[TMP-082] Multi-Relational Contextual Reasoning for Complex Scene Generation for Autonomous Vehicle Data

Contact person:  Loris Bozzato, ( bozzato@fbk.eu)

Internal Partners:

1. Fondazione Bruno Kessler, Loris Bozzato, ghidini@fbk.eu
2. Technical University of Vienna, Thomas Eiter, eiter@kr.tuwien.ac.at 

External Partners:

1. BOSCH Deutschland, Stepanova Daria

The previous requirements fit very well with the capabilities of MR-CKR: on the one hand, we have different contexts in which the inputs need to be modified to suit a different diagnosis of failure of the model. On the other hand, we can exploit the different relations by having one relation that specifies that inputs are more modifiable in one context than another and another relation that describes whether one diagnosis is a special case of another. Additionally, it allows us to incorporate global knowledge such that we can only modify inputs in such a manner that the result is still “”realistic””, i.e., satisfies the axioms in the global knowledge.

In this work, we provide a prototype specialized in generating similar and problematic scenes in the domain of Autonomous Driving.

Results Summary

We show that the proposed approach provides high-quality semantic segmentation from the robot’s perspective, with accuracy comparable to the original one. In addition, we exploited the gained information and improved the recognition performance of the deep network for the lower viewpoints and showed that the small robot alone is capable of generating high-quality semantic maps for the human partner. The computations are close to real time, so the approach enables interactive applications.

Tangible Outcomes

1. Prototype implementation: https://github.com/raki123/MR-CKR