Method and system for controlling unmanned vehicle based on high-precision map

Abstract

The present application discloses a method and system for controlling an unmanned vehicle based on a high-precision map. The method comprises: first obtaining obstacle information around the unmanned vehicle, secondly determining the local driving path of the unmanned vehicle according to the obstacle information, and then obtaining the road curvature and road slope on the local driving path of the unmanned vehicle according to the high-precision map, and correcting the local driving path; then calculating the target speed and target steering wheel angle of the unmanned vehicle using the MPC control algorithm according to the obstacle information, road curvature, road slope and vehicle status information; finally controlling the unmanned vehicle longitudinally using the target speed, and controlling the unmanned vehicle laterally using the target steering wheel angle. The system comprises: a perception module, a local driving path determination module, a road information acquisition module, an inertial navigation device and a control module. Through the present application, the stability of vehicle driving and the comfort of users can be effectively improved.

Description

Technical Field

[0001] The present application relates to the field of unmanned driving control technology, and in particular to a method and system for controlling an unmanned vehicle based on a high-precision map. Background Art

[0002] Unmanned vehicles have been a popular technology in recent years. To ensure the safe operation of unmanned vehicles, how to control them is an important technical issue.

[0003] At present, the control method of unmanned vehicles is mainly based on the vehicle's speed, yaw rate, heading angle and other information, and the vehicle is controlled in the lateral and longitudinal directions through model prediction methods. Specifically, the vehicle's acceleration and steering wheel angle increment are constrained, so as to dynamically solve the current target speed and target steering wheel angle, and use the current target speed and target steering wheel angle to control the unmanned vehicle.

[0004] However, in the current control method of unmanned vehicles, since the vehicle is mainly adjusted based on the current target vehicle speed and target steering wheel angle, the control command can only be issued after obtaining the current information and performing relevant calculations, which will cause a slow control process and affect the stability of the vehicle. Especially when the vehicle is driving on continuous slopes and curves, it is more likely to have problems such as sudden acceleration, sudden deceleration and sudden turn signals, making the driving stability of the unmanned vehicle worse. Summary of the invention

[0005] The present application provides a method and system for controlling an unmanned vehicle based on a high-precision map, so as to solve the problem that the vehicle control method in the prior art causes poor driving stability of the vehicle.

[0006] In order to solve the above technical problems, the embodiments of the present application disclose the following technical solutions:

[0007] A method for controlling an unmanned vehicle based on a high-precision map, the method comprising:

[0008] Obtaining obstacle information around the unmanned vehicle, the obstacle information including: obstacle size, obstacle lateral and longitudinal speeds, and lateral and longitudinal distances of the obstacle from the vehicle;

[0009] Determining a local driving path of the unmanned vehicle according to the obstacle information;

[0010] Obtaining the road curvature and road slope on a local driving path of the unmanned vehicle according to the high-precision map, and correcting the local driving path according to the road curvature and slope information;

[0011] Calculating the target speed and target steering wheel angle of the unmanned vehicle using the MPC control algorithm according to the obstacle information, road curvature, road slope and vehicle state information, wherein the vehicle state information includes: the current speed and current steering wheel angle of the unmanned vehicle;

[0012] Using the target speed to longitudinally control the unmanned vehicle;

[0013] The target steering wheel angle is used to perform lateral control on the unmanned vehicle.

[0014] Optionally, the method for determining a local driving path of an unmanned vehicle according to the obstacle information includes:

[0015] According to the obstacle information, target tracking and prediction are performed on the obstacle to obtain a drivable area of ​​the unmanned vehicle;

[0016] A local driving path of the unmanned vehicle is planned according to the global path of the unmanned vehicle and the drivable area.

[0017] Optionally, the method for calculating the target speed and target steering wheel angle of the unmanned vehicle using an MPC control algorithm according to the obstacle information, road curvature, road slope and vehicle state information includes:

[0018] Define the state of the unmanned vehicle as The control variable is {v r δ f}, the vehicle state space equation is Among them, x is the longitudinal position of the unmanned vehicle at the current moment, and y is the lateral position of the unmanned vehicle at the current moment. is the yaw angle of the unmanned vehicle at the current moment, v r is the target speed of the unmanned vehicle, δ f is the target front wheel turning angle of the unmanned vehicle, l is the wheelbase of the front and rear wheels of the unmanned vehicle;

