Browsing by Author "Lai, TC"

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A fast lane and vehicle detection approach for autonomous vehicles
(2005)
Wu, BF
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Lin, CT
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Chen, CJ
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Lai, TC
;
Liao, HL
This paper proposes a lane detection algorithm, a fuzzy-based vehicle detection approach and a distance measurement method for intelligent autonomous vehicles. The lane detection algorithm reduces computational load and has proven successful on highway and in urban settings at different velocities (110km/h and 50km/h, respectively). The proposed fuzzy-based vehicle detection approach named Contour Size Similarity (CSS), compares the contour size of objects by fuzzy rules. CSS efficiently distinguishes target obstacles, namely vehicles, from other objects detected and differentiates between obstacles and pattern on the road surface. After the nearest vehicle in the lane is found, we estimate its distance to the intelligent autonomous vehicle according to its position in the image. Then the information obtained would sever as aids for automatic driving of the intelligent autonomous vehicles.
A real-time robust lane detection approach for autonomous vehicle environment
(2004)
Wu, BF
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Chen, CJ
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Chiu, CC
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Lai, TC
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電控工程研究所
This paper proposes a real-time lane detection algorithm which provides adaptive region of interesting to reduce computational load and is robust for real road environment, e.g., road inclination, road width calibration. Compared to previous approaches, the flat road assumption is released in our approach so that the result is more close to real road environment. This work has been successfully verified on highway and urban with different velocities 110 km/h and 50 km/h, respectively, in the real vehicle, TAIWAN iTS-1, which is an experimental car of Taiwan ITS project. The algorithm is also demonstrated on the lanes which are with shadows, texts, crooks, or sheltered by other vehicles for sunny and night environment.(1)
Knowledge Management, habitual domains, and innovation dynamics
(2004)
Yu, PL
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Lai, TC
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資訊管理與財務金融系註：原資管所+財金所
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Department of Information Management and Finance
Knowledge Management (KM) with information technology (IT) has made tremendous progresses in recent years. It has helped many people in making decision and transactions. Nevertheless, without continuous expanding and upgrading our habitual domains (HD) and competence set (CS), KM may lead us to decision traps and making wrong decisions. This article introduces the concepts of habitual domains and competence set analysis in such a way that we could see where KM can commit decision traps and how to avoid them. Innovation dynamics, as an overall picture of continued enterprise innovation, is also introduced so that we could know the areas and directions in which KM can make maximum contributions and create value. KM empowered by HD can make KM even more powerful.