Vehicle driving control method, device, vehicle and storage medium

Abstract

The present invention discloses a vehicle driving control method, device, vehicle, and storage medium. The method includes: determining a trajectory search point of a target vehicle within a target predicted driving area within a preset time period; determining, for each time point within the preset time period, an uncertainty value of the target trajectory point corresponding to the current time point in the predicted driving trajectory of the target vehicle due to an obstacle; determining, for each trajectory search point corresponding to the current time point, a speed loss, an acceleration loss, and an obstacle loss of the target vehicle at the current trajectory search point; determining a target loss at the current trajectory search point based on the speed loss, acceleration loss, obstacle loss, and uncertainty value, and using the trajectory search point with the smallest target loss as the target driving trajectory point of the target vehicle at the current time point; and controlling the target vehicle to travel along each target driving trajectory point, thereby taking into account the uncertainty of the obstacle predicted trajectory and expanding the planned path driving space.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a vehicle driving control method, device, vehicle, and storage medium. Background Art

[0002] Autonomous driving vehicles, also known as unmanned vehicles, are intelligent vehicles that are equipped with intelligent perception systems, high-precision positioning systems and planning and control systems to achieve unmanned driving. Autonomous driving vehicles can improve traffic safety and road traffic rates.

[0003] Autonomous vehicles can travel on both closed campus roads and open urban roads, where complex road scenarios and unpredictable traffic conditions exist. Current Level 4 autonomous driving systems handle dynamic obstacles based on the obstacle's intention and predicted future trajectory. These obstacles' trajectories are projected onto the vehicle's path, used to calculate the vehicle's available space, and dynamic planning is performed within this space to search for the optimal path. However, even the most precise prediction models will still have errors compared to the actual situation, making it impossible to fully and accurately predict the future driving intentions and trajectories of other traffic participants, especially in traffic scenarios without lane restrictions, such as intersections and roundabouts. Therefore, calculating the available space based solely on the predicted future trajectory of obstacles will significantly reduce the vehicle's available space, making it prone to erroneous yielding, incorrect braking, and path solution failure. Summary of the Invention

[0004] The present invention provides a vehicle driving control method, device, vehicle and storage medium, which can take into account the uncertainty of obstacle prediction trajectory during the dynamic planning of the target vehicle's driving trajectory, and can not only expand the target vehicle's planned path driving space.

[0005] According to one aspect of the present invention, a vehicle driving control method is provided, comprising:

[0006] During the driving process of the target vehicle, determining a trajectory search point of the target vehicle within a target predicted driving area within a preset time period;

[0007] Determining, for each time point within the preset time period, an uncertainty value of a target trajectory point corresponding to a current time point in the predicted driving trajectory of the obstacle of the target vehicle;

[0008] For each trajectory search point corresponding to the current time point, determining the speed loss, acceleration loss, and obstacle loss of the target vehicle at the current trajectory search point;

[0009] Determining a target loss for the current trajectory search point based on the speed loss, acceleration loss, obstacle loss, and uncertainty value, and using the trajectory search point with the smallest target loss as the target driving trajectory point of the target vehicle at the current time point;

[0010] The target vehicle is controlled to travel along each target driving trajectory point.

[0011] According to another aspect of the present invention, there is provided a vehicle driving control device, comprising:

[0012] A trajectory search point determination module is used to determine the trajectory search points of the target vehicle within the target predicted driving area within a preset time period during the driving process of the target vehicle;

[0013] an uncertainty value determination module, configured to determine, for each time point within the preset time period, an uncertainty value of a target trajectory point corresponding to a current time point in the predicted driving trajectory of the target vehicle with respect to the obstacle;

[0014] a loss amount determination module, configured to determine, for each trajectory search point corresponding to the current time point, a speed loss amount, an acceleration loss amount, and an obstacle loss amount of the target vehicle at the current trajectory search point;

[0015] a target driving trajectory point determination module, configured to determine a target loss amount of the current trajectory search point based on the speed loss amount, the acceleration loss amount, the obstacle loss amount, and the uncertainty value, and to use the trajectory search point with the smallest target loss amount as the target driving trajectory point of the target vehicle at the current time point;

[0016] The vehicle driving control module is used to control the target vehicle to travel along each target driving trajectory point.

[0017] According to another aspect of the present invention, there is provided a vehicle, comprising:

[0018] at least one processor; and

[0019] a memory communicatively connected to the at least one processor; wherein,

[0020] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the vehicle driving control method described in any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle driving control method according to any embodiment of the present invention when executed.

