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Please use this identifier to cite or link to this item: http://hdl.handle.net/1860/2590

Title: Lane-change detection using a computational driver model
Authors: Salvucci, Dario D.
Mandalia, Hiren M.
Kuge, Nobuyuki
Yamamura, Tomohiro
Issue Date: Jun-2007
Publisher: Human Factors & Ergonomics Society
Citation: Human Factors, 49(3): pp. 532-542.
Abstract: Objective: This paper introduces a robust, real-time system for detecting driver lane changes. Background: As intelligent transportation systems evolve to assist drivers in their intended behaviors, the systems have demonstrated a need for methods of inferring driver intentions and detecting intended maneuvers. Method: Using a “model tracing” methodology, our system simulates a set of possible driver intentions and their resulting behaviors using a simplification of a previously validated computational model of driver behavior. The system compares the model’s simulated behavior with a driver’s actual observed behavior and thus continually infers the driver’s unobservable intentions from her or his observable actions. Results: For data collected in a driving simulator, the system detects 82% of lane changes within 0.5 s of maneuver onset (assuming a 5% false alarm rate), 93% within 1 s, and 95% before the vehicle moves one fourth of the lane width laterally. For data collected from an instrumented vehicle, the system detects 61% within 0.5 s, 77% within 1 s, and 84% before the vehicle moves one-fourth of the lane width laterally. Conclusion: The model-tracing system is the first system to demonstrate high sample-by-sample accuracy at low false alarm rates as well as high accuracy over the course of a lane change with respect to time and lateral movement. Application: By providing robust realtime detection of driver lane changes, the system shows good promise for incorporation into the next generation of intelligent transportation systems.
URI: http://dx.doi.org/10.1518/001872007X200157
Appears in Collections:Faculty Research and Publications (Comp Sci)

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