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Tracking Multiple Vehicles Using a Variational Radar Model
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DOI：10.1109/TITS.2018.2879041.png)
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Abstract
En 中文
High-resolution radar sensors are able to resolve multiple detections per object and, therefore, provide valuable information for vehicle environment perception. For instance, multiple detections allow us to infer the size of an object or to measure the object's motion more precisely. Yet, the increased amount of data raises the demands on tracking modules; measurement models that are able to process multiple detections for an object are necessary and measurement-to-object associations become more complex. This paper presents a new variational radar model for tracking vehicles using radar detections and demonstrates how this model can be incorporated into a randomfinite-set-based multi-object filter. The measurement model is learned from actual data using variational Gaussian mixtures and avoids excessive manual engineering. In combination with the multi-object tracker, the entire process chain from raw measurements to the resulting tracks is formulated probabilistically. The presented approach is evaluated on experimental data, and it is demonstrated that the data-driven measurement model outperforms a manually designed model.
Keywords:
Radar
tracking
variational methods
autonomous vehicles
sensor fusion
machine learning
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