Intelligent vehicle path tracking control method based on clothoid curve

Abstract

The application relates to a clothoid curve-based intelligent vehicle path tracking control method, characterized by constructing a motion model of a vehicle tracking a curvature linearly continuous change path; predicting a state of the vehicle after a communication delay time of a control system according to a current state of the vehicle; determining a preview point selection interval and calculating a clothoid curve according to a predicted vehicle position, a vehicle speed and a bending degree of an expected path; finally, taking a curvature change rate of the clothoid curve as a parameter, taking a curvature of a clothoid curve corresponding position after a response delay time of a current vehicle speed previewed line control steering servo system as a target curvature, and calculating a target steering angle of a front wheel of the vehicle. Advantages: the application considers the problem of tracking errors caused by the discontinuous control curve curvature of the intelligent vehicle, the path tracking precision of a curve under different speeds is greatly improved, and the path following precision and the driving stability can be improved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent vehicle path tracking technology, and particularly relates to an intelligent vehicle path tracking control method based on Clothoid curves. Background Technology

[0002] Intelligent driving technology for automobiles mainly comprises three functional modules: environmental perception, path planning, and motion control. Accurate, smooth, and real-time motion control is a key technology for ensuring the safety and comfort of intelligent driving vehicles. Vehicle motion control includes longitudinal speed control and lateral path tracking.

[0003] Path tracking problems can be broadly categorized into two main research areas: control models and control methods. Currently, control models primarily include geometric models, kinematic models, and dynamic models, while control methods include geometric methods, feedforward feedback control methods, Lyapunov direct methods, robust control methods, and intelligent control methods. Geometric models and their control methods are widely used due to their simplicity, few parameters, low computational complexity, and ease of deployment and portability. Pure tracking methods are the most typical and widely researched geometric control methods in recent years; however, their planned control curves are circular arc segments, failing to consider the constraints of actual vehicle control on the continuity of path curvature. Furthermore, the algorithm's deterministic aiming distance selection strategy has poor adaptability to curves with varying curvatures. Although some researchers have used G1-continuous clothoid curves to fit the control curve between the vehicle's current position and the aiming point, and employed fuzzy methods to determine the aiming distance, they have not provided specific instructions on how to use clothoid curves for lateral vehicle control. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the above-mentioned technologies and provide a path tracking control method for intelligent vehicles based on Clothoid curves. This method significantly improves the path tracking accuracy on curves at different speeds, enhances path following accuracy and driving stability, and focuses on solving the problem of lateral path tracking.

[0005] To achieve the above objectives, this invention employs the following technical solution: a path tracking control method for intelligent vehicles based on clothoid curves, which constructs a motion model for the vehicle to track a path with linearly and continuously changing curvature; predicts the vehicle's state after the communication delay time in the control system based on the vehicle's current state; determines the selection interval for the aiming point based on the predicted vehicle position, vehicle speed, and the curvature of the desired path, and calculates the clothoid curve; finally, using the curvature change rate of the clothoid curve as a parameter, and taking the curvature of the corresponding position of the clothoid curve after the response lag time of the current vehicle speed's aiming-by-wire steering servo system as the target curvature, calculates the target steering angle of the vehicle's front wheels. The specific steps are as follows:

[0006] Step 1: Based on the Ackermann steering model, construct a motion model for the vehicle to track a path with linearly and continuously changing curvature.

[0007] Step 2: Based on the current vehicle status [x] v ,y v ,θ v ,κ v [x], predicting the vehicle's state after the control system communication delay time. g ,y g ,θ g ,κ g ];

[0008] Step 3: Locate the vehicle position [x] on the desired path. g ,y g The nearest point is used as the starting point of the aiming point interval, and the ending point of the aiming point interval is determined based on the vehicle speed and the curvature of the desired path.

[0009] Step 4: Using the vehicle location [x] g ,y g Using the above-mentioned preview point interval as the starting point of the clothoid curve, select a preview point as the ending point of the clothoid curve and calculate the clothoid curve.

[0010] Step 5 verifies the optimal clothoid curve using the maximum curvature, the maximum rate of change of curvature, and the curve length as constraints, and obtains the optimal clothoid curve that satisfies the constraints.

[0011] Step 6 uses the curvature change rate of the optimal clothoid curve as a parameter, and takes the curvature of the corresponding position of the optimal clothoid curve after the response lag time of the current vehicle speed steer-by-wire servo system as the target curvature to calculate the target steering angle of the vehicle's front wheels.

[0012] Furthermore, the motion model for the vehicle to track a linearly continuously changing curvature path, constructed based on the Ackermann steering model in step 1, specifically includes:

[0013] S21: From the geometric relationships of the Ackermann steering vehicle, the relationship between the front wheel steering angle, wheelbase, and steering curvature is as follows:

[0014] κ=tanδL

[0015] Where δ represents the front wheel steering angle, L represents the wheelbase, and κ represents the current steering curvature.

[0016] S22: The rate of change of curvature over time is as follows:

[0017]

[0018] Where s represents the distance the vehicle travels, assuming that the vehicle travels at a constant speed of v within this distance, and κ′ is the rate of change of curvature with arc length;

[0019] S23: The rate of change of curvature with arc length is calculated as follows:

[0020]

[0021] The meanings of the parameters in the formula are consistent with those in formulas S21 and S22, and d(δ) / dt is the front wheel steering angular velocity.

[0022] S24: The angular velocity of the steering wheel is calculated as follows:

[0023] ω=k*d(δ) / dt

[0024] The parameters in the formula have the same meaning as those in formula S23, where ω represents the angular velocity of the steering wheel and k is the proportional coefficient of the steering system.

[0025] S25: The relationship between the steering wheel angular velocity and the rate of change of vehicle curvature is calculated as follows:

[0026]

[0027] The meanings of the parameters in the formula are consistent with those in formulas S21, S23, and S24.

[0028] Further, in step 2), the step of determining the current state of the vehicle [x] v ,y v ,θ v ,κ v [x], predicting the vehicle's state after the control system communication delay time. g ,y g ,θ g ,κ g Specifically:

[0029] S31: In the vehicle coordinate system, the vehicle's state after the control system communication delay time:

[0030]

[0031] The parameters in the formula have the same meaning as those in formulas S21 and S22. t1 represents the communication delay time of the control system, s represents the distance traveled in time t1, r represents the turning radius, and [Δx,Δy,Δθ,κ] represents the state of the vehicle in the current vehicle coordinate system after time t1.

[0032] S32: Vehicle state in the global coordinate system after the control system communication delay time:

[0033]

[0034] The parameters in the formula have the same meaning as those in formula S31, [x v ,y v ,θ v ,κ v [x] represents the vehicle's current state in the global coordinate system. g ,y g ,θ g ,κ g [] represents the state of the vehicle in the global coordinate system after time t1.

[0035] Furthermore, in step 3, finding the location [x] of the vehicle along the desired path... g ,y g The nearest point is used as the starting point of the aiming point interval. The ending point of the aiming point interval is determined based on the vehicle speed and the curvature of the desired path, specifically:

[0036] S41: The pre-aiming point intervals are stored in sequence form, numbered starting from 1, with a quantity of m. The pre-aiming point selection intervals are as follows:

[0037] p = [p1p2L p] j L p m ]

[0038] Where, p j Let p1 represent the j-th pre-aiming point, and p1 represent the starting point of the pre-aiming point interval, which is the position of the vehicle on the desired path relative to the above-mentioned vehicle position [x]. g ,y g The nearest point, p m This indicates the endpoint of the pre-aiming point interval, which is obtained based on the vehicle speed and the length and curvature of the desired path.

[0039] S42: The single pre-aiming point is as follows:

[0040] p j =[x j y j θ j κ j ]

[0041] Where, in the formula x j Represents the x-coordinate, y j Represents the ordinate, θ j Indicates the heading angle, κ j This indicates the curvature of the torsion curve.

[0042] Furthermore, in step 4, the vehicle position [x] is described above. g ,y gUsing the aforementioned preview point interval as the starting point of the Clothoid curve, select a preview point as the ending point of the Clothoid curve, and calculate the Clothoid curve as follows:

[0043] S51: The Clothoid curve is calculated as follows:

[0044]

[0045] In the formula, s represents the arc length of the clothoid curve, [x g ,y g ] indicates the starting point of the clothoid curve, θ g κ represents the deflection angle at the starting point of the clothoid curve. g κ′ represents the curvature at the starting point of the clothoid curve, and κ′ represents the rate of change of curvature of the clothoid curve.

[0046] Further, in step 5, the optimal clothoid curve is validated using the maximum curvature, the maximum rate of change of curvature, and the curve length as constraints to obtain an optimal clothoid curve that satisfies the constraints. Specifically:

[0047] S61: The maximum curvature constraint is calculated as follows:

[0048]

[0049] The parameters in the formula have the same meanings as those in formulas S21 and S22, δ max Indicates the maximum steering angle of the front wheels, a y V represents lateral acceleration. l The speed threshold is a constant.

[0050] S62: The maximum rate of change of curvature constraint is calculated as follows:

[0051]

[0052] The parameters in the formula have the same meanings as those in formulas S21, S22, S24, and S61. s ′ tatic An empirical value for the rate of curvature change set at extremely low speeds;

[0053] S63: The length constraint of the Clothoid curve is calculated as follows:

[0054]

[0055] The meanings of the parameters in the formula are consistent with those in formulas S22 and S51.

[0056] Further, in step 6, using the curvature change rate of the optimal clothoid curve as a parameter, and taking the curvature of the corresponding position of the optimal clothoid curve after the response lag time of the current vehicle speed-guided steering servo system as the target curvature, the target steering angle of the vehicle's front wheels is calculated as follows:

[0057] S71: The target angular velocity of the steering wheel is calculated as follows:

[0058] ω=κ′*k*L*v*cos 2 (δ v )

[0059] In this formula, the parameters represent the same meanings as those in formulas S21, S22, and S24, where κ′ is the rate of change of curvature of the optimal clothoid curve, and δ... v The current steering angle of the vehicle's front wheels.

[0060] S72: The calculation of the target steering angle is as follows:

[0061] angle = angle v +ω*t2

[0062] In this formula, the parameters have the same meanings as those in formula S71, where t2 represents the response hysteresis time of the steer-by-wire servo system, and angle... v This represents the current steering wheel angle.

[0063] Beneficial effects: This invention takes into account the problem of tracking error caused by the discontinuity of the curvature of the intelligent vehicle control curve. It significantly improves the path tracking accuracy of curves at different speeds, thereby improving path following accuracy and driving stability. Attached Figure Description

[0064] Figure 1 This is a flowchart of the path tracking control method implementation;

[0065] Figure 2 This is a diagram illustrating vehicle location prediction.

[0066] Figure 3 It is the impact of the aiming point selection on the control path calculation deviation;

[0067] Figure 4 It is the experimental path;

[0068] Figure 5a It is the road curvature corresponding to a driving time axis with a speed limit of 10km / h;

[0069] Figure 5b This is the actual vehicle speed corresponding to the time axis of a 10km / h speed limit driving mode;

[0070] Figure 5c This is the steering angle command corresponding to the driving time axis with a speed limit of 10km / h;

[0071] Figure 5d It is the deviation of the yaw angle relative to the reference curve corresponding to the driving time axis at a speed limit of 10km / h;

[0072] Figure 5e It is the lateral position deviation relative to the reference curve corresponding to the driving time axis at a speed limit of 10km / h;

[0073] Figure 6a It is the road curvature corresponding to the time axis of a 15km / h speed limit driving;

[0074] Figure 6b This is the actual vehicle speed corresponding to the time axis of a 15km / h speed limit driving mode;

[0075] Figure 6c This is the steering angle command corresponding to the driving time axis with a speed limit of 15km / h;

[0076] Figure 6d It is the deviation of the yaw angle relative to the reference curve corresponding to the driving time axis at a speed limit of 15km / h;

[0077] Figure 6e It is the lateral position deviation relative to the reference curve corresponding to the driving time axis at a speed limit of 15km / h;

[0078] Figure 7a It is the road curvature corresponding to the time axis of a speed limit of 20km / h;

[0079] Figure 7b This is the actual vehicle speed corresponding to the time axis of a 20km / h speed limit driving route;

[0080] Figure 7c This is the steering angle command corresponding to the driving time axis with a speed limit of 20km / h;

[0081] Figure 7d It is the deviation of the yaw angle relative to the reference curve corresponding to the driving time axis at a speed limit of 20km / h;

[0082] Figure 7e It is the lateral position deviation of the relative reference curve corresponding to the driving time axis at a speed limit of 20km / h. Detailed Implementation

[0083] To better understand the above-mentioned objects, features, and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. The described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention pertains. The terminology used herein is for the purpose of describing specific embodiments only and is not intended to limit the present invention.

