The VTI National Transport Library Catalogue

Automated accident detection system Harlow, Charles ; Wang, Yu

By: Harlow, CharlesContributor(s): Wang, YuPublication details: Transportation Research Record, 2001Description: nr 1746, s. 90-3Subject(s): USA | Accident | Detection | Automatic | Junction | Traffic flow | Data acquisition | Time | Traffic signal | | Recording | Acoustic signal | 81 | 21Bibl.nr: VTI P8167:1746Location: Abstract: The development of a system for automatically detecting and reporting traffic accidents at intersections was considered. A system with these properties would be beneficial in determining the cause of accidents and could also be useful in determining the features of the intersection that have an impact on safety. A complete system would automatically detect and record traffic conditions associated with accidents such as time of the accident, video of the accident, and the traffic light signal controller parameters. The basic research required to develop the system is considered. This involves developing methods for processing acoustic signals and recognizing accident events from the background traffic events. A database of vehicle crash sounds, car braking sounds, construction sounds, and traffic sounds was created. The mel-frequency cepstral coefficients were computed as a feature vector for input to the classification system. A neural network was used to classify these features into categories of crash and noncrash events. The classification testing results achieved 99% accuracy.
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The development of a system for automatically detecting and reporting traffic accidents at intersections was considered. A system with these properties would be beneficial in determining the cause of accidents and could also be useful in determining the features of the intersection that have an impact on safety. A complete system would automatically detect and record traffic conditions associated with accidents such as time of the accident, video of the accident, and the traffic light signal controller parameters. The basic research required to develop the system is considered. This involves developing methods for processing acoustic signals and recognizing accident events from the background traffic events. A database of vehicle crash sounds, car braking sounds, construction sounds, and traffic sounds was created. The mel-frequency cepstral coefficients were computed as a feature vector for input to the classification system. A neural network was used to classify these features into categories of crash and noncrash events. The classification testing results achieved 99% accuracy.

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