Tracking and data association
Toward a Symbolic Representation of Intensity Changes in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kalman filtering: theory and practice
Kalman filtering: theory and practice
Vision-based vehicle guidance
Vehicles capable of dynamic vision: a new breed of technical beings?
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Robust real-time ground plane motion compensation from a moving vehicle
Machine Vision and Applications
International Journal of Computer Vision
Model-based tracking of complex innercity road intersections
Mathematical and Computer Modelling: An International Journal
FhG-Co-driver: From map-guided automatic driving by machine vision to a cooperative driver support
Mathematical and Computer Modelling: An International Journal
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Turn-Intent Analysis Using Body Pose for Intelligent Driver Assistance
IEEE Pervasive Computing
A sensor fusion framework using multiple particle filters for video-based navigation
IEEE Transactions on Intelligent Transportation Systems
Integrated real-time vision-based preceding vehicle detection in urban roads
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
MARVEL: multiple antenna based relative vehicle localizer
Proceedings of the 18th annual international conference on Mobile computing and networking
MARVEL: multiple antenna based relative vehicle localizer
Proceedings of the 18th annual international conference on Mobile computing and networking
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Currently available driver assistance systems (i) warn the driver based on vehicle state sensors (e.g., door open, outside temperature near or below the freezing point), (ii) offer route guidance information (navigation systems based on GPS and digital road maps), or—in some critical situations—(iii) even actively influence vehicle handling under carefully delimited conditions (anti-blocking-system, electronic-stability-program).This contribution reports about investigations to combine passive GPS- and map-based route guidance with model-based machine vision in order to automatically assess or even execute driving maneuvers in inner-city traffic situations. Information provided by todays route guidance systems is treated as a generic description of lane structure. The schematic description of lane structures extractable from commercially available standard digital maps is automatically instantiated by a machine vision approach which interprets video image sequences recorded by cameras from within a driving vehicle. The resulting model of the lane structure in front of the vehicle is subsequently exploited in order to control vehicle maneuvers in real-time as a proof of principal system competences. The machine-vision-based execution of driving maneuvers can at any time be overridden by the driver.