Vehicle control method and system and vehicle

Abstract

The invention discloses a vehicle control method, and the method comprises the steps: obtaining traffic state information which at least comprises the driving state information of a target vehicle, the road state information and the surrounding environment state information; determining a driving condition corresponding to the target vehicle at the next moment based on the traffic state information; and controlling the target vehicle to run according to the corresponding running track at the next moment based on a control strategy corresponding to the running working condition. Meanwhile, the invention further discloses a control system of the vehicle and the vehicle.

Description

Technical Field

[0001] The present application relates to the field of vehicle control, and in particular to a vehicle control method, system and vehicle. Background Art

[0002] With the implementation of my country's policy of building a strong transportation nation and technological innovation, the road transport industry has developed significantly, greatly stimulating the potential of road transport. However, in the process of automatic tracking and control of vehicles, it is found that traffic accidents are very likely to occur due to the complex driving conditions of vehicles. Therefore, how to improve the safety of vehicle automatic driving is an issue that urgently needs to be addressed. Utility Model Content

[0003] In view of this, the embodiments of the present application hope to provide a vehicle control method, system and vehicle to at least solve the above-mentioned technical problems.

[0004] To achieve the above purpose, the technical solution of this application is implemented as follows:

[0005] According to one aspect of an embodiment of the present application, a vehicle control method is provided, the method comprising:

[0006] Acquiring traffic status information, wherein the traffic status information at least includes driving status information of the target vehicle, road status information, and surrounding environment status information;

[0007] Determine the driving condition corresponding to the target vehicle at the next moment based on the traffic state information;

[0008] Based on the control strategy corresponding to the driving condition, the target vehicle is controlled to travel according to the corresponding driving trajectory at the next moment.

[0009] In the above solution, the obtaining of traffic status information includes:

[0010] The traffic status information is acquired through the target vehicle, the road test equipment and other vehicles around the target vehicle.

[0011] In the above solution, the determining the driving condition corresponding to the target vehicle at the next moment based on the traffic state information includes:

[0012] Based on the traffic state information, generating a traffic state matrix corresponding to the target vehicle at the current moment;

[0013] If it is determined based on the traffic state matrix that there is no obstacle in the driving direction of the target vehicle or the distance condition and speed condition between the target vehicle and the obstacle are met, determining that the driving condition corresponding to the target vehicle at the next moment is a straight line condition;

[0014] If it is determined based on the traffic state matrix that the driving direction of the target vehicle has a curve area, determining that the driving condition corresponding to the target vehicle at the next moment is a curve condition;

[0015] If it is determined based on the traffic state matrix that there is no curve area in the driving direction of the target vehicle, but the distance condition and / or speed condition are not satisfied between the target vehicle and the obstacle, it is determined that the driving condition corresponding to the target vehicle at the next moment is a lane change condition.

[0016] In the above solution, when it is determined that the driving condition corresponding to the target vehicle at the next moment is a straight-line condition, the method further includes at least one of the following:

[0017] If there is no obstacle in the driving direction of the target vehicle, control the target vehicle to be in a uniform driving state, wherein the uniform driving state indicates that the target vehicle maintains the current speed;

[0018] If the distance between the target vehicle and the obstacle is greater than or equal to a first preset distance, and the current speed of the target vehicle is less than the moving speed of the obstacle, the target vehicle is controlled to be in a following vehicle driving state, wherein the following vehicle driving state indicates that the target vehicle first accelerates to a first target speed and then decelerates to the same speed as the moving speed of the obstacle;

[0019] If the distance between the target vehicle and the obstacle is less than the first preset distance but greater than or equal to the second preset distance, and the current speed of the target vehicle is greater than or equal to the moving speed of the obstacle, the target vehicle is controlled to be in a decelerated driving state, wherein the decelerated driving state indicates that the target vehicle first drives at a constant speed at the current speed for a target time, and then decelerates to a second target speed;

[0020] If the distance between the target vehicle and the obstacle is less than the second preset distance, and the current speed of the target vehicle is greater than or equal to the moving speed of the obstacle, the target vehicle is controlled to be in an emergency stop state, and the emergency stop state indicates that the target vehicle decelerates from the current speed to a third target speed.

[0021] In the above solution, when it is determined that the driving condition corresponding to the target vehicle at the next moment is a curved condition, the method further includes:

[0022] Constructing discrete trajectory points under the curve condition based on first constraint information of the curve area, wherein the first constraint information includes a starting position, an end position, a road centerline, a vehicle turning curvature value, a curvature change rate, and lateral and longitudinal accelerations of the curve area;

[0023] The discrete trajectory points are fitted using a quintic polynomial to obtain a curved driving trajectory corresponding to the curved working condition.

[0024] In the above solution, when it is determined that the driving condition corresponding to the target vehicle at the next moment is a lane-changing condition, the method further includes:

[0025] Constructing discrete trajectory points under the lane changing condition based on second constraint information between the target vehicle and the obstacle, the second constraint information including a safe distance between the target vehicle and the obstacle, a longitudinal vehicle position of the target vehicle, a lateral vehicle position of the target vehicle, a center axis distance of a lane to be changed, a center line of an original lane, a maximum lateral speed of the target vehicle, a coordinate position of the obstacle, a vehicle turning curvature value, and a curvature change rate;

[0026] The discrete trajectory points under the lane-changing condition are fitted using a quintic polynomial to obtain a lane-changing driving trajectory corresponding to the lane-changing condition.

[0027] In the above scheme, the control strategy corresponding to the driving condition controls the target vehicle to drive according to the corresponding driving trajectory at the next moment, including:

[0028] Generate a dynamic model of the target vehicle based on the longitudinal motion information, lateral motion information and yaw motion information of the target vehicle;

[0029] If the driving condition corresponding to the target vehicle at the next moment is a straight-line condition, the mass center offset and speed error of the target vehicle are input into the dynamic model as target variables, and a proportional-integral-differential (PID) control strategy is called to control the target vehicle to drive along a straight-line driving trajectory at the next moment;

[0030] If the driving condition corresponding to the target vehicle at the next moment is a curved condition, the mass center offset, speed error, front wheel steering angle and yaw rate error of the target vehicle are input into the dynamic model as target variables, and the model prediction control (MPC) strategy is called to control the target vehicle to drive along the curved driving trajectory at the next moment;

[0031] If the driving condition corresponding to the target vehicle at the next moment is a lane changing condition, the center of mass offset, speed error, front wheel steering angle and yaw angular velocity error of the target vehicle are input into the dynamic model as target variables, and when the front wheel center of mass of the target vehicle coincides with the straight starting stage, the proportional integral differential (PID) control strategy is called to control the target vehicle to drive according to the straight trajectory in the lane changing driving trajectory at the next moment; when the front wheel center of mass of the target vehicle coincides with the curve starting stage, the model prediction (MPC) control strategy is called to control the target vehicle to drive according to the curve trajectory in the lane changing driving trajectory at the next moment.

[0032] In the above scheme, the method further comprises:

[0033] The driving condition and the corresponding driving trajectory are periodically updated according to the traffic status information.

[0034] According to another aspect of the present application, a control system of a vehicle is provided, the system comprising:

[0035] An acquisition unit, used to acquire traffic status information, wherein the traffic status information at least includes driving status information of the target vehicle, road status information and surrounding environment status information;

[0036] A calculation unit, used to determine the driving condition corresponding to the target vehicle at the next moment based on the traffic state information;

[0037] A control unit is used to control the target vehicle to travel according to the corresponding driving trajectory at the next moment based on the control strategy corresponding to the driving condition.

[0038] According to a third aspect of the present application, a vehicle is provided, the vehicle being connected to a vehicle-road cloud platform, configured to send traffic status information to the vehicle-road cloud platform, and to receive a control instruction sent by the vehicle-road cloud platform based on the traffic status information, and to drive according to a corresponding driving trajectory at the next moment based on a control strategy corresponding to a driving condition in the control instruction;

[0039] Wherein, the vehicle at least comprises:

[0040] A tractor and a trailer, each of the tractor and the trailer has at least one set of axles and wheels; one end of the trailer is connected to the tractor for traveling under the traction of the tractor.

[0041] The vehicle control method, system and vehicle provided in the present application analyze the vehicle's driving condition through current traffic status information, and plan and control the vehicle's driving trajectory at the next moment based on the vehicle's current driving condition, which can effectively improve the safety and stability of the vehicle under any conditions during automatic driving. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is a schematic diagram of the vehicle control method process in this application Figure 1 ;

[0043] Figure 2 This is a schematic diagram of the vehicle control method process in this application Figure 2 ;

[0044] Figure 3 This is a schematic diagram of the structure of the control system of the vehicle in this application;

[0045] Figure 4 This is an illustration of the vehicle tracking effect in this application Figure 1 ;

[0046] Figure 5 This is a diagram showing the vehicle tracking results in this application. Figure 2 . DETAILED DESCRIPTION

[0047] The technical solution of the present application is further elaborated in detail below in conjunction with the accompanying drawings and specific embodiments of the specification.

[0048] The various specific technical features in the various embodiments described in the specific implementation methods can be combined in various ways without contradiction. For example, different implementation methods can be formed by combining different specific technical features. In order to avoid unnecessary repetition, the various possible combinations of the specific technical features in this application will not be described separately.

[0049] In the embodiments of the present application, it should be noted that, unless otherwise specified and limited, the term "connection" should be understood in a broad sense. For example, it can be an electrical connection or a connection between two components. It can be a direct connection or an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to the specific circumstances.

[0050] It should be noted that the terms "first\second\third" involved in the embodiments of the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that the specific order or sequence of "first\second\third" can be interchanged where permitted. It should be understood that the objects distinguished by "first\second\third" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.

[0051] Figure 1 This is a schematic diagram of the vehicle control method process in this application Figure 1 The method can be applied to a vehicle-road cloud platform, which is connected to a multi-axle multi-steering vehicle and a road test device. The multi-axle multi-steering vehicle includes but is not limited to a connected automatic semi-trailer train (CAST), a connected automatic trailer, etc. The multi-axle multi-steering vehicle connected to the vehicle-road cloud platform is referred to as a target vehicle. Figure 1 As shown, the method includes:

[0052] Step 101, obtaining traffic status information, wherein the traffic status information at least includes driving status information of the target vehicle, road status information and surrounding environment status information;

[0053] In this application, the vehicle-road cloud platform can obtain traffic status information through the target vehicle, road test equipment and other vehicles around the target vehicle.

[0054] Here, the target vehicle may include a variety of sensors, including but not limited to infrared sensors, global positioning system (GPS) sensors, optical sensors, network communication sensors, etc. When the target vehicle drives into the network communication range, a communication connection can be established with the vehicle-road cloud platform through the various sensors, and the driving status information of the target vehicle can be sent to the vehicle-road cloud platform through the communication connection.

[0055] For example, GPS can be used to obtain the target vehicle's contour driving range, driving speed, position, vehicle azimuth, wheel steering, steering wheel steering, and other information. The contour driving range can be calculated by simplifying the contour trajectory of the target vehicle into a central trajectory (including the tractor and semi-trailer) and using the target vehicle size unit.

[0056] Here, when the road test equipment is directly connected to the vehicle-road cloud platform, the vehicle-road cloud platform can directly obtain road status information through the road test equipment; when the road test equipment is connected to the target vehicle, the target vehicle can obtain the road status information of the current area of ​​the target vehicle from the road test equipment, and then the target vehicle sends the road status information to the vehicle-road cloud platform.

[0057] Here, the state information of obstacles around the target vehicle can also be obtained through various sensors of the target vehicle. The obstacles include but are not limited to other vehicles or objects (such as trees, stones, railings, etc.) around the target vehicle.

[0058] Step 102, determining the driving condition corresponding to the target vehicle at the next moment based on the traffic state information;

[0059] In this application, the vehicle-road cloud platform can generate the traffic state matrix CAST corresponding to the target vehicle at the current moment based on the traffic state information and taking the connection point where the target vehicle establishes a communication connection with the vehicle-road cloud platform as the starting point. i , where i = 1, 2, 3, represents the order of cars entering the network communication range of the vehicle-road cloud platform. For example, the traffic state matrix of the target vehicle generated by the vehicle-road cloud platform is:

[0060] CAST i =[t,x q ,x g ,y q ,y g ,v qx ,v qy ,a qx ,a qy ,γ,l q ,l g ];

[0061] Among them, t represents the current time, x q ,x g Represent the lateral coordinates of the center of mass of the target vehicle, y q ,y g Respectively represent the longitudinal coordinates of the center of mass of the target vehicle, v qx ,v qy ,a qx ,a qy They represent the longitudinal speed and acceleration, lateral speed and acceleration of the target vehicle, γ represents the front wheel turning angle of the tractor in the target vehicle, l q ,l g Respectively represent the longitudinal body length of the target vehicle.

[0062] The vehicle-road-cloud platform can also generate a state matrix of obstacles around the target vehicle, which is:

[0063] OBS i =[t,x o ,y o ,v o ,a o ,l o ].

[0064] Among them, l o represents the longitudinal body length of other vehicles around the target vehicle, x o ,y o ,v o ,a o They respectively represent the lateral coordinates, longitudinal coordinates, speed and acceleration of other vehicles around the target vehicle.

[0065] Here, if it is determined based on the traffic state matrix that there is no obstacle in the driving direction of the target vehicle or the distance condition and speed condition between the target vehicle and the obstacle are satisfied, then it is determined that the driving condition corresponding to the target vehicle at the next moment is a straight line condition;

[0066] Here, if it is determined based on the traffic state matrix that the driving direction of the target vehicle has a curve area, then the driving condition corresponding to the target vehicle at the next moment is determined to be a curve condition;

[0067] Here, if it is determined based on the traffic state matrix that there is no curve area in the driving direction of the target vehicle, but the distance condition and / or the speed condition are not satisfied between the target vehicle and the obstacle, then it is determined that the driving condition corresponding to the target vehicle at the next moment is a lane change condition.

[0068] Step 103: Control the target vehicle to travel along the corresponding driving trajectory at the next moment based on the control strategy corresponding to the driving condition.

[0069] Here, the vehicle-road cloud platform can also obtain the three-degree-of-freedom information of the target vehicle and generate the dynamic model of the target vehicle based on the three-degree-of-freedom information. The three-degree-of-freedom information includes the longitudinal motion information, lateral motion information and yaw motion information of the target vehicle. Among them, the longitudinal motion information refers to the movement of the vehicle in the longitudinal direction perpendicular to the horizontal direction; the lateral motion information refers to the movement of the vehicle in the horizontal direction; and the yaw motion information refers to the rotational motion of the vehicle around an axis perpendicular to the horizontal plane.

[0070] Among them, the longitudinal motion information includes but is not limited to the longitudinal center of mass position, longitudinal speed, and acceleration; the lateral motion information includes but is not limited to the lateral center of mass position, lateral speed, and acceleration; the yaw motion information includes but is not limited to the front wheel turning angle and yaw angular velocity.

[0071] If the driving condition corresponding to the target vehicle at the next moment is a straight-line condition, the vehicle-road cloud platform can input the mass center offset and speed error of the target vehicle into the dynamic model as target variables, and call the proportional-integral-derivative control (PID) control strategy to control the target vehicle to drive along a straight-line trajectory at the next moment;

[0072] If the driving condition corresponding to the target vehicle at the next moment is a curved condition, the vehicle-road cloud platform can input the mass center offset, speed error, front wheel steering angle and yaw rate error of the target vehicle as target variables into the dynamic model, and call the model predictive control (MPC) control strategy to control the target vehicle to drive along the curved driving trajectory at the next moment;

[0073] If the driving condition corresponding to the target vehicle at the next moment is a lane changing condition, the vehicle-road cloud platform can input the target vehicle's center of mass offset, speed error, front wheel steering angle and yaw angular velocity error as target variables into the dynamic model, and when the front wheel center of mass of the target vehicle coincides with the starting stage of the straight line, call the proportional integral differential (PID) control strategy to control the target vehicle to drive along the straight trajectory in the lane changing driving trajectory at the next moment; when the front wheel center of mass of the target vehicle coincides with the starting stage of the curve, call the model prediction (MPC) control strategy to control the target vehicle to drive along the curve trajectory in the lane changing driving trajectory at the next moment.

[0074] By constructing a vehicle dynamics model, the present application can make better use of existing information conditions to find out the input variables and output variables that affect the movement direction and speed of the tractor and trailer, so as to realize trajectory planning and tracking control under multiple working conditions based on the three-degree-of-freedom CAST motion in the subsequent process.

[0075] For example, taking the target vehicle as a connected automatic semi-trailer train, which includes a tractor and a trailer, the tractor dynamics model is constructed using Newton's second law as follows:

[0076] m q v qx (σ' q +ω q )=F q1 +F q2 -F qgy

[0077] I zq ω' q =F q1 l1-F q2 l2+F qgyl3;

[0078] Among them, m q represents the mass of the CAST tractor, v qx represents the speed of the tractor in the longitudinal direction of the vehicle, ω q is the yaw rate of the tractor, F q1 Indicates the lateral force on the front axle tire of the tractor, F q2 Indicates the lateral force on the rear axle tire of the tractor, F qgy It represents the y-direction component of the force on the towing point between the tractor and the trailer in the vehicle coordinate system, I zq represents the moment of inertia of the tractor in the body coordinate system along the plane perpendicular to xoy, σ′ q and ω′ q They respectively represent the derivative functions of the sideslip angle and yaw velocity of the tractor's center of mass at a certain moment, and l1, l2 and l3 respectively represent the distances from the tractor's center of mass to the front wheel axle, rear wheel axle and the towing point of the tractor and trailer.

[0079] Similarly, the trailer dynamics model is constructed as follows:

[0080] m g v gx (σ' g +ω g )=F g +F qgy

[0081] I zg ω' g =-F g l4+F qgy l5;

[0082] Among them, m g Indicates the mass of the CAST trailer, v gx Indicates the speed of the trailer along the longitudinal direction of the vehicle. In this invention, it is assumed that the longitudinal speed of the tractor and the trailer is the same. The longitudinal speed will be expressed as v in the following. qx Indicates that ω g is the trailer yaw rate, F g Indicates the lateral force on the trailer tire, F qgy It represents the y-direction component of the force on the towing point between the tractor and the trailer in the vehicle coordinate system, I zg represents the moment of inertia of the trailer in the body coordinate system along the plane perpendicular to the xoy plane (i.e., the z plane), σ′ g and ω′ g They respectively represent the derivative functions of the sideslip angle and yaw velocity of the trailer's center of mass at a certain moment, l4 and l5 respectively represent the distance from the trailer's center of mass to the axle center and the distance between the tractor and the trailer's towing point.

[0083] Here, the yaw rate relationship between the tractor and trailer is ω q =ω g +ξ', where ξ' represents the rate of change of the articulation angle between the tractor and trailer.

[0084] The lateral speed of the trailer v gy =v qx σ g

[0085] The formula for calculating the side slip angle of the tires on the tractor front axle, tractor rear axle and trailer axle is as follows:

[0086]

[0087] where k i represents the tire stiffness, which can be selected by looking up the tire model factors in the table, and γ represents the front wheel turning angle of the tractor.

[0088] The calculation formula of the sideslip angle of the center of mass of the tractor and trailer is as follows:

[0089] σ q =v qy / v qx

[0090] σ g =σ q -(l3+l5)ω q / v qx +l5ξ' / v qx +ξ;

[0091] Finally, the state space equation of CAST is obtained: Px+Qx=Dγ, where the sideslip angle and yaw acceleration of the tractor are considered as the state vector part, and the state of the trailer is indirectly represented by adding the articulation angle between the tractor and the trailer. Therefore, the state vector is always expressed as x=[σ q ω q ξ' ξ] T .

[0092] Among them, the matrix P is:

[0093]

[0094] Among them, the matrix Q is:

[0095]

[0096] and

[0097] Among them, the matrix D is:

[0098] D=[-k q1 -(l1+l3)kq1 00] T ;

[0099] In this application, if it is determined based on the traffic state matrix that there is no obstacle in front of the target vehicle or it is at a safe distance from the obstacle and does not need to turn, then the driving condition corresponding to the target vehicle at the next moment is determined to be a straight-line condition, and only longitudinal speed control is required to ensure the normal driving safety of the vehicle. Here, the speed and position information of the obstacle or vehicle in front, the speed and position of the target vehicle (CAST) can be obtained through the on-board sensors and laser radar sensing equipment of the target vehicle. The judgment formula for the safety condition of the target vehicle is as follows:

[0100] 0 <v qx ≤v pv or

[0101] where v qx and v pv are the longitudinal speed values ​​of CAST and the obstacle in front respectively; l c_pv is the distance between the vehicle and the obstacle; the binary variable α is introduced to prevent CAST from colliding with the obstacle in front at a very low speed. qx <0.1m / s, α=0, otherwise α=1; h safe is CAST safe headway time; safe CAST safe stationary vehicle distance; d max It is the maximum deceleration of CAST, expressed as a positive value.

[0102] If the current target vehicle meets the above safety conditions, it means that the target vehicle can continue to travel along the current lane direction.

[0103] Here, in order to facilitate the establishment of the model and the discussion and analysis of different working conditions, it is assumed in this application but not limited to that CAST travels along the positive direction of the X-axis of the geodetic coordinate system. At this time, based on the traffic state matrix, when the driving condition corresponding to the target vehicle at the next moment is determined to be a straight line condition, CAST can be divided into four situations: following the vehicle, driving at a constant speed, decelerating, and emergency stopping.

[0104] If there is no obstacle in the direction of the target vehicle, the target vehicle is controlled to be in a uniform speed state, and the uniform speed state indicates that the target vehicle maintains the current speed v px Traveling along the positive direction of the X axis, the trajectory is straight and the speed remains constant;

[0105] If the distance between the target vehicle and the obstacle is greater than or equal to a first preset distance (for example, the first preset distance l c_pv ≥50m), and the current speed of the target vehicle v qxis smaller than the moving speed v of the obstacle pv (For example, v qx <v pv And v pv >v min , v min Indicates the CAST minimum speed), the target vehicle is controlled to be in a following vehicle driving state, which means that the target vehicle first accelerates to a first target speed (for example, greater than the speed of the preceding vehicle), and then decelerates to the same moving speed as the obstacle. In this way, the trajectory planned by CAST can be made smoother.

[0106] Here, if the CAST vehicle speed is less than v min In this case, the vehicle is regarded as parked to improve driving safety and save energy.

[0107] Here, the minimum speed of CAST is not limited, as long as it can meet the requirements of following the vehicle. min It can be set to 0.1m / s, and the specific value can be determined according to the actual situation.

[0108] Here, when the target vehicle is in the following driving state, the distance between the target vehicle and the preceding vehicle can meet the safe driving conditions, namely:

[0109]

[0110] The uniform speed travel time t can be calculated by this formula u , where a c and d c They represent the comfortable acceleration and deceleration of CAST, both of which are positive values ​​and can be set according to actual conditions. In this application, they are 2m / s2 and -1m / s2 respectively. u ≤0, it means that the solution does not meet the conditions and the formula needs to be improved for further trajectory planning. The improved strategy is CAST to accelerate to the target speed v with comfortable acceleration qa , and then decelerate to the same speed as the vehicle in front to achieve following driving. That is:

[0111]

[0112] In this way, the target vehicle speed v can be calculated qa The trajectory is a straight line and the speed increases first, then becomes constant, and then decreases.