[0019] According to the unmanned vehicle state, control variables and vehicle state space equations, the kinematic model of the four-wheel vehicle is determined: Among them, k is the current moment, k+1 is the next moment, is the state of the vehicle at the kth moment, is the control amount of the vehicle at the kth moment, is the state of the vehicle at the k+1th moment,

[0020] According to the kinematic model, the MPC control algorithm is adopted, and the deviation between the predicted posture of the unmanned vehicle and the local driving path is calculated using the formula Y=Ψζ(k)+ΘU(k), wherein Y is the deviation between the trajectory of the unmanned vehicle predicted by the vehicle kinematic model and the local driving path, ψ is the coefficient of the state quantity deviation, Θ is the control quantity coefficient, and U(k) is the control quantity;

[0021] According to the deviation between the predicted position and the local driving path of the unmanned vehicle, the quadratic optimal solution function J=Y T QY+URU T , the target speed and target steering wheel angle of the unmanned vehicle are calculated, where J is the target evaluation function, Q is the vehicle state deviation evaluation coefficient, and R is the control state evaluation coefficient.

[0022] Optionally, a kinematic model of a four-wheel vehicle is determined according to the unmanned vehicle state, control variables and vehicle state space equations: include:

[0023] The unmanned vehicle state and control variables are converted into matrix format to obtain Among them, ζ is the state of the unmanned vehicle, and u is the control quantity;

[0024] According to the unmanned vehicle state and control variables in matrix format, the vehicle space state is linearized in first order to calculate Among them, r is the state of the vehicle at the rth moment, u r is the control amount of the vehicle at the rth moment;

[0025] definition The current time is k, and the formula Discretization can obtain the kinematic model of the four-wheel vehicle

[0026] Optionally, the method for longitudinally controlling the unmanned vehicle using the target speed includes:

[0027] The traction of the unmanned vehicle is calculated using the formula Pt=Pf+Pw+Pi+Pj, where Pt is the traction, Pf is the rolling resistance, Pw is the air resistance, Pi is the slope resistance, and Pj is the inertial resistance.

[0028] According to the actual speed of the unmanned vehicle and the target speed, the formula P1=a1*(v r -v act ), the traction force of the vehicle corrected by speed is calculated, where P1 is the traction force of the vehicle corrected by speed, a1 is the correction coefficient, and v r is the target speed of the driverless vehicle, vact is the actual speed of the vehicle;

[0029] The traction force of the slope-corrected engine is calculated using the formula P2=a2*θ, where a2 is the slope correction parameter, θ is the road slope, and P2 is the traction force of the slope-corrected engine;

[0030] The total traction required by the unmanned vehicle is calculated by summing the traction of the unmanned vehicle, the traction of the speed-corrected vehicle, and the traction of the slope-corrected engine;

[0031] An engine or a braking system of the unmanned vehicle is controlled according to the total traction force.

[0032] Optionally, the method for performing lateral control on an unmanned vehicle using the target steering wheel angle comprises:

[0033] According to the transverse slope of the current local driving path, the formula δ2=a3*φ is used to calculate the steering wheel angle for transverse slope correction, where δ2 is the steering wheel angle for transverse slope correction, a3 is the correction coefficient, and φ is the transverse slope of the road;

[0034] Summing the target steering wheel angle and the lateral slope corrected steering wheel angle to calculate a steering wheel angle to be requested by the unmanned vehicle;

[0035] The requested steering wheel angle is sent to the steering machine of the unmanned vehicle through CAN communication.

[0036] A system for controlling an unmanned vehicle based on a high-precision map, the system comprising:

[0037] A perception module is used to obtain obstacle information around the unmanned vehicle, wherein the obstacle information includes: obstacle size, obstacle lateral and longitudinal speed, and lateral and longitudinal distance of the obstacle from the vehicle;

[0038] A local driving path determination module, used to determine the local driving path of the unmanned vehicle according to the obstacle information;

[0039] A road information acquisition module, used to acquire the road curvature and road slope on the local driving path of the unmanned vehicle according to the high-precision map, and to correct the local driving path according to the road curvature and slope information;

[0040] Inertial navigation equipment, used to obtain vehicle position information and vehicle attitude information of the unmanned vehicle;

[0041] A control module, for calculating a target speed and a target steering wheel angle of the unmanned vehicle using an MPC control algorithm according to the obstacle information, the road curvature, the road slope and the vehicle state information, wherein the vehicle state information includes: a current speed and a current steering wheel angle of the unmanned vehicle;

[0042] The control module is further used to use the target speed to perform longitudinal control on the unmanned vehicle, and to use the target steering wheel angle to perform lateral control on the unmanned vehicle.

