Energy-saving car-following trajectory optimization control method and system

Abstract

The application discloses a kind of energy-saving considering car-following trajectory optimization control method and system, the method includes the following steps: according to future state estimation strategy to determine the historical trajectory of preceding vehicle and future trajectory;The combined trajectory of historical trajectory and future trajectory is used as the preliminary reference trajectory of ego vehicle;Determine state variable based on preliminary reference trajectory, and construct trajectory prediction model of ego vehicle according to state variable;Based on trajectory deviation factor, following stability factor, energy loss factor and obstacle collision factor, determine the optimization objective function of ego vehicle;According to optimization objective function and trajectory prediction model, control ego vehicle to complete the car-following action to preceding vehicle;The application can realize the dynamic response of the future state of preceding vehicle and the expected car-following distance through predictive control, balance between accurate and stable following, safety obstacle avoidance and energy saving, simultaneously consider vehicle rotation energy consumption and brake energy recovery, effectively reduce energy consumption and improve tracking efficiency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of car-following control, in particular to a car-following trajectory optimization control method and system considering energy saving. BACKGROUND

[0002] Vehicle autonomous following technology, i.e. car-following technology, is a core technology in the field of intelligent driving, the essence of which is that the ego vehicle perceives and predicts the speed, position and other information of the target vehicle (front vehicle) through various ways, and controls the lateral and longitudinal motion of the ego vehicle to achieve stable following of the front vehicle. The implementation process is a tracking motion control of locking the front vehicle, or a lateral and longitudinal following control of the rear vehicle on the front vehicle under the condition of platooning. It should be noted that this kind of following control targeted by the present research is significantly different from the conventional adaptive cruise system (sometimes also called car-following), which usually deals with structured roads and only controls the longitudinal vehicle distance, and the front vehicle is not a single lock.

[0003] The existing autonomous car-following technology has the following significant defects, especially in the field of dynamic tracking of specific vehicles in unstructured scenarios:

[0004] (1) Insufficient prediction ability of the dynamic behavior of the front vehicle, for example:

[0005] Chinese patent CN201811033215.8 discloses a method for controlling the adaptive cruise distance of a vehicle and a vehicle-following control device, which proposes to dynamically adjust the acceleration of the ego vehicle based on the acceleration of the front vehicle. However, its prediction model only relies on short-term relative speed and cannot predict long-term behavior changes of the front vehicle in scenarios such as road branching and sudden obstacles, resulting in tracking failure in curved roads or cut-in scenarios.

[0006] Chinese patent CN119099613A discloses an adaptive cruise control method based on neural network approximation, which optimizes the tracking error of the vehicle distance by approximating the vehicle dynamics model through a neural network. However, its training data is limited to regular roads such as highways, and does not cover the random motion characteristics of the front vehicle in unstructured scenarios such as rural roads and construction road sections.

[0007] (2) Poor adaptability to unstructured scenarios, for example:

[0008] Chinese patent CN119659612A discloses a vehicle control method, a vehicle platooning system and a storage medium, which proposes dynamic path planning for vehicles in platooning. However, it relies on V2V communication and predefines the platooning target, and cannot be applied to specific vehicle tracking scenarios without communication.

[0009] Chinese patent CN119337088A discloses a target vehicle tracking method, device, storage medium and product. Although the patent realizes lane-level tracking through semantic segmentation, its core is target recognition in the perception domain, and it does not involve vehicle motion control and energy consumption optimization.

[0010] (3) Energy consumption optimization and motion control decoupling, for example:

[0011] Chinese patent CN201910275899.0 discloses a vehicle automatic following control method and system. The patent adjusts the following distance by adjusting environmental parameters such as slope and visibility, but does not jointly optimize the energy consumption model and vehicle dynamics model, so it cannot minimize energy consumption while ensuring tracking accuracy.

[0012] Chinese patent CN119611363A discloses an adaptive cruise dead zone processing control system and method. The patent only optimizes low-speed following comfort through dead zone compensation, and does not realize energy consumption and tracking coordination from the global path planning level.

[0013] (4) Lack of target-specific tracking technology, for example:

[0014] Chinese patent CN202011464516.3 discloses a vehicle identification and tracking method and system. The patent realizes vehicle identification and monitoring through video backtracking, but its essence is post-data analysis, and it cannot generate tracking control instructions in real time.

