A field study of multimodal alerts for an autonomous threat detection system
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
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- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
Citation
Published In
DOI
EISSN
ISSN
ISBN
Publication Date
Volume
Start / End Page
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences