Vehicle driving control method and device, electronic equipment and storage medium

Abstract

The invention provides a vehicle driving control method and device, electronic equipment and a storage medium, and the method comprises the steps: predicting a road condition according to a to-be-planned road segment in front of a vehicle, and dividing the road segment into a first number of continuous distance segments; according to the vehicle performance parameters, the maximum driving speed of the vehicle is calculated, and then the planned vehicle speed is divided into a second number of vehicle speed gears from zero to the maximum driving speed; according to the performance of each sub-controller, tasks are issued to each sub-controller, so that each sub-controller calculates the vehicle control force of all vehicle speed gear changes and the energy consumption value corresponding to the vehicle control force in parallel within each continuous distance section; and receiving all vehicle control forces and corresponding energy consumption values in each continuous distance section returned by each sub-controller, and calculating a target energy consumption value of each continuous distance section, which enables the overall energy consumption value of the to-be-planned road section to be the lowest, so as to control the vehicle to run according to the vehicle control force corresponding to the target energy consumption value. Therefore, high precision and real-time performance are taken into consideration, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] This disclosure relates to the field of autonomous driving technology, and in particular to a vehicle driving control method, device, electronic device, and storage medium. Background Technology

[0002] In autonomous driving technologies, vehicle trajectory planning typically relies on a single controller to perform dynamic planning for the entire path. Specifically, these technologies often employ a centralized computing architecture to attempt to cover all driving conditions within a single optimization process, with the core being a fine-grained enumeration search of control variables.

[0003] However, to ensure planning accuracy, related technologies need to process massive amounts of computational data and occupy a large amount of storage space. This makes it difficult for the controller to complete the calculation within a limited time window, resulting in a failure to guarantee real-time performance. If computational accuracy is sacrificed to meet real-time performance requirements, i.e., the discrete precision of the control variables is reduced, although the computational burden is alleviated, it will cause a large optimization error, causing the energy consumption control strategy to fail and the expected vehicle energy-saving goals to be unattainable. Summary of the Invention

[0004] In view of this, the present disclosure provides a vehicle driving control method, the method being applied to a main controller, the main controller being used to schedule various sub-controllers, the method comprising: Based on the predicted road conditions of the planned road segment ahead of the vehicle, the planned road segment is divided into a first number of continuous distance segments; Based on the vehicle performance parameters, the maximum driving speed of the vehicle is calculated, and then the planned driving speed of the vehicle is divided into a second number of speed gears between zero and the maximum driving speed. Based on the performance of each sub-controller, a corresponding number of data calculation tasks for continuous distance segments are issued to each sub-controller, so that each sub-controller can calculate in parallel the vehicle control force for all speed gear changes in each continuous distance segment and the energy consumption value corresponding to the vehicle control force, based on the vehicle driving status data, the vehicle's dynamic parameters and predicted road condition data. The system receives the control forces and corresponding energy consumption values ​​of all vehicles within each consecutive distance segment returned by each sub-controller, calculates the target energy consumption value of each consecutive distance segment that minimizes the overall energy consumption value of the road segment to be planned, and controls the vehicle driving according to the vehicle control force corresponding to the target energy consumption value.

[0005] Optionally, the main controller is also connected to an actuator. The calculation of target energy consumption values ​​for each consecutive distance segment that minimizes the overall energy consumption of the planned road segment, and the control of vehicle movement based on the vehicle control force corresponding to the target energy consumption value, includes: The vehicle control forces and corresponding energy consumption values ​​within each continuous distance segment are spliced ​​and integrated according to the segment order of the road segment to be planned, so as to obtain the vehicle control forces and corresponding energy consumption values ​​of all speed gear changes within each continuous distance segment of the road segment to be planned. Based on the energy consumption values ​​corresponding to the control forces of all vehicles within each continuous distance segment of the road segment to be planned, calculate the overall energy consumption value of the road segment to be planned corresponding to different combinations of vehicle speed gear changes within each continuous distance segment of the road segment to be planned. The target energy consumption value and the corresponding target vehicle control force of each continuous distance segment that minimizes the overall energy consumption value of the road segment to be planned are selected. The target vehicle control force is then sent to the actuator, so that the actuator controls the vehicle to drive according to the target vehicle control force.

[0006] Optionally, the step of calculating the overall energy consumption value of the planned road segment corresponding to different combinations of vehicle speed gear changes within each consecutive distance segment of the planned road segment, based on the energy consumption values ​​corresponding to the control forces of all vehicles within each consecutive distance segment of the planned road segment, includes: Based on vehicle dynamics parameters, determine the maximum change in vehicle speed over a continuous distance segment; Based at least on the maximum change in vehicle speed, select a set of vehicle speed gear changes that meet the vehicle dynamics constraints from all speed gear changes in each continuous distance segment of the road segment to be planned. Based on the energy consumption value corresponding to the vehicle control force of each speed gear change in the set of speed gear changes, calculate the overall energy consumption value of the planned road segment corresponding to different combinations of speed gear changes within each continuous distance segment of the planned road segment.

[0007] Optionally, the vehicle control force includes motor drive force and electric motor force, and the step of selecting a set of vehicle speed gear changes that meet vehicle dynamics constraints from all speed gear changes within each consecutive distance segment of the road segment to be planned, based at least on the maximum change in vehicle speed, includes: Obtain parameter constraints, which include upper and lower limits of at least one of the motor driving force, electric motor power, and power battery state of charge. Based on the parameter constraints and the maximum change in vehicle speed, a set of vehicle speed gear changes that meet the parameter constraints and the vehicle dynamics constraints is selected from all speed gear changes within each continuous distance segment of the road segment to be planned.

[0008] Optionally, the step of issuing data calculation tasks for a corresponding number of continuous distance segments to each sub-controller based on the performance of each sub-controller includes: Based on the calculation speed data of each sub-controller, determine the number of continuous distance segments to be processed by each sub-controller; The storage space data of the sub-controller with the lowest computing speed is divided by the number of continuous distance segments allocated to the sub-controller with the lowest computing speed to obtain the maximum computing accuracy of each sub-controller; wherein, the maximum computing accuracy is greater than or equal to the computing accuracy represented by the second number of vehicle speed gears; Based on the number of consecutive distance segments to be processed by each sub-controller and the maximum calculation accuracy, a data calculation task for a corresponding number of consecutive distance segments is issued to each sub-controller.

[0009] Optionally, before issuing data calculation tasks for a corresponding number of continuous distance segments to each sub-controller based on the performance of each sub-controller, the method further includes: Based on the calculation accuracy of the second number of vehicle speed gears and the calculation complexity of a single continuous distance segment, determine the minimum remaining storage space required for a single sub-controller. The minimum data processing requirements for a single sub-controller are determined based on the minimum remaining storage space required for a single sub-controller and the minimum computing time required for vehicle operation. If the data processing capability of a target sub-controller does not meet the minimum data processing requirements, the target sub-controller is removed so that it no longer performs data calculation tasks.

[0010] Optionally, the calculation precision of the second number of vehicle speed gears is less than the maximum calculation precision, and the method further includes: If each sub-controller meets the minimum data processing requirements, the calculation accuracy of the second quantity of vehicle speed gears is gradually increased, but not exceeding the maximum calculation accuracy. During the data processing process of each sub-controller based on the increased calculation accuracy, it is determined whether there are any sub-controllers whose calculation speed does not meet the minimum calculation time requirements for vehicle operation. If the calculation speed of at least one sub-controller does not meet the minimum calculation time requirement, the calculation accuracy of the second number of vehicle speed gears will be stopped.

[0011] This disclosure also provides a vehicle driving control device, the device being a main controller, the device being used to schedule various sub-controllers, the device comprising: A segmentation unit is used to divide the planned road segment into a first number of continuous distance segments based on the predicted road conditions of the planned road segment ahead of the vehicle. The calculation unit is used to calculate the maximum driving speed of the vehicle based on the vehicle performance parameters, and then divide the planned driving speed of the vehicle into a second number of speed gears between zero and the maximum driving speed. The distribution unit is used to distribute data calculation tasks for a corresponding number of continuous distance segments to each sub-controller according to the performance of each sub-controller, so that each sub-controller can calculate in parallel the vehicle control force for all speed gear changes in each continuous distance segment and the energy consumption value corresponding to the vehicle control force based on the vehicle driving status data, the vehicle's dynamic parameters and predicted road condition data. The control unit is used to receive the control forces of all vehicles and their corresponding energy consumption values ​​in each continuous distance segment returned by each sub-controller, calculate the target energy consumption value of each continuous distance segment that minimizes the overall energy consumption value of the road segment to be planned, and control the vehicle driving according to the vehicle control force corresponding to the target energy consumption value.

[0012] This disclosure also provides an electronic device, including a communication interface, a processor, a memory, and a bus, wherein the communication interface, the processor, and the memory are interconnected via the bus; The memory stores machine-readable instructions, and the processor executes the above method by invoking the machine-readable instructions.

[0013] This disclosure also provides a machine-readable storage medium storing machine-readable instructions that, when called and executed by a processor, implement the above-described method.

[0014] Therefore, in the technical solution disclosed herein, the main controller first divides the planned road segment into several continuous distance segments based on the predicted road conditions ahead of the vehicle. Then, it calculates the vehicle's maximum speed using vehicle performance parameters and divides the planned speed into several speed gears within the range from zero to this maximum speed. Subsequently, the main controller calculates tasks based on the performance allocation data of each sub-controller. Each sub-controller, based on vehicle driving state data, dynamic parameters, and road condition data, calculates in parallel the vehicle control force and energy consumption value corresponding to all speed gear changes within each continuous distance segment. After receiving this data, the main controller calculates and filters out the target energy consumption values ​​for each continuous distance segment that minimize the overall energy consumption of the planned road segment. Finally, the main controller implements vehicle driving control based on the vehicle control force corresponding to the target energy consumption value.

[0015] Through the above methods, the technical solution disclosed herein relies on a dynamic planning driving control scheme based on predicted road conditions and vehicle parameters. On the one hand, by dividing the road segment to be planned and the vehicle speed gear into segments, combined with the parallel computing mode of the sub-controller, the single-point computing power and storage pressure are reduced without sacrificing computing accuracy, achieving a balance between high precision and real-time performance. This improves the calculation efficiency of vehicle control force and energy consumption values, solves the problem of computing lag caused by insufficient computing power and storage of traditional single controllers, and ensures efficient and stable vehicle driving. On the other hand, based on the target selection control strategy of minimizing the overall energy consumption of the entire road segment, the energy consumption during vehicle driving is precisely optimized, improving energy utilization efficiency and ensuring economical and energy-saving vehicle driving. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating a vehicle driving control method as an exemplary embodiment; Figure 2 This is an exemplary embodiment illustrating a distance-speed matrix diagram after equally dividing the planned road segment and vehicle speed; Figure 3 This is a schematic diagram illustrating an exemplary embodiment of a method for solving energy consumption optimization paths using inverse dynamic programming; Figure 4 This is an exemplary embodiment illustrating a combination of vehicle speed gear changes for consecutive distance segments with the lowest overall energy consumption. Figure 5 This is a schematic diagram illustrating a method for stitching together the collaborative computing results of multiple controllers, as shown in an exemplary embodiment. Figure 6 This is a schematic diagram illustrating a method for computational task allocation and optimal path arbitration, as shown in an exemplary embodiment. Figure 7 This is an exemplary embodiment illustrating the hardware structure of an electronic device; Figure 8 This is a block diagram illustrating a vehicle driving control device as an exemplary embodiment. Detailed Implementation

[0018] To enable those skilled in the art to better understand the technical solutions in this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0019] It should be noted that the steps of the corresponding methods are not necessarily performed in the order shown and described in this disclosure in other embodiments. In some other embodiments, the methods may include more or fewer steps than those described in this disclosure. Furthermore, a single step described in this disclosure may be broken down into multiple steps in other embodiments; and multiple steps described in this disclosure may be combined into a single step in other embodiments.

[0020] The present disclosure will now be described through specific embodiments and in conjunction with specific application scenarios.

[0021] Please see Figure 1 , Figure 1 This is a flowchart illustrating a vehicle driving control method as an exemplary embodiment. The method may perform the following steps: Step 102: Based on the predicted road conditions of the planned road segment ahead of the vehicle, divide the planned road segment into a first number of continuous distance segments.

[0022] For example, while the vehicle is in motion, information about the future path ahead can be obtained through maps or navigation. Information on road slope changes, speed limits, and traffic congestion within a given km radius is collected by the vehicle's main controller. After obtaining the km information, the total length of the road segment to be planned is divided into equal parts. Section, obtained Segmented continuous distance segments, each segment having a distance of [missing information]. km.

[0023] The "first quantity of continuous distance segments" can be divided by equally dividing the "road segment to be planned," or the length of each continuous distance segment can be adjusted according to predicted road conditions. For example, continuous distance segments in areas with drastic slope changes or dense curves can be relatively short, while those in straight, unobstructed, or stable road areas can be relatively long, thus ensuring calculation accuracy and saving energy. The "road segment to be planned" refers to the route the vehicle will travel in the future, and its length is provided by navigation or high-precision maps. The "first quantity" refers to... This indicates that the road section is divided into The segment is used for the state discretization in subsequent dynamic programming. The value of affects the calculation accuracy and load, and can be dynamically adjusted by the main controller based on the computing power and storage resources of each sub-controller. "Continuous distance segments" means that the distances of each continuous distance segment are connected end to end, without overlap or gaps, forming a complete prediction path coverage.

[0024] Step 104: Calculate the maximum driving speed of the vehicle based on the vehicle performance parameters, and then divide the planned speed of the vehicle into a second number of speed gears between zero and the maximum driving speed.

[0025] For example, the main controller reads pre-calibrated vehicle performance parameters from the motor controller or vehicle controller via the vehicle bus, including the maximum driving force of the motor, braking system capacity, vehicle weight, and drag coefficient. Then, based on the vehicle performance parameters and predicted road conditions (gradient, friction coefficient, speed limit, congestion, etc.), the main controller calculates the maximum speed the vehicle can achieve on the current road surface. Subsequently, the main controller will switch from 0 to... Speed ​​range is divided into equal parts There are 1 gear, and the interval between each speed gear is 1. This forms a discrete set of vehicle speed gears, which is used to construct the state space in dynamic programming.

