Michael Ortiz, Jannik Fritsch, Franz Kummert, Alexander Gepperth, "Behavior prediction at multiple time-scales in inner-city scenarios", IEEE Intelligent Vehicles Symposium (IV), 2011.

We present a flexible and scalable architecture that can learn to predict the future behavior of a vehicle in inner-city traffic. While behavior prediction studies have mainly been focusing on lane change events on highways, we apply our approach to a simple inner-city scenario: approaching a traffic light. Our system employs dynamic information about the current ego-vehicle state as well as static information about the scene, in this case position and state of nearby traffic lights. Our approach differs from previous work in several aspects. First of all, we hold that predicting the precise sequence of physical and actuator states of a car driving in dynamic innercity traffic is both challenging and unnecessary. We therefore represent predicted behavior as a sequence of few elementary states, termed behavior primitives. As a second aspect, behavior prediction is treated as a multi-class learning problem since there are multiple behavior primitives. Rather than disturbing the system, we show that this can be exploited for computing information-theoretic measures of prediction confidence, thereby allowing to identify and reject unreliable predictions. We show that the horizon of predictions can be extended up to 6s, and that uncertain predictions can be detected and eliminated efficiently. We consider this a significant result since typical prediction horizons are usually in the range of 1 to 2s. The main message of this paper is that simple learning methods can achieve excellent prediction quality at long time horizons by operating purely on the “system-level”, i.e., using an abstract, low-dimensional situation representation. Since the learning approach greatly reduces the design effort, and since we show that the prediction of multiple behavior classes is feasible, we expect our architecture to be scalable to more complex scenarios in inner-city traffic.