[0022] The vehicle driving control scheme provided by an embodiment of the present invention includes: determining a trajectory search point within a target predicted driving area of ​​a preset time period during the driving process of a target vehicle; determining, for each time point within the preset time period, the uncertainty value of the target trajectory point corresponding to the current time point in the predicted driving trajectory of the target vehicle due to obstacles; determining, for each trajectory search point corresponding to the current time point, the speed loss, acceleration loss, and obstacle loss of the target vehicle at the current trajectory search point; determining the target loss of the current trajectory search point based on the speed loss, acceleration loss, obstacle loss, and the uncertainty value, and selecting the trajectory search point with the smallest target loss as the target driving trajectory point of the target vehicle at the current time point; and controlling the target vehicle to drive along each target driving trajectory point. The technical scheme provided by the embodiment of the present invention can take into account the uncertainty of the obstacle predicted trajectory during the dynamic planning of the target vehicle's driving trajectory, thereby not only expanding the target vehicle's planned path driving space, but also improving the accuracy of the target vehicle's driving trajectory planning and enhancing the efficiency of the planned path driving.

[0023] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 This is a flow chart of a vehicle driving control method provided according to the first embodiment of the present invention;

[0026] Figure 2 This is a schematic diagram of a target predicted driving area provided by an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of the effect of a target vehicle crossing an obstacle boundary provided by an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram of a target vehicle and an obstacle driving process provided by an embodiment of the present invention;

[0029] Figure 5a1 is a schematic diagram of a decision effect of a target vehicle overtaking an obstacle provided by an embodiment of the present invention;

[0030] Figure 5b 1 is a schematic diagram of a decision effect of a target vehicle yielding to an obstacle provided by an embodiment of the present invention;

[0031] Figure 6 This is a schematic diagram of a target vehicle's driving trajectory planning effect provided by an embodiment of the present invention;

[0032] Figure 7 This is a schematic diagram of a vehicle driving effect provided by an embodiment of the present invention;

[0033] Figure 8 This is a flow chart of a vehicle driving control method provided according to a second embodiment of the present invention;

[0034] Figure 9 This is a schematic structural diagram of a vehicle driving control device provided according to a third embodiment of the present invention;

[0035] Figure 10 It is a schematic structural diagram of a vehicle for implementing the vehicle driving control method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0037] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order 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, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0038] Example 1

[0039] Figure 1A flow chart of a vehicle driving control method is provided for the first embodiment of the present invention. This embodiment is applicable to the case of controlling the vehicle driving trajectory. The method can be executed by a vehicle driving control device, which can be implemented in the form of hardware and / or software and can be configured in a vehicle. Figure 1 As shown, the method includes:

[0040] S110 . During the driving process of the target vehicle, determining a trajectory search point of the target vehicle within a target predicted driving area within a preset time period.

[0041] The target vehicle can be understood as the ego vehicle, that is, the vehicle itself. The preset time period can be a pre-set time period, which is a preset time period from the current time point backward, such as 7 seconds or 15 seconds. The embodiment of the present invention does not limit the length of the preset time period.

[0042] Exemplarily, during the driving process of the target vehicle, a target predicted driving area of ​​the target vehicle within a preset time period is obtained, where the target predicted driving area may be all areas where the target vehicle may travel within the preset time period. Trajectory search points within the target driving area are determined, where the trajectory search points may be trajectory search points generated by randomly dividing the target driving area, or may be possible trajectory search points pre-planned by a user within the target driving area.

[0043] Optionally, determining the trajectory search points within the target predicted driving area of ​​the target vehicle in a preset time period includes: determining the target predicted driving area of ​​the target vehicle in the preset time period based on the target predicted driving path of the target vehicle; dividing the target predicted driving area by set time intervals and set distance intervals, and determining the trajectory search points within the target predicted driving area. Specifically, obtaining the target predicted driving path of the target vehicle, and determining the target predicted driving area of ​​the target vehicle based on the target predicted driving path, wherein the target predicted driving area is the drivable area of ​​the target vehicle in the preset time period determined based on the target predicted driving path. Optionally, obtaining the target predicted driving path of the target vehicle and the obstacle predicted driving path of the obstacle of the target vehicle in the preset time period, projecting the obstacle predicted driving path onto the target predicted driving path of the target vehicle, and using the area of ​​the target predicted driving path excluding the obstacle predicted driving path as the target predicted driving area of ​​the target vehicle. Figure 2 A schematic diagram of a target predicted driving area provided by an embodiment of the present invention. Figure 2As shown in the figure, the target driving area is divided into predetermined time intervals (delta_t) and distance intervals (delta_s), and the points where the dashed lines intersect are used as trajectory search points. The trajectory search points corresponding to the time points at the predetermined time intervals can be understood as the possible trajectory points of the target vehicle at the current time point.

[0044] S120. Determine, for each time point within the preset time period, an uncertainty value of a target trajectory point corresponding to a current time point in the predicted driving trajectory of the obstacle of the target vehicle.