[0026] It should be noted that the "vehicle speed gears" can be divided equally between zero and the maximum driving speed, or they can be divided unequally. For example, more densely packed vehicle speed gears can be set in the commonly used vehicle speed range (such as 40-80km / h), while sparser vehicle speed gears can be set in other vehicle speed ranges, thereby ensuring calculation accuracy and saving energy.

[0027] To help those skilled in the art better understand this solution, the following example uses equal division, combined with... Figure 2 This section provides a clear explanation of how planned road segments and planned speed limits are divided. Please refer to [link / reference]. Figure 2 , Figure 2 This is an exemplary embodiment illustrating a distance-speed matrix diagram after equally dividing the planned road segment and vehicle speed. For example... Figure 2 As shown, the total length of the road segment to be planned Divided into equal parts There are 3 consecutive distance segments, each with a distance of 1. They are arranged sequentially along the horizontal axis; meanwhile, the planned speed range of the vehicles is from 0 to the maximum speed. Divided into equal parts There are several speed gears, with each speed gear spaced apart by a certain interval. The distance-vehicle speed matrix is ​​formed by uniformly distributing data along the vertical axis, creating a two-dimensional grid. Each grid point in the matrix represents a possible state, i.e., the discrete vehicle speed corresponding to a certain distance traveled (e.g., speed). Indicates that the vehicle has traveled to At the location, the vehicle speed is The matrix has 10 rows. The number of columns is The whole constitutes a discretized representation of the distance-velocity state space, which is used for the traversal of state transitions and energy consumption calculations in dynamic programming algorithms, and supports the subsequent parallel solution of the optimal control sequence on each sub-controller.

[0028] Among them, "vehicle performance parameters" refer to physical quantities that affect the vehicle's dynamic characteristics, including but not limited to the peak driving force of the motor, the maximum braking force of the braking system, the vehicle's mass, the air resistance coefficient, and the rolling resistance coefficient, which can be obtained through bench testing, simulation calibration, or online identification by on-board sensors. "Maximum driving speed" is the theoretically highest speed that can be maintained based on the current vehicle configuration and environmental conditions (such as zero slope and no wind), and is usually limited by the balance between motor power and resistance. "Second quantity" refers to... , representing the discrete precision of the vehicle speed dimension. The main controller dynamically sets the parameters based on the storage capacity and real-time scheduling feedback of each sub-controller to balance accuracy and computational timeliness.

[0029] Step 106: Based on the performance of each sub-controller, send a corresponding number of data calculation tasks for continuous distance segments to each sub-controller, so that each sub-controller can calculate in parallel the vehicle control force for all speed gear changes within each continuous distance segment and the energy consumption value corresponding to the vehicle control force, based on the vehicle driving status data, the vehicle's dynamic parameters, and the predicted road condition data.

[0030] For example, the main controller first obtains the current computing load, remaining memory, and communication latency status of each sub-controller through the vehicle network; then, it combines the pre-defined... consecutive distance segments and For each speed gear, the entire distance-speed matrix is ​​dynamically allocated to each sub-controller by column (i.e., by distance segment) or row (i.e., by speed gear segment). After receiving the allocated road segment range and speed gear segment range, each sub-controller, based on the vehicle's current driving state (such as initial speed, battery state of charge, brake pedal opening), dynamic parameters (such as vehicle mass, frontal area, air drag coefficient, rolling resistance coefficient, motor efficiency curve, etc.), and road condition data of the current road segment (such as wind resistance, road slope, road friction coefficient), traverses all feasible speed gear transfer paths within the road segment range, calculates the vehicle control force (including motor driving force, electric motor power, and mechanical braking force) required for each transfer, and the corresponding energy consumption value, and sends the results back to the main controller.

[0031] Here, "sub-controller" refers to the on-board electronic control unit participating in distributed dynamic programming calculations, which can be a computing node in a domain controller or a dedicated coprocessor. "Vehicle control force" refers to the longitudinal resultant force required to achieve a specific vehicle speed change, usually determined by the driving force output by the vehicle motor, the motor power, and the mechanical braking force. "Energy consumption value" is the energy consumed by the vehicle (electric energy or fuel) under corresponding road segments and speed changes. It can be calculated based on the vehicle's longitudinal dynamics equations, and combined with energy conversion models such as motor efficiency, transmission efficiency, and battery charging and discharging characteristics, the product of control force and vehicle speed (i.e., instantaneous power) can be integrated in the time domain. In discretization implementation, it can also be approximated by weighted summation of the product of control force and average vehicle speed for each distance segment.

[0032] In the embodiments of this disclosure, for ease of calculation and optimization, both vehicle speed and control force are discretized: vehicle speed is divided into several levels (e.g., 0, ...). , ...up to the maximum vehicle speed), forming a discrete state sequence; the control force is quantified into a finite number of discrete values ​​based on the motor's capability, constituting a discrete control force set, including positive motor driving force and negative motor driving force. Within each distance segment, after the system calculates the theoretically required control force based on vehicle dynamics and road condition data, it does not directly use this value, but instead selects the closest discrete value from the preset discrete control force set as the actual control command. This discretization method transforms the continuous optimization problem into a finite state transition problem, reducing computational complexity and ensuring that the generated control sequence can be accurately implemented by the motor controller in actual execution.

[0033] Step 108: Receive the control forces of all vehicles and their corresponding energy consumption values ​​within each consecutive distance segment returned by each sub-controller, calculate the target energy consumption value of each consecutive distance segment that minimizes the overall energy consumption value of the road segment to be planned, and control the vehicle driving according to the vehicle control force corresponding to the target energy consumption value.

[0034] For example, see Figure 3 , Figure 3 This is a schematic diagram illustrating an exemplary embodiment of a method for solving energy consumption optimization paths using inverse dynamic programming. For example... Figure 3 As shown, after receiving the control forces and corresponding energy consumption values ​​of all vehicles within each consecutive distance segment from the sub-controllers, the main controller summarizes the vehicle control forces and their energy consumption values ​​corresponding to all speed gear changes within each consecutive distance segment. Subsequently, the main controller uses a backward recursive method of dynamic programming to select the combination of speed gear changes for each consecutive distance segment that minimizes the overall energy consumption value of the planned road segment, starting from the last segment, and records the target energy consumption value and corresponding vehicle control force for each consecutive distance segment on the path.

[0035] Please see Figure 4 , Figure 4 This is an exemplary embodiment illustrating a combination of vehicle speed gear changes for consecutive distance segments with the lowest overall energy consumption. (See diagram below.) Figure 4 As shown, after the main controller integrates the data returned by all sub-controllers and performs a global energy consumption assessment, it selects the speed gear change sequence that minimizes the overall energy consumption of the planned road segment and maps it to a distance-speed matrix. The arrows marked with bold solid lines in the figure represent this optimal speed gear change combination: the lines connecting adjacent points on the path represent the allowed speed gear shifts (i.e., changes from one speed gear to the next) within the corresponding distance segment. The entire path is composed of the optimal speed gear changes within each consecutive distance segment, and includes the target energy consumption value and corresponding vehicle control force for each consecutive distance segment.

