Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning

In this paper we demonstrate a driver intent inference system (DIIS) based on lane positional information, vehicle parameters, and driver head motion. We present robust computer vision methods for identifying and tracking freeway lanes and driver head motion. These algorithms are then applied and evaluated on real-world data collected in a modular intelligent vehicle test-bed. Analysis of the data for lane change intent is performed using a sparse Bayesian learning methodology. Finally, the system as a whole is evaluated using a novel metric and real-world data of vehicle parameters, lane position, and driver head motion.