Acceleration determination method and device for vehicle trajectory simulation

Abstract

The invention discloses an acceleration determination method and device for vehicle track simulation. The method comprises the following steps: acquiring a first adjustment vector matched with a current road environment; obtaining a second adjustment vector matched with the object portrait of the vehicle object; according to the first adjustment vector and the second adjustment vector, adjusting at least one acceleration state parameter in the acceleration state parameter set to obtain a target acceleration state parameter set; and determining the current acceleration according to the current speed of the vehicle object and the target acceleration state parameter set. The technical problem that the matching degree of the acceleration determined by the related method and the actual driving scene is low is solved.

Description

Technical Field

[0001] This application relates to the field of intelligent driving, and more specifically, to a method and apparatus for determining acceleration for vehicle trajectory simulation. Background Technology

[0002] With the continuous improvement of vehicle intelligence, high-level autonomous driving has become a core area of ​​competition in the global automotive industry. The decision-making and planning system, as the "brain" of autonomous driving, undertakes the crucial task of integrating perception, localization, and prediction information to generate safe, comfortable, and efficient driving trajectories. Especially in structured road environments, vehicles need to frequently handle complex interaction scenarios such as lane changes and merging, placing higher demands on the real-time response capability, safety reliability, and intelligent adaptability of the decision-making system.

[0003] In the process of generating motion trajectories, forward simulation is typically used to sequentially determine the vehicle acceleration corresponding to each trajectory point. However, existing methods usually determine the acceleration corresponding to different trajectory points based on a following model with fixed parameters. These methods typically only consider the relative speed between the vehicle and the vehicle in front, ignoring other factors that may affect driving behavior. Therefore, the accelerations determined by these methods often do not match the actual driving scenario well and cannot accurately reflect real-world driving conditions.

[0004] There is currently no widely accepted and effective solution to this problem. Summary of the Invention

[0005] This application provides an acceleration determination method and apparatus for vehicle trajectory simulation, to at least solve the technical problem that the acceleration determined by related methods has a low degree of matching with the actual driving scenario.

[0006] According to one aspect of the embodiments of this application, an acceleration determination method for vehicle trajectory simulation is provided, comprising: acquiring a first adjustment vector matching a current road environment, wherein the current road environment is determined based on at least one driving state of at least one other vehicle object in a first driving cycle, the first adjustment vector including at least one first adjustment coefficient, and the at least one first adjustment coefficient being used to perform a first adjustment operation on at least one corresponding acceleration state parameter; acquiring a second adjustment vector matching an object profile of a self-vehicle object, wherein the object profile is determined based on at least one driving operation of the self-vehicle object in a second driving cycle, and the second adjustment vector including at least one second adjustment coefficient; and so on. One of the aforementioned second adjustment coefficients is used to perform a second adjustment operation on at least one corresponding acceleration state parameter; based on the aforementioned first adjustment vector and the aforementioned second adjustment vector, at least one acceleration state parameter in the acceleration state parameter set is adjusted to obtain a target acceleration state parameter set, wherein the aforementioned acceleration state parameter set includes a first distance parameter, a second distance parameter, a deceleration parameter, and a maximum acceleration parameter, wherein the aforementioned first distance parameter is the safe distance between the self-vehicle object and the preceding vehicle object in a stationary state, and the aforementioned second distance parameter is the safe time distance between the self-vehicle object and the preceding vehicle object in a moving state; based on the current speed of the self-vehicle object and the aforementioned target acceleration state parameter set, the current acceleration is determined.

[0007] According to another aspect of the embodiments of this application, an acceleration determination device for vehicle trajectory simulation is also provided, comprising: a first acquisition unit, configured to acquire a first adjustment vector matching the current road environment, wherein the current road environment is determined based on at least one driving state of at least one other vehicle object in a first driving cycle, the first adjustment vector including at least one first adjustment coefficient, and the at least one first adjustment coefficient being used to perform a first adjustment operation on at least one corresponding acceleration state parameter; and a second acquisition unit, configured to acquire a second adjustment vector matching the object profile of the self-vehicle object, wherein the object profile is determined based on at least one driving operation of the self-vehicle object in a second driving cycle, the second adjustment vector including at least one second adjustment coefficient, and at least one first adjustment coefficient being used to perform a first adjustment operation on at least one corresponding acceleration state parameter; and a second acquisition unit, configured to acquire a second adjustment vector matching the object profile of the self-vehicle object, wherein the object profile is determined based on at least one driving operation of the self-vehicle object in a second driving cycle, and the second adjustment vector including at least one second adjustment coefficient, and at least one first adjustment coefficient being used to perform a first adjustment operation on at least one corresponding acceleration state parameter; and a third acquisition unit, configured to acquire a second adjustment vector matching the object profile of the self-vehicle object, wherein the object profile is determined based on at least one driving operation of the self-vehicle object in a second driving cycle, and the second adjustment vector including at least one second adjustment coefficient, and at least one first adjustment coefficient being used to perform a first adjustment operation on at least one corresponding acceleration state parameter; and a fourth acquisition unit, configured to acquire a second adjustment vector matching the object profile of the self-vehicle object in a second driving cycle, the second adjustment vector including at least one second adjustment coefficient, and a fifth ... The aforementioned second adjustment coefficient is used to perform a second adjustment operation on at least one corresponding acceleration state parameter; the first determining unit is used to adjust at least one acceleration state parameter in the acceleration state parameter set according to the aforementioned first adjustment vector and the aforementioned second adjustment vector to obtain a target acceleration state parameter set, wherein the aforementioned acceleration state parameter set includes a first distance parameter, a second distance parameter, a deceleration parameter, and a maximum acceleration parameter, wherein the aforementioned first distance parameter is the safe distance between the self-vehicle object and the preceding vehicle object in a stationary state, and the aforementioned second distance parameter is the safe time distance between the self-vehicle object and the preceding vehicle object in a moving state; the second determining unit is used to determine the current acceleration according to the current speed of the self-vehicle object and the aforementioned target acceleration state parameter set.

[0008] According to another aspect of the embodiments of this application, a computer program product or computer program is provided, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the acceleration determination method for vehicle trajectory simulation as described above.

[0009] According to another aspect of the embodiments of this application, an electronic device is also provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the above-described acceleration determination method for vehicle trajectory simulation through the computer program.

[0010] In this implementation, a first adjustment vector matching the current road environment is first obtained. This vector is statistically derived from the driving status of at least one other vehicle within the first driving cycle and contains several first adjustment coefficients. These coefficients perform a first adjustment operation on the first distance parameter, second distance parameter, deceleration parameter, and maximum acceleration parameter in the acceleration state parameter set. This embeds the dynamic characteristics of the surrounding traffic flow into the originally static acceleration state parameters in real time, enabling the safe distance and safe time distance to expand and contract instantly with the behavior of other vehicles, significantly improving the model's response sensitivity to the external environment.

[0011] In addition, a second adjustment vector matching the vehicle's profile is obtained. This vector is determined based on the actual driving operation of the vehicle during the second driving cycle and contains several second adjustment coefficients. These coefficients perform the second adjustment operation on the same set of acceleration state parameters, quantify the vehicle's driving style and feed it back to the parameter level, thereby realizing personalized correction of the driving style and avoiding the problems of being too conservative or too aggressive caused by uniform parameters.

[0012] Furthermore, after fusing the first adjustment vector and the second adjustment vector, the acceleration state parameter set is synchronously corrected to the target acceleration state parameter set. The current acceleration is calculated using the current speed of the vehicle and the target acceleration state parameter set. Since all key parameters have been dynamically updated, the determined current acceleration can maintain synchronization with traffic flow on a macroscopic level and conform to the vehicle's handling characteristics on a microscopic level, thus better matching the actual driving scenario and driving experience. This solves the technical problem of low matching degree between the determined acceleration and the actual scenario in related technologies. Attached Figure Description

[0013] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0014] Figure 1 This is a schematic diagram of an application environment for an optional acceleration determination method for vehicle trajectory simulation according to an embodiment of this application;

[0015] Figure 2 This is a flowchart of an optional acceleration determination method for vehicle trajectory simulation according to an embodiment of this application;

[0016] Figure 3 This is a schematic diagram of an optional driving state according to an embodiment of this application;

[0017] Figure 4 This is a schematic diagram of an optional state machine according to an embodiment of this application;

[0018] Figure 5This is a flowchart of an optional simulation process according to an embodiment of this application;

[0019] Figure 6 This is a schematic diagram of an optional simulation process according to an embodiment of this application;

[0020] Figure 7 This is a schematic diagram of an acceleration determination device for vehicle trajectory simulation according to an embodiment of this application;

[0021] Figure 8 This is a schematic diagram of the structure of an optional electronic device according to an embodiment of this application. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] According to one aspect of the embodiments of this application, an acceleration determination method for vehicle trajectory simulation is provided. This acceleration determination method for vehicle trajectory simulation can be applied to, but is not limited to, [various applications]. Figure 1 The hardware environment shown. Optionally, the acceleration determination method for vehicle trajectory simulation provided in this application can be applied to a vehicle terminal. Figure 1A side view of a vehicle terminal 101 is shown, which can travel on a driving surface 113. The vehicle terminal 101 includes a memory 102 storing an onboard navigation system 103 and a digital road map 104, a spatial monitoring system 117, a vehicle controller 109, a GPS (Global Positioning System) sensor 110, an HMI (Human / Machine Interface) device 111, and also includes an autonomous controller 112 and a telematics controller 114.

[0025] In one embodiment, the space monitoring system 117 includes one or more space sensors and systems for monitoring a visible area 105 in front of the vehicle terminal 101. The space monitoring system 117 also includes a space monitoring controller 118. The space sensors for monitoring the visible area 105 include a lidar sensor 106, a radar sensor 107, a camera 108, etc. The space monitoring controller 118 can be used to generate data related to the visible area 105 based on data input from the space sensors. The space monitoring controller 118 can determine the linear range, relative speed, and trajectory of the vehicle terminal 101 based on the input from the space sensors, for example, determining the vehicle's current speed and its relative speed to a vehicle in front. The space sensors of the vehicle terminal space monitoring system 117 may include object positioning sensing devices, which may include range sensors that can be used to locate objects in front, such as vehicles in front.

[0026] Camera 108 is advantageously mounted and positioned on vehicle terminal 101 in a location that allows for the capture of images of a visible area 105, wherein at least a portion of the visible area 105 includes the area in front of vehicle terminal 101 and a portion of the travel surface 113 of the trajectory of vehicle terminal 101. The visible area 105 may also include the surrounding environment. Other cameras may also be employed, for example, including a second camera positioned on the rear or side portion of vehicle terminal 101 to monitor the rear of vehicle terminal 101 and one of the right or left sides of vehicle terminal 101.

[0027] The autonomous controller 112 is configured to implement autonomous driving or advanced driver assistance system (ADAS) vehicle terminal functionality. Such functionality may include an onboard vehicle terminal control system capable of providing a certain level of driving automation. Driving automation may include a series of dynamic driving and vehicle terminal operations. Driving automation may include a certain level of automated control or intervention involving individual vehicle terminal functions (e.g., steering, acceleration, and / or braking). For example, the aforementioned autonomous controller may be used to determine a target pose point indicating the current position of the vehicle terminal by performing the following steps:

[0028] S102, Obtain the first adjustment vector that matches the current road environment;

[0029] The current road environment is determined based on at least one driving state of at least one other vehicle object in the first driving cycle. The first adjustment vector includes at least one first adjustment coefficient, which is used to perform a first adjustment operation on at least one corresponding acceleration state parameter.

