技术领域Technical field
本公开涉及车辆自动驾驶技术领域，具体而言，涉及端对端轮胎性能裕度辨识模型建模、使用方法及装置、计算机可读存储介质和电子设备。The present disclosure relates to the field of vehicle automatic driving technology, and specifically to end-to-end tire performance margin identification model modeling, usage methods and devices, computer-readable storage media and electronic devices.
背景技术Background technique
随着汽车工业的发展，车辆智能化水平逐渐提高，越来越多先进的驾驶员辅助系统和主动安全系统安装在车辆上，辅助驾驶员控制车辆，提高车辆的稳定性和安全性。然而，轮胎作为整车与环境的主要连接点，轮胎工作状态会直接影响车辆动力学的变化，轮胎性能裕度作为表征轮胎工作状态的指标，直接影响车辆控制器的控制效率。With the development of the automobile industry, the level of vehicle intelligence is gradually improving. More and more advanced driver assistance systems and active safety systems are installed on vehicles to assist the driver in controlling the vehicle and improve the stability and safety of the vehicle. However, as the tire is the main connection point between the vehicle and the environment, the tire working state will directly affect the changes in vehicle dynamics. The tire performance margin, as an indicator of the tire working state, directly affects the control efficiency of the vehicle controller.
需要说明的是，在上述背景技术部分公开的信息仅用于加强对本公开的背景的理解，因此可以包括不构成对本领域普通技术人员已知的现有技术的信息。It should be noted that the information disclosed in the above background section is only used to enhance understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to those of ordinary skill in the art.
发明内容Contents of the invention
本公开实施例提供端对端轮胎性能裕度辨识模型建模、使用方法及装置、计算机可读存储介质和电子设备，能够实现轮胎性能裕度的辨识模型的建模。Embodiments of the present disclosure provide end-to-end tire performance margin identification model modeling, usage methods and devices, computer-readable storage media and electronic devices, which can realize modeling of tire performance margin identification models.
本公开的其他特性和优点将通过下面的详细描述变得显然，或部分地通过本公开的实践而习得。Additional features and advantages of the disclosure will be apparent from the following detailed description, or, in part, may be learned by practice of the disclosure.
根据本公开的一个方面，提供一种端对端轮胎性能裕度辨识模型建模方法，包括：获取车辆实验数据；根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度；将所述车辆实验数据进行轮胎性能裕度标注；将完成标注的所述车辆实验数据进行数据分段；将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模。According to one aspect of the present disclosure, an end-to-end tire performance margin identification model modeling method is provided, including: obtaining vehicle experimental data; obtaining a performance margin corresponding to the tire of the vehicle according to the vehicle experimental data; The vehicle experimental data is marked with tire performance margin; the marked vehicle experimental data is segmented into data segments; the segmented vehicle experimental data with completed annotation is trained using artificial intelligence methods to complete end-to-end pairing End tire performance margin identification model modeling.
在一个实施例中，所述车辆实验数据包括横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩、期望横摆角速度和期望质心侧偏角中的一项或多项；获取车辆实验数据包括：通过所述车辆的传感器获取横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩中的一项或多项；通过单轨车辆模型获取期望横摆角速度和期望质心侧偏角中的一项或多项。In one embodiment, the vehicle experimental data includes one of yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, four wheel moments, desired yaw angular velocity and desired center of mass slip angle. One or more items; obtaining vehicle experimental data includes: obtaining one or more of yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, and four wheel moments through the vehicle's sensors; Obtain one or more of the desired yaw angular velocity and desired center-of-mass side slip angle through the single-track vehicle model.
在一个实施例中，根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度包括：根据所述车辆实验数据获取与所述车辆的轮胎对应的线性区域、过渡区域、饱和区域或滑移区域的性能裕度。In one embodiment, obtaining the performance margin corresponding to the tire of the vehicle according to the vehicle experimental data includes: obtaining a linear region, a transition region, a saturation region or a linear region corresponding to the tire of the vehicle according to the vehicle experimental data. Performance margin in slip region.
在一个实施例中，将完成标注的所述车辆实验数据进行数据分段包括：基于滑动时间窗将完成标注的所述车辆实验数据进行数据分段。In one embodiment, performing data segmentation on the annotated vehicle experimental data includes: performing data segmentation on the annotated vehicle experimental data based on a sliding time window.
在一个实施例中，基于滑动时间窗将完成标注的所述车辆实验数据进行数据分段包括：以N作为分段步长，其中N是大于等于2的自然数；将完成标注的所述车辆实验数据前补充N-1个全零数据；以N为单位顺序基于滑动时间窗将所述车辆实验数据进行成段；其中，基于滑动时间窗将完成标注的所述车辆实验数据分成的段数与所述车辆实验数据的数量相等。In one embodiment, segmenting the labeled vehicle experiment data based on the sliding time window includes: using N as the segmentation step size, where N is a natural number greater than or equal to 2; segmenting the labeled vehicle experiment data Supplement N-1 all-zero data before the data; divide the vehicle experimental data into segments based on the sliding time window sequentially in units of N; wherein the number of segments that the labeled vehicle experimental data is divided into based on the sliding time window is the same as the number of segments. The amount of experimental data for the above vehicles is equal.
在一个实施例中，将所述车辆实验数据进行轮胎性能裕度标注包括：以每段所述车辆实验数据中最后一个或一组数据对应的性能裕度作为该段所述车辆实验数据的性能裕度。In one embodiment, annotating the tire performance margin of the vehicle experimental data includes: taking the performance margin corresponding to the last one or a group of data in each segment of the vehicle experimental data as the performance of the vehicle experimental data in that segment. Margin.
在一个实施例中，将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模包括：将分段后的标注完成的所述车辆实验数据通过长短期记忆网络模型LSTM进行训练，以完成端对端轮胎性能裕度辨识模型建模。In one embodiment, training the segmented and annotated vehicle experimental data through an artificial intelligence method to complete the end-to-end tire performance margin identification model modeling includes: training all the segmented and annotated vehicle experimental data. The vehicle experimental data described above are trained through the long short-term memory network model LSTM to complete the end-to-end tire performance margin identification model modeling.
根据本公开的一个方面，提供一种端对端轮胎性能裕度辨识模型使用方法，其特征在于，包括：获取车辆数据；将所述车辆数据输入至完成训练的端对端轮胎性能裕度辨识模型以获取轮胎的性能裕度；其中，所述端对端轮胎性能裕度辨识模型是通过如上实施例中任一方法获取的模型。According to one aspect of the present disclosure, a method for using an end-to-end tire performance margin identification model is provided, which includes: acquiring vehicle data; inputting the vehicle data into the trained end-to-end tire performance margin identification model model to obtain the performance margin of the tire; wherein the end-to-end tire performance margin identification model is a model obtained by any method in the above embodiments.
根据本公开的一个方面，提供一种端对端轮胎性能裕度辨识模型建模装置，包括：第一获取模块，配置为获取车辆实验数据；第二获取模块，配置为根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度；标注模块，配置为将所述车辆实验数据进行轮胎性能裕度标注；分段模块，配置为将完成标注的所述车辆实验数据进行数据分段；训练模块，配置为将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模。According to one aspect of the present disclosure, an end-to-end tire performance margin identification model modeling device is provided, including: a first acquisition module configured to acquire vehicle experimental data; a second acquisition module configured to acquire vehicle experimental data based on Obtain the performance margin corresponding to the tire of the vehicle; a labeling module configured to label the vehicle experimental data with tire performance margin; a segmentation module configured to perform data segmentation on the labeled vehicle experimental data ; The training module is configured to train the segmented and annotated vehicle experimental data through artificial intelligence methods to complete the end-to-end tire performance margin identification model modeling.
根据本公开的一个方面，提供一种端对端轮胎性能裕度辨识装置，包括：第三获取模块，配置为获取车辆数据；识别模块，配置为将所述车辆数据输入至完成训练的端对端轮胎性能裕度辨识模型以获取轮胎的性能裕度；其中，所述端对端轮胎性能裕度辨识模型是通过如上实施例中任一方法所述获取的模型。According to one aspect of the present disclosure, an end-to-end tire performance margin identification device is provided, including: a third acquisition module configured to acquire vehicle data; and an identification module configured to input the vehicle data to an end-to-end tire that has completed training. An end-to-end tire performance margin identification model is used to obtain the performance margin of the tire; wherein the end-to-end tire performance margin identification model is a model obtained by any method as described in the above embodiments.
根据本公开的一个方面，提供一种电子设备，包括：一个或多个处理器；存储装置，配置为存储一个或多个程序，当所述一个或多个程序被所述一个或多个处理器执行时，使得所述一个或多个处理器实现如上实施例中任一项所述的方法。According to an aspect of the present disclosure, an electronic device is provided, including: one or more processors; a storage device configured to store one or more programs. When the one or more programs are processed by the one or more When the processor is executed, the one or more processors are caused to implement the method described in any one of the above embodiments.
根据本公开的一个方面，提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机程序，所述计算机程序被处理器执行时实现如上实施例中任一项所述的方法。According to one aspect of the present disclosure, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the method described in any one of the above embodiments is implemented.
本申请的端对端轮胎性能裕度辨识模型建模方法，通过获取车辆实验数据；根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度；将所述车辆实验数据进行轮胎性能裕度标注；将完成标注的所述车辆实验数据进行数据分段；将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模，能够实现端对端轮胎性能裕度辨识模型的建模。The end-to-end tire performance margin identification model modeling method of the present application obtains vehicle experimental data; obtains the performance margin corresponding to the tire of the vehicle according to the vehicle experimental data; and performs tire performance on the vehicle experimental data margin annotation; perform data segmentation on the annotated vehicle experimental data; train the segmented annotated vehicle experimental data through artificial intelligence methods to complete the end-to-end tire performance margin identification model construction The model can realize the modeling of end-to-end tire performance margin identification model.
