Steering wheel non-follow-up control crawler unmanned vehicle man-machine co-driving control method

Abstract

The invention belongs to the technical field of man-machine co-driving, and particularly relates to a steering wheel non-follow-up control crawler unmanned vehicle man-machine co-driving control method, and a man-machine co-driving system relying on the method comprises a remote control end and an unmanned vehicle end; the remote control end identifies the intention of a driver, sends the intention to the crawler-type unmanned vehicle end, receives environment information and vehicle state information sent by the unmanned vehicle end, and performs visual prediction display through transmission delay resistant vehicle motion state prediction; the unmanned vehicle end calculates and obtains a final vehicle control target through the man-machine fusion controller; the method comprises the steps of driver intention recognition, man-machine fusion control and anti-transmission delay vehicle motion state prediction. A driver and an autonomous maneuvering system are allowed to participate in vehicle maneuvering control together, two-way information exchange and control are formed through enhancement and cooperation of man-machine mixing, and the effect of '1 + 1gt, 2' is generated. Safe and stable transition of driving authority is guaranteed, delay-resistant display of remote control pictures is achieved, and closed-loop stability when a driver remotely controls a vehicle is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of human-machine co-driving, and in particular relates to a human-machine co-driving control method for a tracked unmanned vehicle with non-follow-up steering wheel control. Background Art

[0002] In recent years, with the improvement of intelligent technology and planning and control technologies, unmanned equipment in various countries around the world is developing rapidly. As a member of the unmanned equipment system, unmanned vehicles play an important role on the battlefield. Unmanned vehicles integrate perception sensors, planning and control related software. According to the mission requirements, they perceive terrain, obstacles and other factors, plan the maneuvering path, complete tracking control, and finally reach the designated target to complete the autonomous maneuvering task. Although unmanned vehicles can realize the automation of cognition, judgment and operation required for vehicle driving, in a complex combat environment, it is still difficult for unmanned driving technology to make correct judgments on all complex working conditions at this stage, and the comprehensive handling capabilities are not perfect. The human-machine collaborative driving control technology allows the driver and the autonomous maneuvering system to jointly participate in the vehicle maneuvering control. It can form a two-way information exchange and control through the enhancement and collaboration of human-machine hybrid, and produce the effect of "1+1>2". This technology is an important technical path before realizing full autonomous driving. Summary of the invention

[0003] 1. Technical issues to be resolved

[0004] The technical problems to be solved by the present invention include:

[0005] Given that current autonomous unmanned vehicles have poor adaptability in complex environments, they require human intervention for remote control. Unmanned vehicles currently generally use either remote control or autonomous maneuvering modes, requiring the vehicle to stop and switch modes for use, making it impossible to achieve human-machine integrated control.

[0006] How to achieve dynamic fusion control of the driver and autonomous maneuvers to ensure a safe and smooth transition of driving authority; in order to solve the problem of anti-delay display of the remote control screen, how to improve the closed-loop stability of the driver during remote control.

[0007] (II) Technical solution

[0008] In order to solve the above technical problems, the present invention provides a human-machine co-driving control method for a tracked unmanned vehicle with non-follow-up steering wheel control, wherein the human-machine co-driving system relied on by the method includes a remote control terminal and an unmanned vehicle terminal;

[0009] The remote control terminal integrates the display, steering wheel, accelerator and brake pedal devices required for driving control, which are used to identify the driver's intention and send it to the crawler unmanned vehicle terminal through the inter-vehicle communication network;

[0010] At the same time, the remote control terminal is also used to receive environmental information and vehicle status information sent from the unmanned vehicle terminal, and to perform visual prediction and display on the display through the vehicle motion state prediction with anti-transmission delay;

[0011] The unmanned vehicle end is used to calculate and obtain the final vehicle control target through the human-machine fusion controller according to the received driver intention information and the autonomous maneuvering control target;

[0012] The method for controlling a human-machine co-driving tracked unmanned vehicle with a non-follow-up steering wheel control comprises the following steps:

[0013] Step 1: Driver intention recognition;

[0014] Step 2: Human-machine fusion control;

[0015] Step 3: Prediction of vehicle motion state with resistance to transmission delay.

