Comparing datasets for generalizing models of driver intent in dynamic environments

In light of growing attention of intelligent vehicle systems, we have present an assessment of methods for driver models that predict driver behaviors. This work looks at varying datasets to see the affects on intent detection algorithms. The motivation is to understand and assess how data is mapped from datasets to discrete states or modes of intent. Using a model of a human driver's decision making process to estimate intent, we build techniques for analyzing and learning human behaviors to improve understanding. We derive models based off of human perception and interaction with the environment (e.g. other vehicles on the road), that is generalizable and flexible enough to detect intent across different drivers. The resulting detection scheme is able to determine driver intent with high accuracy across multiple drivers, relying on a large dataset consisting of lane changes under varying environmental constraints. By comparing different labeling methods, we assess the effectiveness of learned models under different class variations. This allows us to derive accurate and general models for detecting intent that rely on the subtle variations and behaviors that humans exhibit while driving.