CN116901981A - Online self-learning Markov vehicle speed prediction method - Google Patents
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Abstract
Description
技术领域Technical field
本发明属于自动驾驶领域,尤其涉及一种在线自学习型的马尔可夫车辆速度预测方法。The invention belongs to the field of automatic driving, and in particular relates to an online self-learning Markov vehicle speed prediction method.
背景技术Background technique
随着车-路-网技术的发展,地面车辆能够获取周围交通信息,用于设计预测运动控制,实现先进辅助驾驶功能甚至是高等级的无人驾驶技术。其中,车辆速度预测已经成为预测能量管理、自适应巡航与自动变道等若干功能的前置条件,其预测精度直接影响到车辆控制的安全性与燃油经济性。因此,准确的车辆速度预测方法对实现安全、高效的智能驾驶具有重要意义。With the development of vehicle-road-network technology, ground vehicles can obtain surrounding traffic information, which can be used to design predictive motion control, realize advanced assisted driving functions and even high-level driverless technology. Among them, vehicle speed prediction has become a prerequisite for several functions such as predictive energy management, adaptive cruise and automatic lane changing, and its prediction accuracy directly affects the safety and fuel economy of vehicle control. Therefore, accurate vehicle speed prediction methods are of great significance to achieve safe and efficient intelligent driving.
目前,车辆速度预测技术已经得到广泛研究,包括多项式拟合法、传统马尔科夫链和基于神经网络的一类深度学习方法。然而,上述方法分别存在着预测精度差、工况适应性差和离线训练资源浪费、在线计算时效低的问题。现有车辆速度预测方法无法兼顾高精度、自适应性与高实时性,需要进一步改进。At present, vehicle speed prediction technology has been widely studied, including polynomial fitting methods, traditional Markov chains and a type of deep learning methods based on neural networks. However, the above methods have problems such as poor prediction accuracy, poor adaptability to working conditions, waste of offline training resources, and low online calculation timeliness. Existing vehicle speed prediction methods cannot take into account high accuracy, adaptability and high real-time performance, and need further improvement.
发明内容Contents of the invention
本发明的目的在于针对目前速度预测方法无法兼顾高精度、自适应性与高实时性,提供一种在线自学习型的马尔可夫车辆速度预测方法,有效提高预测精度,保证实时性。The purpose of the present invention is to provide an online self-learning Markov vehicle speed prediction method that effectively improves prediction accuracy and ensures real-time performance because current speed prediction methods cannot take into account high accuracy, adaptability and high real-time performance.
实现本发明目的的技术解决方案为:The technical solution to achieve the purpose of the present invention is:
一种在线自学习型的马尔可夫车辆速度预测方法,包括如下步骤:An online self-learning Markov vehicle speed prediction method, including the following steps:
(10)离线数据库构建:选取标准工况,对所述标准工况的数据进行整合,构建车辆行驶速度离线数据库。(10) Offline database construction: Select standard working conditions, integrate the data of the standard working conditions, and build an offline database of vehicle driving speeds.
(20)状态转移概率矩阵离线训练:将车辆加速度作为马尔可夫事件中的状态,定义其状态网格,确定预测范围,根据所述离线数据库获取加速度数据,统计状态转移事件,建立状态转移矩阵,计算并存储转移概率矩阵。(20) Offline training of state transition probability matrix: use vehicle acceleration as a state in a Markov event, define its state grid, determine the prediction range, obtain acceleration data according to the offline database, count state transition events, and establish a state transition matrix , calculate and store the transition probability matrix.
(30)设计状态转移矩阵实时更新算法:推导状态转移矩阵递归形式,定义自学习因子,利用车辆历史加速度转移信息,结合离线状态转移矩阵,实现状态转移矩阵的在线更新。(30) Design a real-time update algorithm for the state transfer matrix: derive the recursive form of the state transfer matrix, define the self-learning factor, use the vehicle's historical acceleration transfer information, and combine with the offline state transfer matrix to achieve online update of the state transfer matrix.