[0016] Among them, in the step 1, a driver intention recognition algorithm is proposed; for the remote control end integrated display, steering wheel, accelerator and brake pedals, the driver controls the vehicle lateral and longitudinal remotely by operating the steering wheel, accelerator and brake pedals; in order to facilitate human-machine fusion control, the steering angle signal and pedal opening information of the remote control end are intention mapped and analyzed, including the driver's target vehicle speed and the driver's target steering curvature, so as to be unified with the autonomous maneuvering control target.

[0017] Wherein, in said step 1, the driver's intention is identified;

[0018] The specific input information of the driver intention recognition process is: accelerator pedal opening α, brake pedal opening β, steering wheel angle γ; the output information is: driver's target speed Driver Target Curvature

[0019] Directly mapping the pedal signal to the driver's target longitudinal acceleration and obtaining the driver's target vehicle speed through integral calculation helps to accurately reflect the driver's intention. Assuming α 0 is the free travel of the accelerator pedal, α max is the maximum travel of the accelerator pedal, is the maximum acceleration allowed, β 0 is the free travel of the brake pedal, β max is the maximum travel of the brake pedal, is the maximum allowable braking deceleration. The deceleration generated by combining the opening of the accelerator pedal and the brake pedal and the friction resistance and slope resistance under different road conditions is Define the driver's target longitudinal acceleration as follows:

[0020]

[0021] Let the current longitudinal speed of the vehicle be v t , the corresponding control period of the left and right driving wheels is △t, then the driver's target speed is obtained for:

[0022]

[0023] The driver's target speed is also affected by the maximum speed v lim The final target speed is expressed as:

[0024]

[0025] Similarly, assuming γ 0 is the free travel of the steering wheel angle, γ max is the maximum travel of the steering wheel angle, ρ max is the maximum steering curvature allowed at the current vehicle speed; the driver’s target steering curvature Parses as:

[0026]

[0027] Among them, the specific ρ max and v lim Set based on experience.

[0028] Wherein, in said step 2, human-machine fusion control is performed;

[0029] In human-machine fusion control, longitudinal and lateral control are decoupled; assuming that the reference vehicle speed issued by the autonomous maneuvering controller is The reference curvature is The current longitudinal speed of the vehicle is v t , the curvature is ρ t ; First, define the vertical human-machine fusion control target as:

[0030]

[0031] In the formula, μ v is the driver's control right in longitudinal human-machine co-driving, satisfying 0≤μ v ≤1, when μ v = 0, it means the vehicle is in a fully autonomous maneuvering state. v =1, it means that the vehicle is completely controlled by the driver; the core of the human-machine fusion control method is to perform μ v Solution of; define μ v The value of is related to the driver's target longitudinal acceleration as follows:

[0032]

[0033] In the formula, a c is the acceleration reference, which is a positive constant; is the driver’s target longitudinal acceleration;

[0034] At the same time, the horizontal human-machine fusion control goal is defined as:

[0035]

[0036] In the formula, μ ρ is the driver's control right in lateral human-machine co-driving, satisfying 0≤μ ρ ≤1, when μ ρ =1, it also means that the vehicle is completely controlled by the driver;

[0037] First, the definition of the human-vehicle consistency index τ is as follows:

[0038]

[0039] In the formula, ρ max is the maximum steering curvature allowed at the current vehicle speed, satisfying ρ max =-ρ min ; When the vehicle's current curvature ρ t and the driver's target steering curvature The closer they are, the closer τ is to 0, which also indicates that the status of the person and the car is highly consistent, otherwise it indicates that the person and the car are inconsistent;

[0040] At the same time, the driver's activity is related to the steering wheel angle, and the activity is defined as follows:

[0041]

[0042] In the formula, γ c is the steering angle reference value, which is a positive constant;

[0043] Finally, the driver's control right μ in lateral human-machine co-driving is defined by combining the human-vehicle consistency index and the driver's activity ρ ,as follows:

[0044]

[0045] Where b is a positive constant. The above method can ensure that when the status of the driver and the vehicle tends to be consistent and the driver's activity is high, the driver's corresponding lateral control authority increases accordingly. It avoids the control instability caused by immediately handing over the vehicle control to the driver when there is a large deviation between the driver's target curvature and the vehicle's current curvature.

