@inproceedings {pub3319,
	title = {Continuous Risk Measures for ADAS and AD},
	author = {Julian Eggert AND Tim Puphal},
	year = {2017},
	month = {September},
	abstract = {In this paper, we compare three different model-based risk measures by evaluating their stengths and weaknesses qualitatively and testing them quantitatively on a set of real
longitudinal and intersection scenarios. We start with the traditional heuristic Time-To-Collision (TTC), which we extend towards 2D operation and non-crash cases to retrieve the Time-To-
Closest-Encounter (TTCE). The second risk measure models position uncertainty with a Gaussian distribution and uses spatial occupancy probabilities for collision risks. We then derive a novel
risk measure based on the statistics of sparse critical events and so-called {\textquotedblleft}survival{\textquotedblright} conditions. The resulting survival analysis shows to have an earlier detection time of crashes and less false positive detections in near-crash and non-crash cases supported by its solid theoretical grounding. It can be seen as a generalization of TTCE and the Gaussian method which is suitable for the validation of ADAS and AD.
},
	publisher = {Society of Automotive Engineers of Japan},
	booktitle = {FAST-zero Symposium 2017}
}