Accelerating Cooperative Planning for Automated Vehicles with Learned Heuristics and Monte Carlo Tree Search

Efficient driving in urban traffic scenarios requires foresight. The observation of other traffic participants and the inference of their possible next actions depending on the own action is considered cooperative prediction and planning. Humans are well equipped with the capability to predict the actions of multiple interacting traffic participants and plan accordingly, without the need to directly communicate with others. Prior work has shown that it is possible to achieve effective cooperative planning without the need for explicit communication. However, the search space for cooperative plans is so large that most of the computational budget is spent on exploring the search space in unpromising regions that are far away from the solution. To accelerate the planning process, we combined learned heuristics with a cooperative planning method to guide the search towards regions with promising actions, yielding better solutions at lower computational costs

Decentralized Cooperative Planning for Automated Vehicles with Continuous Monte Carlo Tree Search

Urban traffic scenarios often require a high degree of cooperation between traffic participants to ensure safety and efficiency. Observing the behavior of others, humans infer whether or not others are cooperating. This work aims to extend the capabilities of automated vehicles, enabling them to cooperate implicitly in heterogeneous environments. Continuous actions allow for arbitrary trajectories and hence are applicable to a much wider class of problems than existing cooperative approaches with discrete action spaces. Based on cooperative modeling of other agents, Monte Carlo Tree Search (MCTS) in conjunction with Decoupled-UCT evaluates the action-values of each agent in a cooperative and decentralized way, respecting the interdependence of actions among traffic participants. The extension to continuous action spaces is addressed by incorporating novel MCTS-specific enhancements for efficient search space exploration. The proposed algorithm is evaluated under different scenarios, showing that the algorithm is able to achieve effective cooperative planning and generate solutions egocentric planning fails to identify

Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search

Today's automated vehicles lack the ability to cooperate implicitly with others. This work presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative planning using macro-actions for automated vehicles in heterogeneous environments. Based on cooperative modeling of other agents and Decoupled-UCT (a variant of MCTS), the algorithm evaluates the state-action-values of each agent in a cooperative and decentralized manner, explicitly modeling the interdependence of actions between traffic participants. Macro-actions allow for temporal extension over multiple time steps and increase the effective search depth requiring fewer iterations to plan over longer horizons. Without predefined policies for macro-actions, the algorithm simultaneously learns policies over and within macro-actions. The proposed method is evaluated under several conflict scenarios, showing that the algorithm can achieve effective cooperative planning with learned macro-actions in heterogeneous environments

The Contrast of Covariational Reasoning and Other Problem Solving Methods of a Calculus Student

Research has shown that covariational reasoning is indicative of success across a variety of mathematics topics, especially calculus. This paper will build on the prior research by examining one calculus student’s covariational reasoning over a multiple term teaching experiment. The tasks associated with the teaching experiment appear in a variety of mathematical forms, and in varying contexts, so that the student’s techniques for each task provide results that can be interpreted using existing frameworks. Analyzing the covariational reasoning of the student with these frameworks reveals relationships between the methods the student uses to solve tasks involving covariational reasoning and the student’s abilities to successfully solve the tasks

Same Story Every Time / Being Black is Not a Crime : Gun Regulations and Recurrent Patterns of Government Control of Black Americans in the Nineteenth and Twentieth Centuries

Since the shooting death of Michael Brown in Ferguson, Missouri in August 2014, there has been a renewed national conversation on relations between law enforcement and communities of color. Subsequent shooting deaths of Black individuals, followed by grand jury non-indictments, have shifted the conversation to a systemic critique, revealing to some, and reminding others, of the deeply racialized nature of criminal justice in the United States. This thesis project is a work of American Political Development that analyzes the racialized developmental of the criminal justice system in the United States, providing context to the recent national conversation. Its purpose is to make sense of the institutionalized racism present in today’s criminal justice system by identifying concrete and detailed instances of institutionalized racism shaping the development of the criminal justice system historically. It identifies a specific racialized pattern in the system’s history: at moments of significant racial progress, there often occurs, through the criminal justice system, a “colorblind” backlash, designed to reassert and maintain control over those Black individuals enjoying racial progress. The scope of the project is limited both in policy area and time—it considers the suspect passage and implementation of “colorblind” gun laws with racialized effects at two critical junctures in America racial history, Reconstruction and the Civil Rights movement. The goal of the project is to at least in part explain the racialized nature of today’s criminal justice system by exploring how colorblindness and institutionalized racism have shaped its development and growth historically

Embedded vs. Drop-in Tutors in Developmental Writing Contexts: Course/Tutoring Perceptions and Impact on Student Writing Efficacy

Many higher education institutions offer drop-in tutoring programs hosted by writing specialists to support struggling students while others may also/alternatively embed tutors directly into courses. In this quasi-experimental study, we compared survey results from 100 students in basic/developmental courses that featured embedded peer tutors with 78 students who experienced tutoring via a walk-in writing center. Variables explored included writing efficacy and course/tutor perception survey items. While students generally found both embedded and walk-in tutoring to be helpful, the ratings for embedding tutoring tended to be statistically stronger for most variables we investigated, suggesting that students responded more positively to embedded tutoring

Videostreaming für mobile Endgeräte mit Schwerpunkt auf iOS

Ziel dieser Bachelorarbeit ist es, eine Applikation zu entwickeln, über welche Videos auf mobile Endgeräte gestreamt werden können. Bei der Programmierung werden Smartphones und Tablet-Computer betrachtet. Darüber hinaus wird das Hauptaugenmerk auf die Geräte gelegt, welche mit dem mobilen Betriebssystem iOS von Apple ausgestattet sind. Die Anwendung soll für verschiedene Unternehmen zur Verfügung stehen, wobei jedem Unternehmen Videos seiner Veranstaltungen zugeordnet werden. Die Umsetzung der Arbeit erfordert einen Einblick in den technischen Stand der Mobilgeräte sowie in die Voraussetzungen für Streaming Media.The aim of this bachelor thesis is to develop an application for getting videos streamed to a mobile device. The smartphones and tablet-computer which are focused on, are considered to use Apples mobile operating system iOS. The application has to be available for different companies whereas each company receive their specific content and videos. For realization an insight into both the development of mobile devices and the requirements for streaming media must be given