应当理解的是，以上的一般描述和后文的细节描述仅是示例性和解释性的，并不能限制本公开。It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and do not limit the present disclosure.
附图说明Description of the drawings
为了更清楚地说明本公开实施例或现有技术中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。In order to explain the embodiments of the present disclosure or the technical solutions in the prior art more clearly, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are only These are some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.
图1示出了可以应用本公开实施方式的端对端轮胎性能裕度辨识模型建模方法的示例性系统架构的示意图；FIG. 1 shows a schematic diagram of an exemplary system architecture in which the end-to-end tire performance margin identification model modeling method according to embodiments of the present disclosure can be applied;
图2是本公开实施例提供的一种端对端轮胎性能裕度辨识模型建模方法的流程图；Figure 2 is a flow chart of an end-to-end tire performance margin identification model modeling method provided by an embodiment of the present disclosure;
图3是本公开实施例提供的一种获取车辆实验数据方法的流程图；Figure 3 is a flow chart of a method for obtaining vehicle experimental data provided by an embodiment of the present disclosure;
图4是本公开实施例提供的一种获单轨车辆模型示意图；Figure 4 is a schematic diagram of a monorail vehicle model provided by an embodiment of the present disclosure;
图5是本公开实施例提供的一种获单轨车辆模型的期望横摆角速度、期望质心侧偏角、真实横摆角速度和真实质心侧偏角的示意图；Figure 5 is a schematic diagram of obtaining the expected yaw angular velocity, expected center of mass side slip angle, true yaw angular velocity and true center of mass side slip angle of a monorail vehicle model provided by an embodiment of the present disclosure;
图6是本公开实施例提供的一种基于滑动时间窗将完成标注的所述车辆实验数据进行数据分段方法的流程图；Figure 6 is a flow chart of a method for segmenting the annotated vehicle experimental data based on a sliding time window provided by an embodiment of the present disclosure;
图7是本公开实施例提供的一种基于滑动时间窗将完成标注的所述车辆实验数据进行数据分段方法的示意图；Figure 7 is a schematic diagram of a data segmentation method for the annotated vehicle experimental data based on a sliding time window provided by an embodiment of the present disclosure;
图8是本公开实施例提供的一种完成数据分类后的车辆实验数据示意图；Figure 8 is a schematic diagram of vehicle experimental data after data classification provided by an embodiment of the present disclosure;
图9是本公开实施例提供的基于深度学习的端对端训练的主要网络架构示意图；Figure 9 is a schematic diagram of the main network architecture of end-to-end training based on deep learning provided by an embodiment of the present disclosure;
图10为左前轮测试集误分类情况示意图；Figure 10 is a schematic diagram of the misclassification situation of the left front wheel test set;
图11为左前轮测试集轮胎性能裕度辨识结果在不同路面摩擦系数下的侧偏角和侧向力关系的三维示意图；Figure 11 is a three-dimensional schematic diagram of the relationship between side slip angle and lateral force under different road friction coefficients under the tire performance margin identification results of the left front wheel test set;
图12为左前轮测试集轮胎性能裕度辨识结果在不同路面摩擦系数下的滑移率和纵向力关系的三维示意图；Figure 12 is a three-dimensional schematic diagram of the relationship between slip rate and longitudinal force under different road friction coefficients under the tire performance margin identification results of the left front wheel test set;
图13为左前轮测试集轮胎性能裕度辨识结果在路面摩擦系数0.3下的归一化纵向力和归一化侧向力关系的示意图；Figure 13 is a schematic diagram of the relationship between the normalized longitudinal force and the normalized lateral force under the road friction coefficient of 0.3 under the tire performance margin identification results of the left front wheel test set;
图14为左前轮测试集轮胎性能裕度辨识结果在路面摩擦系数0.6下的归一化纵向力和归一化侧向力关系的示意图；Figure 14 is a schematic diagram of the relationship between the normalized longitudinal force and the normalized lateral force under the road friction coefficient of 0.6 under the tire performance margin identification results of the left front wheel test set;
图15为左前轮测试集轮胎性能裕度辨识结果在路面摩擦系数1.0下的归一化纵向力和归一化侧向力关系的示意图；Figure 15 is a schematic diagram of the relationship between the normalized longitudinal force and the normalized lateral force under the road friction coefficient of 1.0 under the tire performance margin identification results of the left front wheel test set;
图16为四个车轮测试集轮胎性能裕度辨识结果在不同路面摩擦系数下的总滑移率和归一化力关系的三维示意图；Figure 16 is a three-dimensional schematic diagram of the relationship between the total slip rate and the normalized force under different road friction coefficients under the tire performance margin identification results of the four wheel test set;
图17为联合仿真验证质心处特征变化规律；Figure 17 shows the joint simulation verification feature change pattern at the center of mass;
图18为路面附着系数为1.0时的四个车轮联合仿真验证辨识结果；Figure 18 shows the joint simulation verification and identification results of the four wheels when the road adhesion coefficient is 1.0;
图19为路面附着系数为0.6时的四个车轮联合仿真验证辨识结果；Figure 19 shows the joint simulation verification and identification results of the four wheels when the road adhesion coefficient is 0.6;
图20为路面附着系数为0.3时的四个车轮联合仿真验证辨识结果；Figure 20 shows the joint simulation verification and identification results of the four wheels when the road adhesion coefficient is 0.3;
图21是本公开实施例提供的一种端对端轮胎性能裕度辨识模型使用方法的流程图；Figure 21 is a flow chart of a method for using an end-to-end tire performance margin identification model provided by an embodiment of the present disclosure;
图22是本公开实施例提供的一种端对端轮胎性能裕度辨识模型建模装置的结构示意图；Figure 22 is a schematic structural diagram of an end-to-end tire performance margin identification model modeling device provided by an embodiment of the present disclosure;
图23是本公开实施例提供的一种端对端轮胎性能裕度辨识装置的结构示意图；Figure 23 is a schematic structural diagram of an end-to-end tire performance margin identification device provided by an embodiment of the present disclosure;
图24是本公开实施例提供的一种计算机设备2400的结构示意图。Figure 24 is a schematic structural diagram of a computer device 2400 provided by an embodiment of the present disclosure.
具体实施方式Detailed ways
现在将参考附图更全面地描述示例实施方式。然而，示例实施方式能够以多种形式实施，且不应被理解为限于在此阐述的范例；相反，提供这些实施方式使得本公开将更加全面和完整，并将示例实施方式的构思全面地传达给本领域的技术人员。Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in various forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concepts of the example embodiments. To those skilled in the art.
此外，所描述的特征、结构或特性可以以任何合适的方式结合在一个或更多实施方式中。在下面的描述中，提供许多具体细节从而给出对本公开的实施方式的充分理解。然而，本领域技术人员将意识到，可以实践本公开的技术方案而没有特定细节中的一个或更多，或者可以采用其它的方法、组元、装置、步骤等。在其它情况下，不详细示出或描述公知方法、装置、实现或者操作以避免模糊本公开的各方面。Furthermore, the described features, structures or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other instances, well-known methods, apparatus, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
附图中所示的方框图仅仅是功能实体，不一定必须与物理上独立的实体相对应。即，可以采用软件形式来实现这些功能实体，或在一个或多个硬件模块或集成电路中实现这些功能实体，或在不同网络和/或处理器装置和/或微控制器装置中实现这些功能实体。The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and/or processor devices and/or microcontroller devices. entity.
附图中所示的流程图仅是示例性说明，不是必须包括所有的内容和操作/步骤，也不是必须按所描述的顺序执行。例如，有的操作/步骤还可以分解，而有的操作/步骤可以合并或部分合并，因此实际执行的顺序有可能根据实际情况改变。The flowcharts shown in the drawings are only illustrative, and do not necessarily include all contents and operations/steps, nor must they be performed in the order described. For example, some operations/steps can be decomposed, and some operations/steps can be merged or partially merged, so the actual order of execution may change according to the actual situation.
在本公开实施例中，可以基于单轨车辆模型和长短期记忆网络模型等技术，获取车辆实验数据；根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度；将所述车辆实验数据进行轮胎性能裕度标注；将完成标注的所述车辆实验数据进行数据分段；将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模。In the embodiment of the present disclosure, vehicle experimental data can be obtained based on technologies such as a single-track vehicle model and a long-short-term memory network model; the performance margin corresponding to the tires of the vehicle can be obtained according to the vehicle experimental data; and the vehicle experimental data can be obtained based on the vehicle experimental data. The data is marked with tire performance margin; the marked vehicle experimental data is segmented into data segments; the segmented vehicle experimental data with completed annotation is trained using artificial intelligence methods to complete the end-to-end tire performance margin Degree recognition model modeling.
下面首先对本公开的一些术语进行说明：Some terms used in this disclosure are first explained below:
单轨车辆模型，又称二自由度动力学模型，通常用于表征车辆在稳定时的状态，该模型的输出，期望横摆角速度和期望质心侧偏角则反应的车辆稳态时的动力学变化，随着实际车辆的非线性程度增加，期望值与实际值的对比能直观的反应车辆非线性特性的变化，有利于深度学习网络提取特征。The single-track vehicle model, also known as the two-degree-of-freedom dynamic model, is usually used to characterize the vehicle's stable state. The output of the model, the expected yaw angular velocity and the expected center-of-mass slip angle, reflect the dynamic changes of the vehicle at steady state. , as the degree of nonlinearity of the actual vehicle increases, the comparison between the expected value and the actual value can intuitively reflect the changes in the nonlinear characteristics of the vehicle, which is conducive to feature extraction by the deep learning network.
