Automobile adaptive cruise system, control method, control device, medium and vehicle

Abstract

The application provides an automobile adaptive cruise system, a control method, a control device, a medium and a vehicle, and the automobile adaptive cruise system comprises a road surface preview system, a road surface parameter updating system, a defensive adaptive cruise controller, a driving actuator, a braking actuator and a sensor group, wherein the sensor group comprises a wheel speed sensor and a combined inertial navigation system, the wheel speed sensor acquires wheel speed, and the combined inertial navigation system acquires the longitudinal speed and acceleration of the vehicle. Since the proposed defensive adaptive cruise control strategy can consider the adverse road surface conditions in advance, no obvious speed or torque step change will be generated when driving in the transition area of the road surface with different adhesion conditions, and the control is relatively smooth.

Description

Technical Field

[0001] This invention relates to the field of control technology, specifically to an adaptive cruise control system for automobiles. Background Technology

[0002] Adaptive Cruise Control (ACC) is a driver assistance system that automatically adjusts the cruise speed and maintains a safe distance from the vehicle in front. When following closely, sudden changes in road conditions can cause the drive wheels to slip, leading to vehicle instability and a decrease in the performance of the adaptive cruise control system. This can result in reduced accuracy in maintaining distance and even rear-end collisions. Therefore, it is necessary to accurately identify road conditions and adjust the ACC strategy accordingly.

[0003] Current research has taken into account the impact of changes in road surface parameters on the longitudinal control system of ACC. Based on different identification methods, ACC that takes into account changes in road surface parameters can be classified into 1) ACC based on result identification method and 2) ACC based on cause identification method.

[0004] Results-based identification methods refer to evaluating road conditions by analyzing changes in vehicle dynamic response caused by variations in road conditions. This method primarily establishes the relationship between the road surface adhesion coefficient and the vehicle's dynamic response through a tire model. For example, Hu et al. (Hu J, Rakheja S, Zhang Y. Real-time estimation of tire-road friction coefficient based on lateral vehicle dynamics[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2020, 234(10-11): 2444-2457) established a cascaded estimator of extended Kalman filter and dual unscented Kalman filter based on a brush tire model. Yang Xinjing (Yang Xinjing. Research on Adaptive Cruise Control Strategy Considering Road Surface Adhesion Coefficient[D]. Chongqing Jiaotong University, 2023) used the Dugoff tire model to estimate the road surface adhesion coefficient and designed an ACC control system using a hierarchical architecture.

[0005] The above result-based identification method has the following limitations: 1) It requires continuous excitation conditions, meaning the vehicle must be running on the road surface to be identified and cause changes in the vehicle's dynamic response. Therefore, this method cannot predict road conditions in areas the vehicle has not entered, and lacks assessment of potential adverse road conditions. 2) When directly using the estimated values ​​for controller design, fluctuations or overshoot when the estimated values ​​do not converge can significantly affect longitudinal control performance; in some sudden adverse environments, it is difficult to quickly and accurately assess abrupt changes in road adhesion conditions, thus lacking adaptability to sudden scenarios. 3) The above method only considers changes in the road adhesion coefficient, while ignoring the impact of the rolling resistance coefficient on the vehicle's longitudinal dynamics.

[0006] Cause-based identification methods refer to methods that assess road conditions by identifying road surface physical features using sensors such as cameras and lidar. Classified by sensor type, these methods include single-sensor-based schemes and multi-sensor fusion-based schemes. The mainstream single-sensor-based scheme uses cameras to identify road surface features. For example, Liu Jiemei (Liu Jiemei. Research on Longitudinal Speed ​​Planning and Control Strategy of Intelligent Vehicles Based on Road Surface Adhesion Coefficient Prediction [D]. Jilin University, 2023) uses machine vision to predict the road surface adhesion coefficient, designs an adaptive safety distance model considering the adhesion coefficient, and uses a Model Predictive Control (MPC) to generate the desired control law. Multi-sensor fusion-based solutions often employ sensors such as cameras, lidar, and microphones to acquire multi-source information and fuse multi-source feature data to identify the road surface adhesion coefficient, fully leveraging the advantages of multiple sensors. For example, the research by Holzmann et al. (Holzmann F, Bellino M, Siegwart R, et al. Predictive estimation of the road-tire friction coefficient [C] / / 2006 IEEE conference on computer aided control system design, 2006 IEEE international conference on control applications, 2006 IEEE international symposium on intelligent control. IEEE, 2006: 885-890).

[0007] While causal identification methods can predict road surface conditions ahead, they cannot guarantee absolute accuracy in identifying the road adhesion coefficient or rolling resistance coefficient. On one hand, this approach can only identify the road type but lacks precise estimates of the adhesion coefficient and other parameters. On the other hand, while combining this approach with outcome-based identification methods can achieve accurate real-time estimation of the adhesion coefficient and other parameters to some extent, inaccurate estimations due to overshoot or fluctuations in extreme environments will significantly impact ACC control performance, thus still lacking countermeasures for emergency situations. Summary of the Invention

[0008] Current ACC systems struggle to adapt to sudden, severe environmental scenarios and lack effective preventative measures against inaccurate identification and control instability under potentially adverse road conditions. To address this, this invention proposes a defensive adaptive cruise control system, control method, control device, medium, and vehicle. The defensive adaptive cruise control system primarily comprises a road surface prediction system, a road surface parameter update system, and a defensive adaptive cruise controller. The defensive adaptive cruise control method proposed in this invention consistently considers the impact of latent adverse road surface factors and road surface parameter identification errors on longitudinal control performance, thereby improving the vehicle's control accuracy and stability in adverse environmental scenarios and progressively reducing the conservatism of defensive driving behavior.

[0009] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0010] This invention first provides a defensive adaptive cruise system, comprising:

[0011] Wheel speed sensors acquire wheel rotation speed;

[0012] Combined inertial navigation system to obtain vehicle longitudinal velocity and acceleration;

[0013] The road surface prediction system acquires real-time road surface image information and outputs a conservative estimate of the adhesion coefficient for the rough road section ahead. Conservative estimate of rolling resistance coefficient The road surface parameter update system consists of a road surface parameter estimator and a parameter update mechanism. The road surface parameter estimator obtains the vehicle's longitudinal speed v and the drive wheel speed ω, and outputs an estimated value of the road surface adhesion coefficient. and estimated rolling resistance coefficient and the residual boundary for road surface adhesion coefficient estimation And rolling resistance coefficient estimation residual boundary The parameter update mechanism is based on a conservative estimate of the adhesion coefficient output by the road surface prediction system. Conservative estimate of rolling resistance coefficient And the estimated value of the road adhesion coefficient obtained online by the road parameter estimator. Rolling resistance coefficient estimate Road surface adhesion coefficient estimation residual boundary And rolling resistance coefficient estimation residual boundary And determine the worst-case road adhesion coefficient to be considered based on the region where different road types are located. and rolling resistance coefficient The defensive adaptive cruise controller obtains an estimate of the road surface adhesion coefficient. Rolling resistance coefficient estimate Conservative estimate of worst road surface adhesion coefficient Conservative estimate of worst-case rolling resistance coefficient The desired torque T is obtained based on the defensive MPC control method. d ;

[0014] The drive actuator, by controlling the drive torque of the drive wheels in real time, achieves the desired torque T obtained by the defensive adaptive cruise controller. d Track;

[0015] The brake actuator, by controlling the magnitude of the wheel-end braking force in real time, controls the desired torque T obtained by the defensive adaptive cruise controller. d Track it.

[0016] This invention utilizes a road preview module to obtain conservative estimates of potential worst road conditions and employs an online identification strategy that can simultaneously estimate road parameters and residual boundaries. The parameters of the defensive adaptive cruise controller are updated in segments, enabling the ACC system to always have remedial measures in place for the worst road conditions and estimation errors.

[0017] This invention enables the joint estimation of road surface adhesion coefficient and rolling resistance coefficient, comprehensively considering the impact of potential parameter changes on the performance of the ACC control system.

[0018] The defensive adaptive cruise controller designed in this invention has a drive anti-slip function, which can balance longitudinal control accuracy and vehicle stability.