[0036] Ultimately, the main controller extracts the target vehicle control force corresponding to each segment based on the path, and sends the control force sequence composed of each segment to the motor controller or braking system to actually control the vehicle's acceleration and deceleration behavior.

[0037] Here, "target energy consumption value" refers to the local energy consumption component corresponding to a continuous distance segment in the globally optimal path. It is not optimized independently but is determined as part of the "overall minimum energy consumption path." "Vehicle control force" includes the motor driving force under driving conditions and the electric motor force and mechanical braking force under braking conditions, which are control inputs that directly act on the longitudinal movement of the vehicle. "Minimum overall energy consumption value" is achieved through the optimality principle of dynamic programming, that is: if the path from the starting point to the ending point is optimal, then any sub-path of it must also be the optimal solution within that sub-interval.

[0038] In some embodiments, although there are alternative methods for computing the “global minimum energy path” such as forward dynamic programming, graph search or reinforcement learning, inverse dynamic programming has a natural recursive structure advantage when dealing with finite-time optimal control problems with explicit terminal constraints. It is easy to combine state pruning with distributed parallel computing and is suitable for multi-controller collaborative architectures.

[0039] In one embodiment shown, the main controller is also connected to an actuator. The calculation of target energy consumption values ​​for each consecutive distance segment that minimizes the overall energy consumption of the planned road segment, and the control of vehicle movement based on the vehicle control force corresponding to the target energy consumption value, includes: concatenating and integrating all vehicle control forces and their corresponding energy consumption values ​​within each consecutive distance segment according to the segment order of the planned road segment, to obtain vehicle control forces for all speed gear changes within each consecutive distance segment of the planned road segment and the corresponding energy consumption values; calculating the overall energy consumption value of the planned road segment corresponding to different speed gear changes within each consecutive distance segment of the planned road segment based on the energy consumption values ​​corresponding to all vehicle control forces within each consecutive distance segment of the planned road segment; selecting the target energy consumption values ​​and corresponding target vehicle control forces for each consecutive distance segment that minimizes the overall energy consumption of the planned road segment; and sending the target vehicle control forces to the actuator, so that the actuator controls vehicle movement based on the target vehicle control forces.

[0040] For example, see Figure 5 , Figure 5 This is a schematic diagram illustrating a method for stitching together the results of collaborative computation by multiple controllers, as shown in an exemplary embodiment. Figure 5 As shown, the main controller receives control forces and corresponding energy consumption values ​​for all vehicles within their respective continuous distance segments from multiple sub-controllers (such as controller 1 to controller n); subsequently, the main controller follows the segment sequence of the road segment to be planned (from segment 1 to segment n). (segment) at the specified splicing point (such as splicing point) The data is then spliced ​​and integrated at point ) to form a control force-energy consumption set covering the entire road segment to be planned. Then, the main controller uses a dynamic programming inverse recursive method, starting from the last segment of the road segment to be planned (the ) The process begins with backtracking forward from the last segment. In the last segment, since there is no subsequent state dependency, it is necessary to traverse all speed gear changes within that continuous distance segment and record their corresponding energy consumption. Starting from the second-to-last segment, based on the minimum cumulative energy consumption value calculated for the next stage, the speed gear change that minimizes the sum of "energy consumption of this segment + minimum cumulative energy consumption of the next stage" is selected. This process is repeated segment by segment until the first segment. Finally, the main controller selects the combination of speed gear changes with the lowest overall energy consumption and extracts the target vehicle control force corresponding to it in each segment. This target vehicle control force is then sent to the actuators via the vehicle network, enabling the actuators to adjust the driving force or braking force accordingly to achieve vehicle driving control.

[0041] Please see Figure 6 , Figure 6 This is a schematic diagram illustrating a method for computational task allocation and optimal path arbitration, as shown in an exemplary embodiment. Figure 6As shown, the main controller sends calculation tasks to sub-controllers 1 and 2 respectively, and each sub-controller independently performs dynamic programming calculations within its assigned road segment. Sub-controller 1 completes the calculations for segment 1 to segment 2. After calculating segment -1, feedback is given for segments 1 to 1. -1 segment contains all speed and gear changes within each consecutive distance segment, along with the corresponding vehicle control force and energy consumption values; simultaneously, sub-controller 2 completes the first... Section to the first After the segment calculation, feedback is also provided. At the "power splicing point," the main controller integrates the calculation results of the two sub-controllers and, based on the principle of minimizing global energy consumption, determines the combination of vehicle speed gear changes that minimizes the overall energy consumption of the planned road segment (marked as "optimal path" in the figure). The "optimal path" consists of multiple consecutive vehicle speed gear changes, with each speed gear change corresponding to a vehicle control force.

[0042] Here, "actuator" refers to the electronically controlled device that receives commands from the main controller and directly adjusts the longitudinal power output of the vehicle, including the execution module in the motor controller, brake controller, or vehicle control unit. "Vehicle control force" is the driving force or braking force required to achieve a specific vehicle speed change, generated by motor torque or regenerative braking strategy. "Splicing and integration" refers to the main controller performing spatiotemporal alignment of the discretized state-control pairs returned by each sub-controller according to the distance segment sequence, reconstructing a complete state transition diagram.

[0043] In some embodiments, to more accurately reflect vehicle energy flow and improve energy efficiency, the calculation of energy consumption further considers regenerative braking. Specifically, vehicle control forces include motor drive force, motor power, and mechanical braking force. Motor drive force is used to drive the vehicle, motor power is generated by the motor in regenerative braking mode and can be used for energy recovery, while mechanical braking force is provided by the friction braking system and does not have energy recovery capability. Each sub-controller determines the vehicle's rolling resistance, wind resistance, gradient resistance, and acceleration resistance based on the vehicle's dynamic parameters and road condition data, and then calculates the motor drive force required to achieve specific speed gear changes within each continuous distance segment. Simultaneously, based on the current vehicle speed and brake pedal opening in the vehicle's driving state data, it determines the motor power that can be applied within that continuous distance segment. Subsequently, the motor drive force is integrated within that continuous distance segment to obtain the drive energy consumption; the motor power is integrated within that continuous distance segment and, combined with a preset motor braking energy recovery efficiency model, the braking energy recovery consumption is calculated. Finally, the difference between the drive energy consumption and the braking energy recovery consumption is calculated to obtain the actual energy consumption value corresponding to that continuous distance segment.

[0044] In one embodiment, the step of calculating the overall energy consumption value of the planned road segment corresponding to different speed gear change combinations within each consecutive distance segment of the planned road segment, based on the energy consumption values ​​corresponding to the control forces of all vehicles within each consecutive distance segment of the planned road segment, includes: determining the maximum speed change of a vehicle within a consecutive distance segment based on vehicle dynamics parameters; at least based on the maximum speed change, selecting a set of speed gear changes that meet vehicle dynamics constraints from all speed gear changes within each consecutive distance segment of the planned road segment; and calculating the overall energy consumption value of the planned road segment corresponding to different speed gear change combinations within each consecutive distance segment of the planned road segment based on the energy consumption values ​​corresponding to the vehicle control forces of each speed gear change in the set of speed gear changes.

