Mind the Gap: Developments in Autonomous Driving Research and the Sustainability Challenge

Scientific knowledge on autonomous-driving technology is expanding at a faster-than-ever pace. As a result, the likelihood of incurring information overload is particularly notable for researchers, who can struggle to overcome the gap between information processing requirements and information processing capacity. We address this issue by adopting a multi-granulation approach to latent knowledge discovery and synthesis in large-scale research domains. The proposed methodology combines citation-based community detection methods and topic modeling techniques to give a concise but comprehensive overview of how the autonomous vehicle (AV) research field is conceptually structured. Thirteen core thematic areas are extracted and presented by mining the large data-rich environments resulting from 50 years of AV research. The analysis demonstrates that this research field is strongly oriented towards examining the technological developments needed to enable the widespread rollout of AVs, whereas it largely overlooks the wide-ranging sustainability implications of this sociotechnical transition. On account of these findings, we call for a broader engagement of AV researchers with the sustainability concept and we invite them to increase their commitment to conducting systematic investigations into the sustainability of AV deployment. Sustainability research is urgently required to produce an evidence-based understanding of what new sociotechnical arrangements are needed to ensure that the systemic technological change introduced by AV-based transport systems can fulfill societal functions while meeting the urgent need for more sustainable transport solutions

Modelling active bio-inspired object recognition in autonomous mobile agents

Object recognition is arguably one of the main tasks carried out by the visual cortex. This task has been studied for decades and is one of the main topics being investigated in the computer vision field. While vertebrates perform this task with exceptional reliability and in very short amounts of time, the visual processes involved are still not completely understood. Considering the desirable properties of the visual systems in nature, many models have been proposed to not only match their performance in object recognition tasks, but also to study and understand the object recognition processes in the brain. One important point most of the classical models have failed to consider when modelling object recognition is the fact that all the visual systems in nature are active. Active object recognition opens different perspectives in contrast with the classical isolated way of modelling neural processes such as the exploitation of the body to aid the perceptual processes. Biologically inspired models are a good alternative to study embodied object recognition since animals are a working example that demonstrates that object recognition can be performed with great efficiency in an active manner. In this thesis I study biologically inspired models for object recognition from an active perspective. I demonstrate that by considering the problem of object recognition from this perspective, the computational complexity present in some of the classical models of object recognition can be reduced. In particular, chapter 3 compares a simple V1-like model (RBF model) with a complex hierarchical model (HMAX model) under certain conditions which make the RBF model perform as the HMAX model when using a simple attentional mechanism. Additionally, I compare the RBF and HMAX model with some other visual systems using well-known object libraries. This comparison demonstrates that the performance of the implementations of the RBF and HMAX models employed in this thesis is similar to the performance of other state-of-the-art visual systems. In chapter 4, I study the role of sensors in the neural dynamics of controllers and the behaviour of simulated agents. I also show how to employ an Evolutionary Robotics approach to study autonomous mobile agents performing visually guided tasks. In addition, in chapter 5 I investigate whether the variation in the visual information, which is determined by simple movements of an agent, can impact the performance of the RBF and HMAX models. In chapter 6 I investigate the impact of several movement strategies in the recognition performance of the models. In particular I study the impact of the variation in visual information using different movement strategies to collect training views. In addition, I show that temporal information can be exploited to improve the object recognition performance using movement strategies. In chapter 7 experiments to study the exploitation of movement and temporal information are carried out in a real world scenario using a robot. These experiments validate the results obtained in simulations in the previous chapters. Finally, in chapter 8 I show that by exploiting regularities in the visual input imposed by movement in the selection of training views, the complexity of the RBF model can be reduced in a real robot. The approach of this work proposes to gradually increase the complexity of the processes involved in active object recognition, from studying the role of moving the focus of attention while comparing object recognition models in static tasks, to analysing the exploitation of an active approach in the selection of training views for a object recognition task in a real world robot