Trajectory optimization method and device for automobile automatic driving emergency obstacle avoidance, terminal and storage medium

Abstract

The invention discloses a trajectory optimization method and device for automobile automatic driving emergency obstacle avoidance, a terminal and a storage medium, and the method comprises the steps: receiving preset basic parameters which comprise a starting point of an automobile driving trajectory, a terminal point of the trajectory and a speed planning equation; establishing a trajectory equation of the trajectory, setting a plurality of trajectory calculation points from the starting point to the end point according to the trajectory equation, and calculating the longitudinal velocity and the longitudinal acceleration of each trajectory calculation point according to a velocity planning equation; combining the trajectory equation and the speed and the longitudinal acceleration of each trajectory calculation point to calculate the comprehensive acceleration of each trajectory calculation point, and screening to obtain the maximum comprehensive acceleration; and performing optimization iteration on each trajectory polynomial coefficient of the trajectory equation in combination with the maximum comprehensive acceleration to obtain an optimal polynomial coefficient, and performing trajectory planning in combination with the optimal polynomial coefficient. According to the method, the maximum comprehensive acceleration of the planned trajectory is minimized, so that the safety level of the emergency obstacle avoidance process is improved.

Description

Technical Field

[0001] The present application relates to the field of automobile autonomous driving (ADAS), and in particular to a trajectory optimization method, device, terminal and storage medium for emergency obstacle avoidance of automobile autonomous driving. Background Art

[0002] ADAS trajectory planning has two tasks: one is speed planning, that is, to reasonably arrange the speed of the vehicle, which can be uniform or variable; the second is to reasonably arrange the curvature of the trajectory, because the greater the curvature, the greater the steering angle of the car, the greater the lateral acceleration, that is, the greater the centrifugal force, when the centrifugal force reaches a certain level, the combined force of the centrifugal force and the longitudinal force will cause slip between the tire and the ground, causing the vehicle to lose control. Therefore, an important task of trajectory planning is to ensure a reasonable trajectory curvature, so that when the vehicle travels along the planned trajectory, the vector sum of the lateral acceleration and the longitudinal acceleration (combined acceleration) is minimized and kept within a safe range.

[0003] At present, the trajectory planning of ADAS is calculated according to the traditional quintic polynomial. Under the constraints of the position, speed direction and curvature of the starting and ending points, the coefficients of the quintic polynomial are completely solvable, that is, the curvature of the trajectory does not change with the change of speed planning. In fact, when different speed planning is carried out, the speed of each point on the trajectory will change. In order to ensure that the lateral acceleration (or comprehensive acceleration) is within a certain safety range, the curvature of the point with high speed should be smaller, and the curvature of the point with low speed can be moderately larger. The calculation method of the quintic polynomial cannot optimize the curvature under different speed planning conditions, and it is also impossible to control the maximum comprehensive acceleration of the vehicle to the minimum when driving along the planned trajectory.

[0004] Therefore, how to control the maximum integrated acceleration of a vehicle to a minimum when the vehicle travels along a planned trajectory is an urgent problem to be solved in this field. Summary of the invention

[0005] In order to solve the problem that the existing obstacle avoidance trajectory planning technology cannot reasonably design the trajectory curvature according to the speed planning, the present application represents the trajectory curve based on the trajectory multi-time polynomial and adopts optimization calculation to minimize the comprehensive acceleration of the vehicle along the trajectory, thereby improving the safety of the emergency obstacle avoidance function.

[0006] In the first aspect, the present application provides a trajectory optimization method for emergency obstacle avoidance of an autonomous vehicle, which adopts the following technical solution: A trajectory optimization method for emergency obstacle avoidance of an automatic driving vehicle comprises the following steps: Receiving preset basic parameters, the basic parameters including a starting point of a vehicle's driving trajectory and a starting point parameter of the starting point, an end point of the trajectory and an end point parameter of the end point, and receiving a pre-established speed planning equation of the trajectory; Establishing a trajectory equation of the trajectory, setting a plurality of trajectory calculation points between the starting point and the end point according to the trajectory equation, and calculating the longitudinal velocity and longitudinal acceleration of each of the trajectory calculation points according to the velocity planning equation; The trajectory equation and the velocity and longitudinal acceleration of each trajectory calculation point are combined to calculate the comprehensive acceleration of each trajectory calculation point and obtain a comprehensive acceleration set, and the maximum comprehensive acceleration is obtained from the comprehensive acceleration set; The trajectory equation includes multiple trajectory polynomial coefficients. The trajectory polynomial coefficients are optimized and iterated in combination with the maximum comprehensive acceleration to obtain the optimal polynomial coefficients. The trajectory equation is updated in combination with the optimal polynomial coefficients, and the updated trajectory equation is used for trajectory planning.

[0007] By adopting the above-mentioned technical means, detailed trajectory equations and speed planning equations were established, and combined with the data of the actual trajectory calculation points, highly precise control of the vehicle's motion state was achieved; at the same time, by optimizing the trajectory polynomial coefficients of the trajectory equation, the maximum comprehensive acceleration of the vehicle at each point during the emergency obstacle avoidance process was minimized, reducing the risk of tire slippage caused by excessive lateral acceleration and improving driving safety.

[0008] Preferably, the step of establishing the trajectory equation of the trajectory and setting a plurality of trajectory calculation points between the starting point and the end point according to the trajectory equation specifically includes the following steps: Establishing a trajectory equation of the trajectory, wherein the trajectory is a driving curve of the vehicle from the starting point to the end point, and the trajectory equation is a multi-order polynomial equation, and the trajectory equation includes a plurality of trajectory polynomial coefficients; A plurality of trajectory calculation points between the starting point and the end point are set according to the trajectory equation, all of the trajectory calculation points are located on the trajectory, and the first-order derivative and the second-order derivative of each of the trajectory calculation points are calculated respectively.

[0009] By adopting the above-mentioned technical means, the first-order derivatives and second-order derivatives of each calculation point on the trajectory can be accurately calculated, thereby accurately reflecting the changes in the vehicle's speed and acceleration at these calculation points, which helps to more finely control the vehicle's driving path and ensure that the vehicle always remains within a safe comprehensive acceleration range during emergency obstacle avoidance, thereby improving the safety and reliability of the obstacle avoidance process.

[0010] Preferably, the trajectory equation is, Y=A×XM 6 +B×X M 5 +C×X M 4 +D×X M 3 +E×X M 2 +F×X M +G; Among them, X M Calculate the longitudinal coordinate of any point on the trajectory between the starting point and the end point, where Y is the longitudinal coordinate of X M The corresponding lateral coordinates of the trajectory calculation points, A, B, C, D, E, F, G are trajectory polynomial coefficients of the trajectory equation; The calculation formulas for the first-order derivative and the second-order derivative of the trajectory calculation point are: Y'=6A×X M 5 +5B×X M 4 +4C×X M 3 +3D×X M 2 +2E×X M +F; Y''=30A×X M 4 +20B×X M 3 +12C×X M 2 +6D×X M +2E; Among them, Y' is X M The first-order derivative of the corresponding trajectory calculation point, Y'' is X M The second-order derivative of the corresponding trajectory calculation point.

[0011] By adopting the above technical means and calculating the first-order and second-order derivatives of the trajectory equation, the curvature of each trajectory calculation point can be accurately obtained, thereby better controlling the vehicle's steering angle and lateral acceleration. The detailed derivative calculation formula provides solid data support for subsequent optimization calculations, ensuring that the dynamic performance indicators of each point can be accurately evaluated during the optimization process.

[0012] Preferably, the calculating of the longitudinal velocity and longitudinal acceleration of each trajectory calculation point according to the velocity planning equation specifically comprises the following steps: The speed planning equation includes, V M =Q×t 3 +R×t2 +U×t+W; a xM =3×Q×t 2 +2×R×t+U; Among them, t is any time after the vehicle starts to avoid obstacles, Q, R, U, W are the coefficients of the speed planning polynomial, V M is the longitudinal velocity of the trajectory calculation point corresponding to time t, a xM is the longitudinal acceleration of the trajectory calculation point corresponding to time t; The longitudinal velocity and longitudinal acceleration of each trajectory calculation point in the trajectory are calculated according to the velocity planning equation.

[0013] By adopting the above technical means and introducing the speed planning equation, the longitudinal speed and longitudinal acceleration of each trajectory calculation point can be accurately calculated, providing an accurate data basis for the subsequent calculation of the comprehensive acceleration.

[0014] Preferably, the step of combining the trajectory equation and the velocity and longitudinal acceleration of each trajectory calculation point to obtain a comprehensive acceleration of each trajectory calculation point and a comprehensive acceleration set, and screening the maximum comprehensive acceleration from the comprehensive acceleration set specifically comprises the following steps: The curvature K of the current trajectory calculation point is calculated based on the first-order derivative and the second-order derivative of any trajectory calculation point. M ; According to the longitudinal velocity and the curvature of the current trajectory calculation point, the lateral acceleration a of the current trajectory calculation point is calculated. yM , the calculation formula is, a yM =V M 2 ×K M ; According to the lateral acceleration and longitudinal acceleration of the current trajectory calculation point, the comprehensive acceleration a of the current trajectory calculation point is calculated. SM , the calculation formula is, a SM =(a yM 2 +a xM 2 ) 1 / 2 ; The comprehensive acceleration of each trajectory calculation point is calculated and summarized to obtain a comprehensive acceleration set, and the maximum comprehensive acceleration is obtained from the comprehensive acceleration set. The calculation formula is: a SMAX =Max(a SM ); Among them, a SMAXThe maximum integrated acceleration among the integrated accelerations corresponding to all the trajectory calculation points.

[0015] By adopting the above-mentioned technical means, the curvature, lateral acceleration and comprehensive acceleration of each trajectory calculation point can be accurately calculated, and the maximum comprehensive acceleration can be screened out. This helps to ensure that the maximum comprehensive acceleration of the vehicle when traveling along the planned trajectory during emergency obstacle avoidance is always within a safe range, thereby improving driving safety.

[0016] Preferably, the step of iteratively optimizing the trajectory polynomial coefficients in combination with the maximum comprehensive acceleration to obtain the optimal polynomial coefficients specifically comprises the following steps: The basic parameters also include a coefficient threshold of the trajectory polynomial coefficient A, and an initial value of the trajectory polynomial coefficient A is determined according to the coefficient threshold; Optimizing and iterating each trajectory polynomial coefficient according to preset constraint rules and the initial value, the optimization and iteration process includes: According to the constraint rules and the initial values, the relationship between trajectory polynomial coefficient B, trajectory polynomial coefficient C, trajectory polynomial coefficient D, trajectory polynomial coefficient E, trajectory polynomial coefficient F and trajectory polynomial coefficient G is obtained, an optimization objective function is established, and the optimal polynomial coefficients of the trajectory equation are obtained according to the optimization objective function.

[0017] By adopting the above technical means, by introducing the initial value of the trajectory polynomial coefficient A and multiple constraint rules, each trajectory polynomial coefficient is optimized and iterated, thus achieving more accurate trajectory planning.

[0018] Preferably, the starting point parameters include starting point coordinates, starting point curvature and initial heading angle, and the end point parameters include end point coordinates, end point curvature and end point heading angle; The constraint rules include multiple constraint conditions. Wherein, the first constraint condition is that the curve represented by the trajectory equation passes through the starting point coordinates; The second constraint condition is that the slope of the curve of the starting point coordinates is a tangent function of the initial heading angle; The third constraint condition is that the curve represented by the trajectory equation passes through the end point coordinates; The fourth constraint condition is that the slope of the curve of the end point coordinates is a tangent function of the end point heading angle; According to the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition, combined with the curvature of the starting point and the end point, a relationship between the trajectory polynomial coefficients B, C, D, E, F and G is obtained.

[0019] By adopting the above-mentioned technical means and concretizing a variety of constraints, it is ensured that the trajectory equation can accurately reflect the actual driving needs. At the same time, the relational expression of the trajectory polynomial coefficients is obtained to optimize the coefficients. The optimized trajectory equation helps to ensure that the comprehensive acceleration of the vehicle under different speed plans always remains within a safe range, significantly improving the safety and reliability of emergency obstacle avoidance.