[0046] Wherein, in said step 2, a vehicle motion state prediction is performed to resist transmission delay;

[0047] Assuming that the driver is looking at the display at time k+1, since there is a larger delay in transmitting the driving picture from the unmanned vehicle to the remote control end compared to simple data transmission, the position and posture of the vehicle relative to the environment displayed on the screen are both in the state at time k. The specific delay time is recorded as T, which is the time difference between time k+1 and time k. Therefore, it is necessary to adopt anti-transmission delay vehicle motion state prediction, combined with predictive image rendering related technologies, to achieve anti-delay display of the remote control image, so as to ensure the closed-loop stability of remote control driving.

[0048] The method for predicting the motion state of a vehicle with anti-transmission delay is as follows:

[0049] The vehicle kinematic model can be described by the following equation:

[0050]

[0051] Where x and y are the longitudinal and lateral positions of the unmanned vehicle in the global coordinate system. are the longitudinal and lateral velocities of the unmanned vehicle in the global coordinate system, v is the speed of the unmanned vehicle, is the heading angle of the vehicle, and ω are both vehicle yaw angular velocity, ρ is vehicle curvature;

[0052] Since the longitudinal and lateral positions x of the vehicle at time k k and k , heading angle Speed k and the curvature ρ k It is known that, assuming that the speed and steering curvature of the tracked vehicle are constant in the time window from k to k+1, by integrating the above state equation, the predicted results of the longitudinal and lateral positions and heading angles of the vehicle at time k+1 are obtained as follows:

[0053]

[0054] Among them, x k and k is the longitudinal and lateral position of the unmanned vehicle in the global coordinate system at time k, x k+1 and k+1 is the longitudinal and lateral position of the unmanned vehicle in the global coordinate system at time k+1, v k is the speed of the unmanned vehicle at time k, ρ k is the curvature of the unmanned vehicle at time k, is the heading angle of the unmanned vehicle at time k, is the heading angle of the unmanned vehicle at time k+1, T is the delay time, that is, the time difference between time k+1 and time k;

[0055] When predicting the vehicle motion state at the (k + 1)-th moment, based on the vehicle state prediction result and combined with relevant techniques of screen rendering, the relative position of the vehicle in the environment in the display screen of the remote control terminal is corrected in real time to ensure the dynamic consistency between the remote control driving screen and the actual state of the vehicle, which can help the driver significantly reduce the sense of lag during remote control, perceive the position of the vehicle in the environment in real time, and react to dangers in a timely manner.

[0056] Among them, the unmanned vehicle terminal is a tracked unmanned vehicle terminal.

[0057] Among them, the unmanned vehicle is a tracked autonomous mobile unmanned vehicle.

[0058] Among them, the method realizes the dynamic fusion control of the driver and the autonomous mobility, ensures the safe and stable transition of the driving authority, and improves the closed-loop stability of the driver during remote control.

[0059] (III) Beneficial effects

[0060] Compared with the prior art, the present invention proposes a human-machine co-driving control method for a tracked unmanned vehicle with non-follow-up steering wheel control, which allows the driver and the autonomous mobility system to jointly participate in the vehicle mobility control. Through the enhancement and cooperation of human-machine mixing, two-way information exchange and control are formed, resulting in an effect of "1 + 1 > 2". It ensures the safe and stable transition of the driving authority, solves the anti-time-delay display of the vehicle state, and improves the closed-loop stability of the driver during remote control. Description of the drawings

[0061] Figure 1 It is a schematic diagram of the framework of the human-machine co-driving control method of the present invention. Specific embodiments

[0062] To make the objectives, contents, and advantages of the present invention clearer, the following further describes the specific embodiments of the present invention in detail with reference to the drawings and embodiments.