[0113] If the distance between the target vehicle and the obstacle is less than the first preset distance but greater than or equal to the second preset distance (for example, the second preset distance is 25m≤l c_pv <50m), and the current speed of the target vehicle v qx Greater than or equal to the moving speed of the obstacle (i.e. v qx ≥vpv And v pv >v min ), in order to reduce the fluctuation of CAST trajectory, the target vehicle is controlled to be in a deceleration state, which means that the target vehicle first drives at a constant speed at the current speed to the target time t u , and then decelerate to the second target speed;

[0114] Here, the uniform speed travel time t can be calculated by the following formula: u :

[0115]

[0116] Here, if t u ≤0, it means that the solution does not meet the safe driving conditions, and the formula needs to be improved for further trajectory planning. The improved strategy is that CAST directly decelerates to the same speed as the vehicle in front to achieve deceleration. In this way, the target deceleration d can be obtained qx The trajectory is a straight line and the speed is constant at first and then decreases.

[0117] If the distance between the target vehicle and the obstacle is less than the second preset distance (for example, l c_pv <25m), and the current speed of the target vehicle is greater than or equal to the moving speed of the obstacle (such as CAST speed v qx Greater than the speed of the vehicle ahead and 0≤v pv ≤v min ), indicating that the obstacle avoidance action cannot be completed at present, the target vehicle is controlled to be in an emergency stop state, and the emergency stop state represents that the target vehicle decelerates from the current speed to a third target speed (for example, 0).

[0118] Here, the target deceleration d can be calculated by the following formula: px :

[0119]

[0120] At this time, the target vehicle's trajectory is straight and its speed decreases.

[0121] It should be noted that the timing of straight line trajectory planning is after the communication connection is established. If the current road section is identified as a straight line section and obstacle avoidance is not required, the straight line condition planning starts from the current trajectory point and the planning range is to the end of the straight line condition.

[0122] Here, the trajectory planning points under the straight line condition are in a discrete state, which contains not only the position information of CAST, but also the speed information to ensure that CAST can drive safely under various straight line conditions.

[0123] Here, the longitudinal speed of the target vehicle is mainly planned under the straight-line working condition. Therefore, after obtaining the target vehicle speed, according to the kinematic formula The longitudinal displacement can be determined to determine the discrete trajectory points under straight-line conditions. The lateral position is along the center line of the current straight lane. At the same time, the corresponding speed value v is also generated at each discrete trajectory point t =v0+at. Among them, x0 and x t Represent the longitudinal position of the target vehicle at the current moment and after time t, v0 and v t They represent the longitudinal speed of the target vehicle at the current moment and after t time respectively, a represents the acceleration of the target vehicle, and t represents the time the target vehicle travels at acceleration a.

[0124] In the present application, if it is determined based on the traffic state matrix that the driving condition corresponding to the target vehicle at the next moment is a curved condition, discrete trajectory points under the curved condition can also be constructed based on the first constraint information of the curved area (including but not limited to arc curves and intersection turning lanes) in the driving direction of the target vehicle, wherein the first constraint information includes but is not limited to the starting position, end position, road centerline, vehicle turning curvature value, curvature change rate and lateral and longitudinal acceleration of the curved area; and then the discrete trajectory points are fitted using a fifth-order polynomial to obtain the curved driving trajectory corresponding to the curved condition.

[0125] Since there is no interference from other vehicles or obstacles when driving on this curve, it is only limited by terrain factors such as curve width and intersection range. Therefore, for curve planning, first, the starting and ending discrete points of the planned curve can be determined based on the location information of the starting and ending points of the curve. Secondly, the center line of the driving curve or the connecting line between the entrance and exit roads of the intersection is used as the initial driving trajectory discrete points of the center particle of the tractor and trailer. Then, the vehicle turning curvature value, curvature change rate, and lateral and longitudinal acceleration are introduced as maximum restrictions to further constrain vehicle driving safety and update the trajectory discrete points. When there is a conflict between the center particle trajectory discrete points and the above-mentioned restrictions, the limit value of the restriction condition should be used as the standard value, and the trajectory discrete points should be re-planned, and the vehicle and the road edge line position should be further introduced to maintain at least a safe distance d in the lateral direction. safe , and be aware of the difference between the safe static distance and the vehicle's driving distance. When planning the trajectory, there is no specific restriction on the speed of the tractor and trailer, as long as it is below the road and vehicle speed limit, that is, Among them, v v_max 、v r_maxThey are CAST speed limit and road speed limit respectively. It is worth noting that when there are discrete trajectory points to choose from at the same time, the discrete points with larger curvature values ​​can be selected first, the curvature change rate is second, and finally the lateral and longitudinal accelerations are compared to ensure the safety and comfort of vehicle driving and reduce the difficulty of subsequent tracking control.

[0126] Here, the central mass point is the curve position along the center line of the road, not the actual mass point position. Therefore, the target vehicle cannot exceed the speed range, the lateral and longitudinal acceleration range, and the curvature change rate when actually driving. If it exceeds the constraint range, the discrete point with a larger curvature value will be given priority; if there are multiple discrete points with a larger curvature value, the discrete point with a smaller curvature change rate will be given priority; if there are still many discrete points with a smaller curvature change rate, the discrete point with a smaller lateral and longitudinal acceleration vector will be given priority.

[0127] Here, when forming discrete trajectory planning points for the target vehicle in the future, considering that the number of planning points is limited by the length of the planning cycle, and when there are more planning discrete points, the precision error is large and it is easy to cause fluctuations during vehicle driving. Therefore, according to the planning trajectory discrete points that meet the above restrictions, a fifth-order polynomial can be used to fit a smooth curve that is more suitable for vehicle road driving. The expression formula of the fifth-order polynomial curve is y=a0+a1x+a2x 2 +a3x 3 +a4x 4 +a5x 5 , where a i ,i=0,1,2,3,4,5 represents the coefficients of the quintic polynomial, and x,y represent the coordinates (x,y) of the discrete trajectory points respectively.

[0128] After the discrete points are screened out in this application, a quintic polynomial can be used to fit all the discrete points into a smooth curve according to the coordinate information, which is used as the planned trajectory curve. This can ensure the stability of the target vehicle while driving and prevent the vehicle from turning back and forth due to excessive dispersion of discrete points.

[0129] In the present application, when it is determined that the driving condition corresponding to the target vehicle at the next moment is a lane changing condition, discrete trajectory points under the lane changing condition can be constructed based on the second constraint information between the target vehicle and the obstacle, wherein the second constraint information includes but is not limited to the safety distance between the target vehicle and the obstacle, the longitudinal vehicle position of the target vehicle (i.e., the length from the front to the rear of the vehicle), the lateral vehicle position of the target vehicle (i.e., the width of the vehicle body), the center axis distance of the lane to be changed, the center line of the original lane, the maximum lateral speed of the target vehicle, the coordinate position of the obstacle, the vehicle turning curvature value and the curvature change rate; then the discrete trajectory points under the lane changing condition are fitted using a fifth-order polynomial to obtain the lane changing driving trajectory corresponding to the lane changing condition.

[0130] Here, obstacles include static obstacles and dynamic obstacles. In this type of trajectory planning, two types of working conditions are mainly considered: obstacle avoidance and lateral target lane change. Considering the simplicity of the problem, this application focuses on analyzing the trajectory planning and tracking control during the obstacle avoidance process. This process includes five stages of planning and tracking control, the first stage: straight driving close to the obstacle before obstacle avoidance, the second stage: curved driving to the adjacent lane when avoiding obstacles, the third stage: straight driving after changing to the adjacent lane, the fourth stage: curved driving to the original lane after overtaking, and the fifth stage: straight driving after changing to the original lane.

