Predicting Velocity Profiles of Road Users at Intersections Using Configurations

Abstract

Intersections are among the most complex traffic situations that motorists encounter, which is reflected by the fact that in Europe more than 40 percent of accidents resulting in injury occur at intersections. In order to support the driver in crossing an intersection an advanced driver assistance system is required to predict the behavior of other drivers, like acceleration and braking maneuvers, as accurately as possible. Such a prediction is a challenging task when considering the complexity and variability of situations encountered at urban intersections. We propose to tackle this problem using a two-staged approach. In the first stage the situation is decomposed into small, more manageable sets of related road users to prevent a combinatorial explosion of possibilities. For each set the road user's driving situation is estimated. In the second stage the velocity profiles of all road users are predicted, taking advantage of the previously estimated driving situation by employing prediction models that are specific to the situation type. The proposed method is evaluated on a simulated intersection situation where the two-staged approach clearly outperforms prediction methods that work without assessing driving situations first. We also show qualitative results on real-world data that confirm the benefits of our approach.