FURI | Spring 2020
A Probabilistic and Confidence-Driven Approach to Theory of Mind Models in Autonomous Agents
By nature, humans can be very unpredictable with their actions, which makes it difficult to create a perfect Theory of Mind (ToM) model to attempt to predict those actions. Having such a model is extremely important to an autonomous car’s motion planning for safe and efficient interaction with other vehicles. The researcher’s work addresses this uncertainty by introducing a Bayesian confidence value within the model to predict the location of other vehicles with a probability distribution. The model will be validated using the Berkeley INTERACTION dataset. The long-term goal is to create a ToM model with mutual intent inference.
Hometown: O'Fallon, Missouri, United States
Graduation date: Spring 2021