Lane-change detection using a computational driver model.
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.


METHODS
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.


CONCLUSIONS
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.


CONCLUSIONS
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.
Salvucci
Dario D
DD
Department of Computer Science, Drexel University, 3141 Chestnut St., Philadelphia, PA 19104, USA. salvucci@cs.drexel.edu
Mandalia
Hiren M
HM
Kuge
Nobuyuki
N
Yamamura
Tomohiro
T
D001334:Automobile Driving / Q000523:psychology; D003071:Cognition; D006801:Humans; D008960:Models, Psychological