[0084] In the various embodiments of the present invention, for ease of description and not limitation of the invention, the term "connection" used in the present invention patent application specification and claims is not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Above," "below," "underneath," "left," "right," etc., are only used to indicate relative positional relationships, and when the absolute position of the described object changes, the relative positional relationship also changes accordingly.

[0085] As shown in the attached diagram, this embodiment provides a path tracking control method based on a clothoid curve. It constructs a motion model for the vehicle to track a path with linearly and continuously changing curvature. Based on the vehicle's current state, it predicts the vehicle's state after the communication delay time with the control system. Based on the predicted vehicle position, vehicle speed, and the curvature of the desired path, it determines the selection interval for the aiming point and calculates the clothoid curve. Finally, using the curvature change rate of the clothoid curve as a parameter, and taking the curvature of the corresponding position of the clothoid curve after the response lag time of the current vehicle speed steer-by-wire servo system as the target curvature, it calculates the target steering angle of the vehicle's front wheels. The specific steps are as follows:

[0086] Step 1): Construct a motion model for the vehicle to track a linearly continuous curvature change path based on the Ackerman steering model; from the geometric relationship of the Ackerman-steering vehicle, the relationship between the front wheel angle, wheelbase, and curvature can be obtained. Calculate the rate of change of curvature with time and the rate of change of curvature with arc length. Based on the linear proportional relationship between the steering wheel angular velocity and the front wheel angular velocity, calculate the relationship between the steering wheel angular velocity and the rate of change of vehicle curvature, specifically:

[0087] S21: From the geometric relationships of the Ackermann steering vehicle, the relationship between the front wheel steering angle, wheelbase, and steering curvature is as follows:

[0088] κ=tanδ / L

[0089] Where δ represents the front wheel steering angle, L represents the wheelbase, and κ represents the current steering curvature.

[0090] S22: The rate of change of curvature over time is as follows:

[0091]

[0092] Where s represents the distance the vehicle travels, assuming that the vehicle travels at a constant speed of v within this distance, and κ′ is the rate of change of curvature with arc length;

[0093] S23: The rate of change of curvature with arc length is calculated as follows:

[0094]

[0095] The meanings of the parameters in the formula are consistent with those in formulas S21 and S22, and d(δ) / dt is the front wheel steering angular velocity.

[0096] S24: The angular velocity of the steering wheel is calculated as follows:

[0097] ω=k*d(δ) / dt

[0098] The parameters in the formula have the same meaning as those in formula S23, where ω represents the angular velocity of the steering wheel and k is the proportional coefficient of the steering system.

[0099] S25: The relationship between the steering wheel angular velocity and the rate of change of vehicle curvature is calculated as follows:

[0100]

[0101] The meanings of the parameters in the formula are consistent with those in formulas S21, S23, and S24.

[0102] Step 2): Based on the vehicle's current status [x] v ,y v ,θ v ,κ v [x], predicting the vehicle's state after the control system communication delay time. g ,y g ,θ g ,κ gThe control system communication delay mainly refers to the communication delay caused by multiple stages, including the forwarding of steering control commands generated by the control module through the interface driver and transmission via the CAN (Controller Area Network) bus. Due to the existence of the control system communication delay t1, the control command issued at the current moment will not receive a response until after time t1. Therefore, the currently issued control command should also be calculated based on the state relationship between the vehicle and the reference path after time t1, thus requiring prediction of the vehicle's state after time t1. Figure 2 As shown, firstly, the vehicle's state after time t1 is predicted in the current vehicle coordinate system. Then, the predicted vehicle state after time t1 in the current vehicle coordinate system is transformed to the global coordinate system. Specifically:

[0103] S31: In the vehicle coordinate system, the vehicle's state after the control system communication delay time:

[0104]

[0105] The parameters in the formula have the same meaning as those in formulas S21 and S22. t1 represents the communication delay time of the control system, s represents the distance traveled in time t1, r represents the turning radius, and [Δx,Δy,Δθ,κ] represents the state of the vehicle in the current vehicle coordinate system after time t1.

