Vehicle following track optimization control method and system considering energy saving

Abstract

The invention discloses a car following track optimization control method and system considering energy saving. The method comprises the following steps: determining a historical track and a future track of a front car according to a future state calculation strategy; taking a combined track of the historical track and the future track as a preliminary reference track of the vehicle; determining a state variable based on the preliminary reference trajectory, and constructing a trajectory prediction model of the vehicle according to the state variable; based on a trajectory deviation factor, a following stability factor, an energy loss factor and an obstacle collision factor, determining an optimization objective function of the vehicle; according to the optimized target function and the trajectory prediction model, controlling the self-vehicle to complete the vehicle following action of the front vehicle; the method can achieve the dynamic response of the future state of the front vehicle and the expected vehicle following distance through prediction control, achieves the balance among accurate and stable following, safe obstacle avoidance and energy saving, synchronously considers the vehicle rotation energy consumption and braking energy recovery, effectively reduces the energy consumption, and improves the tracking efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle following control, and in particular to a vehicle following trajectory optimization control method and system taking energy saving into consideration. Background Art

[0002] Autonomous vehicle following technology, also known as car-following technology, is a core technology in the field of intelligent driving. Its essence is that the vehicle uses various methods to perceive and predict the speed, position, and other information of the target vehicle (the preceding vehicle), and then controls its lateral and longitudinal motion to achieve stable following of the preceding vehicle. This is achieved through a form of tracking motion control that locks onto the preceding vehicle, or, in platooning conditions, the following vehicle performs lateral and longitudinal following control of the preceding vehicle. It should be noted that the type of following control targeted in this study differs significantly from conventional adaptive cruise control systems (sometimes also called car-following systems), which typically operate on structured roads, only handle longitudinal distance control, and do not solely lock onto the preceding vehicle.

[0003] Existing autonomous vehicle-following technology has significant shortcomings in the following areas, particularly in the dynamic tracking of specific vehicles in unstructured scenarios: (1) Insufficient ability to predict the dynamic behavior of the preceding vehicle, for example: Chinese patent CN201811033215.8 discloses a method for controlling vehicle adaptive cruise distance and a vehicle following control device. The patent proposes dynamically adjusting the acceleration of the vehicle using the acceleration of the preceding vehicle. However, its prediction model relies only on short-term relative speed and cannot predict long-term behavior changes of the preceding vehicle due to road bifurcations, sudden obstacles, and other scenarios, resulting in tracking failure in curves or cut-in scenarios.

[0004] Chinese patent CN119099613A discloses an adaptive cruise control method based on neural network approximation. This patent optimizes vehicle tracking error 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 preceding vehicle in unstructured scenarios such as rural roads and construction sections.

[0005] (2) Poor adaptability to unstructured scenarios, for example: Chinese patent CN119659612A discloses a vehicle control method, a vehicle platooning system, and a storage medium. The patent proposes dynamic path planning for vehicles within a platoon, but it relies on V2V communication and preset platoon targets, making it unsuitable for specific vehicle tracking scenarios without communication.

[0006] Chinese patent CN119337088A discloses a target vehicle tracking method, device, storage medium and product. Although this patent achieves lane-level tracking through semantic segmentation, its core is target recognition in the perception domain and does not involve vehicle motion control and energy consumption optimization.

[0007] (3) Decoupling energy consumption optimization from motion control, for example: Chinese patent CN201910275899.0 discloses a vehicle automatic following control method and system. This patent adjusts the following distance based on environmental parameters (such as slope and visibility), but does not jointly optimize the energy consumption model with the vehicle dynamics model, and is unable to minimize energy consumption while ensuring tracking accuracy.

[0008] Chinese patent CN119611363A discloses an adaptive cruise dead zone processing control system and method. This patent only optimizes low-speed following comfort through dead zone compensation, and does not achieve coordination between energy consumption and tracking from the global path planning level.

[0009] (4) Lack of target-specific tracking technology, for example: 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.