[0131] Here, when there is an abnormal obstacle in front of CAST and it is driving at a low speed, it is necessary to change lanes and overtake in advance, start trajectory planning, and use it as the starting point for planning. Here, it is assumed that CAST maintains a safe distance from the obstacle in front before performing lateral obstacle avoidance and lane changing and always maintains the current vehicle speed. When performing obstacle avoidance, CAST should maintain at least the necessary safe distance for lane change with the obstacle in front, that is:

[0132]

[0133] The above formula can ensure that the collision with the obstacle ahead can be avoided during the lane change process, which is used as the end point of the first stage planning and the starting point of the second stage planning, where l lane is the distance between the center axes of adjacent lanes, v qy_max is the maximum lateral velocity of CAST, y pv is the y-axis coordinate of the obstacle ahead, and the idle lane with fewer vehicles and a larger longitudinal distance between the front and rear vehicles and the CAST vehicle is preferred. Here, taking the lane change to the right as an example, the longitudinal vehicle position is:

[0134]

[0135] The lateral vehicle position is:

[0136] x q (t+1)=x q (t)+v qy_max .

[0137] After that, CAST changes lanes at the maximum lateral acceleration, and introduces the vehicle turning curvature value and curvature change rate as the maximum restriction conditions to further constrain vehicle driving safety. The position when the CAST body is flush with the lane is used as the planning end point of the second stage and the planning starting point of the third stage. After entering the third stage, the longitudinal position of the CAST tail needs to exceed the obstacle head position by a certain safety distance, that is:

[0138]

[0139] Among them lg and l pv are the lengths of the trailer and the obstacle in front, respectively, s pv_safe Indicates the static safety distance of the obstacle ahead, which is the end point of the third stage planning and the starting point of the fourth stage planning. When CAST overtakes the obstacle, it needs to return to the original lane and enter the fourth stage. The center line of the original lane is used as the target trajectory point, and the longitudinal vehicle position is:

[0140]

[0141] The lateral vehicle position is: x q (t+1)=x q (t)-v qy_max .

[0142] After that, CAST changes lanes at the maximum lateral acceleration, and introduces the vehicle turning curvature value and curvature change rate as the maximum restriction conditions to further constrain vehicle driving safety. The position when the CAST body is flush with the lane is used as the planning end point of the fourth stage and the planning start point of the fifth stage. Finally, CAST enters the fifth stage and keeps driving in a straight line. All stages are fitted into a smooth curve that is more suitable for vehicle road driving using a fifth-order polynomial.

[0143] It should be noted that for the lane change analysis of the lateral target lane, it is only necessary to assume that there is a static obstacle at a suitable position in front of CAST (v pv =0), including straight-line driving before lane changing, curved driving during lane changing, and straight-line driving after lane changing.

[0144] In this application, when it is determined that the driving condition corresponding to the target vehicle at the next moment is a straight-line condition, in order to ensure the driving stability of the vehicle, a longer preview time and a larger preview distance should be selected, and the lateral deviation does not need to be considered too much when driving in a straight line, so as to achieve the purpose of improving the lateral stability of CAST. Here, according to the Ackerman steering relationship, tanγ=l1 / R safe , x q =v qx / R safe , where γ is the turning angle of the tractor, l1 is the wheelbase of the front wheel of the tractor, and R safe is the turning radius of CAST to maintain a stable state. According to the kinematic formula, the time T of CAST can be obtained. pc The preview position is The CAST target vehicle speed can be driven according to the discrete speed planned in the straight line condition, and the center of mass moves along the current straight line driving direction. Then the PID controller is used for speed tracking control, where the continuous form formula is Where u(t) is the speed value output by the PID controller, and e(t) is the deviation signal, which specifically refers to the difference between the expected speed given in trajectory planning and the actual speed. Similarly, the same PID control is performed on the sideslip angle of the center of mass, K p , K i , K d They are proportional coefficient, integral coefficient and differential coefficient respectively, which can not only eliminate and reduce steady-state error but also reduce system oscillation caused by excessive error value change rate. In this way, safe following, deceleration and emergency braking of CAST straight-line driving conditions can be achieved through PID control.

[0145] In this application, when it is determined that the driving condition corresponding to the target vehicle at the next moment is a curve condition, it is necessary to consider the errors between the vehicle's center of mass position, vehicle speed, yaw rate and the expected center of mass trajectory and yaw rate. In addition, the front wheel steering angle change rate should be reduced as much as possible to improve the vehicle's lateral stability and prevent folding and tipping. Therefore, in the curve tracking control process of the target vehicle, in order to ensure that the CAST trajectory tracking is more accurate and stable, this application can adopt MPC trajectory tracking control, combining the center of mass error, speed error, yaw rate error and front wheel steering angle change rate as the control objective function, constraining the stability and safety, and outputting a suitable front wheel steering angle for CAST.

[0146] Here, the control objective function of MPC is:

[0147]

[0148] Among them, minC con Take the minimum value of the objective function, C i , i = 1, 2, 3, 4 represent the weight coefficients of the mass center side deviation, velocity vector error, yaw rate error and front wheel turning angle change rate respectively, x q (t), x q0 (t), y q (t), y q0 (t) represents the actual and planned horizontal and vertical coordinates of the center of mass of the target vehicle (such as a tractor), v q 、v q0 denote the actual and planned velocity vectors of the target vehicle (e.g., tractor), ω q , ω0 represent the actual value and planned value of the yaw angular velocity respectively, and γ(t) represents the wheel turning angle.

[0149] The constraints of MPC are:

[0150]

[0151] Among them, a ymax is the maximum lateral acceleration, vqymax 、v qxmax is the maximum lateral and longitudinal velocity, γ max is the maximum wheel angle change rate, ω max is the maximum yaw angular velocity, and the limiting braking torque N b And driving torque N a Maximum braking torque N bmax and driving torque N amax .

[0152] Here, according to the kinematic formula, there are the following constraints:

[0153]

[0154] Here, when CAST is at the starting point of the curve trajectory planning, let t = 0, after solving the above variables and outputting them to CAST, trajectory tracking control is performed, and in the next step, T con The system solves the problem in a loop and continuously uses the information obtained by the information acquisition module to perform dynamic rolling tracking control and feedback correction of the actual driving trajectory.

[0155] In this application, when it is determined that the driving condition corresponding to the target vehicle at the next moment is a lane change condition, the focus is on the place where the straight line and the curve are combined, and different planning modules and tracking control modules are accurately called by clearly distinguishing the connection between the straight line and the curve. Considering that the intelligent multi-axle steering car train has a long body, the tracking control models for straight lines and curves are different.

[0156] For example, for a straight line + curve lane change condition, when the center of mass of the front wheel of the CAST tractor coincides with the beginning of the straight line, the PID control model is called to control the CAST to drive in a straight line to ensure driving safety; when the center of mass of the front wheel of the tractor coincides with the end of the straight line, the MPC control model is called to control the CAST to turn. This ensures that the vehicle maintains lateral stability when driving in a straight line and reduces excessive lateral oscillation caused by the overly sensitive MPC model.

[0157] For the curve + straight lane changing condition. When the center of mass of the front wheel of the tractor coincides with the curve stage, the MPC control model is called to control the CAST steering; when the center of mass of the trailer wheel coincides with the end stage of the curve, the PID control model is called to control the entire CAST body to complete the curve and then enter the straight line. This can ensure that the vehicle drives along the planned route when driving on the curve, and prevent collisions with obstacles or vehicles inside the curve.

[0158] For a complete lane changing condition, it will experience complex changes of straight line + curve, curve + straight line, straight line + curve, and curve + straight line. Therefore, PID control is used in this application to simplify the control under straight line conditions and focus on the tracking control of the longitudinal speed. The use of MPC under curved conditions can achieve precise control of the lateral and longitudinal directions and keep CAST stable. In this way, the needs of smooth and safe driving of the target vehicle under different conditions can be met.

[0159] In the present application, the vehicle-road cloud platform can also periodically update the driving conditions and corresponding driving trajectories based on the traffic status information.