长短期记忆网络模型(LSTM，Long short-term memory)，能够学习时间序列数据段中的信息，对长时间序列数据具有较好的学习能力。The long short-term memory network model (LSTM, Long short-term memory) can learn information in time series data segments and has good learning ability for long-term series data.
图1示出了可以应用本公开实施方式的端对端轮胎性能裕度辨识模型建模方法的示例性系统架构100的示意图。FIG. 1 shows a schematic diagram of an exemplary system architecture 100 to which an end-to-end tire performance margin identification model modeling method according to embodiments of the present disclosure may be applied.
如图1所示，系统架构100可以包括终端101、102、103中的一种或多种，网络104和服务器105。网络104是用以在终端101、102、103和服务器105之间提供通信链路的介质。网络104可以包括各种连接类型，例如有线、无线通信链路或者光纤电缆等等。As shown in Figure 1, the system architecture 100 may include one or more of terminals 101, 102, 103, a network 104 and a server 105. Network 104 is the medium used to provide communication links between terminals 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
应该理解，图1中的终端、网络和服务器的数目仅仅是示意性的。根据实现需要，可以具有任意数目的终端、网络和服务器。比如服务器105可以是多个服务器组成的服务器集群等。It should be understood that the number of terminals, networks and servers in Figure 1 is only illustrative. You can have any number of endpoints, networks, and servers depending on your implementation needs. For example, the server 105 may be a server cluster composed of multiple servers.
终端101、102、103通过网络104与服务器105交互，可以接收或发送消息等。终端101、102、103可以是具有显示屏的各种电子设备，包括但不限于智能手机、平板电脑、便携式计算机和台式计算机等等。The terminals 101, 102, and 103 interact with the server 105 through the network 104 and can receive or send messages, etc. The terminals 101, 102, and 103 can be various electronic devices with display screens, including but not limited to smart phones, tablet computers, portable computers, desktop computers, and so on.
服务器105可以是提供各种服务的服务器。例如工作人员利用终端设备103(也可以是终端设备101或102)向服务器105发送轮胎性能裕度的辨识模型的建模请求。服务器105可以获取车辆实验数据；根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度；将所述车辆实验数据进行轮胎性能裕度标注；将完成标注的所述车辆实验数据进行数据分段；将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模。服务器105可以将训练完成的轮胎性能裕度的辨识模型显示于终端设备103，进而工作人员可以基于终端设备103上显示的内容查看所述轮胎性能裕度的辨识模型。The server 105 may be a server that provides various services. For example, the staff uses the terminal device 103 (which may also be the terminal device 101 or 102) to send a modeling request for an identification model of the tire performance margin to the server 105. The server 105 can obtain the vehicle experiment data; obtain the performance margin corresponding to the tire of the vehicle according to the vehicle experiment data; label the vehicle experiment data with the tire performance margin; and perform the annotation on the vehicle experiment data. Data segmentation; train the segmented and annotated vehicle experimental data through artificial intelligence methods to complete the end-to-end tire performance margin identification model modeling. The server 105 can display the trained tire performance margin identification model on the terminal device 103, and then the staff can view the tire performance margin identification model based on the content displayed on the terminal device 103.
其中，终端可以是手机(如终端101)或平板电脑(如终端102)，还可以是台式计算机(如终端101)等，在此不做限制。其中，终端中可以显示应用程序，该应用程序可以是端对端轮胎性能裕度辨识模型建模的应用程序等。其中，图1中的终端仅为例举出的部分设备，在本公开中终端并不仅限于该图1中所例举的设备。The terminal may be a mobile phone (such as terminal 101), a tablet computer (such as terminal 102), or a desktop computer (such as terminal 101), etc., which is not limited here. Wherein, an application program may be displayed in the terminal, and the application program may be an application program for end-to-end tire performance margin identification model modeling, etc. The terminal in FIG. 1 is only an example of some of the equipment, and in the present disclosure, the terminal is not limited to the equipment illustrated in FIG. 1 .
可以理解的是，本公开实施例中所提及的终端可以是一种用户设备，本公开实施例中的服务器包括但不限于服务器或服务器组成的集群。其中，以上所提及的终端可以是一种电子设备，包括但不限于手机、平板电脑、智能语音交互设备、智能家电、车载终端、台式电脑、笔记本电脑、掌上电脑、车载设备、增强现实/虚拟现实(Augmented Reality/Virtual Reality，AR/VR)设备、头盔显示器、智能电视、可穿戴设备、智能音箱、数码相机、摄像头及其他具备网络接入能力的移动互联网设备(mobile internet device，MID)，或者火车、轮船、飞行等场景下的终端设备等。It can be understood that the terminal mentioned in the embodiment of the present disclosure may be a user equipment, and the server in the embodiment of the present disclosure includes but is not limited to a server or a cluster of servers. Among them, the terminal mentioned above can be an electronic device, including but not limited to mobile phones, tablet computers, intelligent voice interaction devices, smart home appliances, vehicle-mounted terminals, desktop computers, notebook computers, handheld computers, vehicle-mounted equipment, augmented reality/ Augmented Reality/Virtual Reality (AR/VR) equipment, helmet-mounted displays, smart TVs, wearable devices, smart speakers, digital cameras, cameras and other mobile internet devices (MID) with network access capabilities , or terminal equipment in scenarios such as trains, ships, flights, etc.
其中，以上所提及的服务器可以是提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、中间件服务、域名服务、安全服务、车路协同、内容分发网络(ContentDelivery Network，CDN)、以及大数据和人工智能平台等基础云计算服务的云服务器，还可以是独立的物理服务器，也可以是多个物理服务器构成的服务器集群或者分布式系统。Among them, the servers mentioned above can provide cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, vehicle-road collaboration, and content distribution networks ( Cloud servers for basic cloud computing services such as Content Delivery Network (CDN) and big data and artificial intelligence platforms can also be independent physical servers, or a server cluster or distributed system composed of multiple physical servers.
可选的，本公开实施例中所涉及的数据可以存储在云平台中，或者可以基于云存储技术、区块链技术对该数据进行存储，在此不做限制。Optionally, the data involved in the embodiments of the present disclosure can be stored in a cloud platform, or the data can be stored based on cloud storage technology or blockchain technology, which is not limited here.
轮胎状态无法通过传感器直接获取，通常需要先估计纵向速度、轮胎滑移率和轮胎力信息并基于这类信息进行再进行估计。此外，轮胎性能裕度信息也直接受到路面摩擦系数的影响，可以通过对路面摩擦系数的估计进而估计轮胎性能裕度，但路面摩擦系数也是无法直接获得的。Tire status cannot be obtained directly through sensors. It is usually necessary to estimate longitudinal speed, tire slip rate and tire force information first and then estimate based on this information. In addition, the tire performance margin information is also directly affected by the road friction coefficient. The tire performance margin can be estimated by estimating the road friction coefficient, but the road friction coefficient cannot be obtained directly.
图2是本公开实施例提供的一种端对端轮胎性能裕度辨识模型建模方法的流程图。本公开实施例提供的方法可以由图1实施例中的终端或服务器执行，或由终端和服务器交互执行。Figure 2 is a flow chart of an end-to-end tire performance margin identification model modeling method provided by an embodiment of the present disclosure. The method provided by the embodiment of the present disclosure can be executed by the terminal or the server in the embodiment of Figure 1, or interactively executed by the terminal and the server.
如图2所示，本公开实施例提供的方法可以包括如下步骤。As shown in Figure 2, the method provided by the embodiment of the present disclosure may include the following steps.
在步骤S210中，获取车辆实验数据。In step S210, vehicle experimental data is obtained.
在该步骤中，终端或服务器获取车辆实验数据。In this step, the terminal or server obtains vehicle experimental data.
其中，所述车辆实验数据包括横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩、期望横摆角速度和期望质心侧偏角中的一项或多项。其中，四个车轮轮速、四个车轮力矩属于轮胎处的数据。车辆实验数据例如可以通过实验获取。Wherein, the vehicle experimental data includes one or more of yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, four wheel moments, desired yaw angular velocity and desired center of mass side slip angle. . Among them, the four wheel speeds and the four wheel torques belong to the data of the tires. Vehicle test data can be obtained through experiments, for example.
在步骤S220中，根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度。In step S220, a performance margin corresponding to the tires of the vehicle is obtained according to the vehicle experimental data.
在该步骤中，终端或服务器根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度。In this step, the terminal or server obtains the performance margin corresponding to the tires of the vehicle based on the vehicle experimental data.
其中，在一个实施例中，根据所述车辆实验数据获取与所述车辆的轮胎对应的线性区域、过渡区域、饱和区域或滑移区域的性能裕度。例如某一时刻的一组数据，例如横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩、期望横摆角速度和期望质心侧偏角中的一项或多项所对应的轮胎对应的线性区域、过渡区域、饱和区域或滑移区域的性能裕度。In one embodiment, the performance margin of the linear region, transition region, saturation region or slip region corresponding to the tire of the vehicle is obtained according to the vehicle experimental data. For example, a set of data at a certain moment, such as one of yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, four wheel moments, desired yaw angular velocity and desired center of mass slip angle, or The performance margin of the linear region, transition region, saturation region or slip region of the tire corresponding to the multiple items.
在步骤S230中，将所述车辆实验数据进行轮胎性能裕度标注。In step S230, the vehicle experimental data is labeled with tire performance margin.
在该步骤中，终端或服务器将所述车辆实验数据进行轮胎性能裕度标注。In this step, the terminal or server labels the tire performance margin on the vehicle experimental data.
