In unfamiliar environments, people need assistance to find their way. One predominant form of such assistance is maps. In constructing these maps, there is a conflict between concentrating on the essential information for wayfinding, namely the route, and providing overview information of the environment. The former eases information extraction by reducing visual clutter, the latter allows for reorientation in the environment even if the route has been left. In this paper we present route aware maps, an approach that combines the best of both (map) worlds. We argue how route information can be embedded in its surrounding environment, i.e., the global spatial context, without introducing unnecessary visual clutter. We present a construction process that results in route aware maps and detail each step of this process. Route aware maps shall ease information extraction by focusing on the route as the crucial piece of information and at the same time impart the feeling of efficient and safe navigation by keeping the wayfinder in global context. Providing a global context in route following invokes spatial awareness with respect to the overall environment and, thus, decreases the (felt) risks of making wayfinding errors.

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This paper proposes a new method for estimating Automatic Identification System (AIS) coverage empirically from received transmissions. The method is appropriate for stationary coverage assets, as distinct from aircraft and satellites. The key idea behind the method is to interpolate probabilistically between AIS reports in order to reconstruct where the missed transmissions might have occurred. These hypothetical missed transmissions then supplement the received ones in a coverage estimate based on a Bayesian treatment of a binomial model of reception. The final estimate of the coverage is implemented over a spatial grid. The method is demonstrated on simulated AIS data and was found to have lower mean squared error than a previously published method. Assumptions and potential weaknesses of the new method are discussed.

We present a method for addressing probabilistic queries about the location of a vessel in the time interval between two position reports, such as from the Automatic Identification System (AIS). The heart of the method is the random generation of physically feasible paths connecting the two reports. The method empowers operators to answer probabilistic questions about any characteristic of the unknown true path. For illustrative purposes, we demonstrate the use of the method to identify which of several vessels is the most likely perpetrator, in a fictitious scenario in which illegal dumping of waste matter has taken place.K E Y WO R D S 1. Bayesian inference.2. Situational awareness. 3. AIS.

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