mAPH

I believe you have read itWaymoStudents who test papers on the dataset will find itmAPH这个指标,But I checked the information on the whole network and there is no relevant explanation（Except you need to climb the wall to enterWaymo官网下的What’s Next）.

So in order to help the next person who wants to understand but can't find the relevant explanation,Here is the official introduction screenshot and my understanding（其实不难）

First, a screenshot of the official website is given,It is good if the students with better foundation can read it by themselves：

首先我们回顾一下$mAP$：计算出$Precision$与$Recall$after two parameters,以$Precision$和$Recall$are the vertical and horizontal axes, respectively,就可以画出 $Precision-Recall（P-R）$曲线,如图：

$P-R$The area enclosed by the curve is called $AveragePrecision（AP）$.
其中：
$Precision=TP/(TP+FP)$
$Recall=TP/(TP+FN)$

至于$TP、TN、FP、FN$The sum of the quantities of the four parameters is the total sample size,The classification results they represent are shown in the table below：

Finally, we look back to the official website to explain,不难知道,它的计算方法与mAPThe difference lies in the drawingP-RThe graph is predicted by headingAccuracy weighted,即TPparameters will be predictedheading加权.predictedheading与真实的headingThe angle difference is A（A值域为$[0,\Pi ]$）,for every real exampleTP计算方法如下：
$TP=1-A/\Pi$
（$mAP$of each target$TP$为1,即混淆矩阵中TPThe number of and here becomes the weighted for each targetTP之和）
All other indicators remain unchanged,Draw this way$P-R$figure and calculatemAP就是$mAPH$了.