Abstract:
This paper proposes an approach for object
identification and tracking for autonomous vehicle application.
In this scheme, data from the vehicle’s onboard vision
and motion sensors are fused to identify the target 3D
dynamic features in the world coordinate. Here several simple
and basic linear dynamic models are combined to make
the approximation of the target’s unpredicted or complex
motion properties. With these basic linear dynamic models
a detailed description of the 3D target tracking system with
the interacting multiple models (IMM) for Extended Kalman
Filtering is presented. The target’s final state estimates are
obtained as a weighted combination of the outputs from
each different model. Performance of the proposed interacting
multiple dynamic model tracking algorithm is demonstrated
through experimental results.