[0043] Optionally, the perception module includes: a main laser radar, two blind-point laser radars and two millimeter-wave radars, the main laser radar is arranged above the roof of the unmanned vehicle, the two blind-point laser radars are respectively arranged on the left front side and the right front side of the unmanned vehicle, and the two millimeter-wave radars are respectively arranged on the front and rear sides of the unmanned vehicle.

[0044] Optionally, the inertial navigation device includes a GNSS (Global Navigation Satellite System) and an IMU (Inertial measurement unit), the GNSS is used to obtain vehicle position information of the unmanned vehicle, and the IMU is used to obtain vehicle posture information of the unmanned vehicle.

[0045] Optionally, the unmanned vehicle is a controlled-by-wire vehicle.

[0046] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0047] The present application provides a method for controlling an unmanned vehicle based on a high-precision map. The method first obtains obstacle information around the unmanned vehicle, then determines the local driving path of the unmanned vehicle according to the obstacle information, and then obtains the road curvature and road slope on the local driving path of the unmanned vehicle according to the high-precision map, and corrects the local driving path according to the road curvature and slope information; then, according to the obstacle information, road curvature, road slope and vehicle status information, the MPC control algorithm is used to calculate the target speed and target steering wheel angle of the unmanned vehicle; finally, the target speed is used to control the unmanned vehicle longitudinally, and the target steering wheel angle is used to control the unmanned vehicle laterally. According to the high-precision map, this embodiment can obtain the road curvature and road slope on the local driving path of the unmanned vehicle in advance, that is, obtain the road information ahead in advance, so that the vehicle can be controlled laterally and longitudinally earlier. Compared with the method of making decisions based on current information in the prior art, it can effectively improve the stability of vehicle operation and the comfort of users. In this embodiment, the local driving path is also corrected based on the road curvature and road slope information, and then the target speed and target steering wheel angle are calculated using the MPC control algorithm. Finally, the target speed and target steering wheel angle are used to perform longitudinal control and lateral control respectively. By correcting the local driving path, the vehicle's lateral and longitudinal compensatory control can be performed, thereby improving the accuracy of vehicle control, which is conducive to further improving the stability of vehicle operation and user comfort.

[0048] The present application also provides a system for controlling an unmanned vehicle based on a high-precision map, the system mainly comprising: a perception module, a local driving path determination module, a road information acquisition module, an inertial navigation device and a control module. The setting of the road information acquisition module can obtain the road curvature and road slope on the local driving path of the unmanned vehicle through the high-precision map, so as to obtain the road information ahead in advance, buy time for subsequent vehicle control, and help improve the stability of vehicle driving and the comfort of users. The road information acquisition module in this embodiment also corrects the local driving path according to the road curvature and slope information, so as to further improve the accuracy and precision of vehicle control, and then improve the stability of vehicle operation.

[0049] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0051] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0052] Figure 1 A flowchart of a method for controlling an unmanned vehicle based on a high-precision map provided in an embodiment of the present application;

[0053] Figure 2 A schematic diagram of the structure of a system for controlling an unmanned vehicle based on a high-precision map provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0055] In order to better understand the present application, the implementation methods of the present application are explained in detail below with reference to the accompanying drawings.

[0056] Embodiment 1

[0057] See also Figure 1 , Figure 1 A flowchart of a method for controlling an unmanned vehicle based on a high-precision map provided in an embodiment of the present application. Figure 1 It can be seen that the method for controlling an unmanned vehicle based on a high-precision map in this embodiment mainly includes the following processes:

[0058] S1: Obtain obstacle information around the unmanned vehicle, the obstacle information including: obstacle size, obstacle lateral and longitudinal speeds, and lateral and longitudinal distances of the obstacle from the vehicle.

[0059] S2: Determine the local driving path of the unmanned vehicle based on the obstacle information.

[0060] Specifically, step S2 includes the following process:

[0061] S21: According to the obstacle information, the obstacle is tracked and predicted to obtain the drivable area of ​​the unmanned vehicle.

[0062] S22: Planning a local driving path of the unmanned vehicle based on the global path and drivable area of ​​the unmanned vehicle.