[0015] Chinese patent CN114690760A discloses a communication method, device and equipment for platoon vehicles, and a platoon vehicle. The patent optimizes platoon performance through communication link packet loss detection, but it relies on fixed communication protocols and cannot adapt to specific vehicle tracking requirements without communication.

[0016] In view of the above defects, a specific vehicle dynamic tracking and energy consumption coordination optimization technology in unstructured scenarios is needed. SUMMARY

[0017] The purpose of the present application is to provide a follow-up trajectory optimization control method and system considering energy saving, thereby solving all or one of the above problems in the prior art.

[0018] To solve the above technical problems, the specific technical solutions of the present application are as follows:

[0019] On the one hand, the present application provides a follow-up trajectory optimization control method considering energy saving, comprising the following steps:

[0020] Future state prediction step of the preceding vehicle:

[0021] Determine the control period, and determine the future state calculation strategy of the preceding vehicle according to the control period;

[0022] a self-vehicle reference trajectory calculation step:

[0023] determining a history trajectory and a future trajectory of the front vehicle according to the future state estimation strategy; and taking a combined trajectory of the history trajectory and the future trajectory as a preliminary reference trajectory of the self-vehicle;

[0024] a self-vehicle trajectory prediction model construction step:

[0025] determining a state variable based on the preliminary reference trajectory, and constructing a trajectory prediction model of the self-vehicle according to the state variable;

[0026] an optimization objective function determination step:

[0027] determining an optimization objective function of the self-vehicle based on a trajectory deviation factor, a following stability factor, an energy loss factor and an obstacle collision factor existing when the self-vehicle follows the front vehicle according to the preliminary reference trajectory;

[0028] an optimal trajectory calculation step:

[0029] calculating an optimal control sequence according to the optimization objective function and the trajectory prediction model, and controlling the self-vehicle to complete a following action on the front vehicle according to the optimal control sequence.

[0030] Further, the future state estimation strategy of the front vehicle according to the control period includes:

[0031] setting an estimation premise that the front vehicle maintains an acceleration and an angular acceleration at the first time point within the control period;

[0032] analyzing a position coordinate estimation formula, a vehicle speed estimation formula and a vehicle heading angle estimation formula of the front vehicle according to the estimation premise;

[0033] integrating the estimation premise, the position coordinate estimation formula, the vehicle speed estimation formula and the vehicle heading angle estimation formula as the future state estimation strategy.

[0034] Further, the determination of the history trajectory and the future trajectory of the front vehicle according to the future state estimation strategy includes:

[0035] determining a current position of the front vehicle;

[0036] traversing a nearest position point of the front vehicle and the self-vehicle at the first time point according to the future state estimation strategy, and estimating a future position of the front vehicle after the control period according to the future state estimation strategy;

[0037] a trajectory between the current position of the preceding vehicle and the future position as a future trajectory of the preceding vehicle.

[0038] Further, the historical trajectory and the future trajectory are both composed of a plurality of waypoints.

[0039] In each of the waypoints, position coordinate information and a heading angle information are included.

[0040] Further, the determination of the state variables based on the preliminary reference trajectory comprises:

[0041] Based on the preliminary reference trajectory, the ego vehicle position coordinates, the ego vehicle speed, and the ego vehicle heading angle are taken as state variables of the trajectory prediction model.

[0042] Further, the determination of the optimization objective function of the ego vehicle based on the trajectory deviation factor, the following stability factor, the energy loss factor, and the obstacle collision factor when the ego vehicle follows the preliminary reference trajectory comprises:

[0043] Based on the trajectory deviation factor, a deviation degree optimization function is determined.

[0044] Based on the following stability factor, a following stability optimization function is determined.

[0045] Based on the energy loss factor, an energy loss optimization function is determined.

[0046] Based on the obstacle collision factor, an obstacle collision optimization function is determined.

[0047] The deviation degree optimization function, the following stability optimization function, the energy loss optimization function, and the obstacle collision optimization function are taken as the optimization objective function.

[0048] Further, the determination of the following stability optimization function based on the following stability factor further comprises:

[0049] According to the deviation between the actual following distance of the ego vehicle and the expected following distance, the following stability optimization function is determined.

