The predicted steering path is displayed via the vehicle's head-up display; The vehicle's head-up display shows vehicle outline information and surrounding environment information, and the display position of the predicted steering path is related to the display position of the vehicle outline information and surrounding environment information.

10. A vehicle, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to execute the executable instructions to implement the steering assistance method as described in any one of claims 1 to 9.

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