Online Learning for Energy Efficient Navigation in Stochastic Transport Networks

Reducing the dependence on fossil fuels in the transport sector is crucial to have a realistic chance of halting climate change. The automotive industry is, therefore, transitioning towards an electrified future at an unprecedented pace. However, in order for electric vehicles to be an attractive alternative to conventional vehicles, some issues, like range anxiety, need to be mitigated. One way to address these problems is by developing more accurate and robust navigation systems for electric vehicles. Furthermore, with highly stochastic and changing traffic conditions, it is useful to continuously update prior knowledge about the traffic environment by gathering data. Passively collecting energy consumption data from vehicles in the traffic network might lead to insufficient information gathered in places where there are few vehicles. Hence, in this thesis, we study the possibility of adapting the routes presented by the navigation system to adequately explore the road network, and properly learn the underlying energy model.The first part of the thesis introduces an online machine learning framework for navigation of electric vehicles, with the objective of adaptively and efficiently navigating the vehicle in a stochastic traffic environment. We assume that the road-specific probability distributions of vehicle energy consumption are unknown, and thus, we need to learn their parameters through observations. Furthermore, we take a Bayesian approach and assign prior beliefs to the parameters based on longitudinal vehicle dynamics. We view the task as a combinatorial multi-armed bandit problem, and utilize Bayesian bandit algorithms, such as Thompson Sampling, to address it. We establish theoretical performance guarantees for Thompson Sampling, in the form of upper bounds on the Bayesian regret, on single-agent, multi-agent and batched feedback variants of the problem. To demonstrate the effectiveness of the framework, we perform simulation experiments on various real-life road networks.In the second half of the thesis, we extend the online learning framework to find paths which minimize or avoid bottlenecks. Solutions to the online minimax path problem represent risk-averse behaviors, by avoiding road segments with high variance in costs. We derive upper bounds on the Bayesian regret of Thompson Sampling adapted to this problem, by carefully handling the non-linear path cost function. We identify computational tractability issues with the original problem formulation, and propose an alternative approximate objective with an associated algorithm based on Thompson Sampling. Finally, we conduct several experimental studies to evaluate the performance of the approximate algorithm

An Efficient Monte Carlo-based Probabilistic Time-Dependent Routing Calculation Targeting a Server-Side Car Navigation System

Incorporating speed probability distribution to the computation of the route planning in car navigation systems guarantees more accurate and precise responses. In this paper, we propose a novel approach for dynamically selecting the number of samples used for the Monte Carlo simulation to solve the Probabilistic Time-Dependent Routing (PTDR) problem, thus improving the computation efficiency. The proposed method is used to determine in a proactive manner the number of simulations to be done to extract the travel-time estimation for each specific request while respecting an error threshold as output quality level. The methodology requires a reduced effort on the application development side. We adopted an aspect-oriented programming language (LARA) together with a flexible dynamic autotuning library (mARGOt) respectively to instrument the code and to take tuning decisions on the number of samples improving the execution efficiency. Experimental results demonstrate that the proposed adaptive approach saves a large fraction of simulations (between 36% and 81%) with respect to a static approach while considering different traffic situations, paths and error requirements. Given the negligible runtime overhead of the proposed approach, it results in an execution-time speedup between 1.5x and 5.1x. This speedup is reflected at infrastructure-level in terms of a reduction of around 36% of the computing resources needed to support the whole navigation pipeline

When the path is never shortest: a reality check on shortest path biocomputation

Shortest path problems are a touchstone for evaluating the computing performance and functional range of novel computing substrates. Much has been published in recent years regarding the use of biocomputers to solve minimal path problems such as route optimisation and labyrinth navigation, but their outputs are typically difficult to reproduce and somewhat abstract in nature, suggesting that both experimental design and analysis in the field require standardising. This chapter details laboratory experimental data which probe the path finding process in two single-celled protistic model organisms, Physarum polycephalum and Paramecium caudatum, comprising a shortest path problem and labyrinth navigation, respectively. The results presented illustrate several of the key difficulties that are encountered in categorising biological behaviours in the language of computing, including biological variability, non-halting operations and adverse reactions to experimental stimuli. It is concluded that neither organism examined are able to efficiently or reproducibly solve shortest path problems in the specific experimental conditions that were tested. Data presented are contextualised with biological theory and design principles for maximising the usefulness of experimental biocomputer prototypes.Comment: To appear in: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Optimized Data Representation for Interactive Multiview Navigation

In contrary to traditional media streaming services where a unique media content is delivered to different users, interactive multiview navigation applications enable users to choose their own viewpoints and freely navigate in a 3-D scene. The interactivity brings new challenges in addition to the classical rate-distortion trade-off, which considers only the compression performance and viewing quality. On the one hand, interactivity necessitates sufficient viewpoints for richer navigation; on the other hand, it requires to provide low bandwidth and delay costs for smooth navigation during view transitions. In this paper, we formally describe the novel trade-offs posed by the navigation interactivity and classical rate-distortion criterion. Based on an original formulation, we look for the optimal design of the data representation by introducing novel rate and distortion models and practical solving algorithms. Experiments show that the proposed data representation method outperforms the baseline solution by providing lower resource consumptions and higher visual quality in all navigation configurations, which certainly confirms the potential of the proposed data representation in practical interactive navigation systems

How to establish a well-functioning guidance system in a complex building structure : Modeling with multi-objective optimization

Wayfinding is a fundamental aspect of our daily lives, encompassing the activities involved in navigating from one place to another. In the context of architectural spaces, effective wayfinding is essential for ensuring a positive user experience and reducing frustration. This issue was brought to light by Solli Distriktpsykiatriske senter (DPS), as they observed difficulties faced by their patients in exiting the building after treatment sessions. Thereby introducing an interesting research question on how to establish a well-functioning guidance system within a complex building structure, with application to finding the way out. In this thesis, an optimization approach has been taken to define the simplest path. Four multi-objective optimization methods are utilized to provide different perspectives on simplicity. The methods consider different weights and rankings of architectural features and the occupants' familiarity with the building, as these factors have been recognized as the most influential factors in daily wayfinding. Comparing the optimizations form the basis for concluding the most suitable method to define the simplest path. Interestingly, three out of four methods occasionally generate paths that contradict human instincts, which negatively affect orientation ability. This serves as the basis for making trade-offs between the methods. As a result, the weighted sum approach with equal weights is found to be the optimal method for defining the simplest path. The findings of the optimization approach lay the foundation for establishing a wellfunctioning guidance system. When applicable, it is recommended to provide signage for the nearest optimal exit, using the simplest path, and the reception. This means that if the path to the reception, despite being longer, aligns with human instinct, it should be clearly indicated. Signage that confirms that the optimal exit route does not involve the main entrance will give the patients more confidence in following the designated path. This is important to ensure trust and reliability in the guidance system. Furthermore, when utilizing the results, consistency in placement and design and the signs' readability are critical to establishing a comprehensive guidance system. It is recommended to incorporate a combination of directional and reassurance signs.nhhma