[0050] Further, the determination of the energy loss optimization function based on the energy loss factor further comprises:

[0051] According to the longitudinal energy consumption, the steering energy consumption, and the preceding vehicle wind-breaking effect energy consumption of the vehicle, the energy loss optimization function is determined.

[0052] Further, the determination of the obstacle collision optimization function based on the obstacle collision factor comprises:

[0053] determining a passing obstacle list, determining an evaluation function for evaluating a probability of obstacle collision according to the passing obstacle list;

[0054] determining the obstacle collision optimization function based on the evaluation function.

[0055] In another aspect, the application also provides a vehicle-following trajectory optimization control system considering energy saving, comprising:

[0056] a future state prediction module of a preceding vehicle, configured to determine a control period and determine a future state prediction strategy of the preceding vehicle according to the control period;

[0057] a reference trajectory calculation module of a subject vehicle, configured to determine a historical trajectory and a future trajectory of the preceding vehicle according to the future state prediction strategy, and take a combined trajectory of the historical trajectory and the future trajectory as a preliminary reference trajectory of the subject vehicle;

[0058] a trajectory prediction model construction module of the subject vehicle, configured to determine state variables based on the preliminary reference trajectory, and construct a trajectory prediction model of the subject vehicle according to the state variables;

[0059] an optimization objective function determination module, configured to determine an optimization objective function of the subject vehicle based on a trajectory deviation factor, a following stability factor, an energy loss factor and an obstacle collision factor existing when the subject vehicle follows the preceding vehicle according to the preliminary reference trajectory;

[0060] an optimal trajectory calculation module, configured to calculate an optimal control sequence according to the optimization objective function and the trajectory prediction model, and control the subject vehicle to complete a vehicle-following action on the preceding vehicle according to the optimal control sequence.

[0061] The application has the following beneficial effects:

[0062] 1. The vehicle-following trajectory optimization control method considering energy saving can realize dynamic response of a future state of a preceding vehicle and an expected following distance through predictive control, quantitatively plan a trajectory of a subject vehicle, balance between accurate and stable following, safe obstacle avoidance and energy saving, simultaneously consider vehicle rotation energy consumption and brake energy recovery, effectively reduce energy consumption and improve tracking efficiency.

[0063] 2. The vehicle-following trajectory optimization control system considering energy saving can realize the vehicle-following trajectory optimization control method considering energy saving through mutual cooperation of system modules. BRIEF DESCRIPTION OF DRAWINGS

[0064] In order to more clearly illustrate the technical solutions in the specific embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the specific embodiments or prior art description. Obviously, the drawings described below are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0065] Figure 1 is a schematic diagram of the rectangular coordinate system in the energy-saving following vehicle trajectory optimization control method described in embodiment 1 of the present application;

[0066] Figure 2 is a schematic diagram of the reference trajectory in the energy-saving following vehicle trajectory optimization control method described in embodiment 1 of the present application;

[0067] Figure 3 is a flowchart of the energy-saving following vehicle trajectory optimization control method described in embodiment 1 of the present application;

[0068] Figure 4 is a detailed flowchart of the energy-saving following vehicle trajectory optimization control method described in embodiment 1 of the present application.

[0069] Figure 5 is a schematic diagram of the architecture of the energy-saving following vehicle trajectory optimization control system described in embodiment 2 of the present application. DETAILED DESCRIPTION

[0070] The preferred embodiments of the present application will be described in detail below with reference to the drawings, so that the advantages and features of the present application can be more easily understood by those skilled in the art, and the scope of protection of the present application can be more clearly defined.

[0071] In the description of the present application, it should be noted that the embodiments described in the present application are part of the embodiments of the present application, not all the embodiments; all other embodiments obtained by those skilled in the art without any creative effort on the basis of the embodiments in the present application belong to the scope of protection of the present application.

[0072] The terms "first," "second," etc., used in this specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0073] In the description of this invention, it should be noted that "vehicle following" in this patent specifically refers to tracking and tracing a particular vehicle, and this vehicle is not necessarily traveling on a structured road, but can travel freely; the main purpose of this patent is to perform specific operations on the vehicle's speed and steering, thereby achieving flexible and dynamic tracking of a vehicle ahead with known state information.