[0030] S104, Obtain the second adjustment vector that matches the object profile of the vehicle object;

[0031] The object profile is determined based on at least one driving operation of the vehicle object in the second driving cycle, and the second adjustment vector includes at least one second adjustment coefficient, which is used to perform a second adjustment operation on at least one corresponding acceleration state parameter.

[0032] S106, adjust at least one acceleration state parameter in the acceleration state parameter set according to the first adjustment vector and the second adjustment vector to obtain the target acceleration state parameter set;

[0033] The acceleration state parameter set includes a first distance parameter, a second distance parameter, a deceleration parameter, and a maximum acceleration parameter. The first distance parameter is the safe distance between the self-vehicle and the vehicle in front when they are stationary. The second distance parameter is the safe time distance between the self-vehicle and the vehicle in front when they are in motion.

[0034] S108, determine the current acceleration based on the current speed of the vehicle and the target acceleration state parameter set.

[0035] HMI device 111 provides human-machine interaction for guiding the operation of infotainment systems, GPS (Global Positioning System) sensors 110, airborne navigation systems 103, and similar systems, and includes controllers. HMI device 111 monitors operator requests and provides the operator with status, service, and maintenance information about the vehicle terminal system. HMI device 111 communicates with and / or controls the operation of multiple operator interface devices. HMI device 111 may also communicate with one or more devices that monitor biometric data associated with the vehicle terminal operator. For simplicity, HMI device 111 is depicted as a single device, but in embodiments of the system described herein, it may be configured as multiple controllers and associated sensing devices.

[0036] Operator controls may be included in the passenger compartment of vehicle terminal 101 and, by way of non-limiting example, may include a steering wheel, accelerator pedal, brake pedal, and operator input device, which is an element of HMI device 111. The operator controls enable a vehicle terminal operator to interact with and instruct the operation of vehicle terminal 101 to provide passenger transport.

[0037] The airborne navigation system 103 uses a digital road map 104 for the purpose of providing navigation support and information to the vehicle terminal operator. The autonomous controller 112 uses the digital road map 104 for the purpose of controlling the operation of the autonomous vehicle terminal or the functions of the ADAS vehicle terminal.

[0038] The vehicle terminal 101 may include a telematics controller 114, which includes a wireless telematics communication system capable of communicating outside the vehicle terminal (including communicating with a communication network 115 with both wireless and wired communication capabilities). The wireless telematics communication system includes a non-airborne server 116 capable of short-range wireless communication with mobile terminals.

[0039] Through the above-described embodiments of this application, after fusing the first adjustment vector and the second adjustment vector, the acceleration state parameter set is synchronously corrected to the target acceleration state parameter set. The current acceleration is calculated using the current speed of the vehicle object and the target acceleration state parameter set. Since all key parameters have been dynamically updated, the determined current acceleration can maintain synchronization with traffic flow on a macroscopic level and conform to the vehicle's handling characteristics on a microscopic level, thus better matching the actual driving scenario and driving experience. This solves the technical problem of low matching degree between the determined acceleration and the actual scenario in related technologies.

[0040] According to one aspect of the embodiments of this application, an acceleration determination method for vehicle trajectory simulation is provided, such as... Figure 2 As shown, the acceleration determination method for vehicle trajectory simulation can be derived from, for example... Figure 1 The electronic devices included in the vehicle terminal shown execute the following specific steps:

[0041] S202, Obtain the first adjustment vector that matches the current road environment;

[0042] The current road environment is determined based on at least one driving state of at least one other vehicle object in the first driving cycle. The first adjustment vector includes at least one first adjustment coefficient, which is used to perform a first adjustment operation on at least one corresponding acceleration state parameter.

[0043] S204, Obtain the second adjustment vector that matches the object profile of the vehicle object;

[0044] The object profile is determined based on at least one driving operation of the vehicle object in the second driving cycle, and the second adjustment vector includes at least one second adjustment coefficient, which is used to perform a second adjustment operation on at least one corresponding acceleration state parameter.

[0045] S206, According to the first adjustment vector and the second adjustment vector, at least one acceleration state parameter in the acceleration state parameter set is adjusted to obtain the target acceleration state parameter set;

[0046] The acceleration state parameter set includes a first distance parameter, a second distance parameter, a deceleration parameter, and a maximum acceleration parameter. The first distance parameter is the safe distance between the self-vehicle and the vehicle in front when they are stationary. The second distance parameter is the safe time distance between the self-vehicle and the vehicle in front when they are in motion.

[0047] S208, determine the current acceleration based on the current speed of the vehicle and the target acceleration state parameter set.

[0048] In the above embodiments, the self-vehicle object can be the vehicle of interest in this embodiment, that is, the vehicle that needs to be located and driven. The other vehicle object can be any vehicle object in the road environment where the self-vehicle object is currently located, other than the self-vehicle object. In addition, the preceding vehicle object can be any vehicle object located in front of the self-vehicle object among the other vehicle objects.

[0049] It should be noted that the above implementation method can be applied to the process of a vehicle driving in intelligent or autonomous driving mode, and to estimate the acceleration at various time points during subsequent driving based on forward simulation. Correspondingly, the current acceleration determined by the above implementation method can be the acceleration corresponding to different estimated timestamps.

[0050] For example, if the current time is "12:00:00", and the vehicle is in intelligent or autonomous driving mode, it is necessary to estimate the acceleration every 0.1 seconds from "12:00:00" to "12:00:10". Then, the current acceleration corresponding to each timestamp can be determined using the methods described in steps S202 to S208 above, with a period of 0.1 seconds.

[0051] It is understood that, upon identifying the current road environment, the aforementioned first adjustment vector can be determined based on the current road environment. The aforementioned first adjustment vector includes at least one first adjustment coefficient for adjusting the acceleration state parameters. The adjustment relationship between the aforementioned first adjustment coefficient and the acceleration state parameters can be a one-to-one correspondence, i.e., one first adjustment coefficient corresponds to adjusting one acceleration state parameter; it can also be a one-to-many relationship, i.e., one first adjustment coefficient can correspond to adjusting multiple acceleration state parameters; it can also be a many-to-one relationship, i.e., multiple first adjustment coefficients correspond to adjusting one acceleration state parameter; or it can be a many-to-many relationship, i.e., multiple first adjustment coefficients correspond to adjusting multiple acceleration state parameters. The above embodiments of this application do not limit the specific adjustment relationship.

[0052] Taking a one-to-one correspondence as an example, each first adjustment coefficient corresponds one-to-one with the acceleration state parameter, used to translate the characteristics of the traffic flow into corrections for safe following distance, safe following time, deceleration, and maximum acceleration. For example, if the average following time obtained statistically during the first driving cycle is lower than a preset threshold, a first adjustment coefficient less than 1 can be generated and applied to the second distance parameter, so that the vehicle subsequently adopts a more compact but still safe following time.

[0053] The first distance parameter included in the acceleration state parameters (which can be specifically expressed as) ), the second distance parameter (which can be specifically expressed as ), deceleration parameters (which can be specifically expressed as ), maximum acceleration parameter (which can be specifically expressed as It can be used to determine real-time acceleration.

[0054] The following sections explain how multiple acceleration state parameters affect the current acceleration.

[0055] Maximum acceleration parameters It can be used to characterize the upper limit of a vehicle's driving capability. On the one hand, when the maximum acceleration parameter... Increasing the maximum acceleration parameter allows for a corresponding increase in the acceleration margin, enabling the vehicle to approach the target speed more quickly; conversely, decreasing the maximum acceleration parameter... This can reduce acceleration power, resulting in a more conservative driving style.

[0056] deceleration parameters It can be used to control the comfort of the braking process. When the deceleration parameter... When the setting is larger, the absolute value of deceleration is smaller when deceleration is triggered, resulting in a smoother ride. When the deceleration parameter... When the setting is smaller, the vehicle can decelerate later and more abruptly, and the peak negative acceleration will increase accordingly, which can cope with emergency situations, but the comfort will decrease.

[0057] First distance parameter It can be used to determine the minimum parking distance. A larger value allows for a greater following distance in low-speed or congested crawling scenarios. Decrease the first distance parameter. This allows for closer parking distances and increases road capacity, but places higher demands on sensor error and execution latency.

[0058] Second distance parameter It can be used to determine the following distance during driving. Increasing the second distance parameter... This means proportionally scaling up the desired distance at any speed, causing the vehicle to exhibit "long-distance" driving behavior, which is beneficial for safety and energy conservation. The second distance parameter is reduced. This reduces following distance, but may increase the risk of collision and lead to more frequent acceleration and deceleration.

[0059] It is evident that the four acceleration state parameters mentioned above can be combined to achieve a fine balance among safety, efficiency, and comfort.

[0060] Correspondingly, the first adjustment coefficient in step S202 above can perform the first adjustment operation on the above four acceleration state parameters, thereby embedding the dynamic characteristics of the surrounding traffic flow into the original static acceleration state parameters in real time, so that the safe distance and safe time distance can be adjusted in real time according to the behavior of other vehicles, significantly improving the model's response sensitivity to the external environment.

[0061] It is understood that the object profile in step S204 above can be determined based on at least one driving operation performed by the vehicle in the second driving cycle. This driving operation can include, but is not limited to, one or more of the following, acceleration, deceleration, and lane-changing operations. It is understood that these driving operations can reflect the driving preferences of the vehicle's driver. For example, in a second driving cycle lasting 5km, if statistics show that the average following distance is maintained at 1.1s, and 80% of deceleration events have peak decelerations below 2m / s², while no emergency lane changes occur, the system determines that the driver is of a high-patience, low-aggression type. Correspondingly, the coefficient corresponding to the deceleration parameter in the second adjustment vector can be increased to 1.2, and the coefficient corresponding to the maximum acceleration parameter can be compressed to 0.85, thus making subsequent trajectory planning more inclined towards gentle acceleration and deceleration and longer headway. Conversely, if the vehicle frequently performs short-distance following maneuvers of less than 0.9 seconds within the same cycle, accompanied by multiple rapid accelerations exceeding 3.5 m / s², the profile is labeled as aggressive. The second adjustment coefficient will shift towards increasing the maximum acceleration and shortening the safe following distance to meet the driver's expectation of rapid response. By aggregating micro-operational statistics and macro-style labels layer by layer, the object profile can provide quantifiable and interpretable personalized input for step S206, enabling personalized fine-tuning of acceleration state parameters in the "human-vehicle co-driving" scenario.

[0062] In the above embodiments of this application, the vehicle's acceleration, deceleration, lane changing, or turning operations can be recorded during the second driving cycle, and a vehicle profile can be constructed accordingly. Then, a second set of adjustment coefficients can be determined based on the above vehicle profile to inject individual driving styles into the same set of acceleration state parameters.

[0063] In the above embodiments, the second adjustment vector includes at least one second adjustment coefficient for adjusting the acceleration state parameters. The adjustment relationship between the second adjustment coefficient and the acceleration state parameters can be a one-to-one correspondence, where one second adjustment coefficient adjusts one acceleration state parameter; it can also be a one-to-many relationship, where one second adjustment coefficient can adjust multiple acceleration state parameters; it can be a many-to-one relationship, where multiple second adjustment coefficients adjust one acceleration state parameter; or it can be a many-to-many relationship, where multiple second adjustment coefficients adjust multiple acceleration state parameters. The above embodiments of this application do not limit the specific adjustment relationship.

