Fusion of Object Tracking and Dynamic Occupancy Grid Map

TL;DR

This paper presents a fusion method combining grid-based and feature-based environment modeling for autonomous driving, improving object tracking and environment understanding without needing to adapt models.

Contribution

It introduces a novel fusion approach that associates and combines different environment representations, enhancing object tracking and hypothesis generation without relying on object model assumptions.

Findings

Fusion improves object tracking over longer periods.

The method is evaluated in real-time on real sequences.

Fusion generates more accurate environment hypotheses.

Abstract

Environment modeling in autonomous driving is realized by two fundamental approaches, grid-based and feature-based approach. Both methods interpret the environment differently and show some situation-dependent beneficial realizations. In order to use the advantages of both methods, a combination makes sense. This work presents a fusion, which establishes an association between the representations of environment modeling and then decoupled from this performs a fusion of the information. Thus, there is no need to adapt the environment models. The developed fusion generates new hypotheses, which are closer to reality than a representation alone. This algorithm itself does not use object model assumptions, in effect this fusion can be applied to different object hypotheses. In addition, this combination allows the objects to be tracked over a longer period of time. This is evaluated with a quantitative evaluation on real sequences in real-time.