[0020] In the second aspect, the present application provides a trajectory optimization device for emergency obstacle avoidance of an automatic driving vehicle, which adopts the following technical solution: A trajectory optimization device for emergency obstacle avoidance of an automatic driving vehicle, comprising the following modules: A basic parameter acquisition module is used to receive preset basic parameters, wherein the basic parameters include a starting point of a vehicle's driving trajectory and a starting point parameter of the starting point, an end point of the trajectory and an end point parameter of the end point, and receive a pre-established speed planning equation of the trajectory; A speed planning module, used to establish a trajectory equation of the trajectory, set a plurality of trajectory calculation points between the starting point and the end point according to the trajectory equation, and calculate the longitudinal speed and longitudinal acceleration of each of the trajectory calculation points according to the speed planning equation; a maximum comprehensive acceleration calculation module, used to calculate the comprehensive acceleration of each trajectory calculation point by combining the trajectory equation and the velocity and longitudinal acceleration of each trajectory calculation point and obtain a comprehensive acceleration set, and to screen the maximum comprehensive acceleration from the comprehensive acceleration set; A coefficient optimization module is used for the trajectory equation to include multiple trajectory polynomial coefficients, and each of the trajectory polynomial coefficients is optimized and iteratively optimized in combination with the maximum comprehensive acceleration to obtain the optimal polynomial coefficient. The trajectory equation is updated in combination with the optimal polynomial coefficient, and the updated trajectory equation is used for trajectory planning.

[0021] By adopting the above-mentioned technical means, a complete trajectory optimization system for emergency obstacle avoidance has been built, which provides the necessary software technical support for the precise operation of emergency obstacle avoidance of autonomous driving vehicles, significantly improves the safety and stability of the obstacle avoidance process, effectively reduces the risk of vehicle loss of control due to high lateral acceleration, and meets the requirements of technological progress.

[0022] In a third aspect, the present application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle as described above.

[0023] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium, wherein at least one instruction, at least one program, code set or instruction set is stored in the readable storage medium, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle as described above.

[0024] In summary, this application has at least the following beneficial effects: (1) This application uses multiple polynomial equations for trajectory planning and combines them with optimization calculation methods to achieve the optimal trajectory curvature of the vehicle under different speed planning, thereby reducing the maximum comprehensive acceleration and ensuring that the vehicle is always within a safe range during emergency obstacle avoidance.

[0025] (2) The present application can automatically adjust the trajectory curvature under different speed planning conditions, so that the lateral acceleration of the vehicle is reduced when driving at high speed, and the curvature is appropriately increased when driving at low speed, thereby achieving more flexible and efficient path planning.

[0026] (3) This application introduces multiple constraints to optimize the calculation of the polynomial coefficients of the trajectory, ensuring that the trajectory planning meets the requirements of various parameters, improving the stability and robustness of the system, and reducing the probability of unexpected situations. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is one of the flow charts of the trajectory optimization method for emergency obstacle avoidance of an automatic driving vehicle according to an embodiment of the present application; Figure 2 This is the second flow chart of the trajectory optimization method for emergency obstacle avoidance of an automatic driving vehicle according to an embodiment of the present application; Figure 3 It is a schematic diagram of a path curve of a trajectory optimization method for emergency obstacle avoidance of an automatic driving vehicle according to an embodiment of the present application; Figure 4 is a path and comprehensive acceleration curve diagram of the optimized and non-optimized comparison of Example 1 of the present application; Figure 5 is a graph of longitudinal velocity and longitudinal acceleration in Example 1 of the present application; Figure 6 is a path and comprehensive acceleration curve diagram of the optimized and non-optimized comparison of Example 2 of the present application; Figure 7 is a graph of longitudinal velocity and longitudinal acceleration in Example 2 of the present application; Figure 8 It is a structural diagram of trajectory optimization for emergency obstacle avoidance of automatic driving of a car according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] The present application provides a trajectory optimization method, device, terminal and storage medium for emergency obstacle avoidance of automatic driving of a vehicle. In order to make the purpose, technical solution and advantages of the present application clearer, the implementation method of the present application will be further described in detail below.

[0029] The following is a further detailed description of an embodiment of a trajectory optimization method for emergency obstacle avoidance of an automatic driving vehicle of the present application in conjunction with the drawings in the specification.

[0030] The present application provides a trajectory optimization method for emergency obstacle avoidance of an automatic driving vehicle, such as Figure 1 and Figure 2 As shown, the following steps are included: S1. Receive preset basic parameters, which include the starting point of the vehicle's driving trajectory and the starting point parameters of the starting point, and the end point of the trajectory and the end point parameters of the end point, and receive the speed planning equation of the pre-established trajectory.

[0031] S11. Receive preset basic parameters.

[0032] Specifically, the starting point parameters include starting point coordinates, starting point curvature, and initial heading angle.

[0033] The endpoint parameters include the endpoint coordinates, endpoint curvature, and endpoint heading angle; The basic parameters also include a coefficient threshold of the trajectory polynomial coefficient A of the trajectory equation that needs to be established.

[0034] S12. Receive a pre-established speed planning equation.

[0035] The speed planning equation is used to plan the speed of the vehicle. In this embodiment, the speed planning equation is obtained from the upstream system and is established in the upstream system.

[0036] The speed planning equation in this embodiment is a cubic polynomial.

[0037] In this embodiment, the speed planning equation includes, V M =Q×t 3 +R×t 2 +U×t+W; a xM =3×Q×t 2 +2×R×t+U; Among them, t is any time after the vehicle starts to avoid obstacles, Q, R, U, W are the coefficients of the speed planning polynomial, V M is the longitudinal velocity of the trajectory calculation point corresponding to time t, a xMThe longitudinal acceleration of the trajectory calculation point corresponding to time t.

[0038] Basic parameters also include collision time TTC and speed planning end time Ts.

[0039] In this embodiment, the starting point is set to O and the end point is set to S; The coordinates of the starting point O are (0.0, 0.0), the coordinates of the end point S are (Lg, Hg), the speed planning curve coefficients are Q, R, U, W, the speed planning end time is Ts, the collision time is TTC (Time to Collision), and the starting heading angle of the starting point O is α O , the starting curvature is K O , the terminal heading angle of the end point S is α S The end curvature is K S .

[0040] Among them, TTC (Time to Collision) is the input of the previous module, which indicates the time when the vehicle collides with the obstacle. When the previous module finds that there is a dangerous obstacle in front of the vehicle, it will calculate how long it will take for the vehicle to collide with the obstacle without taking avoidance measures. This time is TTC. After obtaining TTC, the speed planning equation for speed planning is given within the time range of 0-TTC, and then the speed planning polynomial coefficients and TTC are input into the next module to optimize the obstacle avoidance trajectory. Since the issues discussed in this application are not related to the calculation of TTC, the content of this module will not be repeated here.