[0045] For example, the main controller calculates the maximum acceleration and deceleration that the vehicle can achieve within a single continuous distance segment based on vehicle dynamics parameters, and then determines the maximum allowable increase and decrease in vehicle speed within that segment. Based on this, the main controller performs feasibility screening on all speed gear change combinations within each continuous distance segment, retaining only the set of speed gear changes whose changes do not exceed the maximum increase and decrease in speed within a single continuous distance segment. Subsequently, the main controller iterates through the set of speed gear changes within each continuous distance segment, summing the energy consumption values ​​of the speed gear changes encountered in each segment to obtain the overall energy consumption value for the planned road segment.

[0046] The "maximum change in vehicle speed" refers to the upper limit of vehicle speed change, constrained by physical limits, under given distance and current vehicle state conditions, and can be calculated using kinematic formulas. "Vehicle dynamics constraints" include maximum acceleration / deceleration constraints and driving / braking force boundaries, used to eliminate infeasible speed / gear changes. By pre-screening the set of feasible speed / gear changes before path search, invalid state traversal can be reduced, distributed computing efficiency can be improved, and the selected control sequence can be executed on a real vehicle.

[0047] In one embodiment shown, the vehicle control force includes motor driving force and electric motor power. The step of selecting a set of vehicle speed gear changes that conform to vehicle dynamics constraints from all speed gear changes within each consecutive distance segment of the road segment to be planned, based at least on the maximum change in vehicle speed, includes: obtaining parameter constraints, which include upper and lower limits of at least one of motor driving force, electric motor power, and the state of charge of the power battery; and selecting a set of vehicle speed gear changes that conform to both the parameter constraints and the vehicle dynamics constraints from all speed gear changes within each consecutive distance segment of the road segment to be planned, based on the parameter constraints and the maximum change in vehicle speed.

[0048] For example, to screen feasible speed gear changes, the main controller acquires the vehicle's current parameter constraints, including the maximum driving force that the motor can provide, the maximum braking force that the motor can provide, and the upper and lower limits of the battery's state of charge. Then, for each speed gear change within each consecutive distance segment of the planned road segment (e.g., accelerating from 40 km / h to 50 km / h, or decelerating from 60 km / h to 50 km / h), the main controller first determines the required motor driving force or motor power for the change and checks whether this force exceeds the current upper and lower limits of the motor's output. Simultaneously, the main controller estimates the impact of this segment's driving on the battery charge; if it would lead to overcharging or over-discharging of the battery, the speed gear change is also considered infeasible. Finally, the main controller retains only speed gear changes that simultaneously satisfy the physical limits of the speed change and all system parameter constraints, forming a set of speed gear changes for subsequent evaluation.

[0049] Among them, "motor driving force" is the positive force output by the drive motor when the vehicle accelerates or maintains its speed; "motor power" refers to the negative force generated by the motor braking during the deceleration process; "power battery state of charge" is the remaining percentage of battery charge, and its safe operating range is usually set by the battery management system; "parameter constraints" are used to reflect the real-time capability boundary of the whole vehicle system and to eliminate unexecutable control commands in advance during the path planning stage, thereby ensuring that the combination of vehicle speed and gear changes that minimizes the overall energy consumption of the planned road segment is both energy-saving and engineering feasible.

[0050] In one embodiment, the step of issuing a corresponding number of continuous distance segment data calculation tasks to each sub-controller based on the performance of each sub-controller includes: determining the number of continuous distance segments to be processed by each sub-controller based on the calculation speed data of each sub-controller; dividing the storage space data of the sub-controller with the lowest calculation speed by the number of continuous distance segments allocated to the sub-controller with the lowest calculation speed to obtain the maximum calculation accuracy of each sub-controller; wherein the maximum calculation accuracy is greater than or equal to the calculation accuracy represented by the second number of vehicle speed gears; and issuing a corresponding number of continuous distance segment data calculation tasks to each sub-controller based on the number of continuous distance segments to be processed by each sub-controller and the maximum calculation accuracy.

[0051] For example, before task allocation, the main controller first obtains the real-time performance parameters of each sub-controller via the vehicle communication bus, including computing speed and available storage space. Assuming there are three sub-controllers in the system: sub-controller A has a computing speed of 100,000 operations per second, sub-controller B 60,000 operations per second, and sub-controller C 40,000 operations per second, the main controller identifies sub-controller C as having the lowest computing speed. If the total length of the planned road segment is divided into 30 consecutive distance segments, the main controller initially allocates sub-controller A to handle 12 segments, B to handle 10 segments, and C to handle 8 segments based on the computing speed ratio of each sub-controller. Subsequently, the main controller reads that sub-controller C has 12 MB of available storage space and, combined with its allocated 8 distance segments, calculates the maximum allowable state data volume for each segment to be 1.5 MB; then, based on the approximate memory occupied by each speed gear combination, it deduces that each segment supports a maximum of 60 speed gears, i.e., the maximum calculation accuracy is 60 gears. If the second number of speed gears required by the current plan is 50 (less than 60), then the accuracy requirement is met. Based on this, the main controller finally determines the task range and speed discretization accuracy of each sub-controller, and sends out the corresponding road segment data, speed gear set and dynamic parameters, so that each sub-controller can perform calculations in parallel.

[0052] Among them, "computation speed data" refers to the amount of state transition or dynamic programming node computation that a sub-controller can complete per unit time, reflecting the real-time computing power level; "storage space data" refers to the available memory capacity of the sub-controller that can be used to cache intermediate computation results; "maximum computation accuracy" is the upper limit of vehicle speed discretization, determined by the storage and task load of the weakest node (the one with the lowest computation speed), under the premise that all sub-controller tasks can be completed. Through the above mechanism, the main controller achieves load balancing and accuracy adaptation of computation tasks, taking into account both global optimization quality and the real-time constraints of the distributed system.

[0053] It is understandable that the higher the calculation precision represented by the second number of speed gears, the slower the calculation speed. Therefore, in some embodiments, if the current calculation speed cannot meet the real-time driving needs of the vehicle, the number of speed gears needs to be reduced in the next calculation cycle, thereby lowering the calculation precision represented by the second number of speed gears.

[0054] In one embodiment shown, before issuing data calculation tasks for a corresponding number of continuous distance segments to each sub-controller based on the performance of each sub-controller, the method further includes: determining the minimum remaining storage space required by a single sub-controller based on the calculation accuracy represented by the second number of vehicle speed gears and the calculation complexity of a single continuous distance segment; determining the minimum data processing requirements of a single sub-controller based on the minimum remaining storage space required by a single sub-controller and the minimum calculation time requirement for vehicle travel; if the data processing capability of a target sub-controller does not meet the minimum data processing requirements, the target sub-controller is removed so that it no longer performs data calculation task processing.