[0063] To solve the above technical problems, the present invention provides a human-machine co-driving control method for a tracked unmanned vehicle with non-follow-up steering wheel control, as Figure 1 shown, the human-machine co-driving system on which the method relies includes a remote control terminal and an unmanned vehicle terminal;

[0064] Among them, the remote control terminal integrates a display, a steering wheel, an accelerator, and a brake pedal device required for driving control, is used for identifying the driver's intention, and sends it to the tracked unmanned vehicle terminal through the vehicle-to-vehicle communication network;

[0065] At the same time, the remote control terminal is also used to receive environmental information and vehicle status information sent from the unmanned vehicle terminal, and to perform visual prediction and display on the display through the vehicle motion state prediction with anti-transmission delay;

[0066] The unmanned vehicle end is used to calculate and obtain the final vehicle control target through the human-machine fusion controller according to the received driver intention information and the autonomous maneuvering control target;

[0067] The method for controlling a human-machine co-driving tracked unmanned vehicle with a non-follow-up steering wheel control comprises the following steps:

[0068] Step 1: Driver intention recognition;

[0069] Step 2: Human-machine fusion control;

[0070] Step 3: Prediction of vehicle motion state with resistance to transmission delay.

[0071] Among them, in the step 1, a driver intention recognition algorithm is proposed; for the remote control end integrated display, steering wheel, accelerator and brake pedals, the driver controls the vehicle lateral and longitudinal remotely by operating the steering wheel, accelerator and brake pedals; in order to facilitate human-machine fusion control, the steering angle signal and pedal opening information of the remote control end are intention mapped and analyzed, including the driver's target vehicle speed and the driver's target steering curvature, so as to be unified with the autonomous maneuvering control target.

[0072] Wherein, in said step 1, the driver's intention is identified;

[0073] The specific input information of the driver intention recognition process is: accelerator pedal opening α, brake pedal opening β, steering wheel angle γ; the output information is: driver's target speed Driver Target Curvature

[0074] Directly mapping the pedal signal to the driver's target longitudinal acceleration and obtaining the driver's target vehicle speed through integral calculation helps to accurately reflect the driver's intention. Assuming α 0 is the free travel of the accelerator pedal, α max is the maximum travel of the accelerator pedal, is the maximum acceleration allowed, β 0 is the free travel of the brake pedal, β max is the maximum travel of the brake pedal, is the maximum allowable braking deceleration. The deceleration generated by combining the opening of the accelerator pedal and the brake pedal and the friction resistance and slope resistance under different road conditions is Define the driver's target longitudinal acceleration as follows:

[0075]

[0076] Let the current longitudinal speed of the vehicle be v t , the corresponding control period of the left and right driving wheels is △t, then the driver's target speed is obtained for:

[0077]

[0078] The driver's target speed is also affected by the maximum speed v lim The final target speed is expressed as:

[0079]

[0080] Similarly, assuming γ 0 is the free travel of the steering wheel angle, γ max is the maximum travel of the steering wheel angle, ρ max is the maximum steering curvature allowed at the current vehicle speed; the driver’s target steering curvature Parses as:

[0081]

[0082] Among them, the specific ρ max and v lim Set based on experience.

[0083] Wherein, in said step 2, human-machine fusion control is performed;

[0084] For traditional tracked vehicles that only have autonomous maneuvering functions, their autonomous maneuvering controller receives and processes environmental information through perception sensors, makes decisions and plans, outputs reference curvature and reference vehicle speed information to the brake controller and drive controller, and completes lateral and longitudinal maneuvering control by adjusting the output torque of the left and right drive motors and the opening of the left and right brake valves;

[0085] For tracked unmanned vehicles equipped with a human-machine co-driving system, the brake controller and the drive controller receive control instructions from the human-machine fusion controller, and the human-machine fusion controller simultaneously receives control targets from the autonomous maneuvering controller and the remote control terminal. The human-machine fusion control algorithm is used to make a comprehensive determination of the chassis tracking target based on the two sets of control targets and combined with the vehicle status to ensure that when the driver intervenes or exits the remote control command, the driving authority can be transferred in a timely and smooth manner to ensure that the vehicle is stable and controlled;

