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<p>The general problem of recognizing both horizontal and vertical road curvature parameters while driving along the road has been solved recursively. A differential geometry representation decoupled for the two curvature components has been selected. Based on the planar solution of E.D. Dickmanns and A. Zapp (1986) and its refinements, a simple spatio-temporal model of the driving process makes it possible to take both spatial and temporal constraints into account effectively. The estimation process determines nine road and vehicle state parameters recursively at 25 Hz (40 ms) using four Intel 80286 and one 386 microprocessors. Results with the test vehicle (VaMoRs), which is a 5-ton van, are given for a hilly country road.</p>
recursive 3D road curvature recognition; computer vision; pattern recognition; Intel 386; spatial constraints; computerised navigation; ego-state recognition; differential geometry representation; spatio-temporal model; temporal constraints; Intel 80286; VaMoRs; computational geometry; computer vision; computerised navigation; computerised pattern recognition; road vehicles

B. Mysliwetz and E. Dickmanns, "Recursive 3-D Road and Relative Ego-State Recognition," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 199-213, 1992.
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