A multi-objective approach for inbound trajectory planning of an automated driving bus at a bay-type stop

Abstract

The present invention relates to a multi-objective approach trajectory planning method for an automatic driving bus at a bay-type bus stop, which includes the following steps: Step S1: Set a control area and establish a plane coordinate system; Step S2: Determine the basic operation rules of the bus; Step S3: The multi-objective approach trajectory planning model includes determining the trajectory planning objectives and determining the trajectory planning constraint conditions; Step S31: Select the minimum weighted value of punctuality P, energy consumption E, and comfort C as the objective of the bus approach trajectory planning; Step S32: Determine the constraint conditions of the trajectory planning, considering speed, acceleration, curvature, driving distance, start and end positions, and driving obstacle avoidance constraints; Step S4: Solve the planning model through a sequential quadratic optimization algorithm to obtain the optimal driving trajectory of the bus vehicle; Step S5: Execute the vehicle trajectory according to Step S4; Step S6: Wait for the next round of decision trigger. It can effectively implement the whole process decision-making of the automatic driving bus approaching the station in the bay-type bus stop scenario.

Description

Technical Field

[0001] The present invention relates to the field of autonomous vehicle control, and more particularly, to a multi-objective in-station trajectory planning method for an autonomous bus bay-type stop. Background Art

[0002] In recent years, autonomous vehicles have achieved information sharing between vehicles and between vehicles and roads with the help of advanced sensing devices, showing great potential in improving driving safety and traffic efficiency. The behavior decision-making and planning of autonomous vehicles have become a research hotspot. In the scenario of a bus bay-type stop, the bus in-station behavior is a typical high-frequency vehicle decision-making behavior. The core problem of the autonomous bus in-station system lies in trajectory planning. The vehicle collects information on its body position and obstacles in the approaching station environment through sensors, and thus plans the optimal path from the initial vehicle position to the target parking space. Different from private cars, buses have an operational nature. During the bus in-station process, not only the enterprise operation cost needs to be considered, but also the passenger experience should be taken into account. Therefore, in-station trajectory planning needs to consider factors such as arrival punctuality, energy consumption, and comfort, and the control range should not be limited to the local area of the bay-type stop. Existing research has not comprehensively considered the entire process of vehicle in-station, and the control objectives are relatively single. Based on this, on the basis of using autonomous driving technology, how to effectively control bus in-station while considering multiple factors is an urgent problem for those skilled in the art. Summary of the Invention

[0003] The purpose of the present invention is to overcome the above-mentioned defects in the prior art, and provide a multi-objective in-station trajectory planning method for an autonomous bus bay-type stop. This method involves the application of autonomous driving technology. On the basis of determining the control area, a multi-objective trajectory planning model is constructed, and the sequential quadratic programming algorithm is used for solution to obtain the optimal in-station driving trajectory of the bus vehicle.

[0004] To achieve the above object, the present invention provides a multi-objective in-station trajectory planning method for an autonomous bus bay-type stop, specifically including the following steps:

[0005] Step S1: Set the control area and establish a plane coordinate system;

[0006] The applicable scenario is a bay-type bus stop under the condition of a bus-only lane. The total length of the autonomous bus control area is , including the length of the road control area , the deceleration area length of the bay-type stop, and the parking area length . The lane width is , the width of the parking area is , the included angle between the platform and the deceleration area is , and the length of the bus body , Width , Height , Wheelbase , Front overhang , Rear overhang , The four vertices of the bus are respectively , The angle between the vehicle body and the horizontal plane is the vehicle body direction angle , Establish a plane coordinate system, the starting point of the horizontal axis is the starting point of the control area along the driving direction of the bus on the road, and the starting point of the vertical axis is the curbstone on the outer edge of the bus stop;

[0007] Step S2: Determine the basic operation rules of the bus;

[0008] Step S3: The multi-objective in-station trajectory planning model, including determining the trajectory planning objective and determining the trajectory planning constraint conditions;

[0009] Step S4: Solve the planning model through the sequential quadratic optimization algorithm to obtain the optimal driving trajectory of the bus;

[0010] Step S5: Execute the vehicle trajectory according to the trajectory output in Step S4;

[0011] Step S6: Wait for the next round of decision trigger.

[0012] As a preference of the present invention, the following steps are further included in Step S2:

[0013] Step S21: The moment when the bus arrives at the starting point of the control area is =0, and at this time the bus is already driving in the outermost lane, and the speed is , The safety distance between the vehicle body and the lane boundary before entering the station , The safety distance between the vehicle body and the platform boundary when docking , The distance from the front end of the vehicle to the front end of the platform when docking , The distance between the vehicle and the platform after docking , Take the trajectory of the center point of the front axle of the bus as the vehicle trajectory, and define The state of the bus at time as:

[0014] (1);

[0015] Wherein are respectively The horizontal and vertical coordinates of the bus at time, are respectively The horizontal and vertical speeds of the bus at time, and represent at time and The accelerations in the directions of.