[0010] Chinese patent CN114690760A discloses a communication method, device, equipment and platoon vehicles. The patent optimizes platoon performance through communication link packet loss detection, but it relies on a fixed communication protocol and cannot adapt to the tracking needs of specific vehicles without communication.

[0011] To address the above-mentioned shortcomings, it is necessary to propose a technology for collaborative optimization of specific vehicle dynamic tracking and energy consumption in unstructured scenarios. Summary of the Invention

[0012] The object of the present invention is to provide a vehicle following trajectory optimization control method and system taking energy saving into consideration, thereby solving all or one of the above-mentioned problems existing in the prior art.

[0013] In order to solve the above technical problems, the specific technical solutions of the present invention are as follows: In one aspect, the present invention provides a vehicle following trajectory optimization control method considering energy conservation, comprising the following steps: Steps for predicting the future state of the preceding vehicle: determining a control period, and determining a strategy for estimating a future state of a preceding vehicle based on the control period; Calculation steps for the ego vehicle reference trajectory: Determining the historical trajectory and the future trajectory of the preceding vehicle according to the future state estimation strategy; using the combined trajectory of the historical trajectory and the future trajectory as a preliminary reference trajectory of the ego vehicle; Steps to build the vehicle trajectory prediction model: determining state variables based on the preliminary reference trajectory, and constructing a trajectory prediction model for the vehicle according to the state variables; Steps to determine the optimization objective function: determining an optimization objective function for the ego vehicle based on a trajectory deviation factor, a following stability factor, an energy loss factor, and an obstacle collision factor when the ego vehicle follows the preliminary reference trajectory; Optimal trajectory calculation steps: An optimal control sequence is calculated according to the optimization objective function and the trajectory prediction model, and the ego vehicle is controlled according to the optimal control sequence to complete the following action of the preceding vehicle.

[0014] Furthermore, the determining of the future state estimation strategy of the preceding vehicle according to the control period includes: The calculation premise is set as follows: the preceding vehicle maintains the acceleration and angular acceleration at the first moment during the control period; Analyze the preceding vehicle's position coordinates calculation formula, vehicle speed calculation formula, and vehicle heading angle calculation formula according to the calculation premise; The calculation premise, the position coordinate calculation formula, the vehicle speed calculation formula, and the vehicle heading angle calculation formula are integrated as the future state calculation strategy.

[0015] Furthermore, determining 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 closest position points between the preceding vehicle and the self-vehicle at the first moment according to the future state estimation strategy, and estimating the future position of the preceding vehicle after the control period according to the future state estimation strategy; The trajectory between the closest position point and the current position of the preceding vehicle is used as the historical trajectory sequence of the preceding vehicle, and the trajectory between the current position of the preceding vehicle and the future position is used as the future trajectory of the preceding vehicle.

[0016] Furthermore, both the historical trajectory and the future trajectory are composed of a number of waypoints; Each of the waypoints includes position coordinate information and heading angle information.

[0017] Furthermore, determining the state variable based on the preliminary reference trajectory includes: Based on the preliminary reference trajectory, the vehicle position coordinates, vehicle speed, and vehicle heading angle are used as state variables of the trajectory prediction model.

[0018] Furthermore, determining the optimization objective function of the ego vehicle based on the trajectory deviation factor, following stability factor, energy loss factor, and obstacle collision factor when the ego vehicle follows the preliminary reference trajectory includes: Determining a deviation degree optimization function based on the trajectory deviation factor; Determining a following stability optimization function based on the following stability factor; Based on the energy loss factors, determining an energy loss optimization function; Determining an obstacle collision optimization function based on the obstacle collision factor; The deviation degree optimization function, the following stability optimization function, the energy loss optimization function and the obstacle collision optimization function are used as the optimization objective functions.

[0019] Furthermore, determining a following stability optimization function based on the following stability factor further includes: The following stability optimization function is determined according to a deviation between an actual following distance of the ego vehicle and a desired following distance of the ego vehicle.

