Short-term motion prediction of autonomous vehicles in complex environments: A Deep Learning approach

Complex environments manifest a high level of complexity and it is of critical importance that the safety systems embedded within autonomous vehicles (AVs) are able to accurately anticipate short-term future motion of agents in close proximity. This problem can be further understood as generating a sequence of coordinates describing the plausible future motion of the tracked agent. Number of recently proposed techniques that present satisfactory performance exploit the learning capabilities of novel deep learning (DL) architectures to tackle the discussed task. Nonetheless, there still exists a vast number of challenging issues that must be resolved to further advance capabilities of motion prediction models.This thesis explores novel deep learning techniques within the area of short-term motion prediction of on-road participants, specifically other vehicles from a points of autonomous vehicles. First and foremost, various approaches in the literature demonstrate significant benefits of using a rasterised top-down image of the road to encode the context of tracked vehicle’s surroundings which generally encapsulates a large, global portion of the environment. This work on the other hand explores a use of local regions of the rasterised map to more explicitly focus on the encoding of the tracked vehicle’s state. The proposed technique demonstrates plausible results against several baseline models and in addition outperforms the same model that instead uses global maps. Next, the typical method for extracting features from rasterised maps involves employing one of the popular vision models (e.g. ResNet-50) that has been previously pre-trained on a distinct task such as image classification. Recently however, it has been demonstrated that this approach can be sub-optimal for tasks that strongly rely on precise localisation of features and it can be more advantageous to train the model from scratch directly on the task at hand. In contrast, the subsequent part of this thesis investigates an alternative method for processing and encoding of spatial data based on the capsule networks in order to eradicate several issues that standard vision models exhibit. Through several experiments it is established that the novel capsule based motion predictor that is trained from scratch is able to achieve competitive results against numerous popular vision models. Finally, the proposed model is further extended with the use of generative framework to account for the fact that the space of possible movements of the tracked vehicle is not strictly limited to single trajectory. More specifically, to account for the multi-modality of the problem a conditional variational auto-encoder (CVAE) is employed which enables to sample an arbitrary amount of diverse trajectories. The final model is examined against methods from literature on a publicly available dataset and as presented it significantly outperforms other models whilst drastically reducing the number of trainable parameters

Solid-State Mechanochemical Syntheses of Perovskites

The chapter presents the possibility of applying high-energy ball milling techniques to carry out the synthesis of ceramics with perovskite structure, thereby eliminating prolonged use of high temperatures in their preparation

The Surface Distribution of Oil Spills, the Environmental Status of the Visoka Oilfield, Its Rehabilitation Ways: Case Study

Visoka oilfield was explored in 1963 through the well G-622. In this oilfield had been drilled more than 170 production wells. Through all this years of its exploitation, mainly because of the outdated technology, the environmental pollution reached really dramatic values. The pollution extends on the Visoka village, affecting so the life for the entire community. The oil spills, the gas emissions and the formation water affected and polluted and ―k illed‖ the Gjanica River, its delta and the Adriatic coast in the Semani beach. Due to the technology and the completely wrong policies, used in the past, the wells had been equipped with a pit, whose actually are filled with oil, water and solid wastes. Now these mixtures are more similar with TAR‘s. This is the main evidence which proves that, the area had never been cleaned up properly. Actually the reservoir had been given in use to a foreigner company, based in a petroleum agreement. The concessionary has already preparing the rehabilitation plan. Based on the best international experience the cleaning up ways will be based on the oil spill treatment, mainly through dispersants, the water treatment through dispersants, filtrations processes and reinjection and the emissions cleaning up mainly through reinjection. The polluted soil will be treated through thermal methods, or will be deposited in a closed landfill. Evaluations, calculations and spatial distribution of the oil spills of the Gjanica River and Adriatic Sea are presented in this paper