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Sensors 2010, 10(1), 860-875; doi:10.3390/s100100860
Article

Vision-Based Traffic Data Collection Sensor for Automotive Applications

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Received: 1 December 2009; in revised form: 19 January 2010 / Accepted: 20 January 2010 / Published: 22 January 2010
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Abstract: This paper presents a complete vision sensor onboard a moving vehicle which collects the traffic data in its local area in daytime conditions. The sensor comprises a rear looking and a forward looking camera. Thus, a representative description of the traffic conditions in the local area of the host vehicle can be computed. The proposed sensor detects the number of vehicles (traffic load), their relative positions and their relative velocities in a four-stage process: lane detection, candidates selection, vehicles classification and tracking. Absolute velocities (average road speed) and global positioning are obtained after combining the outputs provided by the vision sensor with the data supplied by the CAN Bus and a GPS sensor. The presented experiments are promising in terms of detection performance and accuracy in order to be validated for applications in the context of the automotive industry.
Keywords: automotive sensor; vehicle detection; computer vision; distance accuracy automotive sensor; vehicle detection; computer vision; distance accuracy
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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MDPI and ACS Style

Llorca, D.F.; Sánchez, S.; Ocaña, M.; Sotelo, M.A. Vision-Based Traffic Data Collection Sensor for Automotive Applications. Sensors 2010, 10, 860-875.

AMA Style

Llorca DF, Sánchez S, Ocaña M, Sotelo MA. Vision-Based Traffic Data Collection Sensor for Automotive Applications. Sensors. 2010; 10(1):860-875.

Chicago/Turabian Style

Llorca, David F.; Sánchez, Sergio; Ocaña, Manuel; Sotelo, Miguel. A. 2010. "Vision-Based Traffic Data Collection Sensor for Automotive Applications." Sensors 10, no. 1: 860-875.


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