[0019] The present invention also provides a defensive adaptive cruise control method, comprising:

[0020] Acquire real-time road surface image information and output it as a conservative estimate of the adhesion coefficient for the adverse road conditions ahead. Conservative estimate of rolling resistance coefficient Obtain the vehicle's longitudinal speed v and drive wheel speed ω, and output an estimated value of the road surface adhesion coefficient. and estimated rolling resistance coefficient and the residual boundary for road surface adhesion coefficient estimation And rolling resistance coefficient estimation residual boundary Based on a conservative estimate of the output adhesion coefficient Conservative estimate of rolling resistance coefficient And the estimated value of the road surface adhesion coefficient obtained online. Rolling resistance coefficient estimate Road surface adhesion coefficient estimation residual boundary And rolling resistance coefficient estimation residual boundary And determine the worst-case road adhesion coefficient to be considered based on the region where different road types are located. and worst rolling resistance coefficient Obtain the estimated value of road surface adhesion coefficient Rolling resistance coefficient estimate Conservative estimate of the worst-case road adhesion coefficient Conservative estimate of the worst-case rolling resistance coefficient The desired torque T is obtained based on the defensive MPC control method. d ;

[0021] For the obtained desired torque T d Perform tracking and based on the desired torque T d To control the speed of the vehicle.

[0022] The present invention also provides a defensive adaptive cruise control device, including a processor and a memory; the memory stores a program or instructions, which are loaded and executed by the processor to implement the steps of the defensive adaptive cruise control method provided above.

[0023] The present invention also provides a computer-readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the defensive adaptive cruise control method described above.

[0024] The present invention also provides a vehicle having the defensive adaptive cruise system provided above or using a defensive adaptive cruise control method for cruise control.

[0025] Beneficial effects:

[0026] The adaptive cruise control system designed in this invention possesses the ability to proactively anticipate and identify road conditions online, always considering the potential impact of the worst road conditions or estimated residuals on the system's control performance. When driving on roads with good adhesion, the control system maximizes control accuracy and smoothness, and always reserves evasion strategies for emergency events. When adverse road conditions are detected, the system can promptly take evasive action, ensuring vehicle safety and stability. Moreover, because the proposed defensive adaptive cruise control strategy anticipates adverse road conditions, there are no significant speed or torque abrupt changes during transitions between different adhesion conditions, resulting in smoother control. Furthermore, the designed joint estimation strategy for rolling resistance coefficient and road adhesion coefficient possesses a decreasing residual boundary, thus enabling the constructed defensive controller to progressively reduce the conservatism of emergency branches. Attached Figure Description

[0027] Figure 1 Defensive adaptive cruise control system architecture;

[0028] Figure 2 Scenario 1 diagram;

[0029] Figure 3 Schematic diagram of scenario two;

[0030] Figure 4 Schematic diagram of scenario 3;

[0031] Figure 5 Schematic diagram for scenario four. Detailed Implementation

[0032] Example 1

[0033] This embodiment provides an adaptive cruise system, mainly composed of a road surface prediction system, a road surface parameter update system, a defensive adaptive cruise controller, a drive actuator, a braking actuator, and sensors, such as... Figure 1 As shown. The sensors include wheel speed sensors and a combined inertial navigation system. The wheel speed sensors acquire the wheel rotation speed; the combined inertial navigation system acquires the vehicle's longitudinal velocity and acceleration.

[0034] The road surface pre-aiming system acquires real-time road surface image information, which, after denoising and distortion correction, is transmitted to a trained road surface adhesion parameter recognition network. The network outputs the road surface condition region division and type. The expert knowledge base refers to a priori mapping table between road surface parameters and road surface types. Its output is a conservative estimate of the adhesion coefficient and rolling resistance coefficient for the upcoming challenging road section, denoted as... and

[0035] The road surface parameter update system consists of a road surface parameter estimator and a parameter update mechanism. The road surface parameter estimator acquires the vehicle's longitudinal speed v and the drive wheel speed ω, and outputs estimated values ​​for the road surface adhesion coefficient and rolling resistance coefficient. and and their estimated residual boundaries and The designed parameter update mechanism can update the parameters based on the output of the road surface prediction system. and and online estimation information of road surface parameters and And based on the region where different road surface types are located, determine the worst-case road surface parameters to be considered at this time. and

[0036] Defensive adaptive cruise control can obtain and Information is used to obtain the desired torque T based on a defensive MPC control method. d Finally, the drive and brake actuator is able to track the desired torque.

