  Perception algorithms in autonomous driving systems confront great challenges
in long-tail traffic scenarios, where the problems of Safety of the Intended
Functionality (SOTIF) could be triggered by the algorithm performance
insufficiencies and dynamic operational environment. However, such scenarios
are not systematically included in current open-source datasets, and this paper
fills the gap accordingly. Based on the analysis and enumeration of trigger
conditions, a high-quality diverse dataset is released, including various
long-tail traffic scenarios collected from multiple resources. Considering the
development of probabilistic object detection (POD), this dataset marks trigger
sources that may cause perception SOTIF problems in the scenarios as key
objects. In addition, an evaluation protocol is suggested to verify the
effectiveness of POD algorithms in identifying the key objects via uncertainty.
The dataset never stops expanding, and the first batch of open-source data
includes 1126 frames with an average of 2.27 key objects and 2.47 normal
objects in each frame. To demonstrate how to use this dataset for SOTIF
research, this paper further quantifies the perception SOTIF entropy to confirm
whether a scenario is unknown and unsafe for a perception system. The
experimental results show that the quantified entropy can effectively and
efficiently reflect the failure of the perception algorithm.