[0041] For this embodiment, TTC is the end time of the speed planning equation, and Ts is the actual end time of the speed planning, so Ts can be equal to TTC, but if Ts>TTC, the vehicle speed that exceeds TTC will remain constant. For example, if TTC is 4 seconds, the speed of the vehicle that is less than 4 seconds is calculated according to the speed planning equation, and the speed of the vehicle that is greater than 4 seconds is the speed calculated according to the speed planning equation at 4 seconds.

[0042] S2, establishing a trajectory equation of the trajectory, setting multiple trajectory calculation points between the starting point and the end point according to the trajectory equation, establishing a velocity planning equation of the trajectory, and calculating the longitudinal velocity and longitudinal acceleration of each trajectory calculation point according to the velocity planning equation, specifically including the following steps: S21. Establish a trajectory equation of the trajectory, where the trajectory is a driving curve of the car from the starting point to the end point.

[0043] like Figure 3 The path curve is shown in the figure. The coordinates of the starting point O are (L 0 , H 0 ), starting heading angle α O, the coordinates of the end point S are (Lg, Hg), and the end point heading angle is α S , M (X, Y) is any point on the curve, and the longitudinal velocity of M is V M , the longitudinal acceleration is a xM , the lateral acceleration is a yM , Z is the end point of speed planning.

[0044] The trajectory equation is a multi-order polynomial equation, and the trajectory equation contains multiple trajectory polynomial coefficients.

[0045] In this embodiment, a sixth-order polynomial is used as an example to establish the trajectory equation. In practical applications, a seventh-order polynomial or even an eighth-order polynomial may also be used to establish the trajectory equation.

[0046] The trajectory equation in this embodiment is, Y=A×X M 6 +B×X M 5 +C×X M 4 +D×X M 3 +E×X M 2 +F×X M +G, Among them, X M Calculate the longitudinal coordinates of any point on the trajectory between the starting point and the end point, where Y is the vertical coordinate of X. M The corresponding lateral coordinates of the trajectory calculation points, A, B, C, D, E, F, G are the trajectory polynomial coefficients of the trajectory equation.

[0047] S22. Setting a plurality of trajectory calculation points between the starting point and the end point according to the trajectory equation, wherein all trajectory calculation points are located on the trajectory.

[0048] The more trajectory calculation points there are between the starting point and the end point, the higher the accuracy is, but the amount of calculation is also greater, and the more system resources are consumed. In this embodiment, 100 trajectory calculation points are taken between the starting point and the end point.

[0049] The number of trajectory calculation points is determined according to the specific application situation, but the calculation results must differ by less than 5%. For example, in a specific implementation, the calculation result of 1000 trajectory calculation points is α, and the calculation result of 100 trajectory calculation points is β. If (α-β) / β<5%, then 100 trajectory calculation points meet the requirements.

[0050] S23, respectively calculating the first-order derivative and the second-order derivative of each trajectory calculation point.

[0051] In this embodiment, the calculation formulas for the first-order derivative and the second-order derivative of the trajectory calculation point are: Y'=6A×X M 5 +5B×X M 4 +4C×X M 3 +3D×X M 2 +2E×X M +F; Y''=30A×X M 4 +20B×X M 3 +12C×X M 2 +6D×X M +2E; Among them, Y' is X M The first-order derivative of the corresponding trajectory calculation point, Y'' is X M The second-order derivatives of the corresponding trajectory calculation points.

[0052] S24. Calculate the longitudinal velocity and longitudinal acceleration of each trajectory calculation point in the trajectory according to the obtained velocity planning equation.

[0053] S3, combining the trajectory equation and the velocity and longitudinal acceleration of each trajectory calculation point to calculate the comprehensive acceleration of each trajectory calculation point and obtain a comprehensive acceleration set, and screening the maximum comprehensive acceleration from the comprehensive acceleration set, specifically including the following steps: S31, calculate the curvature K of the current trajectory calculation point based on the first-order derivative and second-order derivative of any trajectory calculation point M , In this embodiment, the curvature of any trajectory calculation point M(X,Y) on the trajectory is calculated as follows: K M =Y'' / (1+Y' 2 ) 3 / 2 ; Among them, K M Compute the curvature of a point M for any trajectory.

[0054] S32, based on the longitudinal velocity and curvature of the current trajectory calculation point, calculate the lateral acceleration a of the current trajectory calculation point M yM , the calculation formula is, a yM =V M 2 ×K M ; Among them, a yMCalculate the lateral acceleration of point M for the current trajectory.

[0055] S33, based on the lateral acceleration and longitudinal acceleration of the current trajectory calculation point, calculate the comprehensive acceleration a of the current trajectory calculation point M SM , the calculation formula is, a SM =(a yM 2 + a xM 2 ) 1 / 2 ; Among them, a SM Calculate the integrated acceleration of point M for the current trajectory.

[0056] S34, calculate the comprehensive acceleration of each trajectory calculation point and summarize them to obtain a comprehensive acceleration set, and filter the maximum comprehensive acceleration from the comprehensive acceleration set. The calculation formula is: a SMAX =Max(a SM ); Among them, a SMAX It is the maximum comprehensive acceleration among the comprehensive accelerations corresponding to all trajectory calculation points.

[0057] S4. The trajectory equation includes multiple trajectory polynomial coefficients. The optimal polynomial coefficients are obtained by optimizing and iterating each trajectory polynomial coefficient in combination with the maximum comprehensive acceleration, which specifically includes the following steps.

[0058] S41, the basic parameters obtained in step S1 include a coefficient threshold of the trajectory polynomial coefficient A, and an initial value of the trajectory polynomial coefficient A is determined according to the coefficient threshold, specifically including the following steps: S411. The coefficient threshold includes the upper limit and the lower limit of the trajectory polynomial coefficient A. Usually, the range of this coefficient threshold is set by empirical value.