[0037] The road parameter estimator designed in the road parameter update system of this invention can achieve joint estimation of rolling resistance coefficient and adhesion coefficient, and at the same time obtain the estimated residual boundary that decreases with time.

[0038] Establish a longitudinal dynamics model for the vehicle:

[0039]

[0040] Where m is the total mass of the vehicle, v and F represents the vehicle's longitudinal velocity and its derivative with respect to time, respectively. x This represents the tangential reaction force exerted on the tire by the ground, where ρ is the air density and C is the air density. d Let θ be the air resistance coefficient, A be the frontal area, g be the acceleration due to gravity, and θ be the road slope.

[0041] Establish the rolling dynamics of the drive wheel:

[0042]

[0043] Where I is the moment of inertia of the wheel. T is the derivative of the angular velocity of the wheel. d R is the wheel torque, and R is the effective radius of the wheel.

[0044] The Dugoff tire model was used to calculate the tangential reaction force on the tire from the ground:

[0045]

[0046] Among them, C x For the longitudinal stiffness of the wheel, κ is the nonlinear characteristic boundary value of the tire longitudinal force, s is the speed influence factor, and s is the driving slip ratio or braking slip ratio. The calculation method is as follows:

[0047]

[0048] To estimate pavement parameters, the following observation equation is defined:

[0049] y k =h k ξ+w k (25)

[0050] Among them, y k with h k All data are system information observed at time k, where ξ is the parameter to be estimated, and w... k The uncertainty is for an unknown but bounded system. Assume w k boundary It is known that, satisfying Specifically, the observation equation for estimating the rolling resistance coefficient is as follows:

[0051]

[0052] The observation equation for estimating the road surface adhesion coefficient is as follows:

[0053]

[0054] Define the set of road surface parameter estimation residuals as follows: in Refers to the estimated residual boundary at time k and The least squares-based set membership estimation strategy is adopted as follows:

[0055]

[0056] Among them, P k r represents the covariance of the parameter estimate at time k. k The adaptive coefficients at time k are... Let k be the estimated values ​​of the road surface parameters at time k. This reflects the residual boundary information related to the covariance at time k. This represents the residual boundary for estimating pavement parameters. This estimation strategy possesses the following two characteristics: 1) It is bounded and non-increasing.

[0057] This invention updates parameters by region based on the vehicle's spatial location and the sensor's sensing range. Specifically:

[0058] 1) When the vehicle is traveling on a road with good traction, the road surface prediction system fails to detect potentially adverse road conditions, such as... Figure 2 As shown. This is a conservative estimate of the worst-case road surface parameters. and The update is as follows:

[0059]

[0060] Among them, the estimated residual boundaries of the road adhesion coefficient and the rolling resistance coefficient are respectively used as and To represent this. At this point, the conservative estimate... and It is obtained through a joint road surface parameter estimator.

[0061] 2) When the vehicle is traveling on a road with good traction, the road surface warning system has detected potentially adverse road conditions, such as... Figure 3 As shown. This is a conservative estimate of the worst-case road surface parameters. and The update is as follows:

[0062]

[0063] At this point, the conservative estimate and It is obtained by identifying low-adhesion road sections through a road surface pre-aiming system.

[0064] 3) When the vehicle is traveling on a low-traction road surface, the road surface prediction system fails to detect a good-traction road surface, such as... Figure 4 As shown. This is a conservative estimate of the worst-case road surface parameters. and The update is as follows:

[0065]

[0066] At this point, the conservative estimate and It is obtained through online identification of low-adhesion pavements using a joint pavement parameter estimator.

[0067] 4) When the vehicle is traveling on a low-traction road surface, the road surface prediction system has detected a good-traction road surface, such as... Figure 5 As shown. This is a conservative estimate of the worst-case road surface parameters. and The update is as follows:

[0068]

[0069] At this point, the conservative estimate and It is obtained by identifying good road sections through a road surface pre-aiming system.