[0106] S32: Vehicle state in the global coordinate system after the control system communication delay time:

[0107]

[0108] The parameters in the formula have the same meaning as those in formula S31, [x v ,y v ,θ v ,κ v [x] represents the vehicle's current state in the global coordinate system. g ,y g ,θ g ,κ g [] represents the state of the vehicle in the global coordinate system after time t1.

[0109] Step 3): Locate the vehicle position [x] on the desired path. g ,y g The nearest point is used as the starting point of the preview point interval, and the ending point of the preview point interval is determined based on the vehicle speed and the curvature of the desired path; since the selection of preview points will affect the deviation between the planned path and the desired path, such as... Figure 3As shown, the closer the pre-aiming point is, the smaller the area enclosed by the tracking control path and the desired path, meaning the overall process deviation is smaller. However, an excessively short tracking control path can lead to excessively large curvature and rate of curvature change of the calculated Clothid curve, exceeding the control range of the vehicle's steering wheel angle and angular velocity, or causing drastic changes in the steering wheel angle, thus affecting vehicle stability. Therefore, this invention designs a pre-aiming point scanning interval, first finding the area on the desired path that corresponds to the predicted vehicle position [x]. g ,y g The nearest point is used as the starting point of the pre-aiming point interval. Then, the initial value of the pre-aiming point interval length s_length is set, and then adjusted according to the vehicle speed and the length and curvature information of the desired path. Then, starting from the starting point (the point on the desired path closest to the predicted vehicle position), the point along the desired path whose route length is closest to s_length is used as the ending point of the pre-aiming point interval. Specifically:

[0110] S41: The pre-aiming point intervals are stored in sequence form, numbered starting from 1, with a quantity of m. The pre-aiming point selection intervals are as follows:

[0111] p = [p1p2L p] j L p m ]

[0112] Where, p j Let p1 represent the j-th pre-aiming point, and p1 represent the starting point of the pre-aiming point interval, which is the position of the vehicle on the desired path relative to the above-mentioned vehicle position [x]. g ,y g The nearest point, p m This indicates the endpoint of the pre-aiming point interval, which is obtained based on the vehicle speed and the length and curvature of the desired path.

[0113] S42: The single pre-aiming point is as follows:

[0114] p j =[x j y j θ j κ j ]

[0115] Where, in the formula x j Represents the x-coordinate, y j Represents the ordinate, θ j Indicates the heading angle, κ j This indicates the curvature of the torsion curve.

[0116] Step 4): Using the vehicle location [x] g ,y gAs the starting point of the Clothoid curve, select a preview point within the aforementioned preview point interval as the endpoint of the Clothoid curve, and calculate the Clothoid curve; after determining the selected interval for the endpoint of the Clothoid curve, calculate the Clothoid curve from the points within the selected interval, starting from the furthest point, specifically as follows:

[0117] S51: The Clothoid curve is calculated as follows:

[0118]

[0119] In the formula, s represents the arc length of the control curve, [x g ,y g ] is the starting point of the clothoid curve, θ g It is the deflection angle at the starting point of the clothoid curve, κ. g κ′ represents the curvature at the starting point of the clothoid curve, and κ′ represents the rate of change of curvature of the clothoid curve.

[0120] Step 5): The optimal clothoid curve is validated using maximum curvature, maximum rate of change of curvature, and curve length as constraints to obtain the optimal clothoid curve that satisfies the constraints. This invention is applicable to unmanned vehicle platforms with Ackerman steering. The motion constraints include maximum curvature and maximum rate of change of curvature. Furthermore, the control path length obtained using the clothoid curve planning must be sufficiently long; otherwise, directional jitter will occur during continuous control. The maximum curvature constraint is discussed in two cases: when the vehicle is stationary and when it is moving. When the vehicle is stationary or moving at extremely low speeds, its motion curvature constraint is calculated using the maximum front wheel steering angle parameter. When the vehicle is moving, the maximum motion curvature is constrained by limiting lateral acceleration. When the speed is very low or the vehicle is stationary, the maximum rate of change of curvature of the constraint curve is set to 0.05 / m. 2 The larger this value, the more pronounced the vehicle's vibration during start-up. It requires a sufficiently long Clothoid curve control path; if the Clothoid control path is too short, a "dragon-drawing" problem will occur due to large changes in the curvature of the continuous control curve. Specifically:

[0121] S61: The maximum curvature constraint is calculated as follows:

[0122]

[0123] The parameters in the formula have the same meanings as those in formulas S21 and S22, δ max Indicates the maximum steering angle of the front wheels, a y V represents lateral acceleration. l The speed threshold is a constant.