[0160] For example, the planning cycle is T, and the traffic status information and road-related information of the intelligent multi-axle steering car train are actively obtained every Ts, and then steps 101-103 are looped to continuously call the CAST path planning and tracking control module. In this way, the models and rules constructed in this application are carried out in a dynamic rolling periodicity, which can obtain traffic status information in a more real-time, accurate and efficient manner, provide trajectory planning solutions under complex working conditions for intelligent multi-axle steering car trains and perform tracking control, so that the intelligent multi-axle steering car train can be safer, more stable and reliable during driving, while saving computing resources and meeting real-time computing needs.

[0161] The vehicle control method provided in the present application adopts different control strategies based on different types of working conditions, which solves the problem in the prior art that a single control method causes unstable driving of the vehicle at the junction of two different working conditions, so that the vehicle can drive smoothly and safely under any working condition.

[0162] Figure 2 The process diagram of the vehicle control method in this application is shown as follows Figure 2 ,like Figure 2 As shown, the method includes:

[0163] Step 201, determining whether the target vehicle enters the network connection area;

[0164] If yes, go to step 202, if no, go to step 201;

[0165] Step 202, obtaining traffic status information;

[0166] Here, road status information and environmental status information of other surrounding vehicles and objects can be obtained through laser radar and intelligent road test equipment; GPS and sensors can also be used to obtain the speed, position and steering angle information of the target vehicle.

[0167] Step 203, based on the traffic state information, generating a traffic state matrix corresponding to the target vehicle at the current moment;

[0168] Step 204, generating a dynamic model of the target vehicle based on the longitudinal motion information, lateral motion information and yaw motion information of the target vehicle;

[0169] Step 205, determining the driving condition corresponding to the target vehicle at the next moment based on the traffic state matrix and the dynamic model;

[0170] If it is a straight line condition, execute step 206; if it is a curve condition, execute step 207; if it is a lane change, execute step 208;

[0171] Step 206, constructing discrete trajectory points of the straight line working condition;

[0172] Step 207 , constructing discrete trajectory points of the curved working condition, and fitting the discrete trajectory points using a fifth-order polynomial to obtain a smooth curved driving trajectory corresponding to the curved working condition.

[0173] Step 208: construct discrete trajectory points of the lane changing condition, and use a quintic polynomial to fit the discrete trajectory points under the lane changing condition to obtain a smooth lane changing driving trajectory corresponding to the lane changing condition.

[0174] Step 209, under the straight line condition, calling the PID control strategy to control the target vehicle to travel along the straight line trajectory at the next moment;

[0175] Step 210, under the curve condition, calling the MPC control strategy to control the target vehicle to travel along the curve trajectory at the next moment;

[0176] Step 211, in the case of a straight path under the lane-changing condition, the PID control strategy is called to control the target vehicle to travel along the straight driving trajectory at the next moment; and in the case of a curved path under the lane-changing condition, the MPC control strategy is called to control the target vehicle to travel along the curved driving trajectory at the next moment.

[0177] The vehicle control method provided in the present application can perform different trajectory planning under different types of working conditions, and consider the straight-line driving scene, the curved driving scene, and the combination of curves and straight lines separately. When planning the trajectory, multiple planning discrete points are generated as the future trajectory of the intelligent multi-axle steering vehicle train, and a fifth-order polynomial is used to fit a smooth curve that is more suitable for the vehicle's road driving conditions under the curved working conditions. In combination with the high computational efficiency under PID control and the high model accuracy under MPC control, a model conversion rule is designed. A PID model with high computational efficiency is used under straight-line working conditions, and an MPC model with high model accuracy is used under curved working conditions. A tracking control model conversion rule is designed under the working conditions of the combination of curves and straight lines, and the center of mass position of the tractor and trailer is used to solve the continuity problem of the junction point of the two working conditions.

[0178] Figure 3 The schematic diagram of the structure of the vehicle control system in this application is as follows: Figure 3 As shown, the system includes:

[0179] The acquisition unit 301 is used to acquire traffic status information, where the traffic status information at least includes driving status information of the target vehicle, road status information and surrounding environment status information;

[0180] A calculation unit 302, configured to determine a driving condition corresponding to the target vehicle at a next moment based on the traffic state information;

[0181] The control unit 303 is used to control the target vehicle to travel according to the corresponding driving trajectory at the next moment based on the control strategy corresponding to the driving condition.

[0182] In a preferred solution, the acquisition unit 301 can acquire the traffic status information through the target vehicle, the road test equipment and other vehicles around the target vehicle.

[0183] In a preferred embodiment, the system further comprises: a generating unit 304;

[0184] Wherein, the generating unit 304 is used to generate a traffic state matrix corresponding to the target vehicle at the current moment based on the traffic state information;

[0185] The calculation unit 302 is used to determine that the driving condition corresponding to the target vehicle at the next moment is a straight-line condition if it is determined based on the traffic state matrix that there is no obstacle in the driving direction of the target vehicle or the target vehicle and the obstacle meet preset distance conditions and speed conditions; and to determine that the driving condition corresponding to the target vehicle at the next moment is a curved condition if it is determined based on the traffic state matrix that there is a curve area in the driving direction of the target vehicle; and to determine that the driving condition corresponding to the target vehicle at the next moment is a lane-changing condition if it is determined based on the traffic state matrix that there is no curve area in the driving direction of the target vehicle, but the distance condition and / or speed condition are not met between the target vehicle and the obstacle.

[0186] In the preferred embodiment, when it is determined that the driving condition corresponding to the target vehicle at the next moment is a straight-line condition, the control unit 303 is also used to control the target vehicle to be in a uniform driving state if there is no obstacle in the driving direction of the target vehicle, and the uniform driving state indicates that the target vehicle maintains the current speed; and to control the target vehicle to be in a following driving state if the distance between the target vehicle and the obstacle is greater than or equal to a first preset distance, and the current speed of the target vehicle is less than the moving speed of the obstacle, and the following driving state indicates that the target vehicle first accelerates to a first target speed and then decelerates to the same speed as the moving speed of the obstacle; and to control the target vehicle to be in a following driving state if the distance between the target vehicle and the obstacle is greater than or equal to a first preset distance, and the current speed of the target vehicle is less than the moving speed of the obstacle. If the distance between the target vehicle and the obstacle is less than the first preset distance but greater than or equal to the second preset distance, and the current speed of the target vehicle is greater than or equal to the moving speed of the obstacle, the target vehicle is controlled to be in a decelerated driving state, and the decelerated driving state indicates that the target vehicle first drives at a constant speed at the current speed to a target time length, and then decelerates to a second target speed; and if the distance between the target vehicle and the obstacle is less than the second preset distance, and the current speed of the target vehicle is greater than or equal to the moving speed of the obstacle, the target vehicle is controlled to be in an emergency stop state, and the emergency stop state indicates that the target vehicle decelerates from the current speed to a third target speed.

[0187] In a preferred embodiment, when it is determined that the driving condition corresponding to the target vehicle at the next moment is a curved condition, the system further includes: a construction unit 305;

[0188] A construction unit 305 is used to construct discrete trajectory points under the curve condition based on first constraint information of the curve area, wherein the first constraint information includes a starting position, an end position, a road centerline, a vehicle turning curvature value, a curvature change rate, and lateral and longitudinal accelerations of the curve area;

[0189] The calculation unit 302 is further used to fit the discrete trajectory points using a quintic polynomial to obtain a curved driving trajectory corresponding to the curved working condition.

[0190] In a preferred embodiment, when it is determined that the driving condition corresponding to the target vehicle at the next moment is a lane-changing condition, the construction unit 305 is further used to construct discrete trajectory points under the lane-changing condition based on second constraint information between the target vehicle and the obstacle, wherein the second constraint information includes a safety distance between the target vehicle and the obstacle, a longitudinal vehicle position of the target vehicle, a lateral vehicle position of the target vehicle, a centerline distance of the lane to be changed, a centerline of the original lane, a maximum lateral speed of the target vehicle, a coordinate position of the obstacle, a vehicle turning curvature value, and a curvature change rate;

[0191] The calculation unit 302 is further used to fit the discrete trajectory points under the lane changing condition using a quintic polynomial to obtain a lane changing driving trajectory corresponding to the lane changing condition.