(40)在线预测:获取车辆当前时刻下的加速度历史数据,参考所述加速度状态网格进行状态转移事件辨识,在线更新转移概率矩阵,预测车辆加速度,进而获取车辆预测速度。(40) Online prediction: Obtain the acceleration history data of the vehicle at the current moment, identify the state transition event with reference to the acceleration state grid, update the transition probability matrix online, predict the vehicle acceleration, and then obtain the predicted vehicle speed.
与现有技术相比,本发明的显著优点是:Compared with the prior art, the significant advantages of the present invention are:
1、计算效率高:基于对离线状态转移矩阵的训练,在线自学习的速度预测方法只需通过实时地对离线数据进行替换和更新,不需要繁琐的在线算法,大幅提升在线计算时效,使得该方法能够更快地适应新的环境和变化。1. High computational efficiency: Based on the training of the offline state transfer matrix, the online self-learning speed prediction method only needs to replace and update the offline data in real time, without the need for cumbersome online algorithms, which greatly improves the online calculation timeliness, making the Methods can adapt to new environments and changes more quickly.
2、自适应性强:传统的速度预测方法通过离线训练模型,收集大量的数据进行离线训练,用离线数据进行预测,这往往存在着滞后性和精度低的缺点,而本发明在线自学习型的车辆速度预测方法在综合利用传统马尔可夫链与车辆历史数据的基础下对新数据进行实时学习和预测,通过与实际观测结果的对比,系统能够调整和改进预测模型,这种自我优化的能力可以使预测模型不断升级,适应不同的驾驶环境。2. Strong adaptability: The traditional speed prediction method collects a large amount of data for offline training through offline training models, and uses offline data for prediction. This often has the shortcomings of lag and low accuracy, while the online self-learning model of the present invention The vehicle speed prediction method performs real-time learning and prediction on new data based on the comprehensive utilization of traditional Markov chains and vehicle historical data. By comparing with actual observation results, the system can adjust and improve the prediction model. This self-optimizing The ability allows the prediction model to be continuously upgraded to adapt to different driving environments.
附图说明Description of the drawings
图1是本发明一种在线自学习型的马尔可夫车辆速度预测方法的流程示意图。Figure 1 is a schematic flow chart of an online self-learning Markov vehicle speed prediction method of the present invention.
图2是离线工况组合图。Figure 2 is a combination diagram of offline working conditions.
图3是转移概率矩阵离线训练流程图。Figure 3 is the flow chart of offline training of transition probability matrix.
图4是多步马尔可夫转移概率矩阵图。Figure 4 is a multi-step Markov transition probability matrix diagram.
图4(a)是当前时刻1秒后加速度转移概率矩阵图。Figure 4(a) is the acceleration transition probability matrix diagram 1 second after the current time.
图4(b)是当前时刻3秒后加速度转移概率矩阵图。Figure 4(b) is an acceleration transfer probability matrix diagram 3 seconds from the current moment.
图4(c)是当前时刻7秒后加速度转移概率矩阵图。Figure 4(c) is the acceleration transfer probability matrix diagram 7 seconds after the current time.
图4(d)是当前时刻10秒后加速度转移概率矩阵图。Figure 4(d) is an acceleration transfer probability matrix diagram 10 seconds from the current moment.
图5是转移概率矩阵实时更新流程图。Figure 5 is a real-time update flow chart of the transition probability matrix.
图6是传统马尔可夫模型的速度预测轨迹和均方根误差图。Figure 6 is the speed prediction trajectory and root mean square error diagram of the traditional Markov model.
图6(a)是传统马尔可夫模型的速度预测轨迹误差图。Figure 6(a) is the speed prediction trajectory error diagram of the traditional Markov model.
图6(b)是传统马尔可夫模型的速度预测数据均方根误差图。Figure 6(b) is the root mean square error diagram of the speed prediction data of the traditional Markov model.
图7是在线自学习型的速度预测轨迹和均方根误差图。Figure 7 is the online self-learning speed prediction trajectory and root mean square error diagram.
