In this paper, we propose a novel approach to knowledge representation for automotive environment modelling based on Vector Symbolic Architectures (VSAs). We build a vector representation describing structured information and relations within the current scene based on high-level object-lists perceived by individual sensors. Such a representation can be applied to different tasks with little modifications. In a sample instantiation, we focus on two example tasks, namely driving context classification and simple behavior prediction, to demonstrate the general applicability of our approach. Allowing efficient implementation in Spiking Neural Networks (SNNs), we envision to improve task performance of our approach through online-learning.