@article{ref1,
title="Lane-change detection using a computational driver model",
journal="Human factors",
year="2007",
author="Salvucci, Dario D. and Mandalia, Hiren M. and Kuge, Nobuyuki and Yamamura, T.",
volume="49",
number="3",
pages="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 &quot;model tracing&quot; 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 real-time detection of driver lane changes, the system shows good promise for incorporation into the next generation of intelligent transportation systems.<p /> <p>Language: en</p>",
language="en",
issn="0018-7208",
doi="",
url="http://dx.doi.org/"
}