Abstract
(a) Objective. This paper introduces a robust, real-time system for detecting driver lane changes. (b) 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. (c) 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/his observable actions. (d) 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 1/4 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
Abstract
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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.
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