[0020] Furthermore, determining the energy loss optimization function based on the energy loss factor further includes: The energy loss optimization function is determined according to the longitudinal energy consumption, steering energy consumption and wind-breaking effect energy consumption of the preceding vehicle.

[0021] Furthermore, determining an obstacle collision optimization function based on the obstacle collision factor includes: Determining a list of passable obstacles, and determining an evaluation function for evaluating obstacle collision probability based on the list of passable obstacles; The obstacle collision optimization function is determined based on the evaluation function.

[0022] On the other hand, the present invention also provides a vehicle following trajectory optimization control system considering energy saving, comprising: A leading vehicle future state prediction module is used to: determine a control period and determine a strategy for estimating the future state of the leading vehicle based on the control period; The ego vehicle reference trajectory calculation module is configured to: determine the historical trajectory and future trajectory of the preceding vehicle according to the future state estimation strategy; and use the combined trajectory of the historical trajectory and the future trajectory as the preliminary reference trajectory of the ego vehicle; a vehicle trajectory prediction model building module, configured to: determine state variables based on the preliminary reference trajectory, and build the vehicle trajectory prediction model according to the state variables; an optimization objective function determination module, configured to determine an optimization objective function of the ego vehicle based on a trajectory deviation factor, a following stability factor, an energy loss factor, and an obstacle collision factor when the ego vehicle follows the preliminary reference trajectory; The optimal trajectory calculation module is used to calculate an 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 according to the optimal control sequence.

[0023] The beneficial effects of the technical solution of the present invention are: 1. The energy-saving vehicle-following trajectory optimization control method described in the present invention can achieve dynamic response to the future state of the leading vehicle and the expected following distance through predictive control, quantitatively plan the vehicle's trajectory, and achieve a balance between accurate and stable following, safe obstacle avoidance, and energy conservation. It also simultaneously considers the vehicle's rotational energy consumption and braking energy recovery, effectively reducing energy consumption and improving tracking efficiency.

[0024] 2. The energy-saving vehicle-following trajectory optimization control system described in the present invention can realize the energy-saving vehicle-following trajectory optimization control method described in the present invention through the mutual cooperation of system modules. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 Schematic diagram of the rectangular coordinate system described in the vehicle following trajectory optimization control method considering energy saving described in Example 1 of the present invention; Figure 2 is a schematic diagram of the reference trajectory in the vehicle-following trajectory optimization control method considering energy saving described in Example 1 of the present invention; Figure 3 1 is a flow chart of the vehicle following trajectory optimization control method considering energy saving according to Example 1 of the present invention; Figure 4 Detailed flowchart of the vehicle following trajectory optimization control method considering energy saving described in Example 1 of the present invention.

[0027] Figure 5 Schematic diagram of the architecture of the vehicle following trajectory optimization control system considering energy saving described in Example 2 of the present invention. DETAILED DESCRIPTION

[0028] The preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more precise definition of the protection scope of the present invention.

[0029] In the description of the present invention, it should be noted that the embodiments described in the present invention are only part of the embodiments of the present invention, rather than all of the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work are within the scope of protection of the present invention.

[0030] The terms "first," "second," and the like in the specification and claims herein and in the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in orders other than those illustrated or described herein. In addition, the terms "including" and "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising 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 process, method, product, or device.

[0031] In the description of the present invention, it should be noted that the following vehicle in this patent refers specifically to tracking and tracing a specific vehicle, and the vehicle does not necessarily have to be traveling on a structured road, but can be traveling at will; the main purpose of this patent is to perform specific operations on the vehicle's speed and steering, and then to achieve flexible dynamic tracking of a preceding vehicle with a known state information.

