| Budget Amount *help |
¥13,910,000 (Direct Cost: ¥10,700,000、Indirect Cost: ¥3,210,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥4,160,000 (Direct Cost: ¥3,200,000、Indirect Cost: ¥960,000)
Fiscal Year 2016: ¥6,890,000 (Direct Cost: ¥5,300,000、Indirect Cost: ¥1,590,000)
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| Outline of Final Research Achievements |
This study focuses on technology to realize an automotive robot that moves intelligently while recognizing the surrounding environment. There are three contributions. First, a design method of a signal shaping mechanism, called prediction governor, was developed. The prediction governor shapes predicted reference signals to reduce the influence of prediction errors on automatic driving. In this study, the prediction governor's usefulness was confirmed through lane-keeping control experiments with a miniature scale experimental apparatus. Then, a data compaction method of deep neural networks, which are used in environmental prediction, was proposed. Finally, a method to control the surrounding lighting environment itself and make it work on automotive robots was presented.
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