Emerging driver assistance systems, such as look-ahead cruise controllers for heavy duty vehicles, require high precision digital maps. This contribution presents a road grade estimation algorithm for fusion of GPS and vehicle real-time sensor data, with measurements from previous runs over the same road segment. The resulting road grade estimate is thus enhanced using measurements from additional traversals of known roads. Distributed data fusion is utilized to ensure that the storage requirement of known roads does not increase when additional measurements are processed. The implemented algorithm, which is based on extended Kalman filtering and smoothing, is described in detail. Experiments on a Scania test vehicle show the advantages and some of the challenges with the proposed approach.

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We consider the optimization of multicast over packet-switched communication networks with a non-zero packet-loss probability.For the system setup consisting of a number of multiple-description coders, we jointly optimize these coders. We propose an analytic solution, asymptotically optimal in the number of multiple-description coders. The analytic solution allows for fast system adaptation to changing network conditions. A locally optimal optimization algorithm that is useful when the number of multicast groups is small is derived. Simulations show that the utilization of the analytic solution incurs a low overhead on the performance when compared to the locally optimal solution, even for a small number of multipledescription coders.