图7(a)是在线自学习型的速度预测轨迹误差。Figure 7(a) shows the online self-learning speed prediction trajectory error.
图7(b)是在线自学习型的速度预测数据均方根误差图。Figure 7(b) is the root mean square error diagram of online self-learning speed prediction data.
表1是本发明与现有两种方法的性能对比。Table 1 shows the performance comparison between the present invention and the two existing methods.
具体实施方式Detailed ways
下面将结合附图对本发明进行描述,以便于本领域的技术人员可由说明书更好的理解本发明。The present invention will be described below in conjunction with the accompanying drawings, so that those skilled in the art can better understand the present invention from the description.
图1示出了本发明一种在线自学习型的马尔可夫速度预测方法的流程图。Figure 1 shows a flow chart of an online self-learning Markov speed prediction method of the present invention.
在本实施例中,如图1所示,一种在线自学习型的马尔可夫速度预测方法步骤包括:In this embodiment, as shown in Figure 1, the steps of an online self-learning Markov speed prediction method include:
(10)离线数据库构建:选取标准工况,对所述标准工况的数据进行整合,构建车辆行驶速度离线数据库。(10) Offline database construction: Select standard working conditions, integrate the data of the standard working conditions, and build an offline database of vehicle driving speeds.
为了更全面的覆盖到各种典型路段,使预测结果更加精确,本实施例对几种典型工况进行整合,从中进行离线数据获取。In order to more comprehensively cover various typical road sections and make the prediction results more accurate, this embodiment integrates several typical working conditions and obtains offline data from them.
图2示出了离线工况组合图。Figure 2 shows the offline working condition combination diagram.
(20)状态转移概率矩阵离线训练:将车辆加速度作为马尔可夫事件中的状态,定义其状态网格,确定预测范围,根据所述离线数据库获取加速度数据,统计状态转移事件,建立状态转移矩阵,计算并存储转移概率矩阵。(20) Offline training of state transition probability matrix: use vehicle acceleration as a state in a Markov event, define its state grid, determine the prediction range, obtain acceleration data according to the offline database, count state transition events, and establish a state transition matrix , calculate and store the transition probability matrix.
图3示出了所述(20)状态转移概率矩阵离线训练步骤包括:Figure 3 shows the (20) state transition probability matrix offline training steps including:
(21)状态网格定义:以车辆加速度作为马尔可夫事件中的状态,即加速度状态空间可定义为X={a1,a2,......,ap},a1和ap分别表示最小加速度和最大加速度,p为状态空间大小。定义加速度状态网格,加速度a(-4≤a≤4)m/s2,以网格大小0.1m/s2划分变量。(21) State grid definition: Taking vehicle acceleration as the state in the Markov event, that is, the acceleration state space can be defined as X={a 1 , a 2 ,..., a p }, a 1 and a p represents the minimum acceleration and maximum acceleration respectively, and p is the size of the state space. Define the acceleration state grid, the acceleration a(-4≤a≤4)m/s 2 , and divide the variables with a grid size of 0.1m/s 2 .
(22)预测时间步长确定:取预测时间范围为10秒。(22) Determine the prediction time step: take the prediction time range as 10 seconds.
