Abstract
A vehicle emits sound as it travels along the road, which can be used as a kind of robust feature for traffic monitoring. In this paper, an acoustic-based lane detection approach is introduced for a multilane traffic monitoring system. First, a microphone array is designed according to a typical Chinese highway configuration. The design is based on the cross-array structure, and the cross-correlation matrix from the two subarrays in the selected working frequency band is calculated for the subsequent traffic monitoring operations. Then, a cross section across the road is constructed by beamforming, in which the single-source assumption can be applied, and the passing vehicle azimuth is detected by the proposed rank-1 Multiple Signal Classification (MUSIC) algorithm. Finally, a Parzen-window-based technique is proposed to estimate the vehicle azimuth probability density function (pdf) from the individual azimuth observations. Lane centers and boundaries can be revealed from the peak and valley patterns of the estimated pdf. A prototype traffic monitoring system is developed, and several lane detection approaches are compared in both simulated and real-world environments in the developed system framework. The experimental results exhibit the efficiency of the proposed approach.
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