[0016] As a preference of the present invention, the following steps are further included in step S21:

[0017] Step S22: Within the control area, set the driving distance of the autonomous driving bus as , where the driving distance during the approach preparation stage is . The bus experiences two driving states successively during the approach preparation stage and is divided into two parts. First, the first part is that the bus travels at a constant speed of for a driving time of , and the driving distance is ; The second part is that the bus then decelerates at a deceleration of for a driving time of , and the driving distance is . The final speed at this stage is set as . The parameter expressions of these two parts are as follows:

[0018] The expression of the driving distance is as follows:

[0019] (2);

[0020] The expression of the driving distance is as follows:

[0021] (3);

[0022] The expression of the driving distance during the approach preparation stage is as follows:

[0023] (4);

[0024] The expression of the speed in the second part is as follows:

[0025] (5);

[0026] The expression of the acceleration in the second part is as follows:

[0027] (6).

[0028] As a preference of the present invention, the following steps are further included in step S22:

[0029] Step S23: The driving distance for the bus to change lanes and drive into the bay stop, the driving time , and the driving distance . In this stage, a quintic polynomial trajectory planning model is selected to express the lane-changing trajectory, as follows:

[0030] (7);

[0031] , are the coefficients of the fifth-degree polynomial. The first derivative and the second derivative of the above formula are obtained respectively:

[0032] (8);

[0033] (9);

[0034] The bus state at the end of the bus approach preparation stage is the starting point of the fifth-degree polynomial trajectory, which is expressed as:

[0035] (10);

[0036] The termination state after the bus stops at the station is:

[0037] (11);

[0038] When the initial time and the end time of the bus lane-changing approach and its state are all known, the trajectory coefficients of the vehicle are obtained, and then the optimal trajectory is selected through the approach trajectory planning model.

[0039] As a preference of the present invention, the step S3 further includes the following steps:

[0040] Step S31: Select that the weighted value of punctuality , energy consumption , and comfort is the smallest as the goal of bus approach trajectory planning. After normalization, the objective function expression is as follows:

[0041] min (12);

[0042] Among them, , , are the weight coefficients corresponding to each goal, satisfying , , , are the reference values;

[0043] In step S31, the expression of punctuality is as follows:

[0044] (13);

[0045] Among them, is the expected arrival and stop time of the bus, and a punctuality reference value is set .

[0046] As an optimization of the present invention, the step S31 further includes the following steps:

[0047] In step S31, the energy consumption has the following expression:

[0048] (14);

[0049] where and are the energy consumptions during the constant-speed and deceleration operations of the bus respectively;

[0050] During the constant-speed driving stage, the energy consumption of the bus has the following calculation formula:

[0051] (15);

[0052] Considering the efficiency loss, the motor output power during the constant-speed driving stage of the bus is:

[0053] (16);

[0054] where is the mechanical transmission efficiency, is the motor efficiency, is the battery efficiency;

[0055] The constant-speed driving power of the bus is the product of the traction force and the speed :

[0056] (17).

[0057] As an optimization of the present invention, the step S31 further includes the following steps:

[0058] During the constant-speed driving stage, the traction force for the longitudinal driving of the battery electric bus overcomes the rolling resistance, slope resistance, and air resistance. The calculation formula for the traction force is as follows:

[0059] (18);

[0060] (19);

[0061] In the formula: is the traction force, is the rolling resistance, is the slope resistance, is the air resistance, is the angle between the vehicle body and the horizontal plane, is the air resistance coefficient, is the speed, is the frontal area, is the speed, is the total mass of the vehicle and passengers, is the acceleration due to gravity, is the rolling resistance coefficient;

[0062] Therefore, the energy consumption of the bus during the constant-speed driving stage The calculation formula is:

[0063] (20);

[0064] During the deceleration stage, the pure electric bus has energy recovery. Part of the kinetic energy is used to offset the driving resistance, and the other part is converted into electrical energy by the drive motor to charge the power battery, and the remaining part is dissipated as heat. According to the kinematic relationship, the braking energy of the pure electric bus during the deceleration time is expressed as:

[0065] (21);

[0066] The kinetic energy reduced to offset the driving resistance is expressed as:

[0067] (22);

[0068] The energy recovered by the pure electric bus during deceleration is expressed as:

[0069] (23);

[0070] In the formula: is the brake distribution ratio;

[0071] The braking energy recovery efficiency coefficient can be expressed as:

[0072] (24);

[0073] During the deceleration stage, the braking energy of the pure electric bus is expressed

[0074] (25);

[0075] Let the energy consumption reference value be the energy consumed by the bus when driving a distance at the initial speed .

[0076] As a preference of the present invention, the following steps are further included in step S31:

[0077] In step S31, the comfort is expressed by the following formula:

[0078] (26);

[0079] wherein, means dividing the carriage into areas, and the numbers of standing and sitting passengers in area are and respectively, and the comforts are and respectively;

[0080] The comfort of standing passengers is calculated by the following formula:

[0081] (27);

[0082] wherein, the comprehensive weighted acceleration of standing passengers at is expressed as:

[0083] (28);

[0084] wherein, and respectively represent the accelerations in the directions of axis and axis at ;

[0085] The comfort of sitting passengers is calculated by the following formula:

[0086] (29);

[0087] The comprehensive weighted acceleration of sitting passengers at is expressed as:

[0088] (30);

[0089] Let the comfort reference value be the comfort value calculated when the bus decelerates at the maximum acceleration .

[0090] As a preference of the present invention, the following steps are further included in step S3:

[0091] Step S32: Determine the constraint conditions for trajectory planning, considering speed , acceleration , jerk , curvature , driving distance, start and end positions, driving obstacle avoidance constraints;

[0092] Among them, the speed constraint is expressed as:

[0093] (31);

[0094] The acceleration constraint is expressed as:

[0095] (32);

[0096] The jerk constraint is expressed as:

[0097] (33);

[0098] Among them, , , are the maximum speed, maximum acceleration, and maximum jerk values respectively;

[0099] The curvature is calculated by the formula:

[0100] (34);

[0101] The curvature constraint is expressed as:

[0102] (35);

[0103] is the minimum turning radius of the bus, with a value range of 8m - 12m;

[0104] When the bus is in the straight - driving stage, that is, at the end moment of changing lanes and entering the bay - side stop, there is no wheel steering, that is:

[0105] (36).

[0106] As an optimization of the present invention, step S32 further includes the following steps:

[0107] The horizontal and vertical coordinates of the four vertices of the bus at moment are respectively expressed as ([[]]END]] , ), ([[]]END]] , ), ([[]]END]] , ), ([[]]END]] , ), obtain the real-time coordinate positions of the four vertices according to the Ackerman steering model; there is a safety distance between the outer edge of the bus and the road and platform boundary during the approach process, and the obstacle avoidance constraint is expressed as:

[0108] (1) Control safety, the vehicle travels within the controllable range; the straight-line uniform driving distance constraint , the straight-line decelerating driving distance constraint ;

[0109] (3) The rear of the vehicle has not entered the bay, and the front of the vehicle has not entered the bay, that is, when , satisfy , , , ;

[0110] (4) The rear of the vehicle has not entered the bay, and the front of the vehicle is in the deceleration zone, that is, when , , satisfy , , , , ;

[0111] Among them, is the steering angle of the bus, and its expression is as follows:

[0112] (37);

[0113] Among them, is the relationship between the longitudinal running trajectory of the bus and time, is the relationship between the lateral running trajectory of the bus and time;

[0114] (5) The rear of the vehicle has not entered the bay, and the front of the vehicle is in the docking area, that is, when , , satisfy , , , , ;

[0115] (6) The rear of the vehicle is in the deceleration zone, and the front of the vehicle is in the deceleration zone. When , , satisfy , , , ;

[0116] (7) The rear of the vehicle is in the deceleration zone, and the front of the vehicle is in the docking area, that is, when , , satisfy , , , ;

[0117] (8) The rear of the bus is in the docking area and the front of the bus is in the docking area, that is, when , , it satisfies , , , ;

[0118] In addition, the state constraints at the bus starting and ending points are expressed as:

[0119] (1) The initial state of the bus is ;

[0120] (2) The terminal state of the bus is .

[0121] The beneficial effects of the present invention are as follows:

[0122] 1. A multi-objective inbound trajectory planning method for an autonomous driving bus bay-type stop of the present invention involves innovative applications of autonomous driving technology and can effectively achieve the whole process decision-making of an autonomous driving bus entering the station in a bay-type stop scenario.

[0123] 2. By constructing a trajectory planning model, the present invention divides the whole inbound process into a constant speed stage, a variable deceleration stage, and a lane-changing and inbound stage, defines the operating states of each stage in combination with the bus operation characteristics, and pays attention to the front-back association and mutual influence of each driving stage, so that the trajectory planning result can reach the global optimum.

[0124] 3. The trajectory planning model constructed by the present invention fully considers the enterprise operation cost and the passenger riding experience, proposes a multi-objective optimization function including punctuality, energy consumption, and comfort, and fully considers the bus vehicle characteristics, operation characteristics, and safety factors in terms of constraint conditions, so that the trajectory planning result can reach the optimum of comprehensive elements.