[0059] S412, setting multiple reference points of the trajectory polynomial coefficient A within the upper limit and lower limit of the coefficient threshold, In a specific embodiment, the coefficient threshold of coefficient A is set to a range of (-10 -6 , 10 -6 ), and set 13 reference points of coefficient A within the coefficient threshold range: In this embodiment, the 13 reference points are (-10 -6 ,-10 -7 ,-10 -8 ,-10 -9 ,-10 -10 ,-10 -11 ,0.0,10 -11 , 10-10 ,10 -9 ,10 -8 ,10 -7 ,10 -6 ).

[0060] S413, calculate the maximum integrated acceleration a corresponding to each reference point of coefficient A SMAX ,like Figure 2 As shown, "Select the initial value and range of the trajectory polynomial coefficients" will give the reference point for the coefficient A input in the previous step, and the previous step returns the calculation result, that is, the maximum integrated acceleration corresponding to the reference point.

[0061] S414. Filter out the minimum value from all the maximum integrated accelerations, and set the coefficient A reference point corresponding to the minimum value as the initial value of the sixth-order polynomial coefficient optimization calculation.

[0062] S42, optimizing and iterating each trajectory polynomial coefficient according to preset constraint rules and initial values, the optimization and iterative process includes: S421. Obtain the relationship between trajectory polynomial coefficient B, trajectory polynomial coefficient C, trajectory polynomial coefficient D, trajectory polynomial coefficient E, trajectory polynomial coefficient F and trajectory polynomial coefficient G according to the constraint rules and the initial value.

[0063] Constraint rules include multiple constraints. Among them, the first constraint condition is that the curve represented by the trajectory equation passes through the starting point coordinates.

[0064] In this embodiment, the curve passes through the starting point O (0.0, 0.0), The first constraint equation is obtained from the first constraint condition: G=0.0; G is the trajectory polynomial coefficient.

[0065] The second constraint is that the slope of the curve of the starting point coordinates is a tangent function of the initial heading angle.

[0066] In this embodiment, the slope of the curve at the starting point O is the initial heading angle α 0 The tangent function of The second constraint equation is obtained from the second constraint condition: F = tg(α 0 ); F is the trajectory polynomial coefficient.

[0067] The third constraint is that the curve represented by the trajectory equation passes through the end point coordinates.

[0068] In this embodiment, the curve passes through the end point S (Lg, Hg); The third constraint equation is obtained from the third constraint condition: A×L 6 +B×L 5 +C×L 4 +D×L 3 +E×L 2 +L×tg(α 0 )+G=Hg; A, B, C, D, E, F, and G are all trajectory polynomial coefficients.

[0069] The fourth constraint condition is that the slope of the curve of the end point coordinates is a tangent function of the end point heading angle.

[0070] In this embodiment, the slope of the curve of the end point S is the end point heading angle α S The tangent function of The fourth constraint equation is obtained from the fourth constraint condition: tg(α S ) = 6A×L 5 +5B×L 4 +4C×L 3 +3D×L 2 +2E×L+tg(α 0 ).

[0071] S422. The above four constraint equations are obtained according to the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition.

[0072] Combining the curvatures of the starting point O and the end point S, the fifth constraint equation and the sixth constraint equation are obtained respectively.

[0073] That is, the fifth constraint equation is: K O =2E / (1+F 2 ) 3 / 2 ; Among them, K O is the curvature of the starting point O.

[0074] The sixth constraint equation is: K S =(30A×L 4 +20B×L 3 +12C×L 2 +6D×L+2E) / (1+tg 2 (α S )) 3 / 2 ; Among them, K S is the curvature of the end point S.

[0075] S423. Using the above six constraint equations, the following relationship between trajectory polynomial coefficients B, C, D, E, F and G can be obtained: B={-3A×L 6 +6Hg-3[tg(α 0 )+tg(α S )]×L+(0.5PE)×L 2} / L 5 ; C={3A×L 6 -15Hg+[8tg(α 0 )+7tg(α S )]×L-(P-3E)×L 2} / L 4 ; D = {-A × L 6 +10Hg-[6tg(α 0 )+4tg(α S )]×L+(0.5P-3E)×L 2} / L 3 ; E=0.5×K O ×[1+tg 2 (α 0 )] 3 / 2 ; F = tg(α 0 ); G=0.0; Where, L=Lg; P=K S ×[1+tg 2 (α S )] 3 / 2 .

[0076] S423, establish an optimization objective function, and obtain the optimal polynomial coefficients of the trajectory equation according to the optimization objective function. The optimal polynomial coefficients are the trajectory polynomial coefficients that minimize the maximum comprehensive acceleration. The optimization objective function is: Y=Min(a SMAX ); Where Y represents the minimum (optimal) maximum integrated acceleration corresponding to the optimal polynomial coefficients of the trajectory equation.

[0077] Within the coefficient threshold range of all trajectory polynomial coefficients, solve the sixth-order polynomial coefficient that minimizes the maximum integrated acceleration of the trajectory.

[0078] In the above coefficient calculation process, given the coefficient A, we can get a trajectory, calculate the comprehensive acceleration of each trajectory calculation point on this trajectory, and find the maximum comprehensive acceleration aSMAX , which is the maximum integrated acceleration of this trajectory (a SMAX ). Reference Figure 2 , "Optimize the calculation of the sixth-order polynomial coefficients of the trajectory", the maximum integrated acceleration calculation is repeatedly requested.

[0079] The purpose of this optimization iteration step is to find the coefficient A so that the maximum integrated acceleration (a SMAX ) minimum.

[0080] S5. Output the optimized sixth-order polynomial coefficients of each trajectory to the downstream planning module, update the trajectory equation in combination with the optimal polynomial coefficients, and use the updated trajectory equation for trajectory planning, which can minimize the maximum comprehensive acceleration and thus achieve trajectory optimization.

[0081] In summary, the method of the present application is a trajectory optimization algorithm based on minimizing the vehicle's comprehensive acceleration (the vector sum of longitudinal acceleration and lateral acceleration) for the ADAS emergency obstacle avoidance function (AES). The trajectory is expressed by a sixth-order polynomial, and the speed planning is expressed by a third-order polynomial. Through the constraints of the position, speed direction, and curvature of the starting and end points, the minimization of the comprehensive acceleration is used as the optimization goal. After optimization calculation, the optimal trajectory sixth-order polynomial coefficients are obtained, thereby providing the ADAS emergency obstacle avoidance function with a trajectory planning with the minimum comprehensive acceleration, thereby improving the safety of the ADAS emergency obstacle avoidance function.

