Route maps, which depict a path from one location to another, have emerged as one of the most popular applications on the Web. Current computer-generated route maps, however, are often very difficult to use. In this paper we present a set of cartographic generalization techniques specifically designed to improve the usability of route maps. Our generalization techniques are based both on cognitive psychology research studying how route maps are used and on an analysis of the generalizations commonly found in handdrawn route maps. We describe algorithmic implementations of these generalization techniques within BeeLine, a real-time system for automatically designing and rendering route maps. We show that Bee-Line produces route maps that are much more usable than those produced by current computer-based route map rendering systems. Feedback from over 1100 users indicates that over 99 % believe BeeLine maps are preferable to using standard computer-generated route maps alone.

Route directions are usually conveyed either by graphical means, i.e.

We describe a hybrid building navigation system consisting of stationary information booths and a mobile communication infrastructure feeding small portable devices. The graphical presentations for both the booths and the mobile devices are generated from a common source and for the common task of way finding, but they use different techniques to convey possibly different subsets of the relevant information. The form of the presentations is depending on technical limitations of the output media, accuracy of location information, and cognitive restrictions of the user. We analyze what information needs to be conveyed, how limited resources influence the presentation of this information, and argue, that by generating all different presentations in a common framework, a consistent appearance across devices can be achieved and that the different device classes can complement each other in facilitating the navigation task. Keywords: hybrid user interfaces, navigation, resource adaptivity, user adaptivity

Human activity takes place in space. To act effectively, people need mental representations of space. People’s mental representations of space differ from space as conceived of by physicists, geometers, and cartographers. Mental representations of space are constructions based on elements, the things in space, and the spatial relations among them relative to a reference frame. People act in different spaces, depending on the task at hand. The spaces considered here are the space of the body, the space around the body, the space of navigation, and the space of graphics. Different elements and spatial relations are central for functioning in the different spaces, yielding different mental representations. Mental Spaces 3

Numerous cognitive studies have indicated that the form and complexity of route instructions may be as important to human navigators as the overall length of route. Almost without exception, automated navigation systems rely on computing the solution to the shortest path problem, and not the problem of finding the "simplest" path. This paper addresses the issue of finding the "simplest" paths through a network, in terms of their ease of description. We propose a "simplest" paths algorithm that is computationally as e#cient as conventional shortest paths algorithms, at least for planar graphs. An empirical study of the algorithm 's performance, based on an established cognitive model of navigation instruction complexity, revealed that the lengths of a simplest path was on average only 16% longer than the length of the corresponding shortest path. In return for marginally longer routes, the simplest path algorithm seems to o#er considerable advantages over shortest paths in terms of their ease of description. The conclusions indicate several areas for future research; in particular cognitive studies are needed to verify these initial computational results. Potentially, the simplest paths algorithm could be used to replace shortest paths algorithms in any automated system for generating human navigation instructions, including in-car navigation systems, Internet driving direction servers, and other location-based services.

Today’s wayfinding assistance systems provide route directions that are significantly different to those generated by humans, resulting in a gap between what users expect and what the system delivers. This dissertation contributes to closing this gap by presenting a process that adapts instructions to environmental characteristics and a route’s properties, thereby implementing principles of human direction giving. The process generates an abstract, relational specification of route directions, which can, for example, be externalized verbally. 1

Following verbal route instructions requires knowledge of language, space, action and perception. We present MARCO, an agent that follows free-form, natural language route instructions by representing and executing a sequence of compound action specifications that model which actions to take under which conditions. MARCO infers implicit actions from knowledge of both linguistic conditional phrases and from spatial action and local configurations. Thus, MARCO performs explicit actions, implicit actions necessary to achieve the stated conditions, and exploratory actions to learn about the world. We gathered a corpus of 786 route instructions from six people in three large-scale virtual indoor environments. Thirtysix other people followed these instructions and rated them for quality. These human participants finished at the intended destination on 69 % of the trials. MARCO followed the same instructions in the same environments, with a success rate of 61%. We measured the efficacy of action inference with MARCO variants lacking action inference: executing only explicit actions, MARCO succeeded on just 28 % of the trials. For this task, inferring implicit actions is essential to follow poor instructions, but is also crucial for many highly-rated route instructions.

Assistive technology for wayfinding will significantly improve the quality of life for many individuals with cognitive impairments. The user interface of such a system is as crucial as the underlying implementation and localization technology. We built a system using the Wizard-of-Oz technique that let us experiment with many guidance strategies and interface modalities. Through user studies, we evaluated various configurations of the user interface for accuracy of route completion, time to completion, and user preferences. We used a counter-balanced design that included different modalities (images, audio, and text) and different routes. We found that although users were able to use all types of modalities to find their way indoors, they varied significantly in their preferred modalities. We also found that timing of directions requires careful attention, as does providing users with confirmation messages at appropriate times. Our findings suggest that the ability to adapt indoor wayfinding devices for specific users ’ preferences and needs will be particularly important.

Designing effective instructions for everyday products is challenging. One reason is that designers lack a set of design principles for producing visually comprehensible and accessible instructions. We describe an approach for identifying such design principles through experiments investigating the production, preference, and comprehension of assembly instructions for furniture. We instantiate these principles into an algorithm that automatically generates assembly instructions. Finally, we perform a user study comparing our computergenerated instructions to factory-provided and highly rated hand-designed instructions. Our results indicate that the computer-generated instructions informed by our cognitive design principles significantly reduce assembly time an average of 35 % and error by 50%. Details of the experimental methodology and the implementation of the automated system are described.

This work is about the integration of the skills robot control, landmark recognition, and qualitative reasoning in a single autonomous mobile system. It deals with the transfer of coarse qualitative route descriptions usually given by humans into the domain of mobile robot navigation. An approach is proposed that enables the Bremen Autonomous Wheelchair to follow a route in a building, based on a description such as "Follow this corridor, take the second corridor branching off on the right-hand side and stop at its end." The landmark recognition uses a new method taken from the field of image processing for detecting significant places along a route.