[0045] The various time points within the preset time period may be time points starting from the current time point and spaced apart by one or more preset time intervals, or may be time points randomly divided into the preset time period.

[0046] In an embodiment of the present invention, obstacles encountered by the target vehicle within a preset time period are obtained, wherein the obstacle may be one or more than one. For example, the obstacle may be multiple obstacle vehicles, i.e., vehicles other than the target vehicle. The predicted driving trajectory of the obstacle within the preset time period is obtained, wherein, since the predicted driving trajectory of the obstacle may also have prediction deviations, generally, the later the time point, the lower the credibility of the predicted driving trajectory corresponding to the time point, that is, the higher the uncertainty of the predicted driving trajectory corresponding to the time point. Therefore, the uncertainty value of the target trajectory point corresponding to each time point in the predicted driving trajectory of the obstacle is determined. For example, the time length from each time point to the current moment can be calculated, and the ratio of the time length to the preset time period can be used as the uncertainty value of the target trajectory at the corresponding time point.

[0047] Optionally, for each time point within the preset time period, determining the uncertainty value of the target trajectory point corresponding to the current time point in the predicted driving trajectory of the obstacle of the target vehicle, including: obtaining the predicted driving trajectory of the obstacle of the target vehicle within the preset time period; dividing the preset time period into equal time intervals based on a set time interval, and determining each time point within the preset time period; for each time point within the preset time period, determining the uncertainty value of the target trajectory point corresponding to the current time point according to the following formula: p=k*t; wherein p represents the uncertainty value of the target driving trajectory point corresponding to the current time point, k represents a preset uncertainty coefficient, and t represents the length of time from the current time point to the start time of the preset time period.

[0048] Exemplarily, the predicted driving trajectory of the obstacle within a preset time period can be obtained through the interaction between the target vehicle and the obstacle. The preset time period is divided into equal time intervals based on the set time interval, and the driving trajectory points corresponding to each time point in the predicted driving trajectory are determined. For example, if the preset time period is 7s and the set time interval is 0.1s, 71 driving trajectory points in the predicted driving trajectory of the obstacle within 7s can be obtained, wherein the later the time distance, the lower the credibility of the driving trajectory point, that is, the higher the uncertainty of the driving trajectory point. Therefore, the uncertainty value of the driving trajectory point corresponding to each time point is obtained. For example, the uncertainty value of the driving trajectory point corresponding to each time point can be calculated according to the following formula: p = k*t, wherein k represents a pre-set uncertainty coefficient, t represents the length of time from the time point corresponding to the driving trajectory point to the current moment (that is, the start time of the preset time period), and p represents the uncertainty value of the driving trajectory point.

[0049] S130 . For each trajectory search point corresponding to the current time point, determine a speed loss, an acceleration loss, and an obstacle loss of the target vehicle at the current trajectory search point.

[0050] In an embodiment of the present invention, each time point in the target predicted driving area may correspond to multiple trajectory search points. Therefore, it is necessary to plan the optimal driving trajectory point of the target vehicle at each time point from the multiple trajectory search points corresponding to the same time point, thereby planning the optimal driving trajectory of the target vehicle within a preset time period.

[0051] Each time point within a preset time period is polled, with the current polled time point being the current time point. For each trajectory search point corresponding to the current time point, the target vehicle's speed loss, acceleration loss, and obstacle loss at the current trajectory search point are calculated. Exemplarily, the speed loss of the target vehicle at the current trajectory search point may be calculated as follows: 1. Based on the distance delta_s_i between the current trajectory search point and the target driving trajectory point corresponding to the previous time point and the target vehicle's speed v0 at the previous time point, calculate the target vehicle's speed v1 at the current time point, where v1 = 2*delta_s_i / delta_t – v0. This method allows the speed information of all reachable trajectory search points to be calculated based on the target vehicle's initial speed. 2. Based on the target vehicle's v1 at the current time point and a preset reference speed v, calculate the target vehicle's speed loss at the current trajectory search point: cost_v = abs(v1-v)*weight_v, where weight_v is a preset speed loss weight.

[0052] The target vehicle's acceleration loss at the current trajectory search point can be calculated using the following methods: 1. Calculate the target vehicle's acceleration a1 at the current time point based on its velocity v1 at the current trajectory search point and its velocity v0 at the previous time point, where a1 = (v1 – v0) / delta_t. 2. Calculate the target vehicle's acceleration loss at the current trajectory search point based on a1 at the current time point and a pre-set acceleration loss weight: cost_a = abs(a1) * weight_a, where weight_a is the pre-set acceleration loss weight.

