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| Title | Usecase-Based Generic Framework For Data Fusion And Decision Making In Autonomous Driving |
|---|---|
| ID_Doc | 60387 |
| Authors | Ignatious H.A.; El-Sayed H. |
| Year | 2024 |
| Published | 20th International Wireless Communications and Mobile Computing Conference, IWCMC 2024 |
| DOI | http://dx.doi.org/10.1109/IWCMC61514.2024.10592422 |
| Abstract | In the upcoming era influenced by autonomous vehicle (AV) technology, potential outcomes encompass reduced traffic congestion, enhanced road safety, and interconnected vehicles. Projections suggest a substantial upsurge in the AV technology market, anticipated to reach a value of USD 556.67 billion by 2026, indicating rapid expansion. Streamlined analysis and incorporation of key usage scenarios are imperative for the effective operation of autonomous vehicle technology. Advanced networking capabilities such as 5G, latency management, Smart Cities, Internet of Things (IoT), efficient data management, and Vehicle-to-Everything (V2X) communication emerge as critical scenarios necessitating precise solutions to ensure safety and optimize AV performance. Specifically focusing on V2X and managing multiple roadside events, ensuring the precision of data and meticulous contextual information remains crucial for achieving its primary goals. Taking this groundwork into account, this research presents a flexible framework designed to preprocess and merge a range of sensory data gathered from AVs, establishing an accurate context. This context becomes the cornerstone for swift decision-making by AVs, enabling them to adeptly navigate through different roadside scenarios. © 2024 IEEE. |
| Author Keywords | Autonomous Vehicles (AVs); Contextual Awareness; Data Fusion; Data-Preprocessing; Machine Learning (ML) |
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