[0064] Further, the adjustment method described in step S206 above can be as follows: first, perform a first adjustment operation on at least one acceleration state parameter based on a first adjustment vector, and then perform a second adjustment operation on at least one acceleration state parameter obtained after the first adjustment operation based on a second adjustment vector; or first, perform a second adjustment operation on at least one acceleration state parameter based on a second adjustment vector, and then perform a first adjustment operation on at least one acceleration state parameter obtained after the second adjustment operation based on a first adjustment vector; or, optionally, perform a first adjustment operation and a second adjustment operation on at least one acceleration state parameter based on a first adjustment vector and a second adjustment vector respectively, and then perform aggregation processing on the operation results of the first adjustment operation and the second adjustment operation to obtain a target acceleration state parameter set, and determine the current acceleration based on the target acceleration state parameter set and the current speed of the vehicle object.

[0065] By implementing the above method, a first adjustment vector matching the current road environment is first obtained. This vector is obtained by statistically analyzing the driving status of at least one other vehicle object within the first driving cycle and contains several first adjustment coefficients. These coefficients perform first adjustment operations on the first distance parameter, second distance parameter, deceleration parameter, and maximum acceleration parameter in the acceleration state parameter set, thereby embedding the dynamic characteristics of the surrounding traffic flow into the originally static acceleration state parameters in real time. This allows the safe distance and safe time distance to be adjusted in real time according to the behavior of other vehicles, significantly improving the model's response sensitivity to the external environment.

[0066] In addition, a second adjustment vector matching the vehicle's profile is obtained. This vector is determined based on the actual driving operation of the vehicle during the second driving cycle and contains several second adjustment coefficients. These coefficients perform the second adjustment operation on the same set of acceleration state parameters, quantify the vehicle's driving style and feed it back to the parameter level, thereby realizing personalized correction of the driving style and avoiding the problems of being too conservative or too aggressive caused by uniform parameters.

[0067] Furthermore, after fusing the first adjustment vector and the second adjustment vector, the acceleration state parameter set is synchronously corrected to the target acceleration state parameter set. The current acceleration is calculated using the current speed of the vehicle and the target acceleration state parameter set. Since all key parameters have been dynamically updated, the determined current acceleration can maintain synchronization with traffic flow on a macroscopic level and conform to the vehicle's handling characteristics on a microscopic level, thus better matching the actual driving scenario and driving experience. This solves the technical problem of low matching degree between the determined acceleration and the actual scenario in related technologies.

[0068] The following provides a further explanation of step S202 and the corresponding first adjustment operation. In an optional implementation, obtaining the first adjustment vector matching the current road environment includes:

[0069] S1, obtain congestion description parameters, average lane speed, and road type parameters that match the current road environment. The congestion description parameters are used to indicate the congestion status of the current road environment, the average lane speed is used to indicate the average speed of vehicles in the current lane, and the road type parameters are used to indicate the road type corresponding to the current road environment.

[0070] S2, based on congestion description parameters, average lane speed, and road type parameters, determine the current mode identifier that matches the current road environment from multiple candidate mode identifiers, with each candidate mode identifier corresponding to an adjustment vector;

[0071] S3. Based on the current mode identifier, determine the first adjustment vector, which includes a distance adjustment coefficient, a time-distance adjustment coefficient, a deceleration adjustment coefficient, and an acceleration adjustment coefficient. The distance adjustment coefficient is used to adjust the first distance parameter, the time-distance adjustment coefficient is used to adjust the second distance parameter, the deceleration adjustment coefficient is used to adjust the deceleration parameter, and the acceleration adjustment coefficient is used to adjust the maximum acceleration parameter.

[0072] In the above embodiments of this application, the congestion description parameter can be expressed as: This can be used to quantify the congestion status of the current road environment. In one alternative approach, the aforementioned congestion description parameters... The congestion level can be determined directly from V2X messages received in real time by the vehicle (such as the congestion level field in SPAT / MAP), or by calculating the average headway between vehicles using the vehicle's sensors within a specific spatial window. For example, if the average headway between vehicles is less than 1.8 seconds and the speed variance is less than 5 km / h within a 300m window, it is classified as "severe congestion," and the congestion description parameters are... The value is set to 3; if the average headway is between 1.8 and 2.5 seconds and the speed variance is between 5 and 15 km / h, it is judged as "mild congestion". The value is set to 2; all other cases are identified as "smooth," and the congestion description parameter is set accordingly. The value is set to 1.

[0073] The average speed of a lane can be described as This is used to reflect the longitudinal movement intensity of vehicles in the current lane. It can be obtained by performing a time-space weighted average of the speed sequences of other vehicles output by forward radar or cameras, or by directly referencing real-time traffic speeds provided by navigation maps. It should be noted that the above-mentioned lane average speed... The speed values ​​and congestion description parameters are not simply linearly correlated: under the same level of congestion, an average speed of 80 km / h on a highway is still considered "smooth", while 35 km / h on a city branch road may be identified as congested. Therefore, the two can form a two-dimensional orthogonal index in the pattern table, which can efficiently distinguish between the two typical scenarios of "high-speed slow traffic" and "low-speed dense traffic".

[0074] Road type parameters can be described as This can be output in real time from a high-precision map or vehicle positioning unit, and its enumerated values ​​can include at least two of the following road types: "highway arterial road", "urban expressway", "urban ordinary road", "ramp", "auxiliary road", and "elevated road". By introducing... This ensures that subsequent adjustment coefficients maintain a baseline that matches the road design speed and legal speed limit under the same traffic conditions, avoiding abnormal combinations such as "high-speed close distance" or "low-speed long distance".

[0075] In phase S2, the system uses the aforementioned three-dimensional parameters as a joint index to perform nearest neighbor matching from a pre-defined pattern table to obtain the current pattern identifier. This pattern table can be generated offline from historical natural driving data clustering or obtained through traffic simulation calibration. Each pattern identifier corresponds to a pre-calibrated adjustment vector, which contains distance adjustment coefficients, time-distance adjustment coefficients, deceleration adjustment coefficients, and acceleration adjustment coefficients, all of which act on the first distance parameter. Second distance parameter deceleration parameters Maximum acceleration parameters .

[0076] In one alternative implementation, the current mode identifier can be determined in the following way:

[0077]

[0078] The current mode identifier is determined through the above implementation method. Given the specific values, the corresponding first adjustment vector can be determined by looking up the values ​​in Table 1 below. :

[0079] Table 1

[0080]

[0081] For example, in determining through the above implementation method In this case, the first adjustment vector can be determined accordingly. .

[0082] Furthermore, after determining the first adjustment vector In this case, the first adjustment method mentioned above can be implemented in the following way:

[0083]

[0084]

[0085]

[0086]

[0087] in, These are the first distance parameter, the second distance parameter, the deceleration parameter, and the maximum acceleration parameter before the first adjustment operation, respectively.

[0088] These are the first distance parameter, the second distance parameter, the deceleration parameter, and the maximum acceleration parameter after the first adjustment operation.

[0089] In an optional implementation, if the adjustment method of the second adjustment operation on the acceleration state parameter set is to keep the original acceleration state parameter set unchanged, the above step S208 can be implemented in the following way:

[0090]

[0091] in, This represents the vehicle's current speed. For the desired speed of the vehicle, The acceleration index is used to adjust the smoothness of acceleration behavior. This represents the actual headway between the vehicle and the vehicle in front. Furthermore, in the above formula... The acceleration state parameter set adjusted by the first adjustment operation can be determined in the following way:

[0092]

[0093] in, The speed difference between the vehicle and the vehicle in front is, i.e. .

[0094] Through the above-described embodiments of this application, a hierarchical road scene recognition and parameter mapping mechanism is constructed. By analyzing multi-dimensional features such as traffic density, average speed, and road type in real time, the acceleration state parameter sets of different driving modes, such as highways, urban roads, and congested sections, are automatically switched. Core parameters such as safe distance, maximum acceleration, and comfortable deceleration are dynamically adjusted using parameter scaling factors, thus solving the problem of poor scene adaptability caused by the fixation of original parameters.

[0095] The second adjustment vector and the second adjustment operation in step S204 will be further explained below.

[0096] In one optional implementation, obtaining the second adjustment vector that matches the object profile of the vehicle object includes:

[0097] S1, determine a driving description vector based on at least one operation data sequence matched with the second driving cycle, wherein the operation data sequence includes operation quantities corresponding to multiple timestamps respectively, and at least one description parameter included in the driving description vector is determined based on at least one operation data sequence respectively;

[0098] S2, Based on the similarity between the driving description vector and multiple image description vectors, determine the target image description vector from the multiple image description vectors;

[0099] S3, the adjustment vector that matches the target profile description vector is determined as the second adjustment vector, wherein the second adjustment vector includes an aggression adjustment coefficient, a patience adjustment coefficient, an economic adjustment coefficient, and a risk propensity adjustment coefficient.

[0100] In the above embodiments, at least one operational data sequence can be used to indicate multiple driving decision behaviors of the current driving object within the first driving cycle; the at least one operational data sequence includes parameters and data reflecting the driving behavior of the current driving object, such as lane change frequency, braking / acceleration frequency, average absolute acceleration / deceleration, average / standard deviation of acceleration, standard deviation of lane centering error, following distance, and TTC longitudinal speed with the preceding vehicle, etc., and the driving data of the current driving object can be collected in real time by vehicle sensors (such as cameras, radar, ABS systems, etc.).

[0101] Furthermore, a driving description vector for describing the object profile can be determined based on at least one of the above-mentioned operational data sequences, and a target profile description vector can be determined based on the driving description vector.

[0102] In one optional implementation, the driving description vector can be input into a driving style classification model to obtain a target profile description vector corresponding to the vehicle object. It is understood that the object profile indicated by the target profile description vector can be, but is not limited to, one or more of the following types: aggressive, moderate, and economical. Aggressive characteristics include rapid lane changes, frequent acceleration and deceleration, higher average speed, shorter following distance, and a tendency to take aggressive measures when facing risks; attempting to overtake quickly in traffic congestion, and being more aggressive in judging green and yellow lights. Moderate characteristics include smooth driving, fewer lane changes, gradual acceleration and deceleration, and a larger following distance. Economical characteristics include low-speed driving, avoiding unnecessary acceleration and deceleration, preferring to use cruise control, minimizing energy consumption, focusing more on fuel efficiency or electricity consumption while driving, tending to choose economy mode, and reducing unnecessary operations to lower driving costs.

[0103] In another optional implementation, a target profile description vector can be determined based on the similarity between the driving description vector and a set of preset profile description vectors. These preset profile description vectors can be clustering vectors that describe each category after clustering using the unsupervised learning K-Means algorithm or the Gaussian Mixture Model (GMM).

[0104] The following section further explains the process of determining clustering and profile description vectors. In one optional implementation, a large amount of actual driving data of the driving subject over several weeks is first collected. The data includes vehicle speed, acceleration, jerk, headway to the vehicle in front, relative speed, collision time, accelerator pedal opening, brake pedal opening, and steering angular velocity. This raw data can be continuously recorded at a frequency of 10 Hz and aggregated and stored via the vehicle or the cloud.

[0105] Subsequently, the original time series data of each driving object is segmented, for example, by using a sliding window slice with a window length of 120 seconds and a step size of 60 seconds. Multiple statistical features are calculated in each window, including average headway, acceleration standard deviation, maximum braking deceleration, 5th percentile of collision time, frequency of rapid acceleration and deceleration, and proportion of high-speed driving time, thereby constructing a driving description vector that can reflect the characteristics of individual driving behavior.

[0106] After the driving description vectors are constructed, unsupervised learning algorithms such as K-Means are used to cluster the driving description vectors of all driving objects.

