The desire to produce artificial vision systems which behave in an intelligent, humanlike way or which can autonomously and automatically perform tasks currently only performed by humans has been a goal of Artificial Intelligence research for many decades. Until recently much of the research concentrated on extracting visual representations of objects from single, static scenes. The last decade has seen an increase in interest concerning mobile robotics for navigation, planning and autonomous control as well as for the interpretation of events in real, dynamic scenes. Presented in this thesis is research on artificial vision systems from two different, but both necessary, standpoints. One concerns low-level vision-based behaviour of object tracking based upon a naturalistic theory of perception and behaviour within living systems. The other takes a more application and engineering based approach and its goal is to address high-level scene interpretation and control of processing resources. Numerous experiments are presented to demonstrate the various issues. The two main experiments, corresponding to the two research streams, are a system which is able to fixate complex multi-coloured objects and a fully integrated vision system for predicting and following, with a mobile sensor, events in a dynamic scene.

This thesis is concerned with the problem of situational awareness in autonomous vehicles. In this context, situational awareness refers to the ability of an autonomous vehicle to perceive the road layout ahead, interpret the implied semantics and gain an awareness of its surrounding - thus reading the road ahead. Autonomous vehicles require a high level of situational awareness in order to operate safely and efficiently in real-world dynamic environments. A system is therefore needed that is able to model the expected road layout in terms of semantics, both under normal and roadwork conditions. This thesis takes a three-pronged approach to this problem: Firstly, we consider reading the road surface. This is formulated in terms of probabilistic road marking classification and interpretation. We then derive the road boundaries using only a 2D laser and algorithms based on geometric priors from Highway Traffic Engineering principles. Secondly, we consider reading the road scene. Here, we formulate a roadwork scene recognition framework based on opponent colour vision in humans. Finally, we provide a data representation for situational awareness that unifies reading the road surface and reading the road scene. This thesis therefore frames situational awareness in autonomous vehicles in terms of both static and dynamic road semantics - and detailed formulations and algorithms are discussed. We test our algorithms on several benchmarking datasets collected using our autonomous vehicle on both rural and urban roads. The results illustrate that our road boundary estimation, road marking classification, and roadwork scene recognition frameworks allow autonomous vehicles to truly and meaningfully read the semantics of the road ahead, thus gaining a valuable sense of situational awareness even at challenging layouts, roadwork sites, and along unknown roadways.

Cette thèse comprend deux axes de recherche. D´abord, un nouveau cadre méthodologique pour évaluer la conformité des données RSO (Radar à Synthèse d’Ouverture) multivariées à haute résolution spatiale est proposé en termes de statistique asymptotique par rapport au modèle produit. Plus précisément, la symétrie sphérique est étudiée en appliquant un test d'hypothèses sur la structure de la matrice de quadri-covariance. Deux jeux de données, simulées et réelles, sont prises en considération pour étudier la performance du test obtenu par l’analyse qualitative et quantitative des résultats. La conclusion la plus importante, en ce qui concerne la méthodologie employée dans l'analyse des données RSO multivariées, est que, selon les différents cas d’usages, une partie considérable de données hétérogènes peut ne pas s’ajuster asymptotiquement au modèle produit. Par conséquent, les algorithmes de classification et/ou détection conventionnels développés sur la base de celui-ci deviennent sub-optimaux. Cette observation met en évidence la nécessité de développer de modèles plus sophistiqués comme l'Analyse en Composantes Indépendantes, ce qui conduit à la deuxième partie de cette thèse qui consiste en l’étude du biais d’estimation des paramètres TSVM (Target Scattering Vector Model) lorsque l’ACP est utilisée. Enfin, les performances de l'algorithme sont également évaluées sous l'hypothèse du bruit gaussien corrélé spatialement. L’évaluation théorique de l'ACI comme un outil de type ICTD (In Coherent Target Decomposition) polarimétrique permet une analyse plus efficace de l’apport d’information fourni. A ce but, deux espaces de représentation sont utilisé, notamment H /alpha et TSVM / This thesis comprises two research axes. First, a new methodological framework to assess the conformity of multivariate high-resolution Synthetic Aperture Radar (SAR) data with respect to the Spherically Invariant Random Vector model in terms of asymptotic statistics is proposed. More precisely, spherical symmetry is investigated by applying statistical hypotheses testing on the structure of the quadricovariance matrix. Both simulated and real data are taken into consideration to investigate the performance of the derived test by a detailed qualitative and quantitative analysis. The most important conclusion drawn, regarding the methodology employed in analysing SAR data, is that, depending on the scenario under study, a considerable portion of high heterogeneous data may not fit the aforementioned model. Therefore, traditional detection and classification algorithms developed based on the latter become sub-optimal when applied in such kind of regions. This assertion highlights for the need of the development of model independent algorithms, like the Independent Component Analysis, what leads to the second part of the thesis. A Monte Carlo approach is performed in order to investigate the bias in estimating the Touzi's Target Scattering Vector Model (TSVM) parameters when ICA is employed using a sliding window approach under different scenarios. Finally, the performance of the algorithm is also evaluated under Gaussian clutter assumption and when spatial correlation is introduced in the model. These theoretical assessment of ICA based ICTD enables a more efficient analysis of the potential new information provided by the ICA based ICTD. Both Touzi TSVM as well as Cloude and Pottier H/alpha feature space are then taken into consideration for that purpose. The combined use of ICA and Touzi TSVM is straightforward, indicating new, but not groundbreaking information, when compared to the Eigenvector approach. Nevertheless, the analysis of the combined use of ICA and Cloude and Pottier H/alpha feature space revealed a potential aspect of the Independent Component Analysis based ICTD, which can not be matched by the Eigenvector approach. ICA does not introduce any unfeasible region in the H/alpha plane, increasing the range of possible natural phenomenons depicted in the aforementioned feature space.