[0074] Example 1: This example provides a vehicle following trajectory optimization control method that considers energy saving, such as... Figures 1-4 As shown, it includes the following steps:

[0075] S1. Steps for predicting the future state of the vehicle ahead:

[0076] In this step, the state of the preceding vehicle over the next N control cycles is predicted, serving as the data basis for subsequent prediction processes, as detailed below:

[0077] S101. First, based on the vehicle and the vehicle in front, establish a rectangular coordinate system XOY fixed to the ground, as follows: Figure 1 As shown; where O is a fixed point on the ground, subscript l (i.e., leader) represents the leading vehicle (leader vehicle), subscript f (i.e., follower) represents the following vehicle (follower vehicle), (x, y) refers to the position coordinates of the corresponding vehicle, v refers to the velocity of the corresponding vehicle, a refers to the acceleration of the corresponding vehicle, θ refers to the heading angle of the corresponding vehicle, and ω refers to the angular acceleration of the corresponding vehicle.

[0078] S102. Based on the state of the preceding vehicle at time t. Define the acceleration and angular acceleration of the vehicle at time t (i.e., the first time) to remain constant for the next N control cycles. Based on this premise, the motion state value of the vehicle ahead is recursively calculated, and the corresponding recursive relationship is described as follows:

[0079] ;

[0080] Wherein, the above recursive relationship is the estimation strategy for determining the future state of the preceding vehicle, k represents the control cycle count in the future (i.e. taking the absolute time at this moment as the 0 point, k steps in the future), k = 0 is the current time, is the duration of each control cycle; it should be noted that in the case where the future operation expected signal of the preceding vehicle cannot be obtained, the state of the preceding vehicle can be predicted according to the Kalman filter or the constant acceleration method; in this embodiment, only an example of a case is represented, i.e. one of several choices, this embodiment is not necessarily the most ideal, but in the case where the future working state information of the preceding vehicle is directly fed back, the estimation method of this embodiment is faster, and in specific applications, adaptive selection can be made according to specific needs.

[0081] S2, self-vehicle reference trajectory calculation step:

[0082] In this step, the historical trajectory and the future trajectory of the preceding vehicle are determined according to the above recursive relationship, and finally the reference trajectory suitable for the self-vehicle is calculated according to the historical trajectory and the future trajectory of the preceding vehicle, as follows:

[0083] S201, determining the historical trajectory of the preceding vehicle according to the recursive relationship is , which includes a sequence of x coordinate positions, y coordinate positions and θ heading angles; in this historical trajectory, t represents the real absolute time (i.e. the position of the preceding vehicle in the process of moving according to the real time, which contains a plurality of time units in the process of time passing), so the above historical trajectory contains a plurality of coordinate position points.

[0084] S202, by looping through the historical trajectory of the preceding vehicle , the point A closest to the current position M of the self-vehicle is determined; wherein, as shown in Figure 2 , B is the current position of the preceding vehicle, M is the current position of the self-vehicle, C is the future position of the preceding vehicle after N control cycles according to the recursive relationship, and A is the position closest to the current position M of the self-vehicle in the historical trajectory of the preceding vehicle.

[0085] S203, starting from the A position, the AB segment trajectory is selected according to the preset interval (usually set to not more than 0.3m), and the AB segment trajectory is the historical trajectory of the preceding vehicle.

[0086] S204, after the AB segment trajectory is selected, the B point is obtained, and then the BC segment trajectory is obtained by recursively predicting from the B point to the C point according to the recursive relationship, and the BC segment trajectory is the future trajectory of the preceding vehicle.

[0087] S205, taking the AB+BC segment trajectory as the reference trajectory of the ego vehicle, the reference trajectory being a continuous sequence of spatial coordinate points, denoted as TrajectoryRef; correspondingly, the [x, y, θ] coordinates of the reference trajectory are composed of a plurality of point coordinates on the AC route; without considering energy saving, TrajectoryRef is the optimal reference trajectory, which is necessarily obtained on the premise that the preceding vehicle actually travels, and has the best passability; however, in the case of considering energy saving, TrajectoryRef is not necessarily the optimal following trajectory, and therefore needs to be optimized in combination with the prediction model and the cost function.

