Control method for vehicle human-machine remote cooperation based on safe operation boundary

Abstract

The application discloses a vehicle man-machine remote cooperation control method based on safe operation boundary and belongs to the technical field of intelligent vehicles. Operation steps are as follows: (1) collecting vehicle information; (2) calculating a steering wheel rotation angle safe boundary, calculating the steering wheel rotation angle and the brake deceleration safe operation boundary allowed by an auxiliary system according to the vehicle state, the lane boundary, the vehicle-lane deviation, the brake safe distance and the collision time; (3) estimating the driver's intended steering wheel rotation angle theta c * ; (4) calculating a brake deceleration safe boundary, formulating a corresponding auxiliary decision-making strategy according to the safe operation boundary, and dividing the ego vehicle driving mode into a free driving mode and an active driving mode. The man-machine remote cooperation vehicle longitudinal collision avoidance and lateral lane deviation problems are effectively solved, and the man-machine conflict is minimized through a man-machine driving right distribution strategy.

Description

Technical Field

[0001] This invention belongs to the field of intelligent vehicle technology, specifically a control method for remote human-machine collaboration in intelligent vehicles. Background Technology

[0002] In recent years, intelligent vehicle technology has gradually become a hot research area in vehicle engineering. With its continuous development, it has been successfully applied in many fields, such as intelligent driving vehicles and intelligent logistics vehicles. However, due to the current limitations in vehicle intelligence and capabilities, some form of human-assisted supervision is still necessary. Especially in the context of the current pandemic, to avoid close contact between operators and other personnel and keep them away from potential on-site hazards, intelligent vehicle human-machine remote collaboration technology can effectively solve this problem. It keeps personnel within the control loop while avoiding potential on-site dangers. However, this approach also has limitations. Under remote operation, operators observe the environment through monitors, and their field of vision is limited. They can hardly perceive the motion, force, and vibration received by the vehicle at a remote location, which further restricts their situational awareness of the remote vehicle and significantly impacts safe driving.

[0003] Because humans and intelligent vehicles each have their own advantages and disadvantages, conflicts may arise during human-machine remote collaboration due to discrepancies in the intentions and expectations of the operator and the vehicle. Developing a multimodal interface that allows operators and vehicles to complement and enhance each other through perception, decision-making, planning, and control, while simultaneously monitoring and evaluating the performance and status of both humans and vehicles in real time, and employing a shared control method that adaptively allocates or completely transfers overall control authority between humans and vehicles based on their respective states, has become a research hotspot in remote assisted control technology.

[0004] Remote-sharing assistance for intelligent vehicles can be divided into lateral assistance and longitudinal assistance. Currently, the former mainly includes assistance methods based on steering angle and differential braking. However, differential braking reduces vehicle speed and affects passenger comfort. Steering-based assistance methods can be further divided into two technical approaches: steering angle superposition and steering torque superposition. Steering angle superposition is beneficial for achieving shared vehicle control, while the latter is easily applied to vehicles because most vehicles are equipped with Electric Power Steering (EPS) systems. EPS mainly uses control methods based on braking safety distance and collision time, each with different focuses. For example, the former determines a larger safety distance, ensuring driving safety but reducing traffic efficiency. In conclusion, given the frequent occurrence of intelligent driving accidents today, assistance systems that do not consider human-machine collaborative control are not the best solution for vehicle safety. Summary of the Invention

[0005] In order to enable vehicles to drive safely within lane boundaries and safe distances, and to automatically allocate driving rights between people and vehicles based on their status, thereby minimizing human-machine conflict, this invention provides a remote collaborative control method for intelligent vehicles based on safe control boundaries.

[0006] The hardware environment of the vehicle human-machine remote collaboration control method based on safe control boundaries is an intelligent vehicle human-machine remote collaboration control system, which includes a data acquisition module, a data processing module, a wireless communication module, an auxiliary decision-making module, an auxiliary control module, and an execution control module.

[0007] Based on vehicle status, lane boundaries, vehicle-road deviation, braking safety distance, and collision time, the allowable steering wheel angle (SWA) and the allowable safe control boundary of braking deceleration are calculated by the auxiliary system, and the autonomous vehicle driving mode is divided into free driving mode and active driving mode.

[0008] The free driving mode adopts a human driving method driven by a driver;

[0009] The active driving mode adopts an active driving method without a driver.

[0010] The control method fully considers three factors: human-machine collaboration performance, driver operating comfort, and the motion state of the vehicles in front and behind.

[0011] The operation steps of the control method are as follows:

[0012] Step (1): Collect vehicle information

[0013] The vehicle speed v is collected by the data acquisition module. r acceleration a r Speed ​​of the vehicle in front, v f acceleration a f The actual distance S between the vehicle and the vehicle in front ref d, the lateral deviation of the aiming point on the left lane safety boundary line in the vehicle coordinate system l d, the lateral deviation of the aiming point on the right lane safety boundary line in the vehicle coordinate system r Steering wheel angle θ c Driver torque measurement value T se , centroid side deflection angle β.

[0014] Step (2): Calculate the safety boundary of the steering wheel angle.

[0015] The specific steps are as follows:

[0016] Step (2.1): Establish a nonlinear steering control model

[0017] A nonlinear steering control model is designed based on a linear two-degree-of-freedom vehicle model, and its model is represented by equations (1) and (2).

