A Neural Network-based Multisensor Data Fusion Approach for Enabling Situational Awareness of Vehicles

Albert Budi Christian, Chih Yu Lin, Cheng Wei Lee, Lan Da Van, Yu Chee Tseng

研究成果: Conference contribution同行評審

摘要

With the growing number of research studies on Vehicle-to-Vehicle (V2V) communication applications, situational awareness becomes one of major challenges for autonomous vehicles. Autonomous vehicle needs to predict the movement and trajectories of surrounding vehicles accurately in order to make a better decision making. The ability to recognize vehicles' surroundings has become important in order to enable situational awareness and navigate the vehicle safely. In this paper, we propose a neural network called Mapping Decision Feedback Neural Network (MDFNN) to tackle the vehicle identification (VID) issue in V2V communication. According to the MDFNN infrastructure, two types of MDFNN namely as Grid-based MDFNN and Bounding box-based MDFNN are proposed. The MDFNN fuses image, V2V interface, GPS, magnetometer, and speedometer data (i.e., multi-sensor data and V2V communication) to enable situational awareness. MDFNN utilizes the mapping decision feedback information in the proposed deep learning neural network structure. With this improvement, a greatly improved accuracy can help to resolve the VID issue. Our experiment's result shows 85% of accuracy for Grid-based MDFNN.

原文English
主出版物標題Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面199-205
頁數7
ISBN(電子)9780738142623
DOIs
出版狀態Published - 十二月 2020
事件1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020 - Taipei, Taiwan
持續時間: 3 十二月 20205 十二月 2020

出版系列

名字Proceedings - 2020 International Conference on Pervasive Artificial Intelligence, ICPAI 2020

Conference

Conference1st International Conference on Pervasive Artificial Intelligence, ICPAI 2020
國家Taiwan
城市Taipei
期間3/12/205/12/20

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