[0032] Example 1: This example provides a vehicle trajectory optimization control method considering energy saving. Figures 1 to 4 As shown, the following steps are included: S1. Prediction steps for the future state of the preceding vehicle: In this step, the state of the preceding vehicle in the next N control cycles is predicted as the data basis for the subsequent prediction process, as follows: S101. First, establish a rectangular coordinate system XOY fixed to the earth based on the vehicle and the preceding vehicle. Figure 1 As shown in the figure; where O is a fixed point on the ground, the subscript l (i.e. leader) represents the leading vehicle (pilot vehicle), the subscript f (i.e. follower) represents the ego vehicle (following vehicle), (x, y) refers to the position coordinates of the corresponding vehicle, v refers to the speed 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.

[0033] S102: Based on the status of the preceding vehicle at the current time t , it is defined that the acceleration and angular acceleration at time t (i.e. the first moment) are maintained in the next N control cycles. Based on this premise, the motion state value of the preceding vehicle is recursively deduced. The corresponding recursive relationship is described as: ; The above recursive relationship is the calculation strategy for determining the future state of the preceding vehicle. k represents the future control cycle count (i.e., the absolute time at this moment is 0, and the number of steps in the future is k). k=0 is the current moment. is the duration of each control cycle; it should be noted that, when it is impossible to obtain the expected signal of the future operation of the leading vehicle, the state of the leading vehicle can be predicted by Kalman filtering or the above-mentioned constant acceleration method; this embodiment only represents an example of one situation, that is, one of several options. This embodiment is not necessarily the most ideal, but in the absence of direct feedback from the leading vehicle on its future working state information, the calculation method of this embodiment is faster, and can be adaptively selected according to specific needs in specific applications.

[0034] S2. Calculation steps for the vehicle reference trajectory: In this step, the historical and future trajectories of the preceding vehicle are determined based on the above recursive relationship. Finally, a reference trajectory suitable for the ego vehicle is calculated based on the historical and future trajectories of the preceding vehicle, as follows: S201: Determine the historical trajectory of the preceding vehicle based on the above recursive relationship: , which includes the x-coordinate position, y-coordinate position, and θ heading angle sequence; in this historical trajectory, t represents the real absolute time (that is, the number of time units included in the passage of time when the front vehicle moves according to real time), so the above historical trajectory contains multiple coordinate position points.

[0035] S202, looping through the previous vehicle's historical trajectory , determine the point A that is closest to the current position M of the vehicle in front; where, Figure 2 As shown in the figure, B is defined as the current position of the preceding vehicle, M is the current position of the ego vehicle, C is the future position of the preceding vehicle predicted after N control cycles based on the recursive relationship, and A is the position closest to the current position M of the ego vehicle in the historical trajectory of the preceding vehicle.

[0036] S203 , starting from position A, filter out the AB segment trajectory according to a preset interval (usually set to no more than 0.3 m). The AB segment trajectory is the historical trajectory of the preceding vehicle.

[0037] S204. After the AB segment trajectory is screened out, point B can be obtained. At this time, according to the recursive relationship, point B can be recursively deduced to point C to obtain the BC segment trajectory. The BC segment trajectory is the future trajectory of the preceding vehicle.

[0038] S205. Use the AB+BC segment as the reference trajectory of the ego vehicle. This reference trajectory is a continuous sequence of spatial coordinate points, denoted as TrajectoryRef. Correspondingly, the [x, y, θ] coordinates of this reference trajectory are composed of the coordinates of several waypoints on route AC. Without considering energy conservation, TrajectoryRef is the optimal reference trajectory. This trajectory must be obtained under the premise of the actual driving of the preceding vehicle and has the best passability. However, when considering energy conservation, TrajectoryRef is not necessarily the optimal following trajectory. Therefore, it is necessary to combine the prediction model and cost function to optimize the following trajectory.

[0039] S3. Steps for building the vehicle trajectory prediction model: In this step, a prediction module for controlled changes in the ego vehicle trajectory is established to further optimize the ego vehicle's following performance. The details are as follows: S301: Construct a prediction model for the vehicle trajectory as follows: ; Among them, the state variable of the prediction model is [x f 、y f 、v f ,θ f ], superscript k, k+1, and The meanings are consistent with those in the aforementioned step S1.

