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.

中文翻译：

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通过数据融合方法获取自动驾驶汽车的协作智能以进行目标检测

在自动驾驶汽车（AV）中，为了有效地利用获得的资源，大数据分析将成为从各种传感器和执行器中提取有价值信息的可靠来源。结合远程信息处理和实时调查功能提取的数据构成了自动驾驶汽车的生机勃勃的资产。为了证明大数据分析的重要性，本研究提出了一种用于AV实时大数据分析的强大架构，该架构的确与新兴范式的最新趋势和发展保持同步。为了实现更好的AV性能，需要实现大量的传感器和独立系统，并且所提出的模型着重于区分对象并处理视觉信息以决定路径的独立传感器。为了达到上述目的，提出了一种传感器融合机制，该机制融合了3D摄像机传感器数据和激光雷达传感器信息，为路径选择提供了优化的解决方案。此外，为了提高传感器融合机制的效率，设计了三种算法，即重叠算法，顺序相加算法，距离聚焦算法。所提出的方法是为了最大程度地利用巨大的数据集，以便对AV进行实时处理。距离聚焦算法旨在提高传感器融合机制的效率。所提出的方法是为了最大程度地利用巨大的数据集，以便对AV进行实时处理。距离聚焦算法旨在提高传感器融合机制的效率。所提出的方法是为了最大程度地利用巨大的数据集，以便对AV进行实时处理。