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:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/1329271
• Authors:
  • Salvucci, Dario D
  • Mandalia, Hiren M
  • Kuge, Nobuyuki
  • Yamamura, Tomohiro
• Publication Date: 2007-6

Language

• English

Media Info

• Media Type: Print
• Features: Figures; References;
• Pagination: pp 532-542
• Serial:
  • Human Factors
  • Volume: 49
  • Issue Number: 3
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0018-7208
  • EISSN: 1547-8181
  • Serial URL: http://hfs.sagepub.com/

Subject/Index Terms

• TRT Terms: Accuracy; Behavior; Drivers; Intelligent transportation systems; Lane changing; Mathematical models; Methodology; Real time information; Simulation
• Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting; Safety and Human Factors; I72: Traffic and Transport Planning;

Filing Info

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