[0086] To ensure that autonomous maneuvers can smoothly take over the transition when the driver exits, it is necessary to ensure that the autonomous maneuver control target and the current state of the chassis are smooth at any time without sudden changes;

[0087] In human-machine fusion control, longitudinal and lateral control are decoupled; assuming that the reference vehicle speed issued by the autonomous maneuvering controller is The reference curvature is The current longitudinal speed of the vehicle is v t , the curvature is ρ t ; First, define the vertical human-machine fusion control target as:

[0088]

[0089] In the formula, μ v is the driver's control right in longitudinal human-machine co-driving, satisfying 0≤μ v ≤1, when μ v = 0, it means the vehicle is in a fully autonomous maneuvering state. v =1, it means that the vehicle is completely controlled by the driver; the core of the human-machine fusion control method is to perform μ v Solution of; define μ v The value of is related to the driver's target longitudinal acceleration as follows:

[0090]

[0091] In the formula, a c is the acceleration reference, which is a positive constant; is the driver’s target longitudinal acceleration;

[0092] At the same time, the horizontal human-machine fusion control goal is defined as:

[0093]

[0094] In the formula, μ ρ is the driver's control right in lateral human-machine co-driving, satisfying 0≤μ ρ ≤1, when μ ρ =1, it also means that the vehicle is completely controlled by the driver;

[0095] First, the definition of the human-vehicle consistency index τ is as follows:

[0096]

[0097] In the formula, ρ max is the maximum steering curvature allowed at the current vehicle speed, satisfying ρ max =-ρ min ; When the vehicle's current curvature ρ t and the driver's target steering curvature The closer they are, the closer τ is to 0, which also indicates that the status of the person and the car is highly consistent, otherwise it indicates that the person and the car are inconsistent;

[0098] At the same time, the driver's activity is related to the steering wheel angle, and the activity is defined as follows:

[0099]

[0100] In the formula, γ c is the steering angle reference value, which is a positive constant;

[0101] Finally, the driver's control right μ in lateral human-machine co-driving is defined by combining the human-vehicle consistency index and the driver's activity ρ ,as follows:

[0102]

[0103] Where b is a positive constant. The above method can ensure that when the status of the driver and the vehicle tends to be consistent and the driver's activity is high, the driver's corresponding lateral control authority increases accordingly. It avoids the control instability caused by immediately handing over the vehicle control to the driver when there is a large deviation between the driver's target curvature and the vehicle's current curvature.

[0104] Wherein, in said step 2, a vehicle motion state prediction is performed to resist transmission delay;

[0105] Assuming that the driver is looking at the display at time k+1, since there is a larger delay in transmitting the driving picture from the unmanned vehicle to the remote control end compared to simple data transmission, the position and posture of the vehicle relative to the environment displayed on the screen are both in the state at time k. The specific delay time is recorded as T, which is the time difference between time k+1 and time k. Therefore, it is necessary to adopt anti-transmission delay vehicle motion state prediction, combined with predictive image rendering related technologies, to achieve anti-delay display of the remote control picture, so as to ensure the closed-loop stability of remote control driving.

[0106] The method for predicting the motion state of a vehicle with anti-transmission delay is as follows:

[0107] The vehicle kinematic model can be described by the following equation:

[0108]

[0109] Where x and y are the longitudinal and lateral positions of the unmanned vehicle in the global coordinate system. are the longitudinal and lateral velocities of the unmanned vehicle in the global coordinate system, v is the speed of the unmanned vehicle, is the heading angle of the vehicle, and ω are both vehicle yaw angular velocity, ρ is vehicle curvature;