[0088] S3, ego vehicle trajectory prediction model construction step:

[0089] In this step, a prediction module for controlled changes in the ego vehicle trajectory is established to further optimize the following effect of the ego vehicle, as follows:

[0090] S301, the prediction model for the ego vehicle trajectory is constructed as follows:

[0091] ;

[0092] wherein the state variables of the prediction model are [x f , y f , v f , θ f ], and the superscripts k, k+1 and are consistent with the meanings in the aforementioned step S1.

[0093] S302, according to the above prediction model, it can be known that the sequence is the final control variable, and the control variable all have upper and lower limits (constraints), which are described as follows:

[0094] ;

[0095] wherein the specific upper and lower limit values are set according to specific circumstances.

[0096] S4, optimization objective function determination step:

[0097] In this step, according to the actual characteristics in the following process of the ego vehicle, the energy optimization cost function of the prediction model and the obstacle penalty function are determined, and finally four kinds of optimization objective functions are obtained, as follows:

[0098] S401, determine the deviation degree optimization function J1:

[0099] Since the reference trajectory is the trajectory with better road conditions among many trajectories, it has a certain value, so it is necessary to optimize the deviation of the ego vehicle from the reference trajectory, and the optimization function is as follows:

[0100]

[0101] Wherein, β1 is the difference factor between the handling angle and the dimension difference of the lateral and longitudinal coordinates; Representative point The point closest to TrajectoryRef in the Euclidean distance is obtained by traversal calculation.

[0102] S402, determine the following stability optimization function J2:

[0103] Based on the deviation of the actual following distance and the expected following distance, the optimization function of the following stability of the ego vehicle to the front vehicle is determined, which is as follows:

[0104]

[0105] Wherein, N2 is the step length suitable for calculating the following distance, and N2 k is the optimal following distance, which is usually given by the upper layer of the formation or automatic driving control instruction;

[0106] In addition, it should be noted that in other embodiments, a function form that decays with the growth of the cycle can be set; and for the following task considered in the present patent, J2 is very important, and the optimization goal of the present patent is to firmly lock the specific front vehicle for following and cannot be thrown away by the front vehicle.

[0107] S403, determine the energy loss optimization function J3:

[0108] Although the energy in the longitudinal motion direction can be recovered through braking, some vehicles (such as tracked vehicles) will also consume additional energy when turning, so the actual vehicle characteristics are combined, and the longitudinal and lateral directions, drive and braking are considered together, and the corresponding optimization function is as follows:

[0109]

[0110] Wherein:

[0111] μ1 is a parameter for measuring the energy consumption of the ego vehicle at different longitudinal speeds and accelerations, which is negative when regenerative braking (representing that at this time, the energy consumption can be reduced by reasonably controlling the acceleration);

[0112] ​​​μ2 is a parameter for measuring energy consumption of the ego vehicle at different lateral heading angles and angular velocities, which is used for vehicles with large steering energy consumption, and then reasonably suppresses the steering energy consumption;

[0113] μ3 is a parameter for calculating the energy saving optimization brought by the wind-breaking effect of the preceding vehicle when the ego vehicle follows the preceding vehicle;

[0114] μ1, μ2 and μ3 are all offline calibrated according to the specific properties of the vehicle.

[0115] S404, determine the obstacle collision optimization function J4:

[0116] The optimization function is determined by judging whether the current passing waypoint will collide with the obstacle as follows:

[0117] ;

[0118] Violation k is an evaluation function for evaluating whether the above situation will collide, and ; in the evaluation function, is a list of obstacles blocking the passage in front, which is updated at each loop S1, and the value is usually obtained by the automatic driving perception part of the ego vehicle, and the subscript i is the loop number when the list is traversed, N O is the number of obstacles; in addition, it should be noted that because N calculation steps are traversed in this method, this method has better prediction ability for future collision situations.

[0119] S5, optimal trajectory calculation step:

[0120] In this step, the final optimization target is determined in combination with the above four optimization objective functions, and based on the final optimization target and the aforementioned prediction model, the optimal control sequence is determined in real time, and the ego vehicle is controlled to complete stable following actions according to the real-time optimal control sequence. The specific steps are as follows:

[0121] S501, determine the final optimization target as: ; wherein, is a hyperparameter for balancing the dimension differences of various optimization objective functions, which is determined by debugging according to the actual vehicle state.

