NDLI: Situation-specific learning for ego-vehicle behavior prediction systems

Content Provider	IEEE Xplore Digital Library
Author	Ortiz, M.G. Schmudderich, J. Kummert, F. Gepperth, A.
Copyright Year	2011
Description	Author affiliation: CoR-Lab, Bielefeld University, Bielefeld, Germany (Ortiz, M.G.) \|\| Honda Research Institute Europe GmbH, Offenbach am Main, Germany (Schmudderich, J.; Gepperth, A.) \|\| Faculty of Technology, Bielefeld University, Bielefeld, Germany (Kummert, F.)
Abstract	We present a system able to predict the future behavior of the ego-vehicle in an inner-city environment. Our system learns the mapping between the current perceived scene (information about the ego-vehicle and the preceding vehicle, as well as information about the possible traffic lights) and the future driving behavior of the ego-vehicle. We improve the prediction accuracy by estimating the prediction confidence and by discarding unconfident samples. The behavior of the driver is represented as a sequence of elementary states termed behavior primitives. These behavior primitives are abstractions from the raw actuator states. Behavior prediction is therefore considered to be a multi-class learning problem. In this contribution, we explore the possibilities of situation-specific learning. We show that decomposing the perceived complex situation into a combination of simpler ones, each of them with a dedicated prediction, allows the system to reach a performance equivalent to a system without situation-specificity. We believe that this is advantageous for the scalability of the approach to the number of possible situations that the driver will encounter. The system is tested on a real world scenario, using streams recorded in inner-city scenes. The prediction is evaluated for a prediction horizon of 3s into the future, and the quality of the prediction is measured using established evaluation methods.
Starting Page	1237
Ending Page	1242
File Size	679240
Page Count	6
File Format	PDF
ISBN	9781457721984
ISSN	21530009
e-ISBN	9781457721977
DOI	10.1109/ITSC.2011.6083108
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vehicles Acceleration Learning systems Neurons Training Trajectory Prediction algorithms
Content Type	Text
Resource Type	Article

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