[0082] Specifically, a certain number of trajectory calculation points are set between the starting point and the end point of the trajectory, and the number can meet the requirements of the emergency obstacle avoidance function for calculation and control accuracy. A method for calculating the comprehensive acceleration of each trajectory calculation point is established. The comprehensive acceleration of each trajectory calculation point is the vector sum of the longitudinal acceleration and the lateral acceleration of the point. Then the largest comprehensive acceleration among all the calculation points from the starting point to the end point is found. Finally, an optimization algorithm is used to find a set of trajectory sixth-order polynomial coefficients so that the maximum comprehensive acceleration reaches the minimum value. This set of sixth-order polynomial coefficients is provided for emergency obstacle avoidance to plan the trajectory.

[0083] The following is a comparison of two specific cases to verify the implementation of the method of this application.

[0084] Embodiment 1: This example is an analysis of the calculation results of the sixth-order polynomial (optimized) and the fifth-order polynomial (non-optimized). Figure 4 and Figure 5 As shown, the data of this embodiment are as follows.

[0085] Starting point O coordinates (0.0, 0.0); End point S coordinate (31.0, 2.5); Speed ​​planning polynomial coefficients: Q=1.7422, R=-3.92, U=0.0, W=22.2222; Speed ​​planning end time (Ts): 1.5; Time to Collision (TTC): 1.5; Starting point O heading angle (α O ):0.0; End point S heading angle (α S ):0.0; Starting point O curvature (K O ):0.0; End point S curvature (K S ):0.0;

[0086] Working conditions: Vehicle speed 22.22m / s (80kph), a stationary obstacle appears 31m ahead; Automatic Emergency Braking (AEB) conditions: average deceleration 0.75g, vehicle speed at target obstacle 6.12m / s (22kph); Automatic emergency steering (AES) working condition: lateral displacement of 2.5m to avoid obstacles; Speed ​​change planning comprehensive acceleration peak value (quintic polynomial non-optimization): 7.41 (m / s²) does not meet the safety boundary; Speed ​​change planning comprehensive acceleration peak (sixth-order polynomial optimization): 6.89 (m / s²) meets the safety margin; The maximum peak value difference is 7.55%.

[0087] The maximum comprehensive acceleration peak after optimization is 7.55% lower than that of non-optimization.

[0088] Embodiment 2: This example is an analysis of the calculation results of the sixth-order polynomial (optimized) and the fifth-order polynomial (non-optimized). Figure 6 and Figure 7 As shown, the data of this embodiment are as follows.

[0089] Starting point O coordinates (0.0, 0.0); End point S coordinate (70.0, 15.0); Speed ​​planning curve coefficients: Q=0.4444, R=-2.0, U=0.0, W=22.2222; Speed ​​planning end time (Ts): 5.0; Time to Collision (TTC): 3.0; Starting point O heading angle (α O ):0.00872664626; End point S heading angle (α S):0.0104719755; Starting point O curvature (K O ):0.00202479339; End point S curvature (K S ):0.00369169101.

[0090] Working condition: The vehicle speed is 22.22m / s (80kph), a stationary obstacle appears 70m ahead, and the vehicle moves sideways 15m to avoid the obstacle.

[0091] Speed ​​change planning comprehensive acceleration peak value (quintic polynomial non-optimization): 7.19 (m / s²) does not meet the safety margin; Speed ​​planning comprehensive acceleration peak (sixth-order polynomial optimization): 6.44 (m / s²) meets the safety margin; Maximum peak difference: 11.65%.

[0092] The non-optimized maximum integrated acceleration peak is 11.65% greater than the optimized value.

[0093] Based on the same inventive concept as above, the present application also discloses a trajectory optimization device for emergency obstacle avoidance of an automatic driving vehicle, the architecture of which is as follows: Figure 8 As shown, the device includes the following modules: A basic parameter acquisition module is used to receive preset basic parameters, including the starting point of the vehicle's driving trajectory and the starting point parameters of the starting point, and also includes the end point of the trajectory and the end point parameters of the end point, and receives the speed planning equation of the pre-established trajectory; The speed planning module is used to establish a trajectory equation of the trajectory, set multiple trajectory calculation points between the starting point and the end point according to the trajectory equation, and calculate the longitudinal speed and longitudinal acceleration of each trajectory calculation point according to the speed planning equation; The maximum comprehensive acceleration calculation module is used to calculate the comprehensive acceleration of each trajectory calculation point by combining the trajectory equation and the speed and longitudinal acceleration of each trajectory calculation point and obtain a comprehensive acceleration set, and screen the maximum comprehensive acceleration from the comprehensive acceleration set; The coefficient optimization module is used for the trajectory equation containing multiple trajectory polynomial coefficients. The trajectory polynomial coefficients are optimized and iterated in combination with the maximum comprehensive acceleration to obtain the optimal polynomial coefficients. The trajectory equation is updated in combination with the optimal polynomial coefficients, and the updated trajectory equation is used for trajectory planning.

[0094] In a specific implementation scheme, the speed planning module includes the following units: The first speed planning unit is used to establish a trajectory equation of a trajectory, where the trajectory is a driving curve of the vehicle from a starting point to an end point, and the trajectory equation is a multi-time polynomial equation, which includes multiple trajectory polynomial coefficients.

[0095] A second speed planning unit is used to set a plurality of trajectory calculation points between a starting point and an end point according to the trajectory equation, all of which are located on the trajectory, and to calculate the first-order derivative and the second-order derivative of each trajectory calculation point respectively; The trajectory equation is, Y=A×X M 6 +B×X M 5 +C×X M 4 +D×X M 3 +E×X M 2 +F×X M +G, Among them, X M Calculate the longitudinal coordinates of any point on the trajectory between the starting point and the end point, where Y is the vertical coordinate of X. M The corresponding lateral coordinates of the trajectory calculation points, A, B, C, D, E, F, G are the trajectory polynomial coefficients of the trajectory equation; The calculation formulas for the first-order derivative and second-order derivative of the trajectory calculation point are: Y'=6A×X M 5 +5B×X M 4 +4C×X M 3 +3D×X M 2 +2E×X M +F; Y''=30A×X M 4 +20B×X M 3 +12C×X M 2 +6D×X M +2E; Among them, Y' is X M The first-order derivative of the corresponding trajectory calculation point, Y'' is X M The second-order derivatives of the corresponding trajectory calculation points.