[0107] In this embodiment, driving subjects can be automatically divided into several groups, such as aggressive, conservative, economical, and normal types. Aggressive driving subjects typically have shorter headway, larger acceleration fluctuations, and higher frequency of abrupt changes; conservative driving subjects exhibit longer headway, lower acceleration changes, and milder braking behavior; economical driving subjects show a high ratio of throttle opening to acceleration and a low proportion of high-speed driving; normal types fall between the above types. Each of these categories can be represented using its corresponding cluster center vector, that is, the cluster center of each cluster is used as the profile description vector.

[0108] Furthermore, clustering results can be evaluated using metrics such as silhouette coefficient to ensure that the groups have significant discriminative power and stability.

[0109] In this embodiment of the application, based on the clustering results, a second adjustment vector can be further constructed for adjusting the acceleration state parameters. The second adjustment vector includes an aggressive adjustment coefficient. Patience adjustment coefficient Risk propensity adjustment coefficient and economic adjustment coefficient .

[0110] Aggression adjustment coefficient This can be reflected by the normalized values ​​of the acceleration standard deviation and the jerk frequency; patience adjustment coefficient. It can be measured by the negative correlation between average headway and collision time quantiles; risk propensity adjustment coefficient. The assessment can be based on a combination of maximum braking deceleration and the proportion of short-term collision events; economic adjustment coefficient. This is quantified using throttle efficiency and the percentage of high-speed duration. After normalization, each dimension forms a second adjustment vector used to adjust the acceleration state parameters. .

[0111] After determining the aforementioned second adjustment vector The following section further explains the second adjustment operation that adjusts at least one acceleration state parameter in the acceleration state parameter set.

[0112] In one optional implementation, the first adjustment vector and the second adjustment vector adjust at least one acceleration state parameter in the acceleration state parameter set to obtain a target acceleration state parameter set, including at least one of the following:

[0113] Adjustment Method 1: Determine the first adjustment coefficient based on the weighted sum of the risk propensity adjustment coefficient, the patience adjustment coefficient, and the first benchmark coefficient; determine the target distance parameter based on the product of the first adjustment coefficient and the first reference distance parameter, wherein the first adjustment coefficient and the risk propensity adjustment coefficient have a first negative correlation, and the first adjustment coefficient and the patience adjustment coefficient have a second negative correlation.

[0114] The above adjustment method one can adjust the first reference distance parameter. The first reference distance parameter is obtained by adjusting the first distance parameter according to at least one first adjustment coefficient.

[0115] Specifically, adjustments can be made in the following ways:

[0116]

[0117] in, The patience adjustment coefficient is the second reference distance parameter obtained after the first adjustment operation. The risk propensity adjustment coefficient is used to characterize driver patience and is obtained from the second adjustment vector. Its value range is [0,1], with 0 representing extreme impatience and 1 representing extreme patience; The risk tolerance of the driver is represented by the second adjustment vector. The value range is [0,1], where the endpoint 0 represents risk aversion (very cautious) and the endpoint 1 represents risk preference (willing to take risks). and Risk propensity adjustment coefficient Patience adjustment coefficient Each has its corresponding weighting coefficient.

[0118] Adjustment Method 2: Determine the second adjustment coefficient based on the weighted sum of the patience adjustment coefficient, the aggression adjustment coefficient, and the second baseline coefficient; determine the target time distance parameter based on the product of the second adjustment coefficient and the second reference distance parameter, wherein the second adjustment coefficient has a first positive correlation with the patience adjustment coefficient and a third negative correlation with the aggression adjustment coefficient, and the second reference distance parameter is obtained by adjusting the second distance parameter according to at least one first adjustment coefficient;

[0119] The second adjustment method described above can adjust the second reference distance parameter. The second reference distance parameter is obtained by adjusting the second distance parameter according to at least one first adjustment coefficient.

[0120] Specifically, adjustments can be made in the following ways:

[0121]

[0122] in, The patience adjustment coefficient is the second reference distance parameter obtained after the first adjustment operation. Used to characterize driver patience; aggression adjustment coefficient The value used to characterize the driver's aggression is obtained from the second adjustment vector. The value range is [0,1], with the endpoint 0 representing extremely mild and the endpoint 1 representing extremely aggressive. and Adjust the patience level coefficient and the aggression adjustment coefficient Each has its corresponding weighting coefficient.

[0123] Adjustment Method 3: Determine the third adjustment coefficient based on the weighted sum of the risk propensity adjustment coefficient, the economic adjustment coefficient, and the third benchmark coefficient; determine the target deceleration parameter based on the product of the third adjustment coefficient and the reference deceleration parameter. Among these, the third adjustment coefficient has a fourth negative correlation with the risk propensity adjustment coefficient, and the third adjustment coefficient has a fifth negative correlation with the economic adjustment coefficient.

[0124] The third adjustment method described above can adjust the reference deceleration parameter. The reference deceleration parameter is obtained by adjusting the deceleration parameter according to at least one first adjustment coefficient.

[0125] Specifically, adjustments can be made in the following ways:

[0126]

[0127] in The reference deceleration parameter obtained after the first adjustment operation, and the risk propensity adjustment coefficient. Used to characterize the driver's risk tolerance; economic adjustment coefficient It is used to characterize the driver's economic preference and is obtained from the second adjustment vector. The value range is [0,1], where the endpoint 0 indicates limited performance and the endpoint 1 indicates economic priority. and Risk propensity adjustment coefficient and economic adjustment coefficient Each has its corresponding weighting coefficient.

[0128] Adjustment Method 4: Determine the fourth adjustment coefficient based on the weighted sum of the aggressive adjustment coefficient, the economic adjustment coefficient, and the fourth benchmark coefficient; determine the target acceleration parameter based on the product of the fourth adjustment coefficient and the reference acceleration parameter. Among these, the fourth adjustment coefficient has a second positive correlation with the aggressive adjustment coefficient and a sixth negative correlation with the economic adjustment coefficient.

[0129] The fourth adjustment method described above can adjust the reference acceleration parameter. The reference acceleration parameter is obtained by adjusting the maximum acceleration parameter according to at least one first adjustment coefficient.

[0130] Specifically, adjustments can be made in the following ways:

[0131]

[0132] in The reference acceleration parameter obtained after the first adjustment operation, and the aggressive adjustment coefficient. Used to characterize driver aggression; economic adjustment coefficient Used to characterize a driver's economical tendencies. and Adjustment coefficient for aggression and economic adjustment coefficient Each has its corresponding weighting coefficient.

[0133] In conventional methods of determining acceleration, the relevant acceleration state parameters are fixed, which deviates significantly from actual traffic observations and fails to reflect the differences in driving styles such as aggressive, conservative, and economical driving. Furthermore, in existing simulation technologies, the behavior of all vehicles in traffic flow tends to be similar, failing to simulate the diversity of driving behaviors in the real world. Finally, existing technologies cannot meet the personalized preferences and comfort needs of different users in autonomous driving applications.

[0134] Through the above-described implementation method of this application, a four-dimensional driver profile vector (aggression, patience, risk tolerance, and economic preference) is established. Personalized features are mapped to the spatial transformation of IDM core parameters using a linear weighting function. Based on personalized coefficients calibrated from actual driving data, customized adjustments to parameters such as safe distance, acceleration characteristics, and braking behavior are achieved. This solves the problem of behavioral uniformity caused by neglecting individual differences in existing acceleration determination methods, significantly improving the realism of traffic simulation.

[0135] In an optional implementation, step S208, which determines the current acceleration based on the vehicle's current speed and the target acceleration state parameter set, includes:

[0136] S1, obtain the target distance parameter, target acceleration parameter, target time distance parameter and target deceleration parameter from the target acceleration state parameter set;

[0137] S2, the difference between the target acceleration parameter and the target adjustment value is determined as the current acceleration. The target adjustment value is the difference between 1 and the target adjustment amount. The target adjustment amount includes the expected speed adjustment amount and the expected distance adjustment amount. The expected speed adjustment amount is determined based on the ratio between the current speed of the self-vehicle object and the expected speed. The expected distance adjustment amount is determined based on the ratio between the expected distance between the self-vehicle object and the preceding vehicle object and the current distance. The expected distance is determined based on the target distance parameter, the target acceleration parameter, the target time distance parameter, and the target deceleration parameter.

[0138] It is understood that the above implementation method can combine multiple target acceleration states obtained through the first adjustment operation and the second adjustment operation to determine the current acceleration.

[0139] In one alternative approach, before determining the difference between the target acceleration parameter and the target adjustment value as the current acceleration, the method further includes:

[0140] S1, the ratio of the current speed of the vehicle to the desired speed. The power is determined as the desired speed adjustment amount;

[0141] S2, the square of the ratio between the expected distance between the self-vehicle object and the preceding vehicle object and the current distance is determined as the expected distance adjustment amount.

[0142] In an optional implementation, when the first adjustment operation and the second adjustment operation are performed sequentially on the acceleration state parameter set according to the above implementation method, and the adjustment method of the acceleration state parameter set in the second adjustment operation is to keep the original acceleration state parameter set unchanged, the above step S208 can be implemented in the following way:

[0143]

[0144] in, This represents the vehicle's current speed. For the desired speed of the vehicle, The acceleration index is used to adjust the smoothness of acceleration behavior. This represents the actual headway between the vehicle and the vehicle in front. In the formula above... This is the target adjustment value. This is the desired speed adjustment amount. This is the desired spacing adjustment amount.

[0145] In addition, in the above formula The acceleration state parameter set after the first and second adjustment operations can be determined in the following way:

[0146]

[0147] In an alternative implementation, before determining the difference between the target acceleration parameter and the target adjustment value as the current acceleration, the method further includes:

[0148] S1, the first reference coefficient is determined by the power of the product between the target acceleration parameter and the target deceleration parameter;

[0149] S2, determine the second reference coefficient based on the product of the current speed of the vehicle and the current acceleration of the vehicle in front; determine the acceleration adjustment term of the vehicle in front based on the ratio between the second reference coefficient and the first reference coefficient;

[0150] S3, determine the third reference coefficient based on the product of the current speed of the self-vehicle and the current speed difference; determine the speed difference adjustment term based on the ratio between the third reference coefficient and the first reference coefficient, where the current speed difference is the difference between the current speed of the self-vehicle and the current speed of the preceding vehicle.

[0151] S4, determine the speed adjustment item based on the product between the current speed of the vehicle and the target time distance parameter;

[0152] S5, if the sum of the acceleration adjustment item, speed difference adjustment item, and speed adjustment item of the preceding vehicle is greater than or equal to 0, the sum of the acceleration adjustment item, speed difference adjustment item, speed adjustment item, and target distance parameter of the preceding vehicle is determined as the desired distance.

[0153] In this embodiment, the acceleration term of the preceding vehicle can be introduced into the calculation of the expected distance, enabling the vehicle to respond to the acceleration and deceleration behavior of the preceding vehicle earlier.

[0154] The desired spacing can be determined in the following way. :

[0155]

[0156] Correspondingly, in the above embodiments where the acceleration state parameters have undergone the first and second adjustment operations, the current acceleration in the embodiments of this application is... It can be determined in the following way:

[0157]

[0158] Among them, the expected spacing It can be determined in the following way:

[0159]

[0160] By further introducing a dynamic compensation term for the acceleration of the preceding vehicle through the above-described embodiments of this application, the vehicle can not only respond to changes in relative speed but also anticipate the acceleration and deceleration trends of the preceding vehicle. By calculating the influence coefficient of the preceding vehicle's acceleration on the safe distance in real time and dynamically adjusting the expected following distance, the collision risk prediction is elevated from the speed level to the acceleration level, fundamentally solving the problem of acceleration response delay.

