The main objectives of this work were to investigate Geographical Information Systems techniques for modelling a cross-country trafficability. To accomplished stated tasks, reciprocal relationships between the soil deposits, local hydrology, geology and geomorphology were studied in relation to the study area in South-Eastern Sweden. Growing awareness of nowadays users of GIS in general is being concentrated on understanding an importance of soil conditions changed after cross-country trafficability. Therefore, in this thesis, constructing of the Soil Knowledge Database introduced to the genuine geological soil textural classes a new, modified geotechnical division with desirable for off-road ground reasoning measurable factors, like soil permeability, capillarity or Atterberg’s consistency limits. Digital Elevation Model, the driving force for landscape studies in the thesis, was carefully examined together with the complementary datasets of the investigated area. Testing of the elevation data was done in association to the hydrological modelling, which resulted with the Wetness Index map. The three distinguishable soil wetness conditions: dry, moist and wet, were obtained, and used consequently for creation of the static ground conditions map, a visible medium of soils susceptibility to for example machine compaction. The work resulted with a conceptual scheme for cross-country trafficability modelling, which was put into effect while modeling in GIS. As a final outcome, by combining all processed data together, derivatives were incorporated and draped over the rendered 3D animating scene. A visually aided simulation enabled to concretized theoretical, hypothetical and experimental outcomes into one coherent model of apprised under Multicriterial Evaluation techniques standardized factor maps for ground vehicle maneuverability. Also further steps of research were proposed.

Predicting terrain trafficability of deformable terrain is a difficult task with applications in e.g, forestry, agriculture, exploratory missions. The currently used techniques are neither practical, efficient, nor sufficiently accurate and inadequate for certain soil types. An online method which predicts terrain trafficability is of interest for any vehicle with purpose to reduce ground damage, improve steering and increase mobility. This thesis presents a novel approach for predicting the model parameters used in modelling a virtual terrain. The model parameters include particle stiffness, tangential friction, rolling resistance and two parameters related to particle plasticity and adhesion. Using multi-body dynamics, both vehicle and terrain can be simulated, which allows for an efficient exploration of a great variety of terrains. A vehicle with access to certain sensors can frequently gather sensor data providing information regarding vehicle-terrain interaction. The proposed method develops a statistical model which uses the sensor data in predicting the terrain model parameters. However, these parameters are specified at model particle level and do not directly explain bulk properties measurable on a real terrain. Simulations were carried out of a single tracked bogie constrained to move in one direction when traversing flat, homogeneous terrains. The statistical model with best prediction accuracy was ridge regression using polynomial features and interaction terms of second degree. The model proved capable of predicting particle stiffness, tangential friction and particle plasticity, with moderate accuracy. However, it was deduced that the current predictors and training scenarios were insufficient in estimating particle adhesion and rolling resistance. Nevertheless, this thesis indicates that it should be possible to develop a method which successfully predicts terrain model properties.