Behavior prediction at multiple time-scales in inner-city scenarios

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 traffi...

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Bibliographic Details
Main Authors: Garcia Ortiz, Michael, Fritsch, Jannik, Kummert, Franz, Gepperth, Alexander
Format: Conference Proceeding
Language:English
Subjects:
Actuators
Driver circuits
Learning systems
Neurons
Prediction algorithms
Training
Trajectory
Citations: Items that cite this one
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Summary: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.
ISSN:1931-0587
2642-7214
DOI:10.1109/IVS.2011.5940524