[0096] The third speed planning unit is used to calculate the longitudinal speed and longitudinal acceleration of each trajectory calculation point in the trajectory according to the speed planning equation. The speed planning equation includes: V M =Q×t 3 +R×t 2 +U×t+W; a xM=3×Q×t 2 +2×R×t+U; Among them, t is any time after the vehicle starts to avoid obstacles, Q, R, U, W are the coefficients of the speed planning polynomial, V M is the longitudinal velocity of the trajectory calculation point corresponding to time t, a xM The longitudinal acceleration of the trajectory calculation point corresponding to time t.

[0097] In a specific implementation scheme, the maximum integrated acceleration calculation module includes the following units: The first maximum integrated acceleration calculation unit is used to calculate the curvature K of the current trajectory calculation point based on the first-order derivative and second-order derivative of any trajectory calculation point. M ; The second maximum integrated acceleration calculation unit is used to calculate the lateral acceleration a of the current trajectory calculation point based on the longitudinal velocity and curvature of the current trajectory calculation point. yM , the calculation formula is, a yM =V M 2 ×K M ; The third maximum comprehensive acceleration calculation unit is used to calculate the comprehensive acceleration a of the current trajectory calculation point based on the lateral acceleration and longitudinal acceleration of the current trajectory calculation point. SM , the calculation formula is, a SM =(a yM 2 + a xM 2 ) 1 / 2 ; The fourth maximum comprehensive acceleration calculation unit is used to calculate the comprehensive acceleration of each trajectory calculation point and summarize it to obtain a comprehensive acceleration set, and the maximum comprehensive acceleration is obtained by screening from the comprehensive acceleration set. The calculation formula is: a SMAX =Max(a SM ); Among them, a SMAX It is the maximum comprehensive acceleration among the comprehensive accelerations corresponding to all trajectory calculation points.

[0098] In a specific implementation scheme, the coefficient optimization module includes the following units: A first coefficient optimization unit, for basic parameters including a coefficient threshold of the trajectory polynomial coefficient A, and determining an initial value of the trajectory polynomial coefficient A according to the coefficient threshold; The second coefficient optimization unit is used to optimize and iterate each trajectory polynomial coefficient according to the preset constraint rules and initial values. The optimization and iterative process includes: According to the constraint rules and the initial values, the relationship between trajectory polynomial coefficients B, trajectory polynomial coefficients C, trajectory polynomial coefficients D, trajectory polynomial coefficients E, trajectory polynomial coefficients F and trajectory polynomial coefficients G is obtained, an optimization objective function is established, and the optimal polynomial coefficients of the trajectory equation are obtained according to the optimization objective function.

[0099] In a specific implementation scheme, the second coefficient optimization unit includes the following subunits: The coefficient optimization subunit is used for starting point parameters including starting point coordinates, starting point curvature and initial heading angle, and ending point parameters including ending point coordinates, ending point curvature and ending point heading angle; Constraint rules include multiple constraints. Among them, the first constraint condition is that the curve represented by the trajectory equation passes through the starting point coordinates; The second constraint is that the slope of the curve of the starting point coordinates is the tangent function of the initial heading angle; The third constraint is that the curve represented by the trajectory equation passes through the end point coordinates; The fourth constraint condition is that the slope of the curve of the end point coordinates is the tangent function of the end point heading angle; According to the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition, combined with the curvature of the starting point and the end point, the relationship between the trajectory polynomial coefficients B, C, D, E, F and G is obtained.

[0100] From the above functional introduction, it can be seen that the trajectory optimization device for emergency obstacle avoidance of automatic driving vehicles in this application has built a complete trajectory optimization system for emergency obstacle avoidance, which has improved the intelligence and automation level of emergency obstacle avoidance technology of automatic driving vehicles, enhanced the safety and stability of the obstacle avoidance process, and promoted the sustained, rapid and healthy development of the economy and society.

[0101] Based on the same inventive concept mentioned above, an embodiment of the present application also discloses a computer-readable storage medium, which stores at least one instruction, at least one program, code set or instruction set. The at least one instruction, at least one program, code set or instruction set can be loaded and executed by a processor to implement the trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle provided in the above method embodiment.

[0102] Also based on the same inventive concept mentioned above, an embodiment of the present application also discloses a computer-readable storage medium, in which at least one instruction, at least one program, code set or instruction set is stored. The at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle as described above.

[0103] Those skilled in the art will appreciate that all or part of the steps to implement the above embodiments may be accomplished by hardware or by instructing related hardware through a program, and the program may be stored in the computer-readable storage medium, which may include, for example, various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0104] The above description is only an optional embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A trajectory optimization method for emergency obstacle avoidance of automatic driving, characterized in that: The steps include: Receiving preset basic parameters, the basic parameters including a starting point of a vehicle's driving trajectory and a starting point parameter of the starting point, an end point of the trajectory and an end point parameter of the end point, and receiving a pre-established speed planning equation of the trajectory; Establishing a trajectory equation of the trajectory, setting a plurality of trajectory calculation points between the starting point and the end point according to the trajectory equation, and calculating the longitudinal velocity and longitudinal acceleration of each of the trajectory calculation points according to the velocity planning equation; The trajectory equation and the velocity and longitudinal acceleration of each trajectory calculation point are combined to calculate the comprehensive acceleration of each trajectory calculation point and obtain a comprehensive acceleration set, and the maximum comprehensive acceleration is obtained from the comprehensive acceleration set; The trajectory equation includes multiple trajectory polynomial coefficients. The trajectory polynomial coefficients are optimized and iterated in combination with the maximum comprehensive acceleration to obtain the optimal polynomial coefficients. The trajectory equation is updated in combination with the optimal polynomial coefficients, and the updated trajectory equation is used for trajectory planning.

2. The trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle according to claim 1, characterized in that: The step of establishing the trajectory equation of the trajectory and setting a plurality of trajectory calculation points between the starting point and the end point according to the trajectory equation specifically includes the following steps: Establishing a trajectory equation of the trajectory, wherein the trajectory is a driving curve of the vehicle from the starting point to the end point, and the trajectory equation is a multi-order polynomial equation, and the trajectory equation includes a plurality of trajectory polynomial coefficients; A plurality of trajectory calculation points between the starting point and the end point are set according to the trajectory equation, all of the trajectory calculation points are located on the trajectory, and the first-order derivative and the second-order derivative of each of the trajectory calculation points are calculated respectively.