[0161] It is understood that the acceleration determined in the above embodiments of this application can be used in the trajectory planning process during autonomous driving. In the above embodiments of this application, the driving state corresponding to different state nodes in the predicted trajectory can be determined at a certain period, and then the vehicle motion state corresponding to multiple simulation sub-nodes in different state nodes can be determined based on the state cost.

[0162] The aforementioned driving states may include, but are not limited to, lateral states and longitudinal states. Lateral states may include, but are not limited to, the initial state, the state of preparing to change lanes, the initial state of changing lanes, the later state of changing lanes, and the state of abandoning the lane change. Longitudinal states may include, but are not limited to, the state of acceleration, the state of deceleration, and the state of constant speed.

[0163] Furthermore, the vehicle motion state corresponding to each of the aforementioned multiple simulation sub-nodes may include state parameters such as the velocity value and acceleration value corresponding to the simulation sub-node. The acceleration value corresponding to the aforementioned simulation sub-node can be determined through the above-described implementation method.

[0164] The following explains the method for determining the driving state corresponding to each state node.

[0165] In one alternative implementation, before determining the current acceleration based on the vehicle's current speed and the target acceleration state parameter set, the method further includes:

[0166] S1, Based on the driving state of the vehicle object corresponding to the current state node, determine at least one first reference state node from the candidate state node set, wherein the driving state includes lateral driving state and longitudinal driving state;

[0167] S2, determine the state value corresponding to at least one first reference state node based on the current acceleration;

[0168] S3, based on the sorting results of the state cost values ​​corresponding to each of the at least one first reference state nodes, determine at least one first target state node from the at least one first reference state node.

[0169] In the embodiments of this application, such as Figure 3 As shown, the driving status may include, for example: Figure 3 The default state (lane keeping, stage 0) shown in Figure (a) is as follows: Figure 3 The first lane change state (stage 1) shown in Figure (b) is as follows: Figure 3 The second lane change state (stage 2) shown in diagram (c) is as follows: Figure 3 The abortion state shown in diagram (d) is as follows: Figure 3 The third lane change state (stage3) is shown in Figure (e), and the trajectory decision is constrained by the state machine.

[0170] In the above embodiments of this application, the rules of the five-state lane-changing state machine can be deeply embedded into each step of the decision tree expansion process. Specifically, when the trajectory decision tree expands to any node, the current state of the state machine is first queried, and a set of legal lateral and longitudinal actions is generated according to the mapping relationship shown in Table 2, instead of enumerating all possible actions. Subsequently, forward state deduction is performed only for these legal actions, and the state of the state machine is updated synchronously. This mechanism ensures that all candidate behaviors comply with driving logic and safety regulations from the source of decision sequence generation. For example, when the state machine is in the third lane-changing state (stage 3), the system will prohibit the generation of the "terminate lane change" action, thereby avoiding dangerous disjointed behavior of the vehicle when it has already crossed the lane line.

[0171] Table 2

[0172]

[0173]

[0174] The transitions between different states in the above five-state machine can be achieved through methods such as... Figure 4 The description is as shown.

[0175] Optionally, this application proposes an interaction mechanism for state machine and decision tree node expansion, aiming to achieve efficient joint search of horizontal and vertical actions with limited computing resources. This mechanism operates in each planning cycle and mainly includes four stages: initialization, node selection and expansion loop, forward simulation and intelligent pruning, and loop termination and optimal output. The specific process is as follows:

[0176] During the initialization phase, upon receiving the planning instruction for a new cycle, the state machine either resets or inherits the final state of the previous cycle to form the current state. At the same time, the decision tree establishes its root node. It stores the vehicle's current pose, speed, acceleration, and environmental perception information, serving as the starting point for the entire search tree.

[0177] The system then enters a node selection and expansion loop. This loop continues until the decision tree reaches a preset maximum depth or the computation time exceeds the budget. In each loop, the system first selects a node N_current to be expanded from the leaf node list; then it queries the state machine to obtain the current state S_m corresponding to N_current; based on this state, the action generation module filters out a subset A_legal of legal actions that meet the state constraints from the complete horizontal and vertical action space A_all. Here, A_all covers Cartesian combinations of horizontal {keeping lane, changing lanes left, changing lanes right, aborting lane change} and vertical {accelerating, decelerating, maintaining speed}. Filtering rules include avoiding two consecutive lane changes, prohibiting exceeding road boundaries, and speed limit constraints, ensuring that the generated A_legal is both rich and safe.

[0178] After obtaining A_legal, the system performs an adaptive intelligent IDM forward simulation based on multi-dimensional perception for each legal action pair: using the end state of N_current as the initial value, it runs along the candidate action trajectory in simulation steps of 0.5-1.5s, updating the relative position, speed, and acceleration of the vehicle and surrounding vehicles in real time, and simultaneously calculating the costs of navigation deviation, traffic efficiency, comfort, curvature continuity, and safety. After the simulation is completed, a lightweight heuristic function is used to quickly score all newly generated leaf nodes, sort them from low to high cost, and only the Top-K nodes are retained for the next round of expansion, while the remaining nodes are immediately pruned, thus keeping the search space within an acceptable range.

[0179] When the loop condition is no longer met, the system terminates the expansion and initiates a refined forward simulation on the few remaining candidate trajectories: recalculating the dynamics and costs using a smaller time step to obtain a more accurate total cost; then, the trajectory with the minimum total cost is selected as the optimal trajectory, and its acceleration sequence and lane-changing command are output to the longitudinal and lateral controllers for execution. Simultaneously, the state machine state S_m_final corresponding to the end of this optimal trajectory is recorded and used as the initial state for the next planning cycle, achieving a smooth transition between cycles. Through this interactive mechanism, the decision tree, guided by state machine constraints, completes a rapid search of the joint longitudinal and lateral space, ensuring both real-time performance and improved trajectory quality.

[0180] The following describes the method for determining the state of the child nodes corresponding to each simulation node. In one optional implementation, the above-mentioned method for determining the state value corresponding to at least one first reference state node based on the current acceleration includes:

[0181] S1, determine the current reference state node from at least one first reference state node;

[0182] S2, Repeat the following steps until the number of child node states is greater than or equal to the target number threshold:

[0183] S2-1, Based on the current driving state and current acceleration of the vehicle object, determine the state of the next simulation sub-node;

[0184] S2-2, Update the current acceleration based on the state of the child nodes.

[0185] In this embodiment, during the decision tree expansion process, forward simulation and cost evaluation are performed on each layer of nodes. This not only considers the actual cost of the simulated trajectory but also introduces a heuristic function to estimate future potential costs, thus accurately assessing the long-term potential at intermediate nodes. This method retains only the Top-K nodes with the lowest overall cost for subsequent expansion.

[0186] Specifically, the cost function can include the following five dimensions:

[0187] The navigation deviation cost measures the lateral deviation of the vehicle from the centerline of the target lane, ensuring that the vehicle does not deviate from the lane as much as possible. It can be determined in the following way:

[0188]

[0189] in, Let be the lateral distance between the i-th trajectory point and the center of the lane. This represents the maximum allowed offset.

[0190] Traffic efficiency costs are used to evaluate the difference between a vehicle's average speed and its desired speed during the simulation, preventing vehicles from "dawdling" and encouraging efficient traffic flow. The first cost penalizes low speed, while the second directly rewards traveling a greater distance in the road direction. These costs can be determined as follows:

[0191]

[0192] in, For the target speed, The average velocity of the trajectory points . Let S be the endpoint in the Frent (SL) coordinate system of the current node simulation. This represents the theoretical longitudinal displacement that can be achieved within the current simulation duration.

[0193] The comfort cost, used for smoothness based on acceleration and jerk, ensures a smooth and stable trajectory, avoiding sudden acceleration, braking, and sharp turns. It can be determined as follows:

[0194]

[0195] in: The maximum absolute curvature in the entire trajectory;

[0196] The curvature continuity penalty is used to penalize excessive or drastic changes in trajectory curvature, aiming to generate a dynamically feasible trajectory. It can be determined as follows:

[0197]

[0198] Safety costs are calculated based on minimum distance to surrounding vehicles and time to collision (TTC), using an exponential function to convert collision risk and distance into costs. When TTC is small or the distance is very close, the costs increase sharply, creating a "hazard field" that strongly penalizes any unsafe behavior. This can be determined as follows:

[0199]

[0200] Where N is the number of trajectory points and M is the number of targets. For the TTC of the i-th trajectory point and the j-th obstacle, For TTC security threshold, Let be the relative distance between the i-th trajectory point and the j-th obstacle. The safe distance threshold between targets.

[0201] Furthermore, this application introduces two future cost functions to focus on decision-making flexibility and situational quality. These functions are used to assess the magnitude of the "future possibilities" opened up by the current state.

[0202] Among them, the decision flexibility heuristic cost can quantify the number of all executable subsequent lateral actions starting from the current node based on the vehicle's current state and behavioral state machine. This cost rewards nodes that reserve more future options for the system. The interaction situation heuristic cost is used to comprehensively evaluate the future interaction environment with surrounding traffic participants caused by the current node state, including the overall safety pressure predicted based on relative kinematics.

[0203] The heuristic cost of decision-making flexibility can be determined as follows:

[0204]

[0205] In the formula According to the lane change state machine rules, enumerate the number of all legal lateral actions in the next time step, starting from the current node state. For example, a node in the KeepLane state, its (KeepLane, LeftChange, RightChange can continue). A node in the LeftChange state that has entered stage 3, its... (LeftChange is the only option). Therefore, KeepLane nodes will receive a greater flexibility bonus than stage3 lane-changing nodes.

[0206] The heuristic cost of instantaneous interactive situations can be determined as follows:

[0207] Based on the instantaneous state (vehicle state + environment state) at the end of the simulation at the current node, predict the trend of interaction risk changes in the short term if this state is maintained.

[0208]

[0209] In the formula Let TTC be the rate of change of the vehicle's time relative to the j-th obstacle. A decrease in TTC indicates increased risk, hence this term is positive and incurs a cost. The difference: It is the absolute value of TTC (how dangerous it was in the past and present). It's about observing the changing trends of the TTC (whether it will become more dangerous or safer in the future).

[0210] The following combination Figure 5 A complete process of this application is described.

[0211] S502, the decision tree begins to expand; this marks the official start of the search process for this planning cycle.

[0212] S504, Initialize the root node N_root of the decision tree; establish the root node based on the current vehicle state and environmental perception information, which serves as the starting point of the entire tree.

[0213] S506, Select the node to be expanded N_Current; Select a node to be expanded from the existing leaf nodes according to a strategy (such as lowest cost priority).

[0214] S508, query the state machine to obtain the current state S_M; read the lateral state (lane, lane change phase, etc.) and longitudinal state (speed, acceleration, etc.) of the state machine at time N_Current for subsequent action legality judgment.

[0215] S510: Generate a candidate action set, and based on S_M, fit the action subset A_Legal; filter out legal horizontal-vertical action combinations from the complete action space according to the current state machine constraints (such as not being able to change lanes twice consecutively, not being able to cross the road boundary), and form A_legal.

[0216] S512 extends the current N_Current node based on A_Legal; generates new child nodes for each action in A_legal, preparing for parallel simulation.

[0217] S514: Obtain the initial state of the current N_Current node, including coordinates, heading angle, velocity, and acceleration; extract the end pose and kinematics of the parent node as initial values ​​for the forward simulation of the child node.

[0218] S516 Sort all targets by proximity to the moving object using ObjListSort; sort the surrounding vehicles in the perception list by their longitudinal distance from the vehicle to the farthest, ensuring that the most relevant traffic bodies are processed first.

[0219] S518: Extract the targets from ObjListSort sequentially; extract the vehicles in a loop according to the sorting results and incorporate them into the interactive simulation one by one.