[0124] S62: The maximum rate of change of curvature constraint is calculated as follows:

[0125]

[0126] The parameters in the formula have the same meanings as those in formulas S21, S22, S24, and S61. s ′ tatic An empirical value for the rate of curvature change set at extremely low speeds;

[0127] S63: The control curve length constraint is calculated as follows:

[0128]

[0129] The meanings of the parameters in the formula are consistent with those in formulas S22 and S51.

[0130] Step 6): Using the curvature change rate of the optimal clothoid curve as a parameter, and the curvature at the corresponding position of the output curve after the current vehicle speed's pre-aimed steer-by-wire servo system response lag time as the target curvature, calculate the target steering angle of the vehicle's front wheels. The steer-by-wire servo system also requires a certain amount of time from receiving the steering control CAN command to the drive actuator realizing the target steering angle; this response time is called the steer-by-wire system response lag time. The response lag of the steer-by-wire servo system prevents the control command from being implemented immediately. Without proper compensation, this can lead to system instability or limit the driving speed of the autonomous vehicle. Specifically:

[0131] S71: The target angular velocity of the steering wheel is calculated as follows:

[0132] ω=κ′*k*L*v*cos 2 (δ v )

[0133] In this formula, the parameters represent the same meanings as those in formulas S21, S22, and S24, where κ′ is the rate of change of curvature of the optimal control curve, and δ... v The current steering angle of the vehicle's front wheels.

[0134] S72: The calculation of the target steering angle is as follows:

[0135] angle = angle v +ω*t2

[0136] In this formula, the parameters have the same meanings as those in formula S71, where t2 represents the response hysteresis time of the steer-by-wire servo system, and angle... v This represents the current steering wheel angle.

[0137] Example

[0138] This invention proposes a path tracking control method based on the Clothoid curve to improve path following accuracy and driving stability. First, a motion model is constructed to track a path with linearly and continuously changing curvature. Then, based on the vehicle's current state, the vehicle's state after the communication delay time with the control system is predicted. Next, based on the predicted vehicle position, vehicle speed, and the curvature of the desired path, the selection interval for the aiming point is determined, and the Clothoid curve is calculated. Finally, using the curvature change rate of the Clothoid curve as a parameter, and taking the curvature of the Clothoid curve at the corresponding position after the response lag time of the current vehicle speed steer-by-wire servo system as the target curvature, the target steering angle of the vehicle's front wheels is calculated. This invention is tested on a smart vehicle based on a Wey-VV6 chassis.

[0139] I. Real Vehicle Platform and Expected Path

[0140] The test platform used was a smart car modified from the Wey-VV6 chassis, with a wheelbase of 2.845m. The test path was as follows: Figure 4 As shown, typical road conditions include straight roads, right-angle turns, curves, and lane changes.

[0141] II. Test Results

[0142] The parameters for this invention are set as follows: t1 = 0.1s, t2 = 0.12s, v l =0.3m / s, κ s ′ tatic =0.05.

[0143] The maximum vehicle speeds were limited to 10 km / h, 15 km / h, and 20 km / h, with a pure tracking method used as a control. The results are as follows: Figure 5a -e、 Figure 6a -e is shown Figure 7a As shown in -e. The absolute values ​​of the maximum lateral deviation of the pure tracking method and the method proposed in this invention are 0.109m (0.381m), 0.227m (0.378m), and 0.232m (0.368m), respectively, and the absolute values ​​of the maximum yaw angle deviation are 0.0557rad (0.1390rad), 0.0934rad (0.1753rad), and 0.0864rad (0.1727rad), respectively. The values ​​marked in parentheses above are the test results of the pure tracking algorithm. The intelligent vehicle path tracking method based on the clothoid curve proposed in this invention outperforms the pure tracking method in both lateral deviation and yaw angle deviation, demonstrating higher path following accuracy and better smoothness.