在步骤S240中，将完成标注的所述车辆实验数据进行数据分段。In step S240, the annotated vehicle experimental data is segmented.
在该步骤中，终端或服务器将完成标注的所述车辆实验数据进行数据分段。In this step, the terminal or server performs data segmentation on the annotated vehicle experimental data.
其中，在一个实施例中，基于滑动时间窗将完成标注的所述车辆实验数据进行数据分段。In one embodiment, the annotated vehicle experimental data is segmented based on a sliding time window.
在步骤S250中，将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模。In step S250, the segmented and annotated vehicle experimental data is trained using an artificial intelligence method to complete end-to-end tire performance margin identification model modeling.
在该步骤中，终端或服务器将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模。In this step, the terminal or server trains the segmented and annotated vehicle experimental data through artificial intelligence methods to complete the end-to-end tire performance margin identification model modeling.
其中，在一个实施例中，将标注分段后的完成的所述车辆实验数据通过长短期记忆网络模型LSTM进行训练，以完成端对端轮胎性能裕度辨识模型建模。In one embodiment, the segmented vehicle experimental data is trained through the long short-term memory network model LSTM to complete the end-to-end tire performance margin identification model modeling.
图2的端对端轮胎性能裕度辨识模型建模方法，通过获取车辆实验数据；根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度；将所述车辆实验数据进行轮胎性能裕度标注；将完成标注的所述车辆实验数据进行数据分段；将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模，能够完成辨识轮胎性能裕度的端对端轮胎性能裕度辨识模型的建模。The end-to-end tire performance margin identification model modeling method in Figure 2 obtains vehicle experimental data; obtains the performance margin corresponding to the tire of the vehicle according to the vehicle experimental data; and performs tire performance on the vehicle experimental data margin annotation; perform data segmentation on the annotated vehicle experimental data; train the segmented annotated vehicle experimental data through artificial intelligence methods to complete the end-to-end tire performance margin identification model construction The model can complete the modeling of the end-to-end tire performance margin identification model for identifying tire performance margin.
图3是本公开实施例提供的一种获取车辆实验数据方法的流程图。本公开实施例提供的方法可以由图1实施例中的终端或服务器执行，或由终端和服务器交互执行。其中，所述车辆实验数据包括横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩、期望横摆角速度和期望质心侧偏角中的一项或多项Figure 3 is a flow chart of a method for obtaining vehicle experimental data provided by an embodiment of the present disclosure. The method provided by the embodiment of the present disclosure can be executed by the terminal or the server in the embodiment of Figure 1, or interactively executed by the terminal and the server. Wherein, the vehicle experimental data includes one or more of yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, four wheel moments, desired yaw angular velocity and desired center of mass side slip angle.
如图3所示，本公开实施例提供的方法可以包括如下步骤。As shown in Figure 3, the method provided by the embodiment of the present disclosure may include the following steps.
在步骤S310中，通过所述车辆的传感器获取横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩中的一项或多项。In step S310, one or more of the yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, and four wheel moments are obtained through the vehicle's sensors.
在该步骤中，终端或服务器通过所述车辆的传感器获取横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩中的一项或多项。In this step, the terminal or server obtains one or more of the yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, and four wheel moments through the vehicle's sensors.
其中，横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩属于可以直接通过车辆传感器获得的数据。Among them, yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, and four wheel moments are data that can be obtained directly through vehicle sensors.
在步骤S320中，通过单轨车辆模型获取期望横摆角速度和期望质心侧偏角中的一项或多项。In step S320, one or more of the expected yaw angular velocity and the expected center-of-mass slip angle are obtained through the single-rail vehicle model.
在该步骤中，终端或服务器通过单轨车辆模型获取期望横摆角速度和期望质心侧偏角中的一项或多项。In this step, the terminal or server obtains one or more of the expected yaw angular velocity and the expected center-of-mass slip angle through the monorail vehicle model.
单轨车辆模型的输出是期望横摆角速度和期望质心侧偏角，反应车辆稳态时的动力学变化，随着实际车辆的非线性程度增加，期望横摆角速度和期望质心侧偏角与实际值的对比能直观的反应车辆非线性特性的变化，有利于深度学习网络提取特征。期望横摆角速度和期望质心侧偏角与实际值存在不定的时间迟滞，以往的数据很难使网络模型学习到存在的不定的时间迟滞。时间迟滞主要是期望横摆角速度和期望质心侧偏角与实际的存在迟滞。The output of the monorail vehicle model is the expected yaw angular velocity and the expected center of mass side slip angle, which reflect the dynamic changes of the vehicle in the steady state. As the degree of nonlinearity of the actual vehicle increases, the expected yaw angular velocity and the expected center of mass side slip angle are different from the actual values. The comparison can intuitively reflect the changes in the nonlinear characteristics of the vehicle, which is conducive to feature extraction by the deep learning network. There is an uncertain time lag between the expected yaw angular velocity and the expected center-of-mass side slip angle and the actual values. It is difficult for the network model to learn the existing uncertain time lag from past data. The time lag is mainly due to the lag between the desired yaw angular velocity and desired center-of-mass side slip angle and the actual one.
图4是本公开实施例提供的一种获单轨车辆模型示意图。Figure 4 is a schematic diagram of a monorail vehicle model provided by an embodiment of the present disclosure.
参考图4，为所述基于单轨车辆模型和车载传感器的信号使用的单轨车辆模型，该模型的侧向和横摆方向的运动可以表达为如下公式(1)和公式(2)：Referring to Figure 4, the monorail vehicle model is used based on the signals of the monorail vehicle model and on-board sensors. The movement of the model in the lateral and yaw directions can be expressed as the following formulas (1) and formula (2):
其中，CoG为质心，β为质心侧偏角，γ为横摆角速度，β和γ上的点表示求导，m为簧载质量，Vx为纵向车速，Fyf,Fyr分别为前轴侧向力和后轴侧向力，Fyf＝Kαfαf，Fyr＝Kαrαr，l/f为质心到前轴的距离，lr为质心到后轴的距离，Iz为质心处转动惯量；单轨车辆模型通常假设车速为常数，并基于小角度假设sinδ≈0，cosδ≈1。轮胎的侧偏角定义如下公式(3)和公式(4)：Among them, CoG is the center of mass, β is the side slip angle of the center of mass, γ is the yaw angular velocity, the points on β and γ represent derivation, m is the sprung mass, Vx is the longitudinal speed, Fyf and Fyr are the front axle respectively. Lateral force and rear axle lateral force, Fyf =Kαf αf , Fyr =Kαr αr , l/f is the distance from the center of mass to the front axle, lr is the distance from the center of mass to the rear axle, Iz is Moment of inertia at the center of mass; monorail vehicle models usually assume that the vehicle speed is constant, and assume sinδ≈0 and cosδ≈1 based on small angles. The tire slip angle is defined as follows formula (3) and formula (4):
αf为前轮侧偏角，αr为后轮侧偏角，δ为车轮转角；因此状态空间方程可以表达为如下公式(5)：αf is the front wheel slip angle, αr is the rear wheel slip angle, and δ is the wheel rotation angle; therefore, the state space equation can be expressed as the following formula (5):
其中，a11＝-2(Kαf+Kαr/mVx)，a12＝2(lrKαr+lfKαf/mVx2)-1，a21＝2(lrKαr+lfKαf/Iz)，a22＝-2(lr2Kαf+lr2Kαr/IzVx)，d1＝2Kαf/mVx，d2＝2lfKαf/Iz，Kαf和Kαr分别为前后轮胎的转向刚度，通过式(5)可以得到期望横摆角速度和期望质心侧偏角。Among them, a11 =-2(Kαf +Kαr /mVx ), a12 =2(lr Kαr +lf Kαf /mVx2 )-1, a21 =2(lr Kαr + lf Kαf /Iz ), a22 =-2(lr2 Kαf +lr2 Kαr /Iz Vx ), d1 =2Kαf /mVx , d2 =2lf Kαf / Iz , Kαf and Kαr are the steering stiffness of the front and rear tires respectively. The expected yaw angular velocity and expected center of mass side slip angle can be obtained through Equation (5).
图5是本公开实施例提供的一种获单轨车辆模型的期望横摆角速度、期望质心侧偏角、真实横摆角速度和真实质心侧偏角的示意图。FIG. 5 is a schematic diagram for obtaining the expected yaw angular velocity, expected center of mass side slip angle, real center of mass side slip angle, and real center of mass side slip angle of a monorail vehicle model provided by an embodiment of the present disclosure.
参考图5，第一行中，虚线表示期望横摆角速度，实线表示真实的横摆角速度；第二行中，虚线表示期望质心侧偏角，实线表示真实质心侧偏角；第三行表示轮胎性能裕度，1表示线性区域，2表示过渡区域，3表示饱和区域，4表示滑移区域。参考图5中，真实横摆角速度与期望横摆角速度存在一定滞后；真实质心侧偏角和期望质心侧偏角存在一定滞后。Referring to Figure 5, in the first row, the dotted line represents the expected yaw angular velocity, and the solid line represents the real yaw angular velocity; in the second row, the dotted line represents the desired center of mass side slip angle, and the solid line represents the real center of mass side slip angle; in the third row, Indicates the tire performance margin, 1 represents the linear region, 2 represents the transition region, 3 represents the saturation region, and 4 represents the slip region. Referring to Figure 5, there is a certain lag between the true yaw angular velocity and the desired yaw angular velocity; there is a certain lag between the true center of mass side slip angle and the desired center of mass side slip angle.
