3,839 research outputs found
Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment
Many localization algorithms use a spatiotemporal window of sensory
information in order to recognize spatial locations, and the length of this
window is often a sensitive parameter that must be tuned to the specifics of
the application. This letter presents a general method for environment-driven
variation of the length of the spatiotemporal window based on searching for the
most significant localization hypothesis, to use as much context as is
appropriate but not more. We evaluate this approach on benchmark datasets using
visual and Wi-Fi sensor modalities and a variety of sensory comparison
front-ends under in-order and out-of-order traversals of the environment. Our
results show that the system greatly reduces the maximum distance traveled
without localization compared to a fixed-length approach while achieving
competitive localization accuracy, and our proposed method achieves this
performance without deployment-time tuning.Comment: Pre-print of article appearing in 2017 IEEE Robotics and Automation
Letters. v2: incorporated reviewer feedbac
Hybrid optimizer for expeditious modeling of virtual urban environments
Tese de mestrado. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 200
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
Design, Field Evaluation, and Traffic Analysis of a Competitive Autonomous Driving Model in a Congested Environment
Recently, numerous studies have investigated cooperative traffic systems
using the communication among vehicle-to-everything (V2X). Unfortunately, when
multiple autonomous vehicles are deployed while exposed to communication
failure, there might be a conflict of ideal conditions between various
autonomous vehicles leading to adversarial situation on the roads. In South
Korea, virtual and real-world urban autonomous multi-vehicle races were held in
March and November of 2021, respectively. During the competition, multiple
vehicles were involved simultaneously, which required maneuvers such as
overtaking low-speed vehicles, negotiating intersections, and obeying traffic
laws. In this study, we introduce a fully autonomous driving software stack to
deploy a competitive driving model, which enabled us to win the urban
autonomous multi-vehicle races. We evaluate module-based systems such as
navigation, perception, and planning in real and virtual environments.
Additionally, an analysis of traffic is performed after collecting multiple
vehicle position data over communication to gain additional insight into a
multi-agent autonomous driving scenario. Finally, we propose a method for
analyzing traffic in order to compare the spatial distribution of multiple
autonomous vehicles. We study the similarity distribution between each team's
driving log data to determine the impact of competitive autonomous driving on
the traffic environment
Machine learning applied to the context of Poker
A combinação de princípios da teoria de jogo e metodologias de machine learning aplicados ao contexto de formular estratégias ótimas para jogos está a angariar interesse por parte de uma porção crescentemente significativa da comunidade científica, tornando-se o jogo do Poker num candidato de estudo popular devido à sua natureza de informação imperfeita. Avanços nesta área possuem vastas aplicações em cenários do mundo real, e a área de investigação de inteligência artificial demonstra que o interesse relativo a este objeto de estudo está longe de desaparecer, com investigadores do Facebook e Carnegie Mellon a apresentar, em 2019, o primeiro agente de jogo autónomo de Poker provado como ganhador num cenário com múltiplos jogadores, uma conquista relativamente à anterior especificação do estado da arte, que fora desenvolvida para jogos de apenas 2 jogadores. Este estudo pretende explorar as características de jogos estocásticos de informação imperfeita, recolhendo informação acerca dos avanços nas metodologias disponibilizados por parte de investigadores de forma a desenvolver um agente autónomo de jogo que se pretende inserir na classificação de "utility-maximizing decision-maker".The combination of game theory principles and machine learning methodologies applied to encountering optimal strategies for games is garnering interest from an increasing large portion of the scientific community, with the game of Poker being a popular study subject due to its imperfect information nature. Advancements in this area have a wide array of applications in real-world scenarios, and the field of artificial intelligent studies show that the interest regarding this object of study is yet to fade, with researchers from Facebook and Carnegie Mellon presenting, in 2019, the world’s first autonomous Poker playing agent that is proven to be profitable while confronting multiple players at a time, an achievement in relation to the previous state of the art specification, which was developed for two player games only. This study intends to explore the characteristics of stochastic games of imperfect information, gathering information regarding the advancements in methodologies made available by researchers in order to ultimately develop an autonomous agent intended to adhere to the classification of a utility-maximizing decision-maker
LIMO-Velo: A real-time, robust, centimeter-accurate estimator for vehicle localization and mapping under racing velocities
Treballs recents sobre localització de vehicles i mapeig dels seus entorns es
desenvolupen per a dispositius portàtils o robots terrestres que assumeixen
moviments lents i suaus. Contràriament als entorns de curses d’alta velocitat.
Aquesta tesi proposa un nou model d’SLAM, anomenat LIMO-Velo, capaç
de corregir el seu estat amb una latència extremadament baixa tractant els
punts LiDAR com un flux de dades. Els experiments mostren un salt en
robustesa i en la qualitat del mapa mantenint el requisit de correr en temps
real. El model aconsegueix una millora relativa del 20% en el KITTI dataset
d’odometria respecte al millor rendiment existent; no deriva en un sol esce-
nari. La qualitat del mapa a nivell de centı́metre es manté amb velocitats
que poden arribar a 20 m/s i 500 graus/s. Utilitzant les biblioteques obertes
IKFoM i ikd-Tree, el model funciona x10 més ràpid que la majoria de models
d’última generació. Mostrem que LIMO-Velo es pot generalitzar per exe-
cutar l’eliminació dinàmica d’objectes, com ara altres agents a la carretera,
vianants i altres.Trabajos recientes sobre la localización de vehı́culos y el mapeo de sus en-
tornos se desarrollan para dispositivos portátiles o robots terrestres que
asumen movimientos lentos y suaves. Al contrario de los entornos de carreras
de alta velocidad. Esta tesis propone un nuevo modelo SLAM, LIMO-Velo,
capaz de corregir su estado en latencia extremadamente baja al tratar los
puntos LiDAR como un flujo de datos. Los experimentos muestran un salto
en la solidez y la calidad del mapa mientras se mantiene el requisito de tiempo
real. El modelo logra una mejora relativa del 20% en el conjunto de datos
de KITTI Odometry sobre el mejor desempeño existente; no deriva en un
solo escenario. La calidad del mapa de nivel centimétrico todavı́a se logra a
velocidades de carrera que pueden llegar hasta 20 m/s y 500 grados/s. Us-
ando las bibliotecas abiertas IKFoM e ikd-Tree, el modelo funciona x10 más
rápido que la mayorı́a de los modelos de última generación. Mostramos que
LIMO-Velo se puede generalizar para trabajar bajo la eliminación dinámica
de objetos, como otros agentes en la carretera, peatones y más.Recent works on localizing vehicles and mapping their environments are de-
veloped for handheld devices or terrestrial robots which assume slow and
smooth movements. Contrary to high-velocity racing environments. This
thesis proposes a new SLAM model, LIMO-Velo, capable of correcting its
state at extreme low-latency by treating LiDAR points as a data stream.
Experiments show a jump in robustness and map quality while maintaining
the real-time requirement. The model achieves a 20% relative improvement
on the KITTI Odometry dataset over the existing best performer; it does
not drift in a single scenario. Centimeter-level map quality is still achieved
under racing velocities that can go up to 20m/s and 500deg/s. Using the IKFoM and ikd-Tree open libraries, the model performs x10 faster than most
state-of-the-art models. We show that LIMO-Velo can be generalized to work
under dynamic object removal such as other agents in the road, pedestrians,
and more.Outgoin
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