Abstract-In this paper, we demonstrate a driver intent inference system that is 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 that are 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. Index Terms-Computer vision, driver assistance systems, driver intent inference, intelligent vehicles, sparse Bayesian learning (SBL).
Lane position, vehicle parameters, and driver head motion are utilized in this method REF .
Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Computer Science