[0063] The global path of the unmanned vehicle in this embodiment can be obtained by the following method:

[0064] First, determine the starting point and end point of the unmanned vehicle, and then use the A* algorithm to plan the path to obtain the global path.

[0065] After determining the global path and drivable area, the method for planning the local driving path is as follows: transform the global path and drivable area into the Frenet coordinate system, and fit the horizontal and vertical spline curves through the vehicle kinematic constraints, which is the local driving path of the unmanned vehicle.

[0066] Continue to see Figure 1 It can be seen that after the local driving path of the unmanned vehicle is determined, step S3 is executed: according to the high-precision map, the road curvature and road slope on the local driving path of the unmanned vehicle are obtained, and the local driving path is corrected according to the road curvature and slope information.

[0067] This embodiment uses a high-precision map to obtain the road conditions on the vehicle's local driving path in advance, including the road curvature and road slope, to provide a basis for subsequent vehicle control.

[0068] This embodiment corrects the local driving path according to the road curvature and slope information, mainly including the constraints and restrictions on the maximum vehicle speed under different road conditions.

[0069] In this embodiment, the road curvature is ρ, so the radius r of the driving road is calculated by the formula r=1 / ρ. Then, the formula Calculate the maximum speed of the driverless vehicle on the curve, where a lat is the maximum centripetal acceleration at the curve. The maximum vehicle speed is constrained according to the maximum centripetal acceleration and the road radius, which can effectively improve the stability and comfort of vehicle driving at the curve.

[0070] When driving on unstructured roads, the maximum and minimum speeds of the driverless vehicle on the slope can be determined based on the road slope. Adjusting the speed of the driverless vehicle to between the maximum and minimum speeds in advance can effectively improve the smoothness and stability of the vehicle's driving.

[0071] Continue to see Figure 1It can be seen that after obtaining the road curvature and road slope on the local driving path of the unmanned vehicle and modifying the local driving path, step S4 is executed: according to the obstacle information, road curvature, road slope and vehicle state information, the MPC control algorithm is used to calculate the target speed and target steering wheel angle of the unmanned vehicle. Among them, the vehicle state information includes: the current speed and current steering wheel angle of the unmanned vehicle.

[0072] Specifically, step S4 includes the following process:

[0073] S41: Define the state of the unmanned vehicle as The control variable is {v r δ f}, the vehicle state space equation is Among them, x is the longitudinal position of the unmanned vehicle at the current moment, and y is the lateral position of the unmanned vehicle at the current moment. is the yaw angle of the unmanned vehicle at the current moment, v r is the target speed of the unmanned vehicle, δ f is the target front wheel turning angle of the unmanned vehicle, and l is the wheelbase of the front and rear wheels of the unmanned vehicle.

[0074] After determining the target front wheel angle of the unmanned vehicle, the target front wheel angle can be converted into a target steering wheel angle of the unmanned vehicle according to the steering system transmission ratio of the vehicle.

[0075] S42: Determine the kinematic model of the four-wheel vehicle based on the unmanned vehicle state, control variables and vehicle state space equations: Among them, k is the current moment, k+1 is the next moment, is the state of the vehicle at the kth moment, is the control amount of the vehicle at the kth moment, is the state of the vehicle at the k+1th moment,

[0076] Specifically, step S42 includes the following process:

[0077] S421: Convert the driverless vehicle state and control variables into matrix format to obtain Among them, ζ is the state of the unmanned vehicle and u is the control variable.

[0078] S422: Based on the unmanned vehicle state and control variables in matrix format, the vehicle space state is linearized in first order to calculate Among them, r is the state of the vehicle at the rth moment, u r is the control amount of the vehicle at the rth moment.

[0079] S423: Definition The current time is k, and the formula Discretization can obtain the kinematic model of the four-wheel vehicle

[0080] S43: Based on the kinematic model, the MPC control algorithm is adopted, and the formula Y=Ψζ(k)+ΘU(k) is used to calculate the deviation between the predicted position and the local driving path of the unmanned vehicle.

[0081] Among them, Y is the deviation between the unmanned vehicle trajectory predicted by the vehicle kinematic model and the local driving path, ψ is the coefficient of the state quantity deviation, Θ is the control quantity coefficient, and U(k) is the control quantity.

[0082] According to the kinematic model of the four-wheel vehicle in step S42 above Since the purpose of the MPC algorithm is to make the unmanned vehicle track a given path, the deviations between the predicted position of the unmanned vehicle and the local driving path at time k+1, k+2, k+3, and k+n are calculated according to the kinematic model:

[0083] Convert it into matrix form: Y = Ψζ(k) + ΘU(k).