[0055] For example, before task allocation, the main controller first estimates that a single sub-controller requires approximately 1.2 MB of memory to process a single continuous distance segment of data, based on the vehicle speed discretization scheme (e.g., dividing 0-120km / h into 24 5km / h intervals) and the number of all feasible speed transition combinations within a single segment. Simultaneously, considering the real-time requirements of vehicle control for computational efficiency (e.g., completing path planning within 200 milliseconds), it deduces that the sub-controller must support a capability of at least 50,000 state transition calculations per second. The main controller then queries the performance parameters reported by each sub-controller and determines that sub-controller C's processing speed is only 30,000 calculations per second, below the minimum data processing capability requirement. Therefore, it is removed from the available computing nodes, and no further computing tasks are assigned to it; only sub-controllers A and B, which meet the requirements, undertake the computing tasks.

[0056] In some embodiments, the main controller may first filter the remaining storage space: if the remaining storage space of a sub-controller is lower than the minimum requirement, it is directly determined that it does not have the basic resource conditions to perform the computing task, and there is no need to query its data processing capabilities, thereby reducing communication overhead and scheduling delay.

[0057] Among them, "the calculation precision of the second number of vehicle speed gears" refers to the fine granularity of vehicle speed discretization. The more gears there are, the larger the state space becomes, and the higher the storage and computation requirements. "The computational complexity of a single continuous distance segment" is determined by the number of state transitions and energy consumption integral calculations. "Minimum remaining storage space" is used to cache the vehicle speed-control force-energy consumption triplet and its backtracking pointer within a single segment. "Minimum data processing requirements" integrates the dual constraints of storage capacity and timeliness, reflecting the entry threshold for sub-controllers to participate in collaborative optimization. In this embodiment, by pre-screening and eliminating substandard nodes, the overall planning rhythm can be avoided due to individual weak nodes, ensuring the real-time performance and reliability of the system.

[0058] In one embodiment shown, the calculation accuracy represented by the second number of vehicle speed gears is less than the maximum calculation accuracy. The method further includes: if each sub-controller meets the minimum data processing requirements, gradually increasing the calculation accuracy represented by the second number of vehicle speed gears, but not exceeding the maximum calculation accuracy; during the data processing process of each sub-controller based on the increased calculation accuracy, determining whether there is a sub-controller whose calculation speed does not meet the minimum calculation timeliness requirements for vehicle driving; if there is at least one sub-controller whose calculation speed does not meet the minimum calculation timeliness requirements, stopping the increase of the calculation accuracy represented by the second number of vehicle speed gears.

[0059] For example, the main controller initially divides the vehicle speed range of 0-120 km / h into 24 equally wide speed ranges, with a speed discretization granularity of 5 km / h (i.e., each speed range covers 5 km / h). Evaluation showed that all sub-controllers met the storage and computing power requirements at this granularity. Subsequently, the main controller attempted to increase the modeling granularity, reducing it to 2.5 km / h (48 speed ranges), and each sub-controller began path planning calculations based on a higher-resolution vehicle speed state space. During execution, the main controller detected that sub-controller B took 250ms to complete a single segment calculation, exceeding the minimum computational time threshold of 200ms required for real-time vehicle control. At this point, the main controller immediately terminated the granularity refinement process, reverted to the previous effective configuration (5 km / h granularity), and locked the current speed discretization scheme for subsequent distributed computing tasks.

[0060] Here, "second number of speed gears" refers to the total number of gears after discretizing the speed range. The corresponding granularity of speed discretization determines the size of the state space and the computational complexity; the finer the granularity, the more gears, the higher the model accuracy, but the increased computational load. "Maximum computational accuracy" is not accuracy in the sense of measurement error, but rather the finest discretization granularity that the system can support under the current hardware platform and functional safety constraints, usually determined by the computing power of the sub-controller. "Minimum computational timeliness requirement" refers to the maximum allowable delay of vehicle control to the planning results, stemming from the chassis response bandwidth and high-speed driving stability requirements.

[0061] In this embodiment, the main controller gradually increases the number of gears through online trial and error, and monitors the calculation time of each sub-controller in real time; if any node times out, it will fall back to the previous feasible granularity to ensure a dynamic balance between model refinement and real-time performance.

[0062] In some embodiments, to improve the utilization efficiency of distributed computing resources and shorten the overall task completion time, the main controller executes a dynamic task reallocation strategy when it detects that some sub-controllers have not yet completed their assigned computing tasks, while other sub-controllers have completed their tasks ahead of schedule. Specifically, the main controller divides the remaining computing tasks of the unfinished sub-controllers into two equal parts based on the amount of computation or the size of the state space, and transfers a portion of these sub-tasks to idle sub-controllers that have completed their computations. This mechanism avoids overall waiting caused by the computing power bottleneck of individual sub-controllers, significantly improving the system's load balancing capability and real-time response performance in heterogeneous computing environments.

[0063] In some embodiments, to address the inconsistency between historical predictions and current observations caused by sudden changes in environmental perception or traffic conditions, the main controller introduces a prediction consistency verification mechanism. Specifically, at the beginning of each planning cycle, the main controller compares the current road information output by the current perception module, such as road topology, obstacle locations, and traffic signal status, with the historical prediction information used in the previous cycle. If there are significant differences in key elements (such as lane connectivity, presence of obstacles ahead, and speed limit changes), the prediction is deemed inaccurate. In this case, the main controller maintains the vehicle control force unchanged from the previous moment to ensure the continuity and safety of vehicle behavior. Simultaneously, it regenerates the planning task based on the updated current road information and redistributes this task to each sub-controller for a new round of distributed computation. After the new computation results pass timeliness and feasibility verification, the new control force output is switched. This mechanism avoids control command errors caused by prediction errors, enhancing the system's robustness and driving smoothness in complex dynamic traffic scenarios.

[0064] Corresponding to the embodiments of the vehicle driving control method described above, this disclosure also provides an embodiment of a vehicle driving control device.

[0065] Please see Figure 7 , Figure 7 This is an exemplary embodiment illustrating the hardware structure of an electronic device. At the hardware level, the device includes a processor 702, an internal bus 704, a network interface 706, memory 708, and non-volatile memory 710, and may also include other necessary hardware. One or more embodiments of this disclosure can be implemented in software, for example, the processor 702 reads the corresponding computer program from the non-volatile memory 710 into memory 708 and then runs it. Of course, besides software implementation, one or more embodiments of this disclosure do not exclude other implementation methods, such as logic devices or a combination of hardware and software, etc. That is to say, the execution entity of the following processing flow is not limited to individual logic units, but can also be hardware or logic devices.

