@inproceedings {pub2713,
	title = {Generalized Risk Estimation for intelligent ADAS Functions},
	author = {Julian Eggert},
	year = {2014},
	month = {October},
	abstract = {The estimation of risk is a central cornerstone in the evaluation of traffic scene situations for intelligent ADAS. This applies to all levels of functions ranging from simple advices and warning functions right down to the evaluation of possible behavior alternatives, planning and autonomous driving. Current risk handling includes probabilistic collision measures, behavior planning using spatial and temporal occupancy grids, and time metrics such as Time-To-X (e.g. TTC, Time-To-Collision). Especially TTX measures are often used to quantify the threat of a future event. In this paper, we target to develop a theory of the probability of critical future events such as collisions. We show that by introducing the notion of a "survival probability", we can derive previously heuristic TTX risk measures and generalize them to a temporally continous function and to several different situations such as collision risk, risk of passing nearby without collision, risk at narrow curves and risk of passing a red traffic light. We therefore propose that such a risk measure can be used for planning under general conditions, for comparison of predicted trajectories or by using "risk maps" to evaluate behavior according to their risk.},
	publisher = {IEEE},
	booktitle = {IEEE Conference on Intelligent Transportation Systems (ITSC)},
	pages = {711 - 718}
}