Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning

Authors:
J. C. McCall;D. P. Wipf;M. M. Trivedi;B. D. Rao
Affiliations:
Microsoft Corp., Redmond;-;-;-
Venue:
IEEE Transactions on Intelligent Transportation Systems
Year:
2007

Citing 0
Cited 12

Turn-Intent Analysis Using Body Pose for Intelligent Driver Assistance

IEEE Pervasive Computing
Driver monitoring for a human-centered driver assistance system

Proceedings of the 1st ACM international workshop on Human-centered multimedia
Lane tracking with omnidirectional cameras: algorithms and evaluation

EURASIP Journal on Embedded Systems
IMM-based lane-change prediction in highways with low-cost GPS/INS

IEEE Transactions on Intelligent Transportation Systems
On the roles of eye gaze and head dynamics in predicting driver's intent to change lanes

IEEE Transactions on Intelligent Transportation Systems
Head pose estimation and augmented reality tracking: an integrated system and evaluation for monitoring driver awareness

IEEE Transactions on Intelligent Transportation Systems
Maneuver prediction for road vehicles based on a neuro-fuzzy architecture with a low-cost navigation unit

IEEE Transactions on Intelligent Transportation Systems
A complete chronicle discovery approach: application to activity analysis

Expert Systems: The Journal of Knowledge Engineering
On the design and evaluation of robust head pose for visual user interfaces: algorithms, databases, and comparisons

Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
Road type classification through data mining

Proceedings of the 4th International Conference on Automotive User Interfaces and Interactive Vehicular Applications
An application of a fuzzy classifier extracted from data for collision avoidance support in road vehicles

Engineering Applications of Artificial Intelligence
A comparison of selected simple supervised learning algorithms to predict driver intent based on gaze data

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

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.