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However, it is very challenging to support this adaptive feature, since (1) the internal mechanism of adaptive stereo matching (ASM) has to be accurately modeled, and (2) scheduling ASM tasks on multiprocessors to generate the maximum quality is difficult under strict real-time constraints of smart vehicles. In this article, we propose a framework for constructing an ASM application and optimizing its output quality on smart vehicles. First, we empirically convert stereo matching into ASM by exploiting its inherent characteristics of disparity–cycle correspondence and introduce an exponential quality model that accurately represents the quality–cycle relationship. Second, with the explicit quality model, we propose an efficient quadratic programming-based dynamic voltage\u002Ffrequency scaling (DVFS) algorithm to decide the optimal operating strategy, which maximizes the output quality under timing, energy, and temperature constraints. Third, we propose two novel methods to efficiently estimate the parameters of the quality model, namely location similarity-based feature point thresholding and street scenario-confined CNN prediction. Results show that our DVFS algorithm achieves at least 1.61 times quality improvement compared to the state-of-the-art techniques, and average parameter estimation for the quality model achieves 96.35% accuracy on the straight road.",[11,12,13],"Chen, Fupeng","Yu, Heng","Ha, Yajun",[15,16,17],"0000-0002-1548-8243","0000-0002-0305-2135","0000-0003-4244-5916",2020,"https:\u002F\u002Fdoi.org\u002F10.1145\u002F3372784","W2995463625","ACM Transactions on Embedded Computing Systems",null,[24,25,26,27,28,29,30],"Computer science","Algorithm","Scheduling (production processes)","Feature (linguistics)","Matching (statistics)","Artificial intelligence","Mathematical 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