[0040] S302: According to the above prediction model, The sequence is the variable that is ultimately controlled, and the control variable There are upper and lower limits (constraints), which are described as: ; The specific upper and lower limit values ​​are set according to the specific situation.

[0041] S4. Steps for determining the optimization objective function: In this step, based on the actual characteristics of the ego vehicle following process, the energy optimization cost function and obstacle penalty function of the prediction model are determined, and finally four optimization objective functions are obtained, as follows: S401, determine the deviation degree optimization function J1: Since the reference trajectory is a trajectory with better road conditions among many trajectories and has certain value, it is necessary to optimize the degree of deviation of the vehicle from the reference trajectory. The optimization function is as follows: ; Among them, β1 is the factor of difference between the processing angle and the horizontal and vertical coordinate dimensions; Representative Points The point with the closest Euclidean distance to TrajectoryRef is obtained through traversal calculation.

[0042] S402. Determine the following stability optimization function J2: Based on the deviation between the actual following distance and the desired following distance, determine the optimization function representing the following stability of the host vehicle with respect to the leading vehicle, specifically as follows: ; where N2 is the step size applicable to calculating the following distance, and N2 < N, representing the hope to quickly maintain the following distance, ignoring the following distance deviation after the moment greater than N2, and focusing on the following distance in the initial following stage; d k is the optimal following distance, usually given by the upper-level formation or the automatic driving control instruction; In addition, it should be noted that in other embodiments, a function form that decays with the increase of the cycle can be set; for the following task considered in this patent, J2 is very important. The optimization goal of this patent is for the host vehicle to firmly lock onto a specific leading vehicle for following and not be甩掉 by the leading vehicle.

[0043] S403. Determine the energy loss optimization function J3: Although the energy in the longitudinal motion direction can be recovered through braking, some vehicles (such as tracked vehicles) also consume additional energy during steering. Therefore, considering the actual vehicle characteristics, the longitudinal and lateral directions, as well as the drive and braking, are jointly considered to obtain the corresponding optimization function as follows: ; where: μ1 is a parameter used to measure the energy consumption of the host vehicle at different longitudinal vehicle speeds and accelerations. This value is negative during regenerative braking (representing that energy consumption can be reduced by reasonably controlling the acceleration at this time); μ2 is a parameter used to measure the energy consumption of the host vehicle at different lateral heading angles and angular velocities. This item is for vehicles with relatively large steering energy consumption, and thus reasonably suppresses the steering energy consumption; μ3 is a parameter used to calculate the energy-saving optimization brought by the leading vehicle's wind-breaking effect when the host vehicle follows the leading vehicle; μ1, μ2, and μ3 are all calibrated offline according to the specific properties of the vehicle.

[0044] S404. Determine the obstacle collision optimization function J4: Determine the optimization function as follows by judging whether the currently passed waypoint will collide with an obstacle: ; where, Violation k is an evaluation function used to evaluate whether the above situation results in a collision, and ; In this evaluation function, is a list of obstacles that block the passage ahead. The list is updated in each loop S1. Its value is usually obtained by the autonomous driving perception part of the vehicle. The subscript i is the number of loops taken when traversing the list. N O is the number of obstacles; it should also be noted that, because this method traverses N calculation steps, this method has a better prediction ability for future collision situations.

[0045] S5. Optimal trajectory calculation steps: In this step, the final optimization goal is determined by combining the four optimization objective functions mentioned above. Based on the final optimization goal and the aforementioned prediction model, the optimal control sequence is determined in real time. The ego vehicle is controlled according to the real-time optimal control sequence to complete a stable following maneuver. The details are as follows: S501, determine the final optimization goal as: ;in, In order to balance the dimensional differences of various optimization objective functions, the hyperparameters are debugged and determined according to the actual vehicle status.

