Sensornets for Remote Vehicle Classification (SRVC)

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Project Summary

SRVC is developing automated vehicle classification systems based on networks of small, battery-powered and wireless, intelligent sensors that can be easily deployed with brief setup time (tens of minutes), with accurate vehicle information (as good as or better than human observers), and communicate this information to a central monitoring site. Current approaches are not rapidly deployable, accurate enough, and lack the ability to relay data in real-time to central site. The research challenges we are considering in this work are understanding the communications requirements for traffic monitoring systems (both short-range wireless inside a traffic sensornet, and wide-area to a central Traffic Management System), developing self-configuring traffic monitoring systems (including local- and wide-area communications and sensor configuration and placement), and integrating prior work from SURE-SE and SURE-FT with these new results.


SRVC is supported by USC/CSULB METRANS grant #tba.


Publications and Datasets

  • Chengjie Zhang and John Heidemann. Evaluating Signature Matching in a Multi-Sensor Vehicle Classification System (extended) . Technical Report ISI-TR-2011-675, USC/Information Sciences Institute, November, 2011.
  • For a more complete list of related publications, see the I-LENSE publications page.

    SRVC continues to distribute data collected as part of the SURE projects, including a 1500-detection dataset taken at USC August 6, 2004.

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    Last update: $Date: 2011-11-21 20:15:30 -0800 (Mon, 21 Nov 2011) $.