[0053] In an embodiment of the present invention, the dynamic planning process of the target vehicle's driving trajectory is usually performed outside the obstacle driving route, so the loss term of the trajectory search point that crosses the obstacle is infinite, which can ensure that the searched route will not cross the obstacle and avoid introducing risks such as collisions. In an embodiment of the present invention, in order to prevent the deterministic behavior of the target vehicle caused by obstacle uncertainty, a dynamic planning route is introduced to cross part of the predicted trajectory, and the speed loss, acceleration loss and obstacle loss of the target vehicle at the current trajectory search point are comprehensively considered to evaluate whether the current trajectory search point is the optimal trajectory driving point. Among them, the obstacle loss can be understood as the crossing loss of the target vehicle crossing the obstacle. Set the traversable time to cross_t, that is, it is considered that the obstacle boundary uncertainty after this time is large, and it can be attempted to cross. The obstacle loss is calculated as follows: Where t represents the duration from the start time of the preset time period to the current time point, weight_obs is the obstacle loss weight, and cross_s is the crossing distance, which is the distance from the target vehicle to the boundary of the obstacle's driving path. Figure 3 This is a schematic diagram of the effect of a target vehicle crossing an obstacle boundary provided by an embodiment of the present invention. Figure 3 As shown, for vehicle 1, the trajectory of the vehicle crosses the boundary of its predicted trajectory at 5 seconds and 6 seconds respectively, and the crossing distance cross_s is as follows: Figure 3 shown.

[0054] S140: Determine a target loss for the current trajectory search point based on the speed loss, acceleration loss, obstacle loss, and uncertainty value, and use the trajectory search point with the smallest target loss as the target driving trajectory point of the target vehicle at the current time point.

[0055] In an embodiment of the present invention, a target loss of the target vehicle at the current trajectory search point is determined based on the speed loss, acceleration loss, obstacle loss, and uncertainty value. For example, the target loss can be a weighted sum of the speed loss, acceleration loss, obstacle loss, and uncertainty value.

[0056] Optionally, determining the target loss of the current trajectory search point based on the speed loss, acceleration loss, target loss, and uncertainty value includes: calculating the target loss of the current trajectory search point according to the following formula: cost_t = cost_v + cost_a + p * cost_obs + cost_t_pre; wherein cost_t represents the target loss of the current trajectory search point corresponding to the current time point, cost_v represents the speed loss, cost_a represents the acceleration loss, cost_obs represents the obstacle loss, p represents the uncertainty value of the target trajectory point corresponding to the current time point, and p represents the target loss of the target driving trajectory point corresponding to the previous time point. It can be seen from the above formula that the target loss of the current trajectory search point will accumulate the target loss of the target driving trajectory point corresponding to the previous time point.

[0057] In this embodiment of the present invention, the target loss values ​​for each trajectory search point corresponding to the current time point are compared, and the trajectory search point with the smallest target loss value is determined as the target driving trajectory point of the target vehicle at the current time point. It will be appreciated that the above method can determine the target driving trajectory point corresponding to the target vehicle at each time point, and the determined target driving trajectory point is the optimal driving trajectory that takes into account the uncertainty of the obstacle's driving trajectory.

[0058] S150: Control the target vehicle to travel along each target driving trajectory point.

[0059] In the embodiment of the present invention, after determining the target driving trajectory points of the target vehicle at each time point within the preset time period, it can be understood that the optimal driving trajectory of the target vehicle within the preset time period is determined. Therefore, the target vehicle is controlled to travel along each target driving trajectory point.

[0060] The vehicle driving control method provided by an embodiment of the present invention includes: determining a trajectory search point within a target predicted driving area of ​​a preset time period during the driving process of a target vehicle; determining, for each time point within the preset time period, an uncertainty value of the target trajectory point corresponding to the current time point in the predicted driving trajectory of the target vehicle due to obstacles; determining, for each trajectory search point corresponding to the current time point, a speed loss, an acceleration loss, and an obstacle loss of the target vehicle at the current trajectory search point; determining a target loss for the current trajectory search point based on the speed loss, acceleration loss, obstacle loss, and the uncertainty value, and selecting the trajectory search point with the smallest target loss as the target driving trajectory point of the target vehicle at the current time point; and controlling the target vehicle to travel along each target driving trajectory point. The technical solution provided by the embodiment of the present invention can take into account the uncertainty of the obstacle predicted trajectory during the dynamic planning of the target vehicle's driving trajectory, thereby expanding the target vehicle's planned path driving space, improving the accuracy of the target vehicle's driving trajectory planning, and enhancing the efficiency of the planned path driving.

[0061] In some embodiments, before controlling the target vehicle to travel along each target driving trajectory point, it also includes: determining the center point of the intersection area of ​​the target vehicle and the obstacle within the preset time period within the target predicted driving area, and determining the target time point and center point displacement corresponding to the center point; based on the displacement information of the target driving trajectory point corresponding to each time point within the preset time period, determining the target driving displacement corresponding to the target time point; based on the target driving displacement and the center point displacement, determining the driving mode of the target vehicle relative to the obstacle; controlling the target vehicle to travel along each target driving trajectory point includes: controlling the target vehicle to travel along each target driving trajectory point based on the driving mode. The advantage of this setting is that the driving mode of the target vehicle relative to the obstacle can be accurately determined, thereby making an accurate decision on the driving speed of the target vehicle.