[0125] 4. The energy consumption sub-objective proposed by the present invention considers the energy consumption characteristics of pure electric buses, and the kinetic energy recovery is considered in the deceleration stage; the comfort sub-objective proposed considers the comfort values of passengers in different areas of the carriage and in different standing and sitting states, and the obtained inbound trajectory is more in line with the actual decision-making scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0126] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention.

[0127] Figure 1 It is the process schematic diagram of the multi-objective approach trajectory planning method for an automated driving bus bay-type stop provided by the present invention;

[0128] Figure 2 It is the plan view of the multi-objective approach trajectory planning control area for an automated driving bus bay-type stop provided by the present invention;

[0129] Figure 3 It is the flow chart of the sequential quadratic programming algorithm provided by the present invention;

[0130] Figure 4 It is the schematic diagram of the full-course planned trajectory provided by the present invention;

[0131] Figure 5 It is the schematic diagram of the trajectory of a bus changing lanes and driving into a bay-type stop provided by the present invention;

[0132] Figure 6 It is one of the diagrams related to the experimental data simulation parameters provided by the present invention;

[0133] Figure 7 It is the second diagram related to the experimental data simulation parameters provided by the present invention. Specific Embodiment

[0134] Embodiment 1

[0135] This embodiment provides a multi-objective approach trajectory planning method for an automated driving bus bay-type stop, which specifically includes the following steps:

[0136] Step S1: Set the control area and establish a plane coordinate system;

[0137] The applicable scenario is a bay-type bus stop under the condition of a bus-only lane. The total length of the automated driving bus control area is , including the length of the road control area and the deceleration area length of the bay-type stop and the parking area length , the lane width is , the width of the stopping area is , the angle between the platform and the deceleration area is , the length of the bus body , width , height , wheelbase , front overhang , rear overhang , the four vertices of the bus are respectively , the angle between the bus body and the horizontal plane is the body direction angle , establish a plane coordinate system, the starting point of the horizontal axis is the starting point of the control area where the bus travels along the road, and the starting point of the vertical axis is the roadside curbstone of the outer edge of the bus platform;

[0138] Step S2: Determine the basic operation rules of the bus;

[0139] Step S21: The arrival time of the bus at the starting point of the control area is = 0, and at this time the bus is already driving in the outermost lane, and the speed is , the safe distance between the bus body and the lane boundary before entering the station is , the safe distance between the bus body and the platform boundary when docking is , the distance from the front end of the vehicle to the front end of the platform when docking is , the distance between the vehicle and the platform after docking is , taking the trajectory of the center point of the front axle of the bus as the vehicle trajectory, define the state of the bus at time as:

[0140] (1);

[0141] where are respectively the horizontal and vertical coordinates of the bus at time are respectively the horizontal and vertical speeds of the bus at time and represent the and direction accelerations at time

[0142] Within the control area, let the driving distance of the autonomous driving bus be , among which, the driving distance in the approach preparation stage is , the bus experiences two driving states successively in the approach preparation stage, which are divided into two parts. First, the first part is that the bus travels at a constant speed of , the driving time is , and the driving distance is ; The second part is that the bus then decelerates at a deceleration of , the driving time is (using a trigonometric function type speed curve control), and the driving distance is , and the final speed of this stage is set to , the parameter expressions of these two parts are as follows:

[0143] The expression of the driving distance is as follows:

[0144] (2);

[0145] The expression of the driving distance is as follows:

[0146] (3);

[0147] Driving distance during the approach preparation stage is expressed as follows:

[0148] (4);

[0149] The expression of the speed in the second part is as follows:

[0150] (5);

[0151] The expression of the acceleration in the second part is as follows:

[0152] (6);

[0153] Driving distance for the bus to change lanes and enter the bay stop , driving time , driving distance In this stage, a fifth-degree polynomial trajectory planning model is selected to express the lane-changing trajectory as follows:

[0154] (7);

[0155] , are the coefficients of the fifth-degree polynomial. The first derivative and the second derivative are respectively calculated for the above formula:

[0156] (8);

[0157] (9);

[0158] The bus state at the end of the approach preparation stage is the starting point of the fifth-degree polynomial trajectory, expressed as:

[0159] (10);

[0160] The termination state after the bus stops at the station is:

[0161] (11);

[0162] When the initial time and the end time of the bus changing lanes and entering the station and its state are all known, the trajectory coefficients of the vehicle can be calculated, and the optimal trajectory can be selected through the approach trajectory planning model;

[0163] Step S3: Multi-objective approach trajectory planning model, including determining the trajectory planning objective and determining the trajectory planning constraint conditions;