[0122] S502, use the aforementioned prediction model to set the optimization target to minimize J value, optimize the control instruction of the ego vehicle, and finally obtain the first item of the optimal control sequence as an actual control parameter at the current time; and adjusting a driving element parameter (such as torque and power of a driving motor) according to the actual control parameter, so as to overcome various resistances and resistance torques during vehicle driving, thereby controlling acceleration and angular acceleration of the ego vehicle and realizing dynamic controllability of a motion state of the ego vehicle and stable following of the preceding vehicle.

[0123] In addition, it should be noted that the above S1-S5 are processes that are performed in real time and continuously looped, and based on the continuous looping, dynamic control of trajectory optimization prediction considering energy saving is realized.

[0124] It should be noted that the above examples are only for the purpose of explaining the present application and cannot limit the protection scope of the present application. Embodiment 2

[0125] This embodiment is based on the same inventive concept as the energy-saving following trajectory optimization control method described in Embodiment 1, and provides an energy-saving following trajectory optimization control system, as shown in Figure 5 which comprises:

[0126] a preceding vehicle future state prediction module configured to determine a control period and determine a future state prediction strategy of the preceding vehicle according to the control period;

[0127] an ego vehicle reference trajectory calculation module configured to determine a historical trajectory and a future trajectory of the preceding vehicle according to the future state prediction strategy, and take a combined trajectory of the historical trajectory and the future trajectory as a preliminary reference trajectory of the ego vehicle;

[0128] an ego vehicle trajectory prediction model construction module configured to determine a state variable based on the preliminary reference trajectory, and construct a trajectory prediction model of the ego vehicle according to the state variable;

[0129] an optimization objective function determination module configured to determine an optimization objective function of the ego vehicle based on factors of trajectory deviation, following stability, energy loss and obstacle collision of the ego vehicle when following the preceding vehicle according to the preliminary reference trajectory;

[0130] an optimal trajectory calculation module configured to calculate an optimal control sequence according to the optimization objective function and the trajectory prediction model, and control the ego vehicle to complete a following action on the preceding vehicle according to the optimal control sequence.

[0131] Compared with the prior art, the energy-saving following trajectory optimization control method and system can realize dynamic response of the preceding vehicle future state and the expected following distance through predictive control, quantitatively plan the ego vehicle trajectory, balance between accurate and stable following, safe obstacle avoidance and energy saving, simultaneously consider vehicle rotation energy consumption and brake energy recovery, effectively reduce energy consumption and improve tracking efficiency.

[0132] It should be understood that the size of the sequence number of the above processes in various embodiments herein does not mean the order of execution, and the execution order of the processes should be determined according to its function and inherent logic, and should not constitute any limitation on the implementation process of the embodiments herein.

[0133] It should also be understood that in the embodiments herein, the term "and / or" is only a description of the association relationship of the associated objects, which means that there can be three relationships. For example, A and / or B can represent three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are in an "or" relationship.

[0134] Those skilled in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in the above description in general terms. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this paper.

[0135] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0136] In several embodiments provided herein, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displays or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can also be electrical, mechanical or other forms of connection.

[0137] The units described as separate components can or can not be physically separated, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or they can be distributed on multiple network units. According to actual needs, some or all of the units can be selected to achieve the purpose of the embodiments herein.