[0070] Defensive adaptive cruise control

[0071] Establish a longitudinal kinematic model for the vehicle:

[0072]

[0073] By combining the vehicle's longitudinal dynamics (21), the driving wheel's rolling dynamics (22), the tire model (23), and the kinematic equations (33), the state variable is chosen as X=[xv ω]. T The control quantity is u = T d Construct a unified discrete state-space equation:

[0074] χ k+1 =g(χ) k u k )| μ，f (34)

[0075] The model is affected by the road surface adhesion coefficient μ and the rolling resistance coefficient f.

[0076] This invention employs a defensive MPC controller to achieve adaptive cruise control, ensuring that contingency plans are always available for potentially adverse road conditions. Unlike standard MPC, this strategy includes two open-loop trajectory branches: a state sequence branch under the nominal condition and a branch for the nominal condition. State sequence branches in emergency situations Correspondingly, there are also action sequence branches under the nominal condition. Action sequence branches in emergency situations The nominal branch represents the pavement parameter estimate obtained at time k using the pavement parameter estimator. and The vehicle model (34) is updated, and the nominal trajectory is obtained by recursively advancing N steps based on this model; the emergency branch indicates that the worst-case estimate of the road surface is used at time k. and Open-loop prediction is performed to obtain the emergency trajectory within the predicted time domain N. These two trajectories are connected together by the state and action at time k, i.e.

[0077]

[0078] Where, χ kLet k be the measured state at time k. Equation (35) means that the trajectories of the nominal branch and the emergency branch intersect at time k, and the action decisions at this time are the same. Therefore, the action decision at time k can take into account both nominal operation and emergency avoidance, while the action decisions in the time domain from k+1 to k+N-1 cause the nominal trajectory and the emergency trajectory to differ. MPC is a rolling time-domain optimization strategy that only applies the control action at time k to the controlled object, so the result of this scheme is... or It also exhibits a tendency to evolve towards both standard operation and emergency avoidance.

[0079] Design a cost function of the following form:

[0080]

[0081] Where Q1, Q2, Q3, and R1 are all weight matrices. n This represents the cost function under the nominal branch, where L is the vehicle length and d is the cost function. safe For an ideal and safe following distance, x ref This represents the longitudinal displacement of the vehicle ahead, and the first term on the right side of the equation represents the speed tracking accuracy cost (v). ref The first term represents the reference longitudinal speed output by the planning layer; the second term represents the cost of maintaining vehicle spacing accuracy; and the third term represents the cost of controlling ride comfort. e This represents the cost function under the emergency branch, where the first term on the right-hand side of the equation is the slip ratio (or glide ratio) and the reference value s. ref The weighted L2 norm of the bias, with the second term being the cost of the slack variable ε.

[0082] The following are the design constraints for driving safety in emergency situations:

[0083]

[0084] Where, d min The minimum safe distance to prevent rear-end collisions.

[0085] The following are the design constraints for vehicle stability under emergency conditions:

[0086]

[0087] Where, Δs max For the slip ratio (or rotation ratio) deviating from the reference value s ref The maximum allowed value, ε k+i ≥0 represents the slack variable of this stability constraint.

[0088] Design speed limit and torque limit constraints of the following form:

[0089]

[0090] Among them, v min and v max These are the minimum and maximum speeds allowed on the current road segment, T. min and T max These represent the minimum and maximum torques at the current speed.

[0091] In summary, the designed defensive adaptive cruise controller is as follows:

[0092]

[0093] In this constraint equation, the first two terms are the trajectory evolution dynamics constraints under different branches; the third to sixth terms are the physical constraints that the state and actions must satisfy; the seventh and eighth terms are the driving safety and stability constraints that must be satisfied in emergency situations; and the last two terms are the association constraints between the nominal branch and the emergency branch. Note that the nominal branch cost in the cost function only focuses on the accuracy and smoothness of longitudinal control, while the emergency branch cost focuses on driving stability. In addition to the necessary physical constraints, the emergency branch must also follow hard constraints on driving safety and soft constraints on stability. The two relatively independent branches are linked together through association constraints, ensuring that the control quantity at time k balances the characteristics of precise and efficient control and emergency avoidance. The vehicle does not directly choose between the nominal trajectory and the emergency trajectory, but makes a balanced decision. When no emergency occurs, the vehicle maximizes speed tracking and distance following accuracy while always reserving avoidance strategies for emergency events. Furthermore, since the designed least-squares-based set membership estimation strategy has a residual decreasing property, updating the vehicle dynamics model according to the designed parameter update mechanism can progressively reduce the conservatism inherent in the emergency branch itself.

