The OpenMPD dataset mainly covers complex road scenes, such as road shadows, camera overexposure, rain, fog, lane contamination, etc. The stability of the autonomous driving algorithm is precisely tested by these scenes. All those algorithms that have great performance on other datasets may not work well on OpenMPD.

We encourage researchers to try to test and train your algorithms on OpenMPD to make it more adaptable when facing challenges on the real environment. If you have good results on OpenMPD, we encourage you to submit your code and algorithm to our website, with publishing it on the leaderboard your great results will also be seen by others.

Metrics for 2D object detection

mean Average Precision. mAP is a popular metric in measuring the accuracy of object detectors's performance. mAP is calculated by Precision and Recall , and both of them can be calculated by True Positive(TP) 、 False Positive(FP) 、False Negative (FN)、True Negative (TN) etc.

An intersection Over Union (IOU) threshold Predefined determines whether the prediction is a True Positive(TP) or a False Positive(FP). PASCAL VOC is a dataset in object detection competition. For the PASCAL VOC challenge, a prediction is positive if IoU  0.5.

The general definition for the Average Precision (AP) is finding the area under the precision-recall curve above.

We will use above metrics to evaluate your algorithm. The full information for this content will be on our paper.