[0164] Step S31: Select punctuality , energy consumption , comfort with the minimum weighted value as the goal of bus approach trajectory planning. After normalization, the objective function expression is as follows:

[0165] min (12);

[0166] wherein, , , are the weight coefficients corresponding to each objective respectively, satisfying , , , are the reference values;

[0167] In the step S31, the expression of punctuality is as follows:

[0168] (13);

[0169] wherein, is the expected arrival and stop time of the bus. Let the punctuality reference value ;

[0170] In the step S31, the expression of energy consumption is as follows:

[0171] (14);

[0172] wherein, and are the energy consumptions of the bus during uniform motion and deceleration respectively;

[0173] During the uniform motion stage, the energy consumption of the bus is calculated as:

[0174] (15);

[0175] Considering the efficiency loss, the motor output power during the uniform motion stage of the bus is:

[0176] (16);

[0177] wherein, mechanical transmission efficiency, motor efficiency, battery efficiency;

[0178] The uniform motion power of the bus is the traction force and speed The product of:

[0179] (17);

[0180] During the constant-speed driving stage, the traction force for the longitudinal driving of a pure electric bus must overcome the rolling resistance, the slope resistance, and the air resistance. The traction force calculation formula is as follows:

[0181] (18);

[0182] (19);

[0183] In the formula: is the traction force, is the rolling resistance, is the slope resistance, is the air resistance, is the angle between the vehicle body and the horizontal plane, is the air resistance coefficient, is the speed, is the vehicle mass, and the passenger mass The sum of, is the acceleration due to gravity;

[0184] Therefore, the energy consumption of the bus during the constant-speed driving stage The calculation formula is:

[0185] (20);

[0186] During the deceleration driving stage, the pure electric bus has energy recovery. A part of the kinetic energy is used to offset various driving resistances, another part is converted into electrical energy by the drive motor to charge the power battery, and the remaining part is dissipated as heat; According to the kinematic relationship, the braking energy of the pure electric bus during the deceleration time is expressed as:

[0187] (21);

[0188] The kinetic energy reduced for offsetting various driving resistances is expressed as:

[0189] (22);

[0190] The energy recovered by the pure electric bus during the deceleration process is expressed as:

[0191] (23);

[0192] In the formula: is the brake distribution ratio;

[0193] The braking energy recovery efficiency coefficient can be expressed as:

[0194] (24);

[0195] During the deceleration driving stage, the braking energy of the pure electric bus is expressed as

[0196] (25);

[0197] Let the energy consumption reference value be the energy consumed by the bus when driving a distance at the initial speed ;

[0198] In the step S31, the comfort is expressed by the following formula:

[0199] (26);

[0200] Among them, means dividing the carriage into areas, and the numbers of standing and sitting passengers in area are and respectively, and the comforts are and respectively;

[0201] The comfort of standing passengers is calculated by the following formula:

[0202] (27);

[0203] Among them, the comprehensive weighted acceleration of standing passengers at time is expressed as:

[0204] (28);

[0205] Among them, and respectively represent at time, the accelerations in the axis and

[0206] The comfort of sitting passengers is calculated by the following formula:

[0207] (29);

[0208] Seated and standing passengers The comprehensive weighted acceleration at a moment Expressed as:

[0209] (30);

[0210] Set the comfort reference value As the comfort value calculated when the bus decelerates at the maximum acceleration ;

[0211] Step S32: Determine the constraint conditions for trajectory planning, considering speed , acceleration , jerk , curvature , driving distance, starting and ending positions, driving obstacle avoidance constraints;

[0212] Among them, the speed constraint is expressed as:

[0213] (31);

[0214] The acceleration constraint is expressed as:

[0215] (32);

[0216] The jerk constraint is expressed as:

[0217] (33);

[0218] Among them, , , Are the maximum speed, maximum acceleration, and maximum jerk values respectively;

[0219] Curvature The calculation formula is expressed as:

[0220] (34);

[0221] The curvature constraint is expressed as:

[0222] (35);

[0223] Is the minimum turning radius of the bus, with a value range of 8m - 12m;

[0224] When the bus is in the straight driving stage, that is, at the end moment of changing lanes and entering the bay stop, there is no wheel steering, that is:

[0225] (36);

[0226] The horizontal and vertical coordinates of the four vertices of the bus at moment are respectively expressed as ( , ), ( , ), ( , ), ( , ). According to the Ackerman steering model, the real-time coordinate positions of the four vertices are obtained; during the process of the bus entering the station, a certain safety distance should be maintained between the outer edge of the vehicle and the road and platform boundaries. The obstacle avoidance constraint is expressed as:

[0227] (1) Control safety, and the vehicle travels within the controllable range; the straight-line uniform driving distance constraint , the straight-line decelerating driving distance constraint ;