[0094] Example 2

[0095] This embodiment provides a defensive adaptive cruise control method, which is based on the defensive adaptive cruise control system provided in Embodiment 1, and includes:

[0096] Acquire real-time road surface image information and output it as a conservative estimate of the adhesion coefficient for the adverse road conditions ahead. Conservative estimate of rolling resistance coefficient Obtain the vehicle's longitudinal speed v and drive wheel speed ω, and output an estimated value of the road surface adhesion coefficient. and estimated rolling resistance coefficient and the residual boundary for road surface adhesion coefficient estimation And rolling resistance coefficient estimation residual boundary Based on a conservative estimate of the output adhesion coefficient Conservative estimate of rolling resistance coefficient And the estimated value of the road surface adhesion coefficient obtained online. Rolling resistance coefficient estimate Road surface adhesion coefficient estimation residual boundary And rolling resistance coefficient estimation residual boundary And determine the worst-case road adhesion coefficient to be considered based on the region where different road types are located. and worst rolling resistance coefficient Obtain the estimated value of road surface adhesion coefficient Rolling resistance coefficient estimate Conservative estimate of the worst-case road adhesion coefficient Conservative estimate of the worst-case rolling resistance coefficient The desired torque T is obtained based on the defensive MPC control method. d ;

[0097] For the obtained desired torque T d Perform tracking and based on the desired torque T d To control the speed of the vehicle.

[0098] Example 3

[0099] This embodiment provides a defensive adaptive cruise control device, including a processor and a memory; the memory stores a program or instructions, which are loaded and executed by the processor to implement the steps of the defensive adaptive cruise control method provided in Embodiment 2.

[0100] Example 4

[0101] This embodiment provides a computer-readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the defensive adaptive cruise control method described above.

[0102] Example 5

[0103] This embodiment provides a vehicle that has the defensive adaptive cruise control system provided in Embodiment 1 or uses the defensive adaptive cruise control method of Embodiment 2 for cruise control. The vehicle includes cars, commercial vehicles, etc.

Claims

1. A defensive adaptive cruise system, characterized in that, include: Wheel speed sensors acquire wheel rotation speed; Combined inertial navigation system to obtain vehicle longitudinal velocity and acceleration; The road surface prediction system acquires real-time road surface image information and outputs a conservative estimate of the adhesion coefficient for the rough road section ahead. Conservative estimate of rolling resistance coefficient The road surface parameter update system consists of a road surface parameter estimator and a parameter update mechanism. The road surface parameter estimator obtains the vehicle's longitudinal speed v and the drive wheel speed ω, and outputs an estimated value of the road surface adhesion coefficient. and estimated rolling resistance coefficient and the residual boundary for road surface adhesion coefficient estimation And rolling resistance coefficient estimation residual boundary The parameter update mechanism is based on a conservative estimate of the adhesion coefficient output by the road surface prediction system. Conservative estimate of rolling resistance coefficient And the estimated value of the road adhesion coefficient obtained online by the road parameter estimator. Rolling resistance coefficient estimate Road surface adhesion coefficient estimation residual boundary And rolling resistance coefficient estimation residual boundary And determine the worst-case road adhesion coefficient to be considered based on the region where different road types are located. and rolling resistance coefficient The defensive adaptive cruise controller obtains an estimate of the road surface adhesion coefficient. Rolling resistance coefficient estimate Conservative estimate of worst road surface adhesion coefficient Conservative estimate of worst-case rolling resistance coefficient The desired torque T is obtained based on the defensive MPC control method. d ; The drive actuator, by controlling the drive torque of the drive wheels in real time, achieves the desired torque T obtained by the defensive adaptive cruise controller. d Track; The brake actuator, by controlling the magnitude of the wheel-end braking force in real time, controls the desired torque T obtained by the defensive adaptive cruise controller. d Track it.