[0220] S520 simulates forward dynamics model simulation; based on the candidate actions of the self-vehicle and the motion assumptions of the target vehicle, it runs a bicycle or double integral dynamics model to estimate the initial trajectory draft for the next few seconds.

[0221] S522, a multi-dimensional perception adaptive intelligent IDM vehicle-following simulation; based on dynamic trajectory, it adopts an improved IDM to calculate following acceleration, and integrates multi-target distance, relative speed, congestion description parameters and driver profile in real time to output a more realistic longitudinal acceleration correction value.

[0222] S524, update the state of the next simulation point, coordinates / heading angle / velocity / acceleration; using the acceleration obtained in the previous step, advance the vehicle's pose and kinematic state in 0.15s steps and write it into the new node.

[0223] S526: Has the forward simulation of all targets in ObjListSort been completed? If yes, proceed to S528: Check if the interactive simulation of all neighboring target vehicles has been completed. If not, return to S518 to continue to the next target.

[0224] S528 checks if each node (15 steps, step size 0.15) has completed the current N_Current node; the maximum number of simulation steps per node is 15 steps (approximately 2.25 seconds). If the completion condition is not met even after reaching the required number of steps, the branch is forcibly terminated, and S514 is executed to prevent infinite expansion. If completed, S530 is executed.

[0225] S530 determines the actual cost of the simulated trajectory; based on the completed trajectory, it calculates the actual costs in multiple dimensions such as navigation deviation, traffic efficiency, comfort, and safety, which are then used for subsequent ranking.

[0226] S532, determine the heuristic future cost; use a lightweight heuristic function (such as remaining distance × reciprocal of expected velocity) to quickly estimate the potential cost from the end of the trajectory to the target, and add it to the actual cost to form the total score.

[0227] S534, determine whether the current N_Current node has been expanded; prevent repeated expansion; if the node has already completed the generation of child nodes, skip it.

[0228] S536 evaluates the cost of each action trajectory based on the fusion cost and selects TOP-K as the effective nodes for the next cycle; it sorts all child nodes in ascending order of total cost, retains only the K with the best cost to enter the next round of expansion, and prunes the rest to ensure that computing resources are controllable.

[0229] S538: Determine whether the decision tree has reached its maximum depth or whether the computation time has exceeded the threshold. If the tree depth or CPU time reaches the preset upper limit, terminate the loop and enter the final stage; otherwise, execute S506.

[0230] S540, decision tree expansion complete; output the retained candidate trajectory set for final fine simulation and optimal trajectory selection, and record the corresponding state machine state as the initial value for the next cycle. This concludes the planning cycle expansion process.

[0231] The following combination Figure 6 Another specific embodiment of this application will be described.

[0232] This implementation provides a forward simulation example based on a five-layer decision tree to explain the complete execution process of the joint horizontal and vertical search in a typical high-speed scenario. The decision tree is set to five layers with a 1-second time interval between layers, for a total simulation duration of 4 seconds. Within each 1-second interval, fine-grained forward inference is performed in 0.1-second increments. The horizontal action space includes {KeepLane, LeftChange, RightChange}, and the vertical action space includes {A1, A2, A3}, corresponding to three types of vertical control intensities: "mild acceleration / deceleration," "moderate acceleration / deceleration," and "strong acceleration / deceleration," respectively.

[0233] The initial state of the scenario is as follows: The driver is traveling at a constant speed of 100 km / h in the middle lane. Directly in front of the driver is a slow-moving target vehicle traveling at 90 km / h. To the left rear of the driver is another vehicle rapidly approaching at a speed significantly higher than the driver's. In terms of road geometry, the left lane line is a dashed line, and the right lane line is a solid line; therefore, the state machine prohibits any attempt to change lanes to the right.

[0234] The algorithm execution flow is as follows:

[0235] S1, initialize the root node N0;

[0236] Upon receiving the new planning cycle instruction, a root node N0 is created, which internally records the vehicle's current pose, velocity, and a list of environmental targets, such as... Figure 6 As shown, this serves as the starting point for the entire decision tree.

[0237] S2, first-level extension and state machine filtering;

[0238] When performing a combined horizontal and vertical expansion on N0, the state machine reports the current horizontal state as KeepLane, allowing horizontal actions {KeepLane, LeftChange, RightChange}. Since the right side is a solid line, the state machine immediately marks RightChange as invalid; the three right lane change branches generated as a result are prematurely removed, leaving only the KeepLane and LeftChange branches. Vertically, each of the two branches combines with {A1, A2, A3} to form six first-level child nodes: {KeepLane, A1}, {KeepLane, A2}, {KeepLane, A3}, {LeftChange, A1}, {LeftChange, A2}, and {LeftChange, A3}.

[0239] S3, First layer forward simulation and pruning;

[0240] A 1-second forward simulation was run for each of the six nodes. Due to the continuous pressure from the slower vehicle ahead at 90 km / h, the Time-to-Collision (TTC) of the KeepLane branch rapidly decreased towards the end of the simulation, causing a sharp increase in collision risk. Based on this, the heuristic evaluation module marked {KeepLane,A1}, {KeepLane,A2}, and {KeepLane,A3} as high-risk and pruned them. After the first layer expansion was completed, only the three nodes {LeftChange,A1}, {LeftChange,A2}, and {LeftChange,A3} were retained for the next round.

[0241] S4, second-level extension;

[0242] According to the state machine, the current horizontal state is in the LeftChange phase. Based on state machine constraints, the horizontal actions of child nodes can only continue with LeftChange. Vertically, they continue to combine with {A1, A2, A3}, theoretically forming a total of 9 second-level child nodes (for simplicity, the diagram is incomplete). Figure 6 (Only 3 are shown in the image). Each node advances by 1 second in 0.1-second increments to generate the corresponding trajectory.

[0243] S5, second-layer simulation and secondary pruning;

[0244] In the second-level simulation results, {LeftChange,A3} exhibited excessive longitudinal acceleration, resulting in a significantly higher lateral speed for the vehicle compared to the oncoming vehicle approaching from the left rear. This created a noticeable speed difference, causing a sharp decrease in the following vehicle's TTC (Traffic Time Control), and the overall safety cost exceeded the threshold. The system then pruned this node. The remaining nodes were evaluated and retained.

[0245] S6, progressive expansion from the third to the fifth layer;

[0246] Following the same logic, the algorithm sequentially completes node expansion, forward simulation, and cost evaluation for the third, fourth, and fifth layers. Each layer maintains the LeftChange state under state machine constraints, with the longitudinal direction optimized within the range {A1, A2}. As the number of layers increases, the vehicle gradually completes lateral displacement and establishes a stable following relationship with the vehicle in front in the left lane, balancing the costs of TTC, comfort, and traffic efficiency. Finally, the decision tree outputs several complete trajectories at the end of the fifth layer. The system selects the trajectory with the lowest total cost as the optimal trajectory and sends its corresponding acceleration sequence and lane-changing command to the execution layer.

[0247] Through the aforementioned five-layer, 4-second, 50-step forward deduction and layer-by-layer pruning, the algorithm automatically eliminates high-risk right lane changes and excessive longitudinal maneuvers under multiple constraints, including solid line constraints, slow vehicle suppression, and rapid approach from following vehicles. It prioritizes retaining smooth trajectories of "left lane change + moderate acceleration / deceleration," achieving a comprehensive optimization of safety, comfort, and traffic efficiency. It should be noted that this process is completed entirely within a single cycle of 100 milliseconds. The pruning mechanism reduces the node size from the theoretical 243 nodes to less than 20, significantly reducing the computational load.

[0248] The above-described implementation of this application provides a refined five-state lane-changing state machine, whose rules are deeply embedded in the expansion process of the decision tree. The state machine strictly constrains the generation logic of lateral actions at nodes, fundamentally solving the problems of behavioral sequence rationality defects and safety risks, and ensuring that all candidate behaviors conform to driving logic.

[0249] Furthermore, forward simulation of the actions between nodes is performed. The simulation results evaluate the costs already incurred by the current node, while also assessing the potential costs of future nodes from the perspectives of decision-making flexibility and situational quality. The heuristic function intelligently guesses "how much more cost it will take to reach the destination from here" based on the current node's state. This avoids pruning potential optimal solutions due to "short-sighted" decisions, making the pruning process more forward-looking. It can more accurately identify nodes with potentially higher short-term costs but significant long-term potential, significantly improving the overall quality of the final decision scheme (safer, more efficient, and more reasonable), especially in complex merging and lane-changing interaction scenarios. Before each node expansion, the Top-K nodes with the lowest overall cost are retained for subsequent expansion, reducing computational complexity from exponential to linear while maintaining decision-making quality.

[0250] Furthermore, a multi-level adaptive following acceleration determination algorithm for vehicle trajectory simulation is proposed, which integrates scene recognition, forward vehicle state perception, and driver profile. Specifically, it introduces a forward vehicle acceleration dynamic compensation term, constructs a hierarchical road scene recognition and parameter mapping mechanism, and establishes a four-dimensional driver profile vector (aggression, patience, risk tolerance, and economic tendency), thereby significantly improving the realism of traffic simulation.

[0251] Furthermore, in the forward simulation stage of the decision tree, an innovative vehicle dynamics model incorporating tire slip angle, understeer gradient, and powertrain delay is introduced. By calculating the centroid slip angle and velocity-related curvature, the actual motion posture of the vehicle under medium-to-high speed and large lateral acceleration conditions is accurately simulated. More accurate simulation trajectories are used for cost evaluation, making pruning decisions more reliable. This ensures from the outset that the final planned trajectory conforms to vehicle dynamics constraints, avoiding the risk of "simulation feasible, but not executable in real vehicles," thus improving system safety. This overcomes the problem of simulation trajectories becoming disconnected from real vehicle dynamics caused by the use of simplified kinematic models in traditional decision planning, improving the accuracy of forward simulation predictions.

[0252] In the above implementation, a combination of hard constraints and soft pruning is proposed. The state machine first ensures the safety and rationality of the behavior, and heuristic cost pruning performs soft pruning. On this basis, efficient and optimal solutions are further selected. Finally, fine simulation and optimal solution output are completed from a very small number of candidate trajectories, achieving a balance between computational efficiency and decision quality.

[0253] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0254] According to another aspect of the embodiments of this application, an acceleration determination apparatus for vehicle trajectory simulation is also provided for implementing the above-described acceleration determination method for vehicle trajectory simulation. For example... Figure 7 As shown, the device includes:

[0255] The first acquisition unit 702 is used to acquire a first adjustment vector that matches the current road environment, wherein the current road environment is determined based on at least one driving state of at least one other vehicle object in the first driving cycle, and the first adjustment vector includes at least one first adjustment coefficient, wherein at least one of the first adjustment coefficients is used to perform a first adjustment operation on at least one corresponding acceleration state parameter.

[0256] The second acquisition unit 704 is used to acquire a second adjustment vector that matches the object profile of the vehicle object, wherein the object profile is determined based on at least one driving operation of the vehicle object in the second driving cycle, and the second adjustment vector includes at least one second adjustment coefficient, wherein at least one of the second adjustment coefficients is used to perform a second adjustment operation on at least one corresponding acceleration state parameter.

[0257] The first determining unit 706 is used to adjust at least one acceleration state parameter in the acceleration state parameter set according to the first adjustment vector and the second adjustment vector to obtain a target acceleration state parameter set, wherein the acceleration state parameter set includes a first distance parameter, a second distance parameter, a deceleration parameter, and a maximum acceleration parameter, wherein the first distance parameter is the safe distance between the self-vehicle object and the preceding vehicle object in a stationary state, and the second distance parameter is the safe time distance between the self-vehicle object and the preceding vehicle object in a moving state;

[0258] The second determining unit 708 is used to determine the current acceleration based on the current speed of the aforementioned vehicle object and the aforementioned target acceleration state parameter set.

