Lane-Change Detection Using a Computational Driver Model

Intelligent transportation systems (ITS) need the ability to infer driver intentions and detect intended maneuvers. This study introduces a robust, real-time system for detecting driver lane changes. Using a "model tracing" methodology, the proposed 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. 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. This proposed model-tracing system shows promise for incorporation into the next generation of ITS. It demonstrates both high accuracy over the course of a lane change with respect to time and lateral movement as well as high sample-by-sample accuracy at low false alarm rates.

  • Availability:
  • Authors:
    • Salvucci, Dario D
    • Mandalia, Hiren M
    • Kuge, Nobuyuki
    • Yamamura, Tomohiro
  • Publication Date: 2007-6


  • English

Media Info

  • Media Type: Print
  • Features: Figures; References;
  • Pagination: pp 532-542
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01053658
  • Record Type: Publication
  • Files: TRIS, ATRI
  • Created Date: Jul 8 2007 9:39PM