[0018]

[0019]

[0020] In equations (1) and (2): a1, a2, and a3 are constants related to the structure of the autonomous vehicle system, and a1 = 2C f +2C r a2=2l f C f -2l r C r a3=2l2 fC f +2l2 rC r ω is the yaw rate, β is the sideslip angle of the center of mass, and δ f Let m be the front wheel steering angle, m be the total vehicle mass, and I be the total vehicle mass. z Let l be the moment of inertia of the entire vehicle about its vertical axis. f l is the distance from the vehicle's center of gravity to the front axle. r C is the distance from the vehicle's center of gravity to the rear axle. f C is the side slip angle of the front wheel of the vehicle. r Let v be the rear wheel sideslip angle of the vehicle, and v be the longitudinal velocity of the vehicle. x (t);

[0021] Furthermore, it is assumed that the vehicle's trajectory is ideal and that the data acquisition module provides the correct vehicle state and lane parameters; additionally, it is assumed that over a period of time, the vehicle state parameters—yaw acceleration ω, sideslip angle β, and vehicle composite velocity—are... The velocity remains unchanged; at this point, the vehicle will undergo uniform circular motion, and the resultant velocity V of the vehicle will be tangent to the driving trajectory; under the above assumptions, the vehicle will travel along the desired driving trajectory, thereby obtaining the ideal steering wheel angle θ. c :

[0022]

[0023] In equation (5): G w The steady-state yaw rate gain is given by Δf, where Δf is the lateral aiming distance from P to the X-axis in the vehicle coordinate system, and t is the yaw rate gain. p For the aiming time, w d Ideal yaw rate;

[0024] According to the aforementioned ideal steering wheel angle calculation method, when the vehicle speed is high, an excessively large steering wheel angle can cause the lateral force of the vehicle tires to saturate, leading to lateral instability of the vehicle. To avoid this dangerous situation, based on the adhesion coefficient μ and the vehicle's longitudinal speed v under different road conditions... x(t) is used to set the maximum allowable steering wheel angle limit value:

[0025]

[0026] In equation (8): g is the acceleration due to gravity;

[0027] Step (2.2): Calculate the safety boundary of the steering wheel angle.

[0028] Based on the nonlinear steering control model described in step (2.1), the calculated steering wheel angle safety boundary is shown in equations (9) and (10). To prevent the vehicle from deviating from the lane, the driver's intended steering wheel angle... It must be limited to the safe limits of the steering wheel angle;

[0029]

[0030]

[0031] In equations (9) and (10): d l d represents the lateral deviation of the left lane safety boundary preview point in the vehicle coordinate system. r θ represents the lateral deviation of the right lane safety boundary preview point in the vehicle coordinate system. l The upper boundary of the steering wheel angle, θ r The lower boundary of the steering wheel angle;

[0032] Step (3): Estimate the driver's intended steering wheel angle

[0033] The driver achieves their steering intention by applying torque to the steering wheel; therefore, estimating the driver's intended steering wheel angle directly based on the steering system model is not feasible. Equation (16) is obtained;

[0034]

[0035] In equation (16): J c B is the moment of inertia of the steering column. c θ is the steering column damping coefficient. c (s) represents the steering wheel angle θ c Laplace transform, The steering wheel angle intended by the driver The Laplace transform, T se (s) represents the driver's torque measurement value T. se Laplace transform, t a This is the extrapolation time step.

[0036] Step (4): Calculate the braking deceleration safety boundary

[0037] Step (4.1) Establish a longitudinal collision avoidance safety distance model

[0038] Assuming that the vehicle in front suddenly brakes and decelerates to a stop until both vehicles come to a stop, in order to ensure that the two vehicles do not collide, the vehicle in front must maintain a minimum safe distance S0 from the vehicle in front after braking. Therefore, the longitudinal collision avoidance safety distance model is expressed by equation (17).

[0039] S = S f -S r +S0 (17)

[0040] In equation (17): S f S is the distance traveled after the preceding vehicle stops braking. r S represents the distance traveled after the vehicle stops braking, and S represents the safe distance for the vehicle to avoid a collision.

[0041] Step (4.2), Front vehicle speed and operating condition identification

[0042] Since the vehicle follows the vehicle in front, this invention analyzes the calculation of the safety distance S based on the motion conditions of the vehicle in front. The identification method is as shown in equation (18):

[0043]

[0044] In equation (18): a(k) is the acceleration of the vehicle in front, v(k+1) is the velocity of the vehicle in front at time k+1, and v(k) is the velocity of the vehicle in front at time k.

[0045] The speed of time, T s This refers to the system sampling time.

[0046] Step (4.3): Calculate the safe distance S under each working condition.

[0047] Finally, based on the vehicle speed condition identification results described in step (4.2), the safe distance S for avoiding collision between the front and rear vehicles under each condition is obtained:

[0048]

[0049] In equation (22): t1 is the human reaction time, t2 is the brake clearance elimination time, t3 is the time for linear increase of braking deceleration, and a r’ For the vehicle's acceleration, a f Let Δv be the acceleration of the vehicle in front. rel v is the relative speed between the two vehicles. f v is the speed of the vehicle in front. r For the vehicle's speed;

[0050] Step (4.4): Calculate the braking deceleration safety boundary.

[0051] The control boundary for braking deceleration is defined by the time-to-collision (TTC) as shown in equation (23):

[0052]

[0053] In equation (23): TTC is the collision time;

[0054] According to the longitudinal collision avoidance safety distance model of the vehicle, the collision time TTC is measured by the motion state information of the front and rear vehicles; as in equation (24);

[0055]

[0056] In equation (24): Δa rel S is the relative acceleration between the front and rear vehicles. rel This represents the actual distance between your vehicle and the vehicle in front.