(23)统计状态转移事件:在每个所述预测时间步长下,遍历每个加速度状态网格中的值,根据所述离线加速度轨迹。统计每个当前加速度值ai转移到其对应的下一个时刻加速度值aj的样本数量,记为p为所述状态空间大小,k为未来即时的指数,k∈{1,2,......,10},Q为加速度曲线测试长度。统计离线轨迹中与ai值相等的样本数量,记为/> (23) Statistical state transition events: at each prediction time step, traverse the values in each acceleration state grid according to the offline acceleration trajectory. Count the number of samples that each current acceleration value a i transfers to its corresponding acceleration value a j at the next moment, recorded as p is the size of the state space, k is the future immediate index, k∈{1, 2,...,10}, and Q is the acceleration curve test length. Count the number of samples in the offline trajectory that are equal to the value of a i , recorded as/>
(24)状态转移矩阵计算:根据所述状态事件统计的样本数量在未来某一时刻k下的状态转移矩阵可以计算为:(24) Calculation of state transition matrix: number of samples counted according to the state events The state transition matrix under k at a certain time in the future can be calculated as:
所述(24)状态转移矩阵计算步骤包括:The (24) state transition matrix calculation steps include:
(241)状态转移矩阵建立:初始化转移概率矩阵大小为(Nacc,Nacc,10),Nacc为加速度网格长度,初始化/>矩阵,用于统计每个时间步长加速度ai出现的次数,初始化/>矩阵,用于统计从某个加速度值到另一个加速度值的转移次数。(241) State transition matrix establishment: initialize transition probability matrix The size is (Nacc, Nacc, 10), Nacc is the length of the acceleration grid, initialized/> Matrix, used to count the number of occurrences of acceleration a i at each time step, initialized/> Matrix used to count the number of transitions from one acceleration value to another.
(242)频率矩阵建立:根据所述样本数量二者分别与加速度曲线测试长度Q的比值分别表示两种事件在曲线中出现的频率,即:(242) Frequency matrix establishment: According to the number of samples The ratio of the two to the test length Q of the acceleration curve respectively represents the frequency of the two events appearing in the curve, namely:
建立频率矩阵 Create frequency matrix
(243)状态转移矩阵公式建立:根据所述事件频率未来某一时刻下的状态转移矩阵也可计算为:(243) State transition matrix formula establishment: According to the event frequency The state transition matrix at a certain point in the future can also be calculated as:
(244)转移概率矩阵存储:根据所述加速度ai,aj,记录其在加速度状态网格中的位置索引i、j,将数据存储在所述状态转移矩阵对应位置。(244) Transition probability matrix storage: According to the acceleration a i , a j , record the position index i, j in the acceleration state grid, and store the data in the corresponding position of the state transition matrix.
图4示出了根据所述(20)状态转移概率矩阵离线训练,初步得到的多步马尔可夫转移概率矩阵图,图中所示Pij表示加速度由当前时刻ai转移到下一时刻aj的概率。Figure 4 shows the multi-step Markov transition probability matrix diagram initially obtained according to the offline training of the state transition probability matrix in (20). The P ij shown in the figure represents the acceleration transferred from the current moment a i to the next moment a The probability of j .
图4(a)为当前时刻1秒后加速度转移概率矩阵图。Figure 4(a) is the acceleration transfer probability matrix diagram 1 second after the current time.
图4(b)为当前时刻3秒后加速度转移概率矩阵图。Figure 4(b) is the acceleration transfer probability matrix diagram 3 seconds from the current moment.
图4(c)为当前时刻7秒后加速度转移概率矩阵图。Figure 4(c) is the acceleration transfer probability matrix diagram 7 seconds after the current time.
图4(d)为当前时刻10秒后加速度转移概率矩阵图。Figure 4(d) is the acceleration transfer probability matrix diagram 10 seconds from the current moment.
为了更好地体现本发明比传统方法能够适应更多的道路情况,本实施例选择在未整合入离线数据库中的US06工况下进行实验。In order to better demonstrate that the present invention can adapt to more road conditions than traditional methods, this embodiment chooses to conduct experiments under the US06 working condition that is not integrated into the offline database.
(30)设计状态转移矩阵实时更新算法:推导状态转移矩阵递归形式,定义自学习因子,利用车辆历史加速度转移信息,结合离线状态转移矩阵,实现状态转移矩阵的在线更新。(30) Design a real-time update algorithm for the state transfer matrix: derive the recursive form of the state transfer matrix, define the self-learning factor, use the vehicle's historical acceleration transfer information, and combine with the offline state transfer matrix to achieve online update of the state transfer matrix.