[0144] See appendix for details Figure 5a---5e, Explanation: On the designed test section, the maximum speed limit was set at 10 km / h. During the comparative test, the same longitudinal speed control strategy was used. Comparative analysis showed that the maximum yaw angle deviation of the proposed method and the pure tracking method were 0.0557 rad and 0.1390 ​​rad, respectively, and the maximum lateral deviation was 0.109 m and 0.381 m, respectively. The proposed method significantly outperforms the pure tracking method in both of these indicators.

[0145] See appendix for details Figure 6a ----6e, Explanation: On the designed test section, the maximum speed limit was set at 15 km / h. During the comparative test, the same longitudinal speed control strategy was used. Comparative analysis showed that the maximum yaw angle deviation of the proposed method and the pure tracking method were 0.0934 rad and 0.1753 rad, respectively, and the maximum lateral deviation was 0.227 m and 0.378 m, respectively. The proposed method significantly outperforms the pure tracking method in both of these indicators.

[0146] See appendix for details Figure 7a ---7e, Explanation: On the designed test section, the maximum speed limit was set at 20 km / h. During the comparative test, the same longitudinal speed control strategy was used. Comparative analysis showed that the maximum yaw angle deviation of the proposed method and the pure tracking method were 0.0864 rad and 0.1727 rad, respectively, and the maximum lateral deviation was 0.232 m and 0.368 m, respectively. The proposed method significantly outperforms the pure tracking method in both of these indicators.

[0147] The above detailed description of a path tracking control method for intelligent vehicles based on Clothoid curves, with reference to the embodiments described above, is illustrative rather than limiting. Several embodiments may be listed within the defined scope. Therefore, variations and modifications that do not depart from the overall concept of the present invention should be within the protection scope of the present invention.

Claims

1. A path tracking control method for intelligent vehicles based on Clothoid curves, characterized by: A motion model is constructed to track a linearly continuously changing curvature path for the vehicle. Based on the current vehicle state, the vehicle state after the communication delay time of the control system is predicted. Based on the predicted vehicle position, vehicle speed, and the curvature of the desired path, the selection interval for the aiming point is determined, and the clothoid curve is calculated. Finally, using the curvature change rate of the clothoid curve as a parameter, and taking the curvature of the corresponding position of the clothoid curve after the response lag time of the current vehicle speed steer-by-wire servo system as the target curvature, the target steering angle of the vehicle's front wheels is calculated. The specific steps are as follows: Step 1: Based on the Ackermann steering model, construct a motion model for the vehicle to track a path with linearly and continuously changing curvature. Step 2: Based on the current vehicle status Predicting the vehicle's state after the control system communication delay time. ; Step 3: Locate the vehicle's position along the desired path. The nearest point is used as the starting point of the aiming point interval, and the ending point of the aiming point interval is determined based on the vehicle speed and the curvature of the desired path. The process of finding the location of the aforementioned vehicle on the desired path. The nearest point is used as the starting point of the aiming point interval, and the ending point of the aiming point interval is determined based on the vehicle speed and the curvature of the desired path. Step 4: Based on the above vehicle locations As the starting point of the clothoid curve, select a preview point within the above preview point interval as the ending point of the clothoid curve, and calculate the clothoid curve; Step 5 verifies the optimal clothoid curve using maximum curvature, maximum rate of change of curvature, and curve length as constraints to obtain an optimal clothoid curve that satisfies the constraints. Specifically, this verification process involves: S61: The maximum curvature constraint is calculated as follows: The parameters in the formula have the same meanings as those in formulas S21 and S22. Indicates the maximum steering angle of the front wheels. Indicates lateral acceleration. The speed threshold is a constant. S62: The maximum rate of change of curvature constraint is calculated as follows: The parameters in the formula have the same meanings as those in formulas S21, S22, S24, and S61. An empirical value for the rate of curvature change set at extremely low speeds; S63: The length constraint of the Clothoid curve is calculated as follows: The meanings of the parameters in the formula are consistent with those in formulas S22 and S51. Step 6 uses the curvature change rate of the optimal Clothoid curve as a parameter, and the curvature of the corresponding position of the optimal Clothoid curve after the response lag time of the current vehicle speed steer-by-wire servo system as the target curvature to calculate the target steering angle of the vehicle's front wheels. Specifically: S71: The target angular velocity of the steering wheel is calculated as follows: The parameters in the formula have the same meanings as those in formulas S21, S22, and S24. The rate of change of curvature of the optimal clothoid curve. The current steering angle of the vehicle's front wheels. S72: The calculation of the target steering angle is as follows: The parameters in the formula have the same meaning as those in formula S71. t 2 indicates the response hysteresis time of the steer-by-wire servo system. This represents the current steering wheel angle.