[0062] For example, Figure 4 A schematic diagram of the driving process of a target vehicle and an obstacle provided by an embodiment of the present invention. Figure 4 As shown, during the process of the target vehicle (also known as the ego vehicle) traveling with an obstacle, the target vehicle's path may intersect with the obstacle's path within a preset time period. In this case, the target vehicle and the obstacle are likely to collide. Therefore, it is necessary to plan the target vehicle's driving mode relative to the obstacle to avoid a collision. The target vehicle's driving mode relative to the obstacle includes overtaking and yielding. Overtaking means that the ego vehicle needs to accelerate to overtake the obstacle vehicle, while yielding means that the ego vehicle needs to slow down to avoid the obstacle vehicle.

[0063] In an embodiment of the present invention, within the target predicted driving area, the intersection area between the target vehicle and the obstacle within a preset time period is determined, and the center point of the intersection area is determined. Optionally, determining the center point of the intersection area between the target vehicle and the obstacle within the preset time period includes: obtaining the first predicted driving path of the target vehicle within the preset time period and the second predicted driving path of the obstacle within the preset time period; determining the intersection area between the target vehicle and the obstacle within the preset time period based on the first predicted driving path and the second predicted driving path; and determining the center point of the intersection area. The projection area of ​​the second predicted driving path of the obstacle on the first predicted driving path of the target vehicle is the intersection area between the target vehicle and the obstacle, and the center point of the intersection area is calculated. The target time point and center point displacement corresponding to the center point of the intersection area are determined. The horizontal coordinate corresponding to the center point is the target time point middle_t of the center point, and the vertical coordinate corresponding to the center point is the center point displacement middle_s of the center point.

[0064] Based on the target driving trajectory points corresponding to each time point, a target driving trajectory point sequence is generated, and the displacement information of each target driving trajectory point in the target driving trajectory point sequence relative to the starting position of the target vehicle is determined to generate a target driving displacement sequence corresponding to the target vehicle at each time point. Based on the target driving displacement sequence, the target driving displacement corresponding to the target time point (i.e., the time point corresponding to the center point of the intersection area) is determined. For example, the displacements in the target driving displacement sequence can be interpolated to determine the target driving displacement ego_s corresponding to the target time point. Based on the target driving displacement ego_s and the center point displacement middle_s, the target vehicle's driving mode relative to the obstacle is determined.

[0065] Optionally, based on the target driving displacement and the center point displacement, determining the driving mode of the target vehicle relative to the obstacle includes: when the target driving displacement is greater than the center point displacement, determining that the driving mode of the target vehicle relative to the obstacle is overtaking driving; when the target driving displacement is less than the center point displacement, determining that the driving mode of the target vehicle relative to the obstacle is yielding driving. For example, Figure 5a A schematic diagram of a decision effect of a target vehicle rushing to overtake an obstacle provided by an embodiment of the present invention. Figure 5b A schematic diagram of the decision effect of a target vehicle yielding to an obstacle provided by an embodiment of the present invention. Figure 5a As shown in , the target driving displacement is greater than the center point displacement, that is, ego_s>middle_s. Therefore, the target vehicle's driving mode relative to the obstacle is overtaking driving, that is, the target vehicle accelerates to overtake the obstacle. Figure 5bAs shown, the target driving displacement is smaller than the center point displacement, that is, ego_s<=middle_s. Therefore, the target vehicle's driving mode relative to the obstacle is yielding, that is, the target vehicle slows down to avoid the obstacle.

[0066] After determining the driving mode of the target vehicle relative to the obstacle, the target vehicle is controlled to travel along each target driving trajectory point based on the driving mode. Figure 6 This is a schematic diagram of the effect of target vehicle trajectory planning provided by an embodiment of the present invention. Figure 6 As shown, there are two types of driving trajectory points of the target vehicle (ego vehicle) relative to vehicle 1. Among them, a series of trajectory points above the driving path of vehicle 1 are when the ego vehicle is overtaking vehicle 1, and a series of trajectory points below the driving path of vehicle 1 are when the ego vehicle is yielding to vehicle 1.

[0067] Optionally, before controlling the target vehicle to travel along each target driving trajectory point based on the driving mode, the method further includes: smoothing the driving path of the target vehicle according to each driving trajectory point to obtain a smoothed driving path; controlling the target vehicle to travel along each target driving trajectory point based on the driving mode includes: controlling the target vehicle to travel along the smoothed driving path based on the driving mode. Exemplarily, Figure 7 This is a schematic diagram of a vehicle driving effect provided by an embodiment of the present invention. Figure 7 As shown, the vehicle is traveling along the curve with arrows in the figure, wherein the vehicle is driving in a way of overtaking relative to vehicle 1, is driving in a way of yielding relative to vehicles 2 and 3, and is driving in a way of following relative to vehicle 4.

