Traffic Ground Truth Estimation Using Multisensor Consensus Filter

Abstract

The estimation of traffic ground truth has traditionally been accomplished with a trusted reference detector or by human observation, either live or in time-coded video recordings. These approaches are limited by the error characteristics of any single trusted detector, observation error, or the temporal and spatial resolution of the recorded video used by the human observer. For conventional traffic management purposes, the output of a trusted detector is usually adequate. However, for unbiased performance assessment and comparative ranking of vehicle detectors, an accurate ground truth estimate is essential. With this objective, an automated detector testing system was implemented. This system used consensus filtering methods typically employed in the fusion of data from multisensor networks to optimally estimate ground truth. The algorithm continuously adjusted a level of confidence in each detector under test that was used to form a weighted consensus decision for the presence, speed, and length of each vehicle. Individual detectors under test were then assessed by comparison with the estimated ground truth record and by the confidence factors generated by the algorithm. This paper describes the algorithm and presents the testing methodology to determine its ability to estimate ground truth accurately with the use of synthetic traffic data for which absolute ground truth is known, as well as synthetic detector data derived from the ground truth with known injected errors. The application of this algorithm in the advanced traffic management systems detector testbed of the California Department of Transportation is described.