[0259] Optionally, the first acquisition unit 702 is configured to: acquire congestion description parameters, lane average speed, and road type parameters matching the current road environment, wherein the congestion description parameters indicate the congestion state of the current road environment, the lane average speed indicates the average speed of vehicles corresponding to the current lane, and the road type parameters indicate the road type corresponding to the current road environment; determine a current mode identifier matching the current road environment from multiple candidate mode identifiers based on the congestion description parameters, lane average speed, and road type parameters, wherein each of the multiple candidate mode identifiers corresponds to an adjustment vector; and determine a first adjustment vector based on the current mode identifier, wherein the first adjustment vector includes a distance adjustment coefficient, a time-distance adjustment coefficient, a deceleration adjustment coefficient, and an acceleration adjustment coefficient, wherein the distance adjustment coefficient is used to adjust the first distance parameter, the time-distance adjustment coefficient is used to adjust the second distance parameter, the deceleration adjustment coefficient is used to adjust the deceleration parameter, and the acceleration adjustment coefficient is used to adjust the maximum acceleration parameter.

[0260] Optionally, the second acquisition unit 704 is configured to: determine a driving description vector based on at least one operation data sequence matching the second driving cycle, wherein the operation data sequence includes operation quantities corresponding to multiple timestamps respectively, and at least one description parameter included in the driving description vector is determined based on at least one of the operation data sequences respectively; determine a target profile description vector from the multiple profile description vectors based on the similarity between the driving description vector and the multiple profile description vectors; and determine the adjustment vector matching the target profile description vector as the second adjustment vector, wherein the second adjustment vector includes an aggression adjustment coefficient, a patience adjustment coefficient, an economy adjustment coefficient, and a risk propensity adjustment coefficient.

[0261] Optionally, the first determining unit 706 is configured to: determine a first adjustment coefficient based on a weighted sum of the risk propensity adjustment coefficient, the patience adjustment coefficient, and the first benchmark coefficient; determine a target distance parameter based on the product of the first adjustment coefficient and the first reference distance parameter, wherein the first adjustment coefficient and the risk propensity adjustment coefficient have a first negative correlation, the first adjustment coefficient and the patience adjustment coefficient have a second negative correlation, and the first reference distance parameter is obtained by adjusting the first distance parameter according to at least one of the first adjustment coefficients; and determine the target distance parameter based on the patience adjustment coefficient. The second adjustment coefficient is determined by a weighted sum of the aforementioned aggression adjustment coefficient and the second benchmark coefficient; the target time distance parameter is determined by the product of the aforementioned second adjustment coefficient and the second reference distance parameter, wherein the aforementioned second adjustment coefficient has a first positive correlation with the aforementioned patience adjustment coefficient, and the aforementioned second adjustment coefficient has a third negative correlation with the aforementioned aggression adjustment coefficient; the aforementioned second reference distance parameter is obtained by adjusting the aforementioned second distance parameter according to at least one of the aforementioned first adjustment coefficients; the third adjustment coefficient is determined by a weighted sum of the aforementioned risk propensity adjustment coefficient, the aforementioned economic adjustment coefficient, and the third benchmark coefficient; according to The target deceleration parameter is determined by multiplying the aforementioned third adjustment coefficient and the reference deceleration parameter. The third adjustment coefficient has a fourth negative correlation with the aforementioned risk propensity adjustment coefficient, and a fifth negative correlation with the aforementioned economic adjustment coefficient. The reference deceleration parameter is obtained by adjusting the deceleration parameter based on at least one of the aforementioned first adjustment coefficients. The fourth adjustment coefficient is determined by a weighted sum of the aforementioned aggression adjustment coefficient, the aforementioned economic adjustment coefficient, and the fourth benchmark coefficient. The target acceleration parameter is determined by multiplying the aforementioned fourth adjustment coefficient and the reference acceleration parameter. The first adjustment coefficient has a second positive correlation with the above-mentioned aggression adjustment coefficient, and the second adjustment coefficient has a sixth negative correlation with the above-mentioned economy adjustment coefficient. The reference acceleration parameter is obtained by adjusting the above-mentioned maximum acceleration parameter according to at least one of the above-mentioned first adjustment coefficients. The aggression adjustment coefficient is used to indicate the degree of aggression of the above-mentioned driving operation of the above-mentioned vehicle object. The patience adjustment coefficient is used to indicate the degree of tolerance of the above-mentioned vehicle object for driving waiting time. The economy adjustment coefficient is used to indicate the tendency of the above-mentioned vehicle object to perform energy-saving driving operations. The risk tendency adjustment coefficient is used to indicate the tendency of the above-mentioned vehicle object to perform risky driving operations.

[0262] Optionally, the second determining unit 708 is configured to: obtain target distance parameters, target acceleration parameters, target time distance parameters, and target deceleration parameters from the target acceleration state parameter set; determine the difference between the target acceleration parameters and the target adjustment value as the current acceleration, wherein the target adjustment value is the difference between 1 and the target adjustment amount, the target adjustment amount includes a desired speed adjustment amount and a desired distance adjustment amount, the desired speed adjustment amount is determined based on the ratio between the current speed and the desired speed of the vehicle object, the desired distance adjustment amount is determined based on the ratio between the desired distance and the current distance between the vehicle object and the preceding vehicle object, and the desired distance is determined based on the target... The target distance parameter, the target acceleration parameter, the target time distance parameter, and the target deceleration parameter are determined. The target distance parameter is obtained by adjusting the first distance parameter based on at least one of the first adjustment coefficients and at least one of the second adjustment coefficients. The target time distance parameter is obtained by adjusting the second distance parameter based on at least one of the first adjustment coefficients and at least one of the second distance parameters. The target acceleration parameter is obtained by adjusting the maximum acceleration parameter based on at least one of the first adjustment coefficients and at least one of the second distance parameters. The target deceleration parameter is obtained by adjusting the deceleration parameter based on at least one of the first adjustment coefficients and at least one of the second distance parameters.

[0263] Optionally, the second determining unit 708 is further configured to: calculate the ratio between the current speed and the desired speed of the vehicle object. The power of the power is determined as the expected speed adjustment amount; the square of the ratio between the expected distance between the self-vehicle and the preceding vehicle and the current distance is determined as the expected distance adjustment amount.

[0264] Optionally, the second determining unit 708 is further configured to: determine a first reference coefficient by raising the half power of the product between the target acceleration parameter and the target deceleration parameter; determine a second reference coefficient by raising the product between the current speed of the vehicle and the current acceleration of the preceding vehicle; determine a preceding vehicle acceleration adjustment term by the ratio between the second reference coefficient and the first reference coefficient; determine a third reference coefficient by raising the product between the current speed of the vehicle and the current speed difference; determine a speed difference adjustment term by the ratio between the third reference coefficient and the first reference coefficient, wherein the current speed difference is the difference between the current speed of the vehicle and the current speed of the preceding vehicle; determine a speed adjustment term by raising the product between the current speed of the vehicle and the target time-distance parameter; and determine the desired distance by raising the sum of the preceding vehicle acceleration adjustment term, the speed difference adjustment term, and the speed adjustment term if the sum is greater than or equal to 0.

[0265] Optionally, the acceleration determination device for vehicle trajectory simulation further includes: a node determination unit, configured to determine at least one first reference state node from a set of candidate state nodes based on the driving state of the vehicle object at the current state node, wherein the driving state includes lateral driving state and longitudinal driving state; determine the state cost value corresponding to each of the at least one first reference state node based on the current acceleration; and determine at least one first target state node from the at least one first reference state node based on the sorting result of the state cost values ​​corresponding to each of the at least one first reference state node.

[0266] Optionally, the node determination unit is configured to: determine the current reference state node from at least one of the first reference state nodes; repeat the following steps until the number of child node states is greater than or equal to a target number threshold: determine the child node state corresponding to the next simulation child node based on the current driving state of the vehicle object and the current acceleration; update the current acceleration based on the child node state.

[0267] For specific implementation examples, please refer to the example shown in the above-described method for determining acceleration for vehicle trajectory simulation, which will not be repeated here.

[0268] This application also provides an electronic device, such as... Figure 8As shown, memory 802 can be used to store software programs and modules, such as the program instructions / modules corresponding to the acceleration determination method and device for vehicle trajectory simulation in this embodiment of the invention. Processor 804 executes various functional applications and data processing by running the software programs and modules stored in memory 802, thereby realizing the aforementioned acceleration determination method for vehicle trajectory simulation. Memory 802 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, memory 802 may further include memory remotely located relative to processor 804, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Specifically, memory 802 may be used, but is not limited to, storing file information such as target logical files. As an example, such as... Figure 8 As shown, the memory 802 may include, but is not limited to, the first acquisition unit 702, the second acquisition unit 704, the first determination unit 706, and the second determination unit 708 in the lane change intention generation device. Furthermore, it may include, but is not limited to, other module units in the acceleration determination device for vehicle trajectory simulation described above, which will not be elaborated upon in this example.

[0269] Optionally, the transmission device 806 described above is used to receive or send data via a network. Specific examples of the network described above may include wired networks and wireless networks. In one example, the transmission device 806 includes a Network Interface Controller (NIC), which can be connected to other network devices and a router via a network cable to communicate with the Internet or a local area network. In another example, the transmission device 806 is a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0270] In addition, the above-mentioned electronic device also includes a display 808 and a connection bus 810 for connecting the various module components in the above-mentioned electronic device.

[0271] According to one aspect of this application, a computer program product is provided, comprising a computer program / instructions containing program code for performing the methods shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs various functions provided in embodiments of this application.

[0272] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0273] It should be noted that the computer system of the electronic device is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0274] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication component, and / or installed from a removable medium. When the computer program is executed by a central processing unit, it performs various functions defined in the system of this application.

[0275] According to one aspect of this application, a computer-readable storage medium is provided, wherein a processor of a computer device reads computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0276] Optionally, in this embodiment, the computer-readable storage medium described above may be configured to store a computer program for performing the following steps:

[0277] S1, obtain a first adjustment vector that matches the current road environment, wherein the current road environment is determined based on at least one driving state of at least one other vehicle object in the first driving cycle, and the first adjustment vector includes at least one first adjustment coefficient, wherein at least one of the first adjustment coefficients is used to perform a first adjustment operation on at least one corresponding acceleration state parameter.

[0278] S2, obtain a second adjustment vector that matches the object profile of the vehicle object, wherein the object profile is determined based on at least one driving operation of the vehicle object in the second driving cycle, and the second adjustment vector includes at least one second adjustment coefficient, wherein at least one of the second adjustment coefficients is used to perform a second adjustment operation on at least one corresponding acceleration state parameter.

[0279] S3, according to the first adjustment vector and the second adjustment vector, at least one acceleration state parameter in the acceleration state parameter set is adjusted to obtain the target acceleration state parameter set, wherein the acceleration state parameter set includes a first distance parameter, a second distance parameter, a deceleration parameter, and a maximum acceleration parameter, wherein the first distance parameter is the safe distance between the self-vehicle object and the preceding vehicle object in a stationary state, and the second distance parameter is the safe time distance between the self-vehicle object and the preceding vehicle object in a moving state;

[0280] S4. Determine the current acceleration based on the current speed of the vehicle object and the target acceleration state parameter set.