3. The trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle according to claim 2, characterized in that: The trajectory equation is, Y=A×X M 6 +B×X M 5 +C×X M 4 +D×X M 3 +E×X M 2 +F×X M +G; Among them, X M Calculate the longitudinal coordinate of any point on the trajectory between the starting point and the end point, where Y is the longitudinal coordinate of X M The corresponding lateral coordinates of the trajectory calculation points, A, B, C, D, E, F, G are trajectory polynomial coefficients of the trajectory equation; The calculation formulas for the first-order derivative and the second-order derivative of the trajectory calculation point are: Y’=6A×X M 5 +5B×X M 4 +4C×X M 3 +3D×X M 2 +2E×X M +F; Y’’=30A×X M 4 +20B×X M 3 +12C×X M 2 +6D×X M +2E; Where Y' is X M The first-order derivative of the corresponding trajectory calculation point, Y'' is X M The second-order derivative of the corresponding trajectory calculation point.

4. The trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle according to claim 3 is characterized in that: Calculating the longitudinal velocity and longitudinal acceleration of each trajectory calculation point according to the velocity planning equation specifically includes the following steps: The speed planning equation includes, V M =Q×t 3 +R×t 2 +U×t+W; a xM =3×Q×t 2 +2×R×t+U; Among them, t is any time after the vehicle starts to avoid obstacles, Q, R, U, W are the coefficients of the speed planning polynomial, V M is the longitudinal velocity of the trajectory calculation point corresponding to time t, a xM is the longitudinal acceleration of the trajectory calculation point corresponding to time t; The longitudinal velocity and longitudinal acceleration of each trajectory calculation point in the trajectory are calculated according to the velocity planning equation.

5. The trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle according to claim 4 is characterized in that: The step of combining the trajectory equation and the velocity and longitudinal acceleration of each trajectory calculation point to calculate the comprehensive acceleration of each trajectory calculation point and obtain a comprehensive acceleration set, and selecting the maximum comprehensive acceleration from the comprehensive acceleration set specifically includes the following steps: The curvature K of the current trajectory calculation point is calculated based on the first-order derivative and the second-order derivative of any trajectory calculation point. M ; According to the longitudinal velocity and the curvature of the current trajectory calculation point, the lateral acceleration a of the current trajectory calculation point is calculated. yM , the calculation formula is, and yM =V M 2 ×K M ; According to the lateral acceleration and longitudinal acceleration of the current trajectory calculation point, the comprehensive acceleration a of the current trajectory calculation point is calculated. SM , the calculation formula is, a SM =(a yM 2 + a xM 2 ) 1 / 2 ; The comprehensive acceleration of each trajectory calculation point is calculated and summarized to obtain a comprehensive acceleration set, and the maximum comprehensive acceleration is obtained from the comprehensive acceleration set. The calculation formula is: a SMAX =Max(a SM ); Among them, a SMAX The maximum integrated acceleration among the integrated accelerations corresponding to all the trajectory calculation points.

6. The trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle according to claim 3, characterized in that: The optimizing and iterating each trajectory polynomial coefficient in combination with the maximum comprehensive acceleration to obtain the optimal polynomial coefficient specifically includes the following steps: The basic parameters also include a coefficient threshold of the trajectory polynomial coefficient A, and an initial value of the trajectory polynomial coefficient A is determined according to the coefficient threshold; Optimizing and iterating each trajectory polynomial coefficient according to preset constraint rules and the initial value, the optimization and iteration process includes: According to the constraint rules and the initial values, the relationship between trajectory polynomial coefficient B, trajectory polynomial coefficient C, trajectory polynomial coefficient D, trajectory polynomial coefficient E, trajectory polynomial coefficient F and trajectory polynomial coefficient G is obtained, an optimization objective function is established, and the optimal polynomial coefficients of the trajectory equation are obtained according to the optimization objective function.

7. The trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle according to claim 6, characterized in that: The starting point parameters include starting point coordinates, starting point curvature and initial heading angle, and the end point parameters include end point coordinates, end point curvature and end point heading angle; The constraint rules include multiple constraint conditions. Wherein, the first constraint condition is that the curve represented by the trajectory equation passes through the starting point coordinates; The second constraint condition is that the slope of the curve of the starting point coordinates is a tangent function of the initial heading angle; The third constraint condition is that the curve represented by the trajectory equation passes through the end point coordinates; The fourth constraint condition is that the slope of the curve of the end point coordinates is a tangent function of the end point heading angle; According to the first constraint condition, the second constraint condition, the third constraint condition and the fourth constraint condition, combined with the curvature of the starting point and the end point, a relationship between the trajectory polynomial coefficients B, C, D, E, F and G is obtained.

8. A trajectory optimization device for emergency obstacle avoidance of an automatic driving vehicle, characterized in that: Includes the following modules: A basic parameter acquisition module is used to receive preset basic parameters, wherein the basic parameters include a starting point of a vehicle's driving trajectory and a starting point parameter of the starting point, an end point of the trajectory and an end point parameter of the end point, and receive a pre-established speed planning equation of the trajectory; A speed planning module, used to establish a trajectory equation of the trajectory, set a plurality of trajectory calculation points between the starting point and the end point according to the trajectory equation, and calculate the longitudinal speed and longitudinal acceleration of each of the trajectory calculation points according to the speed planning equation; a maximum comprehensive acceleration calculation module, used to calculate the comprehensive acceleration of each trajectory calculation point by combining the trajectory equation and the velocity and longitudinal acceleration of each trajectory calculation point and obtain a comprehensive acceleration set, and to screen the maximum comprehensive acceleration from the comprehensive acceleration set; A coefficient optimization module is used for the trajectory equation to include multiple trajectory polynomial coefficients, and each of the trajectory polynomial coefficients is optimized and iteratively optimized in combination with the maximum comprehensive acceleration to obtain the optimal polynomial coefficient. The trajectory equation is updated in combination with the optimal polynomial coefficient, and the updated trajectory equation is used for trajectory planning.

9. An intelligent terminal, characterized in that: It includes a memory and a processor, wherein the memory stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The readable storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the trajectory optimization method for emergency obstacle avoidance of automatic driving of a vehicle as described in any one of claims 1 to 7.

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