Along with emerging technologies and increasing demands, autonomation has become a significant trend in current transportation systems. Within this context, the autonomous transportation system (ATS) framework hinges on functions that serve as fundamental units to support its operation. Recognizing the divisions among these function areas can enhance our understanding of their meanings and interrelationships. This study introduces a method for dividing function areas within the ATS framework, grounded in text similarity, to mitigate reliance on subjective experience. Precisely, this method quantifies the similarity between functions based on their textual descriptions, and implements hierarchical clustering to delineate them into distinct function areas. To validate the effectiveness of this proposed method, a case study analyzing a vehicle automatic driving scenario was conducted. The results demonstrate that our approach can efficiently divide function areas, producing clustering outcomes that possess superior accuracy and purity when juxtaposed with reference classifications. Consequently, this method has the potential to facilitate the formulation of function areas within ATS, thereby supporting the autonomous operation and construction of ATS. Moreover, its applicability extends beyond ATS, showing promise for other clustering problems that involve multiple texts, such as in text classification.

This paper describes a fully parallel real-time Mandarin dictation machine which recognizes Mandarin speech with almost unlimited texts and very large vocabulary for the input of Chinese characters to computers. Isolated syllables including the tones are first recognized using specially trained hidden Markov models with special feature parameters, the exact characters are then identified from the syllables using a Markov Chinese language model, because every syllable can represent many different homonym characters. The real-time implementation is in Occam language on a transputer system with 10 T8OO processors operating in parallel. The overall correction rate for the final output characters is about 89%.

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