[0046] S502, using the above prediction model, combined with the standard model predictive control method, set the optimization target to minimize the J value, optimize the control instructions of the vehicle, and finally obtain The optimal control sequence is obtained, and the first item of the optimal control sequence is taken as the actual control parameter at the current moment. The drive component parameters (such as the torque and power of the drive motor) are adjusted according to the actual control parameter to overcome various resistances and resistance torques during vehicle driving, thereby controlling the acceleration and angular acceleration of the ego vehicle, achieving dynamic control of the ego vehicle's motion state and stably following the vehicle in front.

[0047] It should also be noted that the above S1 to S5 are real-time and continuously cyclic processes, and based on this continuous cycle, dynamic control of vehicle-following trajectory optimization prediction taking energy saving into consideration is achieved.

[0048] It should be noted that the above examples are only for explaining the present invention and are not intended to limit the scope of protection of the present invention. Example 2

[0049] This embodiment is based on the same inventive concept as the energy-saving vehicle following trajectory optimization control method described in Example 1, and provides an energy-saving vehicle following trajectory optimization control system. Figure 5 Shown, including: A leading vehicle future state prediction module is used to: determine a control period and determine a strategy for estimating the future state of the leading vehicle based on the control period; The ego vehicle reference trajectory calculation module is configured to: determine the historical trajectory and future trajectory of the preceding vehicle according to the future state estimation strategy; and use the combined trajectory of the historical trajectory and the future trajectory as the preliminary reference trajectory of the ego vehicle; a vehicle trajectory prediction model building module, configured to: determine state variables based on the preliminary reference trajectory, and build the vehicle trajectory prediction model according to the state variables; an optimization objective function determination module, configured to determine an optimization objective function of the ego vehicle based on a trajectory deviation factor, a following stability factor, an energy loss factor, and an obstacle collision factor when the ego vehicle follows the preliminary reference trajectory; The optimal trajectory calculation module is used to calculate an 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 according to the optimal control sequence.

[0050] Different from the existing technology, the present application adopts a vehicle following trajectory optimization control method and system that takes energy saving into consideration. It can realize dynamic response of the future state of the leading vehicle and the expected following distance through predictive control, quantitatively plan the trajectory of the vehicle itself, and achieve a balance between accurate and stable following, safe obstacle avoidance and energy saving. It simultaneously considers the vehicle's rotational energy consumption and braking energy recovery, effectively reducing energy consumption and improving tracking efficiency.

[0051] It should be understood that in the various embodiments of this document, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0052] It should also be understood that in the embodiments herein, the term "and / or" merely describes an association between associated objects, indicating that three possible relationships exist. For example, "A and / or B" could represent: A alone, A and B simultaneously, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0053] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel 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 document.

[0054] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0055] In the several embodiments provided herein, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices, or units, or can be an electrical, mechanical, or other form of connection.

[0056] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments herein.

[0057] In addition, the functional units in the various embodiments herein may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0058] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this article is essentially or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of this article. The aforementioned storage medium includes: various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0059] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention's description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A vehicle following trajectory optimization control method considering energy saving, characterized in that: The following steps are involved: Steps for predicting the future state of the preceding vehicle: determining a control period, and determining a strategy for estimating a future state of a preceding vehicle based on the control period; Calculation steps for the ego vehicle reference trajectory: Determining the historical trajectory and the future trajectory of the preceding vehicle according to the future state estimation strategy; using the combined trajectory of the historical trajectory and the future trajectory as a preliminary reference trajectory of the ego vehicle; Steps to build the vehicle trajectory prediction model: determining state variables based on the preliminary reference trajectory, and constructing a trajectory prediction model for the vehicle according to the state variables; Steps to determine the optimization objective function: determining an optimization objective function for the ego vehicle based on a trajectory deviation factor, a following stability factor, an energy loss factor, and an obstacle collision factor when the ego vehicle follows the preliminary reference trajectory; Optimal trajectory calculation steps: An optimal control sequence is calculated according to the optimization objective function and the trajectory prediction model, and the ego vehicle is controlled according to the optimal control sequence to complete the following action of the preceding vehicle.