[0084] S44: Based on the deviation between the predicted position of the unmanned vehicle and the local driving path, the quadratic optimal solution function J = Y T QY+URU T , the target speed and target steering wheel angle of the unmanned vehicle are calculated.

[0085] Among them, J is the target evaluation function, Q is the vehicle state deviation evaluation coefficient, and R is the control state evaluation coefficient.

[0086] From the above quadratic optimal solution function, we can know that the smaller the deviation between the predicted position of the unmanned vehicle and the local driving path, the closer the driving trajectory of the unmanned vehicle is to the set reference path, the smaller the speed change of the unmanned vehicle, and the smoother the operation of the unmanned vehicle. The speed change is related to the output U. According to the objective function, according to the set reference path, the quadratic optimal solution function J=Y is used. T QY+URU T , the target speed and target front wheel angle of the unmanned vehicle can be calculated, and then the target front wheel angle can be converted into the target steering wheel angle of the unmanned vehicle according to the steering system transmission ratio of the vehicle.

[0087] Continue to see Figure 1 It can be seen that after the target speed and the target steering wheel angle of the driverless vehicle are calculated, steps S5 and S6 are respectively executed.

[0088] Wherein, step S5: using the target speed to perform longitudinal control on the unmanned vehicle.

[0089] Specifically, step S5 includes the following process:

[0090] S51: The traction of the unmanned vehicle is calculated using the formula Pt=Pf+Pw+Pi+Pj, where Pt is the traction, Pf is the rolling resistance, Pw is the air resistance, Pi is the slope resistance, and Pj is the inertial resistance.

[0091] S52: According to the actual speed and target speed of the unmanned vehicle, the formula P1=a1*(v r -v act ), the traction force of the vehicle corrected by speed is calculated, where P1 is the traction force of the vehicle corrected by speed, a1 is the correction coefficient, and v r is the target speed of the driverless vehicle, v act is the actual speed of the vehicle.

[0092] S53: Calculate the traction force of the slope correction engine using the formula P2=a2*θ, where a2 is the slope correction parameter, θ is the road slope, and P2 is the traction force of the slope correction engine.

[0093] By calculating the traction of the slope-corrected engine, the effect of different slopes on the traction of the vehicle engine can be taken into account, which is beneficial to improving the accuracy of vehicle control.

[0094] S54: The traction force of the unmanned vehicle, the traction force of the speed-corrected vehicle, and the traction force of the slope-corrected engine are summed to calculate the total traction force required by the unmanned vehicle.

[0095] S55: Controlling the engine or braking system of the unmanned vehicle according to the total traction force.

[0096] Controlling the engine or braking system of the unmanned vehicle means performing longitudinal control of the unmanned vehicle.

[0097] S6: Laterally control the unmanned vehicle using the target steering wheel angle.

[0098] Specifically, step S6 includes the following process:

[0099] S61: According to the transverse slope of the current local driving path, the formula δ2=a3*φ is used to calculate the steering wheel angle for transverse slope correction, where δ2 is the steering wheel angle for transverse slope correction, a3 is the correction coefficient, and φ is the transverse slope of the road.

[0100] S62: Sum the target steering wheel angle and the steering wheel angle corrected by the lateral slope to calculate the steering wheel angle to be requested by the unmanned vehicle.

[0101] S63: Sending the requested steering wheel angle to the steering machine of the unmanned vehicle via CAN communication.

[0102] Embodiment 2

[0103] exist Figure 1 Based on the embodiment shown, see Figure 2 , Figure 2 A schematic diagram of the structure of a system for controlling an unmanned vehicle based on a high-precision map provided in an embodiment of the present application. Figure 2 It can be seen that the system for controlling the unmanned vehicle based on the high-precision map in this embodiment mainly includes: a perception module, a local driving path determination module, a road information acquisition module, an inertial navigation device and a control module. The unmanned vehicle in this embodiment is a wire-controlled vehicle, that is, a vehicle controlled by CAN communication, and the engine and braking system of the vehicle can be controlled by CAN, and the vehicle status information is fed back to the control module through CAN.