[0110] Since the longitudinal and lateral positions x of the vehicle at time k k and k , heading angle Speed kand the curvature ρ k It is known that, assuming that the speed and turning curvature of the tracked vehicle are constant in the time window from k to k+1, by integrating the above state equation, the prediction results of the longitudinal and lateral positions and heading angles of the vehicle at time k+1 are obtained as follows:

[0111]

[0112] Among them, x k and k is the longitudinal and lateral position of the unmanned vehicle in the global coordinate system at time k, x k+1 and k+1 is the longitudinal and lateral position of the unmanned vehicle in the global coordinate system at time k+1, v k is the speed of the unmanned vehicle at time k, ρ k is the curvature of the unmanned vehicle at time k, is the heading angle of the unmanned vehicle at time k, is the heading angle of the unmanned vehicle at time k+1, T is the delay time, that is, the time difference between time k+1 and time k;

[0113] When the vehicle's motion state at time k+1 is predicted, the relative position of the vehicle in the remote control display screen is corrected in real time based on the vehicle state prediction results and combined with relevant image rendering technologies to ensure the dynamic consistency between the remote control driving screen and the actual state of the vehicle. This can help the driver significantly reduce the lag during remote control, perceive the vehicle's position in the environment in real time, and respond to danger in a timely manner.

[0114] Among them, the unmanned vehicle end is a tracked unmanned vehicle end.

[0115] Wherein, the unmanned vehicle is a tracked autonomous mobile unmanned vehicle.

[0116] Among them, the method achieves dynamic fusion control of the driver and autonomous maneuvers, ensures a safe and smooth transition of driving authority, and improves the closed-loop stability of the driver during remote control.

[0117] Example 1

[0118] The principle of the solution of this embodiment is shown as follows Figure 1 shown.

[0119] 1. Driver intention recognition. The remote control terminal integrates a display, steering wheel, accelerator and brake pedals. The driver controls the vehicle in the lateral and longitudinal directions by operating the steering wheel, accelerator and brake pedals. In order to facilitate human-machine fusion control, the steering angle signal and pedal opening information of the remote control terminal are intentionally mapped and analyzed, including the driver's target vehicle speed and the driver's target steering curvature, so as to be unified with the autonomous maneuvering control target.

[0120] 2. Human-machine fusion control. For tracked autonomous maneuvering unmanned vehicles equipped with a human-machine co-driving system, the brake controller and drive controller receive control instructions from the human-machine fusion controller, and the human-machine fusion controller simultaneously receives control targets from the autonomous maneuvering controller and the remote control terminal. The human-machine fusion control algorithm is used to make a comprehensive judgment on the chassis tracking target based on the two sets of control targets and combined with the vehicle status to ensure that when the driver intervenes or exits the remote control command, the driving authority can be transferred in a timely and smooth manner to ensure that the vehicle is stable and controlled.

[0121] 3. Anti-transmission delay vehicle motion state prediction. Assuming that the driver is looking at the display at time k+1, there will be a large delay in transmitting the driving picture from the tracked unmanned vehicle to the remote control end. Therefore, the position and posture of the vehicle relative to the environment displayed on the screen are all at time k. The specific delay time is recorded as T, which is the time difference between time k+1 and time k. By adopting the anti-transmission delay vehicle motion state prediction and combining it with the predictive picture rendering related technology, the remote control picture anti-delay display is realized to ensure the closed-loop stability of remote control driving.

[0122] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for controlling a human-machine co-driving tracked unmanned vehicle with non-follow-up steering wheel control, characterized in that: The human-machine co-driving system on which the method relies includes a remote control terminal and an unmanned vehicle terminal; The remote control terminal integrates the display, steering wheel, accelerator and brake pedal devices required for driving control, which are used to identify the driver's intention and send it to the crawler unmanned vehicle terminal through the inter-vehicle communication network; At the same time, the remote control terminal is also used to receive environmental information and vehicle status information sent from the unmanned vehicle terminal, and to perform visual prediction and display on the display through the vehicle motion state prediction with anti-transmission delay; The unmanned vehicle end is used to calculate and obtain the final vehicle control target through the human-machine fusion controller according to the received driver intention information and the autonomous maneuvering control target; The method for controlling a human-machine co-driving tracked unmanned vehicle with a non-follow-up steering wheel control comprises the following steps: Step 1: Driver intention recognition; Step 2: Human-machine fusion control; Step 3: Prediction of vehicle motion state with resistance to transmission delay.

