Spatio-Temporal Reasoning within a Neural Network framework for Intelligent Physical Systems

Existing functionality for intelligent physical systems (IPS), such as autonomous vehicles (AV), generally lacks the ability to reason and evaluate the environment and to learn from other intelligent agents in an autonomous fashion. Such capabilities for IPS is required for scenarios where an human intervention is unlikely to be available and robust long-term autonomous operation is necessary in potentially dynamic environments. To address these issues, the IPS will then need to reason about the interactions with these items through time and space. Incorporating spatio-temporal reasoning into the IPS will provide the capability to understand these interactions. This paper describes our proposed neural network framework that incorporates spatio-temporal reasoning for IPS. The preliminary experimental results addressing research challenges related to spatio-temporal reasoning within neural network framework for IPS are promising.