图6是本公开实施例提供的一种基于滑动时间窗将完成标注的所述车辆实验数据进行数据分段方法的流程图。本公开实施例提供的方法可以由图1实施例中的终端或服务器执行，或由终端和服务器交互执行。FIG. 6 is a flow chart of a method for segmenting the annotated vehicle experimental data based on a sliding time window according to an embodiment of the present disclosure. The method provided by the embodiment of the present disclosure can be executed by the terminal or the server in the embodiment of Figure 1, or interactively executed by the terminal and the server.
如图6所示，本公开实施例提供的方法可以包括如下步骤。As shown in Figure 6, the method provided by the embodiment of the present disclosure may include the following steps.
在步骤S610中，以N作为分段步长，其中N是大于等于2的自然数。In step S610, N is used as the segment step size, where N is a natural number greater than or equal to 2.
在该步骤中，终端或服务器以N作为分段步长，其中N是大于等于2的自然数。In this step, the terminal or server uses N as the segment step size, where N is a natural number greater than or equal to 2.
在步骤S220中，将所述车辆实验数据前补充N-1个全零数据。In step S220, add N-1 all-zero data before the vehicle experimental data.
在该步骤中，终端或服务器将所述车辆实验数据前补充N-1个全零数据。例如，车辆实验数据为12秒采集的1200个或组实验数据时，取N为100，在1200个或组实验数据前补充99个或组全零数据。In this step, the terminal or server adds N-1 all-zero data before the vehicle experimental data. For example, when the vehicle experimental data is 1200 pieces or groups of experimental data collected in 12 seconds, take N as 100, and add 99 pieces or groups of all-zero data before the 1200 pieces or groups of experimental data.
在步骤S430中，以N为单位顺序基于滑动时间窗将所述车辆实验数据进行成段；其中，基于滑动时间窗将所述车辆实验数据将分成的段数与所述车辆实验数据的数量相等。在该步骤中，终端或服务器以N为单位顺序基于滑动时间窗将所述车辆实验数据进行成段。In step S430, the vehicle experimental data is sequentially divided into segments based on the sliding time window in units of N; wherein the number of segments into which the vehicle experimental data is divided based on the sliding time window is equal to the number of the vehicle experimental data. In this step, the terminal or server sequentially divides the vehicle experimental data into segments based on the sliding time window in units of N.
其中，基于滑动时间窗将所述车辆实验数据将分成的段数与所述车辆实验数据的数量相等。Wherein, the number of segments into which the vehicle experimental data is divided based on the sliding time window is equal to the number of the vehicle experimental data.
例如，车辆实验数据为12秒采集的1200个或组实验数据时，取N为100，在1200个或组实验数据前补充99个或组全零数据。则以N为单位顺序基于滑动时间窗将所述车辆实验数据进行成段中，第一段数据为在1200个或组实验数据前补充99个或组全零数据加上1200个或组实验数据的第一个或组数据；第二段数据为在1200个或组实验数据前补充98个或组全零数据加上1200个或组实验数据的第一个或组数据以及第二个或组数据；依次类推，将1200个或组实验数据分成1200个数据段，每个数据段的最后一个或组数据彼此不同，可以使每个或组数据都得到训练。For example, when the vehicle experimental data is 1200 pieces or groups of experimental data collected in 12 seconds, take N as 100, and add 99 pieces or groups of all-zero data before the 1200 pieces or groups of experimental data. Then the vehicle experimental data is divided into segments based on the sliding time window in order of N units. The first segment of data is to supplement 99 or groups of all-zero data before 1200 or groups of experimental data plus 1200 or groups of experimental data. The first or group of data; the second period of data is the first or group of data and the second or group of 98 or groups of all-zero data plus 1200 or groups of experimental data before 1200 or groups of experimental data. Data; and by analogy, 1200 or groups of experimental data are divided into 1200 data segments. The last data or group of data in each data segment is different from each other, so that each or group of data can be trained.
图7是本公开实施例提供的一种基于滑动时间窗将完成标注的所述车辆实验数据进行数据分段方法的示意图。FIG. 7 is a schematic diagram of a data segmentation method for the annotated vehicle experimental data based on a sliding time window provided by an embodiment of the present disclosure.
在一个实施例中，以每段所述车辆实验数据中最后一个或一组数据对应的性能裕度作为该段所述车辆实验数据的性能裕度。In one embodiment, the performance margin corresponding to the last one or a group of data in each segment of the vehicle experimental data is used as the performance margin of the vehicle experimental data in that segment.
图8是本公开实施例提供的一种完成数据分类后的车辆实验数据示意图。Figure 8 is a schematic diagram of vehicle experimental data after data classification is completed according to an embodiment of the present disclosure.
参考附图8，将数据分类为四个区域：线性区域(倒三角)、过渡区域(×号)、饱和区域(菱形)和滑移区域(圆形)从而对数据打上标签。Referring to Figure 8, the data is classified into four areas: linear area (inverted triangle), transition area (× sign), saturation area (diamond) and slip area (circle) to label the data.
图9是本公开实施例提供的基于深度学习的端对端训练的主要网络架构示意图。Figure 9 is a schematic diagram of the main network architecture of end-to-end training based on deep learning provided by an embodiment of the present disclosure.
参阅附图9，为基于深度学习的端对端训练模块的主要网络架构，分别为序列输入层，主要负责数据集的输入，在本公开中输入特征为车载传感器的和单轨车辆模型的期望值的不同信号；LSTM层即长短期记忆网络层，能够学习时间序列数据段中的信息，对长时间序列数据具有较好的学习能力。参阅附图5，期望值与实际值间存在一时间迟滞，这一间隔随车辆状态发生变化，很难通过公式表达，本公开通过使用LSTM算法对长时间序列数据的学习能力，学习这一时间迟滞；批量标准化层主要对每个通道小批量数据观测值进行标准化，这样能够加快卷积神经网络的训练速度，降低训练对初始化的敏感性；全连接层起到分类器的作用，将学习到的分布式特征整合到一起输出；sigmoid层则为全连接层的延续，将全连接层的输出转换为0到1间的数值，并保证总和为1；分类层则是通过计算较差熵损失输出最终的类别。Refer to Figure 9, which shows the main network architecture of the end-to-end training module based on deep learning. They are sequence input layers, which are mainly responsible for the input of data sets. In this disclosure, the input features are the expected values of on-board sensors and monorail vehicle models. Different signals; the LSTM layer is the long short-term memory network layer, which can learn the information in the time series data segment and has good learning ability for long-term series data. Referring to Figure 5, there is a time lag between the expected value and the actual value. This interval changes with the vehicle status and is difficult to express through a formula. This disclosure uses the LSTM algorithm to learn this time lag on long-term sequence data. ; The batch normalization layer mainly standardizes the small batch data observations of each channel, which can speed up the training speed of the convolutional neural network and reduce the sensitivity of training to initialization; the fully connected layer plays the role of a classifier and converts the learned The distributed features are integrated together and output; the sigmoid layer is a continuation of the fully connected layer, converting the output of the fully connected layer into a value between 0 and 1, and ensuring that the sum is 1; the classification layer is output by calculating the poor entropy loss final category.
图10为左前轮测试集误分类情况示意图。Figure 10 is a schematic diagram of the misclassification situation of the left front wheel test set.
基于深度学习的端对端训练模块的数据集按70％、15％和15％划分为训练集，验证集和测试集。训练集用于网络训练建立端对端轮胎性能裕度辨识模型，验证集在训练过程进行验证防止训练过拟合的发生，测试集用于测试训练结束得到的模型，测试模型准确率、精准率、召回率和F1-score等系数。参阅附图10，为测试集左前轮误分类情况，四个区域误分类都低于6％，模型具有较高的精度。The data set of the end-to-end training module based on deep learning is divided into training set, validation set and test set by 70%, 15% and 15%. The training set is used for network training to establish an end-to-end tire performance margin identification model. The verification set is verified during the training process to prevent the occurrence of training overfitting. The test set is used to test the model obtained after training to test the accuracy and accuracy of the model. , recall rate and F1-score and other coefficients. Refer to Figure 10, which shows the misclassification of the left front wheel in the test set. The misclassification in the four areas is less than 6%, and the model has high accuracy.
图11为左前轮测试集轮胎性能裕度辨识结果在不同路面摩擦系数下的侧偏角和侧向力关系的三维示意图。Figure 11 is a three-dimensional schematic diagram of the relationship between side slip angle and lateral force under different road friction coefficients under the tire performance margin identification results of the left front wheel test set.
图12为左前轮测试集轮胎性能裕度辨识结果在不同路面摩擦系数下的滑移率和纵向力关系的三维示意图。Figure 12 is a three-dimensional schematic diagram of the relationship between slip rate and longitudinal force under different road friction coefficients under the tire performance margin identification results of the left front wheel test set.
图13为左前轮测试集轮胎性能裕度辨识结果在路面摩擦系数0.3下的归一化纵向力和归一化侧向力关系的示意图。Figure 13 is a schematic diagram of the relationship between the normalized longitudinal force and the normalized lateral force under the road friction coefficient of 0.3 under the tire performance margin identification results of the left front wheel test set.
图14为左前轮测试集轮胎性能裕度辨识结果在路面摩擦系数0.6下的归一化纵向力和归一化侧向力关系的示意图。Figure 14 is a schematic diagram of the relationship between the normalized longitudinal force and the normalized lateral force under the road friction coefficient of 0.6 under the tire performance margin identification results of the left front wheel test set.
图15为左前轮测试集轮胎性能裕度辨识结果在路面摩擦系数1.0下的归一化纵向力和归一化侧向力关系的示意图。Figure 15 is a schematic diagram of the relationship between the normalized longitudinal force and the normalized lateral force under the road friction coefficient of 1.0 under the tire performance margin identification results of the left front wheel test set.
