Tutorial: Knowledge-infused Learning for Autonomous Driving (KL4AD)

Author(s)

Ruwan Wickramarachchi, University of South Carolina - ColumbiaFollow
Cory Henson, Bosch Center for AIFollow
Sebastian Monka, Bosch Center for AIFollow
Daria Stepanova, Bosch Center for AIFollow
Amit Sheth, University of South Carolina - ColumbiaFollow

ORCID iD

Wickramarachchi: 0000-0001-5810-1849

Henson: 0000-0003-3875-3705

Stepanova: 0000-0001-8654-5121

Sheth: 0000-0002-0021-5293

Document Type

Workshop

Abstract

Autonomous Driving (AD) is considered as a testbed for tackling many hard AI problems. Despite the recent advancements in the field, AD is still far from achieving full autonomy due to core technical problems inherent in AD. The emerging field of neuro-symbolic AI and the methods for knowledge-infused learning are showing exciting ways of leveraging external knowledge within machine/deep learning solutions, with the potential benefits for interpretability, explainability, robustness, and transferability. In this tutorial, we will examine the use of knowledge-infused learning for three core state-of-the-art technical achievements within the AD domain. With a collaborative team from both academia and industry, we will demonstrate recent innovations using real-world datasets.

Publication Info

Preprint version The 21st International Semantic Web Conference, 2022.

© The Authors, 2022

APA Citation

Wickramarachchi, R., Henson, C., Monka, S., Stepanova, D., & Sheth, A. (2022). Tutorial: Knowledge-infused Learning for Autonomous Driving (KL4AD) [Conference preprint]. 21st International Semantic Web Conference (ISWC), Virtual/ Hangzhou, China.