@article {pub4285,
	title = {Online and Predictive Warning with Risk Maps for Forced Lane Changes},
	author = {Tim Puphal AND Benedict Flade AND Malte Probst  AND Volker Willert AND J{\"u}rgen Adamy AND Julian Eggert},
	year = {2021},
	abstract = {In the past, we have introduced the survival analysis that is able to holistically evaluate car risks (from e.g. collisions and curves) in driving trajectories. Its probabilistic and predictive nature is beneficial over common Time-To-X indicators. However, we did not demonstrate so far functions online on real test cars. In this paper, we therefore present Risk Maps (RM) for warning
support in forced lane changes. 

In this context, low-cost sensor setups with a GNSS for localization and multiple cameras in object detection are leveraged. By unifying the data in a Relational Local Dynamic Map (R-LDM),
we can apply RM in real-time. RM efficiently probes situations so that we then find safe velocities and paths (take the gap or not, etc.). Here, we focus especially to improve uncertainty-awareness and transparency of both models.

Final experiments show that we can succesfully advise the ego driver in gap and no-gap variations at two-lane roads with other vehicles. We consider different possible situations, including their risks, utilities and comfort in a single system. In turn, this is promising to provide hard and interpretable safety.},
	publisher = {IEEE},
	journal = {Transactions on Intelligent Vehicles}
}