图16为四个车轮测试集轮胎性能裕度辨识结果在不同路面摩擦系数下的总滑移率和归一化力关系的三维示意图。Figure 16 is a three-dimensional schematic diagram of the relationship between the total slip rate and the normalized force under different road friction coefficients under the tire performance margin identification results of the four wheel test set.
图17为联合仿真验证质心处特征变化规律。Figure 17 shows the joint simulation verification feature change pattern at the center of mass.
图18为路面附着系数为1.0时的四个车轮联合仿真验证辨识结果。Figure 18 shows the joint simulation verification and identification results of the four wheels when the road adhesion coefficient is 1.0.
图19为路面附着系数为0.6时的四个车轮联合仿真验证辨识结果。Figure 19 shows the joint simulation verification and identification results of the four wheels when the road adhesion coefficient is 0.6.
图20为路面附着系数为0.3时的四个车轮联合仿真验证辨识结果。Figure 20 shows the joint simulation verification and identification results of the four wheels when the road adhesion coefficient is 0.3.
参阅附图11和附图12，为所述数据集中测试集的测试结果绘制的三维视图，这里以左前轮为例，分别为代表三种摩擦系数下纯侧偏工况轮胎侧向力-侧偏角图和代表纯纵滑工况轮胎纵向力-滑移率图。测试结果显示训练好的模型能够以较高的精确率将纯工况下的轮胎性能裕度划分为四类。参阅附图13，附图14和附图15，分别为复合工况下路面摩擦系数为0.3，0.6和1.0情况下轮胎归一化侧向力和归一化纵向力图，图像证明了模型在复合工况下的准确率。参阅附图16为四个轮胎的测试集的全部工况测试结果。Refer to Figure 11 and Figure 12, which are three-dimensional views of the test results of the test set in the data set. Taking the left front wheel as an example, they represent the tire lateral force in pure cornering conditions under three friction coefficients - The side slip angle diagram and the tire longitudinal force-slip ratio diagram represent pure longitudinal slip conditions. The test results show that the trained model can classify the tire performance margin under pure working conditions into four categories with a high accuracy. Refer to Figure 13, Figure 14 and Figure 15, respectively, which are the normalized lateral force and normalized longitudinal force diagrams of the tire when the road friction coefficient is 0.3, 0.6 and 1.0 under the composite working conditions. The images prove that the model is effective in the composite accuracy under working conditions. Refer to Figure 16 for the test results of all working conditions of the four tire test sets.
参阅附图17至图20，展示了本申请所述的端对端轮胎性能裕度辨识系数的泛化能力。图17展示了输入网络的质心处信号变化，图18至图20展示了模型对未遇见过的工况的辨识能力，该测试为联合仿真测试所有数据均为实时传输，证明其能够用于实时的轮胎性能裕度辨识。在路面摩擦系数分别为1.0，0.6和0.3的工况下均展现的很高的辨识精度，对训练集中未出现的数据表现出了较高的辨识精确率，泛化能力较强。Referring to Figures 17 to 20 of the accompanying drawings, the generalization ability of the end-to-end tire performance margin identification coefficient described in this application is demonstrated. Figure 17 shows the signal changes at the center of mass of the input network. Figures 18 to 20 show the model’s ability to identify unencountered working conditions. This test is a joint simulation test. All data are transmitted in real time, proving that it can be used in real time. Identification of tire performance margin. It shows high recognition accuracy under the conditions where the road friction coefficient is 1.0, 0.6 and 0.3 respectively. It shows high recognition accuracy for data that does not appear in the training set and has strong generalization ability.
图21是本公开实施例提供的一种端对端轮胎性能裕度辨识模型使用方法的流程图。本公开实施例提供的方法可以由车辆里的控制器执行。Figure 21 is a flow chart of a method for using an end-to-end tire performance margin identification model provided by an embodiment of the present disclosure. The method provided by the embodiment of the present disclosure can be executed by the controller in the vehicle.
如图21所示，本公开实施例提供的方法可以包括如下步骤。As shown in Figure 21, the method provided by the embodiment of the present disclosure may include the following steps.
在步骤S2110中，获取车辆数据。In step S2110, vehicle data is obtained.
在该步骤中，车辆里的控制器获取车辆数据。In this step, the controller in the vehicle obtains vehicle data.
其中，车辆数据例如可以包括横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩、期望横摆角速度和期望质心侧偏角中的一项或多项。期望横摆角速度和期望质心侧偏角例如可以通过车辆里的控制器的单轨车辆模型获取。横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩例如通过车载传感器直接获取。The vehicle data may include, for example, one or more of yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, four wheel moments, desired yaw angular velocity and desired center of mass slip angle. The desired yaw rate and the desired center-of-mass slip angle can be obtained, for example, from a single-track vehicle model of the controller in the vehicle. Yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, and four wheel moments are directly obtained through on-board sensors, for example.
在一个实施例中，例如通过车辆总线获取车辆数据。In one embodiment, the vehicle data is acquired via a vehicle bus, for example.
在步骤S2120中，将所述车辆数据输入至完成训练的端对端轮胎性能裕度辨识模型以获取轮胎的性能裕度。；In step S2120, the vehicle data is input into the trained end-to-end tire performance margin identification model to obtain the performance margin of the tire. ;
其中，所述端对端轮胎性能裕度辨识模型是通过如上实施例中任一方法获取的模型。在该步骤中，车辆里的控制器将所述车辆数据输入至完成训练的端对端轮胎性能裕度辨识模型以获取轮胎的性能裕度；其中，所述端对端轮胎性能裕度辨识模型是通过如上实施例中任一方法获取的模型。Wherein, the end-to-end tire performance margin identification model is a model obtained through any method in the above embodiments. In this step, the controller in the vehicle inputs the vehicle data into the trained end-to-end tire performance margin identification model to obtain the tire performance margin; wherein, the end-to-end tire performance margin identification model It is a model obtained through any method in the above embodiments.
图22是本公开实施例提供的一种端对端轮胎性能裕度辨识模型建模装置的结构示意图。Figure 22 is a schematic structural diagram of an end-to-end tire performance margin identification model modeling device provided by an embodiment of the present disclosure.
如图22所示，本公开实施例提供的端对端轮胎性能裕度辨识模型建模装置2200可以包括：As shown in Figure 22, the end-to-end tire performance margin identification model modeling device 2200 provided by the embodiment of the present disclosure may include:
第一获取模块2210，配置为获取车辆实验数据；The first acquisition module 2210 is configured to acquire vehicle experimental data;
第二获取模块2220，配置为根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度；The second acquisition module 2220 is configured to acquire the performance margin corresponding to the tires of the vehicle according to the vehicle experimental data;
标注模块2230，配置为将所述车辆实验数据进行轮胎性能裕度标注；The annotation module 2230 is configured to annotate the tire performance margin of the vehicle experimental data;
分段模块2240，配置为将完成标注的所述车辆实验数据进行数据分段；The segmentation module 2240 is configured to segment the vehicle experimental data that has been annotated;
训练模块2250，配置为将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模。The training module 2250 is configured to train the segmented and annotated vehicle experimental data through artificial intelligence methods to complete the end-to-end tire performance margin identification model modeling.
在一个实施例中，所述车辆实验数据包括横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩、期望横摆角速度和期望质心侧偏角中的一项或多项；第一获取模块2210，配置为通过所述车辆的传感器获取横摆角速度、方向盘转角、纵向加速度、侧向加速度、四个车轮轮速、四个车轮力矩中的一项或多项；通过单轨车辆模型获取期望横摆角速度和期望质心侧偏角中的一项或多项。In one embodiment, the vehicle experimental data includes one of yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, four wheel moments, desired yaw angular velocity and desired center of mass slip angle. One or more items; the first acquisition module 2210 is configured to acquire one or more of yaw angular velocity, steering wheel angle, longitudinal acceleration, lateral acceleration, four wheel speeds, and four wheel moments through the vehicle's sensors. item; obtain one or more of the desired yaw angular velocity and desired center-of-mass side slip angle through the monorail vehicle model.
在一个实施例中，第二获取模块2220，配置为根据所述车辆实验数据获取与所述车辆的轮胎对应的线性区域、过渡区域、饱和区域或滑移区域的性能裕度。In one embodiment, the second acquisition module 2220 is configured to acquire the performance margin of the linear region, transition region, saturation region or slip region corresponding to the tire of the vehicle according to the vehicle experimental data.
在一个实施例中，分段模块2230，配置为基于滑动时间窗将完成标注的所述车辆实验数据进行数据分段。In one embodiment, the segmentation module 2230 is configured to segment the annotated vehicle experimental data based on a sliding time window.
在一个实施例中，分段模块2230，配置为以N作为分段步长，其中N是大于等于2的自然数；将完成标注的所述车辆实验数据前补充N-1个全零数据；以N为单位顺序基于滑动时间窗将完成标注的所述车辆实验数据进行成段；其中，基于滑动时间窗将完成标注的所述车辆实验数据将分成的段数与所述车辆实验数据的数量相等。In one embodiment, the segmentation module 2230 is configured to use N as the segmentation step size, where N is a natural number greater than or equal to 2; supplement N-1 all-zero data before completing the labeled vehicle experimental data; N is the unit sequence for dividing the annotated vehicle experimental data into segments based on the sliding time window; wherein the number of segments that the annotated vehicle experimental data is divided into based on the sliding time window is equal to the number of the vehicle experimental data.
在一个实施例中，标注模块2240，配置为以每段所述车辆实验数据中最后一个或一组数据对应的性能裕度作为该段所述车辆实验数据的性能裕度。In one embodiment, the annotation module 2240 is configured to use the performance margin corresponding to the last one or a group of data in each segment of the vehicle experimental data as the performance margin of the vehicle experimental data in the segment.
