Copyright 2017 - TUE - Mobile Perception Systems

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Sensing the environment around the vehicle requires giving real-world significance to sensory signals. We call this “semantic interpretation”. For example, the vehicle must be able to decide whether a pixel in an image belongs to a tree or to a pedestrian. This semantic interpretation is done by advanced pattern recognition software, of which the principles are taught in this course. For intelligent vehicles to be safe, the interpretation of sensory signals must be done extremely reliably in a wide variety of environmental conditions. This is achieved by fusing signals from multiple and, most importantly, from different sensor modalities, as this allows mitigating the pro's and con's of different sensors. Sensor fusion is performed by probabilistic filtering techniques, of which the most well-known is the Kalman filter. This and other more advanced filtering techniques, such as particle filtering, will be taught in this course. Besides lectures, the course also incorporates programming assignments in which students will develop a pedestrian detection system.

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