People spend a significant amount of time in their cars (US: 86 minutes/day, Europe: 43 minutes/day) while commuting, shopping, or traveling. Hence, the variety of entertainment in the car increases, and many vehicles are already equipped with displays, allowing for watching news, videos, accessing the Internet, or playing games. At the same time, the urbanization caused a massive increase of traffic volume, which led to people spending an ever-increasing amount of their time in front of red traffic lights. An observation of the prevailing forms of entertainment in the car reveals that content such as text, videos, or games are often a mere adaptation of content produced for television, public displays, PCs, or mobile phones and do not adapt to the situation in the car. In this paper we report on a web survey assessing which forms of entertainment and which types of content are considered to be useful for in-car entertainment by drivers. We then introduce an algorithm, which is capable of learning standing times in front of traffic lights based on GPS information only. This, on one hand, allows for providing content of appropriate length, on the other hand, for directing the attention of the driver back to-wards the street at the right time. Finally, we present a prototype implementation and a qualitative evaluation.