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Lane-change detection using a computational driver model.

Dario D Salvucci1, Hiren M Mandalia, Nobuyuki Kuge

  • 1Department of Computer Science, Drexel University, 3141 Chestnut St., Philadelphia, PA 19104, USA. salvucci@cs.drexel.edu

Human Factors
|June 8, 2007
PubMed
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This study presents a real-time system for detecting driver lane changes using model tracing. The system accurately infers driver intentions from observed actions, crucial for advanced driver-assistance systems.

Area of Science:

  • Intelligent Transportation Systems
  • Driver Behavior Modeling
  • Real-time Systems

Background:

  • Evolving intelligent transportation systems (ITS) require methods to infer driver intentions.
  • Detecting intended maneuvers is critical for driver assistance.
  • Current systems need enhanced driver intention inference capabilities.

Purpose of the Study:

  • Introduce a robust, real-time system for detecting driver lane changes.
  • Infer driver intentions from observable actions using a model-tracing methodology.
  • Enhance the safety and functionality of intelligent transportation systems.

Main Methods:

  • Utilized a model-tracing methodology to simulate driver intentions and behaviors.
  • Employed a simplified, validated computational model of driver behavior.

Related Experiment Videos

  • Compared simulated model behavior with observed driver actions for intention inference.

    • Main Results:

    • Achieved 82% lane change detection within 0.5s in a driving simulator (5% false alarm rate).
    • Detected 93% of lane changes within 1s and 95% before significant lateral movement.
    • In real-world data, detected 61% within 0.5s and 84% before substantial lateral movement.

    Conclusions:

    • The model-tracing system demonstrates high accuracy in real-time lane change detection.
    • Achieved high sample-by-sample accuracy with low false alarm rates.
    • Shows significant promise for integration into next-generation intelligent transportation systems.