[0138] In addition, each function unit in each embodiment herein can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0139] When the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on such understanding, the technical solutions herein or the part that essentially contributes to the prior art, or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment herein. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0140] The above is only an embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent flow transformation made by using the content of the specification and drawings, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A vehicle-following trajectory optimization control method considering energy saving, characterized in that, Includes the following steps: Steps for predicting the future state of the vehicle ahead: A control period is determined, and a future state prediction strategy for the preceding vehicle is determined based on the control period. The determination of the future state prediction strategy based on the control period includes: setting the prediction premise as follows: the preceding vehicle maintains its acceleration and angular acceleration at the first moment within the control period; analyzing the position coordinate prediction formula, vehicle speed prediction formula, and vehicle heading angle prediction formula of the preceding vehicle according to the prediction premise; and integrating the prediction premise, the position coordinate prediction formula, the vehicle speed prediction formula, and the vehicle heading angle prediction formula as the future state prediction strategy. Steps for calculating the reference trajectory of a vehicle: The historical trajectory and future trajectory of the preceding vehicle are determined according to the future state estimation strategy; the combined trajectory of the historical trajectory and the future trajectory is used as the preliminary reference trajectory of the voluntary vehicle; the determination of the historical trajectory and future trajectory of the preceding vehicle according to the future state estimation strategy includes: determining the current position of the preceding vehicle; traversing the nearest position points of the preceding vehicle and the voluntary vehicle at the first moment according to the future state estimation strategy, and estimating the future position of the preceding vehicle after the control cycle according to the future state estimation strategy; using the trajectory between the nearest position point and the current position of the preceding vehicle as the historical trajectory sequence of the preceding vehicle, and using the trajectory between the current position and the future position of the preceding vehicle as the future trajectory of the preceding vehicle; Steps for building a vehicle trajectory prediction model: Based on the preliminary reference trajectory, state variables are determined, and a trajectory prediction model for the vehicle is constructed based on the state variables. Steps for determining the objective function: Based on the trajectory deviation factors, following stability factors, energy loss factors, and obstacle collision factors existing when the vehicle follows the preliminary reference trajectory, an optimization objective function for the vehicle is determined. This determination includes: determining a deviation degree optimization function based on the trajectory deviation factors; determining a following stability optimization function based on the following stability factors; determining an energy loss optimization function based on the energy loss factors; and determining an obstacle collision optimization function based on the obstacle collision factors. The deviation degree optimization function, the following stability optimization function, the energy loss optimization function, and the obstacle collision optimization function are then used as the optimization objective function. Steps for calculating the optimal trajectory: The optimal control sequence is calculated based on the optimization objective function and the trajectory prediction model, and the vehicle is controlled to follow the vehicle in front based on the optimal control sequence.

2. The energy-saving following trajectory optimization control method according to claim 1, characterized in that: Both the historical trajectory and the future trajectory are composed of several waypoints; Each waypoint contains: location coordinates and heading angle information.

3. The energy-saving following trajectory optimization control method according to claim 1, characterized in that: The determination of state variables based on the preliminary reference trajectory includes: Based on the preliminary reference trajectory, the vehicle's position coordinates, vehicle speed, and vehicle heading angle are used as the state variables of the trajectory prediction model.

4. The energy-saving following trajectory optimization control method according to claim 1, characterized in that: The step of determining the following stability optimization function based on the aforementioned following stability factors further includes: The following stability optimization function is determined based on the deviation between the actual following distance and the expected following distance of the vehicle.

5. The energy-saving following trajectory optimization control method according to claim 1, characterized in that: The step of determining the energy loss optimization function based on the energy loss factors further includes: The energy loss optimization function is determined based on the vehicle's longitudinal energy consumption, steering energy consumption, and wind-breaking energy consumption of the vehicle ahead.

6. The energy-saving following trajectory optimization control method according to claim 1, characterized in that: The step of determining the obstacle collision optimization function based on the obstacle collision factors includes: A list of obstacles is determined, and an evaluation function for evaluating the probability of obstacle collision is determined based on the list of obstacles. The obstacle collision optimization function is determined based on the evaluation function.

7. A vehicle trajectory optimization control system considering energy saving based on the energy-saving vehicle trajectory optimization control method according to any one of claims 1 to 6, characterized in that, The system includes: The preceding vehicle future state prediction module is used to: determine the control period and determine the preceding vehicle future state prediction strategy based on the control period; The self-vehicle reference trajectory calculation module is used to: determine the historical trajectory and future trajectory of the preceding vehicle based on the future state estimation strategy; and use the combined trajectory of the historical trajectory and the future trajectory as the initial reference trajectory of the self-vehicle. The vehicle trajectory prediction model building module is used to: determine state variables based on the preliminary reference trajectory, and build the vehicle trajectory prediction model based on the state variables; The objective function determination module is used to: determine the objective function of the vehicle based on the trajectory deviation factors, following stability factors, energy loss factors, and obstacle collision factors that exist when the vehicle follows the preliminary reference trajectory. The optimal trajectory calculation module is used to: calculate the optimal control sequence based on the optimization objective function and the trajectory prediction model, and control the vehicle to complete the following action of the preceding vehicle based on the optimal control sequence.

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