Finding the way in known and unknown city environments is a task that all pedestrians carry out regularly. Current technology allows the use of smart devices as aids that can give automatic verbal route directions on the basis of the pedestrian's current position. Many such systems only give route directions, but are unable to interact with the user to answer clarifications or understand other verbal input. Furthermore, they rely mainly on conveying the quantitative information that can be derived directly from geographic map representations: 'In 300 meters, turn into High Street'. However, humans are reasoning about space predominantly in a qualitative manner, and it is less cognitively demanding for them to understand route directions that express such qualitative information, such as 'At the church, turn left' or 'You will see a café'. This thesis addresses three challenges that an interactive wayfinding system faces in the context of natural language generation and understanding: in a given situation, it must decide on whether it is appropriate to give an instruction based on a relative direction, it must be able to select salient landmarks, and it must be able to resolve the user's references to objects. In order to address these challenges, this thesis takes a data-driven approach: data was collected in a large-scale city environment to derive decision-making models from pedestrians' behavior. As a representation for the geographical environment, all studies use the crowd-sourced Openstreetmap database. The thesis presents methodologies on how the geographical and language data can be utilized to derive models that can be incorporated into an automatic route direction system. / <p>QC 20160516</p>

People in unfamiliar environments often need navigation guidance to reach a destination. Research has found that compared to outdoors, people tend to lose orientation much more easily within complex buildings, such as university buildings and hospitals. This paper proposes a category-based method to generate landmark-based route instructions to support people's wayfinding activities in unfamiliar indoor environments. Compared to other methods relying on detailed instance-level data about the visual, semantic, and structural characteristics of individual spatial objects, the proposed method relies on commonly available data about categories of spatial objects, which exist in most indoor spatial databases. With this, instructions like "Turn right after the second door, and use the elevator to go to the second floor" can be generated for indoor navigation. A case study with a university campus shows that the method is feasible in generating landmark-based route instructions for indoor navigation. More importantly, compared to metric-based instructions (i.e., the benchmark for indoor navigation), the generated landmark-based instructions can help users to unambiguously identify the correct decision point where a change of direction is needed, as well as offer information for the users to confirm that they are on the right way to the destination.