[0066] Please see Figure 8 , Figure 8 This is a block diagram illustrating an exemplary embodiment of a vehicle driving control device 800. This vehicle driving control device 800 can be applied to, for example... Figure 7 The illustrated electronic device implements the technical solution of this disclosure. The device is a main controller, used to schedule various sub-controllers, and includes: The segmentation unit 802 is used to divide the planned road segment into a first number of continuous distance segments based on the predicted road conditions of the planned road segment ahead of the vehicle. The calculation unit 804 is used to calculate the maximum driving speed of the vehicle based on the vehicle performance parameters, and then divide the planned driving speed of the vehicle into a second number of speed gears between zero and the maximum driving speed. The sending unit 806 is used to send a corresponding number of data calculation tasks for continuous distance segments to each sub-controller according to the performance of each sub-controller, so that each sub-controller can calculate in parallel the vehicle control force for all speed gear changes in each continuous distance segment and the energy consumption value corresponding to the vehicle control force based on the vehicle driving status data, the vehicle's dynamic parameters and predicted road condition data. The control unit 808 is used to receive the control forces of all vehicles and their corresponding energy consumption values ​​in each continuous distance segment returned by each sub-controller, calculate the target energy consumption value of each continuous distance segment that minimizes the overall energy consumption value of the road segment to be planned, and control the vehicle driving according to the vehicle control force corresponding to the target energy consumption value.

[0067] In some embodiments, the main controller is further connected to an actuator, and the calculation of target energy consumption values ​​for each consecutive distance segment that minimizes the overall energy consumption value of the planned road segment, in order to control vehicle driving according to the vehicle control force corresponding to the target energy consumption value, includes: The vehicle control forces and corresponding energy consumption values ​​within each continuous distance segment are spliced ​​and integrated according to the segment order of the road segment to be planned, so as to obtain the vehicle control forces and corresponding energy consumption values ​​of all speed gear changes within each continuous distance segment of the road segment to be planned. Based on the energy consumption values ​​corresponding to the control forces of all vehicles within each continuous distance segment of the road segment to be planned, calculate the overall energy consumption value of the road segment to be planned corresponding to different combinations of vehicle speed gear changes within each continuous distance segment of the road segment to be planned. The target energy consumption value and the corresponding target vehicle control force of each continuous distance segment that minimizes the overall energy consumption value of the road segment to be planned are selected. The target vehicle control force is then sent to the actuator, so that the actuator controls the vehicle to drive according to the target vehicle control force.

[0068] In some embodiments, calculating the overall energy consumption value of the planned road segment corresponding to different speed gear changes within each consecutive distance segment of the planned road segment, based on the energy consumption values ​​corresponding to the control forces of all vehicles within each consecutive distance segment of the planned road segment, includes: Based on vehicle dynamics parameters, determine the maximum change in vehicle speed over a continuous distance segment; Based at least on the maximum change in vehicle speed, select a set of vehicle speed gear changes that meet the vehicle dynamics constraints from all speed gear changes in each continuous distance segment of the road segment to be planned. Based on the energy consumption value corresponding to the vehicle control force of each speed gear change in the set of speed gear changes, calculate the overall energy consumption value of the planned road segment corresponding to different combinations of speed gear changes within each continuous distance segment of the planned road segment.

[0069] In some embodiments, the vehicle control force includes motor drive force and electric motor force, and the step of selecting a set of vehicle speed gear changes that meet vehicle dynamics constraints from all speed gear changes within each consecutive distance segment of the road segment to be planned, at least based on the maximum change in vehicle speed, includes: Obtain parameter constraints, which include upper and lower limits of at least one of the motor driving force, electric motor power, and power battery state of charge. Based on the parameter constraints and the maximum change in vehicle speed, a set of vehicle speed gear changes that meet the parameter constraints and the vehicle dynamics constraints is selected from all speed gear changes within each continuous distance segment of the road segment to be planned.

[0070] In some embodiments, the step of issuing data calculation tasks for a corresponding number of continuous distance segments to each sub-controller based on the performance of each sub-controller includes: Based on the calculation speed data of each sub-controller, determine the number of continuous distance segments to be processed by each sub-controller; The storage space data of the sub-controller with the lowest computing speed is divided by the number of continuous distance segments allocated to the sub-controller with the lowest computing speed to obtain the maximum computing accuracy of each sub-controller; wherein, the maximum computing accuracy is greater than or equal to the computing accuracy represented by the second number of vehicle speed gears; Based on the number of consecutive distance segments to be processed by each sub-controller and the maximum calculation accuracy, a data calculation task for a corresponding number of consecutive distance segments is issued to each sub-controller.

[0071] In some embodiments, before issuing data calculation tasks for a corresponding number of continuous distance segments to each sub-controller according to the performance of each sub-controller, the apparatus further includes: The first determining unit 810 is used to determine the minimum remaining storage space required by a single sub-controller based on the calculation accuracy of the second quantity of vehicle speed gears and the calculation complexity of a single continuous distance segment. The second determining unit 812 is used to determine the minimum data processing requirements of a single sub-controller based on the minimum remaining storage space required for a single sub-controller and the minimum computing time required for vehicle operation. The elimination unit 814 is used to eliminate the target sub-controller if the data processing capability of the target sub-controller does not meet the minimum data processing requirements, so that the target sub-controller no longer performs data calculation tasks.

[0072] In some embodiments, the calculation precision represented by the second number of vehicle speed gears is less than the maximum calculation precision, and the device further includes: The first addition unit 816 is used to gradually increase the calculation accuracy of the second quantity of vehicle speed gears if each sub-controller meets the minimum data processing requirements, but not exceed the maximum calculation accuracy. The third determining unit 818 is used to determine whether there is a sub-controller whose calculation speed does not meet the minimum calculation time requirement for vehicle driving during the data processing process of each sub-controller based on the increased calculation accuracy. The second addition unit 820 is used to stop increasing the calculation accuracy of the second quantity of vehicle speed gears if the calculation speed of at least one sub-controller does not meet the minimum calculation time requirement.

[0073] The specific implementation process of the functions and roles of each unit in the above device can be found in the implementation process of the corresponding steps in the above method, and will not be repeated here.

[0074] For the device embodiments, since they basically correspond to the method embodiments, the relevant parts can be referred to in the description of the method embodiments. The device embodiments described above are merely illustrative. 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 modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0075] The systems, devices, modules, or units described in the above embodiments can be implemented by a computer or entity, or by a product with a certain function. A typical implementation device is a computer, which can be a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email sending and receiving device, game console, tablet computer, wearable device, or any combination of these devices.

[0076] In a typical configuration, a computer includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0077] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0078] Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, disk storage, quantum memory, graphene-based storage media or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0079] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data shall comply with the relevant laws, regulations and standards of the relevant regions, and corresponding operation portals shall be provided for users to choose to authorize or refuse.

[0080] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0081] The foregoing has described specific embodiments of this disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] The terminology used in one or more embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this disclosure. The singular forms “a,” “the,” and “the” as used in one or more embodiments of this disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0083] It should be understood that although the terms first, second, third, etc., may be used to describe various information in one or more embodiments of this disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first information may also be referred to as second information without departing from the scope of one or more embodiments of this disclosure, and similarly, second information may also be referred to as first information. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0084] The above description is merely a preferred embodiment of one or more embodiments of this disclosure and is not intended to limit the scope of one or more embodiments of this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of one or more embodiments of this disclosure should be included within the scope of protection of one or more embodiments of this disclosure.