[0057] When calculating the time to collision (TTC), the relative acceleration Δa between the front and rear vehicles needs to be considered. rel That is, Δa rel =0 and Δa rel In the two cases where the value is not equal to 0, the lower boundary 'a' of the vehicle's braking acceleration is finally obtained. min for:

[0058]

[0059] Meanwhile, to ensure the vehicle's driving safety, the vehicle's braking deceleration cannot exceed its maximum limit value. Therefore, the upper boundary of the vehicle's braking deceleration, a ≤ a, is obtained. max =a bmax , where a bmax This is the vehicle's maximum braking deceleration.

[0060] Further technical solutions are as follows:

[0061] Based on the lateral safety control boundary of the vehicle obtained in step (2) and the longitudinal safety control boundary of the vehicle obtained in step (4), a corresponding auxiliary decision-making strategy is formulated. The auxiliary decision-making strategy consists of three auxiliary decisions, which not only ensure the lateral and longitudinal movement safety of the vehicle, but also minimize the conflict between the driver and the auxiliary system, thereby giving the driver more driving freedom. The auxiliary system should only be activated when necessary. If the vehicle is to maintain a safe distance within the lane or longitudinally, the driver's intended steering angle is... and braking deceleration a c It should be confined within a safe boundary; therefore, the steering angle should be determined by judging the driver's intended direction. and braking deceleration a cWhether the driver is within its safety boundary allows for the handover of control sovereignty between the driver and the assistance system. To this end, three output variables for the auxiliary decision-making process are designed: the target value of the auxiliary motor's rotation angle θ. md Target value of auxiliary braking deceleration a md And auxiliary weight ρ; where the target value of the auxiliary motor rotation angle θ md and the target value of auxiliary braking deceleration a md The reference value for the lower-level controller is used, and its decision-making process is as follows:

[0062] The lateral decision-making strategy for autonomous vehicles is as follows:

[0063]

[0064] The vehicle's longitudinal decision-making strategy is as follows:

[0065]

[0066] The auxiliary weight ρ is used to switch the control sovereignty of the auxiliary system, and its determination process is as follows:

[0067]

[0068] In equation (30): ρ h The auxiliary weights determined directly by T; max It is the maximum steering torque that a driver can apply when operating a car normally, a bmax This is the maximum braking deceleration that a driver can apply when operating the car normally; to ensure a smooth transition of vehicle control and increase driver comfort, the braking deceleration is adjusted after gaining control. h Then, transfer control permissions ρ h The input is fed into a first-order filter in the auxiliary control module to output a more flexible auxiliary weight ρ.

[0069] Compared with the prior art, the beneficial technical effects of the present invention are reflected in the following aspects:

[0070] 1. Addressing the issue of remote human-machine collaboration, this invention proposes a vehicle remote human-machine collaboration control method based on safe control boundaries. According to the vehicle's designed safe control boundaries, it selects a mode that matches the human and vehicle status between free driving mode and active driving mode based on a pre-defined auxiliary decision-making strategy. This invention not only effectively solves driving safety issues but also reduces human-machine conflict while giving drivers more driving freedom.

[0071] 2. The vehicle safety control boundary defined in this invention is a safety control boundary that changes continuously according to the vehicle's state. For example, vehicle speed, lateral deviation, and aiming time can all affect the width of the safety control boundary. Therefore, this invention can dynamically adjust the control boundary of the steering wheel angle by using aiming time as a design parameter, making it easier for the vehicle to match various working conditions.

[0072] 3. The nonlinear steering control model established in this invention includes the lateral anticipation distance Δf and the longitudinal velocity v. x Both (t) and the centroid sideslip angle β are input variables of the system, while the aiming time t p For design parameters, in addition, the lateral aiming distance x la and steady-state gain G w All of these are related to speed; therefore, the nonlinear steering control model designed in this invention is applicable to any speed and has good generalization performance.

[0073] 4. This invention proposes a method for estimating the driver's intended steering angle directly based on the steering system model. Compared with methods for estimating the driver's intended steering angle based on the driver model or vehicle-road model, this method can reduce the impact of model uncertainty and avoid errors caused by model inaccuracy. Furthermore, by using the driver's intended steering angle instead of the actual steering angle as a condition for judging the dangerous state of the vehicle, the auxiliary system can be activated in advance, allowing the vehicle to anticipate the occurrence of danger and reducing the occurrence of accidents to a certain extent. Attached Figure Description

[0074] Figure 1 This is a diagram of a linear two-degree-of-freedom vehicle model.

[0075] Figure 2 This is a diagram of a nonlinear steering control model;

[0076] Figure 3 To calculate the safe boundary diagram for the steering wheel angle of a car based on the road boundary;

[0077] Figure 4 This is a model diagram of the power steering system;

[0078] Figure 5 The results are from a steering wheel angle experiment.

[0079] Figure 6 This is a diagram of the longitudinal collision avoidance safety distance model;

[0080] Figure 7 This is a flowchart of the vertical auxiliary control process;

[0081] Figure 8 This is a flowchart of the horizontal auxiliary control process;

[0082] Figure 9 This is a diagram illustrating the overall structure of the auxiliary control system. Detailed Implementation

[0083] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0084] Example

[0085] The hardware environment of the intelligent vehicle human-machine remote collaboration control method based on safe control boundaries is the intelligent vehicle human-machine remote collaboration control system, which includes a data acquisition module, a data processing module, a wireless communication module, an auxiliary decision-making module, an auxiliary control module, and an execution control module.

