This paper proposes a method for using previewed road geometry from a high-fidelity map to improve estimates of planar vehicle states in the presence of unmodeled sensor bias errors. Using well-established, linear models for representing human driver behavior and for planar vehicle states, a causal link between previewed road geometry and vehicle states can be derived. Cast as an augmented, closed-loop linear system, the total driver-vehicle-road system’s states are estimated using a Kalman filter. Estimation results from this filter using simulated noisy measurements of vehicle states and map-based measurements of previewed road geometry are compared to standard Kalman filters with identical measurements of vehicle states alone. The effects of errors in driver modeling, vehicle nonlinearity, and measurement disturbances on the estimator’s fidelity are also examined and discussed.
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ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference
October 17–19, 2012
Fort Lauderdale, Florida, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-4531-8
PROCEEDINGS PAPER
Model-Based Vehicle State Estimation Using Previewed Road Geometry and Noisy Sensors
Alexander A. Brown,
Alexander A. Brown
The Pennsylvania State University, University Park, PA
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Sean N. Brennan
Sean N. Brennan
The Pennsylvania State University, University Park, PA
Search for other works by this author on:
Alexander A. Brown
The Pennsylvania State University, University Park, PA
Sean N. Brennan
The Pennsylvania State University, University Park, PA
Paper No:
DSCC2012-MOVIC2012-8762, pp. 591-600; 10 pages
Published Online:
September 17, 2013
Citation
Brown, AA, & Brennan, SN. "Model-Based Vehicle State Estimation Using Previewed Road Geometry and Noisy Sensors." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 3: Renewable Energy Systems; Robotics; Robust Control; Single Track Vehicle Dynamics and Control; Stochastic Models, Control and Algorithms in Robotics; Structure Dynamics and Smart Structures; Surgical Robotics; Tire and Suspension Systems Modeling; Vehicle Dynamics and Control; Vibration and Energy; Vibration Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 591-600. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8762
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