[0068] Example 2

[0069] Figure 8 This is a flow chart of a vehicle driving control method provided in the second embodiment of the present invention, such as Figure 8 As shown, the method includes:

[0070] S810: During the driving process of the target vehicle, determine a trajectory search point of the target vehicle within a target predicted driving area within a preset time period.

[0071] S820: For each time point within a preset time period, determine an uncertainty value of a target trajectory point corresponding to the current time point in the predicted driving trajectory of the obstacle of the target vehicle.

[0072] Among them, for each time point within a preset time period, determining the uncertainty value of the target trajectory point corresponding to the current time point in the predicted driving trajectory of the obstacle of the target vehicle, including: obtaining the predicted driving trajectory of the obstacle of the target vehicle within the preset time period; dividing the preset time period into equal time intervals based on the set time interval, and determining each time point within the preset time period; for each time point within the preset time period, the uncertainty value of the target trajectory point corresponding to the current time point is calculated according to the following formula: p=k*t; wherein p represents the uncertainty value of the target driving trajectory point corresponding to the current time point, k represents a pre-set uncertainty coefficient, and t represents the length of time from the current time point to the start time of the preset time period.

[0073] S830 : For each trajectory search point corresponding to the current time point, determine the speed loss, acceleration loss, and obstacle loss of the target vehicle at the current trajectory search point.

[0074] S840: Determine the target loss of the current trajectory search point based on the speed loss, acceleration loss, obstacle loss, and uncertainty value, and use the trajectory search point with the smallest target loss as the target driving trajectory point of the target vehicle at the current time point.

[0075] Among them, the target loss amount of the current trajectory search point is determined according to the speed loss amount, acceleration loss amount, target loss amount and uncertainty value, including: calculating the target loss amount of the current trajectory search point according to the following formula: cost_t = cost_v + cost_a + p*cost_obs + cost_t_pre; wherein cost_t represents the target loss amount of the current trajectory search point corresponding to the current time point, cost_v represents the speed loss amount, cost_a represents the acceleration loss amount, cost_obs represents the obstacle loss amount, p represents the uncertainty value of the target trajectory point corresponding to the current time point, and represents the target loss amount of the target driving trajectory point corresponding to the previous time point.

[0076] S850: Determine the center point of the intersection area between the target vehicle and the obstacle within a preset time period within the target predicted driving area, and determine the target time point and center point displacement corresponding to the center point.

[0077] S860: Determine the target driving displacement corresponding to the target time point based on the displacement information of the target driving trajectory points corresponding to each time point within the preset time period.

[0078] S870: Determine the driving mode of the target vehicle relative to the obstacle based on the target driving displacement and the center point displacement.

[0079] Among them, based on the target driving displacement and the center point displacement, the driving mode of the target vehicle relative to the obstacle is determined, including: when the target driving displacement is greater than the center point displacement, the driving mode of the target vehicle relative to the obstacle is determined to be overtaking driving; when the target driving displacement is less than the center point displacement, the driving mode of the target vehicle relative to the obstacle is determined to be yielding driving.

[0080] S880: Control the target vehicle to travel along each target driving trajectory point based on the driving mode.

[0081] The vehicle driving control scheme provided by the embodiments of the present invention, on the one hand, can take into account the uncertainty of obstacle prediction during the dynamic planning of the target vehicle's driving trajectory. This not only expands the target vehicle's planned driving path space, but also improves the accuracy of the target vehicle's driving trajectory planning and enhances the efficiency of the planned path. Furthermore, it can accurately determine the target vehicle's driving pattern relative to obstacles, thereby accurately determining the target vehicle's driving speed.

[0082] Example 3

[0083] Figure 9 This is a structural diagram of a vehicle driving control device provided in Example 3 of the present invention.

[0084] like Figure 9 As shown, the device includes:

[0085] The trajectory search point determination module 910 is used to determine the trajectory search points of the target vehicle within the target predicted driving area within a preset time period during the driving process of the target vehicle;

[0086] An uncertainty value determination module 920 is configured to determine, for each time point within the preset time period, an uncertainty value of a target trajectory point corresponding to a current time point in the predicted driving trajectory of the target vehicle with respect to the obstacle;

[0087] The loss amount determination module 930 is used to determine the speed loss amount, acceleration loss amount and obstacle loss amount of the target vehicle at the current trajectory search point for each trajectory search point corresponding to the current time point;

[0088] a target driving trajectory point determination module 940, configured to determine a target loss amount for the current trajectory search point based on the speed loss amount, the acceleration loss amount, the obstacle loss amount, and the uncertainty value, and to select the trajectory search point with the smallest target loss amount as the target driving trajectory point of the target vehicle at the current time point;

[0089] The vehicle driving control module 950 is used to control the target vehicle to travel along each target driving trajectory point.

