Navigation with uncertain spatio-temporal resources

Supporting people with intelligent navigation instructions enables users to efficiently achieve trip-related objectives (e.g., minimum travel time or fuel consumption) and preserves them from making unnecessary detours. This, in turn, enables them to save time, money and, additionally, minimize $CO_2$ emissions. For these reasons, manufacturers integrate navigation systems into almost all modern automobiles. Nevertheless, most of them support only simple routing instructions, i.e., how to drive from location A to B. Albeit, people are regularly faced with more complex decisions, e.g. navigating to a cheap gas station on the route while incorporating dynamic gas price changes. Another example-scenario is after reaching the destination, an available facility to park needs to be found. So far, people cruise almost randomly around the goal area in the search for a parking space. As a consequence, persons valuable time is consumed and unnecessary traffic arises. Besides private persons, transportation companies have to make complex mobility decisions. For instance, taxi drivers have to find out where to move next whenever the taxi is idle. There are plenty possibilities for where the taxi driver could go. In case the last drop-off was in a sparsely populated region, waiting for a call from the taxi office will likely result in a longer drive to the next customer. In turn, customer satisfaction decreases with a longer waiting time and implies a potential loss of customers. Recently, the number of data sources that potentially improve these mobility decisions increased. For instance, on-street parking sensors track the current state of the spaces (e.g. Melbourne), mobile applications collect taxi requests from customers and gas stations publish the current prices all in real-time. This thesis investigates the question of how to design algorithms such that they exploit this volatile data. Standard routing algorithms assume a static world. But the availability of passengers, gas prices and the availability of parking spots change over time in a non-deterministic manner. Hence, we model multiple real-world applications as Markov decision processes (MDP), i.e., a framework for sequential decision making under uncertainty. Depending on the task, we propose to solve the MDP with dynamic programming, replanning and hindsight planning or reinforcement learning. Ultimately, we combine all applications in a single problem domain. Subsequently, we propose a reinforcement learning approach that solves all applications in this domain without modification. Furthermore, it decouples the routing task from solving the application itself. Hence, it is transferable to previously unseen street networks without further training.Durch intelligente Navigationssysteme werden Verkehrsteilnehmer davor bewahrt, Umwege zu fahren. Dadurch sparen sie Zeit, Geld und verringern den $CO_2$-Ausstoß. Aus diesem Grund verbauen Hersteller Navigationssysteme in fast allen Neuwägen. Bis heute unterstützen die meisten Systeme nur einfache Routenplanung, die den kürzesten oder schnellsten Pfad von A nach B berechnen. Dennoch müssen Fahrer regelmäßig Entscheidungen darüber hinaus treffen. Beispielsweise soll eine möglichst günstige Tankstelle auf dem Weg zum eigentlichen Ziel besucht werden. Allerdings kann diese ihre Preise, während der Fahrer oder die Fahrerin auf dem Weg dort hin ist, dynamisch ändern. Anschließend muss, sobald das eigentliche Ziel erreicht ist, ein Parkplatz gefunden werden. Bisher fahren Parkplatzsuchende zufällig durch das Zielgebiet in der Hoffnung möglichst schnell einen freien Parkplatz zu finden. Die Suche verursacht zusätzlichen Verkehr und der Fahrer oder die Fahrerin verbringt mehr Zeit auf der Straße. Neben Privatpersonen müssen auch Transportunternehmen komplexe Entscheidungen über Bewegungen treffen. Zum Beispiel muss ein Taxifahrer, wenn er gerade keinen Fahrgast hat, entscheiden, wo er sich als nächstes positioniert. Zwar könnte er am letzten Zielort warten, bis er einen Anruf der Taxizentrale bekommt. Falls jedoch der letzte Zielort in einem entlegenen Gebiet ist, muss der nächste Fahrgast wahrscheinlich lange warten, bis der Fahrer oder die Fahrerin bei ihm ankommt. Damit sinkt die Kundenzufriedenheit, was wiederum einen potentiellen Verlust der Kunden bedeutet. Seit Kurzem gibt es immer mehr Datenquellen, die Entscheidungen für diese Probleme verbessern. Beispielsweise wird durch Parkplatzsensoren die Verfügbarkeit der Parkplätze verfolgt, mobile Anwendungen sammeln Anfragen über Fahrgäste und Tankstellen veröffentlichen ihren aktuellen Preis in Echtzeit. In dieser Arbeit wird der Forschungsfrage nachgegangen, wie Algorithmen gestaltet werden können, sodass diese veränderlichen Informationen verwendet werden können. Standard-Routing-Algorithmen gehen von einer statischen Welt aus. Aber die Verfügbarkeit von Fahrgästen, die Tankstellenpreise und die Parkplatzzustände ändern sich nicht deterministisch. Aus diesem Grund modellieren wir eine Reihe von Anwendungen als Markov-Entscheidungsproblem (MDP). Applikationsabhängig schlagen wir vor, das MDP mit dynamischer Programmierung, Replanning bzw. Hindsight Planning oder Reinforcement Learning zu lösen. Abschließend fassen wir alle Anwendungen in einer Domäne zusammen. Dadurch können wir einen Reinforcement Learning Ansatz definieren, der alle Anwendungen in dieser Domäne ohne Änderung lösen kann. Dieser Ansatz ermöglicht es, die Routenplanung von der eigentlichen Problemstellung zu lösen. Dadurch ist die gelernte Funktionsapproximation auch auf bisher unbekannte Straßennetze ohne weiteres Training anwendbar

Probably Unknown: Deep Inverse Sensor Modelling In Radar

Radar presents a promising alternative to lidar and vision in autonomous vehicle applications, able to detect objects at long range under a variety of weather conditions. However, distinguishing between occupied and free space from raw radar power returns is challenging due to complex interactions between sensor noise and occlusion. To counter this we propose to learn an Inverse Sensor Model (ISM) converting a raw radar scan to a grid map of occupancy probabilities using a deep neural network. Our network is self-supervised using partial occupancy labels generated by lidar, allowing a robot to learn about world occupancy from past experience without human supervision. We evaluate our approach on five hours of data recorded in a dynamic urban environment. By accounting for the scene context of each grid cell our model is able to successfully segment the world into occupied and free space, outperforming standard CFAR filtering approaches. Additionally by incorporating heteroscedastic uncertainty into our model formulation, we are able to quantify the variance in the uncertainty throughout the sensor observation. Through this mechanism we are able to successfully identify regions of space that are likely to be occluded.Comment: 6 full pages, 1 page of reference

Motion Planning of Uncertain Ordinary Differential Equation Systems

This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems. Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs. The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space

Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page