2. The defensive adaptive cruise system according to claim 1, characterized in that, The road surface prediction system includes a road surface parameter recognition network and an expert knowledge base; The road surface parameter recognition network outputs the road surface condition area division and type based on real-time acquired road surface image information; the expert knowledge base outputs a conservative estimate of the adhesion coefficient for the upcoming severe road section based on a priori mapping table between road surface parameters and road surface types. Conservative estimate of rolling resistance coefficient 3. The defensive adaptive cruise system according to claim 1, characterized in that, The road surface parameter estimator achieves joint estimation of rolling resistance coefficient and adhesion coefficient, while obtaining the estimation residual boundary that decreases over time; Establish a longitudinal dynamics model for the vehicle: Where m is the total mass of the vehicle, v and F represents the vehicle's longitudinal velocity and its derivative with respect to time, respectively. x This represents the tangential reaction force exerted on the tire by the ground, where ρ is the air density and C is the air density. d Where A is the air resistance coefficient, g is the frontal area, θ is the acceleration due to gravity, and θ is the road slope. Establish the rolling dynamics of the drive wheel: Where I is the moment of inertia of the wheel. T is the derivative of the angular velocity of the wheel. d R is the wheel torque, and R is the effective radius of the wheel. The Dugoff tire model was used to calculate the tangential reaction force on the tire from the ground: Among them, C x For the longitudinal stiffness of the wheel, κ represents the nonlinear characteristic boundary value of the tire longitudinal force, s represents the speed influence factor, and s represents the driving slip ratio or braking slip ratio. The calculation method for s is as follows: To estimate pavement parameters, the following observation equation is defined: y k =h k ξ+w k (5) Among them, y k with h k All data are system information observed at time k, where ξ is the parameter to be estimated, and w... k For an unknown but bounded system of uncertainty; assume w k boundary It is known that, satisfying The observation equation for estimating the rolling resistance coefficient is as follows: The observation equation for estimating the road surface adhesion coefficient is as follows: Define the set of road surface parameter estimation residuals as follows: in Refers to the estimated residual boundary at time k and Refers to the estimated value of the road surface adhesion coefficient at time k. and estimated rolling resistance coefficient The least squares-based set membership estimation strategy is adopted as follows: Among them, P k r represents the covariance of the parameter estimate at time k. k The adaptive coefficients at time k are... It reflects the residual boundary information related to the covariance at time k.

4. The defensive adaptive cruise system according to claim 3, characterized in that, The parameters are updated in different regions based on the vehicle's spatial location and the sensor's sensing range, as follows: 1) When the vehicle is traveling on a road with good traction, and the road surface prediction system does not detect potentially adverse road conditions, a conservative estimate of the worst-case road surface parameters is given. and The update is as follows: Among them, the estimated residual boundaries of the road adhesion coefficient and the rolling resistance coefficient are respectively used as and To indicate; 2) When the vehicle is traveling on a road with good traction, and the road surface prediction system has detected potentially adverse road conditions, a conservative estimate of the worst-case road surface parameters is given. and The update is as follows: 3) When the vehicle is traveling on a low-traction road surface and the road surface prediction system does not detect a good-traction road surface, a conservative estimate of the worst-case road surface parameters is given. and The update is as follows: 4) When the vehicle is traveling on a low-traction road, and the road surface prediction system has detected a good-traction road surface, a conservative estimate of the worst-case road surface parameters is given. and The update is as follows: At this point, the conservative estimate and It is obtained by identifying good road sections through a road surface pre-aiming system.

