For satisfactory traffic management of an intelligent transport system, it is vital that traffic\nmicrowave radar detectors (TMRDs) can provide real-time traffic information with high accuracy. In\nthis study, we develop several information-aided smart schemes for traffic detection improvements\nof TMRDs in multiple-lane environments. Specifically, we select appropriate thresholds not only for\nremoving noise from fast Fourier transforms (FFTs) of regional lane contexts but also for reducing\nFFT side lobes within each lane. The resulting FFTs of reflected vehicle signals and those of clutter\nare distinguishable. We exploit FFT and lane-/or time stamp-related information for developing\nsmart schemes, which mitigate adverse effects of lane-crossing FFT side lobes of a vehicle signal.\nAs such, the proposed schemes can enhance the detection accuracy of both lane vehicle flow and\ndirectional traffic volume. On-site experimental results demonstrate the advantages and feasibility of\nthe proposed methods, and suggest the best smart scheme.
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