@inproceedings {pub4259,
	title = {Time-Course Sensitive Collision Probability Model for Risk Estimation},
	author = {Fabian M{\"u}ller AND Julian Eggert},
	year = {2020},
	month = {September},
	abstract = {Avoiding critical situations is a prerequisite for Advanced Driver Assistant Systems and Autonomous Driving (AD) to decrease the number of total hazards and fatal collisions. To guide safe motion behavior in complex scenarios, an appropriate risk measurement system which considers inherent uncertainties is essential. We present a time-course-aware risk model, which estimates collision risks based on the evolution of state distributions along foretasted trajectories, regarding their magnitude based on survival theory and their shape adaptation by truncation of critical regions. We investigate the ability of the presented model for providing a potential field for cost-based motion planning in complex traffic scenes with many other traffic participants like lateral positioning in overtaking scenarios, velocity dependencies in following scenarios and gap finding in intersection scenarios and compare them with other state of the art approaches.},
	publisher = {IEEE},
	booktitle = {Intelligent Transportation Systems Conference (ITSC)}
}