[0086] Based on vehicle status, lane boundaries, vehicle-road deviation, braking safety distance, and collision time, the allowable steering wheel angle (SWA) and the allowable safe control boundary of braking deceleration are calculated by the auxiliary system, and the autonomous vehicle driving mode is divided into free driving mode and active driving mode.

[0087] The free driving mode adopts a human driving method driven by a driver;

[0088] The active driving mode adopts an active driving method without a driver.

[0089] The control method of this invention fully considers three factors: human-machine collaboration performance, driver operating comfort, and the motion state of the vehicles in front and behind.

[0090] The vehicle configurations in the following embodiments are shown in Table 1:

[0091] Table 1 Self-propelled vehicle structural parameters

[0092]

[0093] The operation steps of the control method of the present invention are as follows:

[0094] Step (1): The vehicle speed v is collected by the data acquisition module. r acceleration a r Speed ​​of the vehicle in front, v f acceleration a f The actual distance S between the vehicle and the vehicle in front ref d, the lateral deviation of the aiming point on the left lane safety boundary line in the vehicle coordinate system l d, the lateral deviation of the aiming point on the right lane safety boundary line in the vehicle coordinate system r Actual steering wheel angle θ c Driver torque measurement value T se , centroid side deflection angle β.

[0095] Step (2): Calculate the safe boundary of the steering wheel angle. The operation steps are as follows:

[0096] Step (2.1): Establish a nonlinear steering control model

[0097] Design a nonlinear steering control model based on a linear two-degree-of-freedom vehicle model, such as Figure 1 As shown, its model is represented by equations (1) and (2).

[0098]

[0099]

[0100] In equations (1) and (2): a1, a2, and a3 are constants related to the structure of the autonomous vehicle system, and a1 = 2C f +2C r a2=2l f C f -2l r C r a3=2l2 fC f +2l2 rC r ω is the yaw rate, β is the sideslip angle of the center of mass, and δ f Let m be the front wheel steering angle, m be the total vehicle mass, and I be the total vehicle mass. z Let l be the moment of inertia of the entire vehicle about its vertical axis. f l is the distance from the vehicle's center of gravity to the front axle. r C is the distance from the car's center of gravity to the rear axle. f C is the side slip angle of the front wheel of the vehicle. r Let v be the rear wheel sideslip angle of the vehicle, and v be the longitudinal velocity of the vehicle. x (t);

[0101] Furthermore, it is assumed that the vehicle's trajectory is ideal and that the data acquisition module provides the correct vehicle state and lane parameters; additionally, it is assumed that over a period of time, the vehicle state parameters—yaw acceleration ω, sideslip angle β, and vehicle composite velocity—are... The velocity remains unchanged; at this time, the vehicle will undergo uniform circular motion, and the resultant velocity V of the vehicle will be tangent to the trajectory; under the above assumptions, the vehicle will travel along the desired trajectory. Let G be the vehicle's center of mass at the initial moment, and P be the velocity at the aiming time t. p The predicted point of the vehicle's center of mass G on the desired trajectory. XY is the world coordinate system, X... v -Y v Using the vehicle's coordinate system, and finally based on the geometric relationships of the vehicle's trajectory, such as... Figure 2 As shown, we obtain equation (3);

[0102]

[0103] In equation (3): θ is the self-aiming time t. p The corresponding central angles are θ = ωt. p Δf is the lateral aiming distance from P to the X-axis in the vehicle coordinate system, x la This represents the predicted longitudinal distance traveled by the vehicle in its own coordinate system. In reality, the lateral velocity of the vehicle is much smaller than its longitudinal velocity, i.e., v. x <v y Therefore, the longitudinal predicted distance x la =v x (t)t p Then, according to equation (3), the ideal yaw rate ω is obtained. d for:

[0104]

[0105] Finally, using equations (3) and (4), the ideal steering wheel angle θ is obtained. c :

[0106]

[0107] In equation (5): G w The steady-state yaw rate gain is calculated simply using equation (6);

[0108]

[0109] In equation (6): i sw The steering wheel angle is the transmission ratio between the steering wheel and the front wheel, and L is the vehicle wheelbase, where L = l. f +l r K is the stability factor and is calculated according to equation (7);

[0110]

[0111] According to the designed nonlinear steering control model, the lateral anticipation distance Δf from P to the X-axis in the vehicle coordinate system and the longitudinal velocity v of the vehicle are... x Both (t) and the centroid sideslip angle β are input variables of the system, while the aiming time t p As a design parameter, in this embodiment, t is taken as... p =0.8s;

[0112] According to the aforementioned ideal steering wheel angle calculation method, when the vehicle speed is high, an excessively large steering wheel angle can cause the lateral force on the vehicle tires to saturate, leading to lateral instability. To avoid this dangerous situation, based on the coefficient of friction μ under different road conditions and the vehicle's longitudinal speed v... x (t) is used to set the maximum allowable steering wheel angle limit value:

[0113]

[0114] In equation (8): g is the acceleration due to gravity; in this embodiment, μ = 0.8;

[0115] Step (2.2): Calculate the safety boundary of the steering wheel angle.

