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Estimating and understanding the current scene is an inevitable capability of automated vehicles. Usually, maps are used as prior for interpreting sensor measurements in order to drive safely and comfortably. Only few approaches take into account that maps might be outdated and lead to wrong assumptions on the environment. This work estimates a lanelevel intersection topology without any map prior by observing the trajectories of other traffic participants.We are able to deliver both a coarse lane-level topology as well as the lane course inside and outside of the intersection using Markov chain Monte Carlo sampling. The model is neither limited to a number of lanes or arms nor to the topology of the intersection.We present our results on an evaluation set of 1000 simulated intersections and achieve 99.9% accuracy on the topology estimation that takes only 36ms, when utilizing tracked object detections. The precise lane course on these intersections is estimated with an error of 15cm on average after 140ms. Our approach shows a similar level of precision on 14 realworld intersections with 18cm average deviation on simple intersections and 27cm for more complex scenarios. Here the estimation takes only 113ms in total.

Connectivity is one of the major prerequisites of automated driving. Enabled by numerous connected sensors, new cars offer new functionalities, provide higher security levels and promise to enhance the comfort of travelling. However, by connecting a vehicle with its environment, the car becomes more transparent. The integration of the car into a smart grid seems to conflict with the users' expectation of their car as a private retreat, thus reducing the acceptance and usage adoption of connected cars. This article aims at helping developers and engineers to consider the user's expectations when designing a connected car. Furthermore, this article reviews and compares recent international surveys on user's privacy with our own results on the user's attitude towards connected vehicular services.

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