Affiliation:
1. University of Chinese Academy of Sciences; the Key Laborator
Abstract
<div class="section abstract"><div class="htmlview paragraph">Finding edge hazardous scenarios which appear very infrequently in the dataset
than common hazardous scenarios is essential for implementing scenario-based
testing of autonomous driving systems(ADs). However, it is difficult to evaluate
the rarity of dynamic scenarios with huge scenario space high-dimensional time
series, making it difficult to search for edge hazardous scenarios quickly. To
solve this problem, this paper proposes a Semi-supervised anomaly detection
method combining MiniRocket and DAGMM(Semi-MiniRocket-GMM, SRG), which treats
edge hazardous scenarios as anomalous samples of common hazardous scenarios. SRG
uses a small number of samples of common hazardous scenarios to guide
interpretable feature extraction and clustering of a large amount of
high-dimensional unlabeled temporal data and finds rarer edge hazardous
scenarios based on anomaly evaluation to improve the coverage of test scenarios.
The method is validated in the open-source natural driving dataset HighD.
Compared with DAGMM, the SRG method can find edge hazardous lane change
scenarios more quickly and accurately with a few samples of hazardous scenarios.
The SRG method aimed at discovering edge hazardous scenarios can both guide the
direction of generating scenarios and speed up the testing process.</div></div>