@inproceedings {pub4281,
	title = {TTX Risk Measures Derived from General Survival Theory},
	author = {Julian Eggert},
	year = {2020},
	month = {September},
	abstract = {Although intelligent Advanced Driving Assistance Systems and Autonomous Driving technologies are becoming ubiquitous, efficient and safe driving in dynamic, narrow or congested environment remains a theoretical and technological challenge. The reason mainly lies in the complexity of handling prediction and uncertainty, which are the fundamental ingredients for risk estimation. The theoretical shortcomings can be seen on the level of mesoscopic risk quantifiers like those based on Time-To-Event (TTX, e.g. TTC, TTB) or spatial safe distances, which mainly reflect heuristic engineering practices or worst case deterministic forecasts of a scenario. In both cases, uncertainties are not explicitly considered and worst-case assumptions significantly reduce driving efficiency, like traffic throughput. However uncertainties are ubiquitous in real driving so that a fundamentally different strategy is required, which seeks for a tradeoff between risk and
benefit. In this paper, we introduce a microscopic, probabilistic unified risk estimation framework (PURE), grounded in a first principles probabilistic risk theory, which generalizes to arbitrary risks and situations and which explicitly addresses these uncertainties. To underline the generality of the approach, we show how empirical risk measures like TTX can then be derived, understood and extended starting from the general risk estimation, making them interpretable in terms of their constituting parameters.},
	publisher = {IEEE},
	booktitle = {ITSC 2020},
	editor = {IEEE},
	pages = {1115-1122}
}