2. The energy-saving vehicle following trajectory optimization control method according to claim 1, characterized in that: The determining of the future state estimation strategy of the preceding vehicle according to the control period includes: The calculation premise is set as follows: the preceding vehicle maintains the acceleration and angular acceleration at the first moment during the control period; Analyze the preceding vehicle's position coordinates calculation formula, vehicle speed calculation formula, and vehicle heading angle calculation formula according to the calculation premise; The calculation premise, the position coordinate calculation formula, the vehicle speed calculation formula, and the vehicle heading angle calculation formula are integrated as the future state calculation strategy.

3. The energy-saving vehicle following trajectory optimization control method according to claim 1, characterized in that: The determining of the historical trajectory and the future trajectory of the preceding vehicle according to the future state extrapolation strategy includes: Determining the current position of the preceding vehicle; Traversing the closest position points between the preceding vehicle and the self-vehicle at the first moment according to the future state estimation strategy, and estimating the future position of the preceding vehicle after the control period according to the future state estimation strategy; The trajectory between the closest position point and the current position of the preceding vehicle is used as the historical trajectory sequence of the preceding vehicle, and the trajectory between the current position of the preceding vehicle and the future position is used as the future trajectory of the preceding vehicle.

4. The energy-saving vehicle following trajectory optimization control method according to claim 1, characterized in that: The historical trajectory and the future trajectory are both composed of a number of waypoints; Each of the waypoints includes: position coordinate information and heading angle information.

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

6. The energy-saving vehicle following trajectory optimization control method according to claim 1, characterized in that: The determining of the optimization objective function of the ego vehicle based on the trajectory deviation factor, following stability factor, energy loss factor, and obstacle collision factor when the ego vehicle follows the preliminary reference trajectory includes: Determining a deviation degree optimization function based on the trajectory deviation factor; Determining a following stability optimization function based on the following stability factor; Based on the energy loss factors, determining an energy loss optimization function; Determining an obstacle collision optimization function based on the obstacle collision factor; The deviation degree optimization function, the following stability optimization function, the energy loss optimization function and the obstacle collision optimization function are used as the optimization objective functions.

7. The energy-saving vehicle following trajectory optimization control method according to claim 6, characterized in that: The determining of the following stability optimization function based on the following stability factor further includes: The following stability optimization function is determined according to a deviation between an actual following distance of the ego vehicle and a desired following distance of the ego vehicle.

8. The energy-saving vehicle following trajectory optimization control method according to claim 6, characterized in that: The determining of the energy loss optimization function based on the energy loss factor further includes: The energy loss optimization function is determined according to the longitudinal energy consumption, steering energy consumption and wind-breaking effect energy consumption of the preceding vehicle.

9. The energy-saving vehicle following trajectory optimization control method according to claim 6, characterized in that: The determining of the obstacle collision optimization function based on the obstacle collision factor includes: Determining a list of passable obstacles, and determining an evaluation function for evaluating obstacle collision probability based on the list of passable obstacles; The obstacle collision optimization function is determined based on the evaluation function.

10. A vehicle trajectory optimization control system considering energy saving, characterized in that: include: A leading vehicle future state prediction module is used to: determine a control period and determine a strategy for estimating the future state of the leading vehicle based on the control period; The ego vehicle reference trajectory calculation module is configured to: determine the historical trajectory and future trajectory of the preceding vehicle according to the future state estimation strategy; and use the combined trajectory of the historical trajectory and the future trajectory as the preliminary reference trajectory of the ego vehicle; a vehicle trajectory prediction model building module, configured to: determine state variables based on the preliminary reference trajectory, and build the vehicle trajectory prediction model according to the state variables; an optimization objective function determination module, configured to determine an optimization objective function of the ego vehicle based on a trajectory deviation factor, a following stability factor, an energy loss factor, and an obstacle collision factor when the ego vehicle follows the preliminary reference trajectory; The optimal trajectory calculation module is used to calculate an 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 according to the optimal control sequence.

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