[0192] In the preferred embodiment, the system further comprises: an input unit 306 and a calling unit 307;

[0193] Wherein, the generating unit 304 is further used to generate a dynamic model of the target vehicle based on the longitudinal motion information, the lateral motion information and the yaw motion information of the target vehicle;

[0194] An input unit 306, configured to input the mass center offset and speed error of the target vehicle as target variables into the dynamic model if the driving condition corresponding to the target vehicle at the next moment is a straight-line condition;

[0195] A calling unit 307, used to call a proportional integral derivative (PID) control strategy to control the target vehicle to travel along a straight-line trajectory at the next moment;

[0196] The input unit 306 is further used to input the mass center offset, speed error, front wheel steering angle and yaw rate error of the target vehicle as target variables into the dynamic model if the driving condition corresponding to the target vehicle at the next moment is a curve condition;

[0197] The calling unit 307 is also used to call the model prediction control (MPC) strategy to control the target vehicle to travel along the curved driving trajectory at the next moment.

[0198] The input unit 306 is further configured to input the mass center offset, speed error, front wheel steering angle and yaw rate error of the target vehicle as target variables into the dynamic model if the driving condition corresponding to the target vehicle at the next moment is a lane change condition;

[0199] The calling unit 307 is also used to call the proportional integral differential (PID) control strategy to control the target vehicle to travel along the straight trajectory in the lane changing driving trajectory at the next moment when the center of mass of the front wheels of the target vehicle coincides with the starting stage of the straight line; and to call the model prediction (MPC) control strategy to control the target vehicle to travel along the curved trajectory in the lane changing driving trajectory at the next moment when the center of mass of the front wheels of the target vehicle coincides with the starting stage of the curved line.

[0200] In a preferred embodiment, the system further comprises: an updating unit 308;

[0201] The updating unit 308 is used to periodically update the driving condition and the corresponding driving trajectory according to the traffic status information.

[0202] It should be noted that the vehicle control system provided in the above embodiment is different from the above Figure 1 to Figure 2 The provided vehicle control method belongs to the same concept, and the details can be referred to the above method embodiments, which will not be repeated here.

[0203] The following is an example of this application, for example, in Example 1:

[0204] Assume that there is an intelligent multi-axle steering vehicle train driving in a straight line on the lane of an urban road and communicating with the vehicle-road cloud platform. The positioning system and network communication system of the intelligent multi-axle steering vehicle train send their own traffic status information to the vehicle-road cloud platform. The vehicle-road cloud platform assigns it a unique ID sequence CAST1 and assigns it an updated traffic status initial matrix CAST1 = [60,50,0,0,10,0,0,0,0,10,10], indicating that CAST1 is driving in a straight line in the lane at an initial longitudinal speed of 10m / s. The center of mass positions of the tractor and trailer are 60m and 50m respectively, and the lengths of the tractor and trailer are 10m respectively. Then, CAST1 sends road condition information and other traffic participant information to the vehicle-road cloud platform through on-board sensor equipment, laser radar and other sensing equipment and intelligent roadside equipment through networked equipment, and finds that there is a vehicle driving in front of CAST1, and obtains the speed of the vehicle in front as v pv , and there exists v qx ≥v pv >0, then it is necessary to determine whether CAST1 meets the safety conditions for continuing to drive in a straight line, and obtain the distance between CAST1 and the vehicle in front as l c_pv , and verify the judgment model:

[0205]

[0206] After calculation, the above safety conditions are met. Therefore, CAST1 can continue to drive straight in the current lane.

[0207] For the condition where CAST1 continues to travel in a straight line, due to the existence of v qx ≥v pv >0 and 25m≤l c_pv <50m, so CAST1 needs to decelerate. In order to reduce the fluctuation of CAST trajectory, it first drives at a constant speed at the current speed and then decelerates at a comfortable deceleration to the same speed as the vehicle in front and maintains a safe headway. In other words, it finally drives safely with the speed of the vehicle in front:

[0208]

[0209] This formula can be used to calculate the time t that CAST1 will continue to travel at a constant speed at the current speed.u In this embodiment, t u <0, so the uniform speed driving stage is cancelled and a larger deceleration is used to directly decelerate the driving. The deceleration formula is as follows:

[0210]

[0211] To find the target deceleration d qx The planned trajectory is a discrete trajectory. These discrete trajectory points are connected into a straight line. They do not need to be fitted into a smooth curve like curved driving. For discrete trajectory points, in order to ensure the driving safety of CAST1, the characteristics include both longitudinal speed and position.

[0212] Finally, according to the planned trajectory points, the PID controller is used for speed tracking control, which mainly uses the difference between the discrete trajectory point speed value generated in the planning stage and the actual speed value of the car train to control. The continuous form formula is Where u(t) is the speed value output by the PID controller, and e(t) is the deviation signal, which specifically refers to the difference between the expected speed given in trajectory planning and the actual speed. Similarly, the same PID control is performed on the sideslip angle of the center of mass, K p , K i , K d They are proportional coefficient, integral coefficient and differential coefficient respectively, which can not only eliminate and reduce steady-state error but also reduce system oscillation caused by excessive error value change rate. Through PID control, safe following, deceleration and emergency braking of CAST straight-line driving conditions can be achieved.

[0213] It is worth noting that considering the limitations of computational efficiency and the time-varying characteristics of traffic flow, dynamic rolling periodic updates are adopted in the completed trajectory planning and tracking control process.

[0214] For example, in Example 2:

[0215] Suppose there is an intelligent multi-axle steering car train CAST2 running straight at a constant speed on a city road, and encounters a curve ahead, it needs to turn to drive along the curve. Assume that the initial traffic state of the intelligent multi-axle steering car train is CAST2 = [0,100,90,0,0,10,0,0,0,0,10,10], that is, the center of mass of the trailer is running straight at a longitudinal speed of 10m / s. Suddenly, it is obtained that the vehicle ahead in the same lane slows down and stops, which affects the normal driving of CAST2. If no braking measures are taken, the two vehicles will collide. Taking braking measures but not turning measures will affect the continued driving of CAST2, so it takes the lane change to the adjacent lane on the left. The maximum longitudinal speed of CAST2 is 15m / s, the maximum lateral speed is 1m / s, the maximum longitudinal acceleration is 2m / s2, and the maximum lateral acceleration is 0.5m / s2. The longitudinal distance between CAST2 and the stationary vehicle in front is 100m, which is enough distance to complete the lane change without changing back to the original lane. The distance between the center lines of adjacent lanes is 3.5m. The simulation test environment uses MATLAB / Simulink and Trucksim joint simulation. The simulation results include trajectory tracking effect and speed tracking effect. Figure 4 As shown in Figure 2, the trajectory tracking error and velocity tracking error are as follows: Figure 5 shown.

[0216] Depend on Figure 4 From (a) in the figure, we can see that in the working condition of Example 2, CAST2 first drives in a straight line. After receiving the information that it needs to change lanes at 0s, it starts to execute the lane change strategy to the left, and starts tracking control while performing trajectory planning. First, the steering wheel angle is increased to the left, and then the steering wheel angle is turned to the right when the lane change is halfway through, so that when CAST2 successfully changes lanes to the left lane (that is, the lateral displacement is 3.5m), the body of the vehicle is in a state parallel to the lane, and finally continues to drive in a straight line after completing the lane change. While changing lanes to the left, Figure 4 As can be seen from (b) in the figure, CAST2 needs to brake to reduce the speed first to prevent a rollover accident during the lane change due to excessive speed.

