@article {pub3545,
	title = {Continuous Risk Measures for Driving Support
},
	author = {Julian Eggert AND Tim Puphal},
	year = {2018},
	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},
	journal = {International Journal of Automotive Engineering},
	volume = {9},
	number = {3},
	pages = {130-137}
}