[0116] like Figure 3 As shown, W R For lane width, W E d is the width of the safe zone at the vehicle's center of gravity. l d r These represent the preview points P on the left and right lane safety boundary lines in the vehicle coordinate system, respectively. l P r Lateral deviation, circular arc GP l and GP r These represent the movement of the vehicle to point P. l and P r The trajectory of the center of mass of a point, O l O r R is the center of the corresponding circle. l R r The corresponding radius is used to calculate the safe boundary of the steering wheel angle, as shown in equations (9) and (10). In order to prevent the vehicle from deviating from the lane, the driver's intended steering wheel angle is... It must be limited to the safe limits of the steering wheel angle;

[0117]

[0118]

[0119] In equations (9) and (10): d l d represents the lateral deviation of the left lane safety boundary preview point in the vehicle coordinate system. r θ represents the lateral deviation of the right lane safety boundary preview point in the vehicle coordinate system. l The upper boundary of the steering wheel angle, θ r The lower boundary of the steering wheel angle.

[0120] Step (3): Estimate the driver's intended steering wheel angle The operation steps are as follows:

[0121] The steering wheel serves as the human-machine interface for information transmission between the driver and the car. The driver uses the steering wheel angle to control the car's steering state and applies torque to control the car's movement to achieve their steering intentions. Therefore, directly estimating the driver's intended steering wheel angle based on the steering system model is not feasible. like Figure 4 As shown, considering the forces on the steering column, the steering system model of equation (11) is obtained;

[0122]

[0123] In equation (11): θ c x is the steering wheel angle. r For rack displacement, r p T is the pitch circle radius of the pinion. d For the driver's torque, J c B is the moment of inertia of the steering column. c K is the steering column damping coefficient. c This refers to the steering column stiffness coefficient;

[0124] Then the driver torque measurement value T se Substituting into equation (11), and the driver's torque measurement value T se Calculate according to formula (12);

[0125] T se =K c (θ c -θ m / N) (12)

[0126] In equation (12): N is the transmission ratio of the motor reduction mechanism, θ m This refers to the motor's rotation angle;

[0127] However, when the car is traveling at high speed, F r It is mainly generated by the tire's self-centering torque. To simplify the model, let x... r =θ m r p / N, and finally, by combining equations (11) and (12), we obtain equation (13);

[0128]

[0129] The driver's actual torque T can be seen from equation (13). d and driver torque measurement value T se It affects the steering wheel angle θ c The two main factors, and T d This directly reflects the driver's intention to turn. If Nθ exists at the current moment... c =θ m That is, T se =0, at this moment if the driver suddenly applies a certain steering torque, then the driver's intended steering wheel angle in that instantaneous state will be 0. With driver torque T d The following relationship exists:

[0130]

[0131] In equation (14): for The Laplace transform, T d (s) is T d The Laplace transform of;

[0132] Finally, the linear extrapolation method is used to estimate the driver's intended steering wheel angle θ*c(s), which is defined as Equation (15);

[0133]

[0134] In equation (15): t a To extrapolate the time step, in this embodiment, t is taken as... a =0.5s, With θ c (s) are respectively With θ c The Laplace transform of;

[0135] And because the real driver T d It is difficult to know, and the steering wheel speed is relatively slow. At this time, although the driver's torque measurement value T is difficult to obtain, se Relative to the actual driver torque T d While there is a certain time lag, it fully meets the requirements for estimating the driver's steering intention. Therefore, the driver's torque measurement value T is used. se Alternate driver torque T d To calculate the driver's intended steering wheel angle It is feasible, so equation (15) can be rewritten as:

[0136]

[0137] See Figure 5 The results of a certain steering wheel angle experiment are given, from... Figure 5 The results show that whenever the driver's intended steering angle exceeds the steering angle safety boundary, the assistance system is triggered to prevent the vehicle from deviating from the lane; while when the intended steering angle is within the safety boundary, the assistance control is turned off, and the driver can freely control the car.

[0138] Step (4): Calculate the braking deceleration safety boundary

[0139] The operation steps are as follows:

[0140] Step (4.1) Establish a longitudinal collision avoidance safety distance model, such as Figure 6 As shown, to ensure that the two vehicles do not collide, the vehicle must maintain a minimum safe distance S0 from the vehicle in front after braking. The ideal state of S0 is 0, but considering driving safety, in this embodiment, S0 = 3m is taken; therefore, the longitudinal collision avoidance safety distance model is expressed by equation (17).

[0141] S = S f -S r +S0 (17)

[0142] In equation (17): S f S is the distance traveled after the preceding vehicle stops braking. r S represents the distance traveled after the vehicle stops braking, and S represents the safe distance between the vehicle in front and behind to avoid a collision.

[0143] Step (4.2), Front vehicle speed and operating condition identification

[0144] like Figure 6 As shown, since the vehicle follows the vehicle in front, this invention analyzes the calculation of the safety distance S based on the motion conditions of the vehicle in front. The identification method is as shown in equation (18):

[0145]

[0146] In equation (18): a(k) is the acceleration of the vehicle in front, v(k+1) is the velocity of the vehicle in front at time k+1, v(k) is the velocity of the vehicle in front at time k, and T s In this embodiment, T is taken as the system sampling time. s =0.03s;

[0147] Step (4.3): Calculate the safe distance S under each working condition.

[0148] Based on the identification results of the speed condition of the vehicle in step (4.2), the safe distance S under each condition is calculated. It should be emphasized that since the vehicle in front is in a safe state in most cases when it accelerates, this invention will not discuss it.