2. The method for controlling a human-machine co-driving tracked unmanned vehicle with non-follow-up steering wheel control as claimed in claim 1, characterized in that: In the step 1, a driver intention recognition algorithm is proposed; for the remote control end integrated display, steering wheel, accelerator and brake pedals, the driver controls the vehicle lateral and longitudinally by operating the steering wheel, accelerator and brake pedals; in order to facilitate human-machine fusion control, the steering angle signal and pedal opening information of the remote control end are intentionally mapped and analyzed, including the driver's target vehicle speed and the driver's target steering curvature, so as to be unified with the autonomous maneuvering control target.

3. The method for controlling a human-machine co-driving tracked unmanned vehicle with non-follow-up steering wheel control as claimed in claim 1, characterized in that: In the step 1, the driver's intention is identified; The specific input information of the driver intention recognition process is: accelerator pedal opening α, brake pedal opening β, steering wheel angle γ; the output information is: driver's target speed Driver Target Curvature Directly mapping the pedal signal to the driver's target longitudinal acceleration and obtaining the driver's target vehicle speed through integral calculation helps to accurately reflect the driver's intention. Assuming α0 is the free travel of the accelerator pedal, α max is the maximum travel of the accelerator pedal, is the maximum acceleration allowed, β0 is the free travel of the brake pedal, β max is the maximum travel of the brake pedal, is the maximum allowable braking deceleration. The deceleration generated by combining the opening of the accelerator pedal and the brake pedal and the friction resistance and slope resistance under different road conditions is Define the driver's target longitudinal acceleration as follows: The current longitudinal speed of the vehicle is v t , the corresponding control period of the left and right driving wheels is △t, then the driver's target speed is obtained for: The driver's target speed is also affected by the maximum speed v lim The final target speed is expressed as: Similarly, assuming that γ0 is the free travel of the steering wheel angle, γ max is the maximum travel of the steering wheel angle, ρ max is the maximum steering curvature allowed at the current vehicle speed; the driver’s target steering curvature Parses as:

4. The method for controlling a human-machine co-driving tracked unmanned vehicle with non-follow-up steering wheel control as claimed in claim 3, characterized in that: Specific ρ max and v lim Set based on experience.

5. The method for controlling a human-machine co-driving tracked unmanned vehicle with non-follow-up steering wheel control as claimed in claim 3, characterized in that: In the step 2, human-machine fusion control is performed; In human-machine fusion control, longitudinal and lateral control are decoupled; assuming that the reference vehicle speed issued by the autonomous maneuvering controller is The reference curvature is The current longitudinal speed of the vehicle is v t , the curvature is ρ t ; First, define the vertical human-machine fusion control target as: In the formula, μ v is the driver's control right in longitudinal human-machine co-driving, satisfying 0≤μ v ≤1, when μ v = 0, it means the vehicle is in a fully autonomous maneuvering state. v =1, it means that the vehicle is completely controlled by the driver; the core of the human-machine fusion control method is to perform μ v Solution of; define μ v The value of is related to the driver's target longitudinal acceleration as follows: In the formula, a c is the acceleration reference, which is a positive constant; is the driver’s target longitudinal acceleration; At the same time, the horizontal human-machine fusion control goal is defined as: In the formula, μ ρ is the driver's control right in lateral human-machine co-driving, satisfying 0≤μ ρ ≤1, when μ ρ =1, it also means that the vehicle is completely controlled by the driver; First, the definition of the human-vehicle consistency index τ is as follows: In the formula, ρ max is the maximum steering curvature allowed at the current vehicle speed, satisfying ρ max =-ρ min ; When the vehicle's current curvature ρ t and the driver's target steering curvature The closer they are, the closer τ is to 0, which also indicates that the status of the person and the car is highly consistent, otherwise it indicates that the person and the car are inconsistent; At the same time, the driver's activity is related to the steering wheel angle, and the activity is defined as follows: In the formula, γ c is the steering angle reference value, which is a positive constant; Finally, the driver's control right μ in lateral human-machine co-driving is defined by combining the human-vehicle consistency index and the driver's activity ρ ,as follows: In the formula, b is a positive constant. The above method can ensure that when the status of the driver and the vehicle tends to be consistent and the driver's activity is high, the driver's corresponding lateral control authority increases accordingly. Avoid: Control instability caused by immediately handing over vehicle control to the driver when there is a large deviation between the driver's target curvature and the vehicle's current curvature.

6. The method for controlling a human-machine co-driving tracked unmanned vehicle with non-follow-up steering wheel control as claimed in claim 5, characterized in that: In the step 2, a vehicle motion state prediction is performed to resist transmission delay; Assuming that the driver is looking at the display at time k+1, since there is a larger delay in transmitting the driving picture from the unmanned vehicle to the remote control end compared to simple data transmission, the position and posture of the vehicle relative to the environment displayed on the screen are both in the state at time k. The specific delay time is recorded as T, which is the time difference between time k+1 and time k. Therefore, it is necessary to adopt anti-transmission delay vehicle motion state prediction, combined with predictive image rendering related technologies, to achieve anti-delay display of the remote control picture, so as to ensure the closed-loop stability of remote control driving.

7. The method for controlling a human-machine co-driving tracked unmanned vehicle with a non-follow-up steering wheel control as claimed in claim 6, characterized in that: The method for predicting the motion state of a vehicle with anti-transmission delay is as follows: The vehicle kinematic model can be described by the following equation: Where x and y are the longitudinal and lateral positions of the unmanned vehicle in the global coordinate system. are the longitudinal and lateral velocities of the unmanned vehicle in the global coordinate system, v is the speed of the unmanned vehicle, is the heading angle of the vehicle, and ω are both vehicle yaw angular velocity, ρ is vehicle curvature; Since the longitudinal and lateral positions x of the vehicle at time k k and k , heading angle Speed k and the curvature ρ k It is known that, assuming that the speed and turning curvature of the tracked vehicle are constant in the time window from k to k+1, by integrating the above state equation, the prediction results of the longitudinal and lateral positions and heading angles of the vehicle at time k+1 are obtained as follows: Among them, x k and k is the longitudinal and lateral position of the unmanned vehicle in the global coordinate system at time k, x k+1 and k+1 is the longitudinal and lateral position of the unmanned vehicle in the global coordinate system at time k+1, v k is the speed of the unmanned vehicle at time k, ρ k is the curvature of the unmanned vehicle at time k, is the heading angle of the unmanned vehicle at time k, is the heading angle of the unmanned vehicle at time k+1, T is the delay time, that is, the time difference between time k+1 and time k; When the vehicle's motion state at time k+1 is predicted, the relative position of the vehicle in the remote control display screen is corrected in real time based on the vehicle state prediction results and combined with relevant image rendering technologies to ensure the dynamic consistency between the remote control driving screen and the actual state of the vehicle. This can help the driver significantly reduce the lag during remote control, perceive the vehicle's position in the environment in real time, and respond to danger in a timely manner.

8. The method for controlling a human-machine co-driving tracked unmanned vehicle with a non-follow-up steering wheel control as claimed in claim 1, characterized in that: The unmanned vehicle end is a tracked unmanned vehicle end.

9. The method for controlling a human-machine co-driving tracked unmanned vehicle with non-follow-up steering wheel control as claimed in claim 1, characterized in that: The unmanned vehicle is a tracked autonomous mobile unmanned vehicle.

10. The method for controlling a human-machine co-driving tracked unmanned vehicle with non-follow-up steering wheel control as claimed in claim 1, characterized in that: The method achieves dynamic fusion control of the driver and autonomous maneuvers, ensures a safe and smooth transition of driving authority, and improves the closed-loop stability of the driver during remote control.

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