[0228] (3) The rear of the vehicle has not entered the bay, and the front of the vehicle has not entered the bay, that is, when , it satisfies , , , ;

[0229] (4) The rear of the vehicle has not entered the bay, and the front of the vehicle is in the deceleration area, that is, when , , it satisfies , , , , ;

[0230] Among them, is the steering angle of the bus, and its expression is as follows:

[0231] (37);

[0232] Among them, is the relationship between the longitudinal running trajectory of the bus and time, is the relationship between the lateral running trajectory of the bus and time;

[0233] (5) The rear of the vehicle has not entered the bay, and the front of the vehicle is in the docking area, that is, when , , it satisfies , , , , ;

[0234] (6) The rear of the vehicle is in the deceleration zone and the front of the vehicle is in the deceleration zone. When , is satisfied, , , , ;

[0235] (7) The rear of the vehicle is in the deceleration zone and the front of the vehicle is in the docking zone, that is, when , is satisfied, , , , ;

[0236] (8) The rear of the vehicle is in the docking zone and the front of the vehicle is in the docking zone, that is, when , is satisfied, , , , ;

[0237] In addition, the state constraints at the bus starting and ending points are expressed as:

[0238] (1) The starting state of the bus is ;

[0239] (2) The ending state of the bus is ;

[0240] Step S4: Solve the planning model through the sequential quadratic programming algorithm to obtain the optimal driving trajectory of the bus vehicle;

[0241] Step S5: Execute the vehicle trajectory according to the trajectory output in Step S4;

[0242] Step S6: Wait for the next round of decision trigger.

[0243] Embodiment 2

[0244] Refer to Figure 1-7 This embodiment provides a multi-objective in-station trajectory planning method for an autonomous driving bus bay station, which includes the following steps:

[0245] Step S1: Set the control area and establish a plane coordinate system;

[0246] Among them, please refer to Figure 1 The relevant information of the experimental data simulation parameters in this embodiment can be seen in Figure 6 and Figure 7 .

[0247] Step S2: Perform model solution;

[0248] SeeFigure 3 , the model established in this embodiment is based on the constant-speed driving time , deceleration driving time , the final speed in the deceleration section , lane-changing and parking driving time as a multi-objective optimization model with control variables. In this embodiment, the function in MATLAB is used to solve the global minimum of a non-linear multi-variable model with constraints;

[0249] See Figure 4 , step S3: Execute the vehicle trajectory according to the trajectory output in step S2. The punctuality index is 6, the energy consumption is 2,438,000 joules, the comfort index is 701.25, and the total consumption time is 31 seconds;

[0250] Step S4: Wait for the next round of decision trigger.

[0251] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A multi-objective approach for planning the approach trajectory of an autonomous bus at a bay-type stop, characterized in that, It includes the following steps: Step S1: Set the control area and establish a plane coordinate system; The applicable scenario is a bay-type bus stop under the condition of a bus-only lane, and the total length of the automatic driving bus control area is L A , including the length L of the road control area R , the deceleration area length L of the bay-type stop D and the parking area length L S , the lane width is W1, the width of the stopping area is W2, and the angle between the platform and the deceleration area is the length L of the bus body V , width W, height H, wheelbase L z , front overhang L ` , rear overhang L \ , the four vertices of the bus are D1, D2, D3, and D4 respectively, and the angle between the bus body and the horizontal plane is the body direction angle α. A plane coordinate system is established, with the starting point of the horizontal axis being the starting point of the control area where the bus travels along the road, and the starting point of the vertical axis being the curbstone on the outer edge of the bus platform; Step S2: Determine the basic operation rules of the bus; The bus experiences two driving states successively during the approach preparation stage, which is divided into two parts. First, in the first part, the bus travels at a constant speed of v1 for a driving time of t1 and a driving distance of L1. Second, in the second part, the bus then decelerates at a deceleration of and travels for a driving time of t2 and a driving distance of L2. The final speed at this stage is set as v2. Step S3: Multi-objective in-station trajectory planning model, including determining the trajectory planning objective and determining the trajectory planning constraint conditions; Select the minimum weighted value of punctuality P, energy consumption E, and comfort C as the objective of bus in-station trajectory planning. The expression of comfort C is as follows: where n represents dividing the carriage into n areas, and the numbers of standing and sitting passengers in area i are respectively and The comfort levels are respectively and Comfort C of standing passengers st[n^ The calculation formula is as follows: Among them, the comprehensive weighted acceleration a st[n^ (t) of the standing passenger at time t is expressed as: Among them, and respectively represent the accelerations in the X-axis and Y-axis directions at time t; Comfort C of sitting and standing passengers sit The calculation formula is as follows: The comprehensive weighted acceleration a of the sitting and standing passengers at time t sit (t) is expressed as: Let the comfort reference value C0 be the comfort value calculated when the bus is decelerating at the maximum acceleration a m[x while decelerating; Determine the constraint conditions of trajectory planning, considering speed v(t), acceleration a(t), jerk j(t), curvature K, driving distance, starting and ending positions, and driving obstacle avoidance constraints; Step S4: Solve the planning model through the sequential quadratic optimization algorithm to obtain the optimal driving trajectory of the bus; Step S5: Execute the vehicle trajectory according to the trajectory output in Step S4; Step S6: Wait for the trigger of the next round of decision-making.

