The growing demand for a more efficient maintenance of concrete bridges requires a model that tracks the deterioration of each bridge based on inspection data. Although it has been expected that machine learning could be applied to this problem, inspection data sparsely distributed over time are not suitable for machine learning in contrast to the continuous big data usually targeted. This study applies machine learning to a regression model of crack formation and propagation using inspection data to confirm the applicability. It includes the selection of the optimal algorithm, development of the model based on a novel methodology, and factor analysis using the model. Accordingly, the model was constructed by Gaussian process regression and it could appropriately extract the differences in the progress of crack damage due to multiple influential factors. The results demonstrate the excellent applicability of machine learning even to sparse data.

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Embedded processors for video image recognition require to address both the cost (

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This paper presents IMAPCAR, a 100GOPS programmable highly parallel vision processor LSI consuming less than 2 W of power for in-vehicle vision tasks of driver assistance systems. First, requirements of vision processors for driver assistance systems as well as the characteristics of vision tasks for safety are summarized. Next, features in the design of IMAPCAR are described in detail, which comparing with a previous design, improved the performance for major vision tasks by a factor of 2.5 while reduced 50% of power. Design choices taken by other in-vehicle vision processors are also compared and analyzed. Finally, technology perspectives of future invehicle vision processors are discussed.

SUMMARY This paper describes the real-time implementation of a vision-based overtaking vehicle detection method for driver assistance systems using IMAPCAR, a highly parallel SIMD linear array processor. The implemented overtaking vehicle detection method is based on optical flows detected by block matching using SAD and detection of the flows' vanishing point. The implementation is done efficiently by taking advantage of the parallel SIMD architecture of IMAPCAR. As a result, video-rate (33 frames/s) implementation could be achieved.