Claims

1. A vehicle driving control method, characterized in that, The method is applied to a main controller, which is used to schedule various sub-controllers, and the method includes: Based on the predicted road conditions of the planned road segment ahead of the vehicle, the planned road segment is divided into a first number of continuous distance segments; Based on the vehicle performance parameters, the maximum driving speed of the vehicle is calculated, and then the planned driving speed of the vehicle is divided into a second number of speed gears between zero and the maximum driving speed. Based on the performance of each sub-controller, a corresponding number of data calculation tasks for continuous distance segments are issued to each sub-controller, so that each sub-controller can calculate in parallel the vehicle control force for all speed gear changes in each continuous distance segment and the energy consumption value corresponding to the vehicle control force, based on the vehicle driving status data, the vehicle's dynamic parameters and predicted road condition data. The system receives the control forces and corresponding energy consumption values ​​of all vehicles within each consecutive distance segment returned by each sub-controller, calculates the target energy consumption value of each consecutive distance segment that minimizes the overall energy consumption value of the road segment to be planned, and controls the vehicle driving according to the vehicle control force corresponding to the target energy consumption value.

2. The method according to claim 1, characterized in that, The main controller is also connected to an actuator. The calculation of target energy consumption values ​​for each consecutive distance segment that minimizes the overall energy consumption value of the road segment to be planned, and the control of vehicle movement based on the vehicle control force corresponding to the target energy consumption value, includes: The vehicle control forces and corresponding energy consumption values ​​within each continuous distance segment are spliced ​​and integrated according to the segment order of the road segment to be planned, so as to obtain the vehicle control forces and corresponding energy consumption values ​​of all speed gear changes within each continuous distance segment of the road segment to be planned. Based on the energy consumption values ​​corresponding to the control forces of all vehicles within each continuous distance segment of the road segment to be planned, calculate the overall energy consumption value of the road segment to be planned corresponding to different combinations of vehicle speed gear changes within each continuous distance segment of the road segment to be planned. The target energy consumption value and the corresponding target vehicle control force of each continuous distance segment that minimizes the overall energy consumption value of the road segment to be planned are selected. The target vehicle control force is then sent to the actuator, so that the actuator controls the vehicle to drive according to the target vehicle control force.

3. The method according to claim 2, characterized in that, The step of calculating the overall energy consumption value of the planned road segment corresponding to different combinations of vehicle speed gear changes within each consecutive distance segment of the planned road segment, based on the energy consumption values ​​corresponding to the control forces of all vehicles within each consecutive distance segment of the planned road segment, includes: Based on vehicle dynamics parameters, determine the maximum change in vehicle speed over a continuous distance segment; Based at least on the maximum change in vehicle speed, select a set of vehicle speed gear changes that meet the vehicle dynamics constraints from all speed gear changes in each continuous distance segment of the road segment to be planned. Based on the energy consumption value corresponding to the vehicle control force of each speed gear change in the set of speed gear changes, calculate the overall energy consumption value of the planned road segment corresponding to different combinations of speed gear changes within each continuous distance segment of the planned road segment.

4. The method according to claim 3, characterized in that, The vehicle control force includes motor drive force and electric motor force. The step of selecting a set of vehicle speed gear changes that meet vehicle dynamics constraints from all speed gear changes within each consecutive distance segment of the road segment to be planned, based at least on the maximum change in vehicle speed, includes: Obtain parameter constraints, which include upper and lower limits of at least one of the motor driving force, electric motor power, and power battery state of charge. Based on the parameter constraints and the maximum change in vehicle speed, a set of vehicle speed gear changes that meet the parameter constraints and the vehicle dynamics constraints is selected from all speed gear changes within each continuous distance segment of the road segment to be planned.

5. The method according to claim 1, characterized in that, The step of issuing data calculation tasks for a corresponding number of continuous distance segments to each sub-controller based on their performance includes: Based on the calculation speed data of each sub-controller, determine the number of continuous distance segments to be processed by each sub-controller; Divide the storage space data of the sub-controller with the lowest computing speed by the number of continuous distance segments allocated to the sub-controller with the lowest computing speed to obtain the maximum computing accuracy of each sub-controller; wherein the maximum computing accuracy is greater than or equal to the computing accuracy represented by the second number of vehicle speed gears; Based on the number of consecutive distance segments to be processed by each sub-controller and the maximum calculation accuracy, a data calculation task for a corresponding number of consecutive distance segments is issued to each sub-controller.

6. The method according to claim 1, characterized in that, Before issuing data calculation tasks for a corresponding number of continuous distance segments to each sub-controller based on their performance, the method further includes: Based on the calculation accuracy of the second number of vehicle speed gears and the calculation complexity of a single continuous distance segment, determine the minimum remaining storage space required for a single sub-controller. The minimum data processing requirements for a single sub-controller are determined based on the minimum remaining storage space required for a single sub-controller and the minimum computing time required for vehicle operation. If the data processing capability of a target sub-controller does not meet the minimum data processing requirements, the target sub-controller is removed so that it no longer performs data calculation tasks.

7. The method according to claim 6, characterized in that, The second quantity of vehicle speed gears represents a calculation precision that is less than the maximum calculation precision. The method further includes: If each sub-controller meets the minimum data processing requirements, the calculation accuracy of the second quantity of vehicle speed gears is gradually increased, but not exceeding the maximum calculation accuracy. During the data processing process of each sub-controller based on the increased calculation accuracy, it is determined whether there are any sub-controllers whose calculation speed does not meet the minimum calculation time requirements for vehicle operation. If the calculation speed of at least one sub-controller does not meet the minimum calculation time requirement, the calculation accuracy of the second number of vehicle speed gears will be stopped.

8. A vehicle driving control device, characterized in that, The device is a main controller, and the device is used to schedule various sub-controllers. The device includes: A segmentation unit is used to divide the planned road segment into a first number of continuous distance segments based on the predicted road conditions of the planned road segment ahead of the vehicle. The calculation unit is used to calculate the maximum driving speed of the vehicle based on the vehicle performance parameters, and then divide the planned driving speed of the vehicle into a second number of speed gears between zero and the maximum driving speed. The distribution unit is used to distribute data calculation tasks for a corresponding number of continuous distance segments to each sub-controller according to the performance of each sub-controller, so that each sub-controller can calculate in parallel the vehicle control force for all speed gear changes in each continuous distance segment and the energy consumption value corresponding to the vehicle control force based on the vehicle driving status data, the vehicle's dynamic parameters and predicted road condition data. The control unit is used to receive the control forces of all vehicles and their corresponding energy consumption values ​​in each continuous distance segment returned by each sub-controller, calculate the target energy consumption value of each continuous distance segment that minimizes the overall energy consumption value of the road segment to be planned, and control the vehicle driving according to the vehicle control force corresponding to the target energy consumption value.

9. An electronic device, characterized in that, It includes a communication interface, a processor, a memory, and a bus, wherein the communication interface, the processor, and the memory are interconnected via the bus; The memory stores machine-readable instructions, and the processor executes the method according to any one of claims 1 to 7 by invoking the machine-readable instructions.

10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores machine-readable instructions, which, when invoked and executed by a processor, implement the method described in any one of claims 1 to 7.

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