[0149] (4.3.1) The vehicle in front is stationary

[0150] When the vehicle in front is stationary, an accident can only be avoided when the vehicle brakes to a stop. The safe distance S in this case is:

[0151]

[0152] (4.3.2) The vehicle in front moves at a constant speed

[0153] When the vehicle in front is moving at a constant speed, there are two possible initial speeds between the vehicle and the vehicle in front: v f ≥v r v f <v r When v f ≥v r When the distance between the two vehicles does not decrease, a collision can be avoided; when v f <v r At this point, a collision risk can only be avoided when both vehicles decelerate to the same speed. The safe distance S at this time is:

[0154]

[0155] (4.3.3) The vehicle in front slows down

[0156] In this situation, regardless of whether the vehicle in front, your own vehicle, or both vehicles stop simultaneously, the distance between the two vehicles will be minimized only when your own vehicle comes to a complete stop. Therefore, the critical collision risk state in this situation is when your own vehicle's speed drops to zero. At this point, the safe distance S is:

[0157]

[0158] In equations (19), (20), and (21): t1 is the human reaction time, t2 is the brake clearance elimination time, and t3 is the time for linear increase in braking deceleration. In this embodiment, t1 = 0.3s, t2 = 0.1s, and t3 = 0.4s are taken. f For the acceleration of the vehicle in front, a r’ Let Δv be the acceleration of the vehicle. rel v is the relative speed between the two vehicles. f For the speed of the vehicle in front, v r For the vehicle's speed;

[0159] In summary, the safe distance S under each working condition is:

[0160]

[0161] After obtaining the safe distance S, compare it with the actual distance S between your own vehicle and the vehicle in front. ref The magnitude of the signal can determine whether the vehicle is in a dangerous situation. If it is, the system will activate the auxiliary decision-making module to prevent a collision. For example... Figure 7 As shown;

[0162] Step (4.4): Calculate the braking deceleration safety boundary.

[0163] When a vehicle enters a dangerous situation and the auxiliary system gains control of the vehicle, both the vehicle in front and the vehicle behind must decelerate with appropriate braking before a collision to avoid a collision. Excessive braking deceleration will affect driving safety, while insufficient braking will not prevent a collision with the vehicle in front. This invention defines the control boundary of braking deceleration by collision time TTC, as shown in equation (23);

[0164]

[0165] According to the longitudinal collision avoidance safety distance model of the vehicle described in step (4.1), the collision time TTC is measured by the motion state information of the front and rear vehicles, as shown in equation (24).

[0166]

[0167] In equation (24): Δa rel S is the relative acceleration between the front and rear vehicles. rel This represents the actual distance between your vehicle and the vehicle in front.

[0168] When calculating the time to collision (TTC), the relative acceleration Δa between the front and rear vehicles needs to be considered. rel That is, Δa rel =0 and Δa rel There are two possibilities: ≠0.

[0169] (4.3.1)Δa rel =0

[0170] When the relative acceleration between the front and rear vehicles is 0, the TTC is:

[0171]

[0172] (4.3.2)Δa rel ≠0

[0173] The relative acceleration Δa of the current vehicle and the vehicle behind. rel When ≠0, the above equation is considered as TTC with respect to Δa. rel Δv rel S rel The quadratic state function f(TTC,Δa) rel ,Δv rel ,S rel That is, when Δ=Δv2 rel+2S rel Δa rel A collision can only occur between vehicles when the time to collision (TTC) is ≥0.

[0174]

[0175] Finally, by combining equations (23), (25), and (26), we obtain the lower boundary a of the vehicle's braking acceleration. min

[0176]

[0177] Meanwhile, to ensure the vehicle's driving safety, the vehicle's braking deceleration must be further limited to a certain range, thus obtaining the upper boundary of the vehicle's braking deceleration a≤a max =a bmax , where a bmax a is the maximum braking deceleration of the vehicle. bmax Constrained by the brake and road surface adhesion coefficient, in this embodiment a bmax Take 6-8 m / s 2 .

[0178] Step (5): Decision Support Strategies

[0179] Based on the lateral and longitudinal safety control boundaries of the vehicle described in steps (2) and (4), corresponding auxiliary decision-making strategies are formulated. For example... Figure 7 , 8 As shown, the auxiliary decision-making strategy of the human-machine remote collaborative assistance sharing system studied in this invention not only ensures the safety of the vehicle's lateral and longitudinal movements but also minimizes the conflict between the driver and the assistance system, giving the driver more driving freedom. To this end, this invention designs three output variables for the auxiliary decision-making: θ md a md ρ; where θ md The target value for the auxiliary motor rotation angle; a md ρ represents the target value for the auxiliary braking deceleration; ρ is the auxiliary weight. md a md The reference value for the lower-level controller is used, and its decision-making process is as follows:

[0180] The lateral decision-making strategy for autonomous vehicles is as follows:

[0181]

[0182] The vehicle's longitudinal decision-making strategy is as follows:

[0183]

[0184] The variable ρ is used to switch control sovereignty of the auxiliary system, and its determination process is as follows:

[0185]

[0186] In equation (30): ρ h For the auxiliary weights determined directly, T max It is the maximum steering torque that a driver can apply when operating a car normally, a bmax This is the maximum braking deceleration that a driver can apply when operating the car normally; to ensure a smooth transition of vehicle control and increase driver comfort, the braking deceleration is adjusted after gaining control. h Then, it is input into the first-order filter in the auxiliary control module to output a more flexible auxiliary weight ρ.