2. The multi-objective inbound trajectory planning method for an autonomous bus bay-type stop according to claim 1, wherein Step S2 also includes the following steps: Step S21: The arrival time of the bus at the starting point of the control area is t0 = 0, and at this time, the bus is already driving in the outermost lane, with a speed of v1, a safety distance H1 between the vehicle body and the lane boundary before entering the station, a safety distance H2 between the vehicle body and the platform boundary when docking, a distance H3 from the front end of the vehicle to the front end of the platform when docking, and a distance H4 between the vehicle and the platform after docking. Taking the trajectory of the center point of the front axle of the bus as the vehicle trajectory, define the state Z(t) of the bus at time t as: where \(x(t)\) and \(y(t)\) are the horizontal and vertical coordinates of the bus at time \(t\), are the speeds of the bus in the horizontal and vertical directions at time \(t\), and represent the accelerations in the \(X\) and \(Y\) directions at time \(t\).

3. A multi-objective approach for planning the approach trajectory of an autonomous bus at a bay-type stop, according to claim 2, characterized in that Step S21 also includes the following steps: Step S22: Within the control area, set the driving distance of the autonomous driving bus as L. Among them, the driving distance L during the approach preparation stage 12 , and the parameter expressions of these two parts are as follows: The expression of the driving distance L1 is as follows: L1 = v1×t1 (7); The expression of the driving distance L2 is as follows: Travel distance L during the approach preparation phase 12 is expressed as follows: L 12 = L1 + L2 (9); The expression of the speed of the second part is as follows: The expression of the acceleration of the second part is as follows:

4. The multi-objective approach for planning the inbound trajectory of an automated driving bus at a bay-type stop according to claim 3, wherein Step S22 also includes the following steps: Step S23: The driving distance L3 of the bus changing lanes and driving into the bay stop, the driving time t3, the driving distance L3. In this stage, a quintic polynomial trajectory planning model is selected for the expression of the lane-changing trajectory, as follows: a i (i = 0, 1, 2, 3, 4, 5), b i (i = 0, 1, 2, 3, 4, 5) are the coefficients of a fifth-degree polynomial. The first derivative and the second derivative of the above formula are respectively obtained as follows: The bus state at the end of the in-station preparation stage is the starting point of the quintic polynomial trajectory, expressed as: The termination state of the bus after in-station docking is: Z(t1 + t2 + t3) = {L R + L D + L S - H3 - L z - L ` , H4 + W / 2; 0, 0; 0, 0} (16); When the initial time t1 + t2 and the end time t1 + t2 + t3 of the bus changing lanes and entering the station and its state are known, calculate the trajectory coefficients of the vehicle, and then screen out the optimal trajectory through the in-station trajectory planning model.

5. The multi-objective approach for planning the approach trajectory of an autonomous bus at a bay-type stop according to claim 1, characterized in that Step S3 also includes the following steps: Step S31: The expression of the objective function after normalization is as follows: Among them, ω1, ω2, and ω3 are the weight coefficients corresponding to each objective, satisfying ω1 + ω2 + ω3 = 1, and P0, E0, and C0 are the reference values; In Step S31, the expression of punctuality P is as follows: P = |t1 + t2 + t3 - T _ | (18); Among them, T _ is the expected arrival and stop time of the bus at the stop. Let the on-time reference value P0 = T _ , and t3 is the driving time for the bus to change lanes and enter the bay stop.

6. A multi-objective approach for planning the approach trajectory of an automated driving bus at a bay-type stop, as claimed in claim 5, wherein Step S31 also includes the following steps: In Step S31, the expression of energy consumption E is as follows: E = E u + E r (19); Among them, E u and E r are the energy consumptions during the uniform and decelerated operations of the bus, respectively. During the constant-speed driving stage, the energy consumption E of the bus u The calculation formula is as follows: Considering the efficiency loss, the motor output power P u (t) during the constant-speed driving stage of the bus is as follows: Among them, η t Mechanical transmission efficiency, η m Motor efficiency, η ] Battery efficiency; Power P of a bus moving at a constant speed tr (t) is the traction force F tr (t) and the product of the speed v(t): P tr P(t) = F tr P(t)v(t) (22).