图5示出了所述(30)设计状态转移矩阵实时更新算法步骤包括:Figure 5 shows the (30) design state transition matrix real-time update algorithm steps including:
(31)定义自学习因子:根据所述事件频率推导其递归形式,可得:(31) Define the self-learning factor: according to the event frequency Deriving its recursive form, we can get:
其中表示离线数据中在k时刻后加速度从ai转移到加速度值为aj的样本数量, 同理可得/> in Represents the number of samples in the offline data in which the acceleration is transferred from a i to the acceleration value a j after k time, The same can be said/>
根据所述两式的递归形式,某一时刻下的状态转移矩阵可计算为:According to the recursive forms of the two equations, the state transition matrix at a certain moment can be calculated as:
若离线加速度轨迹中无对应加速度值ai,则即:If there is no corresponding acceleration value a i in the offline acceleration trajectory, then Right now:
其中η为引入的马尔科夫学习系数,在实际应用中可以自由调整以实现较好的预测效果。Among them, eta is the introduced Markov learning coefficient, which can be freely adjusted in practical applications to achieve better prediction results.
(32)状态转移矩阵在线更新:对于每个时间步长,获取最新的一系列加速度数据,根据当前的加速度值和前k个时间步长的加速度值,确定在加速度状态网格中的位置索引j和i,然后更新对应的频率矩阵,最后计算转移概率矩阵。(32) State transition matrix online update: for each time step, obtain the latest series of acceleration data, and determine the position index in the acceleration state grid based on the current acceleration value and the acceleration values of the previous k time steps. j and i, then update the corresponding frequency matrix, and finally calculate the transition probability matrix.
(40)在线预测:获取车辆当前时刻下的加速度历史数据,参考所述加速度状态网格进行状态转移事件辨识,在线更新转移概率矩阵,预测车辆加速度,进而获取车辆预测速度。(40) Online prediction: Obtain the acceleration history data of the vehicle at the current moment, identify the state transition event with reference to the acceleration state grid, update the transition probability matrix online, predict the vehicle acceleration, and then obtain the predicted vehicle speed.
所述(40)在线预测步骤包括:The (40) online prediction step includes:
(41)加速度值、速度值获取:获取当前时刻的速度值和加速度值,通过对当前加速度值做近似处理,在加速度网格中找到最接近的索引ai。(41) Acceleration value and velocity value acquisition: Obtain the velocity value and acceleration value at the current moment, and find the closest index a i in the acceleration grid by approximating the current acceleration value.
(42)初始化加速度和速度预测向量:创建两个零矩阵分别用来存储未来k个时间步长的加速度和速度预测。(42) Initialize acceleration and velocity prediction vectors: Create two zero matrices to store acceleration and velocity predictions for k time steps in the future.
(43)预测结果计算:根据转移概率矩阵,获取第i行,第k列对应的转移概率向量,使用该概率向量与加速度网格的元素逐一相乘并求和,得到加速度预测值/>即:(43) Prediction result calculation: According to the transition probability matrix, obtain the transition probability vector corresponding to the i-th row and k-th column, and use this probability vector Multiply and sum the elements of the acceleration grid one by one to obtain the acceleration prediction value/> Right now:
接着通过将前k个预测加速度值相加,再加上当前速度值v,得到在未来k个时间步长内的速度预测值即:Then by adding the first k predicted acceleration values and adding the current speed value v, the predicted speed value in the next k time steps is obtained. Right now:
如果任何一个速度预测值小于0,将其设置为0,以确保速度值为非负。If any of the speed predictions are less than 0, set them to 0 to ensure that the speed values are non-negative.
(44)预测误差计算:通过对预测速度值和真实速度值求均方根误差,并将其存储在改进后的预测误差向量中的对应时刻。绘制预测结果。(44) Prediction error calculation: Calculate the root mean square error between the predicted speed value and the true speed value, and store it at the corresponding moment in the improved prediction error vector. Plot the prediction results.
为了明确量化速度预测误差,引入均方根误差(RMSE)作为性能评价指标:均方根误差越小,意味着预测精度越高。In order to clearly quantify the speed prediction error, the root mean square error (RMSE) is introduced as a performance evaluation index: the smaller the root mean square error, the higher the prediction accuracy.
