Simulated intersection environment and learning of collision and traffic data in the U & I aware framework

Abstract

Road intersections have become the places of high road incidents and car collisions. Our hypothesis is that a system can be made aware of dangerous situations at road intersections and warn drivers accordingly. Moreover, over time, the system can learn (or re-learn) such "patterns" of danger for specific intersections given a history of rich collision data collected via sensors (that exist today). Based on the assumption that such a history of sensory data about colliding vehicles can be obtained, we show useful patterns that can be extracted. This paper presents our framework for intersection understanding, presenting simulated results suggesting that a fragment of the world (i.e. intersections) can be more deeply understood by mining appropriate sensor data. The simulated environment of the road intersections forming the basis of a real-world implementation and testing of the framework are discussed here. The recent results of mining traffic and collision data generated by the simulation are also included in this paper.