7. A multi-objective approach for planning the approach trajectory of an autonomous bus at a bay-type stop, as claimed in claim 5, characterized in that Step S31 also includes the following steps: In the uniform driving stage, the traction calculation formula is as follows: F tr f(t) = F ` +F i +F w (23); Where: F tr (t) is the traction force, F ` is the rolling resistance, F i is the grade resistance, F w is the air resistance, α is the angle between the vehicle body and the horizontal plane, C D is the air resistance coefficient, A is the frontal area, v(t) is the speed, m [ is the total mass of the vehicle and passengers, g is the acceleration due to gravity, f is the rolling resistance coefficient; Therefore, the energy consumption E of the bus during the constant-speed driving stage u The calculation formula is as follows: During the deceleration stage, the pure electric bus has energy recovery; according to the kinematic relationship, the braking energy E of the pure electric bus during the deceleration time is k expressed as: Kinetic energy E reduced to counteract driving resistance tr Expressed as: The energy E recovered during the deceleration of a pure electric bus r is expressed as: E r = (1 - k)η t η m η r (E k - E tr ) (28); In the formula: k is the brake distribution ratio; Braking energy recovery efficiency coefficient η r It is expressed as: a(t) is the acceleration; During the deceleration stage, the braking energy E of the pure electric bus k denotes Let the energy consumption baseline value E0 be the energy consumed by the bus when traveling a distance L at the initial speed v u traveling distance L Z consumed energy.

8. A multi-objective approach for planning the inbound trajectory of an autonomous bus at a bay-type stop, as claimed in claim 1, wherein Step S3 also includes the following steps: Step S32: The speed constraint is expressed as: 0 ≤ v(t) ≤ v m[x (31); The acceleration constraint is expressed as: |a(t)| ≤ a m[x (32); The jerk constraint is expressed as: |j(t)| ≤ j m[x (33); where v m[x , a m[x , j m[x are the maximum speed, maximum acceleration, and maximum jerk value, respectively; The calculation formula of the curvature K is expressed as: x(t) and y(t) are the horizontal and vertical coordinates of the bus at time t respectively; The curvature constraint is expressed as: K(t) ≤ 1 / R min (35); R min is the minimum turning radius of a bus, with a value range of 8 m to 12 m; When the bus is in the straight - driving stage, that is, at the end moment when it changes lanes and enters the bay - side stop, there is no wheel steering, that is: K(t)=0, when 0≤t≤t1 + t2 or t = t1 + t2 + t3 (36).

9. A multi-objective inbound trajectory planning method for an automated driving bus bay stop according to claim 8, characterized in that The steps in step S32 also include the following steps: The abscissa and ordinate of the four vertices of the bus at time t are respectively expressed as According to the Ackerman steering model, the real-time coordinate positions of the four vertices are obtained; during the process of the bus entering the station, there is a safety distance between the outer edge of the vehicle and the road and platform boundary, and the obstacle avoidance constraint is expressed as: (1) Control safety, the vehicle travels within a controllable range; the straight-line uniform driving distance constraint is L1 ≤ L R +L D +L S -H3-L z -L ` , the straight-line decelerated driving distance constraint is L2 ≤ L R +L D +L S -H3-L z -L ` ; (2) The rear of the vehicle has not entered the bay, and the front of the vehicle has not entered the bay, that is, when is satisfied, (3) The rear of the vehicle has not entered the bay, and the front of the vehicle is in the deceleration zone, that is, when it satisfies Among them, θ is the steering angle of the bus, and its expression is as follows: Among them, is the relationship between the longitudinal running trajectory of the bus and time, is the relationship between the lateral running trajectory of the bus and time; (4) The rear of the vehicle has not entered the bay and the front of the vehicle is in the parking area, that is, when is satisfied (5) The rear of the vehicle is in the deceleration zone, and the front of the vehicle is in the deceleration zone. When time, it satisfies (6) The rear of the vehicle is in the deceleration area and the front of the vehicle is in the docking area, that is, when it satisfies (7) The rear of the vehicle is in the docking area and the front of the vehicle is in the docking area, that is, when it satisfies H1 is the safety distance between the vehicle body and the lane boundary before entering the station, H2 is the safety distance between the vehicle body and the platform boundary when docking, H3 is the distance from the front end of the vehicle to the front - most end of the platform when docking, and H4 is the distance between the vehicle and the platform after docking; In addition, the state constraints at the bus starting and ending points are expressed as: (1) The initial state of the bus is {0, W2 + H1+W / 2; v1, 0; 0, 0}; (2) The termination state of the bus is {L R +L D +L S -H3-L z -L ` ,H4+W / 2; 0,0; 0,0}.

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