[0187] See Figure 9 The specific details of each module in the control system of this invention are described below:

[0188] Data acquisition module: Collects the following data: vehicle speed v r acceleration a r Speed ​​of the vehicle in front, v f acceleration a f The actual distance S between the vehicle and the vehicle in frontref d, the lateral deviation of the aiming point on the left lane safety boundary line in the vehicle coordinate system l d, the lateral deviation of the aiming point on the right lane safety boundary line in the vehicle coordinate system r Actual steering wheel angle θ c Driver torque measurement value T se , centroid side deflection angle β.

[0189] Data processing module: Based on the vehicle motion parameters obtained from the data acquisition module, calculates the vehicle's lateral and longitudinal safety control boundaries and the steering wheel angle intended by the driver. Target value θ of auxiliary motor rotation angle md The target value of auxiliary braking deceleration a md Key parameters, etc.

[0190] Wireless communication module: Responsible for data transmission between the vehicle-mounted terminal and the remote control terminal. For example, it sends the safety control boundaries obtained from the vehicle-mounted terminal to the remote terminal.

[0191] The auxiliary decision-making module, as described in claim 2, is primarily responsible for calculating the auxiliary control weight ρ and outputting the expected value of the auxiliary control module (lower-level controller), such as θ. md a md wait.

[0192] Auxiliary control module: It is equivalent to the lower-level controller and is responsible for tracking the desired motor rotation angle θ. md and the desired braking deceleration a md And output auxiliary torque T a0 and auxiliary braking pressure p a0 .

[0193] Execution control module: Combines the T output from the previous module a0 p a0 These parameters directly control the vehicle's acceleration, deceleration, and steering.

[0194] Those skilled in the art will readily understand that the above embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A control method for remote human-machine collaboration in vehicles based on safe control boundaries, wherein the hardware environment is a control system for remote human-machine collaboration in intelligent vehicles, the control system comprising a data acquisition module, a data processing module, a wireless communication module, an auxiliary decision-making module, an auxiliary control module, and an execution control module, characterized in that: Based on vehicle status, lane boundaries, vehicle-road deviation, braking safety distance, and collision time, the allowable steering wheel angle (SWA) and the allowable safe control boundary of braking deceleration are calculated by the auxiliary system, and the autonomous vehicle driving mode is divided into free driving mode and active driving mode. The free driving mode adopts a human driving method driven by a driver; The active driving mode adopts an active driving method without a driver. The control method fully considers three factors: human-machine collaboration performance, driver operating comfort, and the motion state of the vehicles in front and behind; the operation steps of the control method are as follows. Step (1): Collect vehicle information The vehicle speed v is collected by the data acquisition module. r acceleration a r Speed ​​of the vehicle in front, v f acceleration a f The actual distance S between the vehicle and the vehicle in front ref d, the lateral deviation of the aiming point on the left lane safety boundary line in the vehicle coordinate system l d, the lateral deviation of the aiming point on the right lane safety boundary line in the vehicle coordinate system r Steering wheel angle θ c Driver torque measurement value T se , centroid sideslip angle β; Step (2): Calculate the safety boundary of the steering wheel angle. The specific steps are as follows: Step (2.1): Establish a nonlinear steering control model A nonlinear steering control model is designed based on a linear two-degree-of-freedom vehicle model, and the model is represented by equations (1) and (2). In equations (1) and (2): a1, a2, and a3 are constants related to the structure of the autonomous vehicle system, and a1 = 2C f +2C r a2=2l f C f -2l r C r a3=2l2 fC f +2l2 rC r ω is the yaw rate, β is the sideslip angle of the center of mass, and δ f Let m be the front wheel steering angle, m be the total vehicle mass, and I be the total vehicle mass. z Let l be the moment of inertia of the entire vehicle about its vertical axis. f l is the distance from the vehicle's center of gravity to the front axle. r C is the distance from the vehicle's center of gravity to the rear axle. f C is the side slip angle of the front wheel of the vehicle. r Let v be the rear wheel sideslip angle of the vehicle, and v be the longitudinal velocity of the vehicle. x (t); Furthermore, it is assumed that the vehicle's trajectory is ideal and that the data acquisition module provides the correct vehicle state and lane parameters; additionally, it is assumed that over a period of time, the vehicle state parameters—yaw acceleration ω, sideslip angle β, and vehicle composite velocity—are... The velocity remains unchanged; at this point, the vehicle will undergo uniform circular motion, and the resultant velocity V of the vehicle will be tangent to the driving trajectory; under the above assumptions, the vehicle will travel along the desired driving trajectory, thereby obtaining the ideal steering wheel angle θ. c : In equation (5): G w The steady-state yaw rate gain is given by Δf, where Δf is the lateral aiming distance from P to the X-axis in the vehicle coordinate system, and t is the yaw rate gain. p For the aiming time, w d Ideal yaw rate; According to the aforementioned ideal steering wheel angle calculation method, when the vehicle speed is high, an excessively large steering wheel angle can cause the lateral force of the vehicle tires to saturate, leading to lateral instability of the vehicle. To avoid this dangerous situation, based on the adhesion coefficient μ and the vehicle's longitudinal speed v under different road conditions... x (t) is used to set the maximum allowable steering wheel angle limit value: In equation (8): g is the acceleration due to gravity; Step (2.2) Calculate the safety boundary of the steering wheel angle. Based on the nonlinear steering control model described in step (2.1), the calculated steering wheel angle safety boundary is shown in equations (9) and (10). To prevent the vehicle from deviating from the lane, the driver's intended steering wheel angle θ c * It must be limited to the safe limits of the steering wheel angle; In equations (9) and (10): d l d represents the lateral deviation of the left lane safety boundary preview point in the vehicle coordinate system. r θ represents the lateral deviation of the right lane safety boundary preview point in the vehicle coordinate system. l The upper boundary of the steering wheel angle, θ r The lower boundary of the steering wheel angle; Step (3): Estimate the driver's intended steering wheel angle The driver achieves their steering intention by applying torque to the steering wheel; therefore, estimating the driver's intended steering wheel angle directly based on the steering system model is not feasible. Equation (16) is obtained; In equation (16): J c B is the moment of inertia of the steering column. c θ is the steering column damping coefficient. c (s) represents the steering wheel angle θ c Laplace transform, The steering wheel angle intended by the driver The Laplace transform, T se (s) represents the driver's torque measurement value T. se Laplace transform, t a This is the extrapolation time step; Step (4): Calculate the braking deceleration safety boundary Step (4.1) Establish a longitudinal collision avoidance safety distance model Assuming that the vehicle in front suddenly brakes and decelerates to a stop until both vehicles come to a stop, in order to ensure that the two vehicles do not collide, the vehicle in front must maintain a minimum safe distance S0 from the vehicle in front after braking. Therefore, the longitudinal collision avoidance safety distance model is expressed by equation (17). S=S f -S r +S0 (17) In equation (17): S f S is the distance traveled after the preceding vehicle stops braking. r S represents the distance traveled after the vehicle stops braking, and S represents the safe distance for the vehicle to avoid a collision. Step (4.2), Front vehicle speed and operating condition identification Since the vehicle follows the vehicle in front, this invention analyzes the calculation of the safe distance S based on the motion conditions of the vehicle in front, and the identification method is as shown in equation (18): In equation (18): a(k) is the acceleration of the vehicle in front, v(k+1) is the velocity of the vehicle in front at time k+1, v(k) is the velocity of the vehicle in front at time k, and T s This refers to the system sampling time. Step (4.3): Calculate the safe distance S under each working condition. Finally, based on the vehicle speed condition identification results described in step (4.2), the safe distance S for avoiding collision between the front and rear vehicles under each condition is obtained: In equation (22): t1 is the human reaction time, t2 is the brake clearance elimination time, t3 is the time for linear increase of braking deceleration, and a r’ For the vehicle's acceleration, a f Let Δv be the acceleration of the vehicle in front. rel v is the relative speed between the two vehicles. f v is the speed of the vehicle in front. r For the vehicle's speed; Step (4.4): Calculate the braking deceleration safety boundary. The control boundary for braking deceleration is defined by the time-to-collision (TTC) as shown in equation (23): In equation (23): TTC is the collision time; According to the longitudinal collision avoidance safety distance model of the vehicle, the collision time TTC is measured by the motion state information of the front and rear vehicles; as in equation (24); In equation (24): Δa rel S is the relative acceleration between the front and rear vehicles. rel This represents the actual distance between your vehicle and the vehicle in front. When calculating the time to collision (TTC), the relative acceleration Δa between the front and rear vehicles needs to be considered. rel That is, Δa rel =0 and Δa rel In the two cases where the value is not equal to 0, the lower boundary 'a' of the vehicle's braking acceleration is finally obtained. min for: Meanwhile, to ensure the vehicle's driving safety, the vehicle's braking deceleration cannot exceed its maximum limit value. Therefore, the upper boundary of the vehicle's braking deceleration, a ≤ a, is obtained. max =a bmax , where a bmax This is the vehicle's maximum braking deceleration.