[0090] Optionally, the loss amount determination module is configured to:

[0091] The target loss of the current trajectory search point is calculated according to the following formula:

[0092] cost_t=cost_v+cost_a+p*cost_obs+cost_t_pre;

[0093] Among them, cost_t represents the target loss amount of the current trajectory search point corresponding to the current time point, cost_v represents the speed loss amount, cost_a represents the acceleration loss amount, cost_obs represents the obstacle loss amount, p represents the uncertainty value of the target trajectory point corresponding to the current time point, and represents the target loss amount of the target driving trajectory point corresponding to the previous time point.

[0094] Optionally, the uncertainty value determination module is configured to:

[0095] Obtaining a predicted driving trajectory of the target vehicle with respect to an obstacle within the preset time period;

[0096] Dividing the preset time period into equal time intervals based on a set time interval, and determining each time point within the preset time period;

[0097] For each time point within the preset time period, the uncertainty value of the target trajectory point corresponding to the current time point is calculated according to the following formula:

[0098] p=k*t;

[0099] Among them, p represents the uncertainty value of the target driving trajectory point corresponding to the current time point, k represents a preset uncertainty coefficient, and t represents the distance between the current time point and the start time of the preset time period.

[0100] Optionally, the device further includes:

[0101] a center point determination module, configured to determine, before controlling the target vehicle to travel along each target driving trajectory point, within the target predicted driving area, a center point of an intersection area between the target vehicle and the obstacle within the preset time period, and determine a target time point and a center point displacement corresponding to the center point;

[0102] a target driving displacement determination module, configured to determine a target driving displacement corresponding to a target time point based on displacement information of target driving trajectory points corresponding to respective time points within the preset time period;

[0103] a driving mode determination module, configured to determine a driving mode of the target vehicle relative to the obstacle based on the target driving displacement and the center point displacement;

[0104] The vehicle driving control module is used to:

[0105] The target vehicle is controlled to travel along each target driving trajectory point based on the driving mode.

[0106] The driving mode determination module is configured to:

[0107] When the target driving displacement is greater than the center point displacement, determining that the driving mode of the target vehicle relative to the obstacle is overtaking driving;

[0108] When the target driving displacement is less than the center point displacement, it is determined that the driving mode of the target vehicle relative to the obstacle is yielding driving.

[0109] Optionally, the center point determination module is used to:

[0110] Obtaining a first predicted driving path of the target vehicle within the preset time period and a second predicted driving path of the obstacle within the preset time period;

[0111] determining an intersection area between the target vehicle and the obstacle within the preset time period based on the first predicted driving path and the second predicted driving path;

[0112] The center point of the intersection region is determined.

[0113] Optionally, the trajectory search point determination module is used to:

[0114] Determining a target predicted driving area of ​​the target vehicle within a preset time period based on the target predicted driving path of the target vehicle;

[0115] The target predicted driving area is divided into set time intervals and set distance intervals, and a trajectory search point within the target predicted driving area is determined.

[0116] The vehicle driving control device provided in the embodiment of the present invention can execute the vehicle driving control method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0117] Example 4

[0118] Figure 10A schematic diagram of a vehicle 10 is shown that can be used to implement an embodiment of the present invention. The vehicle is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The vehicle can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0119] like Figure 10 As shown, vehicle 10 includes at least one processor 11 and memory, such as read-only memory (ROM) 12 and random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor, and processor 11 can perform various appropriate actions and processes based on the computer programs stored in ROM 12 or loaded from storage unit 18 into RAM 13. RAM 13 can also store various programs and data required for the operation of vehicle 10. Processor 11, ROM 12, and RAM 13 are interconnected via bus 14. An input / output (I / O) interface 15 is also connected to bus 14.

[0120] Various components in the vehicle 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the vehicle 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0121] The processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors that run machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the vehicle driving control method.

[0122] In some embodiments, the vehicle travel control method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the vehicle 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the vehicle travel control method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the vehicle travel control method in any other suitable manner (e.g., by means of firmware).