[0217] pass Figure 5 As can be seen from (a) in the figure, the error between the actual lateral displacement and the planned lateral displacement increases first and then decreases. This may be due to the large left turn angle of the front wheel during the tracking control process, which leads to an increasing deviation. However, after the wheel is adjusted to the right, the lateral displacement error gradually decreases and is almost 0 when the lane change is completed. During the lane change process, the maximum lateral displacement error is 0.15m. Figure 5As can be seen from (b) in the figure, the longitudinal speed error of CAST2 increases first and then decreases. On the one hand, this may be because when there is a lateral error, the longitudinal speed is increased to eliminate the error so that the actual trajectory is more consistent with the planned trajectory. On the other hand, it may be because the mass of CAST2 is too large, resulting in a large inertia force. The actual braking force makes it impossible for the vehicle speed of CAST2 to immediately meet the planning requirements.

[0218] Through the vehicle control system provided by the present application, different control strategies are selected and called according to the operating characteristics of different driving conditions, which can not only improve the model efficiency while ensuring the driving safety of the vehicle, but also improve the stability of the vehicle.

[0219] In the present application, a vehicle is also provided, the vehicle is connected to a vehicle-road cloud platform, and is used to send traffic status information to the vehicle-road cloud platform, and is used to receive a control instruction sent by the vehicle-road cloud platform based on the traffic status information, and drives according to the corresponding driving trajectory at the next moment based on a control strategy corresponding to the driving condition in the control instruction;

[0220] Wherein, the vehicle at least comprises:

[0221] A tractor and a trailer, each of the tractor and the trailer has at least one set of axles and wheels; one end of the trailer is connected to the tractor for traveling under the traction of the tractor.

[0222] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. In addition, the features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0223] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle control method, characterized in that: The method comprises: Acquiring traffic status information, wherein the traffic status information at least includes driving status information of the target vehicle, road status information, and surrounding environment status information; Determine the driving condition corresponding to the target vehicle at the next moment based on the traffic state information; Based on the control strategy corresponding to the driving condition, the target vehicle is controlled to travel according to the corresponding driving trajectory at the next moment.

2. The method according to claim 1, characterized in that The obtaining of traffic status information includes: The traffic status information is acquired through the target vehicle, the road test equipment and other vehicles around the target vehicle.

3. The method according to claim 1, characterized in that The determining the driving condition corresponding to the target vehicle at the next moment based on the traffic state information includes: Based on the traffic state information, generating a traffic state matrix corresponding to the target vehicle at the current moment; If it is determined based on the traffic state matrix that there is no obstacle in the driving direction of the target vehicle or the distance condition and speed condition between the target vehicle and the obstacle are met, determining that the driving condition corresponding to the target vehicle at the next moment is a straight line condition; If it is determined based on the traffic state matrix that the driving direction of the target vehicle has a curve area, determining that the driving condition corresponding to the target vehicle at the next moment is a curve condition; If it is determined based on the traffic state matrix that there is no curve area in the driving direction of the target vehicle, but the distance condition and / or speed condition are not satisfied between the target vehicle and the obstacle, it is determined that the driving condition corresponding to the target vehicle at the next moment is a lane change condition.

4. The method according to claim 3, characterized in that When it is determined that the driving condition corresponding to the target vehicle at the next moment is a straight-line condition, the method further includes at least one of the following: If there is no obstacle in the driving direction of the target vehicle, control the target vehicle to be in a uniform driving state, wherein the uniform driving state indicates that the target vehicle maintains the current speed; If the distance between the target vehicle and the obstacle is greater than or equal to a first preset distance, and the current speed of the target vehicle is less than the moving speed of the obstacle, the target vehicle is controlled to be in a following vehicle driving state, wherein the following vehicle driving state indicates that the target vehicle first accelerates to a first target speed and then decelerates to the same speed as the moving speed of the obstacle; If the distance between the target vehicle and the obstacle is less than the first preset distance but greater than or equal to the second preset distance, and the current speed of the target vehicle is greater than or equal to the moving speed of the obstacle, the target vehicle is controlled to be in a decelerated driving state, wherein the decelerated driving state indicates that the target vehicle first drives at a constant speed at the current speed for a target time, and then decelerates to a second target speed; If the distance between the target vehicle and the obstacle is less than the second preset distance, and the current speed of the target vehicle is greater than or equal to the moving speed of the obstacle, the target vehicle is controlled to be in an emergency stop state, and the emergency stop state indicates that the target vehicle decelerates from the current speed to a third target speed.

5. The method according to claim 3, characterized in that: When it is determined that the driving condition corresponding to the target vehicle at the next moment is a curved condition, the method further includes: Constructing discrete trajectory points under the curve condition based on first constraint information of the curve area, wherein the first constraint information includes a starting position, an end position, a road centerline, a vehicle turning curvature value, a curvature change rate, and lateral and longitudinal accelerations of the curve area; The discrete trajectory points are fitted using a quintic polynomial to obtain a curved driving trajectory corresponding to the curved working condition.

6. The method according to claim 3, characterized in that When it is determined that the driving condition corresponding to the target vehicle at the next moment is a lane-changing condition, the method further includes: Constructing discrete trajectory points under the lane changing condition based on second constraint information between the target vehicle and the obstacle, the second constraint information including a safe distance between the target vehicle and the obstacle, a longitudinal vehicle position of the target vehicle, a lateral vehicle position of the target vehicle, a center axis distance of a lane to be changed, a center line of an original lane, a maximum lateral speed of the target vehicle, a coordinate position of the obstacle, a vehicle turning curvature value, and a curvature change rate; The discrete trajectory points under the lane-changing condition are fitted using a quintic polynomial to obtain a lane-changing driving trajectory corresponding to the lane-changing condition.

7. The method according to claim 3, characterized in that The control strategy corresponding to the driving condition controls the target vehicle to drive according to the corresponding driving trajectory at the next moment, including: Generate a dynamic model of the target vehicle based on the longitudinal motion information, lateral motion information and yaw motion information of the target vehicle; If the driving condition corresponding to the target vehicle at the next moment is a straight-line condition, the mass center offset and speed error of the target vehicle are input into the dynamic model as target variables, and a proportional-integral-differential (PID) control strategy is called to control the target vehicle to drive along a straight-line driving trajectory at the next moment; If the driving condition corresponding to the target vehicle at the next moment is a curved condition, the mass center offset, speed error, front wheel steering angle and yaw rate error of the target vehicle are input into the dynamic model as target variables, and the model prediction control (MPC) strategy is called to control the target vehicle to drive along the curved driving trajectory at the next moment; If the driving condition corresponding to the target vehicle at the next moment is a lane changing condition, the center of mass offset, speed error, front wheel steering angle and yaw angular velocity error of the target vehicle are input into the dynamic model as target variables, and when the front wheel center of mass of the target vehicle coincides with the straight-line starting stage, the proportional integral differential (PID) control strategy is called to control the target vehicle to drive along the straight-line trajectory in the lane changing driving trajectory at the next moment; when the front wheel center of mass of the target vehicle coincides with the curve starting stage, the model prediction (MPC) control strategy is called to control the target vehicle to drive along the curve trajectory in the lane changing driving trajectory at the next moment.

8. The method according to claim 1, characterized in that The method further comprises: The driving condition and the corresponding driving trajectory are periodically updated according to the traffic status information.

9. A vehicle control system, characterized in that: The system comprises: An acquisition unit, used to acquire traffic status information, wherein the traffic status information at least includes driving status information of the target vehicle, road status information and surrounding environment status information; A calculation unit, used to determine the driving condition corresponding to the target vehicle at the next moment based on the traffic state information; A control unit is used to control the target vehicle to travel according to the corresponding driving trajectory at the next moment based on the control strategy corresponding to the driving condition.

10. A vehicle, characterized in that: The vehicle is connected to the vehicle-road cloud platform, and is used to send traffic status information to the vehicle-road cloud platform, and to receive control instructions sent by the vehicle-road cloud platform based on the traffic status information, and drive according to the corresponding driving trajectory at the next moment based on the control strategy corresponding to the driving condition in the control instruction; Wherein, the vehicle at least comprises: A tractor and a trailer, each of the tractor and the trailer has at least one set of axles and wheels; one end of the trailer is connected to the tractor for traveling under the traction of the tractor.

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