5. The defensive adaptive cruise system according to claim 1, characterized in that, A defensive adaptive cruise controller obtains the desired torque T based on a defensive MPC control method. d ,include: Establish a longitudinal kinematic model for the vehicle: By combining the vehicle longitudinal dynamics model (1), the driving wheel rolling dynamics (2), the tire model (3), and the kinematic equations (13), the state variable is selected as χ=[xv ω]. T The control quantity is u = T d Construct a unified discrete state-space equation: Adaptive cruise control is implemented using a defensive MPC controller, which includes two open-loop trajectory branches: a state sequence branch under nominal conditions and a defensive MPC controller. State sequence branches in emergency situations Action sequence branching under nominal conditions Action sequence branches in emergency situations The nominal branch represents the pavement parameter estimate obtained at time k using the pavement parameter estimator. and Update the vehicle model (14), and based on this model, recursively advance N steps to obtain the nominal trajectory; the emergency branch indicates that the worst-case estimate of the road surface is used at time k. and Open-loop prediction is performed to obtain the emergency trajectory within the predicted time domain N. These two trajectories are connected together by the state and action at time k, i.e.: Where, χ k The measured state at time k; Design a cost function of the following form: Where Q1, Q2, Q3, and R1 are all weight matrices, J n This represents the cost function under the nominal branch, where L is the vehicle length and d is the cost function. safe For an ideal and safe following distance, x ref This represents the longitudinal displacement of the vehicle ahead, and the first term on the right side of the equation represents the speed tracking accuracy cost (v). ref The first term represents the reference longitudinal speed output by the planning layer; the second term represents the cost of maintaining vehicle spacing accuracy; and the third term represents the cost of controlling ride comfort. e This represents the cost function under the emergency branch, where the first term on the right-hand side is the slip ratio or glide ratio and the reference value s. ref The weighted L2 norm of the bias, with the second term being the cost of the slack variable ε; The following are the design constraints for driving safety in emergency situations: Where, d min The minimum safe distance to prevent rear-end collisions; The following are the design constraints for vehicle stability under emergency conditions: Where, Δs max For slip ratio or slew rate deviating from the reference value s ref The maximum allowed value, ε k+i ≥0 represents a slack variable for this stability constraint; Design speed limit and torque limit constraints of the following form: Among them, v min and v max These are the minimum and maximum speeds allowed on the current road segment, T. min and T max These represent the minimum and maximum torques at the current speed.

6. The defensive adaptive cruise system according to claim 5, characterized in that, The designed defensive adaptive cruise controller is shown below: Among them, the first two terms in the constraint equation are the trajectory evolution dynamic constraints under different branches, the third to sixth terms are the physical constraints that the state and action must meet, the seventh and eighth terms are the driving safety and stability constraints that must be met in emergency situations, and the last two terms are the association constraints between the nominal branch and the emergency branch.

7. A defensive adaptive cruise control method, characterized in that, include: Acquire real-time road surface image information and output it as a conservative estimate of the adhesion coefficient for the adverse road conditions ahead. Conservative estimate of rolling resistance coefficient Obtain the vehicle's longitudinal speed v and drive wheel speed ω, and output an estimated value of the road surface adhesion coefficient. and estimated rolling resistance coefficient and the residual boundary for road surface adhesion coefficient estimation And rolling resistance coefficient estimation residual boundary Based on a conservative estimate of the output adhesion coefficient Conservative estimate of rolling resistance coefficient And the estimated value of the road surface adhesion coefficient obtained online. Rolling resistance coefficient estimate Road surface adhesion coefficient estimation residual boundary And rolling resistance coefficient estimation residual boundary And determine the worst-case road adhesion coefficient to be considered based on the region where different road types are located. and worst rolling resistance coefficient Obtain the estimated value of road surface adhesion coefficient Rolling resistance coefficient estimate Conservative estimate of the worst-case road adhesion coefficient Conservative estimate of the worst-case rolling resistance coefficient The desired torque T is obtained based on the defensive MPC control method. d ; For the obtained desired torque T d Perform tracking and based on the desired torque T d To control the speed of the vehicle.

8. A defensive adaptive cruise control device, characterized in that, It includes a processor and a memory; the memory stores a program or instructions which are loaded and executed by the processor to implement the steps of the defensive adaptive cruise control method as described in claim 7.

9. A computer-readable storage medium, characterized in that, The readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the defensive adaptive cruise control method as described in claim 7.

10. A vehicle, characterized in that, Cruise control is performed using the defensive adaptive cruise system according to any one of claims 1-6 or the defensive adaptive cruise control method according to claim 7.

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