[0281] Optionally, in this embodiment, those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware of an electronic device. The program can be stored in a computer-readable storage medium, which may include: flash drive, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0282] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0283] If the integrated units in the above embodiments are implemented as software functional units and sold or used as independent products, they can be stored in the aforementioned computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause one or more computer devices (which may be personal computers, servers, or network devices, etc.) to execute all or part of the steps of the methods of the various embodiments of this application.

[0284] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0285] In the several embodiments provided in this application, it should be understood that the disclosed user equipment can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of units or modules may be electrical or other forms.

[0286] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0287] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0288] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for determining acceleration for vehicle trajectory simulation, characterized in that, include: A first adjustment vector matching the current road environment is obtained, wherein the current road environment is determined based on at least one driving state of at least one other vehicle object in the first driving cycle, and the first adjustment vector includes at least one first adjustment coefficient, wherein at least one first adjustment coefficient is used to perform a first adjustment operation on at least one corresponding acceleration state parameter. A second adjustment vector matching the object profile of the vehicle object is obtained, wherein the object profile is determined based on at least one driving operation of the vehicle object in the second driving cycle, and the second adjustment vector includes at least one second adjustment coefficient, wherein at least one second adjustment coefficient is used to perform a second adjustment operation on at least one corresponding acceleration state parameter. Based on the first adjustment vector and the second adjustment vector, at least one acceleration state parameter in the acceleration state parameter set is adjusted to obtain a target acceleration state parameter set, wherein the acceleration state parameter set includes a first distance parameter, a second distance parameter, a deceleration parameter, and a maximum acceleration parameter. The first distance parameter is the safe distance between the self-vehicle object and the preceding vehicle object in a stationary state, and the second distance parameter is the safe time distance between the self-vehicle object and the preceding vehicle object in a moving state. The current acceleration is determined based on the current speed of the vehicle and the target acceleration state parameter set.

2. The method according to claim 1, characterized in that, The step of obtaining the first adjustment vector that matches the current road environment includes: Obtain congestion description parameters, average lane speed, and road type parameters that match the current road environment, wherein the congestion description parameters are used to indicate the congestion status of the current road environment, the average lane speed is used to indicate the average vehicle speed corresponding to the current lane, and the road type parameters are used to indicate the road type corresponding to the current road environment. Based on the congestion description parameters, average lane speed, and road type parameters, a current mode identifier that matches the current road environment is determined from multiple candidate mode identifiers, and each of the multiple candidate mode identifiers corresponds to an adjustment vector; Based on the current mode identifier, a first adjustment vector is determined, wherein the first adjustment vector includes a distance adjustment coefficient, a time-distance adjustment coefficient, a deceleration adjustment coefficient, and an acceleration adjustment coefficient. The distance adjustment coefficient is used to adjust the first distance parameter, the time-distance adjustment coefficient is used to adjust the second distance parameter, the deceleration adjustment coefficient is used to adjust the deceleration parameter, and the acceleration adjustment coefficient is used to adjust the maximum acceleration parameter.

3. The method according to claim 1, characterized in that, The step of obtaining the second adjustment vector that matches the object profile of the vehicle object includes: A driving description vector is determined based on at least one operation data sequence matching the second driving cycle, wherein the operation data sequence includes operation quantities corresponding to multiple timestamps respectively, and at least one description parameter included in the driving description vector is determined based on at least one of the operation data sequences respectively; Based on the similarity between the driving description vector and multiple image description vectors, a target image description vector is determined from the multiple image description vectors; The adjustment vector that matches the target profile description vector is determined as the second adjustment vector, wherein the second adjustment vector includes an aggression adjustment coefficient, a patience adjustment coefficient, an economic adjustment coefficient, and a risk propensity adjustment coefficient.

4. The method according to claim 3, characterized in that, The step of adjusting at least one acceleration state parameter in the acceleration state parameter set according to the first adjustment vector and the second adjustment vector to obtain the target acceleration state parameter set includes at least one of the following: Based on the risk propensity adjustment coefficient, the weighted sum of the patience adjustment coefficient and the first benchmark coefficient, a first adjustment coefficient is determined; based on the product of the first adjustment coefficient and the first reference distance parameter, a target distance parameter is determined, wherein the first adjustment coefficient and the risk propensity adjustment coefficient have a first negative correlation, the first adjustment coefficient and the patience adjustment coefficient have a second negative correlation, and the first reference distance parameter is obtained by adjusting the first distance parameter according to at least one of the first adjustment coefficients; A second adjustment coefficient is determined based on the weighted sum of the patience adjustment coefficient, the aggression adjustment coefficient, and the second baseline coefficient; a target time distance parameter is determined based on the product of the second adjustment coefficient and the second reference distance parameter, wherein the second adjustment coefficient and the patience adjustment coefficient have a first positive correlation, the second adjustment coefficient and the aggression adjustment coefficient have a third negative correlation, and the second reference distance parameter is obtained by adjusting the second distance parameter according to at least one of the first adjustment coefficients; A third adjustment coefficient is determined based on the weighted sum of the risk propensity adjustment coefficient, the economic adjustment coefficient, and the third benchmark coefficient; a target deceleration parameter is determined based on the product of the third adjustment coefficient and the reference deceleration parameter, wherein the third adjustment coefficient and the risk propensity adjustment coefficient have a fourth negative correlation, the third adjustment coefficient and the economic adjustment coefficient have a fifth negative correlation, and the reference deceleration parameter is obtained by adjusting the deceleration parameter according to at least one of the first adjustment coefficients; A fourth adjustment coefficient is determined based on the weighted sum of the offensive adjustment coefficient, the economic adjustment coefficient, and the fourth benchmark coefficient; a target acceleration parameter is determined based on the product of the fourth adjustment coefficient and the reference acceleration parameter, wherein the fourth adjustment coefficient and the offensive adjustment coefficient have a second positive correlation, and the fourth adjustment coefficient and the economic adjustment coefficient have a sixth negative correlation, and the reference acceleration parameter is obtained by adjusting the maximum acceleration parameter according to at least one of the first adjustment coefficients; The aggression adjustment coefficient is used to indicate the aggressiveness of the driving operation of the autonomous vehicle, the patience adjustment coefficient is used to indicate the tolerance of the autonomous vehicle for driving waiting time, the economy adjustment coefficient is used to indicate the tendency of the autonomous vehicle to perform energy-saving driving operations, and the risk propensity adjustment coefficient is used to indicate the tendency of the autonomous vehicle to perform risky driving operations.

5. The method according to claim 1, characterized in that, Determining the current acceleration based on the current speed of the vehicle and the target acceleration state parameter set includes: Obtain the target distance parameter, target acceleration parameter, target time distance parameter, and target deceleration parameter from the target acceleration state parameter set; The difference between the target acceleration parameter and the target adjustment value is determined as the current acceleration. The target adjustment value is the difference between 1 and the target adjustment amount. The target adjustment amount includes a desired speed adjustment amount and a desired distance adjustment amount. The desired speed adjustment amount is determined based on the ratio between the current speed and the desired speed of the vehicle. The desired distance adjustment amount is determined based on the ratio between the desired distance and the current distance between the vehicle and the preceding vehicle. The desired distance is determined based on the target distance parameter, the target acceleration parameter, the target time-distance parameter, and the target deceleration parameter. The target distance parameter is obtained by adjusting the first distance parameter according to at least one first adjustment coefficient and at least one second adjustment coefficient; the target time distance parameter is obtained by adjusting the second distance parameter according to at least one first adjustment coefficient and at least one second distance parameter; the target acceleration parameter is obtained by adjusting the maximum acceleration parameter according to at least one first adjustment coefficient and at least one second distance parameter; and the target deceleration parameter is obtained by adjusting the deceleration parameter according to at least one first adjustment coefficient and at least one second distance parameter.

6. The method according to claim 5, characterized in that, Before determining the difference between the target acceleration parameter and the target adjustment value as the current acceleration, the method further includes: The ratio of the current speed of the vehicle to its desired speed. The power is determined as the desired speed adjustment amount; The square of the ratio between the desired distance between the self-vehicle object and the preceding vehicle object and the current distance is determined as the desired distance adjustment amount.

7. The method according to claim 5, characterized in that, Before determining the difference between the target acceleration parameter and the target adjustment value as the current acceleration, the method further includes: The first reference coefficient is determined by raising the product of the target acceleration parameter and the target deceleration parameter to the power of 1 / 2. A second reference coefficient is determined based on the product of the current speed of the vehicle and the current acceleration of the vehicle in front; a forward acceleration adjustment term is determined based on the ratio between the second reference coefficient and the first reference coefficient. A third reference coefficient is determined based on the product of the current speed of the self-vehicle and the current speed difference; a speed difference adjustment term is determined based on the ratio between the third reference coefficient and the first reference coefficient, wherein the current speed difference is the difference between the current speed of the self-vehicle and the current speed of the preceding vehicle. The speed adjustment term is determined based on the product between the current speed of the vehicle and the target time distance parameter; If the sum of the preceding vehicle acceleration adjustment item, the speed difference adjustment item, and the speed adjustment item is greater than or equal to 0, the sum of the preceding vehicle acceleration adjustment item, the speed difference adjustment item, the speed adjustment item, and the target distance parameter is determined as the desired distance.

8. The method according to claim 1, characterized in that, Before determining the current acceleration based on the current speed of the vehicle and the target acceleration state parameter set, the method further includes: Based on the driving state of the vehicle object at the current state node, at least one first reference state node is determined from the candidate state node set, wherein the driving state includes lateral driving state and longitudinal driving state. Determine the state value corresponding to at least one of the first reference state nodes based on the current acceleration; Based on the sorting results of the state cost values ​​corresponding to at least one first reference state node, at least one first target state node is determined from at least one first reference state node.

9. The method according to claim 8, characterized in that, Determining the state value corresponding to at least one of the first reference state nodes based on the current acceleration includes: Determine the current reference state node from at least one of the first reference state nodes; Repeat the following steps until the number of child node states is greater than or equal to the target threshold: Based on the current driving state of the vehicle object, the current acceleration determines the state of the next simulation sub-node; The current acceleration is updated based on the state of the child nodes.

10. An acceleration determination device for vehicle trajectory simulation, characterized in that, include: The first acquisition unit is used to acquire a first adjustment vector that matches the current road environment, wherein the current road environment is determined based on at least one driving state of at least one other vehicle object in the first driving cycle, and the first adjustment vector includes at least one first adjustment coefficient, wherein at least one first adjustment coefficient is used to perform a first adjustment operation on at least one corresponding acceleration state parameter. The second acquisition unit is used to acquire a second adjustment vector that matches the object profile of the vehicle object, wherein the object profile is determined based on at least one driving operation of the vehicle object in the second driving cycle, and the second adjustment vector includes at least one second adjustment coefficient, wherein at least one second adjustment coefficient is used to perform a second adjustment operation on at least one corresponding acceleration state parameter. The first determining unit is configured to adjust at least one acceleration state parameter in the acceleration state parameter set according to the first adjustment vector and the second adjustment vector to obtain a target acceleration state parameter set, wherein the acceleration state parameter set includes a first distance parameter, a second distance parameter, a deceleration parameter, and a maximum acceleration parameter, wherein the first distance parameter is the safe distance between the self-vehicle object and the preceding vehicle object in a stationary state, and the second distance parameter is the safe time distance between the self-vehicle object and the preceding vehicle object in a moving state; The second determining unit is used to determine the current acceleration based on the current speed of the vehicle object and the target acceleration state parameter set.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein the program is executed by an electronic device to perform the method according to any one of claims 1 to 9.

12. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to execute the method described in any one of claims 1 to 9 through the computer program.

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