Estimating and tracking the positions of other vehicles in the environment is important for advanced driver assistant systems (ADAS) and even more so for autonomous driving vehicles. For example, evasive strategies or warnings need accurate and reliable information about the positions and movement directions of the observed traffic participants. Although sensor systems are constantly improving, their data will never be noise-free nor fully reliable, especially in harder weather conditions. Thus, the noisy sensory data should be maximally utilized by pre-processing and information fusion techniques. For this we use a augmented version of our spatial object tracking technique that improves Bayesian-based tracking of other vehicles by incorporating environment information about the street ahead. The algorithm applies attractor-based adjustments of the probabilistic forward predictions in a Bayesian grid filter. In this paper we show that context information - such as lane positions gained from online databases similar to open street map (OSM) - can be effectively be used to flexibly activate the attractors in a real-world setting. Besides the improvements in tracking other vehicles, the resulting algorithm can detect medium-time-scale driving behavior like turning, straight driving and overtaking. The behavior is detected by using a new plausibility estimate: Different behavior alternatives of the tracked vehicle are compared probabilistically with the sensor measurement, considering all possible vehicle positions. Thus, risk levels can be inferred considering alternative behaviors. We evaluate the algorithm in a simulated crossing scenario and with real-world intersection data. The results show that the attractor approach can significantly improve the overall performance of the tracking system and can also be used for better inference of the behavior of the observed vehicle.