2. The intelligent vehicle path tracking control method based on Clothoid curves according to claim 1, characterized in that: The motion model for the vehicle to track a linearly and continuously changing curvature path, as described in step 1, is as follows: S21: From the geometric relationships of the Ackermann steering vehicle, the relationship between the front wheel steering angle, wheelbase, and steering curvature is as follows: in, Indicates the front wheel steering angle. Indicates wheelbase. Indicates the current bending rate; S22: The rate of change of curvature over time is as follows: in, This represents the distance the vehicle travels, assuming the vehicle travels at a constant speed of v during this distance. , This represents the rate of change of curvature with respect to arc length. S23: The rate of change of curvature with arc length is calculated as follows: The parameters in the formula have the same meanings as those in formulas S21 and S22. It is the steering angular velocity of the front wheels; S24: The angular velocity of the steering wheel is calculated as follows: The parameters in the formula have the same meaning as those in formula S23. This represents the angular velocity of the steering wheel. This refers to the proportional coefficient of the steering system transmission; S25: The relationship between the steering wheel angular velocity and the rate of change of vehicle curvature is calculated as follows: The meanings of the parameters in the formula are consistent with those in formulas S21, S23, and S24.

3. The intelligent vehicle path tracking control method based on Clothoid curves according to claim 1, characterized in that: In step 2), the step of determining the current state of the vehicle... Predicting the vehicle's state after the control system communication delay time. Specifically: S31: In the vehicle coordinate system, the vehicle's state after the control system communication delay time: The parameters in the formula have the same meanings as those in formulas S21 and S22. t 1 Indicates the communication delay time of the control system. express t 1 The distance traveled in time, r Indicates the turning radius. express t 1 The vehicle's state in the current vehicle coordinate system after a certain time; S32: Vehicle state in the global coordinate system after the control system communication delay time: The parameters in the formula have the same meaning as those in formula S31. This indicates the current state of the vehicle in the global coordinate system. express t 1 The state of the vehicle in the global coordinate system after a certain time.

4. The intelligent vehicle path tracking control method based on Clothoid curves according to claim 1, characterized in that: Step 3 involves finding the location of the vehicle along the desired path. The nearest point is used as the starting point of the aiming point interval, and the ending point of the aiming point interval is determined based on the vehicle speed and the curvature of the desired path. The process of finding the location of the aforementioned vehicle on the desired path. The nearest point is used as the starting point of the aiming point interval, and the ending point of the aiming point interval is determined based on the vehicle speed and the curvature of the desired path, specifically: S41: The pre-aiming point intervals are stored in sequence form, numbered starting from 1, with a quantity of m. The pre-aiming point selection intervals are as follows: in, Indicates the first j One pre-aiming point, This indicates the starting point of the aiming point interval, which is the position of the vehicle on the desired path. The nearest point, This indicates the endpoint of the pre-aiming point interval, which is obtained based on the vehicle speed and the length and curvature of the desired path. S42: Single aiming point as follows: Among them, in the formula x j Represents the x-axis, y j Represents the vertical axis. Indicates the heading angle. This indicates the curvature of the torsion curve.

5. The intelligent vehicle path tracking control method based on Clothoid curves according to claim 1, characterized in that: step In section 4, the aforementioned vehicle location As the starting point of the Clothoid curve, select a preview point within the aforementioned preview point interval as the ending point of the Clothoid curve, and calculate the Clothoid curve as follows: S51: The Clothoid curve is calculated as follows: Among them, in the formula Indicates the arc length of the clothoid curve. Indicates the starting point of the clothoid curve. This indicates the deflection angle at the starting point of the clothoid curve. This represents the curvature at the starting point of the clothoid curve. This represents the rate of change of curvature of the clothoid curve.

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