An integrated ADAS for assessing risky situations in urban driving

Advanced Driver Assistance Systems (ADAS) are becoming more and more popular. Many of these systems though are limited to specific scenes and often detect risky situations very late so they can only mitigate accidents. These effects are mainly caused by the use of simple physical prediction methods, e.g. to estimate the time-to-contact with another vehicle. In this paper we show an ADAS that extends the functionality of physical collision warnings by additionally estimating potential risks based on implicit predictions. As an example we demonstrate the use of vehicle orientation information for classifying situations. Through this, we can in particular assess the risk of static cars, for which physical prediction does not apply, but which can nevertheless easily cause an accident if they start moving into our driving corridor. The proposed system is evaluated online in a test car and is shown to reliably detect classical risky situations as well as those involving static cars.