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A field study of multimodal alerts for an autonomous threat detection system

Publication ,  Conference
Solovey, ET; Powale, P; Cummings, ML
Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
January 1, 2017

Every year, inattentive or impaired drivers strike law enforcement officials, emergency personnel, and other workers by the roadside. Preventative efforts include making at-risk parties more conspicuous to oncoming motorists in order to prompt safer driving behaviors. In contrast, this work evaluates active alerting mechanisms designed to induce defensive action from at-risk roadside personnel once a hazardous situation has been autonomously detected. This paper reports on field investigations with state police to capture their cognitive requirements for this dynamic environment, as well as the design of four alert prototypes for a high noise, low-light environment such as a highway shoulder. We discuss implications for such future autonomous systems and argue that such active defensive alert mechanisms could improve roadside safety and save lives.

Duke Scholars

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319584744

Publication Date

January 1, 2017

Volume

10276 LNAI

Start / End Page

393 / 412

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Solovey, E. T., Powale, P., & Cummings, M. L. (2017). A field study of multimodal alerts for an autonomous threat detection system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10276 LNAI, pp. 393–412). https://doi.org/10.1007/978-3-319-58475-1_29
Solovey, E. T., P. Powale, and M. L. Cummings. “A field study of multimodal alerts for an autonomous threat detection system.” In Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10276 LNAI:393–412, 2017. https://doi.org/10.1007/978-3-319-58475-1_29.
Solovey ET, Powale P, Cummings ML. A field study of multimodal alerts for an autonomous threat detection system. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. p. 393–412.
Solovey, E. T., et al. “A field study of multimodal alerts for an autonomous threat detection system.” Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10276 LNAI, 2017, pp. 393–412. Scopus, doi:10.1007/978-3-319-58475-1_29.
Solovey ET, Powale P, Cummings ML. A field study of multimodal alerts for an autonomous threat detection system. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. p. 393–412.
Journal cover image

Published In

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

DOI

EISSN

1611-3349

ISSN

0302-9743

ISBN

9783319584744

Publication Date

January 1, 2017

Volume

10276 LNAI

Start / End Page

393 / 412

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences