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