[0123] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0124] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0125] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0126] To provide interaction with a user, the systems and techniques described herein can be implemented in a vehicle having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the vehicle. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0127] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0128] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0129] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0130] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A vehicle driving control method, characterized in that: include: During the driving process of the target vehicle, determining a trajectory search point of the target vehicle within a target predicted driving area within a preset time period; Determining, for each time point within the preset time period, an uncertainty value of a target trajectory point corresponding to a current time point in the predicted driving trajectory of the obstacle of the target vehicle; For each trajectory search point corresponding to the current time point, determining the speed loss, acceleration loss, and obstacle loss of the target vehicle at the current trajectory search point; Determining a target loss for the current trajectory search point based on the speed loss, acceleration loss, obstacle loss, and uncertainty value, and using the trajectory search point with the smallest target loss as the target driving trajectory point of the target vehicle at the current time point; The target vehicle is controlled to travel along each target driving trajectory point.

2. The method according to claim 1, characterized in that Determining the target loss amount of the current trajectory search point according to the velocity loss amount, the acceleration loss amount, the target loss amount, and the uncertainty value includes: The target loss of the current trajectory search point is calculated according to the following formula: cost_t=cost_v+cost_a+p*cost_obs+cost_t_pre; Among them, cost_t represents the target loss amount of the current trajectory search point corresponding to the current time point, cost_v represents the speed loss amount, cost_a represents the acceleration loss amount, cost_obs represents the obstacle loss amount, p represents the uncertainty value of the target trajectory point corresponding to the current time point, and represents the target loss amount of the target driving trajectory point corresponding to the previous time point.

3. The method according to claim 1, characterized in that Determining, for each time point within the preset time period, an uncertainty value of a target trajectory point corresponding to the current time point in the predicted driving trajectory of the obstacle of the target vehicle, including: Obtaining a predicted driving trajectory of the target vehicle with respect to an obstacle within the preset time period; Dividing the preset time period into equal time intervals based on a set time interval, and determining each time point within the preset time period; For each time point within the preset time period, the uncertainty value of the target trajectory point corresponding to the current time point is calculated according to the following formula: p=k*t; Among them, p represents the uncertainty value of the target driving trajectory point corresponding to the current time point, k represents a preset uncertainty coefficient, and t represents the distance between the current time point and the start time of the preset time period.

4. The method according to claim 1, wherein Before controlling the target vehicle to travel along each target driving trajectory point, the method further includes: In the target predicted driving area, determining the center point of the intersection area between the target vehicle and the obstacle within the preset time period, and determining the target time point and center point displacement corresponding to the center point; Determining a target driving displacement corresponding to a target time point based on displacement information of a target driving trajectory point corresponding to each time point within the preset time period; determining a driving mode of the target vehicle relative to the obstacle based on the target driving displacement and the center point displacement; Controlling the target vehicle to travel along each target driving trajectory point includes: The target vehicle is controlled to travel along each target driving trajectory point based on the driving mode.

5. The method according to claim 4, characterized in that Determining a driving mode of the target vehicle relative to the obstacle based on the target driving displacement and the center point displacement includes: When the target driving displacement is greater than the center point displacement, determining that the driving mode of the target vehicle relative to the obstacle is overtaking driving; When the target driving displacement is less than the center point displacement, it is determined that the driving mode of the target vehicle relative to the obstacle is yielding driving.

6. The method according to claim 4, characterized in that Determining a center point of an intersection area between the target vehicle and the obstacle within the preset time period includes: Obtaining a first predicted driving path of the target vehicle within the preset time period and a second predicted driving path of the obstacle within the preset time period; determining an intersection area between the target vehicle and the obstacle within the preset time period based on the first predicted driving path and the second predicted driving path; The center point of the intersection region is determined.

7. The method according to any one of claims 1 to 6, characterized in that: Determining a trajectory search point of the target vehicle within a target predicted driving area within a preset time period includes: Determining a target predicted driving area of ​​the target vehicle within a preset time period based on the target predicted driving path of the target vehicle; The target predicted driving area is divided into set time intervals and set distance intervals, and a trajectory search point within the target predicted driving area is determined.

8. A vehicle driving control device, characterized in that: include: A trajectory search point determination module is used to determine the trajectory search points of the target vehicle within the target predicted driving area within a preset time period during the driving process of the target vehicle; an uncertainty value determination module, configured to determine, for each time point within the preset time period, an uncertainty value of a target trajectory point corresponding to a current time point in the predicted driving trajectory of the target vehicle with respect to the obstacle; a loss amount determination module, configured to determine, for each trajectory search point corresponding to the current time point, a speed loss amount, an acceleration loss amount, and an obstacle loss amount of the target vehicle at the current trajectory search point; a target driving trajectory point determination module, configured to determine a target loss amount of the current trajectory search point based on the speed loss amount, the acceleration loss amount, the obstacle loss amount, and the uncertainty value, and to use the trajectory search point with the smallest target loss amount as the target driving trajectory point of the target vehicle at the current time point; The vehicle driving control module is used to control the target vehicle to travel along each target driving trajectory point.

9. A vehicle, characterized in that: The vehicle comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the vehicle driving control method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the vehicle driving control method according to any one of claims 1 to 7 when executed.

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