在一个实施例中，训练模块2250，配置为将分段后的标注完成的所述车辆实验数据通过长短期记忆网络模型LSTM进行训练，以完成端对端轮胎性能裕度辨识模型建模。In one embodiment, the training module 2250 is configured to train the segmented and annotated vehicle experimental data through the long short-term memory network model LSTM to complete the end-to-end tire performance margin identification model modeling.
本申请的端对端轮胎性能裕度辨识模型建模装置，通过第一获取模块，配置为获取车辆实验数据；第二获取模块，配置为根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度；标注模块，配置为将所述车辆实验数据进行轮胎性能裕度标注；分段模块，配置为将完成标注的所述车辆实验数据进行数据分段；训练模块，配置为将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模，可以实现端对端轮胎性能裕度辨识模型的建模。The end-to-end tire performance margin identification model modeling device of the present application is configured to obtain vehicle experimental data through the first acquisition module; the second acquisition module is configured to acquire the tire corresponding to the vehicle according to the vehicle experimental data. performance margin; the annotation module is configured to annotate the tire performance margin of the vehicle experimental data; the segmentation module is configured to segment the annotated vehicle experimental data; the training module is configured to segment the vehicle experimental data. The annotated vehicle experimental data after the segment is trained through artificial intelligence methods to complete the modeling of the end-to-end tire performance margin identification model, which can realize the modeling of the end-to-end tire performance margin identification model.
图23是本公开实施例提供的一种端对端轮胎性能裕度辨识装置的结构示意图。本公开实施例提供的装置可以安装在车辆里的控制器中。Figure 23 is a schematic structural diagram of an end-to-end tire performance margin identification device provided by an embodiment of the present disclosure. The device provided by the embodiment of the present disclosure can be installed in a controller in a vehicle.
如图23所示，本公开实施例提供的端对端轮胎性能裕度辨识装置2300可以包括：As shown in Figure 23, the end-to-end tire performance margin identification device 2300 provided by the embodiment of the present disclosure may include:
第三获取模块2310，配置为获取车辆数据；The third acquisition module 2310 is configured to acquire vehicle data;
识别模块2320，配置为将所述车辆数据输入至完成训练的端对端轮胎性能裕度辨识模型以获取轮胎的性能裕度；The identification module 2320 is configured to input the vehicle data into the trained end-to-end tire performance margin identification model to obtain the performance margin of the tire;
其中，所述端对端轮胎性能裕度辨识模型是通过如上实施例中任一方法所述获取的模型。Wherein, the end-to-end tire performance margin identification model is a model obtained by any method as described in the above embodiments.
参见图24，图24是本公开实施例提供的一种计算机设备2400的结构示意图。如图24所示，本公开实施例中的计算机设备可以包括：一个或多个处理器2401、存储器2402和输入输出接口2403。该处理器2401、存储器2402和输入输出接口2403通过总线2404连接。存储器2402用于存储计算机程序，该计算机程序包括程序指令，输入输出接口2403用于接收数据及输出数据，如用于宿主机与计算机设备之间进行数据交互，或者用于在宿主机中的各个虚拟机之间进行数据交互；处理器2401用于执行存储器2402存储的程序指令。Referring to Figure 24, Figure 24 is a schematic structural diagram of a computer device 2400 provided by an embodiment of the present disclosure. As shown in Figure 24, the computer device in the embodiment of the present disclosure may include: one or more processors 2401, a memory 2402, and an input and output interface 2403. The processor 2401, the memory 2402 and the input/output interface 2403 are connected through a bus 2404. The memory 2402 is used to store computer programs, which include program instructions. The input and output interface 2403 is used to receive data and output data, such as for data interaction between the host and computer equipment, or for each device in the host. Data exchange occurs between virtual machines; the processor 2401 is used to execute program instructions stored in the memory 2402.
其中，该处理器2401可以执行如下操作：Among them, the processor 2401 can perform the following operations:
获取车辆实验数据；根据所述车辆实验数据获取与所述车辆的轮胎对应的性能裕度；将所述车辆实验数据进行轮胎性能裕度标注；将完成标注的所述车辆实验数据进行数据分段；将分段后的标注完成的所述车辆实验数据通过人工智能方法进行训练，以完成端对端轮胎性能裕度辨识模型建模。Obtaining vehicle experimental data; obtaining performance margins corresponding to the tires of the vehicle according to the vehicle experimental data; labeling the vehicle experimental data with tire performance margins; performing data segmentation on the labeled vehicle experimental data ; Train the segmented and annotated vehicle experimental data through artificial intelligence methods to complete the end-to-end tire performance margin identification model modeling.
或执行如下操作：Or do the following:
获取车辆数据；将所述车辆数据输入至完成训练的端对端轮胎性能裕度辨识模型以获取轮胎的性能裕度。Obtain vehicle data; input the vehicle data into the trained end-to-end tire performance margin identification model to obtain the performance margin of the tire.
在一些可行的实施方式中，该处理器2401可以是中央处理单元(centralprocessing unit，CPU)，该处理器还可以是其他通用处理器、数字信号处理器(digitalsignal processor，DSP)、专用集成电路(application specific integrated circuit，ASIC)、现成可编程门阵列(field-programmable gate array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。In some feasible implementations, the processor 2401 can be a central processing unit (CPU), and the processor can also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits ( application specific integrated circuit (ASIC), ready-made field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.
该存储器2402可以包括只读存储器和随机存取存储器，并向处理器2401和输入输出接口2403提供指令和数据。存储器2402的一部分还可以包括非易失性随机存取存储器。例如，存储器2402还可以存储设备类型的信息。The memory 2402 may include read-only memory and random access memory, and provides instructions and data to the processor 2401 and the input-output interface 2403. A portion of memory 2402 may also include non-volatile random access memory. For example, memory 2402 may also store device type information.
具体实现中，该计算机设备可通过其内置的各个功能模块执行如上述实施例中各个步骤所提供的实现方式，具体可参见上述实施例中各个步骤所提供的实现方式，在此不再赘述。In specific implementation, the computer device can execute the implementation provided by each step in the above embodiment through its built-in functional modules. For details, please refer to the implementation provided by each step in the above embodiment, which will not be described again here.
本公开实施例通过提供一种计算机设备，包括：处理器、输入输出接口、存储器，通过处理器获取存储器中的计算机程序，执行上述实施例中所示方法的各个步骤，进行传输操作。The embodiment of the present disclosure provides a computer device, including: a processor, an input and output interface, and a memory. The processor obtains the computer program in the memory, executes each step of the method shown in the above embodiment, and performs a transmission operation.
本公开实施例还提供一种计算机可读存储介质，该计算机可读存储介质存储有计算机程序，该计算机程序适于由该处理器加载并执行上述实施例中各个步骤所提供的方法，具体可参见上述实施例中各个步骤所提供的实现方式，在此不再赘述。另外，对采用相同方法的有益效果描述，也不再进行赘述。对于本公开所涉及的计算机可读存储介质实施例中未披露的技术细节，请参照本公开方法实施例的描述。作为示例，计算机程序可被部署为在一个计算机设备上执行，或者在位于一个地点的多个计算机设备上执行，又或者，在分布在多个地点且通过通信网络互连的多个计算机设备上执行。Embodiments of the present disclosure also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program. The computer program is adapted to be loaded by the processor and execute the method provided by each step in the above embodiment. Specifically, Please refer to the implementation manner provided for each step in the above embodiment, which will not be described again here. In addition, the description of the beneficial effects of using the same method will not be described again. For technical details not disclosed in the computer-readable storage medium embodiments involved in the present disclosure, please refer to the description of the method embodiments of the present disclosure. As examples, a computer program may be deployed to execute on one computer device, or on multiple computer devices located at one location, or on multiple computer devices distributed across multiple locations and interconnected by a communications network. implement.
该计算机可读存储介质可以是前述任一实施例提供的装置或者该计算机设备的内部存储单元，例如计算机设备的硬盘或内存。该计算机可读存储介质也可以是该计算机设备的外部存储设备，例如该计算机设备上配备的插接式硬盘，智能存储卡(smart mediacard，SMC)，安全数字(secure digital，SD)卡，闪存卡(flash card)等。进一步地，该计算机可读存储介质还可以既包括该计算机设备的内部存储单元也包括外部存储设备。该计算机可读存储介质用于存储该计算机程序以及该计算机设备所需的其他程序和数据。该计算机可读存储介质还可以用于暂时地存储已经输出或者将要输出的数据。The computer-readable storage medium may be the device provided in any of the foregoing embodiments or an internal storage unit of the computer device, such as a hard disk or memory of the computer device. The computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (SMC), a secure digital (SD) card, or a flash memory equipped on the computer device. flash card, etc. Further, the computer-readable storage medium may also include both an internal storage unit of the computer device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the computer device. The computer-readable storage medium can also be used to temporarily store data that has been output or is to be output.
本公开实施例还提供了一种计算机程序产品或计算机程序，该计算机程序产品或计算机程序包括计算机指令，该计算机指令存储在计算机可读存储介质中。计算机设备的处理器从计算机可读存储介质读取该计算机指令，处理器执行该计算机指令，使得该计算机设备执行上述实施例中的各种可选方式中所提供的方法。Embodiments of the present disclosure also provide a computer program product or computer program. The computer program product or computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the methods provided in various optional ways in the above embodiments.