图6示出了传统马尔可夫模型的速度预测轨迹和均方根误差图。Figure 6 shows the speed prediction trajectory and root mean square error plot of the traditional Markov model.
图6(a)示出了传统马尔可夫模型的速度预测轨迹误差图。Figure 6(a) shows the speed prediction trajectory error diagram of the traditional Markov model.
图6(b)示出了传统马尔可夫模型的速度预测数据均方根误差图。Figure 6(b) shows the root mean square error plot of the speed prediction data of the traditional Markov model.
图7示出了在线自学习型的速度预测轨迹和均方根误差图。Figure 7 shows the speed prediction trajectory and root mean square error diagram of the online self-learning type.
图7(a)示出了在线自学习型的速度预测轨迹误差。Figure 7(a) shows the online self-learning speed prediction trajectory error.
图7(b)示出了在线自学习型的速度预测数据均方根误差图。Figure 7(b) shows the root mean square error diagram of the online self-learning speed prediction data.
图6(a)、7(a)中红线表示采用本方法每个时刻所预测速度的轨迹走向,粗线表示车辆实际行驶速度曲线,从图中可以看出,采用本发明的速度预测方法所绘制出的速度预测轨迹与实际车辆速度轨迹吻合度更高,图6(b)、7(b)中绿色阴影部分面积表示每一时刻的均方根误差,从图中可以看出,在此次实施例中本发明较传统马尔可夫模型速度预测有着更高的精确度。The red lines in Figures 6(a) and 7(a) represent the trajectory of the predicted speed at each moment using this method, and the thick lines represent the actual vehicle speed curve. It can be seen from the figures that the speed predicted by the speed prediction method of the present invention is The drawn speed prediction trajectory is more consistent with the actual vehicle speed trajectory. The green shaded area in Figures 6(b) and 7(b) represents the root mean square error at each moment. It can be seen from the figure that here In this embodiment, the present invention has higher accuracy than the traditional Markov model speed prediction.
为了进一步验证本发明的优势,本实施例中还使用较先进的长短期记忆网络(LSTM,Long Short-Term Memory)与本发明的预测结果进行比较,表1为三种方法结果对比表:In order to further verify the advantages of the present invention, in this embodiment, a more advanced long short-term memory network (LSTM, Long Short-Term Memory) is also used to compare with the prediction results of the present invention. Table 1 is a comparison table of the results of the three methods:
表1Table 1
表1为本发明与传统马尔可夫、长短期记忆网络速度预测方法在均方根误差以及计算时间两个方面的对比列表,从表1中我们可以得出使用传统马尔可夫预测方法的均方根误差为2.16m/s,使用长短期记忆网络和自学习型的速度预测方法的均方根误差分别为1.86m/s和1.82m/s,预测精度较传统马尔可夫速度预测方法分别提升了13.9%和15.7%,说明本发明预测精度优于两种现存预测技术,从中我们还可以得出,在精度都高于传统马尔可夫预测方法时,本发明的计算时间为0.6ms,而长短期记忆网络预测方法计算时间需要24ms,本发明的预测结果计算时间远小于长短期记忆网络预测方法,说明本发明的计算效率高。体现了本发明的优势。Table 1 is a comparison list between the present invention and the traditional Markov and long-short-term memory network speed prediction methods in terms of root mean square error and calculation time. From Table 1, we can derive the mean mean square error using the traditional Markov prediction method. The root mean square error is 2.16m/s. The root mean square error of the long short-term memory network and the self-learning speed prediction method are 1.86m/s and 1.82m/s respectively. The prediction accuracy is higher than the traditional Markov speed prediction method respectively. The increases are 13.9% and 15.7%, indicating that the prediction accuracy of the present invention is better than the two existing prediction technologies. From this, we can also conclude that when the accuracy is higher than the traditional Markov prediction method, the calculation time of the present invention is 0.6ms. The calculation time of the long short-term memory network prediction method requires 24 ms, and the calculation time of the prediction results of the present invention is much shorter than that of the long short-term memory network prediction method, indicating that the calculation efficiency of the present invention is high. embodies the advantages of the present invention.
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