In an autonomous vehicle (AV), in order to efficiently exploit the acquired resources, big data analyses will be a reliable source for extracting valuable information from various sensors and actuators. The data extracted with the combined ability of telematics and real-time investigation forms the vibrant asset for self-driving cars. To demonstrate the significances of big data analysis, this study proposes a competent architecture for real-time big data analysis for an AV, which indeed keeps pace with the latest trends and advancement concerning an emerging paradigm. There are a massive amount of sensors and independent systems needed to be realised for better competence in an AV, and the proposed model focuses on independent sensors that distinguish objects and handles visual information to decide the path. In order to attain the objective as mentioned above, a sensor fusion mechanism is proposed, which combines 3D camera sensor data and Lidar sensor information to provide an optimised solution for path selection. Furthermore, three algorithms, namely overlapping algorithm, sequential adding algorithm, the distance-focused algorithm is designed for higher efficiency in sensor fusion mechanism. The proposed methodology is for the best exploitation of the enormous dataset, meant for real-time processing for an AV.

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BACKGROUND: For campus workplace secure text mining, robotic assistance with feature optimization is essential. The space model of the vector is usually used to represent texts. Besides, there are still two drawbacks to this basic approach: the curse and lack of semantic knowledge. OBJECTIVES: This paper proposes a new Meta-Heuristic Feature Optimization (MHFO) method for data security in the campus workplace with robotic assistance. Firstly, the terms of the space vector model have been mapped to the concepts of data protection ontology, which statistically calculate conceptual frequency weights by term various weights. Furthermore, according to the designs of data protection ontology, the weight of theoretical identification is allocated. The dimensionality of functional areas is reduced significantly by combining standard frequency weights and weights based on data protection ontology. In addition, semantic knowledge is integrated into this process. RESULTS: The results show that the development of the characteristics of this process significantly improves campus workplace secure text mining. CONCLUSION: The experimental results show that the development of the features of the concept hierarchy structure process significantly enhances data security of campus workplace text mining with robotic assistance.

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