本公开实施例的说明书和权利要求书及附图中的术语“第一”、“第二”等是用于区别不同对象，而非用于描述特定顺序。此外，术语“包括”以及它们任何变形，意图在于覆盖不排他的包含。例如包含了一系列步骤或单元的过程、方法、装置、产品或设备没有限定于已列出的步骤或模块，而是可选地还包括没有列出的步骤或模块，或可选地还包括对于这些过程、方法、装置、产品或设备固有的其他步骤单元。The terms “first”, “second”, etc. in the description, claims, and drawings of the embodiments of the present disclosure are used to distinguish different objects, rather than to describe a specific sequence. Furthermore, the term "includes" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment that includes a series of steps or units is not limited to the listed steps or modules, but optionally also includes unlisted steps or modules, or optionally also includes Other step units inherent to such processes, methods, apparatus, products or equipment.
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的单元及算法步骤，能够以电子硬件、计算机软件或者二者的结合来实现，为了清楚地说明硬件和软件的可互换性，在该说明中已经按照功能一般性地描述了各示例的组成及步骤。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本公开的范围。Those of ordinary skill in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented with electronic hardware, computer software, or a combination of both. In order to clearly illustrate the relationship between hardware and software Interchangeability, in this description the composition and steps of each example have been generally described according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Skilled artisans may implement the described functionality using different methods for each specific application, but such implementations should not be considered to be beyond the scope of this disclosure.
本公开实施例提供的方法及相关装置是参照本公开实施例提供的方法流程图和/或结构示意图来描述的，具体可由计算机程序指令实现方法流程图和/或结构示意图的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。这些计算机程序指令可提供到通用计算机、专用计算机、嵌入式处理机或其他可编程传输设备的处理器以产生一个机器，使得通过计算机或其他可编程传输设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或结构示意图一个方框或多个方框中指定的功能的装置。这些计算机程序指令也可存储在能引导计算机或其他可编程传输设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或结构示意图一个方框或多个方框中指定的功能。这些计算机程序指令也可装载到计算机或其他可编程传输设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或结构示意一个方框或多个方框中指定的功能的步骤。The methods and related devices provided by the embodiments of the present disclosure are described with reference to the method flowcharts and/or structural schematic diagrams provided by the embodiments of the present disclosure. Specifically, each process and/or the method flowcharts and/or structural schematic diagrams can be implemented by computer program instructions. or blocks, and combinations of processes and/or blocks in flowcharts and/or block diagrams. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable transmission device to produce a machine such that the instructions executed by the processor of the computer or other programmable transmission device produce for implementation A means of specifying a function in a process or processes in a flowchart and/or in a block or blocks in a structural diagram. These computer program instructions may also be stored in a computer-readable memory that causes a computer or other programmable transmission device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture that includes instruction means, the instruction means Implement functions specified in a process or processes in a flowchart and/or in a box or boxes in a structural diagram. These computer program instructions may also be loaded onto a computer or other programmable transmission device such that a series of operating steps are performed on the computer or other programmable device to produce computer-implemented processes, thereby causing the instructions to be executed on the computer or other programmable device Provides steps for implementing the functionality specified in a process or processes of a flowchart and/or a block or blocks of a structural representation.
以上所揭露的仅为本公开较佳实施例而已，当然不能以此来限定本公开之权利范围，因此依本公开权利要求所作的等同变化，仍属本公开所涵盖的范围。What is disclosed above is only the preferred embodiment of the present disclosure. Of course, it cannot be used to limit the scope of rights of the present disclosure. Therefore, equivalent changes made according to the claims of the present disclosure still fall within the scope of the present disclosure.
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311452389.9ACN117435925B (en) | 2023-11-02 | 2023-11-02 | End-to-end tire performance margin identification model modeling, using method and related equipment |
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311452389.9ACN117435925B (en) | 2023-11-02 | 2023-11-02 | End-to-end tire performance margin identification model modeling, using method and related equipment |
| Publication Number | Publication Date |
|---|---|
| CN117435925Atrue CN117435925A (en) | 2024-01-23 |
| CN117435925B CN117435925B (en) | 2025-01-07 |
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311452389.9AActiveCN117435925B (en) | 2023-11-02 | 2023-11-02 | End-to-end tire performance margin identification model modeling, using method and related equipment |
| Country | Link |
|---|---|
| CN (1) | CN117435925B (en) |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110106381A1 (en)* | 2009-10-30 | 2011-05-05 | Ford Global Technologies, Llc | Vehicle and method of tuning performance of same |
| CN112034829A (en)* | 2019-05-15 | 2020-12-04 | 广州汽车集团股份有限公司 | An end-to-end autonomous driving method, system and vehicle therefor |
| US20210122340A1 (en)* | 2019-10-28 | 2021-04-29 | Volkswagen Ag | Real-time performance handling virtual tire sensor |
| CN112784483A (en)* | 2021-01-18 | 2021-05-11 | 吉林大学 | Identification model modeling and using method of tire performance margin and related equipment |
| US20210171004A1 (en)* | 2019-12-10 | 2021-06-10 | Hyundai Motor Company | System of modeling anti-lock brake system controller of vehicle |
| CN114670808A (en)* | 2022-03-03 | 2022-06-28 | 江苏大学 | Vehicle stability control method based on piecewise affine identification for tire sliding mode control |
| CN114781048A (en)* | 2022-03-04 | 2022-07-22 | 江苏大学 | Vehicle running state estimation method based on piecewise affine identification tire model |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110106381A1 (en)* | 2009-10-30 | 2011-05-05 | Ford Global Technologies, Llc | Vehicle and method of tuning performance of same |
| CN112034829A (en)* | 2019-05-15 | 2020-12-04 | 广州汽车集团股份有限公司 | An end-to-end autonomous driving method, system and vehicle therefor |
| US20210122340A1 (en)* | 2019-10-28 | 2021-04-29 | Volkswagen Ag | Real-time performance handling virtual tire sensor |
| US20210171004A1 (en)* | 2019-12-10 | 2021-06-10 | Hyundai Motor Company | System of modeling anti-lock brake system controller of vehicle |
| CN112784483A (en)* | 2021-01-18 | 2021-05-11 | 吉林大学 | Identification model modeling and using method of tire performance margin and related equipment |
| WO2022151642A1 (en)* | 2021-01-18 | 2022-07-21 | 吉林大学 | Modeling method and use method for identification model of tire performance margin, and related device |
| CN114670808A (en)* | 2022-03-03 | 2022-06-28 | 江苏大学 | Vehicle stability control method based on piecewise affine identification for tire sliding mode control |
| CN114781048A (en)* | 2022-03-04 | 2022-07-22 | 江苏大学 | Vehicle running state estimation method based on piecewise affine identification tire model |
| Title |
|---|
| JIN IM 等: "Simultaneous Estimation of Unknown Road Roughness Input and Tire Normal Forces Based on a Long Short-Term Memory Model", 《IEEE ACCESS》, 15 February 2022 (2022-02-15), pages 16657 - 16658* |
| 王树凤: "《多轴转向车辆操纵稳定性研究[M]》", 31 December 2017, 哈尔滨：哈尔滨工业大学出版社, pages: 124 - 128* |
| 许男 等: "复合工况下四轮驱动电动汽车操纵稳定性控制", 《机械工程学报》, vol. 57, no. 8, 30 April 2021 (2021-04-30)* |
| Publication number | Publication date |
|---|---|
| CN117435925B (en) | 2025-01-07 |
| Publication | Publication Date | Title |
|---|---|---|
| CN113191241A (en) | Model training method and related equipment | |
| CN112784483B (en) | Identification model modeling and using method of tire performance margin and related equipment | |
| CN107851213A (en) | Shift learning in neutral net | |
| CN111191733B (en) | Data fusion method and device for multiple data sources, electronic equipment and storage medium | |
| CN110097121B (en) | Method and device for classifying driving tracks, electronic equipment and storage medium | |
| CN114067294B (en) | Text feature fusion-based fine-grained vehicle identification system and method | |
| Bhadani et al. | Strym: A python package for real-time can data logging, analysis and visualization to work with usb-can interface | |
| CN114970705A (en) | Driving state analysis method, device, equipment and medium based on multi-sensing data | |
| CN112434746B (en) | Pre-labeling method based on hierarchical migration learning and related equipment thereof | |
| CN116432067A (en) | Automatic driving test scene extraction method and equipment based on deep embedding clustering | |
| CN113850643A (en) | Product recommendation method and device, electronic equipment and readable storage medium | |
| US11610314B2 (en) | Panoptic generative adversarial network with explicit modeling of category and instance information | |
| CN115114408A (en) | Multi-modal emotion classification method, device, equipment and storage medium | |
| CN111985449A (en) | Recognition method, device, equipment and computer medium for rescue scene images | |
| CN112396657A (en) | Neural network-based depth pose estimation method and device and terminal equipment | |
| US20250291559A1 (en) | Language model-based interface for simulation systems and applications | |
| CN117435925A (en) | End-to-end tire performance margin identification model modeling, usage methods and related equipment | |
| CN117787082A (en) | Modeling method, using method and related equipment of tire slip ratio prediction model | |
| CN111767933A (en) | A method and device for identifying the driving state of a vehicle | |
| US11875235B2 (en) | Machine learning voltage fingerprinting for ground truth and controlled message error for message and ECU mapping | |
| CN113268524A (en) | Method and device for detecting abnormal oil consumption data, electronic equipment and storage medium | |
| CN105911984B (en) | The vehicle for reducing study to mistake travels the method and device of the probability of rectilinear direction | |
| CN114299408A (en) | Recognition method and system, storage medium and terminal for tire crane number box number | |
| CN114461072A (en) | A display method, apparatus, electronic device and storage medium | |
| CN111310858B (en) | Method and apparatus for generating information |
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |