Journal article
Inferring Driver Intent: A Case Study in Lane-Change Detection
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, v 48(19), pp 2228-2231
Sep 2004
Abstract
This paper introduces a robust, real-time system for detecting driver lane changes. Under the framework of a “mind-tracking architecture,” the system simulates a set of possible driver intentions and their resulting behaviors using an approximation of a rigorous and validated model of driver behavior. The system compares these simulations with a driver's actual observed behavior, thus inferring the driver's unobservable intentions. The paper demonstrates how this system can detect a driver's intention to change lanes, achieving an accuracy of 85% with a false alarm rate of 4%; detecting 80% of lane changes within 1/2 second and 90% within 1 second; and detecting 90% before the vehicle moves 1/4 of the lane width laterally — that is, approximately when the vehicle first touches the destination lane line.
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Details
- Title
- Inferring Driver Intent: A Case Study in Lane-Change Detection
- Creators
- Dario D. Salvucci - Drexel University
- Publication Details
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting, v 48(19), pp 2228-2231
- Publisher
- Sage
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science (Computing)
- Other Identifier
- 991021868995304721