• Overview of different sensor data fusion taxonomies as well as different ways to model the environment (dynamic object tracking vs. occupancy grid) in the Bayesian framework including uncertainty quantification • Exploiting potential problems of sensor data fusion, e.g. data association, outlier treatment, anomalies, bias, correlation, or out-of-sequence measurements • Propagation of uncertainties from object recognition to decision making based on selected examples, e.g. the real-time vehicle pose estimation based on uncertain measurements of different sources (GPS, odometry, lidar) including the discussion of fault detection and localization (sensor drift, breakdown, outliers etc.) Sensor fusion overcomes the drawbacks of current sensor technology by combining information from many independent sources of limited accuracy and reliability. This makes the system less vulnerable to random and systematic failures of a single component. Multi-source information fusion avoids the perceptual limitations and uncertainties of a single sensor and forms a more comprehensive perception and recognition of the environment including static and dynamic objects. Through sensor fusion we combine readings from different sensors, remove inconsistencies and combine the information into one coherent structure. This kind of processing is a fundamental feature of all animal and human navigation, where multiple information sources such as vision, hearing and balance are combined to determine position and plan a path to a destination. In addition, several readings from the same sensor are combined, making the system less sensitive to noise and anomalous observations. In general, multi-sensor data fusion can achieve an increased classification accuracy of objects, improved state estimation accuracy, improved robustness for instance in adverse weather conditions, an increased availability, and an enlarged field of view. Emerging applications such as autonomous driving systems that are in direct contact and interact with the real world, require reliable and accurate information about their environment in real-time.