2. The vehicle human-machine remote collaboration control method based on safe control boundaries according to claim 1, characterized in that: Based on the lateral safety control boundary of the vehicle obtained in step (2) and the longitudinal safety control boundary of the vehicle obtained in step (4), a corresponding auxiliary decision-making strategy is formulated. The auxiliary decision-making strategy consists of three auxiliary decisions, which not only ensure the lateral and longitudinal movement safety of the vehicle, but also minimize the conflict between the driver and the auxiliary system, thereby giving the driver more driving freedom. The auxiliary system should only be activated when necessary. If the vehicle is to maintain a safe distance within the lane or longitudinally, the driver's intended steering angle is... and braking deceleration a c It should be confined within a safe boundary; therefore, the steering angle should be determined by judging the driver's intended direction. and braking deceleration a c Whether it is within its safety boundary, enabling the switching of control sovereignty between the driver and the assistance system; Therefore, three auxiliary decision-making output variables are designed as follows: the target value of the auxiliary motor rotation angle θ. md Target value of auxiliary braking deceleration a md And auxiliary weight ρ; where the target value of the auxiliary motor rotation angle θ md and the target value of auxiliary braking deceleration a md The reference value for the lower-level controller is used, and its decision-making process is as follows: The lateral decision-making strategy for autonomous vehicles is as follows: The vehicle's longitudinal decision-making strategy is as follows: The auxiliary weight ρ is used to switch the control sovereignty of the auxiliary system, and its determination process is as follows: In equation (30): ρ h These are auxiliary weights determined directly in the decision-making process. T max It is the maximum steering torque that a driver can apply when operating a car normally, a bmax This is the maximum braking deceleration that a driver can apply when operating the car normally; to ensure a smooth transition of vehicle control and increase driver comfort, the braking deceleration is adjusted after gaining control. h Then, transfer control permissions ρ h The input is fed into a first-order filter in the auxiliary control module to output a more flexible auxiliary weight ρ.

Images