Sensor Data Fusion techniques provide higher-level information by spatio-temporal data integration, the exploitation of redundant and complementary information, and the available context. Important applications exist in logistics, advanced driver assistance systems, medical care, public security, defence, aerospace, robotics, industrial production, precision agriculture, trac monitoring, sensor positioning, and resource management.
Key Aspects:
Distributed sensor fusion in complex scenarios
Fusion of heterogeneous sensor information
Exploitation of non-sensor context knowledge
Artificial Intelligence of autonomous systems
Risk analysis / data driven sensor management
10月10日
2017
10月12日
2017
注册截止日期
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