[0104] Among them, the perception module is used to obtain obstacle information around the unmanned vehicle, and the obstacle information includes: obstacle size, obstacle lateral and longitudinal speeds, and lateral and longitudinal distances of the obstacle from the vehicle; the local driving path determination module is used to determine the local driving path of the unmanned vehicle according to the obstacle information; the road information acquisition module is used to obtain the road curvature and road slope on the local driving path of the unmanned vehicle according to the high-precision map, and correct the local driving path according to the road curvature and slope information; the inertial navigation device is used to obtain the vehicle position information and vehicle posture information of the unmanned vehicle; the control module is used to calculate the target speed and target steering wheel angle of the unmanned vehicle according to the obstacle information, road curvature, road slope and vehicle status information using the MPC control algorithm, and the vehicle status information includes: the current speed and current steering wheel angle of the unmanned vehicle; the control module is also used to use the target speed to control the unmanned vehicle longitudinally, and use the target steering wheel angle to control the unmanned vehicle laterally.

[0105] The control module in this embodiment may be an industrial computer.

[0106] The perception module includes: a main laser radar, two blind-point laser radars and two millimeter-wave radars. The main laser radar is set above the roof of the unmanned vehicle, the two blind-point laser radars are respectively set on the left front and right front sides of the unmanned vehicle, and the two millimeter-wave radars are respectively set on the front and rear sides of the unmanned vehicle.

[0107] The inertial navigation equipment includes: GNSS and IMU. GNSS is used to obtain the vehicle position information of the unmanned vehicle, and IMU is used to obtain the vehicle posture information of the unmanned vehicle.

[0108] The working principle and working method of the system for controlling the unmanned vehicle based on the high-precision map in this embodiment are as follows: Figure 1 The embodiments shown have been described in detail, and the two embodiments can refer to each other, which will not be described again here.

[0109] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling an unmanned vehicle based on a high-precision map, characterized in that: The method comprises: Obtaining obstacle information around the unmanned vehicle, the obstacle information including: obstacle size, obstacle lateral and longitudinal speeds, and lateral and longitudinal distances of the obstacle from the vehicle; Determining a local driving path of the unmanned vehicle according to the obstacle information; Obtaining the road curvature and road slope on a local driving path of the unmanned vehicle according to the high-precision map, and correcting the local driving path according to the road curvature and slope information; Calculating the target speed and target steering wheel angle of the unmanned vehicle using the MPC control algorithm according to the obstacle information, road curvature, road slope and vehicle state information, wherein the vehicle state information includes: the current speed and current steering wheel angle of the unmanned vehicle; Using the target speed to longitudinally control the unmanned vehicle; Using the target steering wheel angle to perform lateral control on the unmanned vehicle; The method of calculating the target speed and target steering wheel angle of the unmanned vehicle by using the MPC control algorithm according to the obstacle information, road curvature, road slope and vehicle state information includes: Define the state of the unmanned vehicle as The control variable is {v r δ f }, the vehicle state space equation is Among them, x is the longitudinal position of the unmanned vehicle at the current moment, and y is the lateral position of the unmanned vehicle at the current moment. is the yaw angle of the unmanned vehicle at the current moment, v r is the target speed of the unmanned vehicle, δ f is the target front wheel turning angle of the unmanned vehicle, l is the wheelbase of the front and rear wheels of the unmanned vehicle; According to the unmanned vehicle state, control variables and vehicle state space equations, the kinematic model of the four-wheel vehicle is determined: Among them, k is the current moment, k+1 is the next moment, is the state of the vehicle at the kth moment, is the control amount of the vehicle at the kth moment, is the state of the vehicle at the k+1th moment, According to the kinematic model, the MPC control algorithm is adopted, and the deviation between the predicted posture of the unmanned vehicle and the local driving path is calculated using the formula Y=Ψζ(k)+ΘU(k), wherein Y is the deviation between the trajectory of the unmanned vehicle predicted by the vehicle kinematic model and the local driving path, ψ is the coefficient of the state quantity deviation, Θ is the control quantity coefficient, and U(k) is the control quantity; According to the deviation between the predicted position and the local driving path of the unmanned vehicle, the quadratic optimal solution function J=Y T QY+URU T , the target speed and target steering wheel angle of the unmanned vehicle are calculated, where J is the target evaluation function, Q is the vehicle state deviation evaluation coefficient, and R is the control state evaluation coefficient; According to the unmanned vehicle state, control variables and vehicle state space equations, the kinematic model of the four-wheel vehicle is determined: include: The unmanned vehicle state and control variables are converted into matrix format to obtain Among them, ζ is the state of the unmanned vehicle, and u is the control quantity; According to the unmanned vehicle state and control variables in matrix format, the unmanned vehicle state is linearized in first order to calculate Among them, r is the state of the vehicle at the rth moment, u r is the control amount of the vehicle at the rth moment; definition The current time is k, and the formula Discretization can obtain the kinematic model of the four-wheel vehicle The method for longitudinally controlling an unmanned vehicle using the target speed comprises: The traction of the unmanned vehicle is calculated using the formula Pt=pf+pw+Pi+pj, where Pt is the traction, Pf is the rolling resistance, Pw is the air resistance, Pi is the slope resistance, and Pj is the inertial resistance. According to the actual speed of the unmanned vehicle and the target speed, the formula P1=a1*(v r -v act ), the traction force of the vehicle corrected by speed is calculated, where P1 is the traction force of the vehicle corrected by speed, a1 is the correction coefficient, and v r is the target speed of the driverless vehicle, v act is the actual speed of the vehicle; The traction force of the slope-corrected engine is calculated using the formula P2=a2*θ, where a2 is the slope correction parameter, θ is the road slope, and P2 is the traction force of the slope-corrected engine; The total traction required by the unmanned vehicle is calculated by summing the traction of the unmanned vehicle, the traction of the speed-corrected vehicle, and the traction of the slope-corrected engine; controlling an engine or a braking system of the unmanned vehicle according to the total traction; The method for lateral control of an unmanned vehicle using the target steering wheel angle comprises: According to the transverse slope of the current local driving path, the formula δ2=a3*φ is used to calculate the steering wheel angle for transverse slope correction, where δ2 is the steering wheel angle for transverse slope correction, a3 is the correction coefficient, and φ is the transverse slope of the road; Summing the target steering wheel angle and the lateral slope corrected steering wheel angle to calculate a steering wheel angle to be requested by the unmanned vehicle; The requested steering wheel angle is sent to the steering machine of the unmanned vehicle through CAN communication.

2. The method for controlling an unmanned vehicle based on a high-precision map according to claim 1, characterized in that: The method for determining a local driving path of an unmanned vehicle according to the obstacle information includes: According to the obstacle information, target tracking and prediction are performed on the obstacle to obtain a drivable area of ​​the unmanned vehicle; A local driving path of the unmanned vehicle is planned according to the global path of the unmanned vehicle and the drivable area.

3. A system for controlling an unmanned vehicle based on a high-precision map, characterized in that: The system is used to implement the method according to claim 1, and the system comprises: A perception module is used to obtain obstacle information around the unmanned vehicle, wherein the obstacle information includes: obstacle size, obstacle lateral and longitudinal speed, and lateral and longitudinal distance of the obstacle from the vehicle; A local driving path determination module, used to determine the local driving path of the unmanned vehicle according to the obstacle information; A road information acquisition module, used to acquire the road curvature and road slope on the local driving path of the unmanned vehicle according to the high-precision map, and to correct the local driving path according to the road curvature and slope information; Inertial navigation equipment, used to obtain vehicle position information and vehicle attitude information of the unmanned vehicle; A control module, for calculating a target speed and a target steering wheel angle of the unmanned vehicle using an MPC control algorithm according to the obstacle information, the road curvature, the road slope and the vehicle state information, wherein the vehicle state information includes: a current speed and a current steering wheel angle of the unmanned vehicle; The control module is further used to use the target speed to perform longitudinal control on the unmanned vehicle, and to use the target steering wheel angle to perform lateral control on the unmanned vehicle.

4. The system for controlling an unmanned vehicle based on a high-precision map according to claim 3, characterized in that: The perception module includes: a main laser radar, two blind laser radars and two millimeter-wave radars. The main laser radar is arranged above the roof of the unmanned vehicle, the two blind laser radars are respectively arranged on the left front side and the right front side of the unmanned vehicle, and the two millimeter-wave radars are respectively arranged on the front side and the rear side of the unmanned vehicle.

5. The system for controlling an unmanned vehicle based on a high-precision map according to claim 3, characterized in that: The inertial navigation device includes a GNSS and an IMU, wherein the GNSS is used to obtain vehicle position information of the unmanned vehicle, and the IMU is used to obtain vehicle posture information of the unmanned vehicle.

6. The system for controlling an unmanned vehicle